沉浸式翻译

Point-by-Point Response to Reviewers' Comments
对审稿人意见的逐点回复

Manuscript Number: PGENETICS-D-23-01091R1
稿件编号：PGENETICS-D-23-01091R1  

Title: Tumor-associated macrophage subtypes on cancer immunity along with prognostic analysis and SPP1-mediated interactions between tumor cells and macrophages
标题：《肿瘤相关巨噬细胞亚型对癌症免疫的影响、预后分析以及肿瘤细胞与巨噬细胞之间 SPP1 介导的相互作用》

NOTE: The texts in italics are the reviewer’s comments; our responses are marked in blue in this text. The revised sentences are marked in highlight in the revised manuscript.
注意：斜体文字是审稿人的评论；我们的回复在此文本中用蓝色标记。修改后的句子在修改稿中用高亮显示。

We appreciate the time and efforts spent by the editor and the reviewers. We have responded all the critiques from the reviewers (please see below). Thanks to the insightful comments from the editor and the reviewers, we now can present a considerably improved version of the manuscript.
我们感谢编辑和审稿人付出的时间和精力。我们已经回应了所有审稿人的评论（请见下文）。感谢编辑和审稿人富有见地的评论，我们现在可以提交一份显著改进的稿件版本。

Reviewers' comments:
审稿人评论：

Reviewer #1: The authors used the published single cell data from different cancers and analyzed macrophage regulation and clinical outcomes. In general, this work is descriptive and lack of in-depth mechanism analysis. Additionally, the findings in single cell data lacks validation experiments.
审稿人#1：作者使用了来自不同癌症的已发表的单细胞数据，分析了巨噬细胞的调控和临床结果。总的来说，这项工作具有描述性，缺乏深入的机制分析。此外，单细胞数据中的发现缺乏验证实验。

1. There are many single cell data from cancers, how did the authors select those three datasets? What are selection criteria?
1. 有很多来自癌症的单细胞数据，作者是如何选择这三个数据集的？选择标准是什么？

2. How did the authors integrate the data and remove the batch effects?
2. 作者如何整合数据并消除批次效应？

3. How did the authors select SPP1 for further analysis? The authors should explain the selection process in a more detail.
3. 作者是如何选择 SPP1 进行进一步分析的？作者应该更详细地解释选择过程。

4. Also, the authors mentioned that “Results showed that SPP1 signal pathway plays a major role in the interaction between TAMs (Fig 2A).” I’m not sure how did the authors draw this conclusion?
4. 此外，作者提到“结果显示 SPP1 信号通路在 TAMs（图 2A）相互作用中起主要作用。”我不确定作者是如何得出这个结论的？

5. The authors should do in-depth data analysis especially for the mechanism rather than accumulating figures.
5. 作者应该进行深入的数据分析，特别是针对机制方面，而不是积累大量图表。

6. Many findings in the manuscript lacks validation experiments. The author should use other assays to validate their findings, such as IHC.
6. 文章中的许多发现缺乏验证实验。作者应使用其他检测方法验证其发现，例如免疫组化。

7. Some conclusions are too general, for example, “These results suggested that macrophage subtypes may have either promoting or suppressive effects in specific cancer types, highlighting the complexity of the role of macrophage in cancer.”
7. 部分结论过于笼统，例如，“这些结果提示巨噬细胞亚型在特定癌症类型中可能具有促进或抑制效应，突显了巨噬细胞在癌症中作用的复杂性。”

8. The logic for this manuscript should be further improved. For example, the authors discussed Fig.1I first, then Fig.1H.
8. 这篇手稿的逻辑需要进一步完善。例如，作者先讨论了图 1I，然后又讨论了图 1H。

9. For the Fig. 1F, the authors mentioned that “The remaining subpopulations exhibited different degrees of expression for other TFs, indicating differences in transcriptional regulation among macrophage subpopulations.” However, I didn’t see clear differences, or are they significantly different?
9. 对于图 1F，作者提到“其余亚群在其他转录因子上表现出不同程度的表达，表明巨噬细胞亚群之间存在转录调控的差异。” 然而，我没有看到明显的差异，或者它们是否有显著差异？

10. Page 11 line 225, can the authors elaborate on the input and output patterns? I’m not sure what do these patterns mean?
10. 第 11 页第 225 行，作者能否详细说明输入和输出模式？我不确定这些模式是什么意思？

Response: We appreciate the positive and valuable comments from this referee. We have tried our best to improve our data, added some validation experiments and proofread the entire manuscript following the comments. We hope our work will meet your approval and a favorable consideration can be rendered. Special thanks to you again for your constructive comments.
回复：我们感谢这位审稿人的积极和宝贵的评论。我们已尽力改进我们的数据，根据评论增加了一些验证实验，并通读了整篇手稿。希望我们的工作能满足您的期望，并得到您的青睐。再次特别感谢您的建设性评论。

Response: Thanks for your valuable comments. We referred to the previous literature on pan-cancer single-cell studies and found that none of these articles had a standard for how to select single-cell data. Just having a sufficient number of single cells can reduce the error of the experiment.
回复：感谢您的宝贵评论。我们参考了关于全癌症单细胞研究的先前文献，发现这些文章中都没有关于如何选择单细胞数据的标准。仅仅拥有足够数量的单细胞就可以减少实验误差。

Phillip M. Galbo, Jr et.al 2021 Clinical Cancer Research. Molecular Features of Cancer-associated Fibroblast Subtypes and their Implication on Cancer Pathogenesis, Prognosis, and Immunotherapy Resistance.
Phillip M. Galbo, Jr 等人 2021 临床癌症研究。癌症相关成纤维细胞亚型的分子特征及其对癌症发病机制、预后和免疫治疗耐药性的影响。

Materials and Methods
材料和方法的

Datasets
数据集

“Single-cell RNA-seq datasets containing CAFs from melanoma and HNSC tumors were downloaded from the gene expression omnibus (GEO; GSE72056 and GSE103322, respectively), while dataset describing lung cancer tumors was from https://doi.org/10.1038/s41591-018-0096-5IF: 50.0 Q1 B1. We chose these datasets because they were among the first studies characterizing CAFs using
“包含黑色素瘤和头颈鳞状细胞癌肿瘤中 CAF 的单细胞 RNA 测序数据集从基因表达综合数据库（GEO；GSE72056 和 GSE103322，分别），而描述肺癌肿瘤的数据集来自 https://doi.org/10.1038/s41591-018-0096-5IF: 50.0 Q1。我们选择这些数据集，因为它们是首批使用 GEO 表征 CAF 的研究之一。scRNA-seq.”
“包含黑色素瘤和头颈鳞状细胞癌肿瘤中 CAF 的单细胞 RNA 测序数据集从基因表达综合数据库（GEO；GSE72056 和 GSE103322，分别），而描述肺癌肿瘤的数据集来自 https://doi.org/10.1038/s41591-018-0096-5。我们选择这些数据集是因为它们是首批使用单细胞 RNA 测序技术表征 CAF 的研究之一。”

Zhengquan Wu et.al 2023 Journal of Biomedical Science. A transcriptomic pan-cancer signature for survival prognostication and prediction of immunotherapy response based on endothelial senescence.
Zhengquan Wu 等 2023《生物医学科学杂志》。基于内皮衰老的转录组全癌症签名用于生存预后和免疫治疗反应预测。

Methods
方法

Pan‑cancer scRNAseq datasets and processing
全癌症 scRNAseq 数据集和处理

“To develop a TEC-specific senescence-related transcriptomic signature (EC.SENESCENCE.SIG), we collected 18 scRNAseq datasets containing tumor, stromal, and immune cell data. These 18 scRNAseq datasets included 15 types of cancer, including ovarian cancer (OV), pancreatic cancer (PAAD), prostate cancer (PRAD), melanoma (SKCM), stomach cancer (STAD), ocular melanomas (UVM), basal-cell carcinoma (BCC), bladder cancer (BLCA), breast cancer (BRCA), colorectal cancer (CRC), head and neck cancer (HNSC), kidney clear cell carcinoma (KIRC), lower grade glioma (LGG), liver cancer (LIHC), and lung adenocarcinoma (LUAD). Raw data were downloaded from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), The European Genome-phenome Archive (EGA, https://ega-archive.org/), and Array Express (https://www.ebi.ac.uk/arrayexpress/).”
“为开发一种针对 TEC 的与衰老相关的转录组特征（EC.SENESCENCE.SIG），我们收集了 18 个包含肿瘤、基质和免疫细胞数据的单细胞 RNA 测序（scRNAseq）数据集。这 18 个 scRNAseq 数据集涵盖了 15 种癌症类型，包括卵巢癌（OV）、胰腺癌（PAAD）、前列腺癌（PRAD）、黑色素瘤（SKCM）、胃癌（STAD）、眼黑色素瘤（UVM）、基底细胞癌（BCC）、膀胱癌（BLCA）、乳腺癌（BRCA）、结直肠癌（CRC）、头颈癌（HNSC）、肾透明细胞癌（KIRC）、低级别胶质瘤（LGG）、肝癌（LIHC）和肺腺癌（LUAD）。原始数据从基因表达综合数据库（GEO，https://www.ncbi.nlm.nih.gov/geo/）、欧洲基因组-表型档案（EGA，https://ega-archive.org/）和阵列表达（https://www.ebi.ac.uk/arrayexpress/）下载。”

The selection of specific single-cell datasets we based on the following three considerations.
我们选择特定单细胞数据集的依据基于以下三个考虑。

1. Selection of a unified sequencing platform: we preferred the Illumina NovaSeq 6000 (Homo sapiens), a unified sequencing platform, to minimize the impact of errors caused by sequencing with different instruments and equipment on the results.
1. 统一测序平台的选取：我们优先选择了 Illumina NovaSeq 6000（Homo sapiens）这一统一测序平台，以尽量减少因使用不同仪器和设备进行测序而产生的误差对结果的影响。”

2. Selection of recent years' data: This platform provides a wide range of resources in GEO databases, and we purposely chose recent years' data to reduce the inter-batch variation between different tumor datasets. We have intentionally selected data from recent years to minimize batch-to-batch variation between different tumor data sets and to ensure data consistency and comparability. Single-cell sequencing technologies and platforms have undergone significant development and improvement over the past few years, so the selection of recent data allows for the use of more technologically stable and advanced data, and reduces the impact of possible technological differences on the results.
2. 选择近年数据：该平台在 GEO 数据库中提供了丰富的资源，我们特意选择了近年数据以减少不同肿瘤数据集间的批次差异。我们有意选择近年数据以最小化不同肿瘤数据集间的批次间差异，并确保数据的一致性和可比性。单细胞测序技术和平台在过去几年中经历了显著的发展和改进，因此选择近年数据允许使用更稳定和先进的技术数据，并减少可能的技术差异对结果的影响。

3. Single-cell sequencing data for different types of cancers were selected: the aim was to compare and analyze the differences and commonalities between these tumors. Different types of cancers may differ in their pathophysiology and immune environments, and by understanding the commonalities and differences in macrophages across cancer types, it helps to develop an understanding of pan-cancer traits, which are shared biological characteristics across multiple cancer types.
3. 选择了不同类型癌症的单细胞测序数据：目的是比较和分析这些肿瘤之间的差异和共性。不同类型的癌症可能在病理生理学和免疫环境中存在差异，通过了解不同癌症类型巨噬细胞的共性和差异，有助于理解跨癌症的共性特征，即多种癌症类型共有的生物学特征。

This comprehensive comparison is expected to reveal unique features of macrophages in specific cancer types that could be potential targets or markers for future therapies. Our selection was made to ensure the reliability and representativeness of the selected data and to enhance the credibility and accuracy of our study.
这项全面比较预计将揭示特定癌症类型中巨噬细胞的独特特征，这些特征可能是未来治疗的潜在靶点或标志物。我们的选择旨在确保所选数据的可靠性和代表性，并提高我们研究的可信度和准确性。

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修改稿第 35 页，第 760-776 行：

“The selection of specific single-cell datasets we based on the following three considerations. Firstly, we prioritized Illumina NovaSeq 6000 (Homo sapiens), a unified sequencing platform, to minimize the impact on the results due to the errors associated with sequencing with different instrumentation. Secondly, this platform in the GEO database provides a wide range of resources, and we purposely selected data from recent years to reduce the batch-to-batch variation between different tumor datasets and to ensure the consistency and comparability of the data. Single-cell sequencing technologies and platforms have undergone significant development and improvement over the past few years, so the selection of recent data allows for the use of more technologically stable and advanced data, and reduces the impact of possible technological differences on the results. Finally, single-cell sequencing data for different types of cancers were selected with the goal of comparing and analyzing the differences and commonalities between these tumors. Different types of cancers may differ in their pathophysiology and immune environments, and by understanding the commonalities and differences in macrophages across cancer types, it helps to develop an understanding of pan-cancer traits, which are shared biological characteristics across multiple cancer types.”
我们选择特定的单细胞数据集基于以下三个考虑。首先，我们优先选择了 Illumina NovaSeq 6000（Homo sapiens）这一统一测序平台，以减少因使用不同仪器进行测序而可能对结果产生的影响。其次，该平台在 GEO 数据库中提供了丰富的资源，我们特意选择了近年来的数据，以减少不同肿瘤数据集之间的批次间差异，并确保数据的连贯性和可比性。近年来，单细胞测序技术和平台经历了显著的发展和改进，因此选择近年数据可以采用更稳定和先进的技术数据，并减少可能的技术差异对结果的影响。最后，我们选择了不同类型癌症的单细胞测序数据，旨在比较和分析这些肿瘤之间的差异和共性。不同类型的癌症可能在它们的病理生理学和免疫环境中有所不同，通过了解不同癌症类型中巨噬细胞的共性和差异，有助于理解跨癌症特征，即多种癌症类型共有的生物学特征。”

