Title: ZBTB10 as a potential prognosis biomarker and correlates with tumor immune
microenvironment in stomach adenocarcinoma
Version: 1 Date: 12 Dec 2024
Reviewer's report:
This study presents an interesting investigation into the potential role of ZBTB10 as a
prognostic biomarker for STAD. The authors utilized the TCGA database and performed various
bioinformatic analyses, demonstrating that ZBTB10 plays a significant role in STAD prognosis
and is associated with the immune microenvironment. While there are several concerns that
need to be addressed to enhance the logical flow and strengthen the study's conclusions. The
following comments outline major and minor points that require the authors' attention.
Major Concerns
1. Figure 1B: Please verify that the names of the STAD cell lines are correct. Additionally,
clarify what the sizes of the circles in the lollipop plot represent.
2. Figure 2D: ZBTB10 is strongly stained in normal tissue according to the HPA database. It is
highly recommended to validate these findings by conducting experiments on your own tissue
samples. Furthermore, the image in Figure 2D contains a reflection; please provide a revised
image without this artifact.
3. Figure 3F: The distinction between ZBTB10 expression levels with and without H. pylori
infection is unclear. Please provide raw data and a reasonable explanation for the observed
results, including the p-value used to compare the two groups.
4. Table S1: The logistic regression analysis is missing the odds ratio (OR) and 95% confidence
intervals (CI). Please include this information.
5. Figure 4A: Why was univariate Cox regression not performed prior to multivariate Cox
regression analysis? Please justify this approach.
6. Logistic and Cox Analysis: There is a correlation between high ZBTB10 expression and age
#65 years in the logistic analysis. However, in the Cox multivariate analysis, worse survival
is noted in patients >65 years with high ZBTB10 expression. Please explain the relationship
between ZBTB10 expression, age, and survival outcomes.
7. Lines 288–300: The logic and evidence supporting the relationship between ZBTB10
mutations and STAD are weak. Please provide experimental validation to explore the effect of
ZBTB10 mutations on its expression in STAD.
8. Lines 315–322: The conclusions regarding the impact of ZBTB10 on the immune
microenvironment (using databases such as CIBERSORT) appear inconsistent with earlier
findings. For instance, high ZBTB10 expression is linked to poor prognosis in earlier sections but
is associated with immune activation (e.g., activated NK cells) in this analysis. Please reconcile
this discrepancy with a thorough explanation.
9. Figures 8A-D: The experimental and control groups for the GO/KEGG analysis are not clearly
defined. Please provide raw data and clarify this in the manuscript. The description of “ZBTB10-
related differentially expressed genes (DEGs)” in the Methods section is insufficient to explain
the function of ZBTB10 in STAD.
10. Figure 8E: Clarify in the figure legend what the color gradients and circle sizes represent.
11. Figure 9B: Provide the complete line graph for the CCK-8 assay (Day 1 to Day 5).
12. Figure 9E: Add results from Transwell assays to further illustrate the effect of ZBTB10
overexpression on cell invasion.
13. Figure 10: Please provide enlarged images for better clarity. The color difference between
the left and right portions of the mixed image for Patient 1 is noticeable; ensure the figure is
consistent. Additionally, add scale bars to each mIHC image.
Minor Concerns
1. Add scatter points to all bar charts and box plots to make the data presentation more
convincing.
2. Clearly state in the manuscript whether ZBTB10 expression levels (high vs. low) are based
on the mean or median for each analysis.
3. In Figure 3, specify the comparison objects for statistical analyses.
4. For Figure 6D, consider using red for high expression and blue for low expression to enhance
visual clarity.
5. In Figure 8B, the KEGG information is incomplete. Include details such as Gene Ratio or
Gene Number and provide an updated figure.
6. For Figures 8C-D, include the normalized enrichment score (NES) and p-value for GSEA
results.
