Title: Elucidating the Role of AC026412.3 in Hepatocellular Carcinoma: A Prognostic
disulfidptosis-related lncRNAs Model Perspective
Version: 1 Date: 07 Jun 2025
Reviewer's report:
This manuscript presents a multifaceted investigation into the prognostic significance of a class
of long non-coding RNAs (lncRNAs) associated with disulfidptosis in hepatocellular carcinoma
(HCC). Drawing on publicly available datasets, particularly The Cancer Genome Atlas (TCGA),
the authors systematically identify and model disulfidptosis-related lncRNAs (DRLRs) that
could potentially serve as biomarkers for patient outcomes in HCC—a malignancy known for
its poor survival and limited reliable prognostic tools.
Using a combination of statistical methods—namely univariate Cox regression, least absolute
shrinkage and selection operator (LASSO) regression, and multivariate Cox regression—the
authors narrow their focus to four lncRNAs: AL031985.3, TMCC1-AS1, AL590705.3, and
AC026412.3. These were used to construct a prognostic risk model. The bioinformatic analysis
was followed by experimental validation, including quantitative real-time PCR (qRT-PCR),
functional assays in cultured HCC cells, and in vivo validation using xenograft mouse models.
The study offers a promising integration of computational biology with wet-lab validation,
providing a thorough analysis that could potentially contribute to the understanding and
prognostic stratification of HCC patients. The inclusion of both in silico and biological
experiments strengthens the manuscript’s impact and attempts to bridge a frequently
encountered gap between computational predictions and translational relevance in cancer
biomarker research.
However, the manuscript is not without limitations, and several concerns must be addressed
before it could be considered for publication. The most substantial limitation is the lack
of external validation. The prognostic model is entirely based on a single dataset—TCGA
—without replication or testing in an independent cohort. This significantly limits the
generalizability and clinical translatability of the findings. In the absence of a validation dataset
(e.g., from ICGC, GEO, or clinical cohorts), it is difficult to determine whether the risk signature
would maintain its performance in other populations or clinical settings.
Further, while the statistical approach is appropriate and appears rigorously implemented,
the potential for overfitting is not sufficiently addressed. The model’s robustness remains
uncertain, and additional techniques such as k-fold cross-validation or bootstrapping could
enhance confidence in the model’s predictive power. Additionally, although the authors perform
Kaplan-Meier survival analysis and construct ROC curves, more comprehensive assessments
of model performance, such as calibration curves, decision curve analysis, or time-dependent
AUC comparisons, are not provided.
From a biological standpoint, the role of AC026412.3—which is featured prominently in
the title—is not thoroughly investigated mechanistically. While some cell line experiments
are presented, they largely offer superficial insights into this lncRNA’s functional role in
HCC pathogenesis. The authors claim it contributes to cellular proliferation and migration,
yet no molecular pathway analyses (e.g., western blotting for downstream effectors, RNA
immunoprecipitation, or knockdown-rescue experiments) are conducted to substantiate these
claims. This limits the mechanistic depth and may weaken the rationale for highlighting
AC026412.3 as a central finding.
Additionally, the in vivo data, though appreciated, lack transparency and detail. There is
insufficient reporting regarding animal randomization, sample sizes, statistical treatment of
tumor growth data, and ethical compliance, which raises questions about reproducibility and
scientific rigor.
Another important limitation—unaddressed by the authors—is the absence of a clinical
correlation analysis. Although the risk model stratifies patients based on molecular features,
it is unclear how this classification maps onto established clinical parameters such as tumor
grade, stage, alpha-fetoprotein (AFP) levels, or response to therapy. Without these correlations,
it is difficult to determine whether the proposed model adds value beyond current prognostic
standards.
The presentation of the manuscript also warrants improvement. Several figures lack sufficient
annotation, and some figure legends are vague, requiring readers to infer meaning from
the images themselves. More importantly, the manuscript would benefit significantly from
professional English editing, particularly in the abstract, results, and discussion sections. The
flow of ideas is sometimes hindered by awkward syntax, run-on sentences, and inconsistently
used scientific terms.
Furthermore, the reference list shows inconsistencies in formatting, incomplete citations, and
lack of adherence to the journal’s style. These technical issues, although minor in isolation,
contribute to the impression that the manuscript requires careful editorial attention.
Beyond the specific methodological and editorial issues mentioned, there are broader scientific
limitations that the authors did not discuss. For instance, selection bias in the TCGA cohort may
affect the generalizability of the findings, particularly given the known demographic and clinical
heterogeneity of HCC worldwide. In addition, the impact of tumor microenvironmental features,
immune infiltration patterns, and underlying etiologies of HCC (e.g., viral vs. non-viral) are not
considered in model development or analysis. These factors could critically influence lncRNA
expression and model relevance.
Lastly, compliance with current reporting standards for prognostic biomarker studies (such
as REMARK guidelines) should be ensured. Transparent reporting on model development,
performance, biological validation, and clinical implications is critical for reproducibility and
future application.
