AUTHOR=Tang Rong , Liu Xiaomeng , Wang Wei , Hua Jie , Xu Jin , Liang Chen , Meng Qingcai , Liu Jiang , Zhang Bo , Yu Xianjun , Shi Si 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.643465

DOI=10.3389/fonc.2021.643465

ISSN=2234-943X

ABSTRACT=Background
Many researchers believe that cancer stem cells (CSCs) may contribute to the dismal prognosis of pancreatic ductal adenocarcinoma (PDAC). CSCs share common biological features with adult stem cells, such as longevity, a self-renewal capacity, differentiation, drug resistance and the requirement for a niche, which 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.
Method
  RNA 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). Single-sample gene set enrichment analysis 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.
Results
The overall survival of patients with high mRNA expression-based stiffness index (mRNAsi) was significantly worse than that of their counterparts with low mRNAsi (P=0.003). This survival disadvantage was independent of baseline clinical characteristics. GO analysis, KEGG analysis and GSEA showed that the differentially expressed genes between patients with high and low mRNAsi 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, which featured different mRNAsi and metabolic activities. Univariate Cox regression analysis identified 14 genes beneficial to OS from 95 key genes selected from the 8 independent gene modules from WGCNA. Among them, MAGEH1, MAP3K3 and PODN were downregulated in both pancreatic tissues and cell lines.
Conclusion
  The present study shows that PDAC samples with high mRNAsi demonstrated worse prognosis and aberrant activation of multiple metabolic pathways. Future studies are expected to investigate the underlying mechanism based on the crosstalk between PDAC stemness and metabolic rewiring.