The Prognostic Value of IASPHD1/I and IZBTB12/I in Colorectal Cancer: A Machine Learning-Based Integrated Bioinformatics Approach

• Published in: Cancers 2023-08, Vol.15 (17)
• Main Authors: Asadnia, Alireza, Nazari, Elham, Goshayeshi, Ladan, Zafari, Nima, Moetamani-Ahmadi, Mehrdad, Goshayeshi, Lena, Azari, Haneih, Pourali, Ghazaleh, Khalili-Tanha, Ghazaleh, Abbaszadegan, Mohammad Reza, Khojasteh-Leylakoohi, Fatemeh, Bazyari, MohammadJavad, Kahaei, Mir Salar, Ghorbani, Elnaz, Khazaei, Majid, Hassanian, Seyed Mahdi, Gataa, Ibrahim Saeed, Kiani, Mohammad Ali, Peters, Godefridus J, Ferns, Gordon A, Batra, Jyotsna, Lam, Alfred King-yin, Giovannetti, Elisa, Avan, Amir
• Format: Article
• Language: English
• Subjects: Care and treatment; Colorectal cancer; Gene expression; Genes; Genomics; Health aspects; Machine learning; Medical research; Medicine, Experimental; RNA sequencing
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers15174300

Colorectal cancer (CRC) is among the leading causes of cancer-related deaths. Despite extensive efforts, a limited number of biomarkers and therapeutic targets have been identified. Therefore, novel prognostic and therapeutic targets are needed in the management of patients and to increase the efficacy of current therapy. The majority CRC patients follow the conventional chromosomal instability (CIN), which is started by several mutations such as APC, followed by genetic alterations in KRAS, PIK3CA and SMAD4, as well as the hyperactivation of pathways such as Wnt/TGFβ/PI3K. Although the underlying genetic changes have been well identified, the mutational signature of tumor cells alone does not enable us to subclassify tumor types or to accurately predict patient survival and suppression of those pathways have often not been effective in treatment. Our data showed some new genetic variants in ASPHD1 and ZBTB12 genes, which were associated with a poor prognosis of patients. Introduction: Colorectal cancer (CRC) is a common cancer associated with poor outcomes, underscoring a need for the identification of novel prognostic and therapeutic targets to improve outcomes. This study aimed to identify genetic variants and differentially expressed genes (DEGs) using genome-wide DNA and RNA sequencing followed by validation in a large cohort of patients with CRC. Methods: Whole genome and gene expression profiling were used to identify DEGs and genetic alterations in 146 patients with CRC. Gene Ontology, Reactom, GSEA, and Human Disease Ontology were employed to study the biological process and pathways involved in CRC. Survival analysis on dysregulated genes in patients with CRC was conducted using Cox regression and Kaplan–Meier analysis. The STRING database was used to construct a protein–protein interaction (PPI) network. Moreover, candidate genes were subjected to ML-based analysis and the Receiver operating characteristic (ROC) curve. Subsequently, the expression of the identified genes was evaluated by Real-time PCR (RT-PCR) in another cohort of 64 patients with CRC. Gene variants affecting the regulation of candidate gene expressions were further validated followed by Whole Exome Sequencing (WES) in 15 patients with CRC. Results: A total of 3576 DEGs in the early stages of CRC and 2985 DEGs in the advanced stages of CRC were identified. ASPHD1 and ZBTB12 genes were identified as potential prognostic markers. Moreover, the combination of ASPHD and ZBTB12 genes