Title: Finding key genes (UBE2T, KIF4A, CDCA3, and CDCA5) co-expressed in hepatitis,
cirrhosis and hepatocellular carcinoma based on multiple bioinformatics techniques
Version: 1 Date: 14 Mar 2024
Reviewer's report:
I have completed my review of the manuscript by Yingai Zhang et al., which presents a
comprehensive data analysis on the identification of biomarkers in Hepatocellular Carcinoma
(HCC) progression. While the study is extensive in data handling and presents valuable insights,
there are several concerns that I believe need to be addressed:
1 There is an inconsistency in the "Screening of differentially expressed genes (DEGs)" section
where the authors mention "CAH-B 1293 differential genes between HC and LC with 565 up-
regulated and 728 down-regulated." This description does not align with the methodology part.
It is unclear what the "CAH-B 1293 differential genes between HC and LC" actually represents.
2 The authors state twice that "with adjusted (adj.) p < 0.05 and |logFC| = 1 were regarded as
significant DEGs." However, it should be |logFC| > 1 for a gene to be considered significantly
differentially expressed.
3 In the same DEGs section, the manuscript refers to "33 datasets" obtained from gene expression
matrix GSE114783. This terminology is misleading, as what is described appears to be "33
samples," which is fundamentally different from "datasets."
4 The choice of 20 as the soft threshold in WGCNA, despite an R2 of 0.98, may not necessarily
enhance the biological relevance of the network and could potentially lead to an overly sparse
network. A threshold of 19 might be a more cautious approach.
5 The exclusion of the normal group in WGCNA could limit the ability to distinguish between
disease-specific gene modules and those broadly expressed under different conditions. Including
data from a normal control group could significantly enhance the interpretability of the analysis.
6 The rationale behind intersecting the three modules obtained from WGCNA with TCGA
differential genes is not entirely clear. Additionally, the brown module contains as many as 2779
genes, which may require further subdivision and analysis to determine specific gene relevance.
7 The PPI analysis utilizes the MCODE plugin, selecting the first cluster without discussing the
contents of the second or subsequent clusters. This selection process appears somewhat arbitrary
and may benefit from a more systematic approach.
8 The discussion section of the paper is quite limited and does not adequately address the
findings of this research. Moreover, the implications and significance of the identified common
biomarkers need further elaboration to clarify their role in the context of HCC progression.
I believe that addressing these issues would greatly improve the manuscript and provide clarity
and robustness to the findings. I look forward to the authors' response and an updated version
of the manuscript.
Sincerely,
Li Yin
Title: Finding key genes (UBE2T, KIF4A, CDCA3, and CDCA5) co-expressed in hepatitis,
cirrhosis and hepatocellular carcinoma based on multiple bioinformatics techniques
Version: 1 Date: 14 Mar 2024
Reviewer's report:
I have completed my review of the manuscript by Yingai Zhang et al., which presents a
comprehensive data analysis on the identification of biomarkers in Hepatocellular Carcinoma
(HCC) progression. While the study is extensive in data handling and presents valuable insights,
there are several concerns that I believe need to be addressed:
1 There is an inconsistency in the "Screening of differentially expressed genes (DEGs)" section
where the authors mention "CAH-B 1293 differential genes between HC and LC with 565 up-
regulated and 728 down-regulated." This description does not align with the methodology part.
It is unclear what the "CAH-B 1293 differential genes between HC and LC" actually represents.
2 The authors state twice that "with adjusted (adj.) p < 0.05 and |logFC| = 1 were regarded as
significant DEGs." However, it should be |logFC| > 1 for a gene to be considered significantly
differentially expressed.
3 In the same DEGs section, the manuscript refers to "33 datasets" obtained from gene expression
matrix GSE114783. This terminology is misleading, as what is described appears to be "33
samples," which is fundamentally different from "datasets."
4 The choice of 20 as the soft threshold in WGCNA, despite an R2 of 0.98, may not necessarily
enhance the biological relevance of the network and could potentially lead to an overly sparse
network. A threshold of 19 might be a more cautious approach.
5 The exclusion of the normal group in WGCNA could limit the ability to distinguish between
disease-specific gene modules and those broadly expressed under different conditions. Including
data from a normal control group could significantly enhance the interpretability of the analysis.
6 The rationale behind intersecting the three modules obtained from WGCNA with TCGA
differential genes is not entirely clear. Additionally, the brown module contains as many as 2779
genes, which may require further subdivision and analysis to determine specific gene relevance.
7 The PPI analysis utilizes the MCODE plugin, selecting the first cluster without discussing the
contents of the second or subsequent clusters. This selection process appears somewhat arbitrary
and may benefit from a more systematic approach.
8 The discussion section of the paper is quite limited and does not adequately address the
findings of this research. Moreover, the implications and significance of the identified common
biomarkers need further elaboration to clarify their role in the context of HCC progression.
I believe that addressing these issues would greatly improve the manuscript and provide clarity
and robustness to the findings. I look forward to the authors' response and an updated version
of the manuscript.
Sincerely,
Li Yin
Please review this paper.
user
Title: Finding key genes (UBE2T, KIF4A, CDCA3, and CDCA5) co-expressed in hepatitis,
cirrhosis and hepatocellular carcinoma based on multiple bioinformatics techniques
Version: 1 Date: 14 Mar 2024
Reviewer's report:
I have completed my review of the manuscript by Yingai Zhang et al., which presents a
comprehensive data analysis on the identification of biomarkers in Hepatocellular Carcinoma
(HCC) progression. While the study is extensive in data handling and presents valuable insights,
there are several concerns that I believe need to be addressed:
1 There is an inconsistency in the "Screening of differentially expressed genes (DEGs)" section
where the authors mention "CAH-B 1293 differential genes between HC and LC with 565 up-
regulated and 728 down-regulated." This description does not align with the methodology part.
It is unclear what the "CAH-B 1293 differential genes between HC and LC" actually represents.
2 The authors state twice that "with adjusted (adj.) p < 0.05 and |logFC| = 1 were regarded as
significant DEGs." However, it should be |logFC| > 1 for a gene to be considered significantly
differentially expressed.
3 In the same DEGs section, the manuscript refers to "33 datasets" obtained from gene expression
matrix GSE114783. This terminology is misleading, as what is described appears to be "33
samples," which is fundamentally different from "datasets."
4 The choice of 20 as the soft threshold in WGCNA, despite an R2 of 0.98, may not necessarily
enhance the biological relevance of the network and could potentially lead to an overly sparse
network. A threshold of 19 might be a more cautious approach.
5 The exclusion of the normal group in WGCNA could limit the ability to distinguish between
disease-specific gene modules and those broadly expressed under different conditions. Including
data from a normal control group could significantly enhance the interpretability of the analysis.
6 The rationale behind intersecting the three modules obtained from WGCNA with TCGA
differential genes is not entirely clear. Additionally, the brown module contains as many as 2779
genes, which may require further subdivision and analysis to determine specific gene relevance.
7 The PPI analysis utilizes the MCODE plugin, selecting the first cluster without discussing the
contents of the second or subsequent clusters. This selection process appears somewhat arbitrary
and may benefit from a more systematic approach.
8 The discussion section of the paper is quite limited and does not adequately address the
findings of this research. Moreover, the implications and significance of the identified common
biomarkers need further elaboration to clarify their role in the context of HCC progression.
I believe that addressing these issues would greatly improve the manuscript and provide clarity
and robustness to the findings. I look forward to the authors' response and an updated version
of the manuscript.
Sincerely,
Li Yin
assistant