Prospective Analysis of Proteins Carried in Extracellular Vesicles with Clinical Outcome in Hepatocellular Carcinoma

• Published in: Current genomics Vol. 23; no. 2; pp. 109 - 117
• Main Authors: Chen, Wenbiao, Zhang, Feng, Xu, Huixuan, Hou, Xianliang, Tang, Donge
• Format: Journal Article
• Language: English
• Published: United Arab Emirates Bentham Science Publishers Ltd 10-06-2022; Benham Science Publishers; Bentham Science Publishers
• Subjects: Bioinformatics; Biomarkers; Clinical outcomes; Datasets; Extracellular vesicles; Functional analysis; Genetics & Genomics; Hepatocellular carcinoma; Hypoxia; Immune system; Liver cancer; Macrophages; Mast cells; Modules; Multidimensional methods; Pathogenesis; Predictions; Protein interaction; Proteins; Tumor cells; Tumor microenvironment; Tumorigenesis; Tumors; Proteins; PPI networks; gene; exosome; hepatocellular carcinoma; prognostic signature
• ISSN: 1389-2029; 1875-5488
• DOI: 10.2174/1389202923666220304125458

Background: Extracellular vehicles (EVs) contain different proteins that relay information between tumor cells, thus promoting tumorigenesis. Therefore, EVs can serve as an ideal marker for tumor pathogenesis and clinical application. Objective: Here, we characterised EV-specific proteins in hepatocellular carcinoma (HCC) samples and established their potential protein-protein interaction (PPI) networks. Materials and Methods: We used multi-dimensional bioinformatics methods to mine a network module to use as a prognostic signature and validated the models prediction using additional datasets. The relationship between the prognostic model and tumor immune cells or the tumor microenvironment status was also examined. Results: 1134 proteins from 316 HCC samples were mapped to the exoRBase database. HCC-specific EVs specifically expressed a total of 437 proteins. The PPI network revealed 321 proteins and 938 interaction pathways, which were mined to identify a three network module (3NM) with significant prognostic prediction ability. Validation of the 3NM in two more datasets demonstrated that the model outperformed the other signatures in prognostic prediction ability. Functional analysis revealed that the network proteins were involved in various tumor-related pathways. Additionally, these findings demonstrated a favorable association between the 3NM signature and macrophages, dendritic, and mast cells. Besides, the 3NM revealed the tumor microenvironment status, including hypoxia and inflammation. Conclusion: These findings demonstrate that the 3NM signature reliably predicts HCC pathogenesis. Therefore, the model may be used as an effective prognostic biomarker in managing patients with HCC.