Title: Unveiling the heterogeneity and immunotherapy potency of tumor-associated neutrophils
in the tumor microenvironment of Gastric cancer
Version: 1 Date: 20 Mar 2025
Reviewer's report:
The manuscript investigates the heterogeneity of tumor-associated neutrophils (TANs) in gastric
cancer (GC) using integrated single-cell RNA sequencing (scRNA-seq) data. It analyzes 12 GC
samples (primary tumors, non-tumor tissues, and metastatic tumors) and identifies five TAN
subclusters (N0–N4) and ten epithelial subclusters.
Major Concerns
1. The study relies solely on computational analyses, with no in vivo or in vitro validation of the
proposed RGS2 role in TANs or the LGALS9/CD45 and CD46/JAG1 interactions.
• While bulk transcriptomics data (TCGA-STAD, ACRG) validate differential expression,
no functional assays (e.g., immunohistochemistry, flow cytometry, cytokine release studies)
confirm these findings.
2. The study concludes that TANs promote GC progression through CD46/JAG1 and that RGS2
is a viable immunotherapy target, but these are correlations, not causations.
• N3 neutrophils are classified as anti-tumor based on gene expression, yet no direct evidence
confirms their cytotoxic or antigen-presenting functions.
• The N1 neutrophil subset in metastases is labeled “immune-activated”, though it may simply
reflect an interferon-rich microenvironment.
3. Statistical and Methodological Concerns
• Batch Effect Correction: The study integrates scRNA-seq datasets but lacks a clear description
of batch effect corrections, raising concerns about data consistency.
• Neutrophil Annotations: The method for defining TAN subsets lacks validation by another
approach (e.g., protein-level staining), leaving room for misclassification.
• Neutrophil Abundance Estimation: Only the xCell algorithm showed significant tumor-vs-
normal differences, while other deconvolution methods (e.g., CIBERSORT, MCPcounter) did
not.
• Small Sample Size: The dataset comprises only six primary GC patients and one additional
metastatic patient, limiting generalizability.
4. Therapeutic Feasibility of RGS2 Targeting
• RGS2 is an intracellular signaling regulator, making it a challenging therapeutic target (not
easily druggable).
• Contradictory Role: RGS2 is downregulated in tumors but upregulated in N4 neutrophils,
suggesting it may act as a tumor suppressor in epithelial cells but a tumor promoter in
neutrophils.
• No mention of small-molecule inhibitors, monoclonal antibodies, or CRISPR-based
approaches to target RGS2, making clinical application unclear.
Minor Concerns
1. Writing and Clarity Issues
• Several grammatical errors and typos, e.g., “patients response” # “patients’ responses”,
“includeds” # “includes”.
• Acronyms (e.g., TME, TANs, TCGA-STAD) should be defined upon first use.
2. Figure and Labeling Issues
• Figures are densely packed, making interpretation difficult.
• UMAP plots lack clear distinctions between primary vs metastatic tumors.
• Figure legends need more detail, e.g., explicitly describing color codes in heatmaps.
3. Reference Accuracy and Formatting
• Several citations are outdated, and some statements lack direct references (e.g., JAG1’s role
in tumor cell differentiation).
• Some references appear out of order or contain formatting errors (e.g., misplaced brackets).
4. Data and Terminology Clarifications
• TCGA-STAD and ACRG datasets should be explicitly stated as bulk transcriptomic cohorts.
• The term “chief cells” is ambiguous—do the authors mean gastric gland cells rather than
zymogenic cells?
Title: Unveiling the heterogeneity and immunotherapy potency of tumor-associated neutrophils
in the tumor microenvironment of Gastric cancer
Version: 1 Date: 20 Mar 2025
Reviewer's report:
The manuscript investigates the heterogeneity of tumor-associated neutrophils (TANs) in gastric
cancer (GC) using integrated single-cell RNA sequencing (scRNA-seq) data. It analyzes 12 GC
samples (primary tumors, non-tumor tissues, and metastatic tumors) and identifies five TAN
subclusters (N0–N4) and ten epithelial subclusters.
Major Concerns
1. The study relies solely on computational analyses, with no in vivo or in vitro validation of the
proposed RGS2 role in TANs or the LGALS9/CD45 and CD46/JAG1 interactions.
• While bulk transcriptomics data (TCGA-STAD, ACRG) validate differential expression,
no functional assays (e.g., immunohistochemistry, flow cytometry, cytokine release studies)
confirm these findings.
2. The study concludes that TANs promote GC progression through CD46/JAG1 and that RGS2
is a viable immunotherapy target, but these are correlations, not causations.
• N3 neutrophils are classified as anti-tumor based on gene expression, yet no direct evidence
confirms their cytotoxic or antigen-presenting functions.
• The N1 neutrophil subset in metastases is labeled “immune-activated”, though it may simply
reflect an interferon-rich microenvironment.
3. Statistical and Methodological Concerns
• Batch Effect Correction: The study integrates scRNA-seq datasets but lacks a clear description
of batch effect corrections, raising concerns about data consistency.
• Neutrophil Annotations: The method for defining TAN subsets lacks validation by another
approach (e.g., protein-level staining), leaving room for misclassification.
