Purpose: Liver cancer is one of the most common tumors with the seventh-highest incidence and the third-highest mortality. Many studies have shown that small extracellular vesicles (sEVs) play an important role in liver cancer. Here, we report comprehensive signatures for sEV proteins from plasma obtained from patients with hepatocellular carcinoma (HCC), which might be valuable for the evaluation and diagnosis of HCC. Methods: We extracted sEVs from the plasma of controls and patients with HCC. Differentially expressed proteins in the sEVs were analyzed using label-free quantification and bioinformatic analyses. Western blotting (WB) was used to validate the abovementioned sEV proteins. Results: Proteomic analysis was performed for plasma sEVs from 21 patients with HCC and 15 controls. Among the 335 identified proteins in our study, 27 were significantly dysregulated, including 13 upregulated proteins that were involved predominantly in the complement cascade (complement C1Q subcomponent subunit B (C1QB), complement C1Q subcomponent subunit C (C1QC), C4B-binding protein alpha chain (C4BPA), and C4B-binding protein beta chain (C4BPB)) and the coagulation cascade (F13B, fibrinogen alpha chain (FGA), fibrinogen beta chain (FGB), and fibrinogen gamma chain (FGG)). We verified increased levels of the C1QB, C1QC, C4BPA, and C4BPB proteins in the plasma sEVs from patients with HCC in both the discovery cohort and validation cohort. Conclusions: The complement cascade in sEVs was significantly involved in HCC progression. C1QB, C1QC, C4BPA, and C4BPB were highly abundant in the plasma sEVs from patients with HCC and might represent molecular signatures.
Keywords: Proteomics; Extracellular vesicles; Hepatocellular carcinoma; Complement
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1186/s12957-022-02849-y.
Liver cancer is one of the most common tumors, as liver cancer has the sixth highest incidence and the third highest mortality according to the Global Cancer Statistics 2020 [[
The small extracellular vesicle (sEV) is a single-bilayer vesicle with a diameter ranging from 30 to 200 nm that originally buds at the plasma and endosomal membranes [[
In recent years, accumulating evidence has suggested that sEVs play important roles in chronic hepatitis, cirrhosis, and liver cancer [[
Due to the close relationship between sEVs and HCC, we aimed to explore potential biomarkers from plasma sEVs for the evaluation of HCC. In this study, by comparing the protein expression among the groups of control subjects and patients with HCC, we comprehensively identified differentially expressed proteins from sEVs using a label-free quantification method. Then, we verified the diagnostic candidates for HCC in the independent validation cohort by performing western blotting.
Plasma samples from controls and patients with HCC were collected at the Affiliated Hospital of Guilin Medical University (Guangxi, China) after written informed consent was obtained from the patients and their families, according to a protocol (#YJSL2021129) approved by the Institutional Review Board of the Affiliated Hospital of Guilin Medical University (Guangxi, China). Twenty-eight patients whose confirmed pathological diagnosis was HCC and 22 control patients without liver cancer. The discovery cohort included 21 patients with HCC and 15 controls. The validation cohort included 7 patients with HCC and 7 controls. All blood samples were collected into EDTA anticoagulant tubes. Briefly, the initial blood samples were sequentially centrifuged at 500 × g for 5 min and 2000 × g for 20 min at 4 °C to remove the cells and cell debris. The supernatant was then aliquoted into a 2-ml centrifuge tube and stored at − 80 °C.
The HepG2 cell line was used in this study. HepG2 cells were obtained from the American Type Culture Collection. HepG2 cells were cultured in DMEM (Gibco) supplemented with 10% FBS (Gibco) and 1% penicillin/streptomycin at 37 °C with 5% CO
The clinical blood samples were collected and sequentially centrifuged at 500 × g for 5 min and 2000 × g for 20 min at 4℃ to remove the cells and cell debris. Then, the supernatants were harvested and centrifuged at 10,000 × g for 20 min and subsequently at 12,000 × g for 15 min at 4 °C to harvest the supernatants. Moderate exosome isolation reagents (RiboBio, China) with polyethylene glycol (PEG) for precipitation were added to the supernatants. The mixtures were thoroughly shaken and incubated for 30 min at 4 °C. Finally, the mixtures were centrifuged at 15,000 × g for 2 min at 4 °C, after which the supernatants were discarded. The pellets were resuspended in 200 μl of PBS and stored at − 80 °C.
The size of resuspended sEVs was measured using a NanoSight NS300 instrument (Malvern PANalytical, UK). The distribution and concentration of acquired sEVs were determined and analyzed using NTA software (version 3.1, NanoSight).
