Pan-Cancer Analysis Reveals Distinct Metabolic Reprogramming in Different Epithelial-Mesenchymal Transition Activity States.

Ji-Yong Sung,Jae-Ho Cheong

doi:10.3390/cancers13081778

Abstract

Simple SummaryRecent genomic classification of tumors has stated that clinically refractory cancers aggregate as a distinct molecular subtype associated with epithelial–mesenchymal transition (EMT). EMT subtype tumors are clinically intractable due to shared malignant characteristics such as poor prognosis and metastasis and are resistant to chemotherapy and immune checkpoint blockades. Therefore, there is an urgent clinical need for the identification of potential therapeutic targets for this tumor subtype. Here, we profiled the metabolic signatures of 9452 samples across 31 cancer types based on EMT activity and identified that ~80 to 90% of cancer types had high carbohydrate and energy metabolism associated with the high EMT state. Furthermore, we identified CHST14 as a potential metabolic target for the EMT subtype for stomach cancer associated with reprogramming of energy metabolism. Our analyses identified metabolic reprogramming associated with EMT, suggesting metabolism-associated targets for clinically refractory cancer subtypes.Epithelial–mesenchymal transition (EMT) is critical for cancer development, invasion, and metastasis. Its activity influences metabolic reprogramming, tumor aggressiveness, and patient survival. Abnormal tumor metabolism has been identified as a cancer hallmark and is considered a potential therapeutic target. We profiled distinct metabolic signatures by EMT activity using data from 9452 transcriptomes across 31 different cancer types from The Cancer Genome Atlas. Our results demonstrated that ~80 to 90% of cancer types had high carbohydrate and energy metabolism, which were associated with the high EMT group. Notably, among the distinct EMT activities, metabolic reprogramming in different immune microenvironments was correlated with patient prognosis. Nine cancer types showed a significant difference in survival with the presence of high EMT activity. Stomach cancer showed elevated energy metabolism and was associated with an unfavorable prognosis (p < 0.0068) coupled with high expression of CHST14, indicating that it may serve as a potential drug target. Our analyses highlight the prevalence of cancer type-dependent EMT and metabolic reprogramming activities and identified metabolism-associated genes that may serve as potential therapeutic targets.

Highlights

Epithelial–mesenchymal transition (EMT) is defined as a change in the cellular organizational process in which cells lose their epithelial characteristics and acquire mesenchymal phenotypes
To explore transcriptional metabolic reprogramming based on EMT activity, we analyzed the RNA sequencing (RNA-seq) data of 31 cancer types from The Cancer Genome Atlas (TCGA)
To analyze the transcriptome signatures in metabolic reprogramming, we examined the gene set for seven metabolic signatures based on the reactome annotation [7,20], including amino acid metabolism (348 genes), carbohydrate metabolism (286 genes), integrated energy metabolism (110 genes), lipid metabolism (766 genes), nucleotide metabolism (90 genes), tricarboxylic acid (TCA) cycle (148 genes), and vitamin cofactor metabolism (168 genes) (Table S2)

Summary

Introduction

Epithelial–mesenchymal transition (EMT) is defined as a change in the cellular organizational process in which cells lose their epithelial characteristics and acquire mesenchymal phenotypes. Metabolic reprogramming leads to EMT progression and the development of aggressive tumor phenotypes [2]. Several studies on the EMT have been conducted, the clinical implications of metabolic reprogramming for the development of distinct EMT states (high/low) remain elusive. It is crucial to understand the mechanisms for metabolic reprogramming underlying the EMT states and the impact on patient survival. Our objective was to focus on the common or distinct molecular features mediating the EMT states and to assess their clinical relevance. In view of this goal, we focused on investigating predictive drug targets for the high EMT state associated with energy metabolism in cancers.

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Conclusion