Integrative Transcriptomic and Perturbagen Analyses Reveal Sex-Specific Molecular Signatures Across Glioma Subtypes

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

While the manuscript addresses an important topic, several comments need to be addressed, outlined below:

• The analysis focuses on Chinese database CCGA only, which makes the results not generalized.
• The study ignores hormonal changes, menopause or hormonal therapy, which could affect expression.
• iLINCS predictions are in silico only. There is no experimental validation (e.g., cell lines, organoids, animal models) to confirm that predicted drugs actually reverse sex-specific signatures.
• The predicted drugs need further discussion about the feasibility of using it due to poor BBB penetration and toxicity. More pharmacokinetic feasibility needs more focus in the manuscript.
• The enrichment of synaptic genes may reflect neuronal contamination in bulk samples, especially in peritumoral tissue. This needs single-cell or spatial validation if possible.

Author Response

Reviewer 1:

While the manuscript addresses an important topic, several comments need to be addressed, outlined below:

1. The analysis focuses on Chinese database CCGA only, which makes the results not generalized.

Response: We thank the reviewer for raising this important point. The CGGA was selected because it provides one of the largest publicly available glioma RNA-seq datasets with complete sex and grade information, which enabled the stratified analyses central to our study. However, we agree that reliance on a single, predominantly Chinese cohort limits the generalizability of our findings. To address this concern, we have added a dedicated paragraph in the Discussion to explicitly acknowledge this limitation, clarify the rationale for using CGGA, and emphasize the need for independent validation in non-Chinese datasets such as TCGA. These revisions appear in the revised Discussion Page 19, Lines 512-521

1. The study ignores hormonal changes, menopause or hormonal therapy, which could affect expression.

Response: We agree that hormonal state, including menopausal status and hormone therapy, may influence gene expression and contribute to sex differences in glioma biology. However, endocrine-related metadata are not available in the CGGA dataset, which is consistent with most large, real-world clinical transcriptomic cohorts. We have now explicitly acknowledged this limitation in the Discussion, clarified that we cannot disentangle hormonal from intrinsic sex effects, and emphasized that our findings reflect sex-stratified biology as observed in real-world patient populations. We also reference prior experimental studies demonstrating sex-dependent transcriptional programs in hormone-depleted systems and highlight the need for future studies incorporating hormonal annotations. These revisions appear in the revised Discussion Page 19-20, Lines 531-543

 

3. iLINCS predictions are in silico only. There is no experimental validation (e.g., cell lines, organoids, animal models) to confirm that predicted drugs actually reverse sex-specific signatures.

Response: We fully agree with the reviewer that iLINCS perturbagen predictions are derived entirely from in silico transcriptomic analyses and do not constitute experimental validation. Functional testing in sex-matched in vitro and in vivo glioma models is beyond the scope of the current study. To clarify this point, we have added explicit language in both the Methods and Discussion stating that these results are hypothesis-generating and intended to prioritize candidate drug classes and mechanisms for future validation rather than imply therapeutic efficacy. We also emphasize that systematic perturbagen screening remains valuable as an unbiased approach to link disease-associated transcriptional programs with candidate interventions, thereby informing the design of downstream experimental studies. These clarifications have been incorporated into the revised manuscript on Page 6, Lines 185-186, Page 20, Lines 503-511, and Page 21, Lines 554-555.

4. The predicted drugs need further discussion about the feasibility of using it due to poor BBB penetration and toxicity. More pharmacokinetic feasibility needs more focus in the manuscript.

Response: We agree that pharmacokinetic feasibility is a key limitation, as many transcriptomic predicted compounds exhibit poor or highly variable blood–brain barrier penetration and potential systemic toxicity. Clinical CSF measurements in CNS tumor patients demonstrate that BBB penetration varies widely even among drugs within the same target class, driven by physicochemical properties and efflux transporter liability rather than target specificity alone (Guntner et al., 2020; PMID: 32493453). We have expanded the Discussion (Pages 19-20, Lines 485-494) to emphasize that these predictions nominate biologically relevant candidates.

1. The enrichment of synaptic genes may reflect neuronal contamination in bulk samples, especially in peritumoral tissue. This needs single-cell or spatial validation if possible.

Response: We agree that bulk RNA-seq data cannot definitively distinguish tumor-cell–intrinsic expression from neuronal or microenvironmental admixture, and that synaptic gene enrichment may partially reflect neuronal contamination, particularly in peritumoral or lower-grade samples. We have now explicitly acknowledged this limitation in the Discussion and clarified that single-cell or spatial transcriptomic approaches are required to resolve cellular origin with certainty. At the same time, we contextualize our findings within the growing body of evidence demonstrating biologically meaningful neuron–glioma interactions, emphasizing that these signatures should be interpreted cautiously and validated in future studies. These revisions appear in the revised Discussion Page 17, Lines 416-423.