The extracted sEVs were lysed in RIPA buffer (Solarbio, China), boiled, and loaded onto a 10% Tris–Gly gel. The separated proteins were transferred to polyvinylidene difluoride membranes, which were subsequently blocked with 5% skim milk in TBST for 60 min at room temperature. After three washes, the membrane was incubated overnight at 4 °C with primary antibodies against CD63 (ab134045, Abcam), CD81 (ab109201, Abcam), TSG101 (DF8427, Affinity Biosciences), Calnexin (
Briefly, 5 μl of each sample were placed on Formvar/carbon-coated TEM grids for 3 min at room temperature. The excess fluid at the edge was blotted with filter paper. After rinsing with PBS, the samples were negatively stained with phosphotungstic acid and allowed to dry naturally for 5 min. TEM images of sEVs were captured using a JEM-1200EX TEM (JEOL, Tokyo, Japan).
The proteins in sEVs were extracted by lysis by SDT buffer (4% SDS, 100 mM Tris–HCl, and 1 mM dithiothreitol, pH 7.6). The protein concentration was quantified using a BCA Protein Assay Kit. Two hundred micrograms of protein were mixed with 30 μl of SDT buffer (4% SDS, 100 mM dithiothreitol, and 150 mM Tris–HCl pH 8.0). Then, the samples were ultrafiltrated to remove the detergent dithiothreitol (DTT) and the other low molecular weight components in UA buffer (8 M urea and 150 mM Tris–HCl, pH 8.0). The samples were incubated with 100 μl of iodoacetamide (100 Mm iodoacetamide in UA buffer) for 30 min in the dark to block cysteine residues and subsequently digested with 4 μg of trypsin (Promega) in 25 mM NH
The LC‒MS/MS analysis was performed using a Q Exactive mass spectrometer (Thermo Fisher Scientific) coupled to an Easy-Nlc system (Thermo Fisher Scientific) for 90 min. The peptide mixtures in 0.1% FA were loaded onto a reverse-phase trap column (Thermo Scientific Acclaim PepMap100, 100 μm *2 cm, nanoViper C18), separated using a linear gradient of buffer B (84% acetonitrile and 0.1% FA) at a 300-nL/min flow rate controlled by IntelliFlow technology through a 10-cm-long reverse-phase analytical column (75 μm, 3-μm particle size, C18; Thermo Scientific Easy Column). The mass spectrometer was operated in positive ion mode. Survey MS spectra ranging from m/z 300 to 1800 were acquired at a resolution of 70,000 at m/z 200 using the data-dependent mode to dynamically choose the top 10 most abundant precursor ions for HCD fragmentation (17,500 at m/z 200) with an automatic gain control (AGC) target of 3 × 10
The MS raw data for each sample were combined and searched against the public database UniProt (
The quantitative LC–MS/MS analysis includes the Andromeda score distribution, mass error distribution, and ratio distribution. The criteria for protein identification were a peptide that is unique to a group of highly similar protein sequences (protein group). The criteria for protein contamination were based on the peptide contamination library, which contained the common contaminating protein sequences, such as keratins, bovine serum albumin, and trypsin. These detected contaminants were excluded from further analysis.
InterProScan software was applied to search for protein sequences and align identified protein domain signatures from the InterPro member database Pfam (
NCBI BLAST + client software and InterProScan were applied to query the selected differentially expressed protein sequences and identify homologous sequences. The Blast2GO program was applied to map GO terms and annotate sequences. The GO annotation results were plotted using R scripts.
Following GO annotation steps, sequence alignment was performed against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (
Based on the background dataset of the total quantified proteins, an enrichment analysis was performed using Fisher's exact test for the p value, which was successively adjusted using the Benjamini–Hochberg correction with the false discovery rate (FDR). The functional categories and pathways were significantly enriched and evaluated only if the adjusted p value was less than 0.05.
All data were analyzed with GraphPad Prism version 8, SPSS 19.0, ImageJ, or R software. Depending on the data, Student's t test or Fisher's exact probability test was used to compare differences, and p < 0.05 was considered significant. The statistical tests, p values, and adjusted p values for each experiment are shown in the figure legends.
