Title: Elucidating the Role of AC026412.3 in Hepatocellular Carcinoma: A Prognostic
disulfidptosis-related lncRNAs Model Perspective
Version: 1 Date: 07 Jun 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
1. Overly Broad and Redundant Introduction
-The introduction spans many concepts (epidemiology, disulfidptosis, RCD, lncRNAs, drug
sensitivity, immune response, EMT, etc.) without a tight logical flow.Several paragraphs
reiterate the role of disulfidptosis and lncRNAs in cancer without clear transitions, causing
thematic drift.
-Focus and streamline — clearly introduce HCC, briefly explain disulfidptosis, and then
tightly link lncRNAs to disulfidptosis and HCC prognosis. Remove redundant or tangential
explanations.
2. Terminological/Typographical Errors
The correct term is "disulfidoptosis", not "disulfidptosis" (multiple occurrences).
Terms like “DRLRs” are defined too late and used inconsistently. Also, the acronym is long and
may be cumbersome for readers.
3. Weak Justification for the Focus on AC026412.3
Although the study ends up highlighting AC026412.3, the introduction doesn’t justify its
relevance, uniqueness, or novelty in the context of HCC.Provide a brief rationale for prioritizing
AC026412.3 early in the introduction (e.g., no prior functional characterization, differential
expression pattern, etc.).
4. Incomplete Description of Dataset Curation
The description of RNA-seq data processing lacks key technical details (e.g., normalization
method, removal of low-expression transcripts, batch correction if any).Add details on data
normalization (e.g., FPKM, TPM), filtering thresholds, and potential preprocessing steps (e.g.,
log-transformation, batch correction).
5. Statistical Methods Need Clarification
-There is a lack of information on how multiple testing was controlled (e.g., Benjamini-
Hochberg correction) in univariate Cox and DEG analysis.The method for choosing the LASSO
penalty parameter (e.g., lambda.min or lambda.1se) is unclear.
- Clarify statistical thresholds, correction methods, and model selection criteria to improve
transparency.
6. Model Overfitting Risk
-The training/test split is 1:1, but you used 10-fold cross-validation within LASSO and then
further validated on the same test set (which may have informed model selection). This raises
concerns about overfitting and circular validation.
-Consider an independent external dataset (e.g., ICGC) for validation or apply nested cross-
validation to avoid optimistic bias.
7. Missing Clinical Relevance of Risk Score
-While a risk score is derived, there is limited discussion about how it could be applied clinically
(e.g., cutoff choice, integration with BCLC staging, decision-making impact).
-Clarify clinical thresholds and consider comparison with existing models (e.g., ALBI, AFP,
BCLC).
8. Shallow Integration of Functional Enrichment Results
-While GO, GSEA, and PPI are mentioned, there’s no biological interpretation of what key
pathways or networks imply for HCC biology or treatment.
- Go beyond listing pathways — discuss how specific enriched terms (e.g., oxidative stress,
immune escape) link to disulfidoptosis or HCC progression.
9. Immunological Analyses Are Descriptive Only
-The tumor microenvironment analysis (CIBERSORT, ssgsea, TIDE) lacks biological
hypothesis or linkage to disulfidoptosis or AC026412.3.
Recommendation: Strengthen rationale — why is this lncRNA expected to affect immune
infiltration? Are there known immunomodulatory pathways it may regulate?
10. Lack of Detailed Experimental Validation:
While the manuscript discusses in vitro and in vivo validation, the specifics of these experiments
could be better articulated. For example:
What exactly was the experimental setup for cell-line-based experiments and animal models?
Detailed methodological information could improve the reproducibility and transparency of the
results.
How was the correlation between the expression of these DRLRs and patient prognosis assessed
clinically, aside from qRT-PCR? Including specific clinical trial data or real-world evidence
would bolster the argument.
11. Insufficient Exploration of Molecular Mechanisms:
The molecular mechanisms behind the 4 DRLRs and their roles in HCC need further exploration.
While the manuscript mentions the link to disulfidptosis, EMT, and immune response, it would
be beneficial to include more specific molecular insights.
For instance, what signaling pathways are affected by these lncRNAs, and how does
disulfidptosis interact with them at the molecular level? This could provide a more in-depth
understanding of the pathways involved and how they affect HCC progression.
12. TMB and Immunotherapy Analysis:
The manuscript mentions the relationship between TMB scores and immunotherapy response
but lacks a detailed explanation of how the DRLRs model correlates with TMB and
immunotherapy response. Expanding on this point would allow the paper to position its findings
more strongly in the context of personalized medicine and immune checkpoint inhibitors in
HCC.
Also, how do the TIDE scores specifically relate to these lncRNAs? Is there any evidence linking
the expression of these lncRNAs with immune evasion mechanisms? More explanation would
strengthen the role of these lncRNAs as predictive biomarkers for immunotherapy response.
