Artificial Intelligence Approaches for Predictive Biomarker Discovery in Non-Small Cell Lung Cancer.

IntroductionNon-small cell lung cancer (NSCLC) is the most prevalent and lethal subtype of lung cancer. Most patients are diagnosed at an advanced stage of the disease, resulting in a poor prognosis. Early treatment and clinical intervention for NSCLC following early diagnosis can improve patients' survival rate. It is of considerable significance to develop a more efficient and precise approach for identifying key genes and clinically pertinent biomarkers in NSCLC to enable its early diagnosis.MethodsAn interpretable two-stage analytical framework integrated with advanced artificial intelligence (AI) technology is proposed to enhance the accuracy of biological gene screening for NSCLC. Firstly, gene-level statistical features derived from the GSE19804,GSE30219 and GSE33532 datasets are standardized and dimensionally reduced via principal component analysis (PCA), which reveals two distinct linear distribution patterns of candidate genes in the PCA projection space. Subsequently, these candidate genes are validated using the TCGA and GEPIA platform by evaluating their differential expression profiles and associations with patient survival outcomes, with the goal of identifying robust predictive biomarkers.ResultsThrough AI-driven analytical pipelines, multiple tumor-associated genes are screened and confirmed to be correlated with NSCLC progression. Notably, ADGRD1 (Adhesion G Protein-Coupled Receptor D1) exhibits a close association with pulmonary physiological functions and may serve as a potential biomarker in the initiation and progression of NSCLC.ConclusionThe proposed method combines unsupervised structural discovery with cross-cohort clinical evidence to prioritize NSCLC biomarkers, providing critical support for early diagnosis, prognostic stratification, and biomarker-guided therapeutic strategies. Furthermore, the study provides technical support for biomarker discovery in other cancer types, and highlights the application value of integrating computational intelligence with oncology research.