Machine Learning-derived Multi-omics Prognostic Signature of Pyroptosis-related lncRNA with Regard to ZKSCAN2-DT and Tumor Immune Infiltration in Colorectal Cancer.

Abstract

Colorectal cancer (CRC) has become the most prevalent gastrointestinal malignant tumor, ranking third (10.2%) in incidence and second (9.2%) in death among all malignancies globally. The most common histological subtype of CRC is colon adenocarcinoma (COAD), although the cause of CRC remains unknown, as there are no valid biomarkers. A thorough investigation was used to build a credible biomolecular risk model based on the pyroptosis-associated lncRNAs discovered for COAD prediction. Furthermore, Cibersort and Tumor Immune Dysfunction and Exclusion (TIDE), the methods of exploring tumor immune infiltration, were adopted in our paper to detect the effects of differential lncRNAs on the tumor microenvironment. Finally, quantitative real-time polymerase chain reaction (qPCR), as the approach of exploring expressions, was utilized on four different cell lines. Seven pyroptosis-related lncRNAs have been identified as COAD predictive risk factors. Cox analysis, both univariate and multivariate, revealed that the established signature might serve as a novel independent factor with prognostic meaning in COAD patients. ZKSCAN2-DT was shown to be considerably overexpressed in the COAD cell line when compared to normal human colonic epithelial cells. Furthermore, ssGSEA analysis results revealed that the immune infiltration percentage of most immune cells dropped considerably as ZKSCAN2-DT expression increased, implying that ZKSCAN2-DT may play an important role in COAD immunotherapy. Our research is the first to identify pyroptosis-related lncRNAs connected with COAD patient prognosis and to construct a predictive prognosis signature, directing COAD patient prognosis in therapeutic interventions.