Title: Bioinformatics identification of key microRNA-correlated genes associated with
hepatocellular carcinoma heterogeneity and prognosis
Version: 0 Date: 01 Apr 2025
Reviewer's report:
The manuscript presents a comprehensive bioinformatics analysis combined with experimental
validation to identify miRNA-correlated (MIRcor) genes in hepatocellular carcinoma (HCC).
The study integrates data from multiple databases, applies clustering and regression models,
and validates findings through external datasets and molecular techniques. While the work
addresses a relevant clinical problem and employs a multi-faceted approach, several aspects of
methodology, interpretation, and presentation require critical evaluation.
Methodology
- Data Integration and Filtering: The use of three miRNA databases (miRDB, PITA, TargetScan)
to predict miRNA-mRNA pairs enhances robustness. However, the rationale for selecting
overlapping pairs across all three databases is not explicitly justified. A discussion on potential
biases introduced by database-specific prediction algorithms would strengthen transparency.
- Consensus Clustering: The application of consensus clustering to classify HCC samples into
two subtypes is appropriate. While the stability of clustering over 1,000 iterations is noted, the
choice of k=2 lacks validation against alternative cluster numbers (e.g., silhouette analysis).
The marginal survival difference (p=0.047) between clusters raises questions about clinical
relevance.
- Differential Expression Analysis: The criteria for DEGs (|log2FC| > 2, adjusted p < 0.05) are
stringent. However, the biological rationale for focusing on overlapping DEGs between subtypes
and tumor-normal comparisons is unclear. Overlapping genes may reflect general tumorigenic
processes rather than subtype-specific mechanisms.
- Risk Model Construction: LASSO regression for feature selection is well-suited for high-
dimensional data. The negative coefficients of the five prognostic genes suggest protective
roles, but the model’s moderate predictive accuracy (AUC: 0.659–0.711 in training, 0.639–
0.739 in validation) limits its translational potential. The absence of a comparison with existing
prognostic models (e.g., AFP-based scores) weakens claims of novelty.
- Immune Microenvironment Analysis: The ssGSEA approach for immune cell quantification is
standard, but the lack of orthogonal validation (e.g., immunohistochemistry) reduces confidence
in inferred immune cell proportions. Correlations between prognostic genes and immune cells
are observational, with no mechanistic exploration.
- Experimental Validation: Western blot and RT-PCR confirm lower expression of prognostic
genes in HCC tissues. However, the small sample size (n=5) and absence of quantitative data
(e.g., band intensity ratios) limit statistical power. Primer sequences are provided, but PCR
conditions (cycle numbers, annealing temperatures) are omitted.
Results and Interpretation
- MIRcor Genes and Clustering: The identification of 26 MIRcor genes enriched in
metabolic processes aligns with known HCC pathways. However, the functional annotation
(e.g., "neutrophil degranulation") lacks direct links to miRNA-mediated regulation, leaving
mechanistic gaps.
- Prognostic Model:  The five-gene signature (RDH16, ADH4, ADH1C, CYP2C9, GYS2) is
supported by previous studies, but the model’s reliance on downregulated genes contrasts with
typical oncogenic drivers. The negative coefficients imply that lower expression correlates with
higher risk, yet the biological basis for this relationship is underexplored.
- Immune Cell Associations: The reported differences in immune cell infiltration (e.g., activated
CD4+ T cells) between risk groups are intriguing but speculative. The claim that prognostic
genes "change the tumor microenvironment" is not substantiated by causal evidence.
- TF-miRNA-mRNA Network: The predicted regulatory network involving 186 nodes is overly
complex and lacks experimental validation. The inclusion of miRNAs like hsa-miR-34a-5p,
known to regulate apoptosis, is plausible but requires functional studies to confirm interactions.
Presentation and Clarity
- Figures: While visually detailed, some figures (e.g., t-SNE plots, heatmaps) suffer from low
resolution or overcrowded labels. Figure 6’s immunohistochemistry images lack quantitative
comparisons (e.g., H-scores), weakening conclusions about protein-level differences.
- Tables: Table 2’s correlation analysis between risk scores and clinical traits highlights
associations with stage and T classification but does not adjust for multiple testing, increasing
false-discovery risk.
- Textual Consistency: Discrepancies exist between the abstract ("five prognostic genes") and
results ("five prognostic genes which were used to construct a MIRcor-DEGs-related gene
signature"). The term "MIRcor-DEGs" is ambiguously defined in context.
Theoretical and Practical Contribution
- The study’s primary contribution lies in integrating miRNA-mRNA correlation analysis with
prognostic modeling. However, the incremental value of the five-gene signature over existing
biomarkers (e.g., AFP, PIVKA-II) remains unaddressed.
- The immune microenvironment analysis provides hypothesis-generating insights but falls short
of elucidating actionable therapeutic targets. The TF-miRNA network, while comprehensive, is
descriptive rather than mechanistic.