• Neutrophil Abundance Estimation: Only the xCell algorithm showed significant tumor-vs-
normal differences, while other deconvolution methods (e.g., CIBERSORT, MCPcounter) did
not.
• Small Sample Size: The dataset comprises only six primary GC patients and one additional
metastatic patient, limiting generalizability.
4. Therapeutic Feasibility of RGS2 Targeting
• RGS2 is an intracellular signaling regulator, making it a challenging therapeutic target (not
easily druggable).
• Contradictory Role: RGS2 is downregulated in tumors but upregulated in N4 neutrophils,
suggesting it may act as a tumor suppressor in epithelial cells but a tumor promoter in
neutrophils.
• No mention of small-molecule inhibitors, monoclonal antibodies, or CRISPR-based
approaches to target RGS2, making clinical application unclear.
Minor Concerns
1. Writing and Clarity Issues
• Several grammatical errors and typos, e.g., “patients response” # “patients’ responses”,
“includeds” # “includes”.
• Acronyms (e.g., TME, TANs, TCGA-STAD) should be defined upon first use.
2. Figure and Labeling Issues
• Figures are densely packed, making interpretation difficult.
• UMAP plots lack clear distinctions between primary vs metastatic tumors.
• Figure legends need more detail, e.g., explicitly describing color codes in heatmaps.
3. Reference Accuracy and Formatting
• Several citations are outdated, and some statements lack direct references (e.g., JAG1’s role
in tumor cell differentiation).
• Some references appear out of order or contain formatting errors (e.g., misplaced brackets).
4. Data and Terminology Clarifications
• TCGA-STAD and ACRG datasets should be explicitly stated as bulk transcriptomic cohorts.
• The term “chief cells” is ambiguous—do the authors mean gastric gland cells rather than
zymogenic cells?
Please review this paper.
user
Title: Unveiling the heterogeneity and immunotherapy potency of tumor-associated neutrophils
in the tumor microenvironment of Gastric cancer
Version: 1 Date: 20 Mar 2025
Reviewer's report:
The manuscript investigates the heterogeneity of tumor-associated neutrophils (TANs) in gastric
cancer (GC) using integrated single-cell RNA sequencing (scRNA-seq) data. It analyzes 12 GC
samples (primary tumors, non-tumor tissues, and metastatic tumors) and identifies five TAN
subclusters (N0–N4) and ten epithelial subclusters.
Major Concerns
1. The study relies solely on computational analyses, with no in vivo or in vitro validation of the
proposed RGS2 role in TANs or the LGALS9/CD45 and CD46/JAG1 interactions.
• While bulk transcriptomics data (TCGA-STAD, ACRG) validate differential expression,
no functional assays (e.g., immunohistochemistry, flow cytometry, cytokine release studies)
confirm these findings.
2. The study concludes that TANs promote GC progression through CD46/JAG1 and that RGS2
is a viable immunotherapy target, but these are correlations, not causations.
• N3 neutrophils are classified as anti-tumor based on gene expression, yet no direct evidence
confirms their cytotoxic or antigen-presenting functions.
• The N1 neutrophil subset in metastases is labeled “immune-activated”, though it may simply
reflect an interferon-rich microenvironment.
3. Statistical and Methodological Concerns
• Batch Effect Correction: The study integrates scRNA-seq datasets but lacks a clear description
of batch effect corrections, raising concerns about data consistency.
• Neutrophil Annotations: The method for defining TAN subsets lacks validation by another
approach (e.g., protein-level staining), leaving room for misclassification.
• Neutrophil Abundance Estimation: Only the xCell algorithm showed significant tumor-vs-
normal differences, while other deconvolution methods (e.g., CIBERSORT, MCPcounter) did
not.
• Small Sample Size: The dataset comprises only six primary GC patients and one additional
metastatic patient, limiting generalizability.
4. Therapeutic Feasibility of RGS2 Targeting
• RGS2 is an intracellular signaling regulator, making it a challenging therapeutic target (not
easily druggable).
• Contradictory Role: RGS2 is downregulated in tumors but upregulated in N4 neutrophils,
suggesting it may act as a tumor suppressor in epithelial cells but a tumor promoter in
neutrophils.
• No mention of small-molecule inhibitors, monoclonal antibodies, or CRISPR-based
approaches to target RGS2, making clinical application unclear.
Minor Concerns
1. Writing and Clarity Issues
• Several grammatical errors and typos, e.g., “patients response” # “patients’ responses”,
“includeds” # “includes”.
• Acronyms (e.g., TME, TANs, TCGA-STAD) should be defined upon first use.
2. Figure and Labeling Issues
• Figures are densely packed, making interpretation difficult.
• UMAP plots lack clear distinctions between primary vs metastatic tumors.
• Figure legends need more detail, e.g., explicitly describing color codes in heatmaps.
3. Reference Accuracy and Formatting
• Several citations are outdated, and some statements lack direct references (e.g., JAG1’s role
in tumor cell differentiation).
• Some references appear out of order or contain formatting errors (e.g., misplaced brackets).
4. Data and Terminology Clarifications
• TCGA-STAD and ACRG datasets should be explicitly stated as bulk transcriptomic cohorts.
• The term “chief cells” is ambiguous—do the authors mean gastric gland cells rather than
zymogenic cells?
assistant