Title: Elucidating the Role of AC026412.3 in Hepatocellular Carcinoma: A Prognostic
disulfidptosis-related lncRNAs Model Perspective
Version: 1 Date: 07 Jun 2025
Reviewer's report:
This manuscript presents a multifaceted investigation into the prognostic significance of a class
of long non-coding RNAs (lncRNAs) associated with disulfidptosis in hepatocellular carcinoma
(HCC). Drawing on publicly available datasets, particularly The Cancer Genome Atlas (TCGA),
the authors systematically identify and model disulfidptosis-related lncRNAs (DRLRs) that
could potentially serve as biomarkers for patient outcomes in HCC—a malignancy known for
its poor survival and limited reliable prognostic tools.
Using a combination of statistical methods—namely univariate Cox regression, least absolute
shrinkage and selection operator (LASSO) regression, and multivariate Cox regression—the
authors narrow their focus to four lncRNAs: AL031985.3, TMCC1-AS1, AL590705.3, and
AC026412.3. These were used to construct a prognostic risk model. The bioinformatic analysis
was followed by experimental validation, including quantitative real-time PCR (qRT-PCR),
functional assays in cultured HCC cells, and in vivo validation using xenograft mouse models.
The study offers a promising integration of computational biology with wet-lab validation,
providing a thorough analysis that could potentially contribute to the understanding and
prognostic stratification of HCC patients. The inclusion of both in silico and biological
experiments strengthens the manuscript’s impact and attempts to bridge a frequently
encountered gap between computational predictions and translational relevance in cancer
biomarker research.
However, the manuscript is not without limitations, and several concerns must be addressed
before it could be considered for publication. The most substantial limitation is the lack
of external validation. The prognostic model is entirely based on a single dataset—TCGA
—without replication or testing in an independent cohort. This significantly limits the
generalizability and clinical translatability of the findings. In the absence of a validation dataset
(e.g., from ICGC, GEO, or clinical cohorts), it is difficult to determine whether the risk signature
would maintain its performance in other populations or clinical settings.
Further, while the statistical approach is appropriate and appears rigorously implemented,
the potential for overfitting is not sufficiently addressed. The model’s robustness remains
uncertain, and additional techniques such as k-fold cross-validation or bootstrapping could
enhance confidence in the model’s predictive power. Additionally, although the authors perform
Kaplan-Meier survival analysis and construct ROC curves, more comprehensive assessments
of model performance, such as calibration curves, decision curve analysis, or time-dependent
AUC comparisons, are not provided.
From a biological standpoint, the role of AC026412.3—which is featured prominently in
the title—is not thoroughly investigated mechanistically. While some cell line experiments
are presented, they largely offer superficial insights into this lncRNA’s functional role in
HCC pathogenesis. The authors claim it contributes to cellular proliferation and migration,
yet no molecular pathway analyses (e.g., western blotting for downstream effectors, RNA
immunoprecipitation, or knockdown-rescue experiments) are conducted to substantiate these
claims. This limits the mechanistic depth and may weaken the rationale for highlighting
AC026412.3 as a central finding.
Additionally, the in vivo data, though appreciated, lack transparency and detail. There is
insufficient reporting regarding animal randomization, sample sizes, statistical treatment of
tumor growth data, and ethical compliance, which raises questions about reproducibility and
scientific rigor.
Another important limitation—unaddressed by the authors—is the absence of a clinical
correlation analysis. Although the risk model stratifies patients based on molecular features,
it is unclear how this classification maps onto established clinical parameters such as tumor
grade, stage, alpha-fetoprotein (AFP) levels, or response to therapy. Without these correlations,
it is difficult to determine whether the proposed model adds value beyond current prognostic
standards.
The presentation of the manuscript also warrants improvement. Several figures lack sufficient
annotation, and some figure legends are vague, requiring readers to infer meaning from
the images themselves. More importantly, the manuscript would benefit significantly from
professional English editing, particularly in the abstract, results, and discussion sections. The
flow of ideas is sometimes hindered by awkward syntax, run-on sentences, and inconsistently
used scientific terms.
Furthermore, the reference list shows inconsistencies in formatting, incomplete citations, and
lack of adherence to the journal’s style. These technical issues, although minor in isolation,
contribute to the impression that the manuscript requires careful editorial attention.
Beyond the specific methodological and editorial issues mentioned, there are broader scientific
limitations that the authors did not discuss. For instance, selection bias in the TCGA cohort may
affect the generalizability of the findings, particularly given the known demographic and clinical
heterogeneity of HCC worldwide. In addition, the impact of tumor microenvironmental features,
immune infiltration patterns, and underlying etiologies of HCC (e.g., viral vs. non-viral) are not
considered in model development or analysis. These factors could critically influence lncRNA
expression and model relevance.
