Prognostic Value of Serum Osteoprotegerin Level in Patients With Hepatocellular Carcinoma Following Surgical Resection

Pre-diagnostic plasma inflammatory proteins and risk of hepatocellular carcinoma in three population-based cohort studies from the United States and the United Kingdom

Previous studies suggest a role for inflammation in hepatocarcinogenesis. However, no study has comprehensively evaluated associations between circulating inflammatory proteins and risk of hepatocellular carcinoma (HCC) among the general population. We conducted a nested case-control study in the Nurses' Health Study (NHS) and the Health Professionals Follow-up Study (HPFS) with 56 pairs of incident HCC cases and controls. External validation was performed in the UK Biobank (34 HCC cases and 48,471 non-HCC controls). Inflammatory protein levels were measured in pre-diagnostic plasma using the Olink® Inflammation Panel. We used conditional logistic regression to calculate multivariable odds ratios (ORs) with 95% confidence intervals (CIs) for associations between a 1-standard deviation (SD) increase in biomarker levels and HCC risk, considering a statistically significant threshold of false discovery rate (FDR)-adjusted p < .05. In the NHS/HPFS, among 70 analyzed proteins with call rates >80%, 15 proteins had significant associations with HCC risk (pFDR < .05). Two proteins (stem cell factor, OR per SD = 0.31, 95% CI = 0.16-0.58; tumor necrosis factor superfamily member 12, OR per SD = 0.51, 95% CI = 0.31-0.85) were inversely associated whereas 13 proteins were positively associated with risk of HCC; positive ORs per SD ranged from 1.73 for interleukin (IL)-10 to 2.35 for C-C motif chemokine-19. A total of 11 proteins were further replicated in the UK Biobank. Seven of the eight selected positively associated proteins also showed positive associations with HCC risk by enzyme-linked immunosorbent assay, with ORs ranging from 1.56 for IL-10 to 2.72 for hepatocyte growth factor. More studies are warranted to further investigate the roles of these observed inflammatory proteins in HCC etiology, early detection, risk stratification, and disease treatment.

RANK-RANKL-OPG Axis in MASLD: Current Evidence Linking Bone and Liver Diseases and Future Perspectives

Metabolic dysfunction-associated steatotic liver disease (MASLD)-and its worse form, metabolic-associated steatohepatitis (MASH), characterised by inflammation and liver damage-corresponds to the liver's involvement in metabolic syndrome, which constitutes an economic burden for healthcare systems. However, the biomolecular pathways that contribute to steatotic liver disease are not completely clear. Abnormalities of bone metabolism are frequent in people affected by metabolic liver disease, with reduced bone density and an increased risk of fracture. Receptor activator of NF-κB (RANK), receptor activator of NF-κB ligand (RANKL), and osteoprotegerin(OPG) are critical regulators of bone metabolism, performing pleiotropic effects, and may have potential involvement in metabolic disorders like MASLD, resulting in a topic of great interest and intrigue. This narrative review aims to investigate this potential role and its implications in MASLD development and progression and in hepatocellular carcinoma, which represents its worst complication.

EMT-related gene risk model establishment for prognosis and drug treatment efficiency prediction in hepatocellular carcinoma

This study was designed to evaluate the prognosis and pharmacological therapy sensitivity of epithelial mesenchymal transition-related genes (EMTRGs) that obtained from the EMTome database in hepatocellular carcinoma (HCC) using bioinformatical method. The expression status of EMTRGs were also investigated using the clinical information of HCC patients supported by TCGA database and the ICGC database to establish the TCGA cohort as the training set and the ICGC cohort as the validation set. Analyze the EMTRGs between HCC tissue and liver tissue in the TCGA cohort in the order of univariate COX regression, LASSO regression, and multivariate COX regression, and construct a risk model for EMTRGs. In addition, enrichment pathways, gene mutation status, immune infiltration, and response to drugs were also analyzed in the high-risk and low-risk groups of the TCGA cohort, and the protein expression status of EMTRGs was verified. The results showed a total of 286 differentially expressed EMTRGs in the TCGA cohort, and EZH2, S100A9, TNFRSF11B, SPINK5, and CCL21 were used for modeling. The TCGA cohort was found to have a worse outcome in the high-risk group of HCC patients, and the ICGC cohort confirmed this finding. In addition, EMTRGs risk score was shown to be an independent prognostic factor in both cohorts by univariate and multivariate COX regression. The results of GSEA analysis showed that most of the enriched pathways in the high-risk group were associated with tumor, and the pathways enriched in the low-risk group were mainly associated with metabolism. Patients in various risk groups had varying immunological conditions, and the high-risk group might benefit more from targeted treatments. To sum up, the EMTRGs risk model was developed to forecast the prognosis for HCC patients, and the model might be useful in assisting in the choice of treatment drugs for HCC patients.

The roles of osteoprotegerin in cancer, far beyond a bone player

Osteoprotegerin (OPG), also known as tumor necrosis factor receptor superfamily member 11B (TNFRSF11B), is a member of the tumor necrosis factor (TNF) receptor superfamily. Characterized by its ability to bind to receptor activator of nuclear factor kappa B ligand (RANKL), OPG is critically involved in bone remodeling. Emerging evidence implies that OPG is far beyond a bone-specific modulator, and is involved in multiple physiological and pathological processes, such as immunoregulation, vascular function, and fibrosis. Notably, numerous preclinical and clinical studies have been conducted to assess the participation of OPG in tumorigenesis and cancer development. Mechanistic studies have demonstrated that OPG is involved in multiple hallmarks of cancer, including tumor survival, epithelial to mesenchymal transition (EMT), neo-angiogenesis, invasion, and metastasis. In this review, we systematically summarize the basis and advances of OPG from its molecular structure to translational applications. In addition to its role in bone homeostasis, the physiological and pathological impacts of OPG on human health and its function in cancer progression are reviewed, providing a comprehensive understanding of OPG. We aim to draw more attention to OPG in the field of cancer, and to propose it as a promising diagnostic or prognostic biomarker as well as potential therapeutic target for cancer.

Identification and validation a costimulatory molecule gene signature to predict the prognosis and immunotherapy response for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. Costimulatory molecules have been proven to be the foundation of immunotherapy. However, the