2. How did the authors integrate the data and remove the batch effects?
2. 作者如何整合数据并消除批次效应？

Response: Thanks for your valuable comments. To mitigate batch effects, we firstly employed a 'merge' technique to integrate all the datasets, aiming to combine the information comprehensively. Subsequently, we utilized the canonical correlation analysis (CCA) method (detailed in “Butler A, et.al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411-20.doi:10.1038/nbt.4096IF: 41.7 Q1 B1.” and “Stuart T, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177(7):1888-902 e21.doi:10.1016/j.cell .2019.05.031”), which is a method included in the Seruat software package that eliminates technical bias by constructing pairs of anchoring units. By adopting this approach, we have effectively minimized batch effects and ensured that our analyses are more focused on biological signals than technical artifacts. Using the CCA method allows us to harmonize our datasets to more accurately and reliably assess potential biological differences.
回复：感谢您的宝贵评论。为减少批次效应，我们首先采用“合并”技术整合所有数据集，旨在全面整合信息。随后，我们使用了典型相关分析（CCA）方法（详细见“Butler A, et.al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411-20.doi:10.1038/nbt.4096.”和“Stuart T, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177(7):1888-902 e21.doi:10.1016/j.cell.2019.05.031”），该方法包含在 Seruat 软件包中，通过构建锚定单元对来消除技术偏差。通过采用这种方法，我们有效地最小化了批次效应，并确保我们的分析更侧重于生物信号而非技术伪影。使用 CCA 方法使我们能够协调数据集，以更准确、更可靠地评估潜在的生物学差异。

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“We integrated all datasets using a "merge" technique designed to fully integrate the information. We then reduced batch effects using the canonical correlation analysis (CCA) method [69, 70]. The "CCA" method is a method that comes with the Seruat package and eliminates technical bias by constituting paired anchor cells.”
我们使用了一种名为"合并"的技术来整合所有数据集，旨在全面整合信息。然后我们使用典型相关分析（CCA）方法[69, 70]来减少批次效应。"CCA"方法是一种随 Seruat 包附带的方法，通过构建配对锚细胞来消除技术偏差。

Reference:
参考文献：

69. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, 3rd, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177(7):1888-902 e21.doi:10.1016/j.cell.2019.05.031IF: 42.5 Q1 B1.

70. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411-20.doi:10.1038/nbt.4096IF: 41.7 Q1 B1.
Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. 跨不同条件、技术和物种整合单细胞转录组数据。Nat Biotechnol. 2018;36(5):411-20.doi:10.1038/nbt.4096IF: 41.7 Q1 .

Response: Thanks for your valuable comments. We have incorporated an updated S2 table (revised S2 table in revised manuscript). This table outlined the intensity variations across different pathways observed in various macrophage subpopulations. This comprehensive dataset was the basis for our subsequent study of the SPP1 signaling pathway within macrophages, in which we found that the SPP1 signaling pathway is the maximum proportion of the weight signaling pathway for cellular communication in macrophages. Figure 2A (revised Figure 2A in the revised manuscript) highlights the collated information derived from the intensity ratio pie charts for the different pathways, which were compiled from the S2 Table dataset (revised S2 Table in the revised manuscript). Upon collating and scrutinizing these charts, a distinct pattern emerged indicating the relative strength of intermacrophage communication across multiple signaling pathways. Our observations suggest that among these pathways, the SPP1 pathway exhibits the highest strength, as evidenced by the data summarized in Figure 2A (revised Figure 2A in the revised manuscript). This leads us to conclude that the SPP1 signaling pathway plays a key role in mediating interactions between tumor-associated macrophages (TAMs). Our focus on SPP1 macrophages stemmed from the previous studies, which underscored the significance of SPP1+ macrophages (detailed in “Han H, et al. Macrophage-derived Osteopontin (SPP1) Protects From Nonalcoholic Steatohepatitis. Gastroenterology. 2023;165(1):201-17.doi:10.1053/j.gastro.2023.03.228IF: 25.1 Q1 B1.” and “Bill R, et al. CXCL9:SPP1 macrophage polarity identifies a network of cellular programs that control human cancers. Science. 2023;381(6657):515-24.doi:10.1126/science.ade2292IF: 45.8 Q1 B1.”). We referenced and integrated these findings into our manuscript to emphasize the crucial role played by SPP1 macrophages in the context of our research area. By leveraging the insights gleaned from our updated S2 table (revised S2 table in revised manuscript) and substantiating it with existing literature on SPP1+ macrophages, we strategically opted to delve deeper into the analysis of SPP1 macrophages. This selection was made with careful consideration of both our dataset's findings and the established importance of SPP1 macrophages in the existing body of research.
回复：感谢您的宝贵评论。我们已纳入更新的 S2 表（修订稿中的修订 S2 表）。该表概述了在不同巨噬细胞亚群中观察到的不同通路强度变化。这一全面的数据集是我们后续研究巨噬细胞中 SPP1 信号通路的基础，其中我们发现 SPP1 信号通路是巨噬细胞中细胞通讯的最大权重信号通路。图 2A（修订稿中的修订图 2A）突出了从不同通路强度比饼图中汇总的信息，这些饼图数据来自 S2 表数据集（修订稿中的修订 S2 表）。在汇总和审阅这些图表后，出现了一种明显的模式，表明了跨多种信号通路的巨噬细胞间通讯的相对强度。我们的观察表明，在这些通路中，SPP1 通路表现出最强的强度，如图 2A（修订稿中的修订图 2A）中汇总的数据所示。这使我们得出结论，SPP1 信号通路在介导肿瘤相关巨噬细胞（TAMs）之间的相互作用中起着关键作用。我们对 SPP1 巨噬细胞的关注源于先前的研究，这些研究强调了 SPP1+巨噬细胞的重要性（详细内容见“Han H, et al. Macrophage-derived Osteopontin (SPP1) Protects From Nonalcoholic Steatohepatitis. Gastroenterology. 2023;165(1):201-17.doi:10.1053/j.gastro.2023.03.228.”和“Bill R, et al. CXCL9:SPP1 macrophage polarity identifies a network of cellular programs that control human cancers. Science. 2023;381(6657):515-24.doi:10.1126/science.ade2292.”）。我们将这些研究结果参考并整合到我们的文稿中，以强调 SPP1 巨噬细胞在我们研究领域的核心作用。通过利用我们更新的 S2 表（修订稿中的修订 S2 表）中获得的认识，并结合现有关于 SPP1+巨噬细胞的文献，我们战略性地选择深入分析 SPP1 巨噬细胞。这一选择是基于对数据集发现和现有研究中 SPP1 巨噬细胞重要性进行审慎考虑而做出的。

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“We calculated the weight of each signaling pathway to the overall cellular interactions. Results showed that SPP1 signal pathway showed the maximum proportion of weight in all signaling pathways involved in TAM cluster interactions (Fig 2A and S2 Table).”
“我们计算了每条信号通路对整体细胞相互作用的总权重。结果显示，在参与 TAM 簇相互作用的信号通路中，SPP1 信号通路占所有信号通路中最大的权重比例（图 2A 和 S2 表）。”

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“Since SPP1+ macrophages were shown to be important for the TME in previous studies [30], we further analyzed the SPP1 pathway in macrophages and analyzed SPP1+ macrophages in pan-cancer. We found that SPP1-CD44 made a major contribution to the SPP1 signaling pathway (Figs 2B-D).”
“由于先前研究表明 SPP1+巨噬细胞对肿瘤微环境（TME）至关重要[30]，我们进一步分析了巨噬细胞中的 SPP1 通路，并在全癌症中分析了 SPP1+巨噬细胞。我们发现 SPP1-CD44 对 SPP1 信号通路有主要贡献（图 2B-D）。”

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“Our study identified the SPP1 pathway as the strongest pathway for TAMs to communicate with each other. This finding echoed previous studies which found that SPP1-positive macrophages play an important role in the TME [30, 39]. Based on these findings, we chose the SPP1 pathway as a focal point for interactions within macrophages. This choice aimed to explore in depth the role and impact of this key pathway among macrophages, providing a unique perspective to better understand macrophage interactions in the TME.”
我们的研究确定了 SPP1 通路是 TAMs 之间相互沟通的最强通路。这一发现与先前研究的结果相呼应，这些研究指出 SPP1 阳性巨噬细胞在肿瘤微环境中发挥着重要作用[30, 39]。基于这些发现，我们选择 SPP1 通路作为巨噬细胞相互作用的研究重点。这一选择旨在深入探索这一关键通路在巨噬细胞中的作用和影响，为更好地理解肿瘤微环境中的巨噬细胞相互作用提供独特的视角。

Reference:
参考文献：

30. Bill R, Wirapati P, Messemaker M, Roh W, Zitti B, Duval F, et al. CXCL9:SPP1 macrophage polarity identifies a network of cellular programs that control human cancers. Science. 2023;381(6657):515-24.doi:10.1126/science.ade2292IF: 45.8 Q1 B1.
Bill R, Wirapati P, Messemaker M, Roh W, Zitti B, Duval F, et al. CXCL9:SPP1 巨噬细胞极性识别控制人类癌症的细胞程序网络。Science. 2023;381(6657):515-24.doi:10.1126/science.ade2292IF: 45.8 Q1 .
30. Bill R, Wirapati P, Messemaker M, Roh W, Zitti B, Duval F, 等. CXCL9:SPP1 巨噬细胞极性识别出控制人类癌症的细胞程序网络. 科学. 2023;381(6657):515-24.doi:10.1126/science.ade2292IF: 45.8 Q1 B1.

39. Han H, Ge X, Komakula SSB, Desert R, Das S, Song Z, et al. Macrophage-derived Osteopontin (SPP1) Protects From Nonalcoholic Steatohepatitis. Gastroenterology. 2023;165(1):201-17.doi:10.1053/j.gastro.2023.03.228IF: 25.1 Q1 B1.
韩华，葛晓，Komakula SSB，Desert R，Das S，宋子，等。巨噬细胞来源的骨桥蛋白（SPP1）预防非酒精性脂肪性肝病。胃肠病学。2023；165(1)：201-17.doi:10.1053/j.gastro.2023.03.228IF: 25.1 Q1。
39. Han H, Ge X, Komakula SSB, Desert R, Das S, Song Z, 等. 巨噬细胞来源的骨桥蛋白（SPP1）预防非酒精性脂肪性肝病. 胃肠病学. 2023;165(1):201-17.doi:10.1053/j.gastro.2023.03.228IF: 25.1 Q1 B1.

