Title: Novel Chemokine-Related LncRNA Signature Correlates with the Prognosis, Immune
Landscape, and Therapeutic Sensitivity of Esophageal Squamous Cell Cancer
Version: 1 Date: 25 Jan 2023
Reviewer:  Gopinath M. Sundaram
Reviewer's report:
Chemokines play an important role in tumor microenvironment (TME) and several earlier
studies have demonstrated the link between chemokine expression and cancer progression.
Long non-coding RNAs are emerging regulators of gene expression which functions through
an array of mechanisms and play key roles in development and disease progression. In this
manuscript, authors investigate the prognostic value of lncRNAs that are related to chemokines
in esophageal squamous cell carcinoma (ESCC). Using a published GEO dataset that has
profiled the expression of lnc RNAs in paired normal and tumor samples of 119 ESCC patient
tissues, the authors first identified a total of 677 chemokine related lncRNAs. Using univariate,
LASSO regression and multivariate analysis, authors narrowed down to 6 lncRNA signature
prognostic risk model. Based on this chemokine-related lncRNA signature, the patient dataset
was classified into low and high-risk cohorts. Using multiple algorithms, the predictive efficacy
and validity of the 6 lnc RNA signature model was validated in training and validation sets of
patient cohorts. Based on the analysis, the results revealed that high risk groups display poor
prognosis, increased chemotherapeutic resistance, and increased infiltration of immune cells
in TME. Overall, the authors conclude that the chemokine related lncRNA signature model
elucidated here can provide novel insights in patient prognosis.
Some major concerns are noted in this manuscript which needs to be addressed prior to
publication.
1. There are some key discrepancies in sample size and the nature of samples used for analysis.
Firstly, the distinction between tumor and normal samples is not clear. As per the metadata of
GEO dataset (GSE53624 & GSE 53622) the authors have used in this study, this dataset contains
lncRNA expression profile from 119 paired tumor and normal (non-tumourous tissue derived
from the same patient). But consistently, it is mentioned in the manuscript that “tumor and
normal groups” where the normal group may convey health controls instead of normal tissues
from the same patients. Secondly, there are some inconsistencies in the sample size. In results
section, page 7, line nos 13 to 15, it states that the expression profiling data from 179 tumor
samples were acquired. Where did this come from since the GEO dataset mentioned in method
contain only 119 paired tumor and normal samples.
2. Page 4, line no 23: “ In brief, the GSE53624 dataset includes 119 ESCC patients and 119
normal samples which incorporates 60 ESCC patients and 60 normal samples”. This statement
does not seem to agree with the actual metadata of GSE53624 in NCBI.
3. List of 64 chemokines used in this study and their tumor promoting/inhibitory effect may be
provided as a supplementary table
4. Figure 3E is too small to deduce the actual lncRNA and the chemokines correlation.
5. In Figure 3F, there is a significant up regulation of PRG1 lncRNA in cacner tissues compared
to normal tissue suggesting this may have a pro-tumorigenic role. However, in Figure S1, PRG1
lncRNA is down regulated in high risk group suggesting that this lncRNA may inhibit tumor
progression. How do the authors explain this discrepancy? Similar situation is with ROR1-AS1
which is downregulated in cancer tissues (as per Figure 3F) and shows upregulation in high risk
groups.  While other lncRNAs shows a positive correlation between their differential expression
in cancer and in low/high risk groups, these two lncRNAs appear to deviate. This need to be
discussed in detail.
Title: Novel Chemokine-Related LncRNA Signature Correlates with the Prognosis, Immune
Landscape, and Therapeutic Sensitivity of Esophageal Squamous Cell Cancer
Version: 1 Date: 25 Jan 2023
Reviewer:  Gopinath M. Sundaram
Reviewer's report:
Chemokines play an important role in tumor microenvironment (TME) and several earlier
studies have demonstrated the link between chemokine expression and cancer progression.
Long non-coding RNAs are emerging regulators of gene expression which functions through
an array of mechanisms and play key roles in development and disease progression. In this
manuscript, authors investigate the prognostic value of lncRNAs that are related to chemokines
in esophageal squamous cell carcinoma (ESCC). Using a published GEO dataset that has
profiled the expression of lnc RNAs in paired normal and tumor samples of 119 ESCC patient
tissues, the authors first identified a total of 677 chemokine related lncRNAs. Using univariate,
LASSO regression and multivariate analysis, authors narrowed down to 6 lncRNA signature
prognostic risk model. Based on this chemokine-related lncRNA signature, the patient dataset
was classified into low and high-risk cohorts. Using multiple algorithms, the predictive efficacy
and validity of the 6 lnc RNA signature model was validated in training and validation sets of
patient cohorts. Based on the analysis, the results revealed that high risk groups display poor
prognosis, increased chemotherapeutic resistance, and increased infiltration of immune cells
in TME. Overall, the authors conclude that the chemokine related lncRNA signature model
elucidated here can provide novel insights in patient prognosis.
Some major concerns are noted in this manuscript which needs to be addressed prior to
publication.
