Title: Identification and validation of a lenvatinib resistance-related prognostic signature in
HCC, in which PFKFB4 contributes to tumor progression and lenvatinib resistance
Version: 3 Date: 15 Feb 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the study describes the acquisition of data from TCGA and ICGC, more details on the
preprocessing steps would enhance transparency. For example, the authors should clarify how
they handled batch effects or data normalization across different datasets, as such issues can
influence results, particularly in multi-dataset analyses.
-While PFKFB4 is highlighted as a key gene in lenvatinib resistance, further mechanistic
exploration into how the other identified LRRDEGs contribute to drug resistance would be
valuable. What specific pathways or cellular processes are these genes involved in? Exploring
these connections could provide deeper insight into the mechanisms of resistance and open doors
for future therapeutic strategies.
-The prognostic nomogram developed in the study is an important tool, but the authors
could enhance the clinical relevance by providing more context on how the model could be
implemented in clinical settings. For example, what are the expected benefits of using the
nomogram to guide therapy compared to existing methods? An analysis of its predictive power
in real-world clinical cohorts would further strengthen the practical utility of the model.
-Although the study used external datasets (ICGC) for validation, the ultimate validation of the
prognostic signature in a prospective clinical trial or a larger independent cohort would be crucial
to confirm its robustness and applicability for guiding treatment decisions in clinical practice.
-While the authors provide several findings from their analyses (such as correlations with
immune checkpoint expressions, immune cell infiltration, and gene mutations), the discussion
would benefit from further clarity on the statistical significance of these findings. Including
specific p-values, confidence intervals, or other statistical metrics would strengthen the claims.
For instance, the positive correlations with immune cell types (e.g., M0 macrophages, T cells)
and immune checkpoints could be further substantiated with statistical values to clarify how
robust these correlations are.
-While the authors suggest that further research is needed to elucidate the mechanisms behind
PFKFB4’s role in lenvatinib resistance, the discussion could be enhanced by offering more
concrete suggestions for future investigations. For example, the authors could discuss potential
animal models or in vitro experiments to further validate their findings. They could also elaborate
on how the gene signature might be used in clinical practice, such as by exploring its use in
patient stratification or in combination with other therapies.
-The authors mention the correlation between the risk score and immune cell infiltration but
do not go into sufficient detail about how the tumor microenvironment may be altered in high-
risk patients. While there is mention of angiogenesis and immune cells, additional insights into
how these features contribute to lenvatinib resistance would be valuable. For example, a more
detailed discussion on how immune evasion or the presence of certain immune subsets (e.g.,
Tregs, M2 macrophages) might contribute to treatment resistance could strengthen this aspect
of the discussion.
Title: Identification and validation of a lenvatinib resistance-related prognostic signature in
HCC, in which PFKFB4 contributes to tumor progression and lenvatinib resistance
Version: 3 Date: 15 Feb 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the study describes the acquisition of data from TCGA and ICGC, more details on the
preprocessing steps would enhance transparency. For example, the authors should clarify how
they handled batch effects or data normalization across different datasets, as such issues can
influence results, particularly in multi-dataset analyses.
-While PFKFB4 is highlighted as a key gene in lenvatinib resistance, further mechanistic
exploration into how the other identified LRRDEGs contribute to drug resistance would be
valuable. What specific pathways or cellular processes are these genes involved in? Exploring
these connections could provide deeper insight into the mechanisms of resistance and open doors
for future therapeutic strategies.
-The prognostic nomogram developed in the study is an important tool, but the authors
could enhance the clinical relevance by providing more context on how the model could be
implemented in clinical settings. For example, what are the expected benefits of using the
nomogram to guide therapy compared to existing methods? An analysis of its predictive power
in real-world clinical cohorts would further strengthen the practical utility of the model.
-Although the study used external datasets (ICGC) for validation, the ultimate validation of the
prognostic signature in a prospective clinical trial or a larger independent cohort would be crucial
to confirm its robustness and applicability for guiding treatment decisions in clinical practice.