Revised Figure 2A in revised manuscript:
修改后的稿件图 2A：

Response: Thanks for your valuable comments. In our manuscript, we presented an amplified analysis of various pathways among different macrophage subpopulations, aiming to depict the differential intensities across these pathways. Figure 2A (revised Figure 2A in revised manuscript) highlights the collated information derived from the intensity ratio pie charts of different pathways, which were compiled from the S2 Table dataset (revised S2 table in revised manuscript). Upon collating and scrutinizing these charts, a distinct pattern emerged, indicating the relative intensities of intermacrophage communication across multiple signaling pathways. Our observation revealed that among these pathways, the SPP1 pathway exhibited the highest intensity, as evidenced by the data summarized in Figure 2A (revised Figure 2A in revised manuscript). This led us to the conclusion that the SPP1 signal pathway plays a pivotal role in mediating the interaction between tumor-associated macrophages (TAMs). The comprehensive analysis and comparative assessment of pathway intensities across various macrophage subpopulations supported our assertion regarding the prominence of the SPP1 signaling pathway in facilitating TAM interactions. At the same time, we added wet experiments. We examined the expression of SPP1 in M0 macrophages, tumor cells (breast, lung, and liver cancers), and in vitro TAM by ELISA, and found relatively high levels of SPP1 in TAM. Treatment of MDA-MB 231 cell-induced TAM with recombinant human SPP1 (rh SPP1) protein showed that rh SPP1 protein significantly stimulated tumor-promoting factors TGFβ, IL10, and vascular endothelial growth factor, whereas the anti-SPP1 antibody inhibited tumor-promoting factors TGFβ, IL10, and vascular endothelial growth factor.
回复：感谢您的宝贵评论。在我们的文稿中，我们呈现了不同巨噬细胞亚群之间各种通路的大幅分析，旨在描绘这些通路中的差异强度。图 2A（修订稿中的修订图 2A）突出了从不同通路强度比饼图中汇总的信息，这些饼图是根据 S2 表数据集（修订稿中的修订 S2 表）编制的。在汇总和审阅这些图表后，出现了一种明显的模式，表明了跨多种信号通路的中巨噬细胞间通讯的相对强度。我们的观察发现，在这些通路中，SPP1 通路表现出最高的强度，如图 2A（修订稿中的修订图 2A）中汇总的数据所示。这使我们得出结论，SPP1 信号通路在介导肿瘤相关巨噬细胞（TAMs）之间的相互作用中起着关键作用。对各种巨噬细胞亚群的通路强度进行综合分析和比较评估，支持了我们的观点，即 SPP1 信号通路在促进 TAM 相互作用方面具有重要作用。同时，我们增加了湿实验。通过 ELISA 检测了 M0 巨噬细胞、肿瘤细胞（乳腺癌、肺癌和肝癌）以及体外 TAM 中 SPP1 的表达，发现 TAM 中 SPP1 水平相对较高。用重组人 SPP1 蛋白（rh SPP1）处理 MDA-MB 231 细胞诱导的 TAM，结果显示 rh SPP1 蛋白显著刺激了促进肿瘤的因子 TGFβ、IL10 和血管内皮生长因子，而抗 SPP1 抗体抑制了促进肿瘤的因子 TGFβ、IL10 和血管内皮生长因子。

To investigate the role of SPP1 in macrophages more deeply, we analyzed the cellular communication between macrophages and epithelial cells and found that SPP1 occupies an important part of both macrophages and epithelial cells, and we also analyzed the pathways associated with SPP1 receptors (CD44, ITGB1, ITGB6) in epithelial cells to explore possible tumor progression. We also verified this by wet experiments, measuring the expression of SPP1 in macrophages and tumor cells, and found that in macrophages, the expression of SPP1 was significantly increased compared to tumor cells; meanwhile, we treated breast cancer MDA-MB 231 cells with conditioned medium with SPP1 overexpressing TAM, and detected the tumor stem cell markers by RT-qPCR (SOX2, CMYC, NANOG and OCT4 genes) and PDL1 gene.
为了更深入地研究 SPP1 在巨噬细胞中的作用，我们分析了巨噬细胞和上皮细胞之间的细胞通讯，发现 SPP1 在巨噬细胞和上皮细胞中都占据重要部分，我们还分析了上皮细胞中与 SPP1 受体（CD44、ITGB1、ITGB6）相关的通路，以探索可能的肿瘤进展。我们通过湿实验验证了这一点，测量了巨噬细胞和肿瘤细胞中 SPP1 的表达，发现与肿瘤细胞相比，巨噬细胞中 SPP1 的表达显著增加；同时，我们用过表达 SPP1 的 TAM 条件培养基处理乳腺癌 MDA-MB 231 细胞，并通过 RT-qPCR（SOX2、CMYC、NANOG 和 OCT4 基因）和 PDL1 基因检测肿瘤干细胞标记物。

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“Moreover, we detected SPP1 expression in M0 macrophages, tumor cells (breast, lung and liver cancer) and TAM by ELISA in vitro. TAMs exhibited high levels of SPP1 relative to M0 macrophages and tumor cells (Fig 2F). To validate SPP1 mediated interaction between TAMs, TAMs induced by MDA-MB 231 cells were treated with recombinant human SPP1 (rh SPP1) protein. Tumor-promoting and anti-inflammatory factors TGF β, IL10 and VEGF were significantly stimulated by rh SPP1 protein but inhibited by anti-SPP1 antibody (Fig 2G). These results suggested that SPP1 mediated interaction between macrophage clusters.”
“此外，我们通过 ELISA 在体外检测了 M0 巨噬细胞、肿瘤细胞（乳腺癌、肺癌和肝癌）及 TAM 中 SPP1 的表达。与 M0 巨噬细胞和肿瘤细胞相比，TAMs 表现出高水平的 SPP1（图 2F）。为验证 SPP1 介导的 TAM 相互作用，由 MDA-MB 231 细胞诱导的 TAMs 用重组人 SPP1（rh SPP1）蛋白处理。rh SPP1 蛋白显著刺激了促肿瘤和抗炎因子 TGF β、IL10 和 VEGF，但被抗 SPP1 抗体抑制（图 2G）。这些结果表明 SPP1 介导了巨噬细胞簇之间的相互作用。”

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“To further demonstrate that TAM communicated with tumor cells via SPP1, SPP1 was overexpressed in TAM cells (Fig 4G). Breast cancer MDA-MB 231 cells were treated with conditioned medium from SPP1-overexpressed TAM, and tumor stem cell markers (SOX2, CMYC, NANOG, OCT4 gene) and PDL1 genes were detected by RT-qPCR. Results showed that SPP1-overexpressed TAM obviously induced the five genes expression, whereas blocking SPP1 using antibody significantly decreased the gene expression induced by TAM (Fig 4H). In addition, we also showed that SPP1 antibody inhibited TAM-activated AKT and STAT3 pathways and TAM-induced N-cadherin and β-catenin expression in MDA-MB 231 cells (Fig 4I). These results suggested that SPP1 mediated communication between TAM and tumor cells.”
“为了进一步证明 TAM 通过 SPP1 与肿瘤细胞进行通讯，我们在 TAM 细胞中过表达 SPP1（图 4G）。乳腺癌 MDA-MB 231 细胞被用 SPP1 过表达的 TAM 的培养基处理，并通过 RT-qPCR 检测肿瘤干细胞标记物（SOX2、CMYC、NANOG、OCT4 基因）和 PDL1 基因的表达。结果显示，SPP1 过表达的 TAM 明显诱导了这五种基因的表达，而使用抗体阻断 SPP1 则显著降低了 TAM 诱导的基因表达（图 4H）。此外，我们还证明 SPP1 抗体抑制了 TAM 激活的 AKT 和 STAT3 通路，以及 TAM 在 MDA-MB 231 细胞中诱导的 N-钙粘蛋白和β-连环蛋白的表达（图 4I）。这些结果表明 SPP1 介导了 TAM 与肿瘤细胞之间的通讯。”

Revised Figure 2A in revised manuscript:
修改后的稿件图 2A：

Added Figure 2F-G in revised manuscript:
修订稿中增加了图 2F-G：

Added Figure 4G-H in revised manuscript:
在修订稿中增加了图 4G-H：

Response: Thanks for your valuable comments. We analyzed the cellchat between macrophages and epithelial cells in order to study the role of SPP1 in macrophages more deeply and found that SPP1 occupies an important role in both macrophages and epithelial cells, and we also analyzed the pathways related to SPP1 receptors (CD44, ITGB1, ITGB6) in epithelial cells to explore the possible tumor progression and SPP1 to affect the survival prognosis of tumors. We also validated this by wet experiments, measuring the expression of SPP1 in macrophages and tumor cells and found that in macrophages, there was a significant increase in the expression of SPP1 compared to tumor cells; Also, we treated breast cancer MDA-MB 231 cells with conditioned medium in which SPP1 overexpressed TAM, and detected tumor stem cell markers (SOX2, CMYC, NANOG, and OCT4 genes) and PDL1 gene by RT-qPCR.
回复：感谢您的宝贵评论。我们分析了巨噬细胞和上皮细胞之间的细胞通讯，以更深入地研究 SPP1 在巨噬细胞中的作用，发现 SPP1 在巨噬细胞和上皮细胞中都占据重要角色，我们还分析了上皮细胞中与 SPP1 受体（CD44、ITGB1、ITGB6）相关的通路，以探索肿瘤可能的进展以及 SPP1 对肿瘤生存预后的影响。我们通过湿实验验证了这一点，测量了巨噬细胞和肿瘤细胞中 SPP1 的表达，发现与肿瘤细胞相比，巨噬细胞中 SPP1 的表达显著增加；此外，我们用过表达 SPP1 的肿瘤相关巨噬细胞（TAM）的条件培养基处理乳腺癌 MDA-MB 231 细胞，并通过 RT-qPCR 检测肿瘤干细胞标记物（SOX2、CMYC、NANOG 和 OCT4 基因）以及 PDL1 基因。

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“In order to investigate the possible alteration of tumor status caused by TAM in the TME, we used the "CellChat" package to score the regulatory relationships between different cell types by known receptor-ligand pairs. Among the ligand-receptor pairs related to epithelial cells, TAM was the primary cell type for epithelial cell communication, and SPP1 was the most important ligand for their communication (Figs 4A-B, S3 Table). SPP1 binded strongly to CD44 and the integrin family (encoded by ITGAV/ITGB1, ITGAV/ITGB5, ITGAV/ITGB6, and ITGA5/ITGB1) and the predominance of SPP1-CD44 (Figs 4C-E). We also analyzed the effect of SPP1 on prognosis in 33 cancers and found that high expression of SPP1 was positively associated with poor prognosis in most cancers (S6 Fig), suggesting that cell communication of TAM with tumor epithelial cells via SPP1 may lead to tumor progression. Finally, we performed KEGG enrichment analysis of SPP1 signaling pathway receptors (CD44, ITGB1, ITGB6) that were expressed in epithelial cells in 33 cancers, and extracted the intersections of the enriched pathways (S7-S8 Figs). In all enriched pathways, 17 cancer-related pathways were identified (Fig 4F). Therefore, this raised a possibility that TAM-secreted SPP1 activated these pathways such as the JAK-STAT and PI3K-Akt signaling pathways, reduced focal adhesion protein and induced PDL1 expression in epithelial cells to contribute to tumor prognosis.
为了研究 TAM 可能引起的 TME 中肿瘤状态的改变，我们使用"CellChat"软件包通过已知的受体-配体对来评分不同细胞类型之间的调控关系。在与上皮细胞相关的配体-受体对中，TAM 是上皮细胞通讯的主要细胞类型，而 SPP1 是它们通讯的最重要配体（图 4A-B，S3 表）。SPP1 与 CD44 和整合素家族（由 ITGAV/ITGB1、ITGAV/ITGB5、ITGAV/ITGB6 和 ITGA5/ITGB1 编码）结合强烈，并且 SPP1-CD44 占主导地位（图 4C-E）。我们还分析了 SPP1 对 33 种癌症预后的影响，发现 SPP1 的高表达与大多数癌症的预后不良呈正相关（图 S6），表明 TAM 通过 SPP1 与肿瘤上皮细胞之间的通讯可能导致肿瘤进展。最后，我们对 33 种癌症中上皮细胞表达的 SPP1 信号通路受体（CD44、ITGB1、ITGB6）进行了 KEGG 富集分析，并提取了富集通路之间的交集（图 S7-S8）。在所有富集通路中，鉴定出 17 个与癌症相关的通路（图 4F）。因此，这提出了一个可能性，即 TAM 分泌的 SPP1 激活了这些通路，如 JAK-STAT 和 PI3K-Akt 信号通路，减少了焦点粘附蛋白，并诱导上皮细胞中 PDL1 的表达，从而影响肿瘤预后。

To further demonstrate that TAM communicated with tumor cells via SPP1, SPP1 was overexpressed in TAM cells (Fig 4G). Breast cancer MDA-MB 231 cells were treated with conditioned medium from SPP1-overexpressed TAM, and tumor stem cell markers (SOX2, CMYC, NANOG, OCT4 gene) and PDL1 genes were detected by RT-qPCR. Results showed that SPP1-overexpressed TAM obviously induced the five genes expression, whereas blocking SPP1 using antibody significantly decreased the gene expression induced by TAM (Fig 4H). In addition, we also showed that SPP1 antibody inhibited TAM-activated AKT and STAT3 pathways and TAM-induced N-cadherin and β-catenin expression in MDA-MB 231 cells (Fig 4I). These results suggested that SPP1 mediated communication between TAM and tumor cells.”
为了进一步证明 TAM 通过 SPP1 与肿瘤细胞进行通讯，在 TAM 细胞中过表达 SPP1（图 4G）。用过表达 SPP1 的 TAM 细胞培养液处理乳腺癌 MDA-MB 231 细胞，并通过 RT-qPCR 检测肿瘤干细胞标记物（SOX2、CMYC、NANOG、OCT4 基因）和 PDL1 基因。结果显示，过表达 SPP1 的 TAM 明显诱导了这五种基因的表达，而使用抗体阻断 SPP1 则显著降低了 TAM 诱导的基因表达（图 4H）。此外，我们还表明 SPP1 抗体抑制了 TAM 激活的 AKT 和 STAT3 通路，以及 TAM 诱导的 MDA-MB 231 细胞中 N-钙粘蛋白和β-连环蛋白的表达（图 4I）。这些结果表明 SPP1 介导了 TAM 与肿瘤细胞之间的通讯。

