Potential Prognosis and Diagnostic Value of AKT3, LSM12, MEF2C, and RAB30 in Exosomes in Colorectal Cancer on Spark Framework

Abstract

Colorectal cancer (CRC) is a common malignant tumor and one of the leading causes of cancer-related deaths worldwide. CRC progression is greatly affected by the local microenvironment. In the study, we proposed a deep computational-based model for the classification of mRNA, lncRNA, and circRNA in exosomes. We, first, analyzed mRNA expression levels in CRC tumors and normal tissues. Secondly, we used GO and KEGG to analyze their functional enrichment. Thirdly, we analyzed the composition of immune cells in all TCGA samples and then evaluated the prognostic value of tumor-infiltrating immune cells in CRC. Lastly, we combined the TCGA dataset, i.e., COADN = 449 and ROADN = 6, for analysis and found that the expression levels of AKT3, LSM12, MEF2C, and RAB30 in exosomes were significantly correlated with tumor immune infiltration levels. The performance evaluation has shown that the proposed model based on neural networks performs better as compared to the existing methods. The proposed model can be used as a potential tool for the immune infiltration level and their role in cancer metastasis and progression, which can help us to explore potential strategies for CRC diagnosis, therapy, and prognosis.