Quantitative proteomic analysis in HCV-induced HCC reveals sets of proteins with potential significance for racial disparity

Abstract

Background: The incidence and mortality of hepatitis C virus (HCV)-induced hepatocellular carcinoma (HCC) is higher in African Americans (AA) than other racial/ethnic groups in the U.S., but the reasons for this disparity are unknown. There is an urgent need for the discovery of novel molecular signatures for HCV disease progression to understand the underlying biological basis for this cancer rate disparity to improve the clinical outcome.

Methods: We performed differential proteomics with isobaric labeling tags for relative and absolute quantitation (iTRAQ) and MS/MS analysis to identify proteins differentially expressed in cirrhotic (CIR) and HCC as compared to normal tissues of Caucasian American (CA) patients. The raw data were analyzed using the ProteinPilot v3.0. Searches were performed against all known sequences populating the Swiss-Prot, Refseq, and TrEMBL databases. Quality control analyses were accomplished using pairwise correlation plots, boxplots, principal component analysis, and unsupervised hierarchical clustering. Supervised analysis was carried out to identify differentially expressed proteins. Candidates were validated in independent cohorts of CA and AA tissues by qRT-PCR or Western blotting.

Results: A total of 238 unique proteins were identified. Of those, around 15% were differentially expressed between normal, CIR & HCC groups. Target validation demonstrates racially distinct alteration in the expression of certain proteins. For example, the mRNA expression levels of transferrin (TF) were 2 and18-fold higher in CIR and HCC in AA as compared to CA. Similarly; the expression of Apolipoprotein A1 (APOA1) was 7-fold higher in HCC of AA. This increase was mirrored in the protein expression levels. Interestingly, the level of hepatocyte nuclear factor4a (HNF4a) protein was down regulated in AA, whereas repression of transcription is seen more in CA compared to AA. These data suggest that racial disparities in HCC could be a consequence of differential dysregulation of HNF4a transcriptional activity.

Conclusion: This study identifies novel molecular signatures in HCV-induced HCC using iTRAQ-based tissue proteomics. The proteins identified will further enhance a molecular explanation to the biochemical mechanism(s) that may play a role in HCC racial disparities.

Figure 1

Unsupervised analysis of normalized proteomics data obtained from Normal, Cirrhosis (Cir) and HCC subjects: A) Unsupervised Clustering B) Principal component analysis. Unsupervised Pearson Correlation based cluster of Normal, Cirrhosis (Cir) and HCC subjects depicts two major clusters containing Normal’s and Diseased (Cir, HCC) subjects. B) The first principal component with highest variation (43%) is shown on the X-axis and separates the samples on the basis of Diseased vs Normal status. The second component with median variance (15%) is displayed on the Y-axis and separates the sample on the basis of the disease progression (Cirrhosis vs. HCC). The Normal, Cirrhosis and HCC samples formed three separate clusters on PCA plot.

Figure 2

Identification and comparison of differentially expressed proteins (DEP) identified from different supervised analysis. Heat maps of fifteen differentially expressed genes that were selected by following supervised analysis A) Normal vs. Cirrhosis, B) Normal vs. HCC, and C) Cirrhosis vs. HCC. The columns represent the samples and the rows represent the proteins. Protein expression is depicted with a pseudocolor scale (−2 to 2); red denoting high expression level and green denoting low expression level. D) Venn Diagram comparing the significantly differentially expressed proteins identified from following comparisons i) Normal vs. Cirrhosis, ii) Normal vs. HCC, and iii) Cirrhosis vs. HCC.

Figure 3

Heat map of thirty-two proteins differentially expressed in both Cirrhosis and HCC as compared to Normal. These proteins represent a potential signature depicting progression of disease. The columns represent the samples and the rows represent the proteins. Protein expression is shown with a pseudocolor scale (−2 to 2); red denoting high expression level and green denoting low expression level. Proteins validated using qRT-PCR or immunoblotting are highlighted in the heat map.

Figure 4

Pathways and functional enrichment analysis of proteins differentially expressed both in Cirrhosis and HCC as compared to Normal A) Functional enrichment analysis B) Pathways enrichment analysis. For Figure 4A or Figure 4B each bar represents a significantly enriched pathway or function as determined using the multiple test corrected Fisher’s Exact Test P-value. The P-value is depicted as –log10 (BH P value) on primary X-axis. The analysis for canonical pathways and functions was performed using Ingenuity Systems interactions.

Figure 5

Interactive Network representation of the cellular functions and pathways affected by thirty-two proteins that are commonly altered in Cirrhosis and HCC as compared to Normal. The network is enriched with proteins significantly linked to cell movement, connectivity tissue disorder and cancer. We used the Ingenuity Pathways Analysis tool (http://www.ingenuity.com) to generate the networks of proteins that are only differentially expressed in Cirrhosis and HCC as compared to normal. The intensity of the node color indicates the degree of up-regulation (red), down-regulation (green) or no effect (white) in HCC as compared to Normal samples.

Figure 6

Target validation of DEP in tissue samples of CA and AA. A) Real-time qRT-PCR, B) A representative of Western blotting analysis. A) Real-time qRT-PCR detected the relative mRNA expression levels of transferrin (TF), apolipoprotein A1 (APOA1), hepatocyte nuclear factor4α (HNF4α) and filamin A (FLNA). 18S rRNA was used as the normalization standard. Compared to CA, AA tissues had an obvious up-regulation of TF in CIR and HCC samples (p < 0.05 and 0.001, respectively) and up-regulation of APOA1, and HNF4α in HCC samples (p < 0.001; p < 0.05, respectively). Bars = means ± SD. There was no significant difference in the expression of FLNA between the two groups. B) A representative of immunoblot analysis result of (I) TF and APOA1, (II) HNF4α in tissue samples of AA and CA. GAPDH was used as a loading control. Compared to CA, AA tissue samples (CIR, HCC) show an obvious increase in protein levels of TF and APOA1, but decreased protein levels of HNF4α.