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Thank you for addressing the remaining concerns of the reviewers. The revised manuscript is acceptable now.
[# PeerJ Staff Note - this decision was reviewed and approved by Paula Soares, a PeerJ Section Editor covering this Section #]
Please address remaining concerns of the reviewer #2 who pointed out that to ensure the study adheres to ethical guidelines, you should clearly state in the methods section whether the human samples were approved by an ethics committee.
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The name "GeneSymbol," however, still concerns me, as it should be written separately as "Gene Symbol" rather than combined. This is something I had hoped you would address in your response. Nonetheless, these are merely minor mistakes.
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I noticed that the author has included data from human samples in the revised manuscript. To ensure the study adheres to ethical guidelines, I recommend that the author clearly state in the methods section whether the samples were approved by an ethics committee.
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Overall, the author has adequately addressed the reviewer comments, and the revised manuscript is more rigorous and persuasive.
Plese address concerns of both reviewers and revise manuscript accordingly.
The research uses several bioinformatics analysis methods, analyzes multiple cohorts of data, and provides experimental validation.
The manuscript includes multiple aspects that necessitate elucidation and modification to enhance its scientific integrity. Initially, the rationale for the data capture from TCGA and its subsequent transfer to Xena must be substantiated. The rationale behind the authors' decision not to utilize the TCGA data readily accessible in Xena remains ambiguous, as does the distinction between the downloaded TCGA data and the Xena-integrated TCGA data. Furthermore, it is essential to clarify which data normalization method—logTPM, TPM, or CPM—was employed in the studies. Moreover, clarification is required on the application of batch correction between GTEx normal tissues and tumor tissues. The basis for use a two-tailed t-test to assess differential RNA expression necessitates further explanation, as alternative methods may be more suitable. I recommend for using of the Wilcoxon rank-sum test in gene differential expression analysis, as recent research[1] evidenced its robustness in human population samples, especially in comparison to edgeR, limma, or DESeq2.
The justification for utilizing TARGET data in the survival prognosis analysis section is necessary, as TARGET exclusively comprises pediatric tumor data.
The rationale for incorporating this dataset in the study is unclear and deserves clarification. The inclusion of GSE151530 data is inadequately detailed.
This dataset contains matrix data that may not require upstream reanalysis; however, it is critical to clarify the batch analysis between each sample in the downstream method[2]. Did the authors merge these matrices directly, or did they use batch correction? If batch correction was not performed, it must be indicated clearly in the methods section.
Furthermore, there is variability in the methodology employed for cell communication analysis. The authors use the terms "cellchat" in the methodology, "cellphoneDB" in the findings, and "cellphone" in the supplemental materials. This inconsistency raises concerns regarding the real approach taken, and it is critical to provide clarification. It is particularly critical to identify whether cell communication analysis distinguished between the experimental and control groups, as failing to do so means that results may include communication data from control samples, which could have an impact on the findings' interpretation. The mismatch in tool names compounds the situation. Cell identification information, such as cell marker expression in a cell cluster, should also be provided.
Finally, the IHC comparisons shown in Figure 4C are problematic. The GRN protein appears to locate variably in tumor and surrounding normal tissues, causing staining variability. In liver cancer tissue microarray data, GRN localization was more common in cancer cells than in macrophages[4]. In addition, the control and experimental groups were not paired, and certain tissue sections in the liver cancer cohort did not show detectable GRN expression. These points should be addressed in order to clarify the analysis and avoid drawing incorrect conclusions.
[1] Li Y, Ge X, Peng F, Li W, Li JJ. Exaggerated false positives by popular differential expression methods when analyzing human population samples. Genome Biol. 2022 Mar 15;23(1):79. doi: 10.1186/s13059-022-02648-4. IF: 10.1 Q1. PMID: 35292087; PMCID: PMC8922736.
[2] Satija Lab. Introduction to scRNA-seq integration. Available at: https://satijalab.org/seurat/articles/integration_introduction. Accessed 2023.
[3] Satija Lab. Data visualization methods in Seurat. Available at: https://satijalab.org/seurat/articles/visualization_vignette. Accessed 2023.
[4] GRN IHC in protein atlas database. Available at: https://www.proteinatlas.org/ENSG00000030582-GRN/pathology/liver+cancer#img.
