Abstract 4880: Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics

Abstract This work aims to decipher p73-regulated biomarkers for a prompt diagnosis of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling technologies. Transcriptome profile of HCT116 cell line p53−/− p73+/+ and p53−/− p73 knockdown was performed to identify differentially expressed genes (DEGs) followed by cross-checking with three CRC tissue expression datasets available in Gene Expression Omnibus. KEGG and Gene ontology were performed on differentially expressed transcripts obtained via the transcriptome profile and intersected genes. The PPI network was constructed via Cytoscape to extract hub genes. KM plots assisted in investigating the prognostic significance of the hub genes. The clinicopathological relevance was explored using the GEPIA and UALCAN databases. Finally, machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1, 289 upregulated and 1, 897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. The analysis of gene ontology and KEGG showed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan Meier, GEPIA, and UALCAN analyses uncovered the prognostic and diagnostic relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0. 78 and 0. 75 using 284 DEGs and 20 hub genes, respectively. This is a novel study utilizing transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-relevant genes. These genes are found relevant regarding the patients’ prognosis and diagnosis. The unveiled biomarkers are also found robust in TNM-stage prediction. This is the first study where transcriptomics, publicly available tissue datasets, and machine learning are altogether employed to reveal key genes in CRC pathogenesis. Our research highlights the robust analysis of four independent data sets to find the key hub genes, further predicting the performance of the key genes in stage-wise classification. Citation Format: Chanchal Bareja, Kountay Dwivedi, Apoorva Uboveja, Ankit Mathur, Naveen Kumar, Daman Saluja. Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 4880.