Title: Bioinformatics identification of key microRNA-correlated genes associated with
hepatocellular carcinoma heterogeneity and prognosis
Version: 0 Date: 01 Apr 2025
Reviewer's report:
The manuscript presents a comprehensive bioinformatics analysis combined with experimental
validation to identify miRNA-correlated (MIRcor) genes in hepatocellular carcinoma (HCC).
The study integrates data from multiple databases, applies clustering and regression models,
and validates findings through external datasets and molecular techniques. While the work
addresses a relevant clinical problem and employs a multi-faceted approach, several aspects of
methodology, interpretation, and presentation require critical evaluation.
Methodology
- Data Integration and Filtering: The use of three miRNA databases (miRDB, PITA, TargetScan)
to predict miRNA-mRNA pairs enhances robustness. However, the rationale for selecting
overlapping pairs across all three databases is not explicitly justified. A discussion on potential
biases introduced by database-specific prediction algorithms would strengthen transparency.
- Consensus Clustering: The application of consensus clustering to classify HCC samples into
two subtypes is appropriate. While the stability of clustering over 1,000 iterations is noted, the
choice of k=2 lacks validation against alternative cluster numbers (e.g., silhouette analysis).
The marginal survival difference (p=0.047) between clusters raises questions about clinical
relevance.
- Differential Expression Analysis: The criteria for DEGs (|log2FC| > 2, adjusted p < 0.05) are
stringent. However, the biological rationale for focusing on overlapping DEGs between subtypes
and tumor-normal comparisons is unclear. Overlapping genes may reflect general tumorigenic
processes rather than subtype-specific mechanisms.
- Risk Model Construction: LASSO regression for feature selection is well-suited for high-
dimensional data. The negative coefficients of the five prognostic genes suggest protective
roles, but the model’s moderate predictive accuracy (AUC: 0.659–0.711 in training, 0.639–
0.739 in validation) limits its translational potential. The absence of a comparison with existing
prognostic models (e.g., AFP-based scores) weakens claims of novelty.
- Immune Microenvironment Analysis: The ssGSEA approach for immune cell quantification is
standard, but the lack of orthogonal validation (e.g., immunohistochemistry) reduces confidence
in inferred immune cell proportions. Correlations between prognostic genes and immune cells
are observational, with no mechanistic exploration.
- Experimental Validation: Western blot and RT-PCR confirm lower expression of prognostic
genes in HCC tissues. However, the small sample size (n=5) and absence of quantitative data
(e.g., band intensity ratios) limit statistical power. Primer sequences are provided, but PCR
conditions (cycle numbers, annealing temperatures) are omitted.
Results and Interpretation
- MIRcor Genes and Clustering: The identification of 26 MIRcor genes enriched in
metabolic processes aligns with known HCC pathways. However, the functional annotation
(e.g., "neutrophil degranulation") lacks direct links to miRNA-mediated regulation, leaving
mechanistic gaps.
- Prognostic Model:  The five-gene signature (RDH16, ADH4, ADH1C, CYP2C9, GYS2) is
supported by previous studies, but the model’s reliance on downregulated genes contrasts with
typical oncogenic drivers. The negative coefficients imply that lower expression correlates with
higher risk, yet the biological basis for this relationship is underexplored.
- Immune Cell Associations: The reported differences in immune cell infiltration (e.g., activated
CD4+ T cells) between risk groups are intriguing but speculative. The claim that prognostic
genes "change the tumor microenvironment" is not substantiated by causal evidence.
- TF-miRNA-mRNA Network: The predicted regulatory network involving 186 nodes is overly
complex and lacks experimental validation. The inclusion of miRNAs like hsa-miR-34a-5p,
known to regulate apoptosis, is plausible but requires functional studies to confirm interactions.
Presentation and Clarity
- Figures: While visually detailed, some figures (e.g., t-SNE plots, heatmaps) suffer from low
resolution or overcrowded labels. Figure 6’s immunohistochemistry images lack quantitative
comparisons (e.g., H-scores), weakening conclusions about protein-level differences.
- Tables: Table 2’s correlation analysis between risk scores and clinical traits highlights
associations with stage and T classification but does not adjust for multiple testing, increasing
false-discovery risk.
- Textual Consistency: Discrepancies exist between the abstract ("five prognostic genes") and
results ("five prognostic genes which were used to construct a MIRcor-DEGs-related gene
signature"). The term "MIRcor-DEGs" is ambiguously defined in context.
Theoretical and Practical Contribution
- The study’s primary contribution lies in integrating miRNA-mRNA correlation analysis with
prognostic modeling. However, the incremental value of the five-gene signature over existing
biomarkers (e.g., AFP, PIVKA-II) remains unaddressed.
- The immune microenvironment analysis provides hypothesis-generating insights but falls short
of elucidating actionable therapeutic targets. The TF-miRNA network, while comprehensive, is
descriptive rather than mechanistic.
