Title: Metabolism reprogramming signature associated with stromal cells abundance in tumor
microenvironment improve prognostic risk classification for gastric cancer
Version: 1 Date: 30 Jan 2022
Reviewer:  Hiroaki Kasashima
Reviewer's report:
The work by Huo shows that the risk score which they identified with the metabolic-related
genes in stromal cells predicted the prognosis of GC patients.
This is an interesting study, showing the potential role of metabolism in stromal cells and
interaction with the immune system in gastric cancer. However, this paper lacks a few analyses
and evaluations to support the authors' conclusions.
1. Authors should describe and explain about ESTIMATE algorithm exactly because this is the
first step to identify the risk score which they established in this paper.
2. The datasets analyzed in this paper collected the total RNA from GC samples, and authors
calculated StromalScore. Is there any association between StromalScore and clinicopathological
features?
3. Does The risk score can be applied in the dataset of isolated stromal cells (CAFs etc.) directly
or separated by Laser-microdissection from GC samples?
4. For Immune cells infiltration, the authors should explain the rationale why they introduced
the comparison of the risk score and ICI. Furthermore, the characteristics of each cluster in ICI
and the meaning that cluster C has lowest of the risk score should be addressed.
Title: Metabolism reprogramming signature associated with stromal cells abundance in tumor
microenvironment improve prognostic risk classification for gastric cancer
Version: 1 Date: 30 Jan 2022
Reviewer:  Hiroaki Kasashima
Reviewer's report:
The work by Huo shows that the risk score which they identified with the metabolic-related
genes in stromal cells predicted the prognosis of GC patients.
This is an interesting study, showing the potential role of metabolism in stromal cells and
interaction with the immune system in gastric cancer. However, this paper lacks a few analyses
and evaluations to support the authors' conclusions.
1. Authors should describe and explain about ESTIMATE algorithm exactly because this is the
first step to identify the risk score which they established in this paper.
2. The datasets analyzed in this paper collected the total RNA from GC samples, and authors
calculated StromalScore. Is there any association between StromalScore and clinicopathological
features?
3. Does The risk score can be applied in the dataset of isolated stromal cells (CAFs etc.) directly
or separated by Laser-microdissection from GC samples?
4. For Immune cells infiltration, the authors should explain the rationale why they introduced
the comparison of the risk score and ICI. Furthermore, the characteristics of each cluster in ICI
and the meaning that cluster C has lowest of the risk score should be addressed.
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: 30 Jan 2022
Reviewer:  Hiroaki Kasashima
Reviewer's report:
The work by Huo shows that the risk score which they identified with the metabolic-related
genes in stromal cells predicted the prognosis of GC patients.
This is an interesting study, showing the potential role of metabolism in stromal cells and
interaction with the immune system in gastric cancer. However, this paper lacks a few analyses
and evaluations to support the authors' conclusions.
1. Authors should describe and explain about ESTIMATE algorithm exactly because this is the
first step to identify the risk score which they established in this paper.
2. The datasets analyzed in this paper collected the total RNA from GC samples, and authors
calculated StromalScore. Is there any association between StromalScore and clinicopathological
features?
3. Does The risk score can be applied in the dataset of isolated stromal cells (CAFs etc.) directly
or separated by Laser-microdissection from GC samples?
4. For Immune cells infiltration, the authors should explain the rationale why they introduced
the comparison of the risk score and ICI. Furthermore, the characteristics of each cluster in ICI
and the meaning that cluster C has lowest of the risk score should be addressed.
assistant