Title: Identification and immunological characterization of cuproptosis-related molecular
clusters in Ulcerative Colitis
Version: 2 Date: 20 Feb 2023
Reviewer's report:
In this study, the authors performed systematic bioinformatics and machine learning manner
among ulcerative colitis samples, exploring some diagnostic effects of ulcerative colitis
disease, coppercytosis, and the immune phenotype analysis, proving some new insights to treat
inflammatory bowel disease. There are several limitations to the study which are outlined below.
1. The part of the discussion is needed to extend deeply, not just exhibit your results of research
work equally. For instance, the synergistic and antagonistic effect of CRGs and correlation
analysis of immune cells infiltration in your cohorts is not declared biological significance.
2. It has not explained why you chose 14 genes including DLST etc. as your representative
CRGs in your research.
3. You performed both a consensus cluster and PCA analysis to obtain two relatively
independent groups in 55 UC samples, however, the resultant analysis of coppercytosis and
immune cells infiltration only occurred in PCA analysis, so your purpose of conducting a
consensus cluster is not clear and needed to be more specification.
4. In the WGCNA of 55UC samples, based on the number of your samples, the Soft Threshold
power was up to 16 or 17, you should illustrate whether power value is appropriate or not. In
the WGCNA both in figure 5 and 6, Module Eigengene colors were not annotated further by
biological significance, such as the associated phenotypes and pathways
5. In the part of machine learning, the panel of 5 genes in RF model is not consistent with your
figure 9 A
6. Importantly, since your validation based on the machine model is not more perfected when
the ratio of UC and control approaching closely (2:1 in GSE107597 cohort) than the ratio being
largely (7:1 in GSE92415 cohort), you should analyze or make a specification
Title: Identification and immunological characterization of cuproptosis-related molecular
clusters in Ulcerative Colitis
Version: 2 Date: 20 Feb 2023
Reviewer's report:
In this study, the authors performed systematic bioinformatics and machine learning manner
among ulcerative colitis samples, exploring some diagnostic effects of ulcerative colitis
disease, coppercytosis, and the immune phenotype analysis, proving some new insights to treat
inflammatory bowel disease. There are several limitations to the study which are outlined below.
1. The part of the discussion is needed to extend deeply, not just exhibit your results of research
work equally. For instance, the synergistic and antagonistic effect of CRGs and correlation
analysis of immune cells infiltration in your cohorts is not declared biological significance.
2. It has not explained why you chose 14 genes including DLST etc. as your representative
CRGs in your research.
3. You performed both a consensus cluster and PCA analysis to obtain two relatively
independent groups in 55 UC samples, however, the resultant analysis of coppercytosis and
immune cells infiltration only occurred in PCA analysis, so your purpose of conducting a
consensus cluster is not clear and needed to be more specification.
4. In the WGCNA of 55UC samples, based on the number of your samples, the Soft Threshold
power was up to 16 or 17, you should illustrate whether power value is appropriate or not. In
the WGCNA both in figure 5 and 6, Module Eigengene colors were not annotated further by
biological significance, such as the associated phenotypes and pathways
5. In the part of machine learning, the panel of 5 genes in RF model is not consistent with your
figure 9 A
6. Importantly, since your validation based on the machine model is not more perfected when
the ratio of UC and control approaching closely (2:1 in GSE107597 cohort) than the ratio being
largely (7:1 in GSE92415 cohort), you should analyze or make a specification
Please review this paper.
user
Title: Identification and immunological characterization of cuproptosis-related molecular
clusters in Ulcerative Colitis
Version: 2 Date: 20 Feb 2023
Reviewer's report:
In this study, the authors performed systematic bioinformatics and machine learning manner
among ulcerative colitis samples, exploring some diagnostic effects of ulcerative colitis
disease, coppercytosis, and the immune phenotype analysis, proving some new insights to treat
inflammatory bowel disease. There are several limitations to the study which are outlined below.
1. The part of the discussion is needed to extend deeply, not just exhibit your results of research
work equally. For instance, the synergistic and antagonistic effect of CRGs and correlation
analysis of immune cells infiltration in your cohorts is not declared biological significance.
2. It has not explained why you chose 14 genes including DLST etc. as your representative
CRGs in your research.
3. You performed both a consensus cluster and PCA analysis to obtain two relatively
independent groups in 55 UC samples, however, the resultant analysis of coppercytosis and
immune cells infiltration only occurred in PCA analysis, so your purpose of conducting a
consensus cluster is not clear and needed to be more specification.
4. In the WGCNA of 55UC samples, based on the number of your samples, the Soft Threshold
power was up to 16 or 17, you should illustrate whether power value is appropriate or not. In
the WGCNA both in figure 5 and 6, Module Eigengene colors were not annotated further by
biological significance, such as the associated phenotypes and pathways
5. In the part of machine learning, the panel of 5 genes in RF model is not consistent with your
figure 9 A
6. Importantly, since your validation based on the machine model is not more perfected when
the ratio of UC and control approaching closely (2:1 in GSE107597 cohort) than the ratio being
largely (7:1 in GSE92415 cohort), you should analyze or make a specification
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