7. There is a typographical error in Figure 9E: "wieth" should be corrected to "width."
Title: ZBTB10 as a potential prognosis biomarker and correlates with tumor immune
microenvironment in stomach adenocarcinoma
Version: 1 Date: 12 Dec 2024
Reviewer's report:
This study presents an interesting investigation into the potential role of ZBTB10 as a
prognostic biomarker for STAD. The authors utilized the TCGA database and performed various
bioinformatic analyses, demonstrating that ZBTB10 plays a significant role in STAD prognosis
and is associated with the immune microenvironment. While there are several concerns that
need to be addressed to enhance the logical flow and strengthen the study's conclusions. The
following comments outline major and minor points that require the authors' attention.
Major Concerns
1. Figure 1B: Please verify that the names of the STAD cell lines are correct. Additionally,
clarify what the sizes of the circles in the lollipop plot represent.
2. Figure 2D: ZBTB10 is strongly stained in normal tissue according to the HPA database. It is
highly recommended to validate these findings by conducting experiments on your own tissue
samples. Furthermore, the image in Figure 2D contains a reflection; please provide a revised
image without this artifact.
3. Figure 3F: The distinction between ZBTB10 expression levels with and without H. pylori
infection is unclear. Please provide raw data and a reasonable explanation for the observed
results, including the p-value used to compare the two groups.
4. Table S1: The logistic regression analysis is missing the odds ratio (OR) and 95% confidence
intervals (CI). Please include this information.
5. Figure 4A: Why was univariate Cox regression not performed prior to multivariate Cox
regression analysis? Please justify this approach.
6. Logistic and Cox Analysis: There is a correlation between high ZBTB10 expression and age
#65 years in the logistic analysis. However, in the Cox multivariate analysis, worse survival
is noted in patients >65 years with high ZBTB10 expression. Please explain the relationship
between ZBTB10 expression, age, and survival outcomes.
7. Lines 288–300: The logic and evidence supporting the relationship between ZBTB10
mutations and STAD are weak. Please provide experimental validation to explore the effect of
ZBTB10 mutations on its expression in STAD.
8. Lines 315–322: The conclusions regarding the impact of ZBTB10 on the immune
microenvironment (using databases such as CIBERSORT) appear inconsistent with earlier
findings. For instance, high ZBTB10 expression is linked to poor prognosis in earlier sections but
is associated with immune activation (e.g., activated NK cells) in this analysis. Please reconcile
this discrepancy with a thorough explanation.
9. Figures 8A-D: The experimental and control groups for the GO/KEGG analysis are not clearly
defined. Please provide raw data and clarify this in the manuscript. The description of “ZBTB10-
related differentially expressed genes (DEGs)” in the Methods section is insufficient to explain
the function of ZBTB10 in STAD.
10. Figure 8E: Clarify in the figure legend what the color gradients and circle sizes represent.
11. Figure 9B: Provide the complete line graph for the CCK-8 assay (Day 1 to Day 5).
12. Figure 9E: Add results from Transwell assays to further illustrate the effect of ZBTB10
overexpression on cell invasion.
13. Figure 10: Please provide enlarged images for better clarity. The color difference between
the left and right portions of the mixed image for Patient 1 is noticeable; ensure the figure is
consistent. Additionally, add scale bars to each mIHC image.
Minor Concerns
1. Add scatter points to all bar charts and box plots to make the data presentation more
convincing.
2. Clearly state in the manuscript whether ZBTB10 expression levels (high vs. low) are based
on the mean or median for each analysis.
3. In Figure 3, specify the comparison objects for statistical analyses.
4. For Figure 6D, consider using red for high expression and blue for low expression to enhance
visual clarity.
5. In Figure 8B, the KEGG information is incomplete. Include details such as Gene Ratio or
Gene Number and provide an updated figure.
6. For Figures 8C-D, include the normalized enrichment score (NES) and p-value for GSEA
results.
