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Transcriptome analysis implicates MYC as a driver of early onset colorectal cancer

ABSTRACT: Despite a global decrease in colorectal cancer (CRC) incidence, the prevalence of early onset colorectal cancer (EOCRC), or those occurring in individuals before the age of 50, has been steadily increasing over the past several decades. When compared to later onset colorectal cancers (LOCRCs) in individuals over 50, our understanding of the genetic and molecular underpinnings of EOCRCs is limited. Here, we conducted transcriptomic analysis of patient-matched normal colonic segments and tumors to identify gene expression programs involved in carcinogenesis. Amongst differentially expressed genes, we found increased expression of the c-MYC (MYC) proto-oncogene and its downstream targets in tumor samples. We identify tumors with high and low differential MYC expression, and patients with high-MYC tumors were older and overweight or obese. We also detect elevated expression of the PVT1 long-non-coding RNA (lncRNA) in most tumors and find gains in copy number for both MYC and PVT1 gene loci in 35% of tumors evaluated. Our transcriptome analysis indicates that EOCRC can be sub-classified into groups based on differential MYC expression and suggests that MYC is a critical driver of most CRCs that develop in younger patients.

ORGANISM(S): Homo sapiens

PROVIDER: GSE196006 | GEO | 2022/09/06

REPOSITORIES: GEO

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Project description:Background: The incidence of colorectal cancer (CRC) has been steadily increasing in younger individuals over the past several decades for reasons that are incompletely defined. Identifying differences in gene expression profiles, or transcriptomes, in early-onset colorectal cancer (EOCRC, < 50 years old) patients versus later-onset colorectal cancer (LOCRC, > 50 years old) patients is one approach to understanding molecular and genetic features that distinguish EOCRC. Methods: We performed RNA-sequencing (RNA-seq) to characterize the transcriptomes of patient-matched tumors and adjacent, uninvolved (normal) colonic segments from EOCRC (n=21) and LOCRC (n=22) patients. The EOCRC and LOCRC cohorts were matched for demographic and clinical characteristics. We used The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) database for validation. We used a series of computational and bioinformatic tools to identify EOCRC-specific differentially expressed genes, molecular pathways, predicted cell populations, differential gene splicing events, and predicted neoantigens. Results: We identified an eight-gene signature in EOCRC comprised of ALDOB, FBXL16, IL1RN, MSLN, RAC3, SLC38A11, WBSCR27 and WNT11, from which we developed a score predictive of overall CRC patient survival. On the entire set of genes identified in normal tissues and tumors, cell type deconvolution analysis predicted a differential abundance of immune and non-immune populations in EOCRC versus LOCRC. Gene set enrichment analysis identified increased expression of splicing machinery in EOCRC. We further found differences in alternative splicing (AS) events, including one within the long non-coding RNA, HOTAIRM1. Additional analysis of AS found seven events specific to EOCRC that encode potential neoantigens. Conclusion: Our transcriptome analyses identified genetic and molecular features specific to EOCRC which may inform future screening, development of prognostic indicators, and novel drug targets.

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Transcriptome analysis implicates MYC as a driver of early onset colorectal cancer

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