Proteome Analyses of Hepatocellular Carcinoma

Mass spectrometry-based multi-omics analysis reveals distinct molecular features in early and advanced stages of hepatocellular carcinoma

Hepatocellular Carcinoma (HCC) is a serious primary solid tumor that is prevalent worldwide. Due to its high mortality rate, it is crucial to explore both early diagnosis and advanced treatment for HCC. In recent years, multi-omics approaches have emerged as promising tools to identify biomarkers and investigate molecular mechanisms of biological processes and diseases. In this study, we performed proteomics, phosphoproteomics, metabolomics, and lipidomics to reveal the molecular features of early- and advanced-stage HCC. The data obtained from these omics were analyzed separately and then integrated to provide a comprehensive understanding of the disease. The multi-omics results unveiled intricate biological pathways and interaction networks underlying the initiation and progression of HCC. Moreover, we proposed specific potential biomarker panels for both early- and advanced-stage HCC by overlapping our data with CPTAC database for HCC diagnosis, and deduced novel insights and mechanisms related to HCC origination and development, such as glucose depletion during tumor progression, ROCK1 deactivation and GSK3A activation.

Research advances of MAL family members in tumorigenesis and tumor progression (Review)

The myelin and lymphocyte protein (MAL) family is a novel gene family first identified and characterized in 2002. This family is comprised of seven members, including MAL, MAL2, plasmolipin, MALL, myeloid differentiation‑associated marker (MYADM), MYADML2 and CMTM8, which are located on different chromosomes. In addition to exhibiting extensive activity during transcytosis, the MAL family plays a vital role in the neurological, digestive, respiratory, genitourinary and other physiological systems. Furthermore, the intimate association between MAL and the pathogenesis, progression and metastasis of malignancies, attributable to several mechanisms such as DNA methylation has also been elucidated. In the present review, an overview of the structural and functional properties of the MAL family and the latest research findings regarding the relationship between several MAL members and various cancers is provided. Furthermore, the potential clinical and scientific significance of MAL is discussed and directions for future research are summarized.

Immunopeptidome of hepatocytes isolated from patients with HBV infection and hepatocellular carcinoma

Background & Aims

Antigen-specific immunotherapy is a promising strategy to treat HBV infection and hepatocellular carcinoma (HCC). To facilitate killing of malignant and/or infected hepatocytes, it is vital to know which T cell targets are presented by human leucocyte antigen (HLA)-I complexes on patient-derived hepatocytes. Here, we aimed to reveal the hepatocyte-specific HLA-I peptidome with emphasis on peptides derived from HBV proteins and tumour-associated antigens (TAA) to guide development of antigen-specific immunotherapy.

Methods

Primary human hepatocytes were isolated with high purity from (HBV-infected) non-tumour and HCC tissues using a newly designed perfusion-free procedure. Hepatocyte-derived HLA-bound peptides were identified by unbiased mass spectrometry (MS), after which source proteins were subjected to Gene Ontology and pathway analysis. HBV antigen and TAA-derived HLA peptides were searched for using targeted MS, and a selection of peptides was tested for immunogenicity.

Results

Using unbiased data-dependent acquisition (DDA), we acquired a high-quality HLA-I peptidome of 2 × 10⁵ peptides that contained 8 HBV-derived peptides and 14 peptides from 8 known HCC-associated TAA that were exclusive to tumours. Of these, 3 HBV- and 12 TAA-derived HLA peptides were detected by targeted MS in the sample they were originally identified in by DDA. Moreover, 2 HBV- and 2 TAA-derived HLA peptides were detected in samples in which no identification was made using unbiased MS. Finally, immunogenicity was demonstrated for 5 HBV-derived and 3 TAA-derived peptides.

Conclusions

We present a first HLA-I immunopeptidome of isolated primary human hepatocytes, devoid of immune cells. Identified HBV-derived and TAA-derived peptides directly aid development of antigen-specific immunotherapy for chronic HBV infection and HCC. The described methodology can also be applied to personalise immunotherapeutic treatment of liver diseases in general.

Lay summary

Immunotherapy that aims to induce immune responses against a virus or tumour is a promising novel treatment option to treat chronic HBV infection and liver cancer. For the design of successful therapy, it is essential to know which fragments (i.e. peptides) of virus-derived and tumour-specific proteins are presented to the T cells of the immune system by diseased liver cells and are thus good targets for immunotherapy. Here, we have isolated liver cells from patients who have chronic HBV infection and/or liver cancer, analysed what peptides are presented by these cells, and assessed which peptides are able to drive immune responses.

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We developed a perfusion-free method to isolate primary hepatocytes that are depleted of immune cells.

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We derived a large-scale unbiased hepatocyte HLA ligandome from patients with HBV and/or HCC.

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The ligandome included peptides derived from HBV proteins and tumour-associated antigens (TAA).

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Using a targeted MS regime, the detection sensitivity of several HBV and TAA-derived peptides could be increased.

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Immunogenicity was demonstrated for a selection of TAA- and HBV-derived HLA peptides.

