Title: Identification and Validation of Necroptosis-related Prognostic Gene Signature and
Tumor Immune Microenvironment Infiltration Characterization in Esophageal Carcinoma
Version: 2 Date: 29 Apr 2022
Reviewer:  Xin Fan
Reviewer's report:
This study constructed a prognostic model by screening the differentially expressed NRGs
related to prognosis. The authors further ran lasso regression to further screen three genes to
construct a prognostic model. The model performs well under simple verification. And the
authors also explored the mutation status of 52 differentially expressed NRGs. Correlations
between immune infiltration and model/3 modeled genes were also explored. As a simple and
common study, it basically covers the necessary methods and results. But there have been many
studies of such models. The underlying methods and results already hardly support its scientific
value, so I offer some suggestions that the authors can take to significantly improve the quality
of this study. It is hoped that these suggestions will significantly enhance the scientific value of
this study. These modifications are not very complicated but still take some time to complete. I
hope the author has done all the revisions carefully. I would also be happy to review the revised
manuscript again.
Main recommendations:
1. Figures 3-4 all cover small images. Would you mind a corresponding introduction to each
small figure in the Results section?
Please refer to the small figure in "The Kaplan-Meier survival curves are shown in Fig5.".
Why only show the KM survival curves of these 7 genes? I guess they are obtained by
univariate survival analysis. But the average reader has a hard time connecting them through
your description. Therefore, I think you should first show the results of univariate survival
analysis of differentially expressed NRGs with prognostic value. Forest graphs can be used in
this process.
2. 2. It was described in the Methods section that "Time ROC analysis was employed to predict
accuracy of the riskscore and each gene." But I did not find the ROC curve of each gene in the
Results section. Please match methods and results carefully.
3. Tables 2, 3, and 4 appear to be redundant in this study because you did not interpret their
results. I suggest removing them and the associated description of the method results. As
an addition, I think you can add comparisons of risk scores between subgroups of different
clinical characteristics. For specific practices, you can refer to the following literature and cite
it correctly.
Lu M, Li J, Fan X, Xie F, Fan J, Xiong Y. Novel Immune-Related Ferroptosis Signature
in Esophageal Cancer: An Informatics Exploration of Biological Processes Related to
the TMEM161B-AS1/hsa-miR-27a-3p/GCH1 Regulatory Network. Front Genet. 2022 Feb
24;13:829384. doi: 10.3389/fgene.2022.829384. PMID: 35281840; PMCID: PMC8908453.
4. The performance validation of the model lacks validation on external datasets. This research
must be supplemented with validation on external datasets to determine the performance of the
model. As far as I know currently GEO has esophageal cancer datasets with survival data. You
can get it in the following literature. Lu M, Li J, Fan X, Xie F, Fan J, Xiong Y. Novel Immune-
Related Ferroptosis Signature in Esophageal Cancer: An Informatics Exploration of Biological
Processes Related to the TMEM161B-AS1/hsa-miR-27a-3p/GCH1 Regulatory Network. Front
Genet. 2022 Feb 24;13:829384. doi: 10.3389/fgene.2022.829384. PMID: 35281840; PMCID:
PMC8908453.
5. In addition, I also propose to randomly split the EC data set of TCGA into two data sets to
verify the model performance. Because there are many studies on such models at present. Models
with untested performance seem to have a detrimental effect on future research. You can obtain
the corresponding method from the following literature and cite it correctly. Fan X, Ou Y, Liu
H, Zhan L, Zhu X, Cheng M, Li Q, Yin D, Liao L. A Ferroptosis-Related Prognostic Signature
Based on Antitumor Immunity and Tumor Protein p53 Mutation Exploration for Guiding
Treatment in Patients With Head and Neck Squamous Cell Carcinoma. Front Genet. 2021 Sep
20;12:732211. doi: 10.3389/fgene.2021.732211. PMID: 34616431; PMCID: PMC8488345.
6. It is recommended to use the ROC curve to further verify the performance of the nomogram.
You can obtain the corresponding process and cite it correctly through the following literature.