13. The Role of the Tumor Microenvironment (TME):
The relationship between M0 macrophage infiltration and high-risk groups is interesting but
needs to be expanded. Specifically, how does the presence of M0 macrophages affect tumor
progression in HCC? Are there alternative immune cells or factors involved in this process?
Exploring this would add depth to the TME discussion.
How does disulfidptosis modulate immune responses in the TME? More detailed mechanistic
insights into how disulfidptosis and macrophage infiltration influence HCC progression would
improve the overall discussion.
14. Clinical Relevance and Next Steps:
The study highlights the clinical potential of the 4 DRLRs signature in HCC, but further
discussion on how this model could be integrated into clinical practice is necessary. Can this
model be used in routine clinical settings for personalized treatment selection? Is it feasible for
it to be used for early detection or recurrence monitoring?
It would also be valuable to compare the predictive performance of this model against existing
prognostic models for HCC. How does it compare in terms of sensitivity, specificity, and clinical
applicability?
15. Future Directions and Limitations:
While you mention the need for more in vivo and in vitro experiments, it would be good to
elaborate on future experimental designs to validate the model further. For example, which
additional experiments would be necessary to confirm the role of these lncRNAs in HCC
progression, EMT, and metastasis? Would you consider testing the model in clinical trials or
incorporating it into a clinical diagnostic tool?
Consider expanding on the limitations of using the TCGA database, particularly regarding the
lack of representation of certain ethnic groups or HCC subtypes. More explicit comments on the
generalizability of your results would strengthen the manuscript.
Title: Elucidating the Role of AC026412.3 in Hepatocellular Carcinoma: A Prognostic
disulfidptosis-related lncRNAs Model Perspective
Version: 1 Date: 07 Jun 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
1. Overly Broad and Redundant Introduction
-The introduction spans many concepts (epidemiology, disulfidptosis, RCD, lncRNAs, drug
sensitivity, immune response, EMT, etc.) without a tight logical flow.Several paragraphs
reiterate the role of disulfidptosis and lncRNAs in cancer without clear transitions, causing
thematic drift.
-Focus and streamline — clearly introduce HCC, briefly explain disulfidptosis, and then
tightly link lncRNAs to disulfidptosis and HCC prognosis. Remove redundant or tangential
explanations.
2. Terminological/Typographical Errors
The correct term is "disulfidoptosis", not "disulfidptosis" (multiple occurrences).
Terms like “DRLRs” are defined too late and used inconsistently. Also, the acronym is long and
may be cumbersome for readers.
3. Weak Justification for the Focus on AC026412.3
Although the study ends up highlighting AC026412.3, the introduction doesn’t justify its
relevance, uniqueness, or novelty in the context of HCC.Provide a brief rationale for prioritizing
AC026412.3 early in the introduction (e.g., no prior functional characterization, differential
expression pattern, etc.).
4. Incomplete Description of Dataset Curation
The description of RNA-seq data processing lacks key technical details (e.g., normalization
method, removal of low-expression transcripts, batch correction if any).Add details on data
normalization (e.g., FPKM, TPM), filtering thresholds, and potential preprocessing steps (e.g.,
log-transformation, batch correction).
5. Statistical Methods Need Clarification
-There is a lack of information on how multiple testing was controlled (e.g., Benjamini-
Hochberg correction) in univariate Cox and DEG analysis.The method for choosing the LASSO
penalty parameter (e.g., lambda.min or lambda.1se) is unclear.
- Clarify statistical thresholds, correction methods, and model selection criteria to improve
transparency.
6. Model Overfitting Risk
-The training/test split is 1:1, but you used 10-fold cross-validation within LASSO and then
further validated on the same test set (which may have informed model selection). This raises
concerns about overfitting and circular validation.
-Consider an independent external dataset (e.g., ICGC) for validation or apply nested cross-
validation to avoid optimistic bias.
7. Missing Clinical Relevance of Risk Score
-While a risk score is derived, there is limited discussion about how it could be applied clinically
(e.g., cutoff choice, integration with BCLC staging, decision-making impact).
-Clarify clinical thresholds and consider comparison with existing models (e.g., ALBI, AFP,
BCLC).
8. Shallow Integration of Functional Enrichment Results
-While GO, GSEA, and PPI are mentioned, there’s no biological interpretation of what key
pathways or networks imply for HCC biology or treatment.
- Go beyond listing pathways — discuss how specific enriched terms (e.g., oxidative stress,
immune escape) link to disulfidoptosis or HCC progression.
9. Immunological Analyses Are Descriptive Only
-The tumor microenvironment analysis (CIBERSORT, ssgsea, TIDE) lacks biological
hypothesis or linkage to disulfidoptosis or AC026412.3.
