Work

Unraveling the molecular landscape of non-small cell lung cancer: Integrating bioinformatics and statistical approaches to identify biomarkers and drug repurposing

Year: 2025

Type: article

Abstract: Non-small-cell lung cancer (NSCLC) is one of the most malignant tumors and the leading cause of death from cancer worldwide and is increasing at a massive rate every year. Most NSCLC patients are diagnosed at advanced stages, which is associated with a poor prognosis and a very low 5-year survival rate. Therefore, the purpose of this study is to investigate molecular markers for early diagnosis, prognosis and therapy of NSCLC through the integration of bioinformatics and statistical methods. A total of 93 overlapping differentially expressed genes (oDEGs) were identified between NSCLC and normal samples through Linear Models for Microarray (LIMMA) and Significance Analysis of Microarrays (SAM) methods. Six top-degree oDEGs (CCNA2, CDC6, AURKA, CCNB1, MKI67, and PRC1) were identified as key genes (KGs) through the protein-protein interaction (PPI) network. The predictive accuracy analysis of the identified KGs revealed an accuracy of 96.92 %, with a sensitivity of 91.73 % and a specificity of 98.15 %. KGs associated with 3 molecular functions (MFs), 5 cellular components (CCs), 3 biological processes (BPs), and 4 pathways were identified through FunRich software. Analysis of expression levels using the UALCAN database revealed that KGs are significantly associated with potential early diagnostic biomarkers. Survival analysis using the GEPIA database demonstrated that the KGs possessed strong prognostic power for NSCLC. Finally, seven repurposed candidate drugs ENTRECTINIB, SORAFENIB, CHEMBL1765740, TOZASERTIB, NERVIANO, AZD-1152-HQPA, and SELICICLIB were proposed through molecular docking analysis. In conclusion, the findings of this study have the potential to significantly impact the early diagnosis, prognosis, and treatment of NSCLC. • Investigated molecular markers for early diagnosis, prognosis, and therapy of NSCLC. • A robust set of DEGs was identified from transcriptomic data by LIMMA & SAM methods. • Enrichment analysis revealed NSCLC-associated BPs, MFs, CCs and pathways. • Identified KGs are crucial for early diagnosis and prognosis of NSCLC. • High-potential small molecule drugs were recommended for NSCLC therapy.

Source: Computers in Biology and Medicine

DOI: 10.1016/j.compbiomed.2025.109744

Authors: Adiba Sultana, Md Shahin Alam, Alima Khanam, Huiying Liang

Institutions: Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Soochow University, University of Rajshahi

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,080