Fan X, Song J, Fan Y, Li J, Chen Y, Zhu H, Zhang Z. CSMD1 Mutation Related to Immunity Can
Be Used as a Marker to Evaluate the Clinical Therapeutic Effect and Prognosis of Patients with
Esophageal Cancer. Int J Gen Med . 2021 Nov 23;14:8689-8710. doi: 10.2147/IJGM.S338284.
PMID: 34849012; PMCID: PMC8627272.
7. Fig.S2 looks more like a table. I feel like you should provide it as a table and reference it.
8. It is recommended to increase the analysis of the predictive value of the model in terms
of immunotherapy and chemotherapy efficacy. The specific process can refer to the above
literatures.
9. To increase the scientific value of this study, I also suggest that the authors add simple
experiments to verify the differential expression of the 3 modeled genes between EC and normal
tissues, such as IHC staining or PCR. These experiments can be performed using tissues or cell
lines. Implemented and doesn't cost much.
10. Please standardize and correctly add some headings in Table 1, such as "levels" and
"Overall". Because it's hard for me to directly understand what they represent. And the whole
table layout doesn't look very standardized. Please re-edit the form according to the standard
form.
11. Please add filter conditions for differential analysis in "NRGs Differential Expression
Analysis" in "Materials and Methods". I also observed that the Results section shows all the
differentially expressed genes. This is somewhat different from "The "limma" and "reshape2"
software packages were used to identify the differences in NRG expression between EC and
normal esophageal tissues." described in the Methods section. Please correct the corresponding
method.
12. There is a match error in “As shown in Fig. 2C and Fig.2D, genetic mutations were observed
in 19.02% (35 / 184) of the EC samples. Among them, we found that the most common
classification of variation was missense mutation (Fig. 2C). SNPs was the most common type
of variation, and C > T ranked as the highest level of SNV class. GLUD2, PYGM, JAK3 and
STAT1 as the gene with the highest mutation frequency among the 52 NRGs (Fig. 2D).” of
Results section. I double checked that the pictures did not match the corresponding descriptions.
Please check carefully and match the reference to the image. Similar errors later should also be
carefully checked and corrected.
language problem:
There are many language errors in this manuscript, the following are some of the ones I found.
I think language editing and editing certificates are necessary before acceptance.
1. Please define "GO" and "KEGG" in "For the study, we discussed the relevance between NRGs
and prognosis of EC using UCSC Xena database, GO, KEGG, TIMER, and Kaplan-Meier (KM)
plotter." of the Introduction section ".
2. "The UCSC Xena database was utilized to obtain TCGA and Genotype-Tissue
Expression (GTEX) RNA-seq gene expression and clinical date(https://toil-xena-hub.s3.us-
east-1.amazonaws.com/download/TcgaTargetGtex_rsem_gene_tpm.gz; Full metadata)"  in
"Materials and Methods" should be changed to "The UCSC Xena database was
utilized to obtain TCGA and Genotype-Tissue Expression (GTEX) RNA-seq gene
expression and clinical data (https://toil-xena-hub.s3.us-east-1.amazonaws. com/download/
TcgaTargetGtex_rsem_gene_tpm.gz;Full metadata)". And "(" and the preceding word require
spaces.
3. "Table 1, Clinical characteristics of patients with EC." changed to "Table 1. Clinical
characteristics of patients with EC."
4. There are some unprofessional expressions in the text, including language, terminology, and
descriptions of some specific methods and results.
5. "Figure" and "Fig" are sometimes used in the manuscript. Please be consistent, and Figure
references are not standardized.
6. The description in the Results section "According to our research, we proved the correlation
between the expression of prognostic NRGs (PPIA, SLC25A5, TNFRSF10B) and immune
infiltration." should be changed to "According to our research, we proved the correlation
between the expression of prognostic NRGs (PPIA, SLC25A5 and TNFRSF10B) and immune
infiltration. "
7. "ESCA" appears in Fig.8A. Would you mind defining it? Authors are advised to re-examine
the manuscript and define abbreviations that appear first in the text. In addition, abbreviated
definitions appearing in tables and figures are also necessary.
