Title: Unveiling gastric precancerous stages: metabolomic insights for early detection and
intervention
Version: 3 Date: 03 Mar 2025
Reviewer's report:
Overall Evaluation:
This study investigates metabolic alterations in gastric precancerous lesions (GPL), identifying
potential biomarkers for distinguishing normal, atrophic gastritis (AG), and intestinal metaplasia
(IM) tissues. The integration of pseudotargeted metabolomics with cellular validation represents
a significant effort to bridge clinical observations with mechanistic insights. While the work
addresses an important gap in gastric cancer (GC) early detection, several methodological and
interpretational limitations must be addressed to strengthen the conclusions. Below are detailed
comments and recommendations for improvement.
Major Strengths:
Clinical Relevance and Novelty:
The focus on tissue-specific metabolomics directly at lesion sites is a notable strength, offering
more direct insights into metabolic dysregulation compared to blood/urine-based studies.
The identification of a 5-metabolite panel (glycerophosphocholine, tiglylcarnitine, malate,
sphingosine, #-glutamylglutamic acid) with diagnostic potential for GPL is promising and aligns
with emerging interest in metabolic biomarkers for cancer prevention.
Methodological Rigor:
The use of pseudotargeted metabolomics (UPLC-MS/MS) enhances sensitivity and dynamic
range, addressing limitations of untargeted approaches.
Validation in a second cohort and functional studies in a cell model (MNNG-induced GES-1
cells) add robustness to the findings.
Multidisciplinary Approach:
Combining clinical metabolomics with in vitro experiments to explore metabolite effects
on intestinalization markers (CDX2, KLF4, MUC2) provides a translational framework for
biomarker validation.
Major Concerns and Recommendations:
1. Cohort Design and Statistical Analysis:
Limitation:
The discovery cohort (N=26) and validation cohort (N=71) are relatively small for metabolomics
studies, raising concerns about overfitting and generalizability.
Table 1 lacks clarity on Helicobacter pylori (H. pylori) infection stratification. The term "(+), (+
+), (+++)" is ambiguous; quantitative measures (e.g., ELISA/culture results) should be provided.
No adjustment for confounding factors (e.g., age, H. pylori status) in multivariate analyses.
Recommendation:
Validate the metabolite panel in an independent, larger multicenter cohort with standardized H.
pylori quantification.
Perform multivariate regression (e.g., logistic regression adjusting for age, sex, H. pylori) to
confirm metabolite independence as biomarkers.
2. Technical Validation and Reproducibility:
Limitation:
The pseudotargeted metabolomics workflow lacks critical details: How were metabolites
annotated (e.g., spectral libraries, MS/MS validation)? Were batch effects controlled?
The "relative quantification" method in discovery cohorts vs. "absolute quantification" in
validation cohorts complicates cross-cohort comparisons.
Recommendation:
Provide raw data (e.g., mzML files) and detailed annotation pipelines (e.g., HMDB/KEGG IDs,
fragmentation spectra).
Clarify whether internal standards were used for normalization in pseudotargeted analysis and
how inter-batch variability was addressed.
3. Mechanistic Insights and Functional Validation:
Limitation:
The cell model (MNNG-treated GES-1) is oversimplified. MNNG is a genotoxic agent, but its
relevance to human AG/IM progression is unclear.
Metabolite treatments (e.g., glycerophosphocholine, malate) showed minimal effects on cell
viability and inconsistent gene expression changes (e.g., malate increased CDX2), undermining
their proposed biological roles.
Recommendation:
Validate findings in patient-derived organoids or animal models of gastric metaplasia.
Explore mechanistic links (e.g., malate’s role in oxidative stress via malate dehydrogenase,
sphingosine’s impact on sphingosine-1-phosphate signaling) using knockdown/overexpression
experiments.
4. Biomarker Performance and Clinical Utility:
Limitation:
The combined AUC of 0.916 in the validation cohort is impressive, but individual metabolites
(e.g., malate: AUC=0.619) show poor discriminatory power.
No longitudinal data to assess biomarker utility in predicting progression from AG/IM to
neoplasia.
Recommendation:
Conduct a prospective longitudinal study to evaluate whether the metabolite panel predicts GC
development.
Compare diagnostic performance against established endoscopic/histologic criteria to justify
clinical adoption.
5. Data Interpretation and Presentation:
Limitation:
Figure 2A (PLS-DA) shows overlapping AG/IM clusters, contradicting the claim of "distinct
clusters."
Table 3 and 4 report AUCs with wide confidence intervals (e.g., malate: 0.496–0.795), indicating
instability in biomarker performance.
