Research Article Open Access

CLINICAL AND GENOMIC STRATEGIES FOR DETECTING HEPATOCELLULAR CARCINOMA IN EARLY STAGES: A SYSTEMATIC REVIEW

Abstract

Hepatocellular carcinoma is one of the commonest deadly tumors, and it is usually diagnose at a late stage, when effective treatment is very difficult. Making its discovery before the development of later stage disease is challenging. Hepatocellular carcinoma represents one of the prevalent types cancers with the highest proportion in developing countries. There are many clinical and pathological staging systems for detecting Hepatocellular carcinoma, but none of them includes biological parameters as predictors for prognosis. The various clinical presentations commonly relate to the extent of hepatic reserve at time of diagnosis and so far, there are no studies that conclusively prove that screening hepatocellular carcinoma will reduce the death rate. The development of genome-wide analysis methods has opened the possibility of identifying multiple changes simultaneously in genetic or epigenetic alterations as well as in gene expression affecting the genome of cancer cells. The main issue raised by this work is to determine which alternatives can construed as reliable biomarkers for providing information about the carcinogenesis process rather using screening methods for detect hepatocellular carcinoma. This systematic review opens the door to Bioinformatics to discover novel cancer biomarkers that will help for early diagnosis of hepatocellular carcinoma. Identifying non-invasive and cost-effective biomarkers for early detection and personalized treatment of HCC will be one of the most promising fields of biomarker research. Find an effective, reliable tool for early diagnosis of HCC to increase the number of patients who are appropriate for therapeutic treatment will play a pivotal role in improving HCC patients’ prognosis. The traditional approaches to identify genomic alternations suffered from several inherent disadvantages, Next generation DNA sequence promises to revolutionize cancer research, diagnosis and therapy.

Esraa M. Hashem, Mai S. Mabrouk, Ayman M. Eldeib

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