Added Figure 4 in revised manuscript:
在修订稿中添加了图 4：

Added supplementary Figure 6 in revised manuscript:
在修订稿中增加了补充图 6：

Added supplementary Figure 7 in revised manuscript:
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Added supplementary Figure 8 in revised manuscript:
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Response: Thanks for your valuable comments. We added wet experiments for detection. We examined SPP1 expression in M0 macrophages, tumor cells (breast, lung, and hepatocellular carcinoma), and TAM in vitro by ELISA and found that TAM exhibited relatively high levels of SPP1. TAM induced by MDA-MB 231 cells was treated with recombinant human SPP1 (rh SPP1) protein. rh SPP1 protein significantly stimulated the tumor-promoting factors TGFβ, IL10, and VEGF, and anti-SPP1 antibody inhibited the tumor-promoting factors TGFβ, IL10, and VEGF. Meanwhile, in order to investigate the role of SPP1 in macrophages more deeply, we analyzed the cellular communication between macrophages and epithelial cells and found that SPP1 occupies an important part of both macrophages and epithelial cells, and we also analyzed the pathways associated with SPP1 receptors (CD44, ITGB1, ITGB6) in epithelial cells to explore possible tumor progression. We also verified this by wet experiments, measuring the expression of SPP1 in macrophages and tumor cells, and found that in macrophages, the expression of SPP1 was significantly increased compared to tumor cells; meanwhile, we treated breast cancer MDA-MB 231 cells with conditioned medium with SPP1 overexpressing TAM, and detected the tumor stem cell markers by RT-qPCR (SOX2, CMYC, NANOG and OCT4 genes) and PDL1 gene.
回复：感谢您的宝贵意见。我们增加了湿实验进行检测。我们通过 ELISA 检测了体外 M0 巨噬细胞、肿瘤细胞（乳腺癌、肺癌和肝细胞癌）以及肿瘤相关巨噬细胞（TAM）中的 SPP1 表达，发现 TAM 表现出相对较高的 SPP1 水平。由 MDA-MB 231 细胞诱导的 TAM 用重组人 SPP1（rh SPP1）蛋白处理。rh SPP1 蛋白显著刺激了促进肿瘤的因子 TGFβ、IL10 和 VEGF，而抗 SPP1 抗体抑制了促进肿瘤的因子 TGFβ、IL10 和 VEGF。同时，为了更深入地研究 SPP1 在巨噬细胞中的作用，我们分析了巨噬细胞和上皮细胞之间的细胞通讯，发现 SPP1 在巨噬细胞和上皮细胞中都占据重要部分，我们还分析了上皮细胞中与 SPP1 受体（CD44、ITGB1、ITGB6）相关的通路，以探索可能的肿瘤进展。我们也通过湿实验验证了这一点，测量了巨噬细胞和肿瘤细胞中 SPP1 的表达，发现与肿瘤细胞相比，巨噬细胞中 SPP1 的表达显著增加；同时，我们用过表达 SPP1 的 TAM 条件培养基处理乳腺癌 MDA-MB 231 细胞，并通过 RT-qPCR（SOX2、CMYC、NANOG 和 OCT4 基因）及 PDL1 基因检测肿瘤干细胞标记物。

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Response: Thanks for your valuable comments. We have revised manuscriptfor a more detailed and precise portrayal of our findings regarding the role of macrophage subtypes in cancer. The updated content now offers a more nuanced summary of our results.
回复：感谢您的宝贵评论。我们已修改稿件，以更详细和精确地描述我们关于巨噬细胞亚型在癌症中作用的研究成果。更新后的内容现在提供了对我们结果的更细致的总结。

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“These findings delineated the TAM landscape in various cancer types, revealing the dichotomous impacts of macrophage subtypes, which could either promoted or suppressed specific cancer types. This complexity underscored the multifaceted role of macrophages within the context of cancer. Consequently, there was a pressing need for targeted investigations tailored to individual cancer types to elucidate the precise role of macrophages. Such focused studies holded the potential to guide subsequent prognostic inquiries aimed at understanding the prognostic implications of macrophages in cancer.”
“这些发现描绘了不同癌症类型的 TAM（肿瘤相关巨噬细胞）景观，揭示了巨噬细胞亚型的双重影响，它们可能促进或抑制特定癌症类型。这种复杂性突显了巨噬细胞在癌症背景下的多面性作用。因此，迫切需要针对不同癌症类型进行有针对性的研究，以阐明巨噬细胞的精确作用。此类专注研究有可能指导后续的预后研究，旨在理解巨噬细胞在癌症中的预后意义。”

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“The results of our analyses suggested that these macrophage subtypes exhibited significant differences in their impact on tumor progression, reflecting the differences between macrophage subtypes as well as the heterogeneity of macrophages among different tumors.”
我们的分析结果提示这些巨噬细胞亚型在肿瘤进展中的影响存在显著差异，这反映了巨噬细胞亚型之间的差异以及不同肿瘤中巨噬细胞的异质性。

Response: Thanks for your valuable comments. In response to your suggestions, we have reordered the revised additions as well as the original images accordingly. At the same time, we found that Fig 4 also had an ordering problem, so we changed it as well, and we have changed the legend accordingly.
回复：感谢您的宝贵意见。针对您的建议，我们已相应地重新排序了修订补充内容以及原始图片。同时，我们发现图 4 也存在排序问题，因此我们也进行了修改，并相应地调整了图例。

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“(F) Representation of different cancers in subgroups. (G) Heatmap of gene expression under specific functions. (H) Heatmap of genes encoding cell surface proteins. (I) Heatmap depicting the top 20 transcription factors that differ more significantly between different subpopulations.”
“(F) 不同癌症在亚组中的表现。 (G) 特定功能下的基因表达热图。 (H) 编码细胞表面蛋白的基因热图。 (I) 展示在不同亚群之间差异更显著的 20 个转录因子的热图。”

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Page 57, Line 1215-1216 in revised manuscript:
修订稿第 57 页，第 1215-1216 行：

“(A-W) Kaplan-Meier (KM) curves depicting differences in survival of highly and poorly expressed signature genes in multiple subpopulations in different tumors”
“(A-W) 描述不同肿瘤中多个亚群中高表达和低表达特征基因生存差异的 Kaplan-Meier (KM) 曲线”

Response: Thanks for your valuable comments. In the process of utilizing DoRothEA for transcription factor (TF) analysis, our approach involved selecting the top 20 TFs exhibiting the greatest covariance among different subpopulations. We calculated the covariance based on the average expression values of these TFs within various subpopulations, aiming to identify the TFs with the most substantial variability in these groups.
回复：感谢您的宝贵评论。在使用 DoRothEA 进行转录因子（TF）分析的过程中，我们的方法涉及选择在不同亚群中表现出最大共变性的前 20 个 TF。我们基于这些 TF 在不同亚群中的平均表达值计算共变性，旨在识别这些组中变异最大的 TF。

Indeed, there is a flaw in our initial presentation and visualization of these findings, which might have obscured the apparent differences observed. We have rectified this mistake in the revised manuscript, ensuring a more accurate and comprehensive representation of the TF analysis results.
确实，我们在最初展示和可视化这些结果时存在缺陷，这可能掩盖了观察到的明显差异。我们在修订稿中纠正了这一错误，确保了 TF 分析结果的更准确和全面的呈现。

Page 13 line 272-274 in revised manuscript:
修订稿第 13 页第 272-274 行：

“We found the low expression of these transcription factors in Cluster3. Other transcription factors in Cluster3 also exhibited similar low expression (Fig 1I).”
“我们发现这些转录因子在 Cluster3 中的表达较低。Cluster3 中的其他转录因子也表现出类似的低表达（图 1I）。”

Response: Thank you very much for pointing out this mistake. We check this manuscript and found that according to the previously published article (https://www.nature.com/articles/s41467-021-21246-9), this should read incoming patterns and outgoing patterns. We have revised it in revised manuscript.
回复：非常感谢您指出这个错误。我们检查了这份稿件，发现根据先前发表的文章（https://www.nature.com/articles/s41467-021-21246-9），这里应该读作输入模式和输出模式。我们在修改稿中已经进行了修改。

For example:
例如：

Page 13-14 line 284-286 in revised manuscript:
修改稿第 13-14 页第 284-286 行：

“We observed the number and strength of communication between different subpopulations, which we classified into three outgoing patterns and two incoming patterns (S4C and S4D Figs).”
“我们观察了不同亚群之间交流的数量和强度，我们将这些交流分为三种传出模式和两种传入模式（S4C 和 S4D 图）。”

Reviewer #2: Overall, I believe the manuscript does a good job of highlighting the importance of understanding the role macrophages play and their diversity in the tumor microenvironment. The analysis of public datasets with the markers derived from the single cell analysis was particularly compelling. However, there are some changes that need to be made to the manuscript interpretations and analysis before I would recommend acceptance. In addition, there are numerous typographical or grammar errors that need to be addressed.
审稿人#2：总的来说，我认为稿件很好地突出了理解巨噬细胞在肿瘤微环境中所起的作用及其多样性的重要性。使用单细胞分析得到的标记对公共数据集的分析尤其引人入胜。然而，在推荐接受稿件之前，需要对稿件的解释和分析进行一些修改。此外，还有许多需要解决的拼写或语法错误。

Major concerns:
主要问题：

1. For the analysis of the single cell data:
1. 对于单细胞数据分析：

Line “These macrophage subtypes were all present in the three cancer types with different abundance”
行“这些巨噬细胞亚型在三种癌症中都存在，但丰度不同”

A. This statement could be misleading as the UMAP is generally produced with a subset of highly variable genes and a principal component analysis. This makes it possible that discrete cell types to be bundled into one group as they are grouped together based on the PCA in this specific manifold. In addition, the granularity (settings) you use for the creation can effect this. I do think that for within your clusters those cells in the same cluster share expression markers within each group versus the other groups. It is distinctly possible however that each cluster contains multiple cell types. Indeed, each of the tissues used for the analysis contain well documented tissue resident macrophages and as presently presented one could conclude that alveolar macrophages are present in breast cancer (not likely). I do not think this completely negates the data presented, but some additional analysis should be done to clarify this. For the clusters that were deemed to express tissue resident macrophages, you should group them by sample origin (eg all LUAD in cluster 0, all BRCA macrophages in group 0, ect.) and do a diff-e test between each of these origin groups within that cluster. Do you see any genes that are statistically significant between these cells within the same groups?
A. 这句话可能具有误导性，因为 UMAP 通常是通过使用一组高度可变基因和主成分分析生成的。这使得在特定流形中，根据主成分分析将离散细胞类型分到同一组成为可能。此外，你用于创建过程中的粒度（设置）也会影响这一点。我认为，在你的聚类中，同一聚类内的细胞在每个组内共享表达标记，而与其他组不同。然而，每个聚类包含多种细胞类型是完全可能的。事实上，用于分析的每种组织中都含有文献记载明确的组织驻留巨噬细胞，根据目前呈现的内容，可以得出肺泡巨噬细胞存在于乳腺癌（可能性不大）的结论。我认为这并不完全否定所呈现的数据，但应该进行一些额外的分析来澄清这一点。对于那些被认为表达组织驻留巨噬细胞的聚类，你应该按样本来源进行分组（例如，将所有 LUAD 归入聚类 0，将所有 BRCA 巨噬细胞归入组 0 等），并在该聚类内对这些来源组之间进行 diff-e 测试。你是否看到这些细胞在相同组内存在统计学上显著的基因？

B. Much literature has been devoted to analysis tissue resident macrophages, most notably Alveolar macrophages (which are mentioned) and the very first well documented tissue macrophage of the Kupper Cells in the liver. Recent work as well as documented the present of tissue specific macrophages in the breast (https://www.sciencedirect.com/science/article/pii/S009286742200201X) More care should be taken test for the documented expression profiles and this should be verbally expanded on in the manuscript. Creating a panel of genes known to be expressed in these and checking them against the groups in the UMAP would be of interest.
B. 许多文献致力于分析组织驻留巨噬细胞，尤其是肺泡巨噬细胞（文中已提及）以及肝脏中最早被详细记录的组织巨噬细胞库珀细胞。近期研究以及文献记录了乳腺中存在组织特异性巨噬细胞（https://www.sciencedirect.com/science/article/pii/S009286742200201X）。应更加谨慎地检测已记录的表达谱，并在文稿中口头详细阐述。对已知在这些细胞中表达的基因进行基因面板检测，并与 UMAP 中的组别进行对比，将很有意义。”

C. What were the major differences if you did a direct diff-e test between groups 0 and 2? This should also be done for groups 1/3 and 6/8
C. 如果你直接对组别 0 和 2 进行 diff-e 测试，主要差异是什么？这也应适用于组别 1/3 和 6/8

