Work

Exploring the clinical and pharmacotherapeutic implications of druggable genome signaling in lung adenocarcinoma: An integrated approach with transcriptome and Mendelian randomization analysis

Year: 2026

Type: article

Abstract: This study aimed to improve prognostic models and therapeutic targets for lung adenocarcinoma (LUAD) and explore possible tumor microenvironmental pathways. We focused on long non-coding RNAs (lncRNAs), which have been implicated in LUAD progression. Using data from The Cancer Genome Atlas (TCGA), we developed a prognostic model based on druggable genome-associated lncRNAs. Rigorous validation confirmed its predictive accuracy. We identified Druggable Genome-Associated LncRNAs (DrugGenoLnc) and conducted functional enrichment analysis, revealing their roles in LUAD biology. Furthermore, we conducted Mendelian randomization (MR) and Bayesian weighted Mendelian Randomization (BWMR) analysis using TwoSampleMR to explore possible lung cancer-related pathways. Our assessment of the tumor microenvironment included tumor mutational burden (TMB), the TIDE algorithm, and the “pRRophetic” R package. Additionally, we analyzed stemness indices in LUAD patients. Our lncRNA-centered prognostic model demonstrated significant value for risk stratification. Functional enrichment analysis highlighted lncRNAs’ involvement in vital biological processes. MR and BWMR analysis confirmed the inhibitory effect of the “neutrophil extracellular trap formation” pathway on NSCLC. Immunological analysis identified high-risk pathways related to immune functions, potentially enhancing immunotherapy prospects for high-risk patients. Patients with high TMB had poorer overall survival, while high-risk patients showed increased chemotherapy drug sensitivity. Lastly, mRNA stem cell index (mRNAsi) correlated with LUAD patient prognosis. This study underscores DrugGenoLnc’s utility as a prognostic feature, establishes a robust prognostic model for LUAD, and offers potential for early detection markers and therapeutic target identification. Furthermore, it provides insights into the anti-tumor immune microenvironment, guiding clinical treatment strategies in LUAD.

Source: Medicine in Omics

DOI: 10.1016/j.meomic.2025.100053

Authors: Guanrong Li, Xiao Zhu

Institution: Guangdong Medical College

Language: English

Topic: Cancer-related molecular mechanisms research

Subfield: Cancer Research

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,120