Title: Identification and immunological characterization of cuproptosis-related molecular
clusters in Ulcerative Colitis
Version: 2 Date: 20 Feb 2023
Reviewer's report:
Pu et al. explored the prognostic significance of cupropotosis gene signatures in ulcerative
colitis in this manuscript. They identified distinct clusters in a single dataset model. This is an
interesting manuscript with the potential to be improved.
1. The authors need to expand the methodology in order to gain a better understanding of the
raw data used in the study and the normalization methods employed.
2. Deconvolution algorithms for identifying immune cell distributions from whole RNA-Seq
data are complex. Several algorithms have emerged, and a consensus needs to be established
by comparing results from different methods, such as XCELL, EPIC, MCPCounter, and
ESTIMATE. The authors need to quantify the immune signatures using these parameters.
3. The authors need to expand on the cut-offs and statistical parameters used in CIBERSORT.
4. The authors need to validate their findings in an independent dataset in order to strengthen
their results.
5. The authors should also validate their findings using internal experimental samples, such as
through gene expression analysis techniques like real-time PCR or quantifying relevant proteins.
6. Recently, several gene signatures have been identified. The authors need to compare their
strength with other gene signatures and mention the strength and limitations of their model.
7. The authors can explore several publications for comparison, some of them are: Zhang, L.,
Mao, R., Lau, C.T., Chung, W.C., Chan, J.C., Liang, F., Zhao, C., Zhang, X. and Bian, Z.,
2022. Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis
based on machine learning methods. *Scientific Reports*, *12* (1), pp.1-13. and Ning, Yumei,
Kun Lin, Jun Fang, Xiaojia Chen, Xinyi Hu, Lan Liu, Qiu Zhao, Haizhou Wang, and Fan
Wang. "Pyroptosis-Related Signature Predicts the Progression of Ulcerative Colitis and Colitis-
Associated Colorectal Cancer as well as the Anti-TNF Therapeutic Response." *Journal of
Immunology Research*
 2023 (2023).
8. In the above references and similar others, these types of gene signatures have been identified.
It is important to compare these gene signatures and assess their strengths in order to give better
insights to the readers. This will significantly improve the manuscript.
Title: Identification and immunological characterization of cuproptosis-related molecular
clusters in Ulcerative Colitis
Version: 2 Date: 20 Feb 2023
Reviewer's report:
Pu et al. explored the prognostic significance of cupropotosis gene signatures in ulcerative
colitis in this manuscript. They identified distinct clusters in a single dataset model. This is an
interesting manuscript with the potential to be improved.
1. The authors need to expand the methodology in order to gain a better understanding of the
raw data used in the study and the normalization methods employed.
2. Deconvolution algorithms for identifying immune cell distributions from whole RNA-Seq
data are complex. Several algorithms have emerged, and a consensus needs to be established
by comparing results from different methods, such as XCELL, EPIC, MCPCounter, and
ESTIMATE. The authors need to quantify the immune signatures using these parameters.
3. The authors need to expand on the cut-offs and statistical parameters used in CIBERSORT.
4. The authors need to validate their findings in an independent dataset in order to strengthen
their results.
5. The authors should also validate their findings using internal experimental samples, such as
through gene expression analysis techniques like real-time PCR or quantifying relevant proteins.
6. Recently, several gene signatures have been identified. The authors need to compare their
strength with other gene signatures and mention the strength and limitations of their model.
7. The authors can explore several publications for comparison, some of them are: Zhang, L.,
Mao, R., Lau, C.T., Chung, W.C., Chan, J.C., Liang, F., Zhao, C., Zhang, X. and Bian, Z.,
2022. Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis
based on machine learning methods. *Scientific Reports*, *12* (1), pp.1-13. and Ning, Yumei,
Kun Lin, Jun Fang, Xiaojia Chen, Xinyi Hu, Lan Liu, Qiu Zhao, Haizhou Wang, and Fan
Wang. "Pyroptosis-Related Signature Predicts the Progression of Ulcerative Colitis and Colitis-
Associated Colorectal Cancer as well as the Anti-TNF Therapeutic Response." *Journal of
Immunology Research*
 2023 (2023).
8. In the above references and similar others, these types of gene signatures have been identified.
It is important to compare these gene signatures and assess their strengths in order to give better
insights to the readers. This will significantly improve the manuscript.
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:
Pu et al. explored the prognostic significance of cupropotosis gene signatures in ulcerative
colitis in this manuscript. They identified distinct clusters in a single dataset model. This is an
interesting manuscript with the potential to be improved.
1. The authors need to expand the methodology in order to gain a better understanding of the
raw data used in the study and the normalization methods employed.
2. Deconvolution algorithms for identifying immune cell distributions from whole RNA-Seq
data are complex. Several algorithms have emerged, and a consensus needs to be established
by comparing results from different methods, such as XCELL, EPIC, MCPCounter, and
ESTIMATE. The authors need to quantify the immune signatures using these parameters.
3. The authors need to expand on the cut-offs and statistical parameters used in CIBERSORT.
4. The authors need to validate their findings in an independent dataset in order to strengthen
their results.
5. The authors should also validate their findings using internal experimental samples, such as
through gene expression analysis techniques like real-time PCR or quantifying relevant proteins.
6. Recently, several gene signatures have been identified. The authors need to compare their
strength with other gene signatures and mention the strength and limitations of their model.
7. The authors can explore several publications for comparison, some of them are: Zhang, L.,
Mao, R., Lau, C.T., Chung, W.C., Chan, J.C., Liang, F., Zhao, C., Zhang, X. and Bian, Z.,
2022. Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis
based on machine learning methods. *Scientific Reports*, *12* (1), pp.1-13. and Ning, Yumei,
Kun Lin, Jun Fang, Xiaojia Chen, Xinyi Hu, Lan Liu, Qiu Zhao, Haizhou Wang, and Fan
Wang. "Pyroptosis-Related Signature Predicts the Progression of Ulcerative Colitis and Colitis-
Associated Colorectal Cancer as well as the Anti-TNF Therapeutic Response." *Journal of
Immunology Research*
 2023 (2023).
8. In the above references and similar others, these types of gene signatures have been identified.
It is important to compare these gene signatures and assess their strengths in order to give better
insights to the readers. This will significantly improve the manuscript.
assistant