Title: Identification and Validation of Necroptosis-related Prognostic Gene Signature and
Tumor Immune Microenvironment Infiltration Characterization in Esophageal Carcinoma
Version: 2 Date: 29 Apr 2022
Reviewer:  Xin Fan
Reviewer's report:
This study constructed a prognostic model by screening the differentially expressed NRGs
related to prognosis. The authors further ran lasso regression to further screen three genes to
construct a prognostic model. The model performs well under simple verification. And the
authors also explored the mutation status of 52 differentially expressed NRGs. Correlations
between immune infiltration and model/3 modeled genes were also explored. As a simple and
common study, it basically covers the necessary methods and results. But there have been many
studies of such models. The underlying methods and results already hardly support its scientific
value, so I offer some suggestions that the authors can take to significantly improve the quality
of this study. It is hoped that these suggestions will significantly enhance the scientific value of
this study. These modifications are not very complicated but still take some time to complete. I
hope the author has done all the revisions carefully. I would also be happy to review the revised
manuscript again.
Main recommendations:
1. Figures 3-4 all cover small images. Would you mind a corresponding introduction to each
small figure in the Results section?
Please refer to the small figure in "The Kaplan-Meier survival curves are shown in Fig5.".
Why only show the KM survival curves of these 7 genes? I guess they are obtained by
univariate survival analysis. But the average reader has a hard time connecting them through
your description. Therefore, I think you should first show the results of univariate survival
analysis of differentially expressed NRGs with prognostic value. Forest graphs can be used in
this process.
2. 2. It was described in the Methods section that "Time ROC analysis was employed to predict
accuracy of the riskscore and each gene." But I did not find the ROC curve of each gene in the
Results section. Please match methods and results carefully.
3. Tables 2, 3, and 4 appear to be redundant in this study because you did not interpret their
results. I suggest removing them and the associated description of the method results. As
an addition, I think you can add comparisons of risk scores between subgroups of different
clinical characteristics. For specific practices, you can refer to the following literature and cite
it correctly.
Lu M, Li J, Fan X, Xie F, Fan J, Xiong Y. Novel Immune-Related Ferroptosis Signature
in Esophageal Cancer: An Informatics Exploration of Biological Processes Related to
the TMEM161B-AS1/hsa-miR-27a-3p/GCH1 Regulatory Network. Front Genet. 2022 Feb
24;13:829384. doi: 10.3389/fgene.2022.829384. PMID: 35281840; PMCID: PMC8908453.
4. The performance validation of the model lacks validation on external datasets. This research
must be supplemented with validation on external datasets to determine the performance of the
model. As far as I know currently GEO has esophageal cancer datasets with survival data. You
can get it in the following literature. Lu M, Li J, Fan X, Xie F, Fan J, Xiong Y. Novel Immune-
Related Ferroptosis Signature in Esophageal Cancer: An Informatics Exploration of Biological
Processes Related to the TMEM161B-AS1/hsa-miR-27a-3p/GCH1 Regulatory Network. Front
Genet. 2022 Feb 24;13:829384. doi: 10.3389/fgene.2022.829384. PMID: 35281840; PMCID:
PMC8908453.
5. In addition, I also propose to randomly split the EC data set of TCGA into two data sets to
verify the model performance. Because there are many studies on such models at present. Models
with untested performance seem to have a detrimental effect on future research. You can obtain
the corresponding method from the following literature and cite it correctly. Fan X, Ou Y, Liu
H, Zhan L, Zhu X, Cheng M, Li Q, Yin D, Liao L. A Ferroptosis-Related Prognostic Signature
Based on Antitumor Immunity and Tumor Protein p53 Mutation Exploration for Guiding
Treatment in Patients With Head and Neck Squamous Cell Carcinoma. Front Genet. 2021 Sep
20;12:732211. doi: 10.3389/fgene.2021.732211. PMID: 34616431; PMCID: PMC8488345.
6. It is recommended to use the ROC curve to further verify the performance of the nomogram.
You can obtain the corresponding process and cite it correctly through the following literature.