We extracted plasma sEVs from 21 patients and 15 controls with an sEV extraction kit (Ribo Bio, China) using polyethylene glycol (PEG) precipitation according to the workflow shown in Fig. 1. The extracted sEVs were confirmed using TEM, which detected their round and cup-like spherical shapes (Fig. 2A), and NTA, which analyzed their diameter and distribution. The average size of the sEVs was 147.5 nm, ranging from 17.5 to 300 nm (Fig. 2B). Compared to the HepG2 cell lysates, the proteins in sEVs were detected using western blotting to determine the presence of three positive markers, CD63, CD81, and TSG101 (Fig. 2C), and the absence of one negative marker, Calnexin (Fig. 2C). As shown in our WB, TEM, and NTA results, the sEV extraction kit we used isolated the sEVs efficiently and accurately.
Graph: Fig. 1 The strategy for sEV isolation by PEG precipitation and bioinformatics analysis
Graph: Fig. 2 sEV characterization and verification. A Representative electron microscopy image of plasma sEVs. Scale bar: 200 nm. B The size and concentration distribution of the sEVs in plasma detected using NTA. The particle size distribution of sEVs mainly ranged from 17.5 to 300 nm and peaked at 147.5 nm. C The protein expression of three positive markers and one negative marker of sEVs was detected in the sEVs-NC, sEVs-HCC, and cell lysate groups. The protein samples from sEVs extracted from the plasma of control patients and patients with HCC were labeled "sEVs-NC" and "sEVs-HCC," respectively. The protein sample of the "Cell Lysate" group was extracted from HepG2 cells without any treatment. All samples were normalized by determining the concentration using the BCA assay
After identifying the extracted sEVs, a label-free nano-LC–MS/MS analysis was performed to comprehensively analyze the proteins in sEVs and compare them between the normal and HCC groups. Five sEV samples selected randomly and evenly from 15 controls were pooled together to form three new specimens representing the control group, and seven sEV samples from 21 patients with HCC were selected randomly and evenly to form three new specimens representing the HCC group as a method to eliminate the interindividual variability and save money. Three new specimens from the HCC group or control group were subjected to a proteomic analysis. The characteristics of the participants from the HCC group and control group were listed in Supplementary Table 1. The results revealed 850,808 secondary spectral maps, of which 77,850 were matched to the UniProt public database. Using the criteria for protein identification and contamination described in the "Methods" section, 3872 unique peptide segments were identified in a total of 4167 peptide segments, and 281 proteins were quantified among 335 identified proteins.
The overlap of proteins between groups was visualized in a Venn diagram to assess the reproducibility of proteins identified among the groups. Two hundred ninety-three and 296 sEV proteins were identified in the control and HCC groups, respectively (Fig. 3C). A total of 272 proteins were expressed commonly among the control and HCC groups (Fig. 3C). Two hundred twenty-nine (78.2%) proteins in the control group (Fig. 3A) and 236 (79.7%) proteins in the HCC group (Fig. 3B) appeared in all three replicates.
Graph: Fig. 3 Venn diagrams of the proteins identified in plasma sEVs. A Venn diagram of sEV proteins identified in the 3 independent control groups. B Venn diagram of sEV proteins identified in the 3 independent HCC groups. C Venn diagram of sEV proteins identified in the normal and HCC groups
Compared to the healthy controls, a fold change in HCC sEV proteins ≥ 1.5 or < 0.66 was defined as a differentially expressed protein (DEP) [[
Graph: Fig. 4 Volcano plot and hierarchical clustering analysis of DEPs in the sEVs from patients with HCC. A Volcano plot of protein expression in sEVs from patients with HCC compared to the control group. B Heatmap of the hierarchical clustering analysis of DEPs. Each column represents a set of samples (abscissa for sample information), and each row represents a protein (namely, the ordinate for SDEPs). The color scale shown in the map illustrates the relative protein expression: red represents upregulated proteins, blue represents downregulated proteins, and the blanks represent samples for which no quantitative information was available
We annotated the aforementioned proteins by performing a Gene Ontology (GO) analysis to understand the functions, locations, and biological pathways of the sEV proteins identified in plasma from patients with HCC. GO is a standardized functional classification system that provides a dynamically updated standardized glossary describing the properties of genes and their products in living organisms. The GO functional annotation was divided into three categories: biological process (BP), molecular function (MF), and cellular component (CC).