Proteome Multimarker Panel for the Early Detection of Hepatocellular Carcinoma: Multicenter Derivation, Validation, and Comparison

Conventional methods for the surveillance of hepatocellular carcinoma (HCC) by imaging, with and without serum tumor markers, are suboptimal with regard to accuracy. We aimed to develop and validate a reliable serum biomarker panel for the early detection of HCC using a proteomic technique. This multicenter case-control study comprised 727 patients with HCC and patients with risk factors but no HCC. We developed a multiple reaction monitoring-mass spectrometry (MRM-MS) multimarker panel using 17 proteins from the sera of 398 patients. Area under the receiver operating characteristics curve (AUROC) values of this MRM-MS panel with and without α-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist-II (PIVKA-II) were compared. The combination and standalone MRM-MS panels had higher AUROC values than AFP in the training (0.940 and 0.929 vs 0.775, both P < 0.05), test (0.894 and 0.893 vs 0.593, both P < 0.05), and confirmation sets (0.961 and 0.937 vs 0.806, both P < 0.05) in detecting small single HCC. The combination and standalone MRM-MS panels had significantly higher AUROC values than the GALAD score (0.945 and 0.931 vs 0.829, both P < 0.05). Our proteome 17-protein multimarker panel distinguished HCC patients from high-risk controls and had high accuracy in the early detection of HCC.

A Comprehensive Bioinformatic Analysis for Identification of Myeloid-Associated Differentiation Marker as a Potential Negative Prognostic Biomarker in Non-Small-Cell Lung Cancer

Objectives: This study aimed to identify a molecular marker associated with the prognosis of non-small-cell lung cancer (NSCLC).

Materials and Methods: The RNA sequencing data and clinical information of NSCLC patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression gene modules and differentially expressed genes (DEGs) by comparing gene expression between NSCLC tumor tissues and normal tissues. Subsequently, the functional enrichment analysis of the DEGs was performed. Kaplan-Meier survival analysis and the GEPIA2 online tool were performed to investigate the relationship between the expression of these genes of interest and the survival of NSCLC patients, and to validate one most survival-relevent hub gene, as well as validated the hub gene using independent datasets from the GEO database. Further analysis was carried out to characterize the relationship between the hub gene and tumor immune cell infiltration, tumor mutation burden (TMB), microsatellite instability (MSI), and other known biomarkers of lung cancer. The related genes were screened by analyzing the protein-protein interaction (PPI) network and the survival model was constructed. GEPIA2 was applied in the potential analysis of pan-cancer biomarker of hub gene.

Results: 57 hub genes were found to be involved in intercellular connectivity from the 779 identified differentially co-expressed genes. Myeloid-associated differentiation marker (MYADM) was strongly associated with overall survival (OS) and disease-free survival (DFS) of NSCLC patients, and high MYADM expression was associated with poor prognosis. Thus, MYADM was identified as a risk factor. Additionally, MYADM was validated as a survival risk factor in NSCLC patients in two independent datasets. Further analysis showed that MYADM was nagetively associated with TMB, and was positively correlated with macrophages, neutrophils, and dendritic cells, suggesting its role in regulating tumor immunity. The MYADM expression differed across many types of cancer and had the potential to serve as a pan-cancer marker.

Conclusion:MYADM is an independent prognostic factor for NSCLC patients, which can predict the progression of cancer and play a role in the tumor immune cell infiltration in NSCLC.

Advances in High Throughput Proteomics Profiling in Establishing Potential Biomarkers for Gastrointestinal Cancer

Gastrointestinal cancers (GICs) remain the most diagnosed cancers and accounted for the highest cancer-related death globally. The prognosis and treatment outcomes of many GICs are poor because most of the cases are diagnosed in advanced metastatic stages. This is primarily attributed to the deficiency of effective and reliable early diagnostic biomarkers. The existing biomarkers for GICs diagnosis exhibited inadequate specificity and sensitivity. To improve the early diagnosis of GICs, biomarkers with higher specificity and sensitivity are warranted. Proteomics study and its functional analysis focus on elucidating physiological and biological functions of unknown or annotated proteins and deciphering cellular mechanisms at molecular levels. In addition, quantitative analysis of translational proteomics is a promising approach in enhancing the early identification and proper management of GICs. In this review, we focus on the advances in mass spectrometry along with the quantitative and functional analysis of proteomics data that contributes to the establishment of biomarkers for GICs including, colorectal, gastric, hepatocellular, pancreatic, and esophageal cancer. We also discuss the future challenges in the validation of proteomics-based biomarkers for their translation into clinics.

