Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: UMLS:C0019204 (hepatocellular carcinoma)
 71,386 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Digestive cancers-including gastric cancer (GC), colorectal cancer, hepatocellular carcinoma, esophageal cancer, and pancreatic cancer-accounted for 26% of cancer cases and 35% of cancer deaths worldwide in 2018. It is crucial and urgent to develop biomarkers for the diagnosis, prognosis, and therapeutic benefits of digestive cancers, especially for GC, since the incidence of GC is lower only than lung cancer in China, is hard to detect at an early stage, and is associated with poor prognosis. Mucins, glycoproteins encoded by MUC family genes, act as a part of a physical barrier in the digestive tract and participate in various signaling pathways. Some mucins have been used or proposed as biomarkers for carcinomas, such as MUC16 (CA125) and MUC4. However, there are no systematic investigations on the association of MUC family members with diagnoses and clinical outcomes even though relevant data have been largely accumulated in the past decade. By analyzing transcriptomic and clinical data of digestive cancer samples from TCGA involving colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), liver hepatocellular carcinoma (LIHC), stomach adenocarcinoma (STAD), and pancreatic adenocarcinoma (PAAD), it was found that expressions levels of MUC15, MUC13, and MUC21 were individually associated with survival for digestive cancers, and high expressions of EMCN (MUC14) and MUC15 were correlated with poor survival for STAD. Cox regression analysis indicated the predictive power of an EMCN/MUC15 combination for overall survival (OS) of GC patients, which was validated on an independent dataset from GEO. EMCN/MUC15 correlated genes were identified to be enriched in cancer-related processes, such as vasculature development, mitosis, and immunity. Therefore, we propose that an EMCN/MUC15 combination could be a potential prognostic signature for gastric cancer.
 ...
PMID:Systematical Analysis of the Cancer Genome Atlas Database Reveals EMCN/MUC15 Combination as a Prognostic Signature for Gastric Cancer. 3217 27

In this study, we constructed a new survival model using mRNA expression-based stemness index (mRNAsi) for prognostic prediction in hepatocellular carcinoma (HCC). Weighted correlation network analysis (WGCNA) of HCC transcriptome data (374 HCC and 50 normal liver tissue samples) from the TCGA database revealed 7498 differentially expressed genes (DEGs) that clustered into seven gene modules. LASSO regression analysis of the top two gene modules identified ANGPT2, EMCN, GLDN, USHBP1 and ZNF532 as the top five mRNAsi-related genes. We constructed our survival model with these five genes and tested its performance using 243 HCC and 202 normal liver samples from the ICGC database. Kaplan-Meier survival curve and receive operating characteristic curve analyses showed that the survival model accurately predicted the prognosis and survival of high- and low-risk HCC patients with high sensitivity and specificity. The expression of these five genes was significantly higher in the HCC tissues from the TCGA, ICGC, and GEO datasets (GSE25097 and GSE14520) than in normal liver tissues. These findings demonstrate that a new survival model derived from five strongly correlating mRNAsi-related genes provides highly accurate prognoses for HCC patients.
 ...
PMID:Weighted correlation gene network analysis reveals a new stemness index-related survival model for prognostic prediction in hepatocellular carcinoma. 3264 41