Title: Identification of the angiogenesis related genes for predicting prognosis of patients with
gastric cancer
Version: 1 Date: 03 Jan 2021
Reviewer's report:
In this manuscript Zheng et al. report several angiogenesis related genes to predict prognosis
of gastric cancer by bioinformatic analysis. Initially, TCGA dataset was mined to discover
angiogenesis related genes (ARGs) differentially expressed between normal and tumor tissues.
Then univariate and multivariate Cox regression analysis was used to identify the associations
between differentially expressed ARSs and prognosis of GC patients. 9 genes (AGT, ANGPT1,
SERPINE1, ANGPT2, PVT1, PROCR, KIT, PLAUR and CAV1) were screened out to construct
a risk scoring mode to predict prognosis of gastric cancer patients; this was followed by
validation in TCGA datasets (training group) and in additional external datasets (validation
group). Next, nomogram was constructed based on age, stage, and risk score to predict survival
of patients with gastric cancer.
Several conceptual weaknesses and issues with data presentation weaken this manuscript in its
current format:
1. The title of Supplementary Table I should be gastric cancer, but not colorectal cancer. The
total number of other characteristics is 378, but the number of Stomach adenocarcinoma is 375,
why these two numbers are inconsistent?
2. Generally, TNM stage is considered to be the most important independent prognostic factor,
however, age is not so significant. In Figure 4d, age but not stage is an independent prognostic
factor for GC patients by multivariate COX regression analysis. Could you give a reasonable
explanation? The Authors need to clarify if age evaluation is a continuous variable or categorical
variable; and stage is based on T1/T2-4 or T1/T2/T3/T4.
3. Though the p-value in Figure 5e is statistically significant, the survival curve doesn’t show
obvious difference between two groups, and the AUC in Figure 5f is only 0.603, which is not
good enough to predict patients’ survival.
4. The results from multivariate COX regression analysis in Figure 4d show that stage is not an
independent prognostic factor, why author put the stage in constructing the nomogram model?
Actually, TNM stage is generally acknowledged as the most important prognostic factor. The
authors should clarify if risk score is superior than TNM stage, or combing age, stage and risk
score (nomogram model) is superior than TNM stage alone.
5. In Figure 8, the authors indicated that AGT, ANGPT1, SERPINE1, ANGPT2, PROCR, KIT,
PLAUR and CAV1 were strongly positive in GC tissues compared to normal gastric tissues.
However, the IHC staining of AGT, CAV1, and KIT do not show strongly positive in tumors.
And the staining of ANGPT2 and SERPINE1 are similar in tumor and normal tissues. I suggest
authors use tissue microarrays to examine the IHC staining of these markers in gastric cancer
tissues.
Title: Identification of the angiogenesis related genes for predicting prognosis of patients with
gastric cancer
Version: 1 Date: 03 Jan 2021
Reviewer's report:
In this manuscript Zheng et al. report several angiogenesis related genes to predict prognosis
of gastric cancer by bioinformatic analysis. Initially, TCGA dataset was mined to discover
angiogenesis related genes (ARGs) differentially expressed between normal and tumor tissues.
Then univariate and multivariate Cox regression analysis was used to identify the associations
between differentially expressed ARSs and prognosis of GC patients. 9 genes (AGT, ANGPT1,
SERPINE1, ANGPT2, PVT1, PROCR, KIT, PLAUR and CAV1) were screened out to construct
a risk scoring mode to predict prognosis of gastric cancer patients; this was followed by
validation in TCGA datasets (training group) and in additional external datasets (validation
group). Next, nomogram was constructed based on age, stage, and risk score to predict survival
of patients with gastric cancer.
Several conceptual weaknesses and issues with data presentation weaken this manuscript in its
current format:
1. The title of Supplementary Table I should be gastric cancer, but not colorectal cancer. The
total number of other characteristics is 378, but the number of Stomach adenocarcinoma is 375,
why these two numbers are inconsistent?
