Abstract:
Contemporary approaches targeted towards precision medicine have shown significant achievements in most of the cancers by generating ‘big data’ across a range of high-throughput experimental and analytical podiums, yet significant problems remain. Integrative scrutinization of this data represents one of the greatest bottlenecks in cancer and other diseases. Surmounting this limitation necessitates integrative analysis of multiple layers of molecular information. Cancer represents a growing source and principal cause of morbidity and mortality in the human population, continuing to stymie clinical treatment efforts. Among various cancers, hepatocellular carcinoma (HCC) is now becoming the fastest growing cancer globally, mainly driven by the ageing HCV/HBV population and extremely limited therapies. There is currently considerable imprecision in optimal diagnostic and therapeutic strategies for HCC. The mounting assemblage of high-throughput data available in publicly accessible databases provides valuable source for generating preliminary in-silico data in support of novel conjectures. Bioinformatic initiatives that combine large amounts of cancer data represents an emerging frontier and are likely to play increasingly important roles. Current study endeavored to define an optimal and feasible method in order to gain new insights into HCC. Our comprehensive integrated analyses found seven novel HCC-specific circulating protein biomarkers including HSD11B1, SERPINC1, C8A, ADH6, CYP2A6, MBL2, UPB1, four highly deleterious HCC-associated Single-nucleotide polymorphisms (SNPs) including SCD1 R126S, SCD1 Y218C, BECN1 I403T, LC3B Y113C, seven miRNAs belonging to miR-17-92 cluster (has-miR-17-3p, has-miR-17-5p, has-miR-19b, has-miR-19a, has-miR-18a, has-miR-20a and miR-92) having a significant impact on drug resistance in HCC as well as uncovered new details about the potential
Abstract
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role of circular RNA circ-DNMT1 in HCC. The present study well demonstrated that a comprehensive integrative informatics approach can be employed as an efficient screening stratagem to effectively extract worthwhile insights from a massive amount of complex, multidimensional molecular datasets.
