Title: LINC00634 is a novel prognostic biomarker for colon cancer associated with immune
infiltration
Version: 2 Date: 24 May 2024
Reviewer's report:
PEER REVIEWER ASSESSMENTS:
OBJECTIVE - Full research articles: is there a clear objective that addresses a testable research
question(s) (brief or other article types: is there a clear objective)?
Yes - there is a clear objective
DESIGN - Is the current approach (including controls and analysis protocols) appropriate for
the objective?
Yes - the approach is appropriate
EXECUTION - Are the experiments and analyses performed with technical rigor to allow
confidence in the results?
No - there are minor issues
STATISTICS - Is the use of statistics in the manuscript appropriate?
No - there are issues with the statistics in the study
INTERPRETATION - Is the current interpretation/discussion of the results reasonable and not
overstated?
No - there are minor issues
OVERALL MANUSCRIPT POTENTIAL - Is the current version of this work technically
sound? If not, can revisions be made to make the work technically sound?
Probably - with minor revisions
PEER REVIEWER COMMENTS:
GENERAL COMMENTS: This paper describes an exploration of prognostic biomarkers for
those with colon cancer associated with immune filtration. The authors have adhered to some
best practices, such as using LASSO for variable selection and Cox regression. The authors
could do more to assure readers that the biomarkers add additional prognostic information to
demographic, clinical and treatment variables.
REQUESTED REVISIONS:
Comment 1. The authors could better adhere to the REMARK guidelines (Altman et al.
Reporting Recommendations for Tumor Marker Prognostic Studies [REMARK]: Explanation
and Elaboration. PLoS Med. 2012 May; 9(5): e1001216). Currently, the authors have not
adhered to items on the checklist.
Comment 2. It is important to know if the specimens were measured before or after treatment.
Timing is important since the findings could be related to treatment induced changes rather than
natural variation. Regardless, controlling for treatment would seem necessary, as unmeasured
characteristics of diseases status could confound the association of biomarkers with outcomes.
These unmeasured confounders could be related to treatment selection.
Comment 3. The statistical analysis section was not consistent with the statistics presented in
the paper. Many statistical methods used, such as LASSO and Cox regression, were discussed in
other sections. A number of methods used in the figures (e.g., correlation, Kaplan Meier curves)
are also not mentioned in the statistical methods section. The statistical analysis section should
describe all statistical methods used.
Comment 4. I did not see the C-index for the demographic and clinical factors used alone. How
much the C-index increases when adding the biomarkers would be useful for readers to know. It
is common that biomarkers do not add much predictive value beyond demographic and clinical
variables.
Comment 5. The use of Tukey’s posthoc test is not necessarily consistent with the statement
that p<0.05 was considered statistically significant. The authors could describe in the text how
Tukey’s method impacted inference in the figures (e.g., Figure 5) compared with standard
p-values that do not account for multiple hypothesis testing. The use of Tukey’s method is
excellent, and more details about how it protected the Type I error rate would assure readers that
the findings are robust to multiple hypothesis testing.
Comment 6. A table describing means or proportions of demographic, clinical, and treatment
variables would be a valuable addition to the paper. Readers would better understand the sample
with such a table.
Comment 7. The authors could note if the tumor samples came from different people, or if some
were samples from different locations of the same person. If many were from the same person,
methods for repeated measures should be used.
Comment 8. The authors could note in the manuscript that the “top 25% method” implies the
highest (top) and lowest (bottom) quartiles.
Comment 9 (minor). Multivariate should be changed to multivariable throughout. Technically,
multivariate refers to methods such as principal components analysis, which are different from
regressions.
Comment 10 (minor). The figure legends could be more informative, such as defining the dashed
lines in the Kaplan Meier curves. They may be 95% confidence intervals. The colors in Figure
1 do not seem to be defined (I think they are related to the magnitude of the correlation).  In
Figures 3a and 6b, the colors ranging from -2 to 2 should be defined. They may be T-statistics
or Z-statistics. The densities, lines, and gray area could be better defined in Figures 6D and 6E.
The gray regions may be standard errors or 95% confidence intervals (it seems important to
make the distinction).
ADDITIONAL REQUESTS/SUGGESTIONS:
I have no further comments.