Inconsistent terminology: "pseudotargeted" vs. "targeted" metabolomics is conflated in
methods/results.
Recommendation:
Re-analyze PLS-DA with permutation testing to confirm model validity.
Report AUCs as median ± IQR instead of ranges to better reflect central tendencies.
Clarify methodological distinctions between pseudotargeted (discovery) and targeted
(validation) workflows.
Minor Comments:
Figures and Tables:
Figure 1: Include a schematic of the MNNG-induced cell model.
Table 2: Define "log2(FC)" directionality (e.g., AG vs. Normal).
Supplementary Figures: Ensure all ROC curves include 95% CIs and sensitivity/specificity at
cutoff.
Discussion:
Compare findings to prior tissue-based metabolomics studies (e.g., Wang et al., 2022* [20]) to
contextualize novelty.
Address why glycerophosphocholine decreased in GPL despite its proposed protective role in
other studies [32–36].
Writing:
Avoid overstatements: "First to identify metabolomic profiles in gastric tissue" is inaccurate (see
Wang et al., 2022* [20]).
Define abbreviations at first mention (e.g., MNNG, AUC).
Conclusion:
This study provides valuable insights into metabolic dysregulation during gastric precancerous
progression and identifies a promising biomarker panel for early detection. However, technical
validation, mechanistic depth, and clinical translation require further exploration. Addressing
these concerns would significantly enhance the manuscript's impact.
Recommendation: Major Revision
Title: Unveiling gastric precancerous stages: metabolomic insights for early detection and
intervention
Version: 3 Date: 03 Mar 2025
Reviewer's report:
Overall Evaluation:
This study investigates metabolic alterations in gastric precancerous lesions (GPL), identifying
potential biomarkers for distinguishing normal, atrophic gastritis (AG), and intestinal metaplasia
(IM) tissues. The integration of pseudotargeted metabolomics with cellular validation represents
a significant effort to bridge clinical observations with mechanistic insights. While the work
addresses an important gap in gastric cancer (GC) early detection, several methodological and
interpretational limitations must be addressed to strengthen the conclusions. Below are detailed
comments and recommendations for improvement.
Major Strengths:
Clinical Relevance and Novelty:
The focus on tissue-specific metabolomics directly at lesion sites is a notable strength, offering
more direct insights into metabolic dysregulation compared to blood/urine-based studies.
The identification of a 5-metabolite panel (glycerophosphocholine, tiglylcarnitine, malate,
sphingosine, #-glutamylglutamic acid) with diagnostic potential for GPL is promising and aligns
with emerging interest in metabolic biomarkers for cancer prevention.
Methodological Rigor:
The use of pseudotargeted metabolomics (UPLC-MS/MS) enhances sensitivity and dynamic
range, addressing limitations of untargeted approaches.
Validation in a second cohort and functional studies in a cell model (MNNG-induced GES-1
cells) add robustness to the findings.
Multidisciplinary Approach:
Combining clinical metabolomics with in vitro experiments to explore metabolite effects
on intestinalization markers (CDX2, KLF4, MUC2) provides a translational framework for
biomarker validation.
Major Concerns and Recommendations:
1. Cohort Design and Statistical Analysis:
Limitation:
The discovery cohort (N=26) and validation cohort (N=71) are relatively small for metabolomics
studies, raising concerns about overfitting and generalizability.
Table 1 lacks clarity on Helicobacter pylori (H. pylori) infection stratification. The term "(+), (+
+), (+++)" is ambiguous; quantitative measures (e.g., ELISA/culture results) should be provided.
No adjustment for confounding factors (e.g., age, H. pylori status) in multivariate analyses.
Recommendation:
Validate the metabolite panel in an independent, larger multicenter cohort with standardized H.
pylori quantification.
Perform multivariate regression (e.g., logistic regression adjusting for age, sex, H. pylori) to
confirm metabolite independence as biomarkers.
2. Technical Validation and Reproducibility:
Limitation:
The pseudotargeted metabolomics workflow lacks critical details: How were metabolites
annotated (e.g., spectral libraries, MS/MS validation)? Were batch effects controlled?
The "relative quantification" method in discovery cohorts vs. "absolute quantification" in
validation cohorts complicates cross-cohort comparisons.
Recommendation:
Provide raw data (e.g., mzML files) and detailed annotation pipelines (e.g., HMDB/KEGG IDs,
fragmentation spectra).
Clarify whether internal standards were used for normalization in pseudotargeted analysis and
how inter-batch variability was addressed.
