Identification of Potential Hub Proteins as Theragnostic Targets in Hepatocellular Carcinoma through Comprehensive Quantitative Tissue Proteomics Analysis.
Quratul Abedin, Kulsoom Bibi, Alex von Kriegsheim, Zehra Hashim, Amber Ilyas
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引用次数: 0
Abstract
Objective: Hepatocellular carcinoma (HCC) is the most common primary liver cancer mainly caused by hepatitis viral infection. Early stage diagnosis is still challenging due to its asymptomatic behavior so there is an urgent need for effective biomarkers. This study aimed to identify effective diagnostic biomarker or therapeutic target for HCC.
Method: Label-free quantitative mass spectrometry was performed to analyze protein expression in HCC and control tissues. Protein-protein interaction (PPI) analysis was done using the STRING database and hub proteins were identified by Cytohubba. The survival analysis and expressions profiling of hub proteins were performed by using GEPIA. Functional and pathway enrichment analysis were carried out using Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG).
Results: A total of 1539 proteins were identified, of which 116 were differentially expressed proteins (DEPs). PPI network analysis revealed 10 hub proteins; EGFR, GAPDH, HSP90AA1, MMP9, PTPRC, CD44, ANXA5, PECAM1, MMP2, and CDK1. Among these, GAPDH, MMP9, ANXA5, HSP90AA1, and CDK1 were significantly associated with low survival rate (p ⩽ .05). Moreover, MMP9 and CDK1 were showed significantly increased expression in tumor tissues as compared to control (p ⩽ .05). The GO analysis based on biological process, cellular components and molecular function indicated that DEPs were enriched in stress response, vesicle and extracellular space, protein binding and enzyme activity. The KEGG pathway analysis showed that the thyroid hormone synthesis pathway is the most enriched.
Conclusion: The hub proteins GAPDH, HSP90AA1, MMP9, ANXA5, and CDK1 demonstrated significant prognostic potential, could be used as promising theragnostic biomarkers for HCC.
期刊介绍：
The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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