Abstract
This study was conducted to identify genes that are differentially expressed in paracancerous tissue and to determine the potential predictive value of selected gene panel. Gene transcriptome data of bladder tissue was downloaded from UCSC Xena browser and NCBI GEO repository, including GTEx (the Genotype-Tissue Expression project) data, TCGA (The Cancer Genome Atlas) data, and GEO (Gene Expression Omnibus) data. Differentially Expressed Genes (DEGs) analysis was performed to identify tumor-DEGs candidate genes, using the intersection of tumor-paracancerous DEGs genes and paracancerous-normal DEGs genes. The survival-related genes were screened by Kaplan–Meier (KM) survival analysis and univariable Cox regression with the cutoff criteria of KM < 0.05 and cox p-value < 0.05. The risk model was developed using Lasso regression. The clinical data were analyzed by univariate and multivariate Cox regression analysis. Gene Ontology (GO) and KEGG enrichment analysis were performed in the DEGs genes between the high-risk and low-risk subgroups. We identified six survival-related genes, EMP1, TPM1, NRP2, FGFR1, CAVIN1, and LATS2, found in the DEG analyses of both, tumor-paracancerous and paracancerous-normal differentially expressed data sets. Then, the patients were classified into two clusters, which can be distinguished by specific clinical characteristics. A three-gene risk prediction model (EMP1, FGFR1, and CAVIN1) was constructed in patients within cluster 1. The model was applied to categorize cluster 1 patients into high-risk and low-risk subgroups. The prognostic risk score was considered as an independent prognostic factor. The six identified survival-related genes can be used in molecular characterization of a specific subtype of bladder cancer. This subtype had distinct clinical features of T (topography), N (lymph node), stage, grade, and survival status, compared to the other subtype of bladder cancer. Among the six identified survival-related genes, three-genes, EMP1, FGFR1, and CAVIN1, were identified as potential independent prognostic markers for the specific bladder cancer subtype with clinical features described.
Highlights
IntroductionThe steadily-rising incidence and prevalence make bladder cancer one of the most common urogenital cancers in the word
Bladder cancer is a malignant tumor with high morbidity and high mortality
Dataset included in the analyses For gene transcriptome data, we analyzed a total of 411 cases of TCGA bladder cancer, 19 cases of TCGA paracancerous tissue, nine cases of GTEx healthy bladder, 36 cases of GEO bladder cancer, 29 cases of GEO paracancerous tissue, and five cases of GEO healthy bladder
Summary
The steadily-rising incidence and prevalence make bladder cancer one of the most common urogenital cancers in the word. There were 549,393 new cases and 199,922 deaths reported worldwide in 20181. The most common pathological type of bladder cancer is transitional cell carcinoma, with nonmuscle-invasive bladder cancers (NMIBCs), and muscle-. Official journal of the Cell Death Differentiation Association. Cheng et al Cell Death Discovery (2020)6:58. Paracancerous tissue, the tissue adjacent to a solid tumor, is often used as a histologically normal, control sample in research studies on tumors. A recent study reported that the paracancerous tissue had quite distinct characteristics compared to either tumor tissue or histologically normal healthy tissue, suggesting a unique intermediate state between healthy tissue and tumor[5]. Additional essential information might be obscured when paracancerous tissue was used as a control instead of using healthy normal tissue
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