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

In silico pharmacology Pub Date : 2026-10-17 eCollection Date: 2026-01-01 DOI:10.1007/s40203-026-00767-5
Nazia Zarin, Nafis Fuad Shahir, Tasnim Hosen Tanha, Mimuna Nishad, Shaila Haque, Md Enamul Kabir Talukder, Tanzil Zahan
{"title":"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","doi":"10.1007/s40203-026-00767-5","DOIUrl":null,"url":null,"abstract":"<p><p>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 <i>KIF18A, ANLN, TLR4, EPAS1</i>, and <i>DLC1</i> 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 <i>KIF18A, ANLN, STIL</i>, and <i>GALNT14</i> 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.</p><p><strong>Graphical abstract: </strong></p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at https://doi.org/10.1007/s40203-026-00767-5.</p>","PeriodicalId":94038,"journal":{"name":"In silico pharmacology","volume":"14 3","pages":"260"},"PeriodicalIF":0.0000,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13676611/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"In silico pharmacology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s40203-026-00767-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

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.

Graphical abstract:

Supplementary information: The online version contains supplementary material available at https://doi.org/10.1007/s40203-026-00767-5.

非小细胞肺癌和结核病的共享分子机制和药物再利用的计算机洞察。
非小细胞肺癌(NSCLC)和结核病(TB)是全球主要的健康负担,有证据表明它们之间存在分子相互联系。为了了解它们的共同机制,我们整合了3个NSCLC和2个TB RNA - seq数据集,并鉴定了33个共同差异表达基因(cDEGs)。对所有cdeg进行富集分析、蛋白-蛋白相互作用(PPI)网络作图、癌症特异性验证和生存分析。PPI网络突出显示KIF18A、ANLN、TLR4、EPAS1和dcl1是中心枢纽基因,富集表明免疫炎症信号、上皮重塑和细胞周期调节是NSCLC-TB合并症的关键过程。约占NSCLC 80%的肺腺癌(LUAD)和肺鳞状细胞癌(LUSC)的谱图显示,KIF18A、ANLN、STIL和GALNT14过表达与晚期和低生存率相关,其中4-14%的改变主要来自mRNA上调。利用其网络中心性和预后意义,KIF18A和ANLN被选为受体靶点,进行基于结构的药物筛选。使用AutoDock和Glide对213种抗NSCLC、抗TB和抗菌化合物进行筛选,然后进行药代动力学和毒性评估。左氧氟沙星(靶向ANLN)和卡马替尼(靶向KIF18A)表现出最有利的对接得分和ADME/Tox谱,具有稳定的结合模式,有利的MM-GBSA结合自由能,以及在100 ns分子动力学模拟中精细的动态相互关联和自由能景观,这表明它们是有希望的与NSCLC和TB相关的硅预测药物。我们的计算机转录组学和结构动力学分析揭示了共同的感染-癌症机制,并确定了进一步评估的候选治疗方法。图片摘要:补充信息:在线版本包含补充材料,可在https://doi.org/10.1007/s40203-026-00767-5上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书