Fan X, Song J, Fan Y, Li J, Chen Y, Zhu H, Zhang Z. CSMD1 Mutation Related to Immunity Can
Be Used as a Marker to Evaluate the Clinical Therapeutic Effect and Prognosis of Patients with
Esophageal Cancer. Int J Gen Med . 2021 Nov 23;14:8689-8710. doi: 10.2147/IJGM.S338284.
PMID: 34849012; PMCID: PMC8627272.
7. Fig.S2 looks more like a table. I feel like you should provide it as a table and reference it.
8. It is recommended to increase the analysis of the predictive value of the model in terms
of immunotherapy and chemotherapy efficacy. The specific process can refer to the above
literatures.
9. To increase the scientific value of this study, I also suggest that the authors add simple
experiments to verify the differential expression of the 3 modeled genes between EC and normal
tissues, such as IHC staining or PCR. These experiments can be performed using tissues or cell
lines. Implemented and doesn't cost much.
10. Please standardize and correctly add some headings in Table 1, such as "levels" and
"Overall". Because it's hard for me to directly understand what they represent. And the whole
table layout doesn't look very standardized. Please re-edit the form according to the standard
form.
11. Please add filter conditions for differential analysis in "NRGs Differential Expression
Analysis" in "Materials and Methods". I also observed that the Results section shows all the
differentially expressed genes. This is somewhat different from "The "limma" and "reshape2"
software packages were used to identify the differences in NRG expression between EC and
normal esophageal tissues." described in the Methods section. Please correct the corresponding
method.
12. There is a match error in “As shown in Fig. 2C and Fig.2D, genetic mutations were observed
in 19.02% (35 / 184) of the EC samples. Among them, we found that the most common
classification of variation was missense mutation (Fig. 2C). SNPs was the most common type
of variation, and C > T ranked as the highest level of SNV class. GLUD2, PYGM, JAK3 and
STAT1 as the gene with the highest mutation frequency among the 52 NRGs (Fig. 2D).” of
Results section. I double checked that the pictures did not match the corresponding descriptions.
Please check carefully and match the reference to the image. Similar errors later should also be
carefully checked and corrected.
language problem:
There are many language errors in this manuscript, the following are some of the ones I found.
I think language editing and editing certificates are necessary before acceptance.
1. Please define "GO" and "KEGG" in "For the study, we discussed the relevance between NRGs
and prognosis of EC using UCSC Xena database, GO, KEGG, TIMER, and Kaplan-Meier (KM)
plotter." of the Introduction section ".
2. "The UCSC Xena database was utilized to obtain TCGA and Genotype-Tissue
Expression (GTEX) RNA-seq gene expression and clinical date(https://toil-xena-hub.s3.us-
east-1.amazonaws.com/download/TcgaTargetGtex_rsem_gene_tpm.gz; Full metadata)"  in
"Materials and Methods" should be changed to "The UCSC Xena database was
utilized to obtain TCGA and Genotype-Tissue Expression (GTEX) RNA-seq gene
expression and clinical data (https://toil-xena-hub.s3.us-east-1.amazonaws. com/download/
TcgaTargetGtex_rsem_gene_tpm.gz;Full metadata)". And "(" and the preceding word require
spaces.
3. "Table 1, Clinical characteristics of patients with EC." changed to "Table 1. Clinical
characteristics of patients with EC."
4. There are some unprofessional expressions in the text, including language, terminology, and
descriptions of some specific methods and results.
5. "Figure" and "Fig" are sometimes used in the manuscript. Please be consistent, and Figure
references are not standardized.
6. The description in the Results section "According to our research, we proved the correlation
between the expression of prognostic NRGs (PPIA, SLC25A5, TNFRSF10B) and immune
infiltration." should be changed to "According to our research, we proved the correlation
between the expression of prognostic NRGs (PPIA, SLC25A5 and TNFRSF10B) and immune
infiltration. "
7. "ESCA" appears in Fig.8A. Would you mind defining it? Authors are advised to re-examine
the manuscript and define abbreviations that appear first in the text. In addition, abbreviated
definitions appearing in tables and figures are also necessary.
