ArticleDiscover oncology2026
Establishment and validation of an ADP-ribosylation-related gene signature for prognostic prediction in lung adenocarcinoma.
Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundLung adenocarcinoma (LUAD) remains a leading cause of cancer mortality worldwide. Emerging evidence implicates ADP-ribosylation in cancer progression. We aimed to develop a prognostic model based on ADP-ribosylation to predict survival outcomes in LUAD.
methodsDifferentially expressed genes (DEGs) between normal and LUAD samples from TCGA were analyzed. Intersection with ADP-ribosylation-related genes (ADPRRGs) yielded DE-ADPRRGs. Univariate Cox, LASSO Cox, and multivariate Cox regression were employed to construct a risk model. A nomogram integrating clinical factors and ADP ribosylation-related risk score (ADPRRS) was developed. Predictive performance was evaluated using ROC curves. Immune infiltration, somatic mutations, and chemotherapeutic drug sensitivity were analyzed. In vitro experiments initially explored the potential functions of the key gene ARL6IP1.
resultsA risk model incorporating five signature genes (ARL6, GGA2, ARL14, PARP1, and ARL6IP1) demonstrated robust prognostic accuracy. The low-ADPRRS group exhibited greater immune cell infiltration (B cells, DCs, macrophages, and neutrophils) and a lower tumor mutational burden than the high-ADPRRS group. High ADPRRS patients exhibited potential sensitivity to cisplatin, docetaxel, and gefitinib, whereas low ADPRRS patients may respond better to parthenolide, roscovitine, and crizotinib. qRT-PCR confirmed the upregulated expression of ARL14, PARP1 and ARL6IP1 in A549 cell line. The in vitro functional experiments revealed that ARL6IP1 had significant effects on promoting proliferation, migration and invasion in LUAD cells, and it promoted tumor progression by inhibiting cell apoptosis.
conclusionThis ADP-ribosylation-based prognostic model reliably predicts LUAD survival and identifies potential biomarkers for tailored therapy.
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