We analyzed all 27 SDEPs by determining GO functional annotations compared with the total proteins of reference species and obtained the signature of the difference. The GO analysis of biological processes revealed an enrichment of SDEPs in the "acute inflammatory response," "protein activation cascade," "protein maturation," "humoral immune response," and "protein processing" (Fig. 5A). A chord diagram revealed the links of SDEPs with biological processes, and the data showed that the majority of SDEPs were involved in the "acute inflammatory response," including AHSG, C1QB, C1QC, C4BPA, C4BPB, CFP, IGHV3-7, IGLV3-25, ORM1, ORM2, PROS1, and VTN (Fig. 5B). The GO analysis of molecular functions revealed an enrichment of SDEPs involved in "enzyme inhibitor activity," "peptidase regulator activity," "endopeptidase regulator activity," "peptidase inhibitor activity," and "endopeptidase inhibitor activity" (Fig. 5C). A chord diagram revealed the links of SDEPs with molecular functions, and the data showed that the majority of SDEPs were involved in "endopeptidase inhibitor activity," including AHSG, PROS1, SERPINA10, SERPINA6, and SERPIND1 (Fig. 5D). The GO analysis of cellular components revealed an enrichment of SDEPs in "blood microparticle," "collagen-containing extracellular matrix," "endoplasmic reticulum lumen," "vesicle lumen," and "cytoplasmic vesicle lumen" (Fig. 5E). A chord diagram revealed the links of SDEPs with cellular components, and the data showed that the majority of SDEPs were involved in the "collagen-containing extracellular matrix," including AHSG, AZGP1, C1QB, C1QC, CFP, fibrinogen alpha chain (FGA), fibrinogen beta chain (FGB), fibrinogen gamma chain (FGG), ORM1, ORM2, TGFBI, and VTN (Fig. 5F).
Graph: Fig. 5 GO analysis of DEPs in the sEVs from patients with HCC. A Bubble diagram of the GO functional enrichment analysis of the biological process category. B Chord plot of the GO functional enrichment analysis of the biological process category. C Bubble diagram of the GO functional enrichment analysis of the molecular function category. D Chord plot of the GO functional enrichment analysis of the molecular function category. E Bubble diagram of the GO functional enrichment analysis of the cellular component category. F Chord plot of the GO functional enrichment analysis of the cellular component category. On the horizontal axis of the bubble diagram, the gene ratio is the ratio of the observed protein count to the background protein count. The color gradient in the bubble diagram represents the adjusted p value (-log10), and the size of bubbles represents the number of enriched proteins. DEPs are listed on the left of the chord plot and connected to the GO terms. The fold change (Log2) is represented by the color indicated in the color bar. The color on the right of the chord plot represents each GO term
In addition, SDEPs were analyzed using the KEGG pathway database to predict the related pathways and involved proteins. We found that SDEPs were involved in "complement and coagulation cascades", "pertussis", and "Staphylococcus aureus infection" (Fig. 6A). The clustering analysis of KEGG pathways indicated that the SDEPs from HCC plasma sEVs were mainly involved in the complement and coagulation cascade pathways (Fig. 6B), including C1QB, C1QC, C4BPA, C4BPB, F13B, FGA, FGB, FGG, SERPIND1, PROS1, and VTN. Taken together, the results of GO and KEGG analyses suggested that the complement (C1QB, C1QC, C4BPA, and C4BPB) and coagulation (F13B, FGA, FGB, and FGG) pathways are the major pathways in which SDEPs are involved, and the dysregulated proteins of the complement and coagulation pathways may be potential molecular signatures for HCC. Detailed information on the GO and KEGG analyses is provided in Supplementary Table 3.
Graph: Fig. 6 KEGG analysis of DEPs in the sEVs from patients with HCC. A Bubble diagram of KEGG pathways. On the horizontal axis of the bubble diagram, the gene ratio is the ratio of the observed protein count to the background protein count. The color gradient in the bubble diagram represents the adjusted p value (-log10), and the size of bubbles represents the number of enriched proteins. B Network of KEGG pathways. Lines represent the relationship between KEGG pathways and DEPs
We developed a protein–protein interaction (PPI) network using the Search Tool for Retrieval of Interacting Genes/Proteins (STRING) database to obtain a better understanding of the relationships among these SDEPs and their closely related proteins. Based on previous studies examining these proteins, the relationships among the SDEPs and their closely related proteins were analyzed, and the SDEPs and their closely related proteins were grouped into four clusters, which are labeled in different colors (Fig. 7). Information on these previous studies is included in Supplementary Table 4. In Fig 7, F13B, FGA, FGB, and FGG act as network hubs in the red cluster; C1QB, C1QC, C4BPA, and C4BPB are network hubs in the yellow cluster. These data were consistent with the aforementioned results indicating that F13B, FGA, FGB, FGG, C1QB, C1QC, C4BPA, and C4BPB were upregulated in plasma sEVs from patients with HCC. The raw data from the PPI network are included in Supplementary Tables 5 and 6. Detailed information on the clustering analysis is provided in Supplementary Table 7.
