Title: Identification of a Novel m6A-related lncRNA Pair Signature for Predicting the Prognosis
of Gastric Cancer Patients
Version: 4 Date: 20 Dec 2021
Reviewer's report:
RE: Identification of a novel m6A-Related lncRNA pairs Signature for Predicting the Prognosis
in Gastric Cancer Patients
N6-methyladenosine (m6A) is the most common form modification on mRNA, and m6A
modification gene could be a potential markers in diagnosis and prognosis of several cancers.
This study utilized the TCGA dataset to construct and validate a m6A-related lncRNA pairs
signature (m6A-LPS) for predicting the prognosis of GC patients. The signature established by
paring m6A-related lncRNAs regardless of expression levels showed a highly and independently
clinical prediction value in GC patients. This manuscript has certain novelty and provides a new
insight into m6A-related lncRNAs for GC prognosis, however, the validation was inadequate
and there were some major revisions are required.
1.In Abstract, the method should describe the details in the selection of m6A-related lncRNA
pairs, otherwise it is quite difficult to understand the prognostic model.
2.In my opinion, one of the advantage of the signature is established by paring m6A-related
lncRNAs, which is rather robust to batch effect and more suitable for clinical individualized
application. It would be better to highlight this advantage in the introduction or discussion
sections, which could be refereed to the literature (PMID:16646797, PMID:26254430 etc. ).
3.In Materials and Methods section on page 6: “Total of 21 m6A regulators were acquired from
published studies, and 10 of 21 m6A regulators with hazard ratio (HR)>1 and p<0.05 in GC
patients were further screened in Kaplan-Meier Plotter” The result should not be described in
the methods section.
4.In Materials and Methods section on page 6: “Where the Expr is the expression level of m6A-
related lncRNA pairs” Here, the expression level of lncRNA pairs should be defined more
clearly.
5.The training and validation sets used in this study were derived from the same dataset (TCGA).
The prognostic performance of the model should be validated in multiple independent datasets,
which could be obtained from public databases (such as GEO, ArrayExpress). More important,
the treatment strategy for patients in TCGA should be described more clearly, which is an
important factor for prognosis.
6.Some statistical results should be represented in the main text. For example, the result on page
12: “The results revealed that the dendritic cells resting, macrophage M2, monocytes, and T cell
CD4 memory resting are positively correlated with the risk score, while T cells CD4 memory
activated is just opposite in CIBERSORT dataset (Figure 9).” The p values and correlation
coefficients should be represented.
Title: Identification of a Novel m6A-related lncRNA Pair Signature for Predicting the Prognosis
of Gastric Cancer Patients
Version: 4 Date: 20 Dec 2021
Reviewer's report:
RE: Identification of a novel m6A-Related lncRNA pairs Signature for Predicting the Prognosis
in Gastric Cancer Patients
N6-methyladenosine (m6A) is the most common form modification on mRNA, and m6A
modification gene could be a potential markers in diagnosis and prognosis of several cancers.
This study utilized the TCGA dataset to construct and validate a m6A-related lncRNA pairs
signature (m6A-LPS) for predicting the prognosis of GC patients. The signature established by
paring m6A-related lncRNAs regardless of expression levels showed a highly and independently
clinical prediction value in GC patients. This manuscript has certain novelty and provides a new
insight into m6A-related lncRNAs for GC prognosis, however, the validation was inadequate
and there were some major revisions are required.
1.In Abstract, the method should describe the details in the selection of m6A-related lncRNA
pairs, otherwise it is quite difficult to understand the prognostic model.
2.In my opinion, one of the advantage of the signature is established by paring m6A-related
lncRNAs, which is rather robust to batch effect and more suitable for clinical individualized
application. It would be better to highlight this advantage in the introduction or discussion
sections, which could be refereed to the literature (PMID:16646797, PMID:26254430 etc. ).
3.In Materials and Methods section on page 6: “Total of 21 m6A regulators were acquired from
published studies, and 10 of 21 m6A regulators with hazard ratio (HR)>1 and p<0.05 in GC
patients were further screened in Kaplan-Meier Plotter” The result should not be described in
the methods section.
4.In Materials and Methods section on page 6: “Where the Expr is the expression level of m6A-
related lncRNA pairs” Here, the expression level of lncRNA pairs should be defined more
clearly.
5.The training and validation sets used in this study were derived from the same dataset (TCGA).
The prognostic performance of the model should be validated in multiple independent datasets,
which could be obtained from public databases (such as GEO, ArrayExpress). More important,
the treatment strategy for patients in TCGA should be described more clearly, which is an
important factor for prognosis.
6.Some statistical results should be represented in the main text. For example, the result on page
12: “The results revealed that the dendritic cells resting, macrophage M2, monocytes, and T cell
CD4 memory resting are positively correlated with the risk score, while T cells CD4 memory
activated is just opposite in CIBERSORT dataset (Figure 9).” The p values and correlation
coefficients should be represented.
Please review this paper.
user
Title: Identification of a Novel m6A-related lncRNA Pair Signature for Predicting the Prognosis
of Gastric Cancer Patients
Version: 4 Date: 20 Dec 2021
Reviewer's report:
RE: Identification of a novel m6A-Related lncRNA pairs Signature for Predicting the Prognosis
in Gastric Cancer Patients
N6-methyladenosine (m6A) is the most common form modification on mRNA, and m6A
modification gene could be a potential markers in diagnosis and prognosis of several cancers.
This study utilized the TCGA dataset to construct and validate a m6A-related lncRNA pairs
signature (m6A-LPS) for predicting the prognosis of GC patients. The signature established by
paring m6A-related lncRNAs regardless of expression levels showed a highly and independently
clinical prediction value in GC patients. This manuscript has certain novelty and provides a new
insight into m6A-related lncRNAs for GC prognosis, however, the validation was inadequate
and there were some major revisions are required.
1.In Abstract, the method should describe the details in the selection of m6A-related lncRNA
pairs, otherwise it is quite difficult to understand the prognostic model.
2.In my opinion, one of the advantage of the signature is established by paring m6A-related
lncRNAs, which is rather robust to batch effect and more suitable for clinical individualized
application. It would be better to highlight this advantage in the introduction or discussion
sections, which could be refereed to the literature (PMID:16646797, PMID:26254430 etc. ).
3.In Materials and Methods section on page 6: “Total of 21 m6A regulators were acquired from
published studies, and 10 of 21 m6A regulators with hazard ratio (HR)>1 and p<0.05 in GC
patients were further screened in Kaplan-Meier Plotter” The result should not be described in
the methods section.
4.In Materials and Methods section on page 6: “Where the Expr is the expression level of m6A-
related lncRNA pairs” Here, the expression level of lncRNA pairs should be defined more
clearly.
5.The training and validation sets used in this study were derived from the same dataset (TCGA).
The prognostic performance of the model should be validated in multiple independent datasets,
which could be obtained from public databases (such as GEO, ArrayExpress). More important,
the treatment strategy for patients in TCGA should be described more clearly, which is an
important factor for prognosis.
6.Some statistical results should be represented in the main text. For example, the result on page
12: “The results revealed that the dendritic cells resting, macrophage M2, monocytes, and T cell
CD4 memory resting are positively correlated with the risk score, while T cells CD4 memory
activated is just opposite in CIBERSORT dataset (Figure 9).” The p values and correlation
coefficients should be represented.
assistant