Arabian Journal of Chemistry (Jan 2021)

Metabolomics approach to understand the hepatitis C virus induced hepatocellular carcinoma using LC-ESI-MS/MS

  • Sindhia Kumari,
  • Arslan Ali,
  • Talat Roome,
  • Anam Razzak,
  • Ayesha Iqbal,
  • Amna Jabbar Siddiqui,
  • Syed Muhammad Zahid Azam,
  • Hafeezullah Shaikh,
  • Hesham R. El-Seedi,
  • Syed Ghulam Musharraf

Journal volume & issue
Vol. 14, no. 1
p. 102907

Abstract

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Hepatocellular carcinoma (HCC) is a typical cancer that has region specified analysis with the incidence of hepatitis C virus (HCV) infection. This study was conducted to improve the understanding of metabolic alterations associated with HCV induced HCC which can open up new strategies to monitor the high risk of HCC. Samples of the subjects with HCV, HCV induced chronic liver disease (CLD), HCV induced HCC, and healthy controls (HS) were collected after complete blood count (CBC), hepatitis viral load, α-fetoprotein (AFP), liver function tests, and albumin. A total of 147 serum samples including HCC (n = 11), CLD (n = 24), HCV (n = 71), and HS (n = 41) were analyzed by LC-ESI-MS/MS. The 21 compounds were found to be responsible for group discrimination after the application of chemometric tools. N-fructosyl tyrosine and hydroxyindoleacetic acid showed an increase in level whereas L-aspartyl-L-phenylalanine and thyroxine showed a consistent decrease in the progression of HCV to HCC in comparison with HS indicating their importance for early detection. The biological pathways such as glycerophospholipid metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, phenylalanine metabolism and tryptophan metabolism showed alteration in some metabolites. The method was internally validated by ROC plot showing AUC value for HS, HCV, CLD, and HCC as 0.99, 1, 1, and 0.89, respectively; while 16 blind samples were also validated with 93% specificity. The untargeted metabolomics investigation of HCV, CLD, and HCC can help to understand the progression of HCV-induced HCC. It reveals significant differences in metabolites to predict prognostic and diagnostic markers.

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