グラフェン関連肺腺癌における因果バイオマーカー発見および予後シグネチャ開発のためのトランスクリプトミクス、メンデルランダム化、およびダブル機械学習アルゴリズムの活用

Long non-coding RNA (lncRNA) screening holds promise for elucidating mechanisms behind graphene-related tumor therapy. This study aimed to investigate the role of graphene therapy-related lncRNA signatures (GTLncRNASig) in lung adenocarcinoma (LUAD) and potential pathways within the tumor microenvironment. LUAD transcriptome and clinical data from The Cancer Genome Atlas (TCGA) were analyzed to develop a prognostic risk model for GTLncRNASig using Cox regression. Further analyses included Kaplan-Meier survival analysis, principal component analysis (PCA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, a nomogram risk model, and tumor immune dysfunction and exclusion (TIDE) assessment. Drug sensitivity was explored using this model. Mendelian randomization (MR), Double Machine Learning (DML) and Bayesian weighting validated causal relationships between enriched pathways and LUAD. Supervised and unsupervised machine learning algorithms evaluated robustness and uncovered hidden correlations in MR results. A 35-lncRNA risk model (GTLncRNASig) was established, identifying strong associations with immune pathways, including Type II IFN Response and MHC class I. High-risk subgroups exhibited immune microenvironment-linked prognostic traits. Screening revealed 12 potential chemotherapy agents, and the stem cell index mRNAsi correlated with LUAD prognosis. MR and Bayesian weighting implicated the systemic lupus erythematosus (SLE) pathway as a LUAD risk factor. Machine learning confirmed the reliability of these findings. This study identified 35 lncRNAs that constitute a prognostic signature in the context of graphene-related LUAD treatment, highlighting immune-related processes and the SLE pathway's role in LUAD. These insights link autoimmune diseases with tumorigenesis and provide valuable guidance for immunotherapy predictions.