Identification of Potential Biomarkers through Multi-omics approach for Hepatocellular Carcinoma

Abstract

Hepatocellular carcinoma (HCC) is a form of liver malignancy that accounts for roughly about 90 percent of all liver cancer cases. Due to its asymptomatic early stages, HCC is a difficult and complex disease to manage, making early detection and treatment essential for improving patient outcomes. HCC development is associated with metabolic problems such as diabetes and obesity, Hepatitis virus infection, and

non-alcoholic fatty liver disease (NAFLD). These risk factors exert influence on mul- tiple facets of tumor development, encompassing molecular and genetic alterations

observed within tumors, modulation of the tumor microenvironment, and perturbation of the signaling pathways implicated in tumor progression and metastasis. However, complete understanding of the environmental and risk factors that promote HCC are not well known. In addition, there are currently limited options for diagnosing or treating HCC, highlighting the need for additional biomarkers and therapeutic targets. Sorafenib, a well-known inhibitor, increases overall survival rate for at least 3 months.

Other HCC inhibitors have shown promise in terms of improving overall patient sur- vival rates. However, the efficacy of HCC inhibitors varies according to tumour stage,

underlying liver disease, and individual patient characteristics. Therefore, to reduce the cancer’s impact on public health, research into novel HCC detection and treatment

methods must be conducted. This study aims to discern candidate biomarkers and tran- scriptional binding sites for HCC through the utilization of a multi-omics approach

that encompasses microarray, RNA-seq, Chip-array/seq, and ATAC-seq analysis to ascertain genes and transcriptional binding factors that exhibit differential expression in relation to HCC, as well as pathways that may potentially impact the progression of HCC. In the analysis, differentially expressed genes (DEGs) were identified, and only those with a p-value less than 0.05 were included in the analysis. Similarly, the reported enriched peaks from ChIP-array analysis had a p-value less than 0.05, while for ChIP and ATAC-seq analysis, the enriched peaks were determined based on a

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Abstract q-value less than 0.05. Moreover, it was observed that the identified TOP2A gene was only absent from the ATAC-seq cell-line data, whereas the ROBO1 gene was present in all accessible datasets. Moreover, Enrichment Analysis revealed that the cAMP signaling pathway is the only common pathway among all accessible datasets. The AKR1D1, FAM13A, GPC3, and TPX2 were also detected in the majority of the datasets, and ChIP-seq revealed their transcriptional binding sites. In the absence of

cell-line ATAC-seq data, 59 pathways were shared by all accessible datasets. Path- ways in cancer, FoxO, Rap1, MAPK, the PI3K-Akt and HIF-1 signaling pathway in

cancer have been identified as key pathways that enhance our understanding of HCC through the recognition of DEGs and transcriptional binding sites. This research will facilitate the creation of more effective diagnostic and therapeutic therapies for this difficult cancer.