D. You mentioned the mitochondrial cutoff, what was the minimum number of genes observed to be included in the study.
D. 你提到了线粒体截止值，研究中最少观察到多少个基因被纳入其中。

2. Previous literature has demonstrated the presence of SPP1+ to be predictive of response to immune therapy in NSCLC (https://www.cell.com/cancer-cell/pdf/S1535-6108(21)00560-2.pdf) as well as the expression of PDL1 to be present by certain macrophage populations (https://www.nature.com/articles/nature22396). At a minimum this literature should be cited and discussed in the section regarding immune therapy.
2. 既往文献已证明 SPP1+的表达可预测非小细胞肺癌（NSCLC）对免疫治疗的反应（https://www.cell.com/cancer-cell/pdf/S1535-6108(21)00560-2.pdf），以及某些巨噬细胞群体中存在 PDL1 的表达（https://www.nature.com/articles/nature22396）。至少在关于免疫治疗的章节中，应引用并讨论这些文献。

Minor Typographical errors:
轻微排版错误：

Line 69: Rephrase sentence, a sentence can not begin with the word “And”
第 69 行：重写句子，句子不能以“和”开头

Line 76: should begin with “The” or “A”. You can also make this plural
第 76 行：应以“该”或“一个”开头。你也可以将其改为复数形式

Line 90: suggest changing tumors to TMEs
第 90 行：建议将肿瘤改为 TMEs

Line 101: change TAM to TAMs and is to are
第 101 行：将 TAM 改为 TAMs，将 is 改为 are

Line 102: change to TME for consistency.
第 102 行：改为 TME 以保持一致性。

Line 106: Ideally this statement should be supported by a reference.
第 106 行：理想情况下，这一陈述应有参考文献支持。

Line 137: should be changed to “the TME”
第 137 行：应改为“肿瘤微环境”

Response: We appreciate the positive and valuable comments from this referee. We have tried our best to improve our data and proofread the entire manuscript following the comments. We hope our work will meet your approval and a favorable consideration can be rendered. Special thanks to you again for your constructive comments.
回复：我们感谢这位审稿人提出的积极和有价值的评论。我们已尽力改进我们的数据，并根据评论全文校对。希望我们的工作能够得到您的认可，并得到有利的考虑。再次特别感谢您提出的建设性意见。

Major concerns:
主要问题：

1. For the analysis of the single cell data:
1. 对于单细胞数据分析：

Line “These macrophage subtypes were all present in the three cancer types with different abundance”
行“这些巨噬细胞亚型在三种癌症中都存在，但丰度不同”

Response: Thank you for your constructive suggestions. In addressing the concern about potential misinterpretation regarding alveolar macrophages in breast cancer samples, we conducted thorough additional analyses and made substantial revisions to our manuscript. Upon further examination of the previously derived data, we performed an in-depth analysis and observed a significant number of cells associated with BRCA (breast cancer), LIHC (liver cancer), and LUAD (lung adenocarcinoma) within Cluster6. Consequently, we revised and enhanced the information pertaining to gene expression in this cluster, ensuring accuracy in our representation of these cells and their respective cancer types. Moreover, our exploration into Cluster10 revealed a predominant presence of cells associated with LUAD (as depicted in Fig 1G). Upon meticulous examination of the data from S1 Table, it became evident that only a minimal number of cells corresponded to breast cancer cells within this cluster. Consequently, based on this observation, we hypothesized that the macrophages identified in LUAD within Cluster10 might indeed be related to alveolar macrophages rather than breast cancer cells. We have incorporated these crucial findings and interpretations into the revised manuscript, providing a more detailed and clarified explanation to accurately delineate the distribution and association of macrophage subtypes within different cancer types. Regarding your inquiry about tissue-resident macrophages, we conducted a rigorous analysis segregating them by respective cancer types. Subsequently, we employed the FindMarkers function to perform a comprehensive differential gene expression analysis within these distinct cancer-specific groups of tissue-resident cells. This process enabled us to identify genes exhibiting statistically significant differences among tissue-resident macrophages within different cancer types (revised Figure S2A-B in revised manuscript). The differential genes identified through this analysis have undergone meticulous modifications and incorporation into the manuscript, thereby enriching the discussion on the unique genetic signatures within tissue-resident macrophages across various cancer origins. These refined findings have been incorporated into our revised manuscript to provide a more comprehensive understanding of the distinct molecular profiles characterizing tissue-resident macrophages within specific cancer contexts.
回复：感谢您的建设性建议。针对关于乳腺癌样本中肺泡巨噬细胞可能存在误读的问题，我们进行了深入补充分析，并对文稿进行了重大修订。在重新审视先前数据后，我们发现 Cluster6 中存在大量与 BRCA（乳腺癌）、LIHC（肝癌）和 LUAD（肺腺癌）相关的细胞。因此，我们修订并完善了该集群中基因表达的相关信息，确保对这些细胞及其对应癌症类型的描述准确无误。此外，我们对 Cluster10 的探索显示，该集群中主要存在与 LUAD 相关的细胞（如图 1G 所示）。在仔细审查 S1 表数据后，我们发现该集群中仅少数细胞对应乳腺癌细胞。基于这一观察结果，我们推测 Cluster10 中 LUAD 中识别的巨噬细胞确实可能与肺泡巨噬细胞相关，而非乳腺癌细胞。我们将这些关键发现和解读融入了修订稿中，提供了更详细和清晰的解释，以准确描绘不同癌症类型中巨噬细胞亚型的分布和关联。关于您关于组织驻留巨噬细胞的询问，我们进行了严格的分析，按各自癌症类型进行分离。随后，我们采用 FindMarkers 函数在这些不同的癌症特异性组织驻留细胞组内进行全面的差异基因表达分析。这个过程使我们能够识别出在不同癌症类型中组织驻留巨噬细胞之间表现出统计学显著差异的基因（修订稿中修订的图 S2A-B）。通过这项分析确定的差异基因已经经过细致的修改和融入文稿，从而丰富了关于不同癌症起源中组织驻留巨噬细胞独特遗传特征的讨论。这些精细的研究结果已被纳入我们修订的文稿中，以提供更全面的理解，关于在特定癌症背景下表征组织驻留巨噬细胞的独特分子特征。

Page 10 line 199-204 in revised manuscript:
修订稿第 10 页第 199-204 行：

“Our analysis focused on examining the gene expression profiles of two distinct subpopulations (Cluster0, Cluster2) across various cancer types. Surprisingly, even within the same category of tissue resident macrophages, we observed differential gene expression patterns among macrophages originating from distinct sources (S2A and S2B Figs). This discovery suggested notable variations in the gene expression profiles of analogous macrophages derived from different tumors.”
“我们的分析专注于检查两种不同亚群（Cluster0，Cluster2）在各种癌症类型中的基因表达谱。令人惊讶的是，即使在同一类组织驻留巨噬细胞中，我们也观察到来自不同来源（S2A 和 S2B 图）的巨噬细胞之间存在差异的基因表达模式。这一发现表明，来自不同肿瘤的类似巨噬细胞的基因表达谱存在显著差异。”

Page 12 line 245-252 in revised manuscript:
修订稿第 12 页第 245-252 行：

“Upon analyzing the data, we noticed that Cluster6 harboured a substantial count of LUAD macrophages, accompanied by a considerable presence in liver hepatocellular carcinoma (LIHC) and a lower proportion in breast cancer (BRCA) samples (Fig 1F). Additionally, our observations revealed elevated expression levels of the monocyte signature genes S100A9 and FCN1 within Cluster6 compared to other subpopulations (Fig 1D). Consequently, we suggested that Cluster6 was not just alveolar resident macrophages but might be generalized monocyte-like macrophages.”
在分析数据时，我们注意到 Cluster6 中存在大量 LUAD 巨噬细胞，同时在肝细胞癌（LIHC）中也有显著存在，但在乳腺癌（BRCA）样本中的比例较低（图 1F）。此外，我们的观察发现 Cluster6 中的单核细胞特征基因 S100A9 和 FCN1 的表达水平高于其他亚群（图 1D）。因此，我们推测 Cluster6 不仅包含肺泡驻留巨噬细胞，还可能是泛单核细胞样巨噬细胞。

Page 12 line259-262 in revised manuscript:
修订稿第 12 页第 259-262 行：

“Furthermore, the predominant subset of Cluster10 macrophages originated from LUAD samples, as depicted in Figure 1F and S1 Table. Our supposition was centered on the potential association between the macrophages derived from LUAD within Cluster10 and alveolar macrophages.”
此外，Cluster10 巨噬细胞的主要亚群来源于 LUAD 样本，如图 1F 和 S1 表所示。我们的假设集中在 Cluster10 中来自 LUAD 的巨噬细胞与肺泡巨噬细胞之间可能的关联上。

Page 24 line 518-520 in revised manuscript:
修订稿第 24 页第 518-520 行：

“Moreover, we observed distinct gene expression patterns between these two clusters across different tumors, and there were differences in function between the two clusters.”
“此外，我们观察到这两个簇在不同肿瘤中的基因表达模式存在差异，并且这两个簇之间存在功能差异。”

Added supplementary Figure 2A-B in revised manuscript:
在修订稿中添加了补充图 2A-B：

Response: Thank you for your constructive suggestions. We performed a signature gene screen for specific tissue macrophages in breast, lung and liver cancers. We obtained macrophage signature genes known to be expressed in these cancers by searching for relevant studies, and cite the article mentioned in the manuscript at the 26th reference. Using these marker genes, we created unique gene combinations for different tissue macrophages in specific tumors and analyzed their expression in different macrophage subpopulations. This analysis allowed us to gain insight into the characteristic gene expression of these macrophages in specific tumor environments. We have added counterparts and figures to the manuscript that show in detail the gene expression of these tissue-specific macrophages in different subpopulations. These additions further enrich our discussion on the specific expression characteristics of macrophages in various cancer types.
回复：感谢您的建设性建议。我们对乳腺癌、肺癌和肝癌中的特定组织巨噬细胞进行了特征基因筛选。通过检索相关研究，我们获得了在这些癌症中表达的巨噬细胞特征基因，并在文稿的第 26 条参考文献中引用了该文章。利用这些标志基因，我们为不同肿瘤中的不同组织巨噬细胞创建了独特的基因组合，并分析了它们在不同巨噬细胞亚群中的表达。这项分析使我们能够深入了解这些巨噬细胞在特定肿瘤环境中的特征基因表达。我们已向文稿中添加了对应内容和图表，详细展示了这些组织特异性巨噬细胞在不同亚群中的基因表达。这些补充内容进一步丰富了我们对不同癌症类型中巨噬细胞特定表达特征的讨论。

Page 10 line 204-212 in revised manuscript:
修订文稿第 10 页第 204-212 行：

“In our investigation, we examined the distribution of distinct tissue-resident macrophages across various tumor types. Specifically, we characterized tissue-specific macrophages present in breast cancer, denoted as FOLR2+ macrophages [26], hepatocellular carcinoma tissue-resident macrophages (Kupffer cells)[27], and tissue-resident macrophages within lung cancer recognized as alveolar tissue-resident macrophages [28]. Our analysis revealed a higher expression of tissue-resident macrophages in both Cluster0 and Cluster2 subpopulations. Notably, these different tumor-specific macrophages exhibited similar expression patterns across both subpopulations (S2C Fig).”
在我们的研究中，我们检查了不同肿瘤类型中不同组织驻留巨噬细胞的分布情况。具体来说，我们表征了乳腺癌中存在的组织特异性巨噬细胞，称为 FOLR2+巨噬细胞[26]，肝细胞癌组织驻留巨噬细胞（库普弗细胞）[27]，以及肺癌中识别为肺泡组织驻留巨噬细胞的组织驻留巨噬细胞[28]。我们的分析显示，在 Cluster0 和 Cluster2 亚群中，组织驻留巨噬细胞的表达水平均较高。值得注意的是，这些不同的肿瘤特异性巨噬细胞在两个亚群中表现出相似的表达模式（S2C 图）。

Reference:
参考文献：

26. Ramos RN, Missolo-Koussou Y, Gerber-Ferder Y, Bromley CP, Bugatti M, Núñez NG, et al. Tissue-resident FOLR2 macrophages associate with CD8 T cell infiltration in human breast cancer. Cell. 2022;185(7):1189-+.doi:10.1016/j.cell.2022.02.021IF: 42.5 Q1 B1.
26. Ramos RN, Missolo-Koussou Y, Gerber-Ferder Y, Bromley CP, Bugatti M, Núñez NG, 等. 组织驻留 FOLR2 巨噬细胞与人类乳腺癌中 CD8 T 细胞浸润相关. 细胞. 2022;185(7):1189-+.doi:10.1016/j.cell.2022.02.021IF: 42.5 Q1 B1.

