Evidence map›Paper›PMID 42316306›Full record

ArticleBioData mining2026

Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing.

Md Feroj Ahmed, Ovi Sharma, Tasfia Noor, Md Abdul Latif, Md Foysal Ahmed, Moumita Bashak, Dewan Mumdood Ahmed, Md Sanoar Hossain, Md Nurul Haque Mollah

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Article in BioData mining, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Md Feroj AhmedBioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Ovi SharmaBioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Tasfia NoorBioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Md Abdul LatifBioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Md Foysal AhmedBioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Moumita BashakBioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Dewan Mumdood AhmedBioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Md Sanoar HossainBioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Md Nurul Haque MollahBioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh. mollah.stat.bio@ru.ac.bd.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Type 2 diabetes (T2D) is considered as a risk factor of triple-negative breast cancer (TNBC). So, there is a significant chance of their co-existence. The management of TNBC with T2D becomes more complex than without T2D due to the conflict of therapies, since some T2D drugs may have bad impact on TNBC and vice-versa. Beside this, drug-drug interaction due to the polypharmacy of multiple drugs, during a one-drug one-disease strategy, may create toxicity or side effect to the patients who are suffering from both diseases simultaneously. Therefore, it is required to explore effective unique drugs as the same treatment for both diseases. This study attempted to contribute in this issue. At first, we identified 36 shared differentially expressed genes (sDEGs) that can separate both TNBC and T2D patients from the control group through integrated transcriptomics analysis. Then top-ranked four sDEGs (S100A9, CIRBP, USP10, and PSMD1) were detected as the overlapping dysregulated shared key-genes (sKGs) through the protein-protein interaction (PPI) network analysis and filtering with a machine learning (ML) approach. The gene regulatory network analysis revealed three key transcriptional (TFs proteins) and post-transcriptional (micro-RNAs) factors of sKGs. The enrichment analysis of sKGs with the GO-terms and KEGG-pathways revealed some crucial molecular functions, biological processes, cellular components, and pathways as the key pathogenetic mechanisms for the development and progression of both diseases. Finally, we recommended sKGs-guided two repurposable common candidate drugs (tepotinib and ursodiol) for both diseases by molecular docking, ADME/T analysis and MD simulation studies. Thus, the results of this study could provide useful insights for researchers and medical professionals for improving the diagnostic and therapeutic strategies for the treatment TNBC with T2D as comorbidity.

Indexed as

Drug repurposingMachine learning–based SHAP analysisShared key genes and pathogenetic processesStatistics and bioinformatics analysisTriple-negative breast cancerType 2 diabetes

Identifiers

PMID42316306
PMCPMC13528018

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