Stratification of Colorectal Patients Based on Survival Analysis Shows the Value of Consensus Molecular Subtypes and Reveals the CBLL1 Gene as a Biomarker of CMS2 Tumours.
Gloria AlfonsínAlberto Berral-GonzálezAndrea Rodríguez-AlonsoMacarena QuirogaJavier De Las RivasAngélica FigueroaPublished in: International journal of molecular sciences (2024)
The consensus molecular subtypes (CMSs) classification of colorectal cancer (CRC) is a system for patient stratification that can be potentially applied to therapeutic decisions. Hakai (CBLL1) is an E3 ubiquitin-ligase that induces the ubiquitination and degradation of E-cadherin, inducing epithelial-to-mesenchymal transition (EMT), tumour progression and metastasis. Using bioinformatic methods, we have analysed CBLL1 expression on a large integrated cohort of primary tumour samples from CRC patients. The cohort included survival data and was divided into consensus molecular subtypes. Colon cancer tumourspheres were used to analyse the expression of stem cancer cells markers via RT-PCR and Western blotting. We show that CBLL1 gene expression is specifically associated with canonical subtype CMS2. WNT target genes LGR5 and c-MYC show a similar association with CMS2 as CBLL1. These mRNA levels are highly upregulated in cancer tumourspheres, while CBLL1 silencing shows a clear reduction in tumoursphere size and in stem cell biomarkers. Importantly, CMS2 patients with high CBLL1 expression displayed worse overall survival (OS), which is similar to that associated with CMS4 tumours. Our findings reveal CBLL1 as a specific biomarker for CMS2 and the potential of using CMS2 with high CBLL1 expression to stratify patients with poor OS.
Keyphrases
- poor prognosis
- stem cells
- gene expression
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- genome wide
- peritoneal dialysis
- binding protein
- clinical practice
- machine learning
- long non coding rna
- free survival
- single molecule
- young adults
- risk assessment
- signaling pathway
- bone marrow
- electronic health record
- case report
- big data
- data analysis