Title: Metabolism reprogramming signature associated with stromal cells abundance in tumor
microenvironment improve prognostic risk classification for gastric cancer
Version: 1 Date: 16 Jun 2022
Reviewer's report:
The authors developed a method combined stromal cells with metabolic reprogramming to
predict the overall survival and classify the risk in gastric cancer. The constructed risk model was
usefulness to identify different sub-cohorts in gastric cancer. This study has certain innovation
to some extent, but there are substantive problems to make it less scientific.
1. Extensive editing of English language and style extremely required. For example:
a. A risk score was constructed by LASSO regression analysis combined multivariate Cox
regression analysis.
A risk score was constructed by LASSO regression analysis combined with multivariate Cox
regression analysis.
b. As the risk score growing, the patients’ risk death increased.
As the risk score growing, the patients’ risk of death increased.
c. We obtained 553 intersection genes (Fig 1E) by identified the DEMRGs between the low-
and high stromal score group of the two independent cohorts
We obtained 553 intersection genes (Fig 1E) by identifying the DEMRGs between the low- and
high stromal score group of the two independent cohorts
...
In the abstract, there are many errors like “were intergrated into”, “to verified”, “an scheme”,
and so on.
2. The manuscript format does not show any consistency. There should be a space between the
parentheses and the previous word.
3. In table 1, the sum of number counting for different entries is inconsistent, for example, the
total number of females and males in TCGA cohort is 325.
4. In each part, the description of the result is so sketchy that it looks like a figure legend.
5. Since the TCGA cohort has been included in the training set, those samples can not be enrolled
in the validation set.
6. In the Results 3.2, the authors described the correlation between the metabolic gene cluster and
prognosis, and concluded that these 553 intersection metabolic genes had a significant impact
on the prognosis of GC. Actually, this conclusion was illogical due to lacking of experimental
evidence and we couldn’t affirm whether it was a reason or result. Description as “These
553 intersection metabolic genes had a significant correlation with the OS of GC” was more
reasonable.
7. In the Results 3.3, the authors should detail how they screened 24 genes from 553 overlapping
genes and specific criteria for screening.
8. In the Results 3.4, the authors should address which cohort was used for identifying the
independent prognostic indicators, how many patients were included. Are there any other clinical
indicators that have been tested?
9. In the Results 3.6, the factors showing no significance in univariate analysis should not been
enrolled in the multivariate analysis.
10. In the results 3.7, the authors explored the relationship between the Immune cell infiltration
characterization and the prognostic signature, but the description is obscure. It would be more
precise to elaborate the characteristics in different ICI clusters and the relationship of the
signature with each cluster.
Title: Metabolism reprogramming signature associated with stromal cells abundance in tumor
microenvironment improve prognostic risk classification for gastric cancer
Version: 1 Date: 16 Jun 2022
Reviewer's report:
The authors developed a method combined stromal cells with metabolic reprogramming to
predict the overall survival and classify the risk in gastric cancer. The constructed risk model was
usefulness to identify different sub-cohorts in gastric cancer. This study has certain innovation
to some extent, but there are substantive problems to make it less scientific.
1. Extensive editing of English language and style extremely required. For example:
a. A risk score was constructed by LASSO regression analysis combined multivariate Cox
regression analysis.
A risk score was constructed by LASSO regression analysis combined with multivariate Cox
regression analysis.
b. As the risk score growing, the patients’ risk death increased.
As the risk score growing, the patients’ risk of death increased.
c. We obtained 553 intersection genes (Fig 1E) by identified the DEMRGs between the low-
and high stromal score group of the two independent cohorts
We obtained 553 intersection genes (Fig 1E) by identifying the DEMRGs between the low- and
high stromal score group of the two independent cohorts
...
In the abstract, there are many errors like “were intergrated into”, “to verified”, “an scheme”,
and so on.
2. The manuscript format does not show any consistency. There should be a space between the
parentheses and the previous word.
