Abstract
BACKGROUND: Although oxidative stress and malignancies are intimately connected, it is unknown how lung adenocarcinoma (LUAD) is affected by oxidative stress response-related genes (OSRGs).Our goal in this work was to create a genetic signature based on OSRGs that might both predict prognosis and hint to potential treatment options for LUAD. METHODS: Clinicopathological and transcriptome information on LUAD patients was obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A model for predicting risk was created using LASSO regression. The TCGA, GSE72094, and GSE41271 cohorts all demonstrated the risk model's prediction ability. Immune cell infiltration was measured using the CIBERSORT method, and the TIDE platform was implemented to evaluate the therapeutic efficacy of immune checkpoint inhibition (ICI). Chemotherapy sensitivity was predicted using drug activity data by the Genomics of Drug Sensitivity. An investigation into gene expression was conducted using qRT-PCR. CCK-8 and transwell assays were employed to look into how DKK1 affected the migration and proliferation of LUAD cells. RESULTS: A gene signature consisting of ANLN, FAM83A, DKK1, LOXL2, RHOV, IGFBP1, CCR2, GNG7, and C11orf16 was efficiently determined and used to calculate a patient-specific risk score, this functioned as a stand-alone biomarker for prediction. Correlations were found between risk scores and immune cell infiltration frequency, ICI therapy response rate, estimated chemotherapeutic drug susceptibility and autophagy-related genes.Furthermore, DKK1 knockdown reduced the ability of LUAD cells to multiply and migrate. CONCLUSION: Our thorough transcriptome study of OSRGs generated a biological framework effective in forecasting outcome and responsiveness to therapy in LUAD patients.