-While the authors provide several findings from their analyses (such as correlations with
immune checkpoint expressions, immune cell infiltration, and gene mutations), the discussion
would benefit from further clarity on the statistical significance of these findings. Including
specific p-values, confidence intervals, or other statistical metrics would strengthen the claims.
For instance, the positive correlations with immune cell types (e.g., M0 macrophages, T cells)
and immune checkpoints could be further substantiated with statistical values to clarify how
robust these correlations are.
-While the authors suggest that further research is needed to elucidate the mechanisms behind
PFKFB4’s role in lenvatinib resistance, the discussion could be enhanced by offering more
concrete suggestions for future investigations. For example, the authors could discuss potential
animal models or in vitro experiments to further validate their findings. They could also elaborate
on how the gene signature might be used in clinical practice, such as by exploring its use in
patient stratification or in combination with other therapies.
-The authors mention the correlation between the risk score and immune cell infiltration but
do not go into sufficient detail about how the tumor microenvironment may be altered in high-
risk patients. While there is mention of angiogenesis and immune cells, additional insights into
how these features contribute to lenvatinib resistance would be valuable. For example, a more
detailed discussion on how immune evasion or the presence of certain immune subsets (e.g.,
Tregs, M2 macrophages) might contribute to treatment resistance could strengthen this aspect
of the discussion.
Please review this paper.
user
Title: Identification and validation of a lenvatinib resistance-related prognostic signature in
HCC, in which PFKFB4 contributes to tumor progression and lenvatinib resistance
Version: 3 Date: 15 Feb 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the study describes the acquisition of data from TCGA and ICGC, more details on the
preprocessing steps would enhance transparency. For example, the authors should clarify how
they handled batch effects or data normalization across different datasets, as such issues can
influence results, particularly in multi-dataset analyses.
-While PFKFB4 is highlighted as a key gene in lenvatinib resistance, further mechanistic
exploration into how the other identified LRRDEGs contribute to drug resistance would be
valuable. What specific pathways or cellular processes are these genes involved in? Exploring
these connections could provide deeper insight into the mechanisms of resistance and open doors
for future therapeutic strategies.
-The prognostic nomogram developed in the study is an important tool, but the authors
could enhance the clinical relevance by providing more context on how the model could be
implemented in clinical settings. For example, what are the expected benefits of using the
nomogram to guide therapy compared to existing methods? An analysis of its predictive power
in real-world clinical cohorts would further strengthen the practical utility of the model.
-Although the study used external datasets (ICGC) for validation, the ultimate validation of the
prognostic signature in a prospective clinical trial or a larger independent cohort would be crucial
to confirm its robustness and applicability for guiding treatment decisions in clinical practice.
-While the authors provide several findings from their analyses (such as correlations with
immune checkpoint expressions, immune cell infiltration, and gene mutations), the discussion
would benefit from further clarity on the statistical significance of these findings. Including
specific p-values, confidence intervals, or other statistical metrics would strengthen the claims.
For instance, the positive correlations with immune cell types (e.g., M0 macrophages, T cells)
and immune checkpoints could be further substantiated with statistical values to clarify how
robust these correlations are.
-While the authors suggest that further research is needed to elucidate the mechanisms behind
PFKFB4’s role in lenvatinib resistance, the discussion could be enhanced by offering more
concrete suggestions for future investigations. For example, the authors could discuss potential
animal models or in vitro experiments to further validate their findings. They could also elaborate
on how the gene signature might be used in clinical practice, such as by exploring its use in
patient stratification or in combination with other therapies.
-The authors mention the correlation between the risk score and immune cell infiltration but
do not go into sufficient detail about how the tumor microenvironment may be altered in high-
risk patients. While there is mention of angiogenesis and immune cells, additional insights into
how these features contribute to lenvatinib resistance would be valuable. For example, a more
detailed discussion on how immune evasion or the presence of certain immune subsets (e.g.,
Tregs, M2 macrophages) might contribute to treatment resistance could strengthen this aspect
of the discussion.
assistant