3. In table 1, the sum of number counting for different entries is inconsistent, for example, the
total number of females and males in TCGA cohort is 325.
4. In each part, the description of the result is so sketchy that it looks like a figure legend.
5. Since the TCGA cohort has been included in the training set, those samples can not be enrolled
in the validation set.
6. In the Results 3.2, the authors described the correlation between the metabolic gene cluster and
prognosis, and concluded that these 553 intersection metabolic genes had a significant impact
on the prognosis of GC. Actually, this conclusion was illogical due to lacking of experimental
evidence and we couldn’t affirm whether it was a reason or result. Description as “These
553 intersection metabolic genes had a significant correlation with the OS of GC” was more
reasonable.
7. In the Results 3.3, the authors should detail how they screened 24 genes from 553 overlapping
genes and specific criteria for screening.
8. In the Results 3.4, the authors should address which cohort was used for identifying the
independent prognostic indicators, how many patients were included. Are there any other clinical
indicators that have been tested?
9. In the Results 3.6, the factors showing no significance in univariate analysis should not been
enrolled in the multivariate analysis.
10. In the results 3.7, the authors explored the relationship between the Immune cell infiltration
characterization and the prognostic signature, but the description is obscure. It would be more
precise to elaborate the characteristics in different ICI clusters and the relationship of the
signature with each cluster.
Please review this paper.
user
Title: Metabolism reprogramming signature associated with stromal cells abundance in tumor
microenvironment improve prognostic risk classification for gastric cancer
Version: 1 Date: 16 Jun 2022
Reviewer's report:
The authors developed a method combined stromal cells with metabolic reprogramming to
predict the overall survival and classify the risk in gastric cancer. The constructed risk model was
usefulness to identify different sub-cohorts in gastric cancer. This study has certain innovation
to some extent, but there are substantive problems to make it less scientific.
1. Extensive editing of English language and style extremely required. For example:
a. A risk score was constructed by LASSO regression analysis combined multivariate Cox
regression analysis.
A risk score was constructed by LASSO regression analysis combined with multivariate Cox
regression analysis.
b. As the risk score growing, the patients’ risk death increased.
As the risk score growing, the patients’ risk of death increased.
c. We obtained 553 intersection genes (Fig 1E) by identified the DEMRGs between the low-
and high stromal score group of the two independent cohorts
We obtained 553 intersection genes (Fig 1E) by identifying the DEMRGs between the low- and
high stromal score group of the two independent cohorts
...
In the abstract, there are many errors like “were intergrated into”, “to verified”, “an scheme”,
and so on.
2. The manuscript format does not show any consistency. There should be a space between the
parentheses and the previous word.
3. In table 1, the sum of number counting for different entries is inconsistent, for example, the
total number of females and males in TCGA cohort is 325.
4. In each part, the description of the result is so sketchy that it looks like a figure legend.
5. Since the TCGA cohort has been included in the training set, those samples can not be enrolled
in the validation set.
6. In the Results 3.2, the authors described the correlation between the metabolic gene cluster and
prognosis, and concluded that these 553 intersection metabolic genes had a significant impact
on the prognosis of GC. Actually, this conclusion was illogical due to lacking of experimental
evidence and we couldn’t affirm whether it was a reason or result. Description as “These
553 intersection metabolic genes had a significant correlation with the OS of GC” was more
reasonable.
7. In the Results 3.3, the authors should detail how they screened 24 genes from 553 overlapping
genes and specific criteria for screening.
8. In the Results 3.4, the authors should address which cohort was used for identifying the
independent prognostic indicators, how many patients were included. Are there any other clinical
indicators that have been tested?
9. In the Results 3.6, the factors showing no significance in univariate analysis should not been
enrolled in the multivariate analysis.
10. In the results 3.7, the authors explored the relationship between the Immune cell infiltration
characterization and the prognostic signature, but the description is obscure. It would be more
precise to elaborate the characteristics in different ICI clusters and the relationship of the
signature with each cluster.
assistant