Comprehensive characterization of cancer‐testis genes in testicular germ cell tumor

Abstract

Cancer‐testis (CT) genes are a group of genes restrictedly expressed in testis and multiple cancers and can serve as candidate driver genes participating in the development of cancers. Our previous study identified a number of CT genes in nongerm cell tumors, but their expression pattern in testicular germ cell tumor (TGCT), a cancer type characterized by less genomic alterations, remained largely unknown. In this study, we systematically investigated the expression pattern of CT genes in TGCT samples and evaluated the transcriptome difference between TGCT and normal testis tissues, using datasets from the UCSC Xena platform, The Cancer Genome Atlas (TCGA) and the Genotype‐Tissue Expression (GTEx) project. Pathway enrichment analysis and survival analysis were conducted to evaluate the biological function and prognostic effect of expressed CT genes. We identified that 1036 testis‐specific expressed protein‐coding genes and 863 testis‐specific expressed long noncoding RNAs (lncRNAs) were expressed in TGCT samples, including 883 CT protein‐coding genes and 710 CT lncRNAs defined previously. The number of expressed CT genes was significantly higher in seminomas ( P = 3.48 × 10 −13 ) which were characterized by frequent mutations in driver genes ( KIT , KRAS and NRAS ). In contrast, the number of expressed CT genes showed a moderate negative correlation with the fraction of copy number altered genomes (cor = −0.28, P = 1.20 × 10 −3 ). Unlike other cancers, our analysis revealed that 96.16% of the CT genes were down‐regulated in TGCT samples, while CT genes in stem cell maintenance related pathways were up‐regulated. Further survival analysis provided evidence that CT genes could also predict the prognosis of TGCT patients with both disease‐free interval and progression‐free interval as clinical endpoints. Taken together, our study provided a global view of CT genes in TGCT and provided evidence that CT genes played important roles in the progression and maintenance of TGCT.

Discovery of Pancreatic Ductal Adenocarcinoma-Related Aberrant Glycosylations: A Multilateral Approach of Lectin Microarray-Based Tissue Glycomic Profiling With Public Transcriptomic Datasets

“…To compare the gene expression levels using the data from the TCGA and GTEx databases, we used the University of California Santa Cruz (UCSC) Xena Browser 2 as described previously (33). Briefly, the expected count values of RNA sequencing by Expectation-Maximization (RSEM) for genes of interest were normalized using DESeq2 (version 2018-05-08) to remove the batch effect, and these calculated values were downloaded from the Xena Browser.…”

An Integrated Autophagy-Related Long Noncoding RNA Signature as a Prognostic Biomarker for Human Endometrial Cancer: A Bioinformatics-Based Approach

“…We obtained the list of autophagy-related genes from HADb [ 26 ]. Fragments per kilobase million (FPKM) RNA-seq data of 575 endometrial cancer samples, which included 23 normal samples and 552 tumor samples, were downloaded from the TCGA database ( https://portal.gdc.cancer.gov/ ) [ 25 ]. The Ensembl human genome browser, GRCh38.p13 ( http://asia.ensembl.org/index.html ), was used to annotate and classify the lncRNAs and protein-coding genes.…”

“…Clinical data for endometrial cancer samples were downloaded from UCSC Xena ( https://xenabrowser.net/ ) [ 25 , 27 , 28 ] and organized and merged with the expression matrix of autophagy-related lncRNAs. Using the survival package in R software, univariate and multivariate Cox regression analyses were performed to obtain a list of autophagy-related lncRNAs associated with prognosis.…”

“…Several articles about screening immune-related lncRNAs to construct prognostic models through a coexpression method had been published [ 23 , 24 ]. Likewise, using a coexpression method, we identified five autophagy-related lncRNAs in silico by collating and merging data on endometrial cancer samples in The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/ ) [ 25 ] and data on autophagy-related genes in the Human Autophagy Database (HADb) ( http://www.autophagy.lu/ ) [ 26 ]. We then constructed a prognostic nomogram to estimate the patients' survival rate and concluded that these lncRNAs have potential as novel therapeutic targets and tumor biomarkers.…”

A novel epigenetic signature for predicting the prognosis of pancreatic ductal adenocarcinoma

“…The DNA methylation (n = 189) and clinical data (n = 179) for PDAC used in this study was downloaded from the Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). To compare the mRNA expression levels using the data from the TCGA (n = 182) and Genotype-Tissue Expression (GTEx) (n = 167) databases, we used the University of California Santa Cruz (UCSC) Xena Browser as described previously [11]. In addition, we downloaded methylation data (n = 228) from International Cancer Genome Consortium (ICGC) database (https://icgc.org/).…”