Leveraging Genetic Instrumental Variables and Sequencing Analysis to Identify a Prognostic Signature Based on Epithelial Cell Markers in Lung Adenocarcinoma

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ABSTRACTMain ProblemThe treatment and prognosis of lung adenocarcinoma (LUAD) remain challenging. The study aimed to identify prognostic genes and construct a prognostic model for LUAD.MethodsAfter identifying malignant alveolar type II (AT2) cells using InferCNV, we applied CytoTRACE, pseudo‐time analysis, Mendelian randomization (MR), and univariate Cox regression analysis to identify prognostic genes. A prognostic model was then developed using an optimized subset of these genes, selected through the least absolute shrinkage and selection operator (LASSO) algorithm. Further analyses included Gene Ontology enrichment analysis and the construction of a protein–protein interaction (PPI) network.ResultsPseudo‐time analysis identified 3526 dynamically expressed genes during malignant AT2 cell dedifferentiation. Subsequent multi‐omics integration refined the gene selection, yielding four prognostic genes for the final predictive model. The resulting model achieved area under the receiver operating characteristic (ROC) curve (AUC) values of 0.649, 0.675, and 0.654 for predicting 1, 2, and 3‐year overall survival (OS) in the training set, respectively, and was successfully validated in two external cohorts at the corresponding time points. Moreover, survival analysis demonstrated that patients in the high‐risk group had significantly poorer OS than those in the low‐risk group, both in the training set and the validation sets (p < 0.01).ConclusionsThe study developed a novel signature based on genes dynamically expressed during malignant AT2 cell dedifferentiation, capable of predicting the prognosis of LUAD patients, and offered four accurate prognostic biomarkers (ADM, MARK4, PARVA, and RPS6KA1).