Graph: Fig. 7 Comprehensive PPI network of DEPs. Interconnecting lines between two proteins indicate the interaction, and the thickness of lines represents the interaction score. The four clusters are marked in different colors
We verified the findings from the proteomics analysis by measuring the expression levels of SDEPs involved in the complement cascade using WB in the aforementioned pooled samples. The protein concentrations in sEVs were quantified using the BCA method, and each lane was loaded with the same amount of protein. The SDEPs involved in the complement cascade were increased in plasma sEVs from patients with HCC, including C1QB, C1QC, C4BPA, and C4BPBP (Fig. 8A). The band densities of C1QB (p < 0.05), C1QC (p < 0.05), C4BPA (p < 0.05), and C4BPB (p < 0.01) in the HCC group were significantly higher than those in the control group (Fig. 8B–E). These results were consistent with the data from the proteomic analysis.
Graph: Fig. 8 Validation of complement proteins. A The protein expression levels of C1QB, C1QC, C4BPA, and C4BPBP in plasma sEVs from the discovery cohort. B Column chart showing the comparison of the band densities of C1QB between the control and HCC groups in the discovery cohort. C Column chart showing the comparison of the band densities of C1QC between the control and HCC groups in the discovery cohort. D Column chart showing the comparison of the band densities of C4BPA between the control and HCC groups in the discovery cohort. E Column chart showing the comparison of the band densities of C4BPB between the control and HCC groups in the discovery cohort. F The protein expression levels of C1QB, C1QC, C4BPA, and C4BPBP in plasma sEVs from the validation cohort. G Column chart showing the comparison of the band densities of C1QB between the control and HCC groups in the validation cohort. H Column chart showing the comparison of the band densities of C1QC between the control and HCC groups in the validation cohort. I Column chart showing the comparison of the band densities of C4BPA between the control and HCC groups in the validation cohort. J Column chart showing the comparison of the band densities of C4BPB between the control and HCC groups in the validation cohort. The left ordinate shows the gray value measured using ImageJ software. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001
In addition to the aforementioned pooled samples, we also wanted to detect the protein expression of C1QB, C1QC, C4BPA, and C4BPBP in plasma sEVs from individual patients, and samples from seven patients with HCC and seven normal controls were randomly selected. The results showed increased C1QB, C1QC, C4BPA, and C4BPBP levels in plasma sEVs from patients with HCC (Fig. 8F). The band densities of C1QB (p < 0.0001), C1QC (p < 0.0001), C4BPA (p < 0.0001), and C4BPB (p < 0.001) in the HCC group were significantly higher than those in the control group (Fig. 8G–J).
With in-depth research on sEVs, the compositions and functions of sEVs have gradually received attention from researchers in tumor biology and developmental biology. Among the numerous sEV omics studies, most have focused on the contents of miRNAs, lncRNAs, and circRNAs, but few have focused on proteins. Proteomics research on sEVs lags far behind transcriptomics research. Therefore, by applying a proteomics analysis, we aimed to comprehensively detect the DEPs in the sEVs to discover potential biomarkers for HCC. In our study, we launched a systematic approach for analyzing biomarkers in plasma sEVs from patients with HCC, and the following components were included: (
Our results described the protein signature of plasma sEVs from patients with HCC, including the upregulated pathways of the complement cascade (C1QB, C1QC, C4BPA, and C4BPB) and the coagulation cascade (F13B, FGA, FGB, and FGG). In the coagulation cascade pathway, the mRNA level of FGG and the elevated plasma fibrinogen level are related to the clinical stage, tumor thrombosis, and prognosis of HCC [[
As a defensive immune process, the complement system is crucial in several cancers [[
In addition, we identified 24 HCC-specific proteins that only appeared in sEVs from the HCC group but not in the control group. Among them, only 2 proteins, namely coagulation factor XIII A chain (F13A1) and collectin-11 (COLEC11), were present in all three samples from the HCC group. F13A1 is widely regarded as a cancer-related gene and FDA-approved drug target that is involved in lung cancer [[
In summary, our study discovered a novel protein signature comprising four upregulated components (C1QB, C1QC, C4BPA, and C4BPB) of the complement system in plasma sEVs from patients with HCC. These factors might be used for noninvasive diagnosis and monitoring tumor progression. This finding has been confirmed by previous studies and our experiments. Our data provide useful insights into the crosstalk between extracellular vesicles and the complement system in HCC.