Title: LINC00634 is a novel prognostic biomarker for colon cancer associated with immune
infiltration
Version: 2 Date: 24 May 2024
Reviewer's report:
PEER REVIEWER ASSESSMENTS:
OBJECTIVE - Full research articles: is there a clear objective that addresses a testable research
question(s) (brief or other article types: is there a clear objective)?
Yes - there is a clear objective
DESIGN - Is the current approach (including controls and analysis protocols) appropriate for
the objective?
Yes - the approach is appropriate
EXECUTION - Are the experiments and analyses performed with technical rigor to allow
confidence in the results?
No - there are minor issues
STATISTICS - Is the use of statistics in the manuscript appropriate?
No - there are issues with the statistics in the study
INTERPRETATION - Is the current interpretation/discussion of the results reasonable and not
overstated?
No - there are minor issues
OVERALL MANUSCRIPT POTENTIAL - Is the current version of this work technically
sound? If not, can revisions be made to make the work technically sound?
Probably - with minor revisions
PEER REVIEWER COMMENTS:
GENERAL COMMENTS: This paper describes an exploration of prognostic biomarkers for
those with colon cancer associated with immune filtration. The authors have adhered to some
best practices, such as using LASSO for variable selection and Cox regression. The authors
could do more to assure readers that the biomarkers add additional prognostic information to
demographic, clinical and treatment variables.
REQUESTED REVISIONS:
Comment 1. The authors could better adhere to the REMARK guidelines (Altman et al.
Reporting Recommendations for Tumor Marker Prognostic Studies [REMARK]: Explanation
and Elaboration. PLoS Med. 2012 May; 9(5): e1001216). Currently, the authors have not
adhered to items on the checklist.
Comment 2. It is important to know if the specimens were measured before or after treatment.
Timing is important since the findings could be related to treatment induced changes rather than
natural variation. Regardless, controlling for treatment would seem necessary, as unmeasured
characteristics of diseases status could confound the association of biomarkers with outcomes.
These unmeasured confounders could be related to treatment selection.
Comment 3. The statistical analysis section was not consistent with the statistics presented in
the paper. Many statistical methods used, such as LASSO and Cox regression, were discussed in
other sections. A number of methods used in the figures (e.g., correlation, Kaplan Meier curves)
are also not mentioned in the statistical methods section. The statistical analysis section should
describe all statistical methods used.
Comment 4. I did not see the C-index for the demographic and clinical factors used alone. How
much the C-index increases when adding the biomarkers would be useful for readers to know. It
is common that biomarkers do not add much predictive value beyond demographic and clinical
variables.
Comment 5. The use of Tukey’s posthoc test is not necessarily consistent with the statement
that p<0.05 was considered statistically significant. The authors could describe in the text how
Tukey’s method impacted inference in the figures (e.g., Figure 5) compared with standard
p-values that do not account for multiple hypothesis testing. The use of Tukey’s method is
excellent, and more details about how it protected the Type I error rate would assure readers that
the findings are robust to multiple hypothesis testing.
Comment 6. A table describing means or proportions of demographic, clinical, and treatment
variables would be a valuable addition to the paper. Readers would better understand the sample
with such a table.
Comment 7. The authors could note if the tumor samples came from different people, or if some
were samples from different locations of the same person. If many were from the same person,
methods for repeated measures should be used.
Comment 8. The authors could note in the manuscript that the “top 25% method” implies the
highest (top) and lowest (bottom) quartiles.
Comment 9 (minor). Multivariate should be changed to multivariable throughout. Technically,
multivariate refers to methods such as principal components analysis, which are different from
regressions.
Comment 10 (minor). The figure legends could be more informative, such as defining the dashed
lines in the Kaplan Meier curves. They may be 95% confidence intervals. The colors in Figure
1 do not seem to be defined (I think they are related to the magnitude of the correlation).  In
Figures 3a and 6b, the colors ranging from -2 to 2 should be defined. They may be T-statistics
or Z-statistics. The densities, lines, and gray area could be better defined in Figures 6D and 6E.
The gray regions may be standard errors or 95% confidence intervals (it seems important to
make the distinction).
ADDITIONAL REQUESTS/SUGGESTIONS:
I have no further comments.