Please review this paper.
user
Title: Bioinformatics identification of key microRNA-correlated genes associated with
hepatocellular carcinoma heterogeneity and prognosis
Version: 0 Date: 01 Apr 2025
Reviewer's report:
The manuscript presents a comprehensive bioinformatics analysis combined with experimental
validation to identify miRNA-correlated (MIRcor) genes in hepatocellular carcinoma (HCC).
The study integrates data from multiple databases, applies clustering and regression models,
and validates findings through external datasets and molecular techniques. While the work
addresses a relevant clinical problem and employs a multi-faceted approach, several aspects of
methodology, interpretation, and presentation require critical evaluation.
Methodology
- Data Integration and Filtering: The use of three miRNA databases (miRDB, PITA, TargetScan)
to predict miRNA-mRNA pairs enhances robustness. However, the rationale for selecting
overlapping pairs across all three databases is not explicitly justified. A discussion on potential
biases introduced by database-specific prediction algorithms would strengthen transparency.
- Consensus Clustering: The application of consensus clustering to classify HCC samples into
two subtypes is appropriate. While the stability of clustering over 1,000 iterations is noted, the
choice of k=2 lacks validation against alternative cluster numbers (e.g., silhouette analysis).
The marginal survival difference (p=0.047) between clusters raises questions about clinical
relevance.
- Differential Expression Analysis: The criteria for DEGs (|log2FC| > 2, adjusted p < 0.05) are
stringent. However, the biological rationale for focusing on overlapping DEGs between subtypes
and tumor-normal comparisons is unclear. Overlapping genes may reflect general tumorigenic
processes rather than subtype-specific mechanisms.
- Risk Model Construction: LASSO regression for feature selection is well-suited for high-
dimensional data. The negative coefficients of the five prognostic genes suggest protective
roles, but the model’s moderate predictive accuracy (AUC: 0.659–0.711 in training, 0.639–
0.739 in validation) limits its translational potential. The absence of a comparison with existing
prognostic models (e.g., AFP-based scores) weakens claims of novelty.
- Immune Microenvironment Analysis: The ssGSEA approach for immune cell quantification is
standard, but the lack of orthogonal validation (e.g., immunohistochemistry) reduces confidence
in inferred immune cell proportions. Correlations between prognostic genes and immune cells
are observational, with no mechanistic exploration.
- Experimental Validation: Western blot and RT-PCR confirm lower expression of prognostic
genes in HCC tissues. However, the small sample size (n=5) and absence of quantitative data
(e.g., band intensity ratios) limit statistical power. Primer sequences are provided, but PCR
conditions (cycle numbers, annealing temperatures) are omitted.
Results and Interpretation
- MIRcor Genes and Clustering: The identification of 26 MIRcor genes enriched in
metabolic processes aligns with known HCC pathways. However, the functional annotation
(e.g., "neutrophil degranulation") lacks direct links to miRNA-mediated regulation, leaving
mechanistic gaps.
- Prognostic Model:  The five-gene signature (RDH16, ADH4, ADH1C, CYP2C9, GYS2) is
supported by previous studies, but the model’s reliance on downregulated genes contrasts with
typical oncogenic drivers. The negative coefficients imply that lower expression correlates with
higher risk, yet the biological basis for this relationship is underexplored.
- Immune Cell Associations: The reported differences in immune cell infiltration (e.g., activated
CD4+ T cells) between risk groups are intriguing but speculative. The claim that prognostic
genes "change the tumor microenvironment" is not substantiated by causal evidence.
- TF-miRNA-mRNA Network: The predicted regulatory network involving 186 nodes is overly
complex and lacks experimental validation. The inclusion of miRNAs like hsa-miR-34a-5p,
known to regulate apoptosis, is plausible but requires functional studies to confirm interactions.
Presentation and Clarity
- Figures: While visually detailed, some figures (e.g., t-SNE plots, heatmaps) suffer from low
resolution or overcrowded labels. Figure 6’s immunohistochemistry images lack quantitative
comparisons (e.g., H-scores), weakening conclusions about protein-level differences.
- Tables: Table 2’s correlation analysis between risk scores and clinical traits highlights
associations with stage and T classification but does not adjust for multiple testing, increasing
false-discovery risk.
- Textual Consistency: Discrepancies exist between the abstract ("five prognostic genes") and
results ("five prognostic genes which were used to construct a MIRcor-DEGs-related gene
signature"). The term "MIRcor-DEGs" is ambiguously defined in context.
Theoretical and Practical Contribution
- The study’s primary contribution lies in integrating miRNA-mRNA correlation analysis with
prognostic modeling. However, the incremental value of the five-gene signature over existing
biomarkers (e.g., AFP, PIVKA-II) remains unaddressed.
- The immune microenvironment analysis provides hypothesis-generating insights but falls short
of elucidating actionable therapeutic targets. The TF-miRNA network, while comprehensive, is
descriptive rather than mechanistic.
assistant