Please review this paper.
user
Title: Identification and Validation of Necroptosis-related Prognostic Gene Signature and
Tumor Immune Microenvironment Infiltration Characterization in Esophageal Carcinoma
Version: 2 Date: 29 Apr 2022
Reviewer:  Xin Fan
Reviewer's report:
This study constructed a prognostic model by screening the differentially expressed NRGs
related to prognosis. The authors further ran lasso regression to further screen three genes to
construct a prognostic model. The model performs well under simple verification. And the
authors also explored the mutation status of 52 differentially expressed NRGs. Correlations
between immune infiltration and model/3 modeled genes were also explored. As a simple and
common study, it basically covers the necessary methods and results. But there have been many
studies of such models. The underlying methods and results already hardly support its scientific
value, so I offer some suggestions that the authors can take to significantly improve the quality
of this study. It is hoped that these suggestions will significantly enhance the scientific value of
this study. These modifications are not very complicated but still take some time to complete. I
hope the author has done all the revisions carefully. I would also be happy to review the revised
manuscript again.
Main recommendations:
1. Figures 3-4 all cover small images. Would you mind a corresponding introduction to each
small figure in the Results section?
Please refer to the small figure in "The Kaplan-Meier survival curves are shown in Fig5.".
Why only show the KM survival curves of these 7 genes? I guess they are obtained by
univariate survival analysis. But the average reader has a hard time connecting them through
your description. Therefore, I think you should first show the results of univariate survival
analysis of differentially expressed NRGs with prognostic value. Forest graphs can be used in
this process.
2. 2. It was described in the Methods section that "Time ROC analysis was employed to predict
accuracy of the riskscore and each gene." But I did not find the ROC curve of each gene in the
Results section. Please match methods and results carefully.
3. Tables 2, 3, and 4 appear to be redundant in this study because you did not interpret their
results. I suggest removing them and the associated description of the method results. As
an addition, I think you can add comparisons of risk scores between subgroups of different
clinical characteristics. For specific practices, you can refer to the following literature and cite
it correctly.
Lu M, Li J, Fan X, Xie F, Fan J, Xiong Y. Novel Immune-Related Ferroptosis Signature
in Esophageal Cancer: An Informatics Exploration of Biological Processes Related to
the TMEM161B-AS1/hsa-miR-27a-3p/GCH1 Regulatory Network. Front Genet. 2022 Feb
24;13:829384. doi: 10.3389/fgene.2022.829384. PMID: 35281840; PMCID: PMC8908453.
4. The performance validation of the model lacks validation on external datasets. This research
must be supplemented with validation on external datasets to determine the performance of the
model. As far as I know currently GEO has esophageal cancer datasets with survival data. You
can get it in the following literature. Lu M, Li J, Fan X, Xie F, Fan J, Xiong Y. Novel Immune-
Related Ferroptosis Signature in Esophageal Cancer: An Informatics Exploration of Biological
Processes Related to the TMEM161B-AS1/hsa-miR-27a-3p/GCH1 Regulatory Network. Front
Genet. 2022 Feb 24;13:829384. doi: 10.3389/fgene.2022.829384. PMID: 35281840; PMCID:
PMC8908453.
5. In addition, I also propose to randomly split the EC data set of TCGA into two data sets to
verify the model performance. Because there are many studies on such models at present. Models
with untested performance seem to have a detrimental effect on future research. You can obtain
the corresponding method from the following literature and cite it correctly. Fan X, Ou Y, Liu
H, Zhan L, Zhu X, Cheng M, Li Q, Yin D, Liao L. A Ferroptosis-Related Prognostic Signature
Based on Antitumor Immunity and Tumor Protein p53 Mutation Exploration for Guiding
Treatment in Patients With Head and Neck Squamous Cell Carcinoma. Front Genet. 2021 Sep
20;12:732211. doi: 10.3389/fgene.2021.732211. PMID: 34616431; PMCID: PMC8488345.
6. It is recommended to use the ROC curve to further verify the performance of the nomogram.
You can obtain the corresponding process and cite it correctly through the following literature.
