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All concerns of the reviewers were adequately addressed and revised manuscript is acceptable now.
[# PeerJ Staff Note - this decision was reviewed and approved by Fanglin Guan, a PeerJ Section Editor covering this Section #]
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Please carefully address concerns of the reviewers and revised manuscript accordingly.
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
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This is an interesting article, but some issues still need to be revised.
1. Risk tables should be added to all prognostic analyses to give readers a clearer understanding of the grouping thresholds.
2. Why experimental validation only around TCP1 should be further explained.
3. Data deficiencies were unavoidable shortcomings in the study.
4. More external data should be brought in for validation.
5. It should be validated with more experiments, including multi-angle validation at the clinical, cellular, and animal levels.
6. Phenotypic validation should be performed in at least two or more cell lines, and phenotypic experiments after overexpression of the target gene should also be performed.
7. Recent literature should be cited, and the format of the references needs to be further optimized.
8. Similar studies on breast cancer and its progression should be read and discussed.
9. The authors should consider engaging in a professional language editing service to ensure the clarity and coherence of the manuscript.
It was a pleasure reading your paper, "Single-cell RNA-seq reveals breast cancer heterogeneity and identifies TCP1 as a therapeutic target in breast cancer," and I thank you for your work. I encourage you to address the issues mentioned, as they can enhance the contribution of manuscripts to the field. The findings will fill a key gap in our understanding of cancer prognosis and provide new tools for cancer treatment to improve patient outcomes.
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The main text states to retain "300-8000 genes/cells" (line 82), but Figure 1A UMAP shows that only 20444 cells were included in the analysis. Based on 68 samples, the average number of cells per sample is only 300, which is much lower than the conventional standard for single-cell studies (usually requiring>5000 cells/sample), indicating data loss or excessive filtering. Doubt about batch correction: The batch effect was corrected using Harmony (line 88), but the comparison before and after correction was not shown (as shown in the PCA graph), making it impossible to evaluate the effect.
The "normal reference" (line 99) using T/B cells as inferCNV is unreasonable: lymphocytes in the breast cancer microenvironment may carry somatic mutations, and normal epithelial cells near the cancer or the open normal breast data set should be used as a reference.
The setting of the CNV threshold is arbitrary ("median value" divides malignant/non-malignant, line 101), lacking a biological or statistical basis; GSEA verifies the existence of circulating evidence in malignant cells (line 111) - using "cancer pathway enrichment" to verify that the classification logic of malignant cells is not valid.
The TCP1 shRNA sequence "GCTCTTTACATGATGCACTTT" (line 332) was aligned with BLAST to actually target the CTSV gene (NCBI Gene ID: 1512), rather than TCP1 (Gene ID: 6950), rendering all functional validation conclusions invalid.
Only BT549 (TNBC cell line) was used to verify TCP1, which did not cover other breast cancer subtypes (such as HER2+, HR+). Conclusion: The generalization is insufficient.
Figure 1C shows that normal breast tissue contains 72.39% epithelial cells, but it is known that normal breast epithelial cells are predominant (>90%), which is inconsistent with anatomical knowledge and suggests a clustering error.
The "RPL41+Epi" subgroup marker gene RPL41 in Figure 3A is a ribosome housekeeping gene and does not have cell subtype specificity; thus, the rationality of the grouping is questionable.
Select 200 genes from the KRT17+subgroup to construct a model (line 250), but do not explain why this subgroup was chosen over others (such as HMGB2+).
The 8 genes screened by LASSO regression (Figure 4A) had logical breaks with the 3 genes confirmed by RSF (NFKBIA, PDLIM4, TCP1), and it was not explained why the remaining 5 genes were discarded.
Claiming that "ERG regulates KRT17+subpopulation" (line 368), but based solely on pySCENIC correlation (Figure 3E-F), lacking experimental validation (such as ChIP qPCR or ERG knockout).
The high-risk group's "immune pathway enrichment" (line 289) is interpreted as an "immune suppressive microenvironment", but GSEA cannot distinguish between immune activation/inhibition states and needs to supplement immune checkpoint expression or T cell depletion markers.
The role of TCP1 (chaperone protein) in breast cancer has been reported by many studies (such as Reference 33: Liu et al. Communications Biology 2025). The author has not compared the existing achievements, and is not innovative enough.
It is claimed that KRT17+epithelial cells have the "highest differentiation potential" (line 365), but KRT17 is a basal cell marker that is typically associated with low differentiation levels (reference 23: Tang et al. Biomolecules 2022).
The feasibility of TCP1 targeted therapy has not been discussed, such as whether small molecule inhibitors exist and whether they affect normal tissue folding function.
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