Lastly, compliance with current reporting standards for prognostic biomarker studies (such
as REMARK guidelines) should be ensured. Transparent reporting on model development,
performance, biological validation, and clinical implications is critical for reproducibility and
future application.
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's report:
This manuscript presents a multifaceted investigation into the prognostic significance of a class
of long non-coding RNAs (lncRNAs) associated with disulfidptosis in hepatocellular carcinoma
(HCC). Drawing on publicly available datasets, particularly The Cancer Genome Atlas (TCGA),
the authors systematically identify and model disulfidptosis-related lncRNAs (DRLRs) that
could potentially serve as biomarkers for patient outcomes in HCC—a malignancy known for
its poor survival and limited reliable prognostic tools.
Using a combination of statistical methods—namely univariate Cox regression, least absolute
shrinkage and selection operator (LASSO) regression, and multivariate Cox regression—the
authors narrow their focus to four lncRNAs: AL031985.3, TMCC1-AS1, AL590705.3, and
AC026412.3. These were used to construct a prognostic risk model. The bioinformatic analysis
was followed by experimental validation, including quantitative real-time PCR (qRT-PCR),
functional assays in cultured HCC cells, and in vivo validation using xenograft mouse models.
The study offers a promising integration of computational biology with wet-lab validation,
providing a thorough analysis that could potentially contribute to the understanding and
prognostic stratification of HCC patients. The inclusion of both in silico and biological
experiments strengthens the manuscript’s impact and attempts to bridge a frequently
encountered gap between computational predictions and translational relevance in cancer
biomarker research.
However, the manuscript is not without limitations, and several concerns must be addressed
before it could be considered for publication. The most substantial limitation is the lack
of external validation. The prognostic model is entirely based on a single dataset—TCGA
—without replication or testing in an independent cohort. This significantly limits the
generalizability and clinical translatability of the findings. In the absence of a validation dataset
(e.g., from ICGC, GEO, or clinical cohorts), it is difficult to determine whether the risk signature
would maintain its performance in other populations or clinical settings.
Further, while the statistical approach is appropriate and appears rigorously implemented,
the potential for overfitting is not sufficiently addressed. The model’s robustness remains
uncertain, and additional techniques such as k-fold cross-validation or bootstrapping could
enhance confidence in the model’s predictive power. Additionally, although the authors perform
Kaplan-Meier survival analysis and construct ROC curves, more comprehensive assessments
of model performance, such as calibration curves, decision curve analysis, or time-dependent
AUC comparisons, are not provided.
From a biological standpoint, the role of AC026412.3—which is featured prominently in
the title—is not thoroughly investigated mechanistically. While some cell line experiments
are presented, they largely offer superficial insights into this lncRNA’s functional role in
HCC pathogenesis. The authors claim it contributes to cellular proliferation and migration,
yet no molecular pathway analyses (e.g., western blotting for downstream effectors, RNA
immunoprecipitation, or knockdown-rescue experiments) are conducted to substantiate these
claims. This limits the mechanistic depth and may weaken the rationale for highlighting
AC026412.3 as a central finding.
Additionally, the in vivo data, though appreciated, lack transparency and detail. There is
insufficient reporting regarding animal randomization, sample sizes, statistical treatment of
tumor growth data, and ethical compliance, which raises questions about reproducibility and
scientific rigor.
Another important limitation—unaddressed by the authors—is the absence of a clinical
correlation analysis. Although the risk model stratifies patients based on molecular features,
it is unclear how this classification maps onto established clinical parameters such as tumor
grade, stage, alpha-fetoprotein (AFP) levels, or response to therapy. Without these correlations,
it is difficult to determine whether the proposed model adds value beyond current prognostic
standards.
The presentation of the manuscript also warrants improvement. Several figures lack sufficient
annotation, and some figure legends are vague, requiring readers to infer meaning from
the images themselves. More importantly, the manuscript would benefit significantly from
professional English editing, particularly in the abstract, results, and discussion sections. The
flow of ideas is sometimes hindered by awkward syntax, run-on sentences, and inconsistently
used scientific terms.
Furthermore, the reference list shows inconsistencies in formatting, incomplete citations, and
lack of adherence to the journal’s style. These technical issues, although minor in isolation,
contribute to the impression that the manuscript requires careful editorial attention.
Beyond the specific methodological and editorial issues mentioned, there are broader scientific
limitations that the authors did not discuss. For instance, selection bias in the TCGA cohort may
affect the generalizability of the findings, particularly given the known demographic and clinical
heterogeneity of HCC worldwide. In addition, the impact of tumor microenvironmental features,
immune infiltration patterns, and underlying etiologies of HCC (e.g., viral vs. non-viral) are not
considered in model development or analysis. These factors could critically influence lncRNA
expression and model relevance.
Lastly, compliance with current reporting standards for prognostic biomarker studies (such
as REMARK guidelines) should be ensured. Transparent reporting on model development,
performance, biological validation, and clinical implications is critical for reproducibility and
future application.
assistant