Hiseq rnaseq

Gene expression data and the corresponding clinical datasheets for 56 mCRC samples were obtained from The Cancer Genome Atlas (TCGA) website (https://portal.gdc.cancer.gov/repository) (up to May 1, 2019) as the TCGA mCRC cohort. The sequencing data were obtained using the Illumina HiSeq_RNA-Seq and Illumina HiSeq_miRNA-Seq platforms. The analysis reported herein completely satisfies the TCGA publication requirements (http://cancergenome.nih.gov/publications/publicationguidelines). The gene symbols were annotated based on the Homo_sapiens.GRCh38.91.chr.gtf file (http://asia.ensembl.org/index.html). Log2 transformation was performed for all gene expression data. The function of the trimmed mean of M values (TMM) normalization method of the edgeR package (Version 3.24.3; http://www.bioconductor.org/packages/release/bioc/html/edgeR.html/) of the R software (Version 3.5.2; https://www.r-project.org/) was applied to normalize the data.

A total of 371 patients with HCC participated in this study. Available miRNA-seq data from 371 HCC samples with survival data and 50 adjacent nontumorous samples (including 50 paired HCC samples) and mRNA-seq data for 367 HCC samples with survival data and 50 adjacent nontumorous samples (including 50 paired HCC samples) were obtained from the TCGA database (https://tcga-data.nci.nih.gov/) on May 1, 2018. Approval from the ethics committee was not necessary. This study fully meets the publication requirements of the TCGA. The RNA-seq data were obtained using the Illumina HiSeq_miRNA-Seq and Illumina HiSeq_RNA-Seq platforms. The genes identified via RNA expression profiling were annotated based on the Ensembl Gene ID. Log₂ transformation was performed on all gene expression data. We normalized the downloaded data by using the trimmed mean of M value (TMM) normalization method of the edgeR R package (Version: 3.24.3) in R software (Version: 3.5.2) [16 (link)]. When an RNA had duplicate data, the average RNA expression was used. The lncRNAs, miRNAs and mRNAs with an average expression value > 1 were retained, and low-abundance RNAs were eliminated.

RNA sequencing and matching medical data were obtained from TCGA (https://portal.gdc.cancer.gov/). Raw data was acquired through the RTCGA Toolbox package (R platform). Overall, 406 samples, including 375 GC and 31 normal tissues, were analyzed.
RNA (including LncRNA and mRNA) and miRNA sequences were acquired from Illumina HiSeq_RNASeq and Illumina HiSeq_miRNASeq platforms, respectively. Perl and R were utilized to evaluate findings.

To explore the potential regulating effect of DNA methylation on host gene expression, Spearman correlation analyses were performed between DNA methylation of those key CpG sites and their host gene mRNA expression. Normalized encoding genes’ mRNA expression data (legacy data) based on RNA-Seq (Illumina RNA-Seq HiSeq platform) was downloaded and preprocessed using R/Bioconductor package TCGAbiolinks [12 (link)]. Both mRNA expression level and DNA methylation level (β value) were auto-scaled before down-stream analysis. P-value < 0.05 was set as the criteria for the significant correlation. For those genes whose DNA methylation was correlated with their mRNA expression, the association of their mRNA expression with four clinical endpoints of GC were then examined using Cox regression analysis.
All analyses were performed and visualized using R version 4.0.2.