Title: Identification of SCN7A as the key gene associated with tumor mutation burden in gastric
cancer
Version: 1 Date: 28 Aug 2021
Reviewer's report:
Li et al. provide a bioinformatic approach combining many algorithms to find out the target gene
associated with patient survival using multiple public datasets from TCGA and NCBI/GEO.
Overall, the study design, data analyses, and conclusion seem logical. I have some questions
regarding their methods and data interpretation as follow:
1. Authors performed multiple algorithms for screening potential gene candidate which
associated with patient survival. I think if they can combine these algorithms into a single
pipeline, the readers will be easier to follow their work. The current format is flushing too many
algorithms into the method section, which is not a good enough presentation.
2. why the authors select MM >0.8 as the threshold for a significant gene? is it a golden standard
for MM value or just a threshold to filter out many genes?
3. Author found SCN7A as the hub gene after filtering and correlate it with patient survival.
However, as we know mRNA expression level of the gene may or may not translate to its protein
function. Thus, even authors have found the genes highly correlated with patient survival, it is
not necessary to become a target for therapy.
4. "We calculated variances of 19525 genes from “TCGA-STAD.” Ranked by decreasing
standard deviation (SD), the top 25% of genes (n= 4881) were selected for further analysis".
why authors select top 25% of genes? why not 30% or 40%? Please clarify this point.
5. Figure 6B shown R values all less than 0.4, is it mean there is no correlation between
SCN7A expression and TIICs? authors concluded SCN7A is positively correlated with these
cell processes. How can authors have that conclusion with these R values?
6. I suggest authors review all the values/thresholds for all the algorithms performed in this
study and explain why they select those values/thresholds in their analyses. Because this study
is purely using dry-lab techniques for suggesting potential candidates, so it is of the essence for
clarification of these numbers.
7. Can authors perform the analyses for other members of this SCN family? I am curious whether
these family members also have low expression in the tumor relative to normal tissue. it would
provide a more comprehensive understanding of this gene family in gastric cancer.
8. Can authors perform more analyses of SCN7A protein expression level in correlation with
gastric cancer? it would make the study conclusion stronger.
Title: Identification of SCN7A as the key gene associated with tumor mutation burden in gastric
cancer
Version: 1 Date: 28 Aug 2021
Reviewer's report:
Li et al. provide a bioinformatic approach combining many algorithms to find out the target gene
associated with patient survival using multiple public datasets from TCGA and NCBI/GEO.
Overall, the study design, data analyses, and conclusion seem logical. I have some questions
regarding their methods and data interpretation as follow:
1. Authors performed multiple algorithms for screening potential gene candidate which
associated with patient survival. I think if they can combine these algorithms into a single
pipeline, the readers will be easier to follow their work. The current format is flushing too many
algorithms into the method section, which is not a good enough presentation.
2. why the authors select MM >0.8 as the threshold for a significant gene? is it a golden standard
for MM value or just a threshold to filter out many genes?
3. Author found SCN7A as the hub gene after filtering and correlate it with patient survival.
However, as we know mRNA expression level of the gene may or may not translate to its protein
function. Thus, even authors have found the genes highly correlated with patient survival, it is
not necessary to become a target for therapy.
4. "We calculated variances of 19525 genes from “TCGA-STAD.” Ranked by decreasing
standard deviation (SD), the top 25% of genes (n= 4881) were selected for further analysis".
why authors select top 25% of genes? why not 30% or 40%? Please clarify this point.
5. Figure 6B shown R values all less than 0.4, is it mean there is no correlation between
SCN7A expression and TIICs? authors concluded SCN7A is positively correlated with these
cell processes. How can authors have that conclusion with these R values?
6. I suggest authors review all the values/thresholds for all the algorithms performed in this
study and explain why they select those values/thresholds in their analyses. Because this study
is purely using dry-lab techniques for suggesting potential candidates, so it is of the essence for
clarification of these numbers.
7. Can authors perform the analyses for other members of this SCN family? I am curious whether
these family members also have low expression in the tumor relative to normal tissue. it would
provide a more comprehensive understanding of this gene family in gastric cancer.
8. Can authors perform more analyses of SCN7A protein expression level in correlation with
gastric cancer? it would make the study conclusion stronger.
Please review this paper.
user
Title: Identification of SCN7A as the key gene associated with tumor mutation burden in gastric
cancer
Version: 1 Date: 28 Aug 2021
Reviewer's report:
Li et al. provide a bioinformatic approach combining many algorithms to find out the target gene
associated with patient survival using multiple public datasets from TCGA and NCBI/GEO.
Overall, the study design, data analyses, and conclusion seem logical. I have some questions
regarding their methods and data interpretation as follow:
1. Authors performed multiple algorithms for screening potential gene candidate which
associated with patient survival. I think if they can combine these algorithms into a single
pipeline, the readers will be easier to follow their work. The current format is flushing too many
algorithms into the method section, which is not a good enough presentation.
2. why the authors select MM >0.8 as the threshold for a significant gene? is it a golden standard
for MM value or just a threshold to filter out many genes?
3. Author found SCN7A as the hub gene after filtering and correlate it with patient survival.
However, as we know mRNA expression level of the gene may or may not translate to its protein
function. Thus, even authors have found the genes highly correlated with patient survival, it is
not necessary to become a target for therapy.
4. "We calculated variances of 19525 genes from “TCGA-STAD.” Ranked by decreasing
standard deviation (SD), the top 25% of genes (n= 4881) were selected for further analysis".
why authors select top 25% of genes? why not 30% or 40%? Please clarify this point.
5. Figure 6B shown R values all less than 0.4, is it mean there is no correlation between
SCN7A expression and TIICs? authors concluded SCN7A is positively correlated with these
cell processes. How can authors have that conclusion with these R values?
6. I suggest authors review all the values/thresholds for all the algorithms performed in this
study and explain why they select those values/thresholds in their analyses. Because this study
is purely using dry-lab techniques for suggesting potential candidates, so it is of the essence for
clarification of these numbers.
7. Can authors perform the analyses for other members of this SCN family? I am curious whether
these family members also have low expression in the tumor relative to normal tissue. it would
provide a more comprehensive understanding of this gene family in gastric cancer.
8. Can authors perform more analyses of SCN7A protein expression level in correlation with
gastric cancer? it would make the study conclusion stronger.
assistant