Fan X, Song J, Fan Y, Li J, Chen Y, Zhu H, Zhang Z. CSMD1 Mutation Related to Immunity Can
Be Used as a Marker to Evaluate the Clinical Therapeutic Effect and Prognosis of Patients with
Esophageal Cancer. Int J Gen Med . 2021 Nov 23;14:8689-8710. doi: 10.2147/IJGM.S338284.
PMID: 34849012; PMCID: PMC8627272.
7. Fig.S2 looks more like a table. I feel like you should provide it as a table and reference it.
8. It is recommended to increase the analysis of the predictive value of the model in terms
of immunotherapy and chemotherapy efficacy. The specific process can refer to the above
literatures.
9. To increase the scientific value of this study, I also suggest that the authors add simple
experiments to verify the differential expression of the 3 modeled genes between EC and normal
tissues, such as IHC staining or PCR. These experiments can be performed using tissues or cell
lines. Implemented and doesn't cost much.
10. Please standardize and correctly add some headings in Table 1, such as "levels" and
"Overall". Because it's hard for me to directly understand what they represent. And the whole
table layout doesn't look very standardized. Please re-edit the form according to the standard
form.
11. Please add filter conditions for differential analysis in "NRGs Differential Expression
Analysis" in "Materials and Methods". I also observed that the Results section shows all the
differentially expressed genes. This is somewhat different from "The "limma" and "reshape2"
software packages were used to identify the differences in NRG expression between EC and
normal esophageal tissues." described in the Methods section. Please correct the corresponding
method.
12. There is a match error in “As shown in Fig. 2C and Fig.2D, genetic mutations were observed
in 19.02% (35 / 184) of the EC samples. Among them, we found that the most common
classification of variation was missense mutation (Fig. 2C). SNPs was the most common type
of variation, and C > T ranked as the highest level of SNV class. GLUD2, PYGM, JAK3 and
STAT1 as the gene with the highest mutation frequency among the 52 NRGs (Fig. 2D).” of
Results section. I double checked that the pictures did not match the corresponding descriptions.
Please check carefully and match the reference to the image. Similar errors later should also be
carefully checked and corrected.
language problem:
There are many language errors in this manuscript, the following are some of the ones I found.
I think language editing and editing certificates are necessary before acceptance.
1. Please define "GO" and "KEGG" in "For the study, we discussed the relevance between NRGs
and prognosis of EC using UCSC Xena database, GO, KEGG, TIMER, and Kaplan-Meier (KM)
plotter." of the Introduction section ".
2. "The UCSC Xena database was utilized to obtain TCGA and Genotype-Tissue
Expression (GTEX) RNA-seq gene expression and clinical date(https://toil-xena-hub.s3.us-
east-1.amazonaws.com/download/TcgaTargetGtex_rsem_gene_tpm.gz; Full metadata)"  in
"Materials and Methods" should be changed to "The UCSC Xena database was
utilized to obtain TCGA and Genotype-Tissue Expression (GTEX) RNA-seq gene
expression and clinical data (https://toil-xena-hub.s3.us-east-1.amazonaws. com/download/
TcgaTargetGtex_rsem_gene_tpm.gz;Full metadata)". And "(" and the preceding word require
spaces.
3. "Table 1, Clinical characteristics of patients with EC." changed to "Table 1. Clinical
characteristics of patients with EC."
4. There are some unprofessional expressions in the text, including language, terminology, and
descriptions of some specific methods and results.
5. "Figure" and "Fig" are sometimes used in the manuscript. Please be consistent, and Figure
references are not standardized.
6. The description in the Results section "According to our research, we proved the correlation
between the expression of prognostic NRGs (PPIA, SLC25A5, TNFRSF10B) and immune
infiltration." should be changed to "According to our research, we proved the correlation
between the expression of prognostic NRGs (PPIA, SLC25A5 and TNFRSF10B) and immune
infiltration. "
7. "ESCA" appears in Fig.8A. Would you mind defining it? Authors are advised to re-examine
the manuscript and define abbreviations that appear first in the text. In addition, abbreviated
definitions appearing in tables and figures are also necessary.
assistant