Hub Gene Clusters Reveal Dysregulated Synaptic Neurotransmitter Signaling Pathways and Drug Repurposing Prospect in Brain Tumors
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors
Dear Authors,
Thank you for the opportunity to evaluate this manuscript. The topic here is relevant, and the integration of differential gene expression analysis, co-expression network analysis, pathway enrichment, and drug repurposing provides a structured computational approach to identify potential therapeutic candidates. The manuscript is interesting and fits within the scope of the journal.
My comments:
1. Methodological clarity and reproducibility. The methods section lacks sufficient detail for reproducibility.
The criteria for dataset inclusion are briefly mentioned, but sample sizes, number of tumour vs control samples, and tumour subtype distribution are not clearly described.
It is not clear whether batch effect correction was performed when combining datasets from different GEO studies, which is critical in multi-dataset microarray analysis.
2. Biological interpretation is overstated. The authors conclude that dysregulated synaptic neurotransmitter signalling pathways are involved in brain tumour development and propose several drugs as treatment candidates. However, the study is purely in silico, so statements about therapeutic potential are too strong. The discussion of neurotransmitters and tumour progression is speculative and not sufficiently supported by the presented data.
I think that the conclusions should be toned down and clearly state that findings are predictive and hypothesis-generating.
- Drug repurposing section needs stronger justification. The drug selection process includes DGIdb, GSEA/CMAP validation, and BBB permeability prediction, which is good in principle. The criteria for selecting the final six drugs are not fully clear.
Some drugs identified (e.g., pyrantel) have very limited evidence in brain tumour therapy; stronger literature support is needed.
The BOILED-Egg model only predicts passive diffusion and does not account for active transport, metabolism, or toxicity, which should be acknowledged as a limitation.
- Language and writing: The manuscript contains numerous grammatical errors, awkward phrasing, and repetitive sentences, especially in the Introduction and Discussion sections. The manuscript requires major English editing.
The study is interesting and potentially publishable, but significant revisions are required. I recommend major revision.
Author Response
We are grateful for the reviewer's time and effort in reading and reviewing our manuscript. We have revised our manuscript based on your comments. We hope that we addressed them well. Thank you very much.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for Authors
Paper is all about bioinformatics analysis of brain tumours to determine putative drugs for repurposing for hard to treat tumours of the CNS. The paper is purely bioinformatics based and would benefit from some validation in cell lines.
GSE analysis first used to identify DEGs between tumour and non-tumour material across the 4 datasets. Subsequently, various other online tools were used to build clusters of protein interactions and pathways, before drug targets for these proteins were identified as possible drugs for repurposing.
- The 4 datasets are very different and the non-tumour tissue in each case may not be a good control sample (age range etc). no explanation given for the either of these assumptions. This is a fundamental failing of the paper as it assumes that similar signalling pathways might be differentially regulated in very different tumours. We know that this is not true even in a single type of brain tumour. For example we know that medulloblastoma has at least 4 different molecular sub-types. There needs to be a strong rationale for lumping all the different tumours types together and for assuming that the non-tumour-brain material will be a valid comparison. I would argue the 4 datasets are so different that they cannot be collectively analysed.
2. There is huge amounts of repetition between methods and results. The paper is very long because of this
3. The assumption is that gene expression correlates with protein expression but this assumption needs some strong validation
4. None of the HDEGs have been shown to have expression correlated with outcome in other brain tumour studies. Surprised this hasn’t been done as it might provide some further information of value.
Comments on the Quality of English Language
the introduction starts with some very strange phrasing in the first 2-3 sentences.
Author Response
We are grateful for the reviewer's time and effort in reading and reviewing our manuscript. We have revised our manuscript based on your comments. We hope that we addressed them well. Thank you very much.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for Authors
The article by Avanceña Villanueva et al. is of potential interest in the field of oncology because the authors conducted a broad computer-based study analyzing data from datasets of different brain tumor types to identify preserved clusters and hub gene clusters. These studies are useful for verifying whether drugs already used to treat other diseases could be exploited for the treatment of various types of brain tumors, also because they are capable of crossing the blood-brain barrier.
The project is ambitious. The computer-based analyses were conducted with sufficient accuracy and highlighted several gene hubs as potential targets for some already known drugs. This work lays the foundation for further future pharmacological research and, above all, for the subsequent validation of the efficacy of the identified drugs.
However, the article has several critical points:
- The English is generally quite good; however, some parts of the text require revision.
- The results are described in excessive detail and should be scaled down to highlight the essentials, especially those in Chapter 3. Just to give an example, I report the following sentences I found on page 5.
