Identification of the Roles of a Stemness Index Based on mRNA Expression in the Prognosis and Metabolic Reprograming of Pancreatic Ductal Adenocarcinoma

Abstract

BackgroundCancer stem cells (CSCs) are widely thought to contribute to the dismal prognosis of pancreatic ductal adenocarcinoma (PDAC). CSCs share biological features with adult stem cells, such as longevity, self-renewal capacity, differentiation, drug resistance, and the requirement for a niche; these features play a decisive role in cancer progression. A prominent characteristic of PDAC is metabolic reprogramming, which provides sufficient nutrients to support rapid tumor cell growth. However, whether PDAC stemness is correlated with metabolic reprogramming remains unknown.MethodRNA sequencing data of PDAC, including read counts and fragments per kilobase of transcript per million mapped reads (FPKM), were collected from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA-PAAD) database. Single-sample gene set enrichment analysis (GSEA) was used to calculate the relative activities of metabolic pathways in each PDAC sample. Quantitative real-time PCR was performed to validate the expression levels of genes of interest.ResultsThe overall survival (OS) of patients with high mRNA expression-based stemness index (mRNAsi) values was significantly worse than that of their counterparts with low mRNAsi values (P = 0.003). This survival disadvantage was independent of baseline clinical characteristics. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and GSEA showed that the differentially expressed genes between patients with high and low mRNAsi values were mainly enriched in oncogenic and metabolic pathways. Weighted gene coexpression network analysis (WGCNA) revealed 8 independent gene modules that were significantly associated with mRNAsi and 12 metabolic pathways. Unsupervised clustering based on the key genes in each module identified two PDAC subgroups characterized by different mRNAsi values and metabolic activities. Univariate Cox regression analysis identified 14 genes beneficial to OS from 95 key genes selected from the eight independent gene modules from WGCNA. Among them, MAGEH1, MAP3K3, and PODN were downregulated in both pancreatic tissues and cell lines.ConclusionThe present study showed that PDAC samples with high mRNAsi values exhibited aberrant activation of multiple metabolic pathways, and the patients from whom these samples were obtained had a poor prognosis. Future studies are expected to investigate the underlying mechanism based on the crosstalk between PDAC stemness and metabolic rewiring.

MAGE-A3 regulates tumor stemness in gastric cancer through the PI3K/AKT pathway

“…The cancer stemness index was rapidly applied to study cancer stem cells in different cancer, such as lung adenocarcinoma, breast cancer, pancreatic cancer, etc. [23][24][25][26][27]. In this study, we first applied WGCNA and mRNAsi to construct a coexpression network based on differentially expressed genes to obtain modules with different degrees of correlation with mRNAsi.…”

Key gene network related to primary ciliary dyskinesia in hippocampus of patients with Alzheimer’s disease revealed by weighted gene co-expression network analysis

“…Module membership (kME) measured the correlations between each gene and each ME. The within-module connectivity (kin) for each gene was determined by summing the connectivity of that gene with each of the other gene set in the same module [ 31 , 32 ], showing significant correlations with MEs and high within-module connectivity, and were considered as hub genes of the modules. The hub genes were verified by using Cytoscape’s cytoHubba plugin [ 33 ].…”

LncRNAs specifically overexpressed in endocervical adenocarcinoma are associated with an unfavorable recurrence prognosis and the immune response

“…RNA-Seq and clinical data from TCGA were downloaded from . The RNA-Seq data were reported as fragments per kilobase million (FPKM) ( Cui et al, 2020 ; Gibb et al, 2015 ; Tang et al, 2021 ). The genotype classification was based on the Ensemble dataset ( ).…”

“…gov/. The RNA-Seq data were reported as fragments per kilobase million (FPKM) (Cui et al, 2020;Gibb et al, 2015;Tang et al, 2021). The genotype classification was based on the Ensemble dataset (http://asia.ensembl.org/index.html).…”