Identification of Circulating Biomarker Candidates for Hepatocellular Carcinoma (HCC): An Integrated Prioritization Approach

<div><p>Hepatocellular carcinoma (HCC) is the world’s third most widespread cancer. Currently available circulating biomarkers for this silently progressing malignancy are not sufficiently specific and sensitive to meet all clinical needs. There is an imminent and pressing need for the identification of novel circulating biomarkers to increase disease-free survival rate. In order to facilitate the selection of the most promising circulating protein biomarkers, we attempted to define an objective method likely to have a significant impact on the analysis of vast data generated from cutting-edge technologies. Current study exploits data available in seven publicly accessible gene and protein databases, unveiling 731 liver-specific proteins through initial enrichment analysis. Verification of expression profiles followed by integration of proteomic datasets, enriched for the cancer secretome, filtered out 20 proteins including 6 previously characterized circulating HCC biomarkers. Finally, interactome analysis of these proteins with midkine (MDK), dickkopf-1 (DKK-1), current standard HCC biomarker alpha-fetoprotein (AFP), its interacting partners in conjunction with HCC-specific circulating and liver deregulated miRNAs target filtration highlighted seven novel statistically significant putative biomarkers including complement component 8, alpha (C8A), mannose binding lectin (MBL2), antithrombin III (SERPINC1), 11β-hydroxysteroid dehydrogenase type 1 (HSD11B1), alcohol dehydrogenase 6 (ADH6), beta-ureidopropionase (UPB1) and cytochrome P450, family 2, subfamily A, polypeptide 6 (CYP2A6). Our proposed methodology provides a swift assortment process for biomarker prioritization that eventually reduces the economic burden of experimental evaluation. Further dedicated validation studies of potential putative biomarkers on HCC patient blood samples are warranted. We hope that the use of such integrative secretome, interactome and miRNAs target filtration approach will accelerate the selection of high-priority biomarkers for other diseases as well, that are more amenable to downstream clinical validation experiments.</p></div

Performance and accuracy evaluation (%) of databases.

<p><b>3A</b>. Graphical representation of databases performance has been shown in percentages. BioGPS database revealed 97%, VeryGene database 92%, TiSGeD database 76%, TiGER database 66%, UniGene database 37%, C-It database 5% and the HPA unveiling 45% performance for the identification of liver-specific protein biomarkers. Performance % was calculated by dividing number of proteins identified by each database to total number of proteins that passed the filtering criteria. <b>3B</b>. Graphical representation of accuracy of the initial protein identifications with HPA database showing the highest accuracy of 25%, VeryGene database showing 8% accuracy, TiSGeD database showing 15%, TiGER database showing 8%, UniGene database showing 19%, C-It showing 2% and BioGPS database showing 20% accuracy. The accuracy was calculated by dividing number of proteins that had passed the filtering criteria by each database to the total number of proteins each database initially identified.</p

Schematic outline of multi-step HCC circulating biomarkers prioritization process.

<p>Liver-specific proteins extracted from various databases were screened using SignalP 4.1, SecretomeP 2.0, ExoCarta, TargetP 1.1 and TMHMM v. 2.0 servers to assess their secretory nature. Liver-specific secreted proteins once verified for their expression in liver (HPA and BioGPS) and blood (Plasma Proteome Database) were further prioritized depending upon their presence in secretome proteome of HCC patients, HCC cell lines and primary human hepatocytes. To infer possible involvement of prioritized proteins in HCC pathogenesis, their interactome analysis was done with AFP (as a standard biomarker for the diagnosis of HCC). Interacting proteins were then analysed for their interaction with HCC specific liver deregulated and circulating miRNA. Results were then statistically verified using SurvExpress validation tool to finally prioritize putative circulating biomarkers for HCC.</p

Interactome network analysis (miRNA-gene).

<p>Interactome network analysis of HCC-specific circulating miRNAs and genes encoding candidate protein biomarkers (retrieved using miRWalk, miRTarBase, TargetScan and microRNA.org) were visualized using Cytoscape software. The red colored circles represent seven final prioritized candidate marker proteins in our study.</p

Total number of liver-specific proteins identified in gene and protein databases.

<p>Total number of liver-specific proteins identified in gene and protein databases.</p

Liver-specific secreted/shed proteins identified by each database utilized in this study.

<p>Liver-specific secreted/shed proteins identified by each database utilized in this study.</p

Seven statistically significant putative HCC specific biomarkers prioritized through integrated <i>in-silico</i> approach.

<p>Seven statistically significant putative HCC specific biomarkers prioritized through integrated <i>in-silico</i> approach.</p

Identification of liver-specific secreted proteins.

<p>Liver-specific secreted proteins identified using seven publicly available gene and protein databases. Databases based on microarray data (TiSGeD, BioGPS and VeryGene) unveiled 845; ESTs data (TiGER, UniGene and C-It) revealed 473 and HPA database based on immunohistochemistry data revealed 69 liver-specific proteins. A total of 272 proteins were identified in two or more than two databases and thus selected for further analysis.</p

Relapse-free survival and ROC curve analysis.

<p>Proposed candidate biomarkers better predicted relapse-free survival (p = 0.01191) <b>(A)</b> as compared to AFP (P = 0.1987) <b>(B)</b>. With respect to the discriminating ability of proposed biomarkers, long rank equal curve showed statistically significant p-value (<0.05) for HCC p = 0.02361 while for cirrhotic liver p-value was 0.1985 (which not significant) (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138913#pone.0138913.g009" target="_blank">Fig 9A and 9B</a>).</p

Receiver operating characteristic (ROC) analysis of sensitivity and specificity by proposed seven candidate biomarkers and AFP in predicting disease-free survival (DFS).

<p>The score performance was assessed by calculating the area under the ROC (AUROC) which was 0.861 (KM method) and 0.854 (NNE method), respectively for proposed candidate biomarkers while for AFP; AUROC was 0.354 (KM method) and 0.5 (NNE method) respectively.</p