7. There is a typographical error in Figure 9E: "wieth" should be corrected to "width."
Please review this paper.
user
Title: ZBTB10 as a potential prognosis biomarker and correlates with tumor immune
microenvironment in stomach adenocarcinoma
Version: 1 Date: 12 Dec 2024
Reviewer's report:
This study presents an interesting investigation into the potential role of ZBTB10 as a
prognostic biomarker for STAD. The authors utilized the TCGA database and performed various
bioinformatic analyses, demonstrating that ZBTB10 plays a significant role in STAD prognosis
and is associated with the immune microenvironment. While there are several concerns that
need to be addressed to enhance the logical flow and strengthen the study's conclusions. The
following comments outline major and minor points that require the authors' attention.
Major Concerns
1. Figure 1B: Please verify that the names of the STAD cell lines are correct. Additionally,
clarify what the sizes of the circles in the lollipop plot represent.
2. Figure 2D: ZBTB10 is strongly stained in normal tissue according to the HPA database. It is
highly recommended to validate these findings by conducting experiments on your own tissue
samples. Furthermore, the image in Figure 2D contains a reflection; please provide a revised
image without this artifact.
3. Figure 3F: The distinction between ZBTB10 expression levels with and without H. pylori
infection is unclear. Please provide raw data and a reasonable explanation for the observed
results, including the p-value used to compare the two groups.
4. Table S1: The logistic regression analysis is missing the odds ratio (OR) and 95% confidence
intervals (CI). Please include this information.
5. Figure 4A: Why was univariate Cox regression not performed prior to multivariate Cox
regression analysis? Please justify this approach.
6. Logistic and Cox Analysis: There is a correlation between high ZBTB10 expression and age
#65 years in the logistic analysis. However, in the Cox multivariate analysis, worse survival
is noted in patients >65 years with high ZBTB10 expression. Please explain the relationship
between ZBTB10 expression, age, and survival outcomes.
7. Lines 288–300: The logic and evidence supporting the relationship between ZBTB10
mutations and STAD are weak. Please provide experimental validation to explore the effect of
ZBTB10 mutations on its expression in STAD.
8. Lines 315–322: The conclusions regarding the impact of ZBTB10 on the immune
microenvironment (using databases such as CIBERSORT) appear inconsistent with earlier
findings. For instance, high ZBTB10 expression is linked to poor prognosis in earlier sections but
is associated with immune activation (e.g., activated NK cells) in this analysis. Please reconcile
this discrepancy with a thorough explanation.
9. Figures 8A-D: The experimental and control groups for the GO/KEGG analysis are not clearly
defined. Please provide raw data and clarify this in the manuscript. The description of “ZBTB10-
related differentially expressed genes (DEGs)” in the Methods section is insufficient to explain
the function of ZBTB10 in STAD.
10. Figure 8E: Clarify in the figure legend what the color gradients and circle sizes represent.
11. Figure 9B: Provide the complete line graph for the CCK-8 assay (Day 1 to Day 5).
12. Figure 9E: Add results from Transwell assays to further illustrate the effect of ZBTB10
overexpression on cell invasion.
13. Figure 10: Please provide enlarged images for better clarity. The color difference between
the left and right portions of the mixed image for Patient 1 is noticeable; ensure the figure is
consistent. Additionally, add scale bars to each mIHC image.
Minor Concerns
1. Add scatter points to all bar charts and box plots to make the data presentation more
convincing.
2. Clearly state in the manuscript whether ZBTB10 expression levels (high vs. low) are based
on the mean or median for each analysis.
3. In Figure 3, specify the comparison objects for statistical analyses.
4. For Figure 6D, consider using red for high expression and blue for low expression to enhance
visual clarity.
5. In Figure 8B, the KEGG information is incomplete. Include details such as Gene Ratio or
Gene Number and provide an updated figure.
6. For Figures 8C-D, include the normalized enrichment score (NES) and p-value for GSEA
results.
7. There is a typographical error in Figure 9E: "wieth" should be corrected to "width."
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