1. There are some key discrepancies in sample size and the nature of samples used for analysis.
Firstly, the distinction between tumor and normal samples is not clear. As per the metadata of
GEO dataset (GSE53624 & GSE 53622) the authors have used in this study, this dataset contains
lncRNA expression profile from 119 paired tumor and normal (non-tumourous tissue derived
from the same patient). But consistently, it is mentioned in the manuscript that “tumor and
normal groups” where the normal group may convey health controls instead of normal tissues
from the same patients. Secondly, there are some inconsistencies in the sample size. In results
section, page 7, line nos 13 to 15, it states that the expression profiling data from 179 tumor
samples were acquired. Where did this come from since the GEO dataset mentioned in method
contain only 119 paired tumor and normal samples.
2. Page 4, line no 23: “ In brief, the GSE53624 dataset includes 119 ESCC patients and 119
normal samples which incorporates 60 ESCC patients and 60 normal samples”. This statement
does not seem to agree with the actual metadata of GSE53624 in NCBI.
3. List of 64 chemokines used in this study and their tumor promoting/inhibitory effect may be
provided as a supplementary table
4. Figure 3E is too small to deduce the actual lncRNA and the chemokines correlation.
5. In Figure 3F, there is a significant up regulation of PRG1 lncRNA in cacner tissues compared
to normal tissue suggesting this may have a pro-tumorigenic role. However, in Figure S1, PRG1
lncRNA is down regulated in high risk group suggesting that this lncRNA may inhibit tumor
progression. How do the authors explain this discrepancy? Similar situation is with ROR1-AS1
which is downregulated in cancer tissues (as per Figure 3F) and shows upregulation in high risk
groups.  While other lncRNAs shows a positive correlation between their differential expression
in cancer and in low/high risk groups, these two lncRNAs appear to deviate. This need to be
discussed in detail.
Please review this paper.
user
Title: Novel Chemokine-Related LncRNA Signature Correlates with the Prognosis, Immune
Landscape, and Therapeutic Sensitivity of Esophageal Squamous Cell Cancer
Version: 1 Date: 25 Jan 2023
Reviewer:  Gopinath M. Sundaram
Reviewer's report:
Chemokines play an important role in tumor microenvironment (TME) and several earlier
studies have demonstrated the link between chemokine expression and cancer progression.
Long non-coding RNAs are emerging regulators of gene expression which functions through
an array of mechanisms and play key roles in development and disease progression. In this
manuscript, authors investigate the prognostic value of lncRNAs that are related to chemokines
in esophageal squamous cell carcinoma (ESCC). Using a published GEO dataset that has
profiled the expression of lnc RNAs in paired normal and tumor samples of 119 ESCC patient
tissues, the authors first identified a total of 677 chemokine related lncRNAs. Using univariate,
LASSO regression and multivariate analysis, authors narrowed down to 6 lncRNA signature
prognostic risk model. Based on this chemokine-related lncRNA signature, the patient dataset
was classified into low and high-risk cohorts. Using multiple algorithms, the predictive efficacy
and validity of the 6 lnc RNA signature model was validated in training and validation sets of
patient cohorts. Based on the analysis, the results revealed that high risk groups display poor
prognosis, increased chemotherapeutic resistance, and increased infiltration of immune cells
in TME. Overall, the authors conclude that the chemokine related lncRNA signature model
elucidated here can provide novel insights in patient prognosis.
Some major concerns are noted in this manuscript which needs to be addressed prior to
publication.
1. There are some key discrepancies in sample size and the nature of samples used for analysis.
Firstly, the distinction between tumor and normal samples is not clear. As per the metadata of
GEO dataset (GSE53624 & GSE 53622) the authors have used in this study, this dataset contains
lncRNA expression profile from 119 paired tumor and normal (non-tumourous tissue derived
from the same patient). But consistently, it is mentioned in the manuscript that “tumor and
normal groups” where the normal group may convey health controls instead of normal tissues
from the same patients. Secondly, there are some inconsistencies in the sample size. In results
section, page 7, line nos 13 to 15, it states that the expression profiling data from 179 tumor
samples were acquired. Where did this come from since the GEO dataset mentioned in method
contain only 119 paired tumor and normal samples.
2. Page 4, line no 23: “ In brief, the GSE53624 dataset includes 119 ESCC patients and 119
normal samples which incorporates 60 ESCC patients and 60 normal samples”. This statement
does not seem to agree with the actual metadata of GSE53624 in NCBI.
3. List of 64 chemokines used in this study and their tumor promoting/inhibitory effect may be
provided as a supplementary table
4. Figure 3E is too small to deduce the actual lncRNA and the chemokines correlation.
5. In Figure 3F, there is a significant up regulation of PRG1 lncRNA in cacner tissues compared
to normal tissue suggesting this may have a pro-tumorigenic role. However, in Figure S1, PRG1
lncRNA is down regulated in high risk group suggesting that this lncRNA may inhibit tumor
progression. How do the authors explain this discrepancy? Similar situation is with ROR1-AS1
which is downregulated in cancer tissues (as per Figure 3F) and shows upregulation in high risk
groups.  While other lncRNAs shows a positive correlation between their differential expression
in cancer and in low/high risk groups, these two lncRNAs appear to deviate. This need to be
discussed in detail.
assistant