Development of an accessible gene expression bioinformatics pipeline to study driver mutations of colorectal cancer

Abstract

Colorectal cancer (CRC) is a global cause of cancer-related mortality driven by genetic and environmental factors which influence therapeutic outcomes. The emergence of next-generation sequencing technologies enables the rapid and extensive collection and curation of genetic data for each cancer type into clinical gene expression biobanks.In this study we used a combination of bioinformatics tools to investigate the expression patterns and prognostic significance of two genes, adenomatous polyposis coli (APC) and B-Raf proto-oncogene (BRAF), that are commonly dysregulated in colon cancer. Subsequently, we investigated the pathways and biomolecular effectors implicated in APC and BRAF function.Our results show mutation types, frequency, anatomical location and differential expression patterns for APC and BRAF between colorectal tumour and matched healthy tissue. The prognostic values of APC and BRAF was investigated as a function of expression level in CRC and other cancer types.In the era of precision medicine and with significant advancements in biobanking and data curation, there is significant scope to use existing clinical datasets for evaluating the role of mutational drivers in carcinogenesis. This offers the potential for studying combinations of less well-known genes and the discovery of novel biomarkers or studying the association between various effector proteins and pathways.

Prediction of colon cancer prognosis and treatment based on immune-related lncRNAs and tumor microenvironment

“…Bioinformatics is a new discipline formed by the combination of life science and computer science. It reveals the inherent information contained in large and complex biological data by comprehensively utilizing biology,computer science and information technology [17] .In recent years, bioinformatics methods have become more and more important in academic research, especially in the early prediction of some di cult clinical problems and the analysis of survival prognosis, which often have unexpected effects [18,19] . The Cancer Genome Atlas (TCGA) is a National Cancer Institute project to analyze cases across different tumor types using genomic platforms and make these raw and processed data available to all researchers [20] .Here, we focus on the use of bioinformatics, combined with the TCGA database to analyze the clinical data of colon cancer, through the annotation and screening of genes and lncRNAs related to survival, risk, prognosis, etc., to predict the small molecules of colon cancer therapeutic drugs, to provide new methods and ideas for its clinical treatment.…”