The role of lncRNA PVT1 and hsa-miR-30a-3p in the tumorigenesis of gastric cancer
This data included EXPRESSION, DEGs, GO-KEGG, CO-EXPRESSION ANALYSIS, PREDICT, ceRNA and SURVIVAL ANALYSIS. The RNA-Seq datasets and corresponding clinical information of GC patients were extracted from the TCGA website (https://portal.gdc.cancer.gov/, Project Name: Stomach Adenocarcinoma, Project ID: TCGA-STAD). The raw datasets of RNA sequences were downloaded using the RTCGA Toolbox package in the R platform. Naive Bayes method, from the limma package of R (Version 3.38.3, http://www.bioconductor.org/packages/release/bioc/html/limma.html), was used to normalize the downloaded data, by filtering low expression and screening common samples including mRNA, lncRNA, and miRNA. A total of 367 samples were available from TCGA datasets, which included 335 tumor and 32 normal tissue samples. We calculated the significance of expression changes of RNAs using the Benjamini and Hochberg false discovery rate (FDR) method. Adjusted P values (adj. P) were applied to identify significantly expressed RNAs. The threshold of FDR <0.01 and |log 2 (fold change) |>1 were set as the cut-off criteria. The online software clusterProfiler was used to perform Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional enrichment. The co-expression of YY1, PVT1, and Myc genes was evaluated by determining the Pearson correlation coefficients. Potential competing endogenous RNAs of PVT1-miRNA-Myc were predicted by the Cytoscape tool and Kaplan- Meier curves were generated for YY1, PVT1, and Myc genes.