PONE-D-20-40860

miRGTF-net: integrative miRNA-gene-TF network analysis reveals key drivers of breast cancer recurrence

PLOS ONE

Dear Dr. Nersisyan,

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Comments to the author:

The authors report miRGTF-net, a tool for constructing regulatory networks among miRNAs, genes, and TFs. This tool combines a database-level approach as well as expression profiles given from miRNAs and genes to reduce potential false positives for edge construction. They have applied miRGTF-net on the TCGA-BRCA ER-positive dataset and proposed a classification pipeline to predict recurrence from several independent patient cohorts. The presentation is in a light quality level and in the scope of PLOS ONE. The rationale of the work is interesting, although it’s not completely novel. The implementation can be considered of some significance for the research community. Several concerns should be addressed as stated below that I believe would increase the quality and clarity of the manuscript.

Major comments:

1. P2 L28-33: The two major approaches of network construction are described their respective disadvantages but no advantage is mentioned here. I suggest using “limitations” in the sentence rather than advantages and disadvantages.

2. P2 L29: “disadvantagesr” should be “disadvantages”.

3. P2 L29-30: Why does the database-level analysis usually lack tissue specificity? Please describe it in detail.

4. P3 L82-83: “some other node” should be “some other nodes”.

5. P4 L118: How many genes were used as the input for the enrichment analysis? What is the threshold used here to identify the significantly enriched terms? Please add these in the article. Also, check out the number of 218 significantly enriched terms is correct.

6. P5 L156: Add a comma between “potential” and “we”.

7. P8 L276: “intersect” should be “intersects”.

8. P9 L341-342: Are miRNA IDs unified to the latest version of miRBase when constructing the miRGTF-net databases? Among the databases, miRTarBase 7.0 and TransmiR v2.0 utilize miRBase v21 and v22, respectively. According to the description of the miRNA.diff file from miRBase, there are 12 human mature miRNAs have been changed the IDs between the two versions. Although the impact is slight, still might cause the relationship in a network to unmatch. Additionally, the miRGTF-net input data with miRNA IDs can use an annotation belonging to one of the various versions of miRBase. Once these miRNA IDs use older versions, the number of miRNAs that cannot be mapped to the databases correctly will increase. This case is more common in early miRNA-seq data. The authors should address this issue and propose a feasible approach in miRGTF-net.

9. Figs. 1 and 7: Please use standard flowchart symbols instead of colors to represent elements in the workflows.

Comments for the miRGTF-net improvement:

1. Besides the human databases, the authors can consider establishing the databases of other model organisms for miRGTF-net.

2. It would be good to show the real-time progress/status as well as brief summary while running the miRGTF-net script.

Reviewer #2: In this manuscript, Nerissyan et al. introduced a novel method to analyze miRNA-gene-TF network on breast cancer datasets. This method highlights the use of integrative information (microarray data, TCGA database, miRNA information) to build a classifier for five-year breast cancer recurrence prediction. As a result, they have identified a set of gene signature that can be used to predict the outcome of cancer patients. Finally, the authors discussed the mechanistic roles of ESR1 and E2F1 underlying breast cancer recurrence.

However, I think the following points need to be clarified in the manuscript:

1, Since the results are based on the input databases, and the use of databases may bias the output. I would like to see a comparison between using other miRNA prediction tools, such as TargetScan, to perform the analysis and see how robust the results would be.

2, I can see the potential of applying this software to other species. I suggest the author to include a note on the Github pages and show how to build the input data for other model organisms, such as mouse and Drosophila. If the preparation of those data needs pre-processing, they should also include scripts to perform that.

3, I suggest the authors to compare one or two more machine learning methods in the classification step. Although SVM is considered as a good classifier (AUC value), other algorithm may outperform it due to the use of different strategy, such as tree models (decision tree, random forest), and probability-based method, for example, Naive Bayes classifier.

4, I suggest the authors to provide more analysis/visualization on the gene signatures, for example, how stable are the signature across TCGA samples? Do they have individual variations? A Heatmap with clustering analysis would be a useful way to address these questions.

Also, I have tested the scripts on their GitHub webpage and the software is user friendly. I would suggest the authors to change the default output format to pdf, which is easier to open and process than the current graphml format. Overall, this work provides a new angle for integrative TF and miRNA analysis. I suggest a moderate revision of the manuscript before publication.

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Reviewer #1: No

Reviewer #2: Yes: Yu H. Sun

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miRGTF-net: integrative miRNA-gene-TF network analysis reveals key drivers of breast cancer recurrence

PONE-D-20-40860R1

Dear Dr. Nersisyan,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Eduardo Andrés-León

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this revised version, the authors exhaustively addressed all the raised concerns. I consider these changes made by the authors to be appropriate. The manuscript is overall more curated, therefore, I suggest the publication of the paper in its revised form.

Reviewer #2: In this revised article, the authors have addressed all the questions I raised during the first round of review. In addition, they added more discussions in the main text (lines 269-279) which expands the application of the new method they developed. The updated github files (such as TRRUST, TransmiR, miRTarBase databases, and the instructions of visualization) will also benefit the users to implement their software in their own research. I hope that the authors keep good maintenance of the github repository and regularly check the reported bugs raised by the users. I suggest to accept this manuscript.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Yu H. Sun