Reviewer 2 Report

Comments and Suggestions for Authors

1. How did the authors address samples which contain mixed grade of gliomas? 
2. The data showed DEGs were different in males and females. It is interesting to showcase plausible mechanisms of glioma biology among different sexes. This can provide a clear view on the sets of DEGs contribute to pathologies.
3. The authors have shown drug repurposing data. How do these drugs correlate well with the DEGS in both male and female patients?
4. Please justify that the DEGs are brought about  by glioma pathologies in different sexes and not due to differences in tumor location.

Author Response

1. How did the authors address samples which contain mixed grade of gliomas? 

Response: We thank the reviewer for this important methodological question. To avoid confounding effects from ambiguous grading, samples annotated with mixed or unclear histologic grades were excluded from differential expression analyses. Only samples with unambiguous low-grade or high-grade classification were included in sex-stratified comparisons. We have clarified this explicitly in the Methods section of the revised manuscript on Page 14, Lines 123-126.

 

2. The data showed DEGs were different in males and females. It is interesting to showcase plausible mechanisms of glioma biology among different sexes. This can provide a clear view on the sets of DEGs that contribute to pathologies.

Response: We thank the reviewer for this thoughtful comment. We would like to clarify that mechanistic interpretation of sex-specific differential gene expression represents a central focus and key novelty of our study. In the middle portion of the Discussion, we provide an in-depth analysis linking male- and female-associated transcriptional programs to distinct biological processes relevant to glioma progression, including proliferative signaling, neuronal and synaptic pathways, and tumor–microenvironment interactions. To improve clarity, we have added a brief signposting sentence directing readers on Page 16, Lines 381-382.

 

3. The authors have shown drug repurposing data. How do these drugs correlate well with the DEGS in both male and female patients?

Response: We thank the reviewer for this question. We would like to clarify that the drug repurposing analysis was explicitly anchored to sex-specific differential gene expression profiles. Perturbagen predictions were generated independently for male and female DEG signatures, and candidate compounds were nominated based on their predicted ability to reverse the corresponding sex-stratified transcriptional programs. This direct linkage between DEGs, pathway enrichment, and perturbagen prediction is described in the Methods and discussed in detail in the central portion of the Discussion. To further improve clarity, we have added a brief sentence in the Discussion explicitly highlighting this relationship on Page 19, Lines 456-459.

 

4. Please justify that the DEGs are brought about by glioma pathologies in different sexes and not due to differences in tumor location.

Response: We agree that anatomical tumor location may influence transcriptomic profiles and could potentially confound sex-specific analyses. Unfortunately, detailed and standardized tumor location metadata were not available in the CGGA dataset, precluding formal adjustment for tumor site. We have now explicitly acknowledged this as a limitation in the Discussion. These revisions appear in the revised Discussion Page 20, Lines 526-530.

Reviewer 3 Report

Comments and Suggestions for Authors

Reddy and colleagues performed a differential expression analysis of glioma grades (LGG vs. HGG/GBM), separating samples by sex, thereby unveiling the divergent transcriptional programs that define progression differently in males and females. Despite similar prior sex-specific gene analyses, the present study's incorporation of differing tumor malignancy grades confirms its considerable significance within glioma research. However, while the findings are compelling, the manuscript could be strengthened by considering certain critical nuances that were not fully addressed by the authors:

1 - GBM is already known for its high patient-to-patient heterogeneity, which is amplified when it is included in the broader HGG category. A slight proportional variation of GBM in the male versus female HGG groups could dramatically show differences in sex-specific gene expression findings. Consequently, it makes more sense to analyze LGG versus GBM or Astrocytoma versus GBM. What were the proportions of the various glioma subtypes in the cohort?

2 - Could the authors include DEGs analysis of a Low grade glioma versus a High grade glioma without GBM? or LGG versus astrocytomas?

3 - Is it possible to include the fold change for each gene in the table 1?

4 - GLI1 is a known glioblastoma marker and a well-established indicator of malignancy. What could be the explanation for its downregulation in females? Should transcript variants also be accounted for in this analysis?

5 - To properly contextualize their findings, the authors should discuss other studies that have investigated sex-based differences in glioblastoma gene expression, for example:

Huang Y, Shan Y, Zhang W, Printzis C, Pesce L, Stover D, Stanhope C, Stranger BE, Huang RS. Sex differences in the molecular profile of adult diffuse glioma are shaped by IDH status and tumor microenvironment. Neuro Oncol. 2025 Feb 10;27(2):430-444. doi: 10.1093/neuonc/noae207.

