Molecular Profiling of Tumor Tissue in Mexican Patients with Colorectal Cancer.
Beatriz Armida Flores-LópezMaría de la Luz Ayala-MadrigalJosé Miguel Moreno-OrtizJorge Peregrina-SandovalMiguel Ángel Trujillo-RojasJosé Luis Venegas-RodríguezRosario Hernández-RamírezMartha Alejandra Fernández-GalindoMelva Gutiérrez-AnguloPublished in: Current issues in molecular biology (2022)
Colorectal cancer is a heterogeneous disease with multiple genomic changes that influence the clinical management of patients; thus, the search for new molecular targets remains necessary. The aim of this study was to identify genetic variants in tumor tissues from Mexican patients with colorectal cancer, using massive parallel sequencing. A total of 4813 genes were analyzed in tumoral DNA from colorectal cancer patients, using the TruSight One Sequencing panel. From these, 192 variants with clinical associations were found distributed in 168 different genes, of which 46 variants had not been previous reported in the literature or databases, although genes harboring those variants had already been described in colorectal cancer. Enrichment analysis of the affected genes was performed using Reactome software; pathway over-representation showed significance for disease, signal transduction, and immune system subsets in all patients, while exclusive subsets such as DNA repair, autophagy, and RNA metabolism were also found. Those characteristics, whether individual or shared, could give tumors specific capabilities for survival, aggressiveness, or response to treatment. Our results can be useful for future investigations targeting specific characteristics of tumors in colorectal cancer patients. The identification of exclusive or common pathways in colorectal cancer patients could be important for better diagnosis and personalized cancer treatment.
Keyphrases
- bioinformatics analysis
- dna repair
- copy number
- genome wide
- genome wide identification
- single cell
- dna damage
- systematic review
- end stage renal disease
- gene expression
- single molecule
- ejection fraction
- newly diagnosed
- prognostic factors
- machine learning
- endoplasmic reticulum stress
- transcription factor
- patient reported outcomes
- big data