27. Sierro F, Evrard M, Rizzetto S, Melino M, Mitchell AJ, Florido M, et al. A Liver Capsular Network of Monocyte-Derived Macrophages Restricts Hepatic Dissemination of Intraperitoneal Bacteria by Neutrophil Recruitment. Immunity. 2017;47(2):374-+.doi:10.1016/j.immuni.2017.07.018IF: 26.3 Q1 B1.
27. Sierro F, Evrard M, Rizzetto S, Melino M, Mitchell AJ, Florido M, 等. 肝包膜单核细胞来源巨噬细胞网络通过中性粒细胞募集限制腹腔内细菌的肝内扩散. 免疫学杂志. 2017;47(2):374-+.doi:10.1016/j.immuni.2017.07.018IF: 26.3 Q1 B1.

28. Xiang C, Zhang M, Shang ZX, Chen SN, Zhao JK, Ding BW, et al. Single-cell profiling reveals the trajectory of FOLR2-expressing tumor-associated macrophages to regulatory T cells in the progression of lung adenocarcinoma. Cell Death & Disease. 2023;14(8).doi:ARTN 493
28. Xiang C, Zhang M, Shang ZX, Chen SN, Zhao JK, Ding BW, 等. 单细胞分析揭示肺腺癌进展中表达 FOLR2 的肿瘤相关巨噬细胞向调节性 T 细胞的轨迹. 细胞死亡与疾病. 2023;14(8).doi:ARTN 493

10.1038/s41419-023-06021-6IF: 9.6 Q1 B1.
10.1038/s41419-023-06021-6IF: 9.6 Q1

Page 24 line 520-523 in revised manuscript:
修改稿第 24 页第 520-523 行：

“We characterized the expression of tissue-specific resident macrophage signature genes across distinct macrophage subgroups in various cancer types, revealing that Cluster0 and Cluster2 indeed exhibited higher expression levels of tissue-resident macrophage genes.”
“我们表征了不同癌症类型中不同巨噬细胞亚群中组织特异性驻留巨噬细胞特征基因的表达，揭示 Cluster0 和 Cluster2 确实表现出更高的组织驻留巨噬细胞基因表达水平。”

Added supplementary Figure 2C in revised manuscript:
修改稿中增加了补充图 2C：

C. What were the major differences if you did a direct diff-e test between groups 0 and 2? This should also be done for groups 1/3 and 6/8.
C. 如果在组 0 和组 2 之间进行直接的 diff-e 测试，主要差异是什么？这也应该对组 1/3 和组 6/8 进行。

Response: Thank you for your constructive suggestions. We conducted differential analysis among clusters 0/2, 1/3, and 6/8, identifying distinct sets of differential genes within each comparison. Enrichment analysis of these genes revealed significant differences between clusters 0 and 2, particularly in bacterial or viral infections and immune responses. Cluster0 showed enrichment in systemic lupus erythematosus, staphylococcal infections, and antigen processing and presentation. Conversely, Cluster2 was associated with the NF-κB pathway, IL17 pathway, and Kaposi sarcoma-associated herpesvirus infection, aligning with various bacterial infections. Comparatively, Cluster3 exhibited involvement in bacterial and viral infections alongside antigen processing and presentation, while Cluster6 displayed enrichment in metabolic pathways and bacterial/viral infections, albeit with lower significance. We've meticulously revised and expanded upon these findings in the revised manuscript, providing comprehensive insights into these distinct cluster-specific pathways and infections.
回复：感谢您的建设性建议。我们对 0/2、1/3 和 6/8 簇进行了差异分析，在每个比较中识别出不同的差异基因集。对这些基因的富集分析揭示了簇 0 和 2 之间的显著差异，特别是在细菌或病毒感染和免疫反应方面。簇 0 在系统性红斑狼疮、葡萄球菌感染以及抗原处理和呈递方面表现出富集。相反，簇 2 与 NF-κB 通路、IL17 通路以及卡波西肉瘤相关疱疹病毒感染相关，这与各种细菌感染相吻合。相比之下，簇 3 涉及细菌和病毒感染以及抗原处理和呈递，而簇 6 在代谢通路和细菌/病毒感染方面表现出富集，尽管显著性较低。我们在修订稿中仔细修订和扩展了这些发现，提供了对这些不同簇特异性通路和感染的全面见解。

Page 10-11 line 212-228 in revised manuscript:
修订稿第 10-11 页第 212-228 行：

“We performed differential analysis and KEGG enrichment analysis on several clusters exhibiting similar expression patterns. Specifically, differential analysis was conducted on the following pairs of clusters: Cluster0 versus Cluster2, Cluster1 versus Cluster3, and Cluster6 versus Cluster8. This allowed the identification of distinct sets of differential genes within these clusters. Subsequently, we subjected these differential genes to enrichment analysis, unveiling divergent pathway enrichments between Cluster0 and Cluster2. Cluster0 showed enrichment in pathways associated with bacterial and viral infections, including systemic lupus erythematosus, staphylococcal infections, and antigen processing and presentation. On the other hand, Cluster2 was demonstrated to be involved in pathways related to the NF-κB pathway, the IL17 pathway, and infections linked to Kaposi sarcoma-associated herpesvirus. Both clusters appeared to be associated with distinct bacterial infections, showing divergent enrichment patterns. Relative to Cluster1, Cluster3 displayed pathways involved in bacterial and viral infections, as well as antigen processing and presentation. Conversely, Cluster6 was notably enriched in metabolic pathways alongside bacterial and viral infection-related pathways (S3 Fig).”
我们对表现出相似表达模式的几个聚类进行了差异分析和 KEGG 富集分析。具体而言，我们对以下聚类对进行了差异分析：Cluster0 与 Cluster2、Cluster1 与 Cluster3，以及 Cluster6 与 Cluster8。这使我们能够在这些聚类中识别出不同的差异基因集。随后，我们对这些差异基因进行了富集分析，揭示了 Cluster0 和 Cluster2 之间的不同通路富集。Cluster0 在细菌和病毒感染相关的通路中富集，包括系统性红斑狼疮、葡萄球菌感染以及抗原处理和呈递。另一方面，Cluster2 被证明与 NF-κB 通路、IL17 通路以及卡波西肉瘤相关疱疹病毒感染相关的通路有关。这两个聚类似乎与不同的细菌感染相关，显示出不同的富集模式。与 Cluster1 相比，Cluster3 显示出与细菌和病毒感染以及抗原处理和呈递相关的通路。相反地，Cluster6 在代谢通路以及细菌和病毒感染相关通路中显著富集（S3 图）。”

Page 24 line 518-520 in revised manuscript:
修订稿第 24 页第 518-520 行：

Page 27 line 572-575 in revised manuscript:
修订稿第 27 页第 572-575 行：

“When we performed functional analysis of the differentially generated genes in these two clusters, we found that most of the bacterial and viral infection pathways, as well as antigen processing and presentation, were enriched in Cluster3.”
“当我们对这两个簇中差异生成的基因进行功能分析时，我们发现大多数细菌和病毒感染通路，以及抗原处理和呈递，在 Cluster3 中富集。”

Added supplementary Figure3 in revised manuscript:
在修订稿中添加了补充图 3：

Response: Thanks for your valuable comments. We regreted the oversight in not explicitly stating the criteria for gene expression selection in our study. We identified and included cells expressing genes within the range of above 200 but below 6000. This threshold was applied as a criterion for cell inclusion based on gene expression levels. We have revised the manuscript to include a clear description of this specific threshold used for gene expression selection.
回复：感谢您的宝贵意见。我们遗憾未在研究中明确说明基因表达选择的标准。我们识别并纳入了表达基因数量在 200 以上但低于 6000 的细胞。这一阈值作为基因表达水平的细胞纳入标准。我们已修订稿件，增加了对这一特定基因表达选择阈值的清晰描述。

Page 36 line789-791 in revised manuscript:
修订稿第 36 页第 789-791 行：

“Cells with the number of expressing genes below 200 or above 6000 were removed. In addition, Cells with higher than 10% mitochondrial gene content were removed prior to further analysis.”
“表达基因数量低于 200 或高于 6000 的细胞被移除。此外，在进一步分析前，表达线粒体基因含量超过 10%的细胞也被移除。”

Response: Thank you for your constructive suggestions. We thoroughly reviewed the referenced articles and observed that the findings align with our study outcomes, particularly in the context of immunotherapy in NSCLC. Additionally, we extensively discussed the involvement of PD-1-expressing macrophages within tumors and their implications in immunotherapy, as highlighted in the provided literature. These crucial insights have been incorporated and expanded upon in our revised manuscript, citing them at references 62 and 64, respectively, located in the original manuscript, and emphasizing the relevance and alignment of our study findings with the established literature on immune therapy in NSCLC and the role of PD-L1-expressing macrophage populations.
回复：感谢您的建设性建议。我们仔细审查了所提及的文献，发现其研究结果与我们的研究结论相符，特别是在非小细胞肺癌免疫治疗方面。此外，我们还深入讨论了肿瘤内 PD-1 表达巨噬细胞的参与及其对免疫治疗的启示，正如所提供的文献所强调的那样。这些关键见解已被纳入并扩展到我们修订的文稿中，分别引用为参考文献 62 和 64，位于原始文稿中，并强调了我们的研究结果与已建立的关于非小细胞肺癌免疫治疗和 PD-L1 表达巨噬细胞群体作用的文献的相关性和一致性。

Page 30 line 636-640 in revised manuscript:
修订文稿第 30 页第 636-640 行：

“In previous studies, the treatment targeting SPP1+ macrophages in NSCLC with anti-PDL1 has been noted to enhance the progression-free survival of patients [62]. This finding aligned with our results, indicating an association between SPP1 macrophages and improved response of NSCLC to immunotherapy.”
“在先前的研究中，针对非小细胞肺癌（NSCLC）中 SPP1+巨噬细胞的抗 PDL1 治疗已被发现可提高患者的无进展生存期[62]。这一发现与我们的研究结果一致，表明 SPP1 巨噬细胞与 NSCLC 对免疫疗法的反应改善之间存在关联。”

Page 30 line 647-651 in revised manuscript:
修订文稿第 30 页第 647-651 行：

“Similarly, previous findings have demonstrated that human TAMs express elevated levels of PD-1. The expression of PD-1 in TAMs exhibits a negative correlation with their ability to phagocytose tumor cells. Targeting PDL1 has been observed to restore the function of PD-1 TAMs [64, 65]. This suggested that immunotherapy directed at PDL1 could potentially enhance anti-tumor mechanisms.”
“同样，先前的研究表明，人类 TAMs 表达 PD-1 水平升高。TAMs 中 PD-1 的表达与其吞噬肿瘤细胞的能力呈负相关。靶向 PDL1 已被观察到可恢复 PD-1 TAMs 的功能[64, 65]。这表明针对 PDL1 的免疫疗法可能增强抗肿瘤机制。”

Reference:
参考文献：

62. Leader AM, Grout JA, Maier BB, Nabet BY, Park MD, Tabachnikova A, et al. Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification. Cancer Cell. 2021;39(12):1594-+.doi:10.1016/j.ccell.2021.10.009IF: 44.5 Q1 B1.
分类和患者分层。癌细胞。2021；39(12)：1594-+.doi:10.1016/j.ccell.2021.10.009IF: 44.5 Q1。
62. Leader AM, Grout JA, Maier BB, Nabet BY, Park MD, Tabachnikova A, 等. 人类非小细胞肺癌病变的单细胞分析细化了肿瘤分类和患者分层. 癌细胞. 2021;39(12):1594-+.doi:10.1016/j.ccell.2021.10.009IF: 44.5 Q1 B1.

64. Gordon SR, Aute RLM, Dulken BW, Hutter G, George BM, Ccracken MNM, et al. PD-1 expression by tumour-associated macrophages inhibits phagocytosis and tumour immunity. Nature. 2017;545(7655):495-+.doi:10.1038/nature22396IF: 48.5 Q1 B1.
戈登 SR，奥特 RLM，杜肯 BW，胡特 G，乔治 BM，克拉克 MNM，等。肿瘤相关巨噬细胞表达 PD-1 抑制吞噬作用和肿瘤免疫。自然。2017；545(7655)：495-+.doi:10.1038/nature22396IF: 48.5 Q1。
64. Gordon SR, Aute RLM, Dulken BW, Hutter G, George BM, Ccracken MNM, 等. 肿瘤相关巨噬细胞表达 PD-1 抑制吞噬作用和肿瘤免疫. 自然. 2017;545(7655):495-+.doi:10.1038/nature22396IF: 48.5 Q1 B1.