Growing Human Hepatocellular Tumors Undergo a Global Metabolic Reprogramming

Hepatocellular carcinoma (HCC) is a common malignancy with poor prognosis, high morbidity and mortality concerning with lack of effective diagnosis and high postoperative recurrence. Similar with other cancers, HCC cancer cells have to alter their metabolism to adapt to the changing requirements imposed by the environment of the growing tumor. In less vascularized regions of tumor, cancer cells experience hypoxia and nutrient starvation. Here, we show that HCC undergoes a global metabolic reprogramming during tumor growth. A combined proteomics and metabolomics analysis of paired peritumoral and tumor tissues from 200 HCC patients revealed liver-specific metabolic reprogramming and metabolic alterations with increasing tumor sizes. Several proteins and metabolites associated with glycolysis, the tricarboxylic acid cycle and pyrimidine synthesis were found to be differentially regulated in serum, tumor and peritumoral tissue with increased tumor sizes. Several prognostic metabolite biomarkers involved in HCC metabolic reprogramming were identified and integrated with clinical and pathological data. We built and validated this combined model to discriminate against patients with different recurrence risks. An integrated and comprehensive metabolomic analysis of HCC is provided by our present work. Metabolomic alterations associated with the advanced stage of the disease and poor clinical outcomes, were revealed. Targeting cancer metabolism may deliver effective therapies for HCC.

Bioactive Molecular Discovery Using Deer Antlers as a Model of Mammalian Regeneration

The ability to activate and regulate stem cells during wound healing and tissue regeneration is a promising field that is resulting in innovative approaches in the field of regenerative medicine. The regenerative capacity of invertebrates has been well documented; however, in mammals, stem cells that drive organ regeneration are rare. Deer antlers are the only known mammalian structure that can annually regenerate to produce a tissue containing dermis, blood vessels, nerves, cartilage, and bone. The neural crest derived stem cells that drive this process result in antlers growing at up to 2 cm/day. Deer antlers thus provide superior attributes compared to lower-order animal models, when investigating the regulation of stem cell-based regeneration. Antler stem cells can therefore be used as a model to investigate the bioactive molecules, biological processes, and pathways involved in the maintenance of a stem cell niche, and their activation and differentiation during organ formation. This review examines stem cell-based regeneration with a focus on deer antlers, a neural crest stem cell-based mammalian regenerative structure. It then discusses the omics technical platforms highlighting the proteomics approaches used for investigating the molecular mechanisms underlying stem cell regulation in antler tissues.

Analysis of Time-Series Gene Expression Data to Explore Mechanisms of Chemical-Induced Hepatic Steatosis Toxicity

Non-alcoholic fatty liver disease (NAFLD) represents a wide spectrum of disease, ranging from simple fatty liver through steatosis with inflammation and necrosis to cirrhosis. One of the most challenging problems in biomedical research and within the chemical industry is to understand the underlying mechanisms of complex disease, and complex adverse outcome pathways (AOPs). Based on a set of 28 steatotic chemicals with gene expression data measured on primary hepatocytes at three times (2, 8, and 24 h) and three doses (low, medium, and high), we identified genes and pathways, defined as molecular initiating events (MIEs) and key events (KEs) of steatosis using a combination of a time series and pathway analyses. Among the genes deregulated by these compounds, the study highlighted OSBPL9, ALDH7A1, MYADM, SLC51B, PRDX6, GPAT3, TMEM135, DLGDA5, BCO2, APO10LA, TSPAN6, NEURL1B, and DUSP1. Furthermore, pathway analysis indicated deregulation of pathways related to lipid accumulation, such as fat digestion and absorption, linoleic and linolenic acid metabolism, calcium signaling pathway, fatty acid metabolism, peroxisome, retinol metabolism, and steroid metabolic pathways in a time dependent manner. Such transcription profile analysis can help in the understanding of the steatosis evolution over time generated by chemical exposure.

Evaluation of liver cirrhosis and hepatocellular carcinoma using Protein-Protein Interaction Networks

AIM

In the current study, we analysised only the articles that investigate serum proteome profile of cirrhosis patients or HCC patients versus healthy controls.

BACKGROUND

Increased understanding of cancer biology has enabled identification of molecular events that lead to the discovery of numerous potential biomarkers in diseases. Protein-protein interaction networks is one of aspect that could elevate the understanding level of molecular events and protein connections that lead to the identification of genes and proteins associated with diseases.

METHODS

Gene expression data, including 63 gene or protein names for hepatocellular carcinoma and 29 gene or protein names for cirrhosis, were extracted from a number of previous investigations. The networks of related differentially expressed genes were explored using Cytoscape and the PPI analysis methods such as MCODE and ClueGO. Centrality and cluster screening identified hub genes, including APOE, TTR, CLU, and APOA1 in cirrhosis.

RESULTS

CLU and APOE belong to the regulation of positive regulation of neurofibrillary tangle assembly. HP and APOE involved in cellular oxidant detoxification. C4B and C4BP belong to the complement activation, classical pathway and acute inflammation response pathway. Also, it was reported TTR, TFRC, VWF, CLU, A2M, APOA1, CKAP5, ZNF648, CASP8, and HSP27 as hubs in HCC. In HCC, these include A2M that are corresponding to platelet degranulation, humoral immune response, and negative regulation of immune effector process. CLU belong to the reverse cholesterol transport, platelet degranulation and human immune response. APOA1 corresponds to the reverse cholesterol transport, platelet degranulation and humoral immune response, as well as negative regulation of immune effector process pathway.

CONCLUSION

In conclusion, this study suggests that there is a common molecular relationship between cirrhosis and hepatocellular cancer that may help with identification of target molecules for early treatment that is essential in cancer therapy.