Based on your findings, GRN+ macrophages appear to be mainly M2-type macrophages, implying that the role of GRN+ cells in your work is to demonstrate that M2-type macrophages suppress CD8 T cells in liver cancer. However, I have a few questions about this topic. Are these GRN-positive cells macrophages or Kupffer cells? Furthermore, GRN+ macrophages may not be a new cell type, as GRN expression occurs during the shift of macrophages from M1 to M2. Furthermore, the involvement of M2 macrophages in suppressing CD8 T cells has been widely investigated in the literature. So, what is the key innovation of this study? You may need to revise the discussion and abstract to better showcase your work's novel contributions.
"We extracted the gene expression profile of each tumor, mapped the expression to GeneSymbol, and further used the R packages IOBR (version 0.99.9, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8283787)." : I have never seen a manuscript that directly includes a PMC URL in the main text. It would be more suitable to include this in the reference section. Also, is the phrase "GeneSymbol" commonly used in such contexts?
"Primary Blood Derived Cancer-Bone Marrow, Primary Solid Tumor, and Recurrent Blood Derived Cancer-Bone Marrow samples, in addition to a previous TCGA prognostic study published in Cell" This sentence is too casual and should be updated for clarity and scientific tone.
Finally, one of the primers looks to be a duplicate in Suppl. material. Please correct it.
1、 In the methods section, "Datasets containing tumor and non-tumor tissues were collected from publicly available GEO databases, TCGA databases, and ICGC databases" needs to specify the exact serial numbers used. Additionally, the article should consistently use either "non-tumor tissues" or "adjacent normal tissues." Furthermore, "significance of differential expression was evaluated using a P-value threshold of 0.05" is problematic as using P-values can lead to false positives; please switch to adjusted P-values or q-values.
2、 The primer IL10 is repeated. The authors need to carefully review the details of the manuscript, as such basic errors can lead readers to question the quality and credibility of the article.
3、 Figure 1A shows that GRN is mainly distributed in macrophages for Glioma_GSE84465 and KIRC_GSE111360, while Figure 1B indicates they are distributed in Malignant and Tprolif, which confuses me regarding the authors' results. Also, the labels in Figure 1B are not fully displayed.
4、 Figures S2 and S3 have the same legend, which is confusing; please standardize the figures. Moreover, the labels in this figure are unclear. Several figures, including 1C, D and 2B, D, lack sufficient clarity. Figure 3B also has multiple labels, and the font does not comply. I strongly recommend that the authors strictly revise all figures according to journal requirements.
5、 In Supplementary Figure 4A, the authors state that "GRN was more significantly expressed in the stroma area," which requires statistical results for validation; otherwise, the use of "significantly" may mislead readers.
6、 GSE151530, as described in the GEO database, comes from three platforms. The authors need to explain how the data were integrated and specify filtering parameters during quality control to ensure that the public dataset aligns with their objectives. Additionally, the authors did not use the original data annotations, so it is necessary to provide evidence for the reannotation of cells to ensure readers' trust in the annotation results.
7、 The authors indicate in the methods section that they used CellChat to analyze cell-cell interactions, yet in the results section, they mention using CellPhoneDB, and the figure legend states they used CellPhone, which is very confusing. The authors need to accurately describe the analysis methods used; otherwise, the reliability of their results may be questioned.
8、 The authors state, "A strong communication was present between GRN+ macrophages and CD8+334 T cells, as well as CD4+ T cells," but the results in Figure 1A do not support this claim. The authors need to demonstrate that this cell-cell interaction in HCC differs from that in normal liver tissue. The authors only used HCC sample data from the GSE151530 dataset, which does not show the communication changes between GRN+ macrophages and CD8+334 T cells in HCC versus healthy samples. It is recommended to add healthy samples and use the "cellchat multiple samples" module for analysis.
9、 Figure 7 shows that after IL-4 induces macrophages to differentiate from M0 to M2, GRN significantly increases, indicating that GRN+ is one of the markers for M2. However, since the authors focus on the functional role of GRN+ macrophages, they need to further explain the differences between GRN+ macrophages and M2 macrophages to justify the uniqueness and rationality behind their focus on GRN+ macrophages.
10、 The authors conclude that GRN+ macrophages induce T-cell exhaustion, but they only provide correlation results from Figure 3C and weakened IFN-γ secretion in co-cultured cells, which is a weak support for the conclusion and needs additional evidence. Furthermore, in the abstract, the authors describe "GRN+ macrophages directly contacted with CD8+ T cells," which needs careful consideration as it could either refer to direct contact or ligand interactions.
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Overall, this study demonstrates that GRN+ macrophages in M2 macrophages execute T-cell exhaustion in tumors, which is a valuable finding that is exciting. I hope the authors can provide richer supporting results. It was a pleasure to read this research.
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