2. Generally, TNM stage is considered to be the most important independent prognostic factor,
however, age is not so significant. In Figure 4d, age but not stage is an independent prognostic
factor for GC patients by multivariate COX regression analysis. Could you give a reasonable
explanation? The Authors need to clarify if age evaluation is a continuous variable or categorical
variable; and stage is based on T1/T2-4 or T1/T2/T3/T4.
3. Though the p-value in Figure 5e is statistically significant, the survival curve doesn’t show
obvious difference between two groups, and the AUC in Figure 5f is only 0.603, which is not
good enough to predict patients’ survival.
4. The results from multivariate COX regression analysis in Figure 4d show that stage is not an
independent prognostic factor, why author put the stage in constructing the nomogram model?
Actually, TNM stage is generally acknowledged as the most important prognostic factor. The
authors should clarify if risk score is superior than TNM stage, or combing age, stage and risk
score (nomogram model) is superior than TNM stage alone.
5. In Figure 8, the authors indicated that AGT, ANGPT1, SERPINE1, ANGPT2, PROCR, KIT,
PLAUR and CAV1 were strongly positive in GC tissues compared to normal gastric tissues.
However, the IHC staining of AGT, CAV1, and KIT do not show strongly positive in tumors.
And the staining of ANGPT2 and SERPINE1 are similar in tumor and normal tissues. I suggest
authors use tissue microarrays to examine the IHC staining of these markers in gastric cancer
tissues.
Please review this paper.
user
Title: Identification of the angiogenesis related genes for predicting prognosis of patients with
gastric cancer
Version: 1 Date: 03 Jan 2021
Reviewer's report:
In this manuscript Zheng et al. report several angiogenesis related genes to predict prognosis
of gastric cancer by bioinformatic analysis. Initially, TCGA dataset was mined to discover
angiogenesis related genes (ARGs) differentially expressed between normal and tumor tissues.
Then univariate and multivariate Cox regression analysis was used to identify the associations
between differentially expressed ARSs and prognosis of GC patients. 9 genes (AGT, ANGPT1,
SERPINE1, ANGPT2, PVT1, PROCR, KIT, PLAUR and CAV1) were screened out to construct
a risk scoring mode to predict prognosis of gastric cancer patients; this was followed by
validation in TCGA datasets (training group) and in additional external datasets (validation
group). Next, nomogram was constructed based on age, stage, and risk score to predict survival
of patients with gastric cancer.
Several conceptual weaknesses and issues with data presentation weaken this manuscript in its
current format:
1. The title of Supplementary Table I should be gastric cancer, but not colorectal cancer. The
total number of other characteristics is 378, but the number of Stomach adenocarcinoma is 375,
why these two numbers are inconsistent?
2. Generally, TNM stage is considered to be the most important independent prognostic factor,
however, age is not so significant. In Figure 4d, age but not stage is an independent prognostic
factor for GC patients by multivariate COX regression analysis. Could you give a reasonable
explanation? The Authors need to clarify if age evaluation is a continuous variable or categorical
variable; and stage is based on T1/T2-4 or T1/T2/T3/T4.
3. Though the p-value in Figure 5e is statistically significant, the survival curve doesn’t show
obvious difference between two groups, and the AUC in Figure 5f is only 0.603, which is not
good enough to predict patients’ survival.
4. The results from multivariate COX regression analysis in Figure 4d show that stage is not an
independent prognostic factor, why author put the stage in constructing the nomogram model?
Actually, TNM stage is generally acknowledged as the most important prognostic factor. The
authors should clarify if risk score is superior than TNM stage, or combing age, stage and risk
score (nomogram model) is superior than TNM stage alone.
5. In Figure 8, the authors indicated that AGT, ANGPT1, SERPINE1, ANGPT2, PROCR, KIT,
PLAUR and CAV1 were strongly positive in GC tissues compared to normal gastric tissues.
However, the IHC staining of AGT, CAV1, and KIT do not show strongly positive in tumors.
And the staining of ANGPT2 and SERPINE1 are similar in tumor and normal tissues. I suggest
authors use tissue microarrays to examine the IHC staining of these markers in gastric cancer
tissues.
assistant