3. Mechanistic Insights and Functional Validation:
Limitation:
The cell model (MNNG-treated GES-1) is oversimplified. MNNG is a genotoxic agent, but its
relevance to human AG/IM progression is unclear.
Metabolite treatments (e.g., glycerophosphocholine, malate) showed minimal effects on cell
viability and inconsistent gene expression changes (e.g., malate increased CDX2), undermining
their proposed biological roles.
Recommendation:
Validate findings in patient-derived organoids or animal models of gastric metaplasia.
Explore mechanistic links (e.g., malate’s role in oxidative stress via malate dehydrogenase,
sphingosine’s impact on sphingosine-1-phosphate signaling) using knockdown/overexpression
experiments.
4. Biomarker Performance and Clinical Utility:
Limitation:
The combined AUC of 0.916 in the validation cohort is impressive, but individual metabolites
(e.g., malate: AUC=0.619) show poor discriminatory power.
No longitudinal data to assess biomarker utility in predicting progression from AG/IM to
neoplasia.
Recommendation:
Conduct a prospective longitudinal study to evaluate whether the metabolite panel predicts GC
development.
Compare diagnostic performance against established endoscopic/histologic criteria to justify
clinical adoption.
5. Data Interpretation and Presentation:
Limitation:
Figure 2A (PLS-DA) shows overlapping AG/IM clusters, contradicting the claim of "distinct
clusters."
Table 3 and 4 report AUCs with wide confidence intervals (e.g., malate: 0.496–0.795), indicating
instability in biomarker performance.
Inconsistent terminology: "pseudotargeted" vs. "targeted" metabolomics is conflated in
methods/results.
Recommendation:
Re-analyze PLS-DA with permutation testing to confirm model validity.
Report AUCs as median ± IQR instead of ranges to better reflect central tendencies.
Clarify methodological distinctions between pseudotargeted (discovery) and targeted
(validation) workflows.
Minor Comments:
Figures and Tables:
Figure 1: Include a schematic of the MNNG-induced cell model.
Table 2: Define "log2(FC)" directionality (e.g., AG vs. Normal).
Supplementary Figures: Ensure all ROC curves include 95% CIs and sensitivity/specificity at
cutoff.
Discussion:
Compare findings to prior tissue-based metabolomics studies (e.g., Wang et al., 2022* [20]) to
contextualize novelty.
Address why glycerophosphocholine decreased in GPL despite its proposed protective role in
other studies [32–36].
Writing:
Avoid overstatements: "First to identify metabolomic profiles in gastric tissue" is inaccurate (see
Wang et al., 2022* [20]).
Define abbreviations at first mention (e.g., MNNG, AUC).
Conclusion:
This study provides valuable insights into metabolic dysregulation during gastric precancerous
progression and identifies a promising biomarker panel for early detection. However, technical
validation, mechanistic depth, and clinical translation require further exploration. Addressing
these concerns would significantly enhance the manuscript's impact.
Recommendation: Major Revision
Please review this paper.
user
Title: Unveiling gastric precancerous stages: metabolomic insights for early detection and
intervention
Version: 3 Date: 03 Mar 2025
Reviewer's report:
Overall Evaluation:
This study investigates metabolic alterations in gastric precancerous lesions (GPL), identifying
potential biomarkers for distinguishing normal, atrophic gastritis (AG), and intestinal metaplasia
(IM) tissues. The integration of pseudotargeted metabolomics with cellular validation represents
a significant effort to bridge clinical observations with mechanistic insights. While the work
addresses an important gap in gastric cancer (GC) early detection, several methodological and
interpretational limitations must be addressed to strengthen the conclusions. Below are detailed
comments and recommendations for improvement.
Major Strengths:
Clinical Relevance and Novelty:
The focus on tissue-specific metabolomics directly at lesion sites is a notable strength, offering
more direct insights into metabolic dysregulation compared to blood/urine-based studies.
The identification of a 5-metabolite panel (glycerophosphocholine, tiglylcarnitine, malate,
sphingosine, #-glutamylglutamic acid) with diagnostic potential for GPL is promising and aligns
with emerging interest in metabolic biomarkers for cancer prevention.
Methodological Rigor:
The use of pseudotargeted metabolomics (UPLC-MS/MS) enhances sensitivity and dynamic
range, addressing limitations of untargeted approaches.
Validation in a second cohort and functional studies in a cell model (MNNG-induced GES-1
cells) add robustness to the findings.