65. Chen Y, Jin H, Song Y, Huang T, Cao J, Tang Q, et al. Targeting tumor-associated macrophages: A potential treatment for solid tumors. J Cell Physiol. 2021;236(5):3445-65.doi:10.1002/jcp.30139IF: 4.0 Q1 B3.
65. Chen Y, Jin H, Song Y, Huang T, Cao J, Tang Q, 等. 靶向肿瘤相关巨噬细胞：一种潜在的实体瘤治疗方法. 细胞生理学杂志. 2021;236(5):3445-65.doi:10.1002/jcp.30139IF: 4.0 Q1 B3.

Minor Typographical errors:
轻微排版错误：

Line 69: Rephrase sentence, a sentence can not begin with the word “And”
第 69 行：重写句子，句子不能以“和”开头

Response: Thanks for your valuable comments. We have revised it in revised manuscript.
回复：感谢您的宝贵评论。我们在修改稿中进行了修改。

Page 5 line 90-91 in revised manuscript:
修改稿第 5 页第 90-91 行：

“In addition, we performed gene enrichment analysis using the REACTOME database and identified subpopulations that decrease the sensitivity of melanoma patients to anti-PD-1 therapy.”
“此外，我们使用 REACTOME 数据库进行了基因富集分析，并确定了那些降低黑色素瘤患者对 PD-1 治疗的敏感性的亚群。”

Line 76: should begin with “The” or “A”. You can also make this plural.
第 76 行：应以“The”或“A”开头。你也可以使其变为复数形式。

Response: Thanks for your valuable comments. We have revised it in revised manuscript.
回复：感谢您的宝贵评论。我们在修改稿中进行了修改。

Page 5 line 95 in revised manuscript:
修改稿第 5 页第 95 行：

“The tumor microenvironment (TME) consists of a diverse range of immune and non-immune stromal cells.”
肿瘤微环境（TME）由多种免疫和非免疫基质细胞组成。

Line 90: suggest changing tumors to TMEs
第 90 行：建议将肿瘤改为 TMEs

Response: Thanks for your valuable comments. We have revised it in revised manuscript.
回复：感谢您的宝贵评论。我们在修改稿中进行了修改。

Page 5 line 106-109 in revised manuscript:
修改稿第 5 页第 106-109 行：

“Recent studies have shown that macrophages exhibit functional heterogeneity in different tissue environment, and this affects their ability to respond to metabolism [5-7], implying that the functions performed by macrophages in different TMEs may be different.”
“近期研究表明，巨噬细胞在不同组织环境中表现出功能异质性，这影响了它们对代谢的反应能力[5-7]，这意味着巨噬细胞在不同肿瘤微环境（TMEs）中执行的功能可能不同。”

Line 101: change TAM to TAMs and is to are
第 101 行：将 TAM 改为 TAMs，将 is 改为 are

Response: Thanks for your valuable comments. We have revised it in revised manuscript.
回复：感谢您的宝贵评论。我们在修改稿中进行了修改。

Page 6 line 121 in revised manuscript:
修改稿第 6 页第 121 行：

“TAMs are an important part of the immune cells in the TME of solid tumors”
TAMs 是实体瘤肿瘤微环境中免疫细胞的重要组成部分

Line 102: change to TME for consistency.
第 102 行：改为 TME 以保持一致性。

Response: Thanks for your valuable comments. We have revised it in revised manuscript.
回复：感谢您的宝贵评论。我们在修改稿中进行了修改。

Page 6 line 121 in revised manuscript:
修改稿第 6 页第 121 行：

“TAMs are an important part of the immune cells in the TME of solid tumors”
TAMs 是实体瘤肿瘤微环境中免疫细胞的重要组成部分

Line 106: Ideally this statement should be supported by a reference.
第 106 行：理想情况下，这一陈述应有参考文献支持。

Response: Thanks for your valuable comments. We have added the appropriate references (“Ehlers FAI, et.al. Exploring the potential of combining IL-2-activated NK cells with an anti-PDL1 monoclonal antibody to target multiple myeloma-associated macrophages. Cancer Immunol Immun. 2023;72(6):1789-801.doi:10.1007/s00262-022-03365-4.”) to the article.
回复：感谢您的宝贵评论。我们已将适当的参考文献（“Ehlers FAI, et.al. 探索结合 IL-2 激活的 NK 细胞与抗 PDL1 单克隆抗体靶向多发性骨髓瘤相关巨噬细胞的潜力。Cancer Immunol Immun. 2023;72(6):1789-801.doi:10.1007/s00262-022-03365-4。”）添加到文章中。

Line 137: should be changed to “the TME”
第 137 行：应改为“肿瘤微环境”

Response: Thanks for your valuable comments. We have revised it in revised manuscript.
回复：感谢您的宝贵评论。我们在修改稿中进行了修改。

Page 8 line 156-158 in revised manuscript:
修改稿第 8 页第 156-158 行：

“We found that TAM subtypes in specific cancer types can activate their specific functional pathways to regulate the TME and sensitivity to immunotherapy.”
“我们发现特定癌症类型的 TAM 亚型可以激活其特定的功能通路来调节肿瘤微环境和免疫治疗的敏感性。”

Reviewer #3: The authors utilized a combination of single-cell tumor data from three different cancer types, and described the features of macrophage clusters with mRNA markers derived from their single-cell analysis. The current study gave insight into the biological function of macrophages in pan-cancer, and stated the significance of macrophage-related mRNA markers in immunotherapy outcome prediction. Although the current manuscript was well organized, there were still some points that need to be improved.
审稿人#3：作者结合了三种不同癌症类型的单细胞肿瘤数据，并描述了基于单细胞分析得出的巨噬细胞簇的 mRNA 标记特征。本研究深入了解了巨噬细胞在全癌症中的生物学功能，并阐述了巨噬细胞相关 mRNA 标记在免疫治疗结果预测中的意义。尽管当前文稿组织良好，但仍有一些需要改进的地方。

Major concern:
主要关注点：

1. The authors declared that they performed a systematic analysis to give insight into the role of tumor-associated macrophage in pan-cancer. However, the single-cell analysis only contained based on 7,801 macrophages in 3 cancer types. The following analysis was based on the single-cell clustering results, thus, the robustness of these results should be stated, and datasets containing more cancer types should be involved.
1. 作者声明他们进行了系统分析，以深入了解肿瘤相关巨噬细胞在泛癌中的作用。然而，单细胞分析仅基于 3 种癌症类型中的 7,801 个巨噬细胞。后续分析基于单细胞聚类结果，因此应说明这些结果的稳健性，并应纳入包含更多癌症类型的数据库。

2. The authors declared that SPP1-related pathways were the major communication between macrophage clusters in Figure 2 and declared that SPP1 expressions were related to lower levels of immune cell infiltrations in Figure 3. While SPP1 was also expressed in tumor cells. What’s the relationship between SPP1+ macrophages and various immune cell infiltration levels? Please clarify.
2. 作者声明图 2 中 SPP1 相关通路是巨噬细胞簇之间主要的通讯方式，并声明图 3 中 SPP1 表达与免疫细胞浸润水平较低相关。尽管 SPP1 也表达在肿瘤细胞中。SPP1+巨噬细胞与各种免疫细胞浸润水平之间的关系是什么？请澄清。

3. The results presented in Figure 5 were quite selective. Please clarify the universal patterns related to the macrophage clusters.
3. 图 5 中展示的结果相当有选择性。请阐明与巨噬细胞簇相关的普遍模式。

4. The authors should describe the heterogeneity and homogeneity of macrophages across cancer types to improve the novelty of the current manuscript.
4. 作者应该描述不同癌症类型中巨噬细胞的异质性和同质性，以提高当前文稿的新颖性。

Minor concerns:
小问题：

1. Line 129, ‘to describe the molecular and biological characteristics to describe the molecular and biological characteristics in the tumor’, clarify the object.
1. 第 129 行，“描述肿瘤中的分子和生物学特征来描述分子和生物学特征”，明确对象。

2. Line 177-180, the macrophage subgroups were annotated using previously published gene sets. Does this mean that the markers identified and subjected to the following analysis were coherent with previous studies? What’s the improvement in this paper? Please clarify.
2. 第 177-180 行，巨噬细胞亚组使用先前发表的基因集进行注释。这是否意味着所识别并用于后续分析的标记与先前研究一致？本文的改进之处是什么？请澄清。

3. Line 545, clarify the version of R package ‘CellChat’.
3. 第 545 行，请明确 R 包‘CellChat’的版本。

4. Line 565, ‘Download TCGA data using the TCGAbiolink package’, check the grammar.
4. 第 565 行，“使用 TCGAbiolink 包下载 TCGA 数据”，检查语法。

5. Line 572, clarify the full name of ‘FDR’.
5. 第 572 行，说明“FDR”的全名。

Major concern:
主要关注点：

Response: Thanks for your valuable comments. Our single-cell analysis comprised 7,801 macrophages across three cancer types, chosen from scRNAseq data available in the GEO database. Our selection of data sequenced on the same platform aimed to minimize batch effects, ensuring a more reliable subsequent analysis. However, limitations arose when seeking additional datasets due to the need for data consistency. Despite attempting to include Uveal melanoma data, we encountered significant batch effects when integrating this data with our primary dataset, even after applying CCAfor integration. Consequently, we proceeded with an analysis limited to the three cancer types to maintain robustness and achieve better integration after CCA, effectively minimizing batch effects. While acknowledging the constrained dataset, we ensured rigorous analysis and integration methods to maintain data reliability.In justifying this approach, we also referenced a study in a similar vein that successfully conducted a pan-cancer analysis using data from only three tumors. This study's approach demonstrates the feasibility of drawing meaningful insights from a limited dataset in a pan-cancer context (source: https://aacrjournals.org/clincancerres/article/27/9/2636/672059/Molecular-Features-of-Cancer-associated-Fibroblast). We've outlined these considerations and limitations in our revised manuscript, ensuring transparency regarding the dataset's size and the steps taken to mitigate associated challenges while striving for robust and meaningful analysis.
回复：感谢您的宝贵评论。我们的单细胞分析涵盖了三种癌症类型的 7,801 个巨噬细胞，这些数据来自 GEO 数据库中可用的 scRNAseq 数据。我们选择在同一平台上测序的数据旨在最小化批次效应，确保后续分析的可靠性。然而，在寻求额外数据集时，由于数据一致性的需要，出现了局限性。尽管我们尝试包含葡萄膜黑色素瘤数据，但在将其与主要数据集整合时，即使应用 CCAfor 整合，我们也遇到了显著的批次效应。因此，我们仅对三种癌症类型进行分析，以保持稳健性，并在 CCA 后实现更好的整合，有效最小化批次效应。虽然承认数据集有限，但我们确保了严格的分析和整合方法，以保持数据的可靠性。在论证这一方法时，我们还参考了一项类似的研究，该研究成功地使用仅来自三个肿瘤的数据进行了全癌症分析。这项研究的方法展示了在泛癌背景下从有限数据集中提取有意义的见解的可行性（来源：https://aacrjournals.org/clincancerres/article/27/9/2636/672059/Molecular-Features-of-Cancer-associated-Fibroblast）。我们在修订稿中概述了这些考虑和局限性，确保就数据集的大小和为缓解相关挑战所采取的步骤保持透明，同时力求进行稳健和有意义的分析。

Response: Thanks for your valuable comments. We conducted single-cell data analysis from three tumors (GSE232237, GSE231559, GSE176031) to meticulously analyze and annotate the data. Our findings revealed a predominant expression of SPP1 within the tumor tissues including immune cells and tumor cells, primarily originating from macrophages. While we cannot conclusively assert that only macrophages express SPP1, our analysis indicated a significantly higher expression of SPP1 in macrophages compared to other cell types including epithelial cells (i.e. tumor cells). This observation strongly suggested the substantial contribution of macrophages to the overall SPP1 expression within the tumor microenvironment. While other cell types may also contribute to SPP1 expression, our data clearly highlights the prominent role of macrophages in this context. Therefore, we could only make a rough judgment that various immune cell infiltration level was mainly associated with SPP1+ macrophages. We could not completely rule out the contribution of SPP1 expression of tumor cells. In the discussion section, we supplemented and explained this issue. Also, we examined SPP1 expression in M0 macrophages, tumor cells (breast, lung, and liver cancers), and TAM by ELISA in vitro.TAM exhibited high levels of SPP1 relative to M0 macrophages and tumor cells.
回复：感谢您的宝贵评论。我们从三个肿瘤（GSE232237、GSE231559、GSE176031）中进行了单细胞数据分析，以仔细分析和注释数据。我们的研究结果表明，SPP1 在肿瘤组织中主要表达，包括免疫细胞和肿瘤细胞，主要来源于巨噬细胞。虽然我们不能断言只有巨噬细胞表达 SPP1，但我们的分析显示，与上皮细胞（即肿瘤细胞）等其他细胞类型相比，巨噬细胞中的 SPP1 表达显著更高。这一观察结果强烈表明，巨噬细胞对肿瘤微环境中 SPP1 的整体表达有重要贡献。虽然其他细胞类型也可能对 SPP1 表达有贡献，但我们的数据清楚地突出了巨噬细胞在此背景下的重要作用。因此，我们只能粗略地判断各种免疫细胞浸润水平主要与 SPP1+巨噬细胞相关。我们无法完全排除肿瘤细胞 SPP1 表达的影响。在讨论部分，我们补充并解释了这个问题。此外，我们通过体外 ELISA 检测了 M0 巨噬细胞、肿瘤细胞（乳腺癌、肺癌和肝癌）以及 TAM 中的 SPP1 表达。与 M0 巨噬细胞和肿瘤细胞相比，TAM 表现出高水平的 SPP1。