Recommendation: Strengthen rationale — why is this lncRNA expected to affect immune
infiltration? Are there known immunomodulatory pathways it may regulate?
10. Lack of Detailed Experimental Validation:
While the manuscript discusses in vitro and in vivo validation, the specifics of these experiments
could be better articulated. For example:
What exactly was the experimental setup for cell-line-based experiments and animal models?
Detailed methodological information could improve the reproducibility and transparency of the
results.
How was the correlation between the expression of these DRLRs and patient prognosis assessed
clinically, aside from qRT-PCR? Including specific clinical trial data or real-world evidence
would bolster the argument.
11. Insufficient Exploration of Molecular Mechanisms:
The molecular mechanisms behind the 4 DRLRs and their roles in HCC need further exploration.
While the manuscript mentions the link to disulfidptosis, EMT, and immune response, it would
be beneficial to include more specific molecular insights.
For instance, what signaling pathways are affected by these lncRNAs, and how does
disulfidptosis interact with them at the molecular level? This could provide a more in-depth
understanding of the pathways involved and how they affect HCC progression.
12. TMB and Immunotherapy Analysis:
The manuscript mentions the relationship between TMB scores and immunotherapy response
but lacks a detailed explanation of how the DRLRs model correlates with TMB and
immunotherapy response. Expanding on this point would allow the paper to position its findings
more strongly in the context of personalized medicine and immune checkpoint inhibitors in
HCC.
Also, how do the TIDE scores specifically relate to these lncRNAs? Is there any evidence linking
the expression of these lncRNAs with immune evasion mechanisms? More explanation would
strengthen the role of these lncRNAs as predictive biomarkers for immunotherapy response.
13. The Role of the Tumor Microenvironment (TME):
The relationship between M0 macrophage infiltration and high-risk groups is interesting but
needs to be expanded. Specifically, how does the presence of M0 macrophages affect tumor
progression in HCC? Are there alternative immune cells or factors involved in this process?
Exploring this would add depth to the TME discussion.
How does disulfidptosis modulate immune responses in the TME? More detailed mechanistic
insights into how disulfidptosis and macrophage infiltration influence HCC progression would
improve the overall discussion.
14. Clinical Relevance and Next Steps:
The study highlights the clinical potential of the 4 DRLRs signature in HCC, but further
discussion on how this model could be integrated into clinical practice is necessary. Can this
model be used in routine clinical settings for personalized treatment selection? Is it feasible for
it to be used for early detection or recurrence monitoring?
It would also be valuable to compare the predictive performance of this model against existing
prognostic models for HCC. How does it compare in terms of sensitivity, specificity, and clinical
applicability?
15. Future Directions and Limitations:
While you mention the need for more in vivo and in vitro experiments, it would be good to
elaborate on future experimental designs to validate the model further. For example, which
additional experiments would be necessary to confirm the role of these lncRNAs in HCC
progression, EMT, and metastasis? Would you consider testing the model in clinical trials or
incorporating it into a clinical diagnostic tool?
Consider expanding on the limitations of using the TCGA database, particularly regarding the
lack of representation of certain ethnic groups or HCC subtypes. More explicit comments on the
generalizability of your results would strengthen the manuscript.
Please review this paper.
user
Title: Elucidating the Role of AC026412.3 in Hepatocellular Carcinoma: A Prognostic
disulfidptosis-related lncRNAs Model Perspective
Version: 1 Date: 07 Jun 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
1. Overly Broad and Redundant Introduction
-The introduction spans many concepts (epidemiology, disulfidptosis, RCD, lncRNAs, drug
sensitivity, immune response, EMT, etc.) without a tight logical flow.Several paragraphs
reiterate the role of disulfidptosis and lncRNAs in cancer without clear transitions, causing
thematic drift.
-Focus and streamline — clearly introduce HCC, briefly explain disulfidptosis, and then
tightly link lncRNAs to disulfidptosis and HCC prognosis. Remove redundant or tangential
explanations.
2. Terminological/Typographical Errors
The correct term is "disulfidoptosis", not "disulfidptosis" (multiple occurrences).
Terms like “DRLRs” are defined too late and used inconsistently. Also, the acronym is long and
may be cumbersome for readers.
3. Weak Justification for the Focus on AC026412.3
Although the study ends up highlighting AC026412.3, the introduction doesn’t justify its
relevance, uniqueness, or novelty in the context of HCC.Provide a brief rationale for prioritizing
AC026412.3 early in the introduction (e.g., no prior functional characterization, differential
expression pattern, etc.).
4. Incomplete Description of Dataset Curation
The description of RNA-seq data processing lacks key technical details (e.g., normalization
method, removal of low-expression transcripts, batch correction if any).Add details on data
normalization (e.g., FPKM, TPM), filtering thresholds, and potential preprocessing steps (e.g.,
log-transformation, batch correction).
