Workflow of our GC-MS based untargeted metabolomic and targeted analyses for biomarker discovery.

<p>The number of candidate metabolites analyzed at specific steps is shown in parenthesis.</p

Example of a retrieved EIC for valine.

<p>The inset in the top left shows the expected ratios for the fragments based on the library to guide the visual inspection. The doted vertical lines show the expected and estimated elution time of the analyte. Although, the background signal of 73 from other compounds is reflected in the apex score, its impact on the AUC is diminished by baseline correction.</p

Characteristics of the study cohort.

<p>Characteristics of the study cohort.</p

List of nine analytes confirmed by targeted analysis with their expected retention time and molecular weights for quantifier (M1) and qualifier fragments (M2-M3).

<p>* Number of TMS in Fiehn library</p><p>Fragment with a molecular weight of 73 was also monitored by default for all the analytes.</p

Evaluation of the metabolites from targeted analysis using PLS-DA and OPLS-DA.

<p>A: Score plot obtained by PLS-DA with HCC cases labeled by red triangles and patients with liver cirrhosis by blue dots. Stage II & III HCC cases are labeled with solid triangles. B: Loading plot from PLS-DA. C: S-plot obtained by OPLS-DA. The nine metabolites previously selected by univariate analysis are highlighted in B and C.</p

Metabolites with significant changes in their levels in HCC vs. cirrhosis based on targeted analysis of plasma by GC-SIM-MS.

<p>The metabolites in the top two panels show increasing trend with the progression of HCC. The metabolites in the bottom panel are down-regulated in HCC vs. cirrhosis. While lactic acid and citric acid show decreasing trend with the progression of HCC, sorbose is down-regulated overall in HCC vs. cirrhosis.</p

Pathway and network analysis of 24 metabolites recognized by IPA from the candidates discovered by GC-MS and LC-MS based analyses.

<p>A: top 10 canonical pathways based on 24 metabolites. B: network involving 13 out of the 24 metabolites (up-regulated in HCC vs. cirrhosis marked in red, down-regulated in HCC vs. cirrhosis marked in green).</p

LC–MS Based Serum Metabolomics for Identification of Hepatocellular Carcinoma Biomarkers in Egyptian Cohort

Although hepatocellular carcinoma (HCC) has been subjected to continuous investigation and its symptoms are well-known, early stage diagnosis of this disease remains difficult and the survival rate after diagnosis is typically very low (3–5%). Early and accurate detection of metabolic changes in the sera of patients with liver cirrhosis can help improve the prognosis of HCC and lead to a better understanding of its mechanism at the molecular level, thus providing patients with in-time treatment of the disease. In this study, we compared metabolite levels in sera of 40 HCC patients and 49 cirrhosis patients from Egypt by using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometer (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from cirrhotic controls are selected by statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. The identities of some of the putative identifications are verified by comparing their MS/MS fragmentation patterns and retention times with those from authentic compounds. Finally, the serum samples are reanalyzed for quantitation of selected metabolites as candidate biomarkers of HCC. This quantitation was performed using isotope dilution by selected reaction monitoring (SRM) on a triple quadrupole linear ion trap (QqQLIT) coupled to UPLC. Statistical analysis of the UPLC-QTOF data identified <b>274</b> monoisotopic ion masses with statistically significant differences in ion intensities between HCC cases and cirrhotic controls. Putative identifications were obtained for <b>158</b> ions by mass based search against databases. We verified the identities of selected putative identifications including glycholic acid (GCA), glycodeoxycholic acid (GDCA), 3β, 6β-dihydroxy-5β-cholan-24-oic acid, oleoyl carnitine, and Phe-Phe. SRM-based quantitation confirmed significant differences between HCC and cirrhotic controls in metabolite levels of bile acid metabolites, long chain carnitines and small peptide. Our study provides useful insight into appropriate experimental design and computational methods for serum biomarker discovery using LC–MS/MS based metabolomics. This study has led to the identification of candidate biomarkers with significant changes in metabolite levels between HCC cases and cirrhotic controls. This is the first MS-based metabolic biomarker discovery study on Egyptian subjects that led to the identification of candidate metabolites that discriminate early stage HCC from patients with liver cirrhosis