Qin S, Yuan Y, Liu H, Pu Y, Chen K, Wu Y, Su Z. Identification and characterization of sex-dependent gene expression profile in glioblastoma. Neuropathology. 2023 Feb;43(1):72-83. doi: 10.1111/neup.12845. 

 

Author Response

Reddy and colleagues performed a differential expression analysis of glioma grades (LGG vs. HGG/GBM), separating samples by sex, thereby unveiling the divergent transcriptional programs that define progression differently in males and females. Despite similar prior sex-specific gene analyses, the present study's incorporation of differing tumor malignancy grades confirms its considerable significance within glioma research. However, while the findings are compelling, the manuscript could be strengthened by considering certain critical nuances that were not fully addressed by the authors:

1 - GBM is already known for its high patient-to-patient heterogeneity, which is amplified when it is included in the broader HGG category. A slight proportional variation of GBM in the male versus female HGG groups could dramatically show differences in sex-specific gene expression findings. Consequently, it makes more sense to analyze LGG versus GBM or Astrocytoma versus GBM. What were the proportions of the various glioma subtypes in the cohort?

Response: We appreciate the reviewer’s thoughtful comment regarding GBM heterogeneity and its potential impact on sex-specific differential expression analyses. We agree that proportional differences in GBM representation could influence observed expression patterns when GBM is grouped within a broader high-grade glioma category. To address this concern, we have provided a detailed breakdown of glioma subtypes and their proportions in the cohort, stratified by sex, in Supplementary Table 10. This table allows readers to directly assess subtype composition and evaluate the potential influence of GBM distribution on the reported findings. Importantly, our primary analyses were designed to mitigate this concern by focusing on comparisons that explicitly include GBM as a distinct high-grade entity rather than subsuming it within a heterogeneous HGG group.

2 - Could the authors include DEGs analysis of a Low grade glioma versus a High grade glioma without GBM? or LGG versus astrocytomas?

Response: We appreciate the reviewer’s suggestion and agree that these glioma subtype analyses would be valuable. Unfortunately, limitations in the available metadata preclude reliable implementation within the current study design. We have therefore noted this as a limitation in our manuscript and consider it an important direction for future work on Page 20, Lines 521-530.

3 - Is it possible to include the fold change for each gene in the Table 1

Response: As suggested, we have included the log fold change for each gene in Table 1 in the revised manuscript.

4 - GLI1 is a known glioblastoma marker and a well-established indicator of malignancy. What could be the explanation for its downregulation in females? Should transcript variants also be accounted for in this analysis?

Response: We appreciate the reviewer’s comment. While GLI1 is a well-established marker of glioblastoma and Hedgehog pathway activity, its expression and regulatory dynamics are context-dependent and may differ by sex. The observed downregulation of GLI1 in females may reflect sex-specific pathway modulation, differences in tumor cellular composition, or variation in upstream signaling rather than reduced malignancy per se. In addition, transcript-level regulation and alternative splicing could contribute to these findings; however, the present analysis was performed at the gene level to maintain consistency across cohorts and ensure statistical robustness. Transcript variant–specific analyses represent an important future direction and are now noted as a limitation of this study on Page 19, Lines 479-484.

 

5 - To properly contextualize their findings, the authors should discuss other studies that have investigated sex-based differences in glioblastoma gene expression, for example:Huang Y, Shan Y, Zhang W, Printzis C, Pesce L, Stover D, Stanhope C, Stranger BE, Huang RS. Sex differences in the molecular profile of adult diffuse glioma are shaped by IDH status and tumor microenvironment. Neuro Oncol. 2025 Feb 10;27(2):430-444. doi: 10.1093/neuonc/noae207. Qin S, Yuan Y, Liu H, Pu Y, Chen K, Wu Y, Su Z. Identification and characterization of sex-dependent gene expression profile in glioblastoma. Neuropathology. 2023 Feb;43(1):72-83. doi: 10.1111/neup.12845. 

Response: As suggested, we have now expanded the Discussion to include prior studies that have investigated sex-based differences in glioblastoma gene expression, including work by Huang et al. and Qin et al. These studies demonstrate that sex-specific molecular profiles in glioblastoma are influenced by factors such as IDH status and tumor microenvironmental composition. We now explicitly contextualize our findings within this existing literature and clarify how our pathway-level, systems-oriented approach complements and extends prior gene-centric analyses on Page 17, Lines 405-415.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors answered the reviewer's comments in a sufficient way and the manuscript is acceptable for publication in its current form

Reviewer 2 Report

Comments and Suggestions for Authors

None

Reviewer 3 Report

Comments and Suggestions for Authors

The authors satisfactorily responded to almost all the questions raised and incorporated relevant information and suggestions into the new version of the manuscript, which improved the discussion of the results.