Differential and multivariate proteomics analyses of the plasma sEVs from patients with HCC indicated that sEV proteins were significantly related to HCC. Our results described the signature of plasma sEVs from HCC and the upregulated pathway of complement cascades (C1QB, C1QC, C4BPA, and C4BPB) and the coagulation cascade (F13B, FGA, FGB, and FGG), which might be the major factors contributing to the classification of HCC. We also verified the upregulated expression of C1QB, C1QC, C4BPA, and C4BPB. Taken together, these data suggested that an sEV analysis is a valid approach for the evaluation of HCC and that C1QB, C1QC, C4BPA, and C4BPB might be potential molecular signatures.
We thank Dr. Juan Xiao, Guangxi Key Laboratory of Molecular Medicine in Liver Injury and Repair, the Affiliated Hospital of Guilin Medical University, for her valuable suggestions for our subject.
Wei Dong performed the experiments, analyzed the results, and wrote the manuscript. Zeyu Xia, Zehua Chai, Xuehong Wang, Zebin Yang, Tingrui Zhang, and Junnan Wang collected the clinical specimens and helped perform the experiments. Qinqin Zhang and Zhidong Qiu contributed to the manuscript revision and discussion. Junfei Jin designed and supervised the study and reviewed the paper. The authors read and approved the final manuscript.
The present study was supported in part by grants from the Natural Science Foundation of Guangxi (2020GXNSFDA238006), the Special Fund of the Central Government Guiding Local Scientific and Technological Development by Guangxi Science and Technology Department (ZY21195024), the Medical High Level Talents Training Plan in Guangxi (G202002005), the Science and Technology Planned Project in Guilin (20210102-1), the Guangxi Distinguished Experts Special Fund (2019B12), the Construction Fund of Guangxi Health Commission Key Laboratory of Basic Research in Sphingolipid Metabolism Related Diseases (the Affiliated Hospital of Guilin Medical University, ZJC2020005), and the Construction Fund of Key Disciplines of Medical and Health in Guangxi (2021-8-4-3).
All authors will have full access to the final trial dataset. The data supporting the conclusions of this article is included within the article and its additional file. The full protocol and participant-level dataset will be made available upon request if agreed upon by the corresponding author.
The research involving human plasma sEVs was performed in accordance with the Declaration of Helsinki, and plasma samples from controls and patients with HCC were collected at the Affiliated Hospital of Guilin Medical University (Guangxi, China) after written informed consent was obtained from the patients and their families (#YJSL2021129) and was approved by the Institutional Review Board of the Affiliated Hospital of Guilin Medical University (Guangxi, China).
Not applicable.
The authors declare that they have no competing interests.
Graph: Additional file 1. The general characteristics of our participants.
Graph: Additional file 2. The information and the statistical results for all DEPs in sEVs.
Graph: Additional file 3. Data from the GO and KEGG analyses.
Graph: Additional file 4. The reference publications for the PPI network in the STRING database.
Graph: Additional file 5. The list of relevant proteins in the PPI network.
Graph: Additional file 6. The interaction scores in the PPI network.
Graph: Additional file 7. The data of the clustering analysis based on the STRING database.
Graph: Additional file 8. The raw data of band density in the discovery cohort.
Graph: Additional file 9. The raw data of band density in the validation cohort.
• HCC
- Hepatocellular carcinoma
• AFP
- Alpha-fetoprotein
• sEV
- Small extracellular vesicle
• C1QB
- Complement C1Q subcomponent subunit B
• C1QC
- Complement C1Q subcomponent subunit C
• C4BPA
- C4B-binding protein alpha chain
• C4BPB
- C4B-binding protein beta chain
• FGA
- Fibrinogen alpha chain
• FGB
- Fibrinogen beta chain
• FGG
- Fibrinogen gamma chain
• F13A
- Coagulation factor XIII A chain
• F13B
- Coagulation factor XIII B chain
- LC–MS/MS
- Liquid chromatography-tandem mass spectrometry
• CC
- Cellular component
• MF
- Molecular function
• GO
- Gene Ontology
• KEGG
- Kyoto Encyclopedia of Genes and Genomes
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By Wei Dong; Zeyu Xia; Zehua Chai; Zhidong Qiu; Xuehong Wang; Zebin Yang; Junnan Wang; Tingrui Zhang; Qinqin Zhang and Junfei Jin
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