In-Depth Serum Proteomics Reveals the Trajectory of Hallmarks of Cancer in Hepatitis B Virus-Related Liver Diseases

Abstract

Hepatocellular carcinoma (HCC) is a prevalent cancer in China, with chronic hepatitis B (CHB) and liver cirrhosis (LC) being high-risk factors for developing HCC. Here, we determined the serum proteomes (762 proteins) of 125 healthy controls and Hepatitis B virus-infected CHB, LC, and HCC patients and constructed the first cancerous trajectory of liver diseases. The results not only reveal that the majority of altered biological processes were involved in the hallmarks of cancer (inflammation, metastasis, metabolism, vasculature, and coagulation) but also identify potential therapeutic targets in cancerous pathways (i.e., IL17 signaling pathway). Notably, the biomarker panels for detecting HCC in CHB and LC high-risk populations were further developed using machine learning in two cohorts comprised of 200 samples (discovery cohort = 125 and validation cohort = 75). The protein signatures significantly improved the area under the receiver operating characteristic curve of HCC (CHB discovery and validation cohort = 0.953 and 0.891, respectively; LC discovery and validation cohort = 0.966 and 0.818, respectively) compared to using the traditional biomarker, alpha-fetoprotein, alone. Finally, selected biomarkers were validated with parallel reaction monitoring mass spectrometry in an additional cohort (n = 120). Altogether, our results provide fundamental insights into the continuous changes of cancer biology processes in liver diseases and identify candidate protein targets for early detection and intervention.

Keywords: antibody array; biomarker; drug target; hepatocellular carcinoma; mass spectrometry.

Graphical abstract

Fig. 1

Serum proteome analyses for liver disease patients using the in-depth serum proteome mapping platform.A, study design using in-depth serum proteome mapping (ID-Map). B, distribution of serum proteins detected by an antibody microarray and DIA-MS based on the reference concentrations provided in the human protein atlas (HPA) (https://www.proteinatlas.org/). C, a comparison of the proteins detected with the ID-Map platform and the proteins identified in published articles for four groups of serum samples (HC, CHB, LC, and HCC). D, bubble maps of liver diseases or their complications enriched in biomarkers and therapeutic targets through DisGeNET. CHB, chronic hepatitis B; DIA-MS, data-independent acquisition mass spectrometry; HC, healthy control; HCC, hepatocellular carcinoma; LC, liver cirrhosis.

Fig. 2

Proteomics analysis of the biological trajectory of liver diseases from HCs, CHB, LC to HCC.A, identification of DEPs in the liver disease groups compared to HCs and to each other using volcano plot analysis. The selection of DEPs was performed using the Wilcoxon rank sum test analysis (p value < 0.05). Blue and red dots represent downregulated and upregulated proteins. B, the number and type of biological processes involved in the hallmarks of cancer that are enriched in the DEPs across the different patient groups. C, biological process analysis of DEPs in CHB versus HC, LC versus HC, HCC versus HC, LC versus CHB, HCC versus CHB, and HCC versus LC using Cytoscape and ClueGo version 3.8. (p value < 0.01). The light to dark red color indicates the low to high significance of biological processes, respectively. CHB, chronic hepatitis B; DEP, differentially expressed protein; HCC, hepatocellular carcinoma; HC, healthy control; LC, liver cirrhosis.

Fig. 3

Hierarchical clustering analyses of serum proteomes in liver diseases.A, hierarchical clustering map of the DEPs identified in HC, CHB, LC, and HCC patients (p value < 0.05). A false color scheme from blue to red represents the minimum and maximum Z-score values, respectively. B, proteins clustered into three groups according to their expression patterns, and the Z-scores were plotted over four groups using the gene cluster trend of Hiplot. C, pathway analysis of the DEPs was performed per cluster using the STRING database (version 11.5.). The false discovery rate (FDR) value indicates the significance of pathways, where a lower FDR represents a higher significance. D, the DEPs involved in cholesterol metabolism are summarized by the average Z-score across four groups (HC, CHB, LC, and HCC). CHB, chronic hepatitis B; DEP, differentially expressed protein; HCC, hepatocellular carcinoma; HC, healthy control; LC, liver cirrhosis.

Fig. 4

Potential therapeutic targets of liver diseases identified by in-depth serum proteomics according to the Therapeutic Target Database. Ninety-one drug targets were identified by cross-referencing the upregulated proteins in the three liver disease groups (CHB, LC, and HCC) discovered in this study with the Therapeutic Target Database (TTD) database. Tissue specificity and cellular location were obtained from the HPA database, while the target type, drug name, and disease were obtained from the TTD database. A false color scheme from blue to red represents the minimum and maximum Z-score values, respectively. CHB, chronic hepatitis B; HCC, hepatocellular carcinoma; HPA, human protein atlas; LC, liver cirrhosis.

Fig. 5

Functional analyses of potential drug targets for liver diseases.A, the diseases treated by drugs that target one of 91 proteins according to the TTD. B, the cellular localization of the potential drug targets was obtained from the HPA database (https://www.proteinatlas.org/). C, protein classes of the potential drug targets were identified using PANTHER (http://www.pantherdb.org/). D, enriched signaling pathways in liver disease-related therapeutic targets based on information obtained from DisGeNET. The size of the blue circle represents the number of DEPs in the pathways. E, therapeutic targets involved in the IL17 signaling pathway. Means of the Z-score were used to represent the alterations in HC, CHB, LC, and HCC. CHB, chronic hepatitis B; DEP, differentially expressed protein; HC, healthy control; HCC, hepatocellular carcinoma; HPA, human protein atlas; LC, liver cirrhosis; TDD, Therapeutic Target Database.

Fig. 6

Development of serum protein signatures differentiating liver diseases using machine learning.A, workflow of feature selection and machine learning modeling. B and C, the receiver operating characteristic (ROC) curve (B) and confusion matrix performance (C) of biomarker panels in LC versus CHB, HCC versus CHB, HCC versus LC of the discovery cohort and validation cohort. D, protein-protein network analysis of proteins in the multimarker panels of LC versus CHB, HCC versus CHB, and HCC versus LC. CHB, chronic hepatitis B; HCC, hepatocellular carcinoma; LC, liver cirrhosis.