Multi-omics integration and Mendelian randomization elucidate the PARP16–UPR axis driving chemoresistancein gastric cancer

BackgroundAcquired resistance to cisplatin-based chemotherapy is common in patients with gastric cancer (GC) and significantly limits treatment efficacy. The aim of this study was to investigate molecular features associated with GC chemoresistance using an integrative multi-level analytical framework combined with Mendelian randomization (MR), followed by cellular validation of key candidates.MethodsTranscriptome datasets GSE14210 and GSE31811 were obtained from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs), followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore potential pathways. A total of 113 machine learning model combinations were applied for feature selection. MR analysis integrating expression quantitative trait loci (eQTLs) and genome-wide association study (GWAS) data was conducted to assess causal relationships between candidate genes and chemoresistance. The single-cell dataset GSE183904 was used to examine cell-type-specific expression patterns. Cisplatin-resistant NCI-N87/DDP cells were then established in vitro, and qRT-PCR, Western blotting, and drug sensitivity assays were performed to evaluate gene expression and function. Pathway inhibitors were applied to test the reversal of resistance.ResultsA total of 827 DEGs were identified, mainly enriched in immune response, ECM interactions, metabolic reprogramming, and signaling pathways such as PI3K–Akt and MAPK. Among the machine learning models, the Stepglm[both] + Random Forest (RF) model achieved the best performance [area under the curve (AUC) = 0.865] and identified several core candidate genes. MR analysis supported potential risk associations for TRABD, RXRA, DEFA4, PARP16, SLC12A9, and TMEM132A, with PARP16 consistently highlighted across transcriptomic, machine learning, and MR analyses. In vitro experiments showed that PARP16 expression was elevated by approximately 3.1-fold in NCI-N87/DDP cells, accompanied by activation of the unfolded protein response (UPR) and suppression of apoptosis, and an elevated cisplatin IC50 of 11.82 μg/mL. Inhibition of the PARP16–UPR axis significantly reduced the IC50 to 4.67 μg/mL and restored DNA damage and apoptosis, demonstrating synergistic effects.ConclusionsPARP16 emerged as a key candidate associated with chemoresistance in GC. Its elevated expression in stem-like cell populations and resistant cell models was associated with UPR activation, and targeting the PARP16–UPR axis restored cisplatin sensitivity. Targeting the PARP16–UPR axis effectively reverses resistance, providing new insights and potential therapeutic strategies for overcoming chemoresistance in GC.