Unsupervised Hierarchical Clustering of Pancreatic Adenocarcinoma Dataset from TCGA Defines a Mucin Expression Profile that Impacts Overall Survival

Abstract

Mucins are commonly associated with pancreatic ductal adenocarcinoma (PDAC) that is a deadly disease because of the lack of early diagnosis and efficient therapies. There are 22 mucin genes encoding large O-glycoproteins divided into two major subgroups: membrane-bound and secreted mucins. We investigated mucin expression and their impact on patient survival in the PDAC dataset from The Cancer Genome Atlas (PAAD-TCGA). We observed a statistically significant increased messenger RNA (mRNA) relative level of most of the membrane-bound mucins (MUC1/3A/4/12/13/16/17/20), secreted mucins (MUC5AC/5B), and atypical mucins (MUC14/18) compared to normal pancreas. We show that MUC1/4/5B/14/17/20/21 mRNA levels are associated with poorer survival in the high-expression group compared to the low-expression group. Using unsupervised clustering analysis of mucin gene expression patterns, we identified two major clusters of patients. Cluster #1 harbors a higher expression of MUC15 and atypical MUC14/MUC18, whereas cluster #2 is characterized by a global overexpression of membrane-bound mucins (MUC1/4/16/17/20/21). Cluster #2 is associated with shorter overall survival. The patient stratification appears to be independent of usual clinical features (tumor stage, differentiation grade, lymph node invasion) suggesting that the pattern of membrane-bound mucin expression could be a new prognostic marker for PDAC patients.

Glycolysis-Related Gene Expression Profiling Screen for Prognostic Risk Signature of Pancreatic Ductal Adenocarcinoma

“…The individual characteristics of PDAC patients are of great significance for precision therapy, among which the genetic characteristic of tumor is a very important aspect. There have been many studies on gene signatures based on TCGA and GEO database to predict the prognosis of PDAC patients, including immune-related genes and mucin genes (Wei et al, 2019 ; Wu et al, 2019 , 2020 ; Jonckheere et al, 2020 ). Pilar Espiau-Romera's review summarized the correspondence between the molecular subtypes and metabolic subtypes of PDAC, and the metabolic reprogramming involved in PDAC included lipid metabolism, glycolysis, and amino acid consumption.…”

Mg2+ Transporters in Digestive Cancers

“…Analyses of Mg 2+ transporter expression in cells and tissues by immunochemistry should be systematically completed by other methods of detection such as functional (e.g., electrophysiology and Mg 2+ -imaging) and transcriptomic analysis. In this work, we analyzed Mg 2+ transporters expression in digestive cancers, correlation and their impact on patient overall and disease-free survival using Genotype Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA), as previously described [ 86 ]. Interestingly, our data reveal that the MAGT1 is overexpressed in all digestive cancers.…”

“…We analyzed Mg 2+ transporters’ expression in digestive cancers, correlation and their impact on patient overall and disease-free survival using Genotype Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets using GEPIA2 and RStudio tools, as previously performed [ 86 ].…”

“…For each dataset, we studied the co-expression of Mg 2+ transporters by performing Pearson’s correlation analysis and principal component analysis (PCA) using RStudio (R scripts were previously described in [ 86 ]) ( Supplementary Tables S1–S4 for the whole analysis).…”

“…Kaplan–Meier curves were analyzed using the optimized SurvExpress Maximize algorithm. The number of analyzed patients across time (days) is indicated below the horizontal axis for both conditions, as previously described [ 86 ].…”