5. Statistical Methods Need Clarification
-There is a lack of information on how multiple testing was controlled (e.g., Benjamini-
Hochberg correction) in univariate Cox and DEG analysis.The method for choosing the LASSO
penalty parameter (e.g., lambda.min or lambda.1se) is unclear.
- Clarify statistical thresholds, correction methods, and model selection criteria to improve
transparency.
6. Model Overfitting Risk
-The training/test split is 1:1, but you used 10-fold cross-validation within LASSO and then
further validated on the same test set (which may have informed model selection). This raises
concerns about overfitting and circular validation.
-Consider an independent external dataset (e.g., ICGC) for validation or apply nested cross-
validation to avoid optimistic bias.
7. Missing Clinical Relevance of Risk Score
-While a risk score is derived, there is limited discussion about how it could be applied clinically
(e.g., cutoff choice, integration with BCLC staging, decision-making impact).
-Clarify clinical thresholds and consider comparison with existing models (e.g., ALBI, AFP,
BCLC).
8. Shallow Integration of Functional Enrichment Results
-While GO, GSEA, and PPI are mentioned, there’s no biological interpretation of what key
pathways or networks imply for HCC biology or treatment.
- Go beyond listing pathways — discuss how specific enriched terms (e.g., oxidative stress,
immune escape) link to disulfidoptosis or HCC progression.
9. Immunological Analyses Are Descriptive Only
-The tumor microenvironment analysis (CIBERSORT, ssgsea, TIDE) lacks biological
hypothesis or linkage to disulfidoptosis or AC026412.3.
Recommendation: Strengthen rationale — why is this lncRNA expected to affect immune
infiltration? Are there known immunomodulatory pathways it may regulate?
10. Lack of Detailed Experimental Validation:
While the manuscript discusses in vitro and in vivo validation, the specifics of these experiments
could be better articulated. For example:
What exactly was the experimental setup for cell-line-based experiments and animal models?
Detailed methodological information could improve the reproducibility and transparency of the
results.
How was the correlation between the expression of these DRLRs and patient prognosis assessed
clinically, aside from qRT-PCR? Including specific clinical trial data or real-world evidence
would bolster the argument.
11. Insufficient Exploration of Molecular Mechanisms:
The molecular mechanisms behind the 4 DRLRs and their roles in HCC need further exploration.
While the manuscript mentions the link to disulfidptosis, EMT, and immune response, it would
be beneficial to include more specific molecular insights.
For instance, what signaling pathways are affected by these lncRNAs, and how does
disulfidptosis interact with them at the molecular level? This could provide a more in-depth
understanding of the pathways involved and how they affect HCC progression.
12. TMB and Immunotherapy Analysis:
The manuscript mentions the relationship between TMB scores and immunotherapy response
but lacks a detailed explanation of how the DRLRs model correlates with TMB and
immunotherapy response. Expanding on this point would allow the paper to position its findings
more strongly in the context of personalized medicine and immune checkpoint inhibitors in
HCC.
Also, how do the TIDE scores specifically relate to these lncRNAs? Is there any evidence linking
the expression of these lncRNAs with immune evasion mechanisms? More explanation would
strengthen the role of these lncRNAs as predictive biomarkers for immunotherapy response.
13. The Role of the Tumor Microenvironment (TME):
The relationship between M0 macrophage infiltration and high-risk groups is interesting but
needs to be expanded. Specifically, how does the presence of M0 macrophages affect tumor
progression in HCC? Are there alternative immune cells or factors involved in this process?
Exploring this would add depth to the TME discussion.
How does disulfidptosis modulate immune responses in the TME? More detailed mechanistic
insights into how disulfidptosis and macrophage infiltration influence HCC progression would
improve the overall discussion.
14. Clinical Relevance and Next Steps:
The study highlights the clinical potential of the 4 DRLRs signature in HCC, but further
discussion on how this model could be integrated into clinical practice is necessary. Can this
model be used in routine clinical settings for personalized treatment selection? Is it feasible for
it to be used for early detection or recurrence monitoring?
It would also be valuable to compare the predictive performance of this model against existing
prognostic models for HCC. How does it compare in terms of sensitivity, specificity, and clinical
applicability?
15. Future Directions and Limitations:
While you mention the need for more in vivo and in vitro experiments, it would be good to
elaborate on future experimental designs to validate the model further. For example, which
additional experiments would be necessary to confirm the role of these lncRNAs in HCC
progression, EMT, and metastasis? Would you consider testing the model in clinical trials or
incorporating it into a clinical diagnostic tool?
Consider expanding on the limitations of using the TCGA database, particularly regarding the
lack of representation of certain ethnic groups or HCC subtypes. More explicit comments on the
generalizability of your results would strengthen the manuscript.
assistant