Multidisciplinary Approach:
Combining clinical metabolomics with in vitro experiments to explore metabolite effects
on intestinalization markers (CDX2, KLF4, MUC2) provides a translational framework for
biomarker validation.
Major Concerns and Recommendations:
1. Cohort Design and Statistical Analysis:
Limitation:
The discovery cohort (N=26) and validation cohort (N=71) are relatively small for metabolomics
studies, raising concerns about overfitting and generalizability.
Table 1 lacks clarity on Helicobacter pylori (H. pylori) infection stratification. The term "(+), (+
+), (+++)" is ambiguous; quantitative measures (e.g., ELISA/culture results) should be provided.
No adjustment for confounding factors (e.g., age, H. pylori status) in multivariate analyses.
Recommendation:
Validate the metabolite panel in an independent, larger multicenter cohort with standardized H.
pylori quantification.
Perform multivariate regression (e.g., logistic regression adjusting for age, sex, H. pylori) to
confirm metabolite independence as biomarkers.
2. Technical Validation and Reproducibility:
Limitation:
The pseudotargeted metabolomics workflow lacks critical details: How were metabolites
annotated (e.g., spectral libraries, MS/MS validation)? Were batch effects controlled?
The "relative quantification" method in discovery cohorts vs. "absolute quantification" in
validation cohorts complicates cross-cohort comparisons.
Recommendation:
Provide raw data (e.g., mzML files) and detailed annotation pipelines (e.g., HMDB/KEGG IDs,
fragmentation spectra).
Clarify whether internal standards were used for normalization in pseudotargeted analysis and
how inter-batch variability was addressed.
3. Mechanistic Insights and Functional Validation:
Limitation:
The cell model (MNNG-treated GES-1) is oversimplified. MNNG is a genotoxic agent, but its
relevance to human AG/IM progression is unclear.
Metabolite treatments (e.g., glycerophosphocholine, malate) showed minimal effects on cell
viability and inconsistent gene expression changes (e.g., malate increased CDX2), undermining
their proposed biological roles.
Recommendation:
Validate findings in patient-derived organoids or animal models of gastric metaplasia.
Explore mechanistic links (e.g., malate’s role in oxidative stress via malate dehydrogenase,
sphingosine’s impact on sphingosine-1-phosphate signaling) using knockdown/overexpression
experiments.
4. Biomarker Performance and Clinical Utility:
Limitation:
The combined AUC of 0.916 in the validation cohort is impressive, but individual metabolites
(e.g., malate: AUC=0.619) show poor discriminatory power.
No longitudinal data to assess biomarker utility in predicting progression from AG/IM to
neoplasia.
Recommendation:
Conduct a prospective longitudinal study to evaluate whether the metabolite panel predicts GC
development.
Compare diagnostic performance against established endoscopic/histologic criteria to justify
clinical adoption.
5. Data Interpretation and Presentation:
Limitation:
Figure 2A (PLS-DA) shows overlapping AG/IM clusters, contradicting the claim of "distinct
clusters."
Table 3 and 4 report AUCs with wide confidence intervals (e.g., malate: 0.496–0.795), indicating
instability in biomarker performance.
Inconsistent terminology: "pseudotargeted" vs. "targeted" metabolomics is conflated in
methods/results.
Recommendation:
Re-analyze PLS-DA with permutation testing to confirm model validity.
Report AUCs as median ± IQR instead of ranges to better reflect central tendencies.
Clarify methodological distinctions between pseudotargeted (discovery) and targeted
(validation) workflows.
Minor Comments:
Figures and Tables:
Figure 1: Include a schematic of the MNNG-induced cell model.
Table 2: Define "log2(FC)" directionality (e.g., AG vs. Normal).
Supplementary Figures: Ensure all ROC curves include 95% CIs and sensitivity/specificity at
cutoff.
Discussion:
Compare findings to prior tissue-based metabolomics studies (e.g., Wang et al., 2022* [20]) to
contextualize novelty.
Address why glycerophosphocholine decreased in GPL despite its proposed protective role in
other studies [32–36].
Writing:
Avoid overstatements: "First to identify metabolomic profiles in gastric tissue" is inaccurate (see
Wang et al., 2022* [20]).
Define abbreviations at first mention (e.g., MNNG, AUC).
Conclusion:
This study provides valuable insights into metabolic dysregulation during gastric precancerous
progression and identifies a promising biomarker panel for early detection. However, technical
validation, mechanistic depth, and clinical translation require further exploration. Addressing
these concerns would significantly enhance the manuscript's impact.
Recommendation: Major Revision
assistant