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All issues pointed by the reviewers were addressed and revised manuscript is acceptable now.
[# PeerJ Staff Note - this decision was reviewed and approved by Paula Soares, a PeerJ Section Editor covering this Section #]
The issues have been improved.
The issues have been improved.
The issues have been improved.
Please address the concerns of both reviewers and amend the 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.
The writing is clear and flows nicely throughout the paper. However, the discussion section needs to be reduced as it is a bit lengthy for the reader.
The experimental design is well thought out and fairly detailed with respect to the hypothesis the authors were trying to study. Kindly find detailed review with the attached annotated PDF.
Cuproptosis is not well studied in breast cancer and BRCA-positive breast cancer patients. This paper looks into understanding the relationship between cuproptosis in BRCA-breast cancer patients. The authors have done a detailed study regarding this and will add value to the breast cancer research.
1. Please provide x-axis title or y-axis title for Figures 1 C and D, Figures 3, 4, 6, 7, 8 and 9
2. What are TMB and NA in Figure 1C? Authors should spell out them in some place of the manuscript.
3. In line 90, the authors should introduce the information of the data in detail. E.g. the meaning of M, N, T, stage. Not all the readers are familiar with this simplified writing.
4. In line 94, the typo "GES42568" should be "GSE42568"
5. "OS" in line 120 should be spelled out for the first occurrence
6. Please increase the resolution of Figures 3, 4, 6, 7, 8. Many texts in the figure cannot be seen clearly.
1. The author used RNA-seq data from TCGA and microarray data from GEO. To find differential genes, the author only applied the limma package to the microarray data. There are much more RNA-seq data from TCGA. RNA-seq data has been shown better for gene expression analysis. Why authors do not use DEseq/EdgeR to find differential genes from RNA-seq data?
2. The authors do not provide batch effect analysis for TCGA and GEO datasets. If there are batch effects among the samples, the data should be preprocessed to remove the batch effect. Please ensure the subtypes of the samples are not due to the batch effect.
3. Why use PC1 and PC2 of PCA result for Cox regression in lines 119-122? Only six genes are found in line 253. Reducing the gene dimension is not necessary. I suggest authors use those genes as variables for the regression. In this way, the coefficient of the gene can be directed to interpret how the expression of these genes affects the prognosis. The significant level (p-value) of each coefficient can also support the inclusion or exclusion of the genes. In addition, please check the form of Cusig score in lines 123-124 is line 257 is not consistent. Maybe, the Cusig score in lines 123-124 is wrong and the i represents the sample ID.
1. The author should carefully check the batch effect of the data to ensure the subtype is not due to the batch effect.
2. The author should keep the consistency of differential genes between the results of microarray and RNA-seq data. Otherwise, one of them should be discarded due to the quality or other reasons.
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