It is well established that genes associated with cell death can serve as prognostic markers for patients with cancer. Programmed cell death (PCD) is known to play a role in cancer cell apoptosis and antitumor immunity. With the continuous discovery of new forms of PCD, the roles of PCD in lung adenocarcinoma (LUAD) require ongoing evaluation. In the present study, mRNA expression data and clinical information associated with 15 forms of PCD were extracted from publicly available databases and systematically analyzed. Utilizing these data, a robust risk prediction model was established that incorporates six PCD‑related genes (PRGs). Datasets from the Gene Expression Omnibus database were employed to validate the six genes exhibiting risk‑associated characteristics. The PRG‑based model reliably predicted the prognosis of patients with LUAD, with the high‑risk group showing a poor prognosis, reduced levels of immune infiltration molecules and diminished expression of human leukocyte antigens. Additionally, the relationships among PRGs, somatic mutations, tumor stemness index and immune infiltration were assessed. Based on these risk characteristics, a nomogram was constructed, patient stratification was performed, small‑molecule drug candidates were predicted, and somatic mutations and chemotherapy responses were analyzed. Furthermore, reverse transcription‑quantitative PCR was used to assess the expression of PDGs in vitro, and the critical role of brain‑derived neurotrophic factor in LUAD development was identified through Mendelian randomization, gene knockdown, wound healing, western blot and colony formation assays. These findings offer new insights into the development of targeted therapies for LUAD, particularly in patients with high BDNF expression.

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