In the original text, the authors wrote:
The volcanic plot (see Figure 1) displayed the DEGs in contrast between the brain tumor group and the normal group with an adjusted p-value cutoff of 0.05 and a log2fold threshold (FC) of ≥ 1 for upregulated genes and ≤ -1 for downregulated genes [33] (this sentence has already been reported in the figure legend). Each dot represents a gene; blue dots represent downregulated genes, red dots were upregulated genes, while the black dots represent genes that failed to fit the significance cutoff and were therefore disregarded. Differential gene expression analysis between the tumor and normal groups within each dataset identified the upregulated and downregulated genes, which were initial information to uncover shared tumor genes across brain tumors.
My suggestion is: The volcanic plot (see Figure 1) displays the DEGs in the brain tumor group and the normal group. The blue dots represent downregulated genes, red were upregulated genes, while the black dots represent disregarded genes failing to fit the significance cutoff. This analysis represents initial information to uncover shared tumor genes across brain tumors.
- In my opinion, Table 2, with the description of the function of the most interesting genes, should be displayed before all the other analyses to facilitate understanding the latter.
- Furthermore, a table indicating the pharmacological activities and the relevant bibliographic references for the drugs selected and listed in Table 1 would be needed.
- Finally, sections 4.2 and 4.3 should be reworked into a chapter entitled "Discussion" (which is completely missing). In this chapter, the authors should highlight both the signaling pathways activated by dysregulated genes, emphasizing their importance in contributing to tumorigenesis, and the possible interaction of the selected drugs on genes and potential signaling pathways.
Minor points
- Drug-gene targets should be "drug target genes."
- Many acronyms are explained several times in the text. Just to give some examples, the explanation of the acronym for Differentially expressed genes (DEGs) is repeated (page 3, line 99; page 4, line 145; page 6, line 218). The same applies to the acronym STRING, which is present in both the methods and in section 3.2 of the results, and the same applies to the analyses conducted with Gene Ontology and the Database of Annotation, Visualization, and Integrated Discovery. However, there are several others throughout the article. Conversely, the acronym for Hub differentially expressed genes (HDEGs) was inserted after being used several times in the results text on pages 8 and 9.
- On pag. 9 I found: “See Supplementary Data 3 for topological properties of the clusters' sub-network, hub ranking scores, and fold change values”: where are they? If a data point 3 is indicated, it is assumed that there are at least other data points 1 and 2. Where they are?
- On page 9, Fig. 6 is indicated as Fig. 5. In Figure 6, they forgot to include the letters a, b, and c.
Comments on the Quality of English Language
The English is generally quite good; however, some parts of the text require revision.
Author Response
We are grateful for the reviewer's time and effort in reading and reviewing our manuscript. We have revised our manuscript based on your comments. We hope that we addressed them well. Thank you very much.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for Authors
The authors have corrected and improved the paper sufficiently. It is now ready for acceptance, on my opinion. Thank you.
Author Response
We sincerely thank the reviewer for the positive evaluation of our manuscript and for recognizing the improvements made. We greatly appreciate your time, constructive comments, and support for the acceptance of our work.
Reviewer 2 Report
Comments and Suggestions for Authors
the manuscript has been improved significantly and the majority of my concerns are mitigated by the authors' responses. I have one question that I would value the authors commenting on. The paper uses 4 datasets to arrive at a hypothesis about the value of various drugs in brain tumour therapy. Have they now taken a 5th, different, data set to see if their hypothesis is validated. There are many such datasets available such as the Cavalli mdedulloblastoma dataset of over 700 patients. Would such a validation improve this paper further?
Comments on the Quality of English Language
Author Response
We are grateful for the reviewer's insightful suggestion and comment. We have added the discussion requested and addressed the following concerns. Please see the attached file. Thank you very much for your help.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for Authors
The article by Villanueva et al. has been reviewed, taking sufficient account of the comments by the reviewers, and therefore, overall, it would be acceptable for publication.
However, some concerns remain regarding the authors' verbosity in describing their results.
For example, on page 8 and the beginning of page 9, they repeat three times that the identified clusters contain up- and down-regulated genes. Finally, they reiterate the same concept on page 14, where they again list the genes included in the clusters. This seems truly redundant and should be corrected to avoid unnecessary repetition.
Furthermore, some sentences do not make complete sense (e.g., on page 9, line 268: DAVID – GO terms and KEGG pathways enrichment analysis, as shown in Figure 6. Or on page 12, lines 355-356: “This allowed us to select further drugs suitable for repurposing for brain tumors. Observed in Figure 8”).
Author Response
We sincerely thank the reviewer for the careful evaluation of our manuscript and for acknowledging that the revised version has sufficiently addressed the reviewers’ comments. Please see the attached files for our response. Thank you very much.
Author Response File: Author Response.pdf