Please review this paper.
user
Title: LINC00634 is a novel prognostic biomarker for colon cancer associated with immune
infiltration
Version: 2 Date: 24 May 2024
Reviewer's report:
PEER REVIEWER ASSESSMENTS:
OBJECTIVE - Full research articles: is there a clear objective that addresses a testable research
question(s) (brief or other article types: is there a clear objective)?
Yes - there is a clear objective
DESIGN - Is the current approach (including controls and analysis protocols) appropriate for
the objective?
Yes - the approach is appropriate
EXECUTION - Are the experiments and analyses performed with technical rigor to allow
confidence in the results?
No - there are minor issues
STATISTICS - Is the use of statistics in the manuscript appropriate?
No - there are issues with the statistics in the study
INTERPRETATION - Is the current interpretation/discussion of the results reasonable and not
overstated?
No - there are minor issues
OVERALL MANUSCRIPT POTENTIAL - Is the current version of this work technically
sound? If not, can revisions be made to make the work technically sound?
Probably - with minor revisions
PEER REVIEWER COMMENTS:
GENERAL COMMENTS: This paper describes an exploration of prognostic biomarkers for
those with colon cancer associated with immune filtration. The authors have adhered to some
best practices, such as using LASSO for variable selection and Cox regression. The authors
could do more to assure readers that the biomarkers add additional prognostic information to
demographic, clinical and treatment variables.
REQUESTED REVISIONS:
Comment 1. The authors could better adhere to the REMARK guidelines (Altman et al.
Reporting Recommendations for Tumor Marker Prognostic Studies [REMARK]: Explanation
and Elaboration. PLoS Med. 2012 May; 9(5): e1001216). Currently, the authors have not
adhered to items on the checklist.
Comment 2. It is important to know if the specimens were measured before or after treatment.
Timing is important since the findings could be related to treatment induced changes rather than
natural variation. Regardless, controlling for treatment would seem necessary, as unmeasured
characteristics of diseases status could confound the association of biomarkers with outcomes.
These unmeasured confounders could be related to treatment selection.
Comment 3. The statistical analysis section was not consistent with the statistics presented in
the paper. Many statistical methods used, such as LASSO and Cox regression, were discussed in
other sections. A number of methods used in the figures (e.g., correlation, Kaplan Meier curves)
are also not mentioned in the statistical methods section. The statistical analysis section should
describe all statistical methods used.
Comment 4. I did not see the C-index for the demographic and clinical factors used alone. How
much the C-index increases when adding the biomarkers would be useful for readers to know. It
is common that biomarkers do not add much predictive value beyond demographic and clinical
variables.
Comment 5. The use of Tukey’s posthoc test is not necessarily consistent with the statement
that p<0.05 was considered statistically significant. The authors could describe in the text how
Tukey’s method impacted inference in the figures (e.g., Figure 5) compared with standard
p-values that do not account for multiple hypothesis testing. The use of Tukey’s method is
excellent, and more details about how it protected the Type I error rate would assure readers that
the findings are robust to multiple hypothesis testing.
Comment 6. A table describing means or proportions of demographic, clinical, and treatment
variables would be a valuable addition to the paper. Readers would better understand the sample
with such a table.
Comment 7. The authors could note if the tumor samples came from different people, or if some
were samples from different locations of the same person. If many were from the same person,
methods for repeated measures should be used.
Comment 8. The authors could note in the manuscript that the “top 25% method” implies the
highest (top) and lowest (bottom) quartiles.
Comment 9 (minor). Multivariate should be changed to multivariable throughout. Technically,
multivariate refers to methods such as principal components analysis, which are different from
regressions.
Comment 10 (minor). The figure legends could be more informative, such as defining the dashed
lines in the Kaplan Meier curves. They may be 95% confidence intervals. The colors in Figure
1 do not seem to be defined (I think they are related to the magnitude of the correlation).  In
Figures 3a and 6b, the colors ranging from -2 to 2 should be defined. They may be T-statistics
or Z-statistics. The densities, lines, and gray area could be better defined in Figures 6D and 6E.
The gray regions may be standard errors or 95% confidence intervals (it seems important to
make the distinction).
ADDITIONAL REQUESTS/SUGGESTIONS:
I have no further comments.
assistant