Development and validation of transient receptor potential channel-related signature for predicting prognosis in...

Transient receptor potential (TRP) channels are central to human temperature detection and serve a crucial role in cancer development. However, their predictive potential in patients with lung adenocarcinoma (LUAD) remains unexplored. Differential expression analysis of TRP-related genes was performed using The Cancer Genome Atlas and validated using protein-protein interaction networks. Consensus clustering stratified the LUAD subtypes. A four-gene prognostic model of anillin (ANLN), cellular repressor of E1A stimulated genes 2 (CREG2), Ras homolog family member F, filopodia associated (RHOF) and CUB domain containing protein 1 was constructed using least absolute shrinkage and selection operator-Cox regression analysis and validated in Gene Expression Omnibus and IMvigor210 cohorts. Functional enrichment, immune microenvironment analysis and drug sensitivity prediction were performed. Reverse transcription-quantitative PCR demonstrated differential expression of the prognostic genes in a LUAD cell line. A total of 43 TRP-related differentially expressed genes were identified, with consensus clustering (k=2) revealing distinct survival subgroups (P=0.015). High-risk patients exhibited immunosuppressive microenvironments with reduced B-cell infiltration and lower immunotherapy response rates (P=0.021). Functional analysis demonstrated an association of high-risk profiles with mitotic cytokinesis and chemokine signaling. A total of eight chemotherapeutic agents showed differential sensitivity between risk groups. The model outperformed existing signatures using receiver operating characteristic area under the curve and concordance-index analyses. Experimental validation demonstrated elevated expression of ANLN, CREG2 and RHOF in LUAD cells, aligning with the bioinformatics findings. The present study identified a robust TRP channel-related four-gene signature as an independent prognostic biomarker for LUAD, offering insights into immune modulation and personalized therapeutic strategies. The model's predictive accuracy for survival and treatment response underscores its translational potential in clinical decision-making.