PARProceedings of Anticancer Research2208-35452208-3553Bio-Byword Scientific Publishing Pty. Ltd.10.26689/par.v7i1.4720ArticleBioinformatics Analysis and Experimental Verification of Prognostic and Biological Significance of Autophagy-Related Long Non-Coding RNAs in Gastric Carcinomahttps://artdesignp.com/journal/PAR/7/1/10.26689/par.v7i1.4720JiangJiebang2023712023-03-06Background: Long non-coding RNAs (lncRNAs) play a vital role in autophagy modulation and tumor progression. However, the key lncRNAs and their functions in gastric cancer (GC) remain largely unknown. Methods: A bioinformatic analysis of GC patients’ gene expression profiling data from the Cancer Genome Atlas database was performed to identify autophagy-related lncRNAs that are associated with predictive risk. Through Cox regression and Lasso regression analyses, the autophagy-related lncRNAs that are associated with prognosis were identified, and a novel prognostic model for GC was established. The model was then used to evaluate the clinical features and predictive risk of individuals with GC. By using two datasets, GSE 62254 (n = 300) and GSE 15459 (n = 192), from Gene Expression Omnibus, its effectiveness was verified. Gene set enrichment analysis according to hallmark and Kyoto Encyclopedia of Genes and Genomes were used to determine the possible biological roles of these lncRNAs. Furthermore, the HOXD antisense growth-associated long non-coding RNA (HAGLR) mechanism in GC was discovered through in vitro and in vivo experiments. Results: Six lncRNAs associated with autophagy in GC were identified, and a new prognostic risk model based on these lncRNAs was established. The six-lncRNA signature was significantly associated with adverse clinicopathological features and found to be an independent GC prognostic factor. The model was proven to be effective and robust by GSE62254 and GSE15459. According to gene set enrichment analysis, the six lncRNAs appeared to be tightly linked to autophagy-related and cancer-related mechanisms. HAGLR was also found to promote tumor growth by enhancing autophagy signaling in GC. Conclusion: A novel prognostic model integrating HAGLR that can effectively evaluate and predict the prognostic risk of GC patients was established. The results indicated that HAGLR promotes gastric cancer progression by enhancing autophagy and is anticipated to be a potential new target for the treatment of gastric cancer. 

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