In silico insights into shared molecular mechanisms and drug repurposing for non‑small cell lung cancer and tuberculosis.

Authors

Nazia Zarin, Nafis Fuad Shahir, Tasnim Hosen Tanha, Mimuna Nishad, Shaila Haque, Md Enamul Kabir Talukder, Tanzil Zahan

Published in

In silico pharmacology. Volume 14. Issue 3. Pages 260. Epub Oct 17, 2026.

Abstract

Non‑small cell lung cancer (NSCLC) and tuberculosis (TB) represent major global health burdens, with evidence of a molecular interconnection. To understand their shared mechanisms, we integrated three NSCLC and two TB RNA‑seq datasets and identified 33 common differentially expressed genes (cDEGs). Enrichment analyses, protein-protein interaction (PPI) network mapping, cancer‑specific validations, and survival analyses were performed on all cDEGs. The PPI network highlighted KIF18A, ANLN, TLR4, EPAS1, and DLC1 as central hub genes, with enrichment pointing to immune‑inflammatory signaling, epithelial remodeling, and cell‑cycle regulation as key processes underlying NSCLC-TB comorbidity. Profiling of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), accounting for about 80% of NSCLC, showed KIF18A, ANLN, STIL, and GALNT14 overexpression linked to advanced stage and poor survival, with 4-14% alterations mainly from mRNA upregulation. Leveraging their network centrality and prognostic significance, KIF18A and ANLN were selected as receptor targets for structure‑based drug screening. A library of 213 anti‑NSCLC, anti‑TB, and antimicrobial compounds was screened against these targets using AutoDock and Glide, followed by pharmacokinetic and toxicity assessment. Levofloxacin (targeting ANLN) and Capmatinib (targeting KIF18A) exhibited the most favorable docking scores and ADME/Tox profiles, with stable binding modes, favorable MM-GBSA binding free energies, and refined dynamic cross-correlation and free-energy landscapes over 100 ns molecular dynamics simulations, nominating them as promising in silico predicted drug agents relevant to both NSCLC and TB. Our in silico transcriptomic and structural‑dynamics analysis reveals shared infection-cancer mechanisms and identifies candidate therapeutics for further evaluation.
The online version contains supplementary material available at https://doi.org/10.1007/s40203-026-00767-5.

PMID:
42894563
Bibliographic data and abstract were imported from PubMed on 19 Oct 2026.

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