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• W3043058794 abstract "Background Alternative splicing (AS) is an important mechanism for regulating gene expression and proteome diversity. Tumor-alternative splicing can reveal a large class of new splicing-associated potential new antigens that may affect the immune response and can be used for immunotherapy. Methods The RNA-seq transcriptome data and clinical information of stomach adenocarcinoma (STAD) cohort were downloaded from The Cancer Genome Atlas (TCGA) database data portal, and data of splicing events were obtained from the SpliceSeq database. Predicting genes were validated by Asian cancer research group (ACRG) cohort and Oncomine database. RT-qPCR was used to analysis the expression of ECT2 in STAD. Results A total of 32,166 AS events were identified, among which 2,042 AS events were significantly associated with patients survival. Biological pathway analysis indicated that these genes play an important role in regulating gastric cancer-related processes such as GTPase activity and PI3K-Akt signaling pathway. Next, we derived a risk signature, using alternate acceptor, that is an independent prognostic marker. Moreover, high ECT2 expression was associated with poorer prognosis in STAD. Multivariate survival analysis demonstrated that high ECT2 expression was an independent risk factor for overall survival. Gene set enrichment analysis revealed that high ECT2 expression was enriched for hallmarks of malignant tumors. The ACRG cohort and Oncomine also showed that high ECT2 expression was associated with poorer prognosis in gastric cancer patients. Finally, RT-qPCR showed ECT2 expression was higher in STAD compared to the normal tissues. Conclusion This study excavated the alternative splicing events in gastric cancer, and found ECT2 might be a biomarkers for diagnosis and prognosis." @default.
• W3043058794 created "2020-07-23" @default.
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• W3043058794 date "2020-07-14" @default.
• W3043058794 modified "2023-10-17" @default.
• W3043058794 title "Identification of potential biomarkers and their clinical significance in gastric cancer using bioinformatics analysis methods" @default.
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• W3043058794 doi "https://doi.org/10.7717/peerj.9174" @default.
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