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“Moreover, we detected SPP1 expression in M0 macrophages, tumor cells (breast, lung and liver cancer) and TAM by ELISA in vitro. TAMs exhibited high levels of SPP1 relative to M0 macrophages and tumor cells (Fig 2F).”
“此外，我们通过体外 ELISA 检测了 M0 巨噬细胞、肿瘤细胞（乳腺癌、肺癌和肝癌）以及 TAM 中的 SPP1 表达。与 M0 巨噬细胞和肿瘤细胞相比，TAMs 表现出高水平的 SPP1（图 2F）。”

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修订稿第 16 页第 337-340 行：

“We chose to analyze single-cell sequencing data from three tumors where SPP1 expression was positively correlated with immune infiltration including Thyroid carcinoma (THCA), Colon adenocarcinoma (COAD), and prostate cancer (PRAD), respectively. Results revealed that SPP1 was mostly expressed in macrophages and monocytes, and less in epithelial cells (S5 Fig). This provided a possibility that macrophages-derived SPP1 made a greater contribution to SPP1 expression in the TME.”
“我们选择分析三个肿瘤的单细胞测序数据，其中 SPP1 表达与免疫浸润呈正相关，包括甲状腺癌（THCA）、结肠腺癌（COAD）和前列腺癌（PRAD）。结果揭示 SPP1 主要在巨噬细胞和单核细胞中表达，而在上皮细胞中表达较少（图 S5）。这表明巨噬细胞来源的 SPP1 可能对 TME 中 SPP1 的表达有更大的贡献。”

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“Meanwhile, our analysis in tumors with positive correlation between SPP1 and immune infiltration found that most of the SPP1 expression was present with macrophages or monocytes, so in our subsequent analyses, we roughly considered that SPP1 expression in tumors was mainly contributed by macrophages.”
“与此同时，我们在 SPP1 与免疫浸润呈正相关的肿瘤分析中发现，大多数 SPP1 表达与巨噬细胞或单核细胞存在，因此在后续分析中，我们将肿瘤中的 SPP1 表达大致归因于巨噬细胞。”

Added Figure 2F in revised manuscript:
修订稿中增加了图 2F：

Added supplementary Figure 5 in revised manuscript:
修订稿中增加了补充图 5：

Response: Thanks for your valuable comments. We fully detail and summarize the functions of each subgroup in our analysis. In refining the manuscript, we avoided any initially perceived strong selectivity. Our goal was to provide a careful and comprehensive overview that encompassed the different functions and characteristics of each subgroup, ensuring a more complete description of the findings.
回复：感谢您的宝贵意见。我们在分析中详细阐述了每个亚组的职能，并进行了总结。在修改稿件时，我们避免了最初感知到的强烈选择性。我们的目标是提供一份谨慎而全面的概述，涵盖每个亚组的不同职能和特征，以确保对研究结果的更完整描述。

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“Our investigations unveiled distinct functional enrichments within each cluster, shedding light on their pivotal roles in various cellular activities and biological mechanisms. Cluster0 exhibited functional enrichment indicative of its involvement in intricate intracellular signaling networks, pivotal regulatory mechanisms governing cellular function, metabolic regulation, and responses to external stimuli (Fig 6A). Cluster1 emerged as a central player in diverse cellular processes encompassing protein synthesis, cell cycle regulation, DNA repair, and the development and function of the nervous system (Fig 6B). The functional enrichments observed in Cluster2 suggested its significance in a wide array of biological processes, including gene transcription and regulation, organelle function, and cytoskeletal reorganization (Fig 6C). In Cluster3, enriched functions pointed to its importance in pivotal biological processes such as DNA repair, cell cycle regulation, and RNA processing (Fig 6D). Cluster4 appeared to be significantly involved in RNA metabolism, processing and potentially viral infection, along with cell cycle regulation (S14A Fig). The functional enrichment of Cluster5 highlighted its critical role in sustaining mitochondrial biosynthesis, protein synthesis, and overall cellular activity (S14B Fig). Notably, enrichment in Cluster6 suggested its importance in nervous system development and signaling (S14C Fig). Likewise, the enriched characterization of Cluster7 implied its involvement in vital cellular processes like RNA processing, transcription, translation, and cellular metabolism (S14D Fig). The functional characterization of Cluster8 indicated its involvement in diverse biological processes encompassing cell signaling, stress responses, and regulation of nuclear receptors (S15A Fig). Cluster9's functional characterization suggested its involvement in key biological processes such as cell metabolism, regulation of translation, and remodeling of the extracellular matrix (S15B Fig). Finally, the functional characterization of Cluster10 suggested its potential role in biological processes including nervous system development and regulation of the cell cycle (S15C Fig). The results of our analyses suggested that these macrophage subtypes exhibited significant differences in their impact on tumor progression, reflecting the differences between macrophage subtypes as well as the heterogeneity of macrophages among different tumors.”
我们的研究揭示了每个簇中独特的功能富集，阐明了它们在各种细胞活动和生物机制中的关键作用。Cluster0 表现出功能富集，表明其参与了复杂的细胞内信号网络、调控细胞功能的重大机制、代谢调节以及对外部刺激的反应（图 6A）。Cluster1 成为多种细胞过程的核心参与者，包括蛋白质合成、细胞周期调控、DNA 修复以及神经系统的发育和功能（图 6B）。Cluster2 中观察到的功能富集表明其在广泛的生物过程中的重要性，包括基因转录和调控、细胞器功能和细胞骨架重组（图 6C）。在 Cluster3 中，富集的功能指向其在 DNA 修复、细胞周期调控和 RNA 加工等重大生物过程中的重要性（图 6D）。Cluster4 似乎显著参与了 RNA 代谢、加工以及潜在的病毒感染，以及细胞周期调控（图 S14A）。 Cluster5 的功能富集突出了其在维持线粒体生物合成、蛋白质合成和整体细胞活动中的关键作用（图 S14B）。值得注意的是，Cluster6 的富集表明其在神经系统发育和信号传导中的重要性（图 S14C）。同样，Cluster7 的富集特征暗示其参与 RNA 加工、转录、翻译和细胞代谢等关键细胞过程（图 S14D）。Cluster8 的功能特征表明其参与多种生物学过程，包括细胞信号传导、应激反应和核受体调控（图 S15A）。Cluster9 的功能特征暗示其参与细胞代谢、翻译调控和细胞外基质重塑等关键生物学过程（图 S15B）。最后，Cluster10 的功能特征表明其在神经系统发育和细胞周期调控等生物学过程中的潜在作用（图 S15C）。我们的分析结果提示这些巨噬细胞亚型在肿瘤进展中的影响存在显著差异，这反映了巨噬细胞亚型之间的差异以及不同肿瘤中巨噬细胞的异质性。”

Response: Thanks for your valuable comments. We have taken the reviewer's suggestion into careful consideration and reworked the results section in the revised version to comprehensively delineate the heterogeneity and homogeneity of macrophages across different cancer types. This revised section offers a detailed analysis and comparison of macrophage characteristics, highlighting both their shared features and distinctive attributes across various cancer contexts. By summarizing and refining the results based on previous findings, we have aimed to enhance the manuscript's novelty and provide a more insightful understanding of macrophage heterogeneity and homogeneity in the context of different cancers.
回复：感谢您的宝贵评论。我们已经仔细考虑了审稿人的建议，并在修订稿中重新修改了结果部分，全面阐述了不同癌症类型中巨噬细胞的异质性和同质性。这一修订部分提供了对巨噬细胞特征的详细分析和比较，突出了它们在不同癌症背景下的共同特征和独特属性。通过基于先前研究结果总结和精炼结果，我们旨在增强文稿的新颖性，并提供对不同癌症背景下巨噬细胞异质性和同质性的更深入理解。

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“Our study revealed varying distributions of macrophage subsets across diverse tumors, highlighting the distinct functional properties exhibited by these macrophages in different tumor types (Fig 1F). Notably, our analysis revealed that both Cluster0 and Cluster2 exhibit genes associated with tissue-resident macrophages. These subpopulations were notably prevalent in LIHC, suggesting a potential inclination of LIHC towards the requirement of tissue-resident macrophages. Conversely, Cluster6 and Cluster8, expressing monocyte-like macrophages, exhibited distinct characteristics. Cluster8, prominently represented in lung adenocarcinoma (LUAD), signifies potential functional differences between Cluster6 and Cluster8, potentially influencing their varying proportions in LUAD. Similarly, Cluster1 and Cluster3, expressing genes related to TAMs, displayed contrasting patterns. Cluster3 nearly lacked macrophages in BRCA whereas Cluster1 showed higher macrophage presence in LIHC. These differences in functional traits across subpopulations possibly accounted for their disparate proportions in various cancers.”
我们的研究揭示了不同肿瘤中巨噬细胞亚群的分布差异，突出了这些巨噬细胞在不同肿瘤类型中表现出的不同功能特性（图 1F）。值得注意的是，我们的分析显示 Cluster0 和 Cluster2 均表现出与组织驻留巨噬细胞相关的基因。这些亚群在 LIHC 中尤为普遍，表明 LIHC 可能倾向于对组织驻留巨噬细胞的需求。相反，表达单核细胞样巨噬细胞的 Cluster6 和 Cluster8 表现出不同特征。Cluster8 在肺腺癌（LUAD）中显著存在，这表明 Cluster6 和 Cluster8 之间存在潜在的功能差异，可能影响了它们在 LUAD 中不同的比例。类似地，表达与 TAMs 相关基因的 Cluster1 和 Cluster3 显示出不同的模式。Cluster3 在 BRCA 中几乎缺乏巨噬细胞，而 Cluster1 在 LIHC 中表现出更高的巨噬细胞存在。这些亚群功能特性的差异可能解释了它们在各种癌症中不同的比例。

Minor concerns:
小问题：

Response: Thanks for your valuable comments. This description is really inaccurate. We have revised it in revised manuscript.
回复：感谢您的宝贵评论。这个描述确实不准确。我们在修订的文稿中已进行了修改。

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“In this study, we utilized a combination of single-cell tumor data from three different cancers to characterize the molecular and biological features of different macrophage subtypes in the TME.”
“在本研究中，我们利用来自三种不同癌症的单细胞肿瘤数据，来表征肿瘤微环境中不同巨噬细胞亚型的分子和生物学特征。”

Response: Thanks for your valuable comments. In this study, we performed clustering based on the self-contained function of the Seurat package to obtain 11 macrophage subpopulations and analyzed them to obtain the characteristic genes of each subpopulation. We found some subpopulations with similar characteristic genes through previous studies, but the characteristic genes of each subpopulation were not exactly the same as our original ones.
回答：感谢您的宝贵评论。在本研究中，我们基于 Seurat 包的自带功能进行聚类，获得了 11 个巨噬细胞亚群，并分析了它们以获得每个亚群的特征基因。我们通过先前的研究发现了一些具有相似特征基因的亚群，但每个亚群的特征基因与我们最初的并不完全相同。

3. Line 545, clarify the version of R package ‘CellChat’.
3. 第 545 行，请明确 R 包‘CellChat’的版本。

Response: Thanks for your valuable comments. We have revised it in revised manuscript.
回复：感谢您的宝贵评论。我们在修改稿中进行了修改。

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“We used CellChat (v 1.5.0)”
“我们使用了 CellChat（v 1.5.0）”

4. Line 565, ‘Download TCGA data using the TCGAbiolink package’, check the grammar.
4. 第 565 行，“使用 TCGAbiolink 包下载 TCGA 数据”，检查语法。

Response: Thank you very much for pointing out this error. We have revised it in revised manuscript.
回复：非常感谢您指出这个错误。我们在修改稿中已进行修改。

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第 38 页第 813 行在修改稿中：

“We downloaded TCGA data using the TCGAbiolinks package”
“我们使用 TCGAbiolinks 包下载了 TCGA 数据”

5. Line 572, clarify the full name of ‘FDR’.
5. 第 572 行，说明“FDR”的全名。

Response: Thank you very much for pointing out this error. We have revised it in revised manuscript.
回复：非常感谢您指出这个错误。我们在修改稿中已进行修改。

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第 39 页第 833 行在修改稿中：

“The p-values were converted to false discovery rate (FDR) by R (v 4.1.3)”
p 值通过 R（v 4.1.3）转换为假发现率（FDR）