Identifying important microbial and genomic biomarkers for differentiating right- versus left-sided colorectal cancer using random forest models.

ABSTRACT:

Background

Colorectal cancer (CRC) is a heterogeneous disease, with subtypes that have different clinical behaviours and subsequent prognoses. There is a growing body of evidence suggesting that right-sided colorectal cancer (RCC) and left-sided colorectal cancer (LCC) also differ in treatment success and patient outcomes. Biomarkers that differentiate between RCC and LCC are not well-established. Here, we apply random forest (RF) machine learning methods to identify genomic or microbial biomarkers that differentiate RCC and LCC.

Methods

RNA-seq expression data for 58,677 coding and non-coding human genes and count data for 28,557 human unmapped reads were obtained from 308 patient CRC tumour samples. We created three RF models for datasets of human genes-only, microbes-only

SUBMITTER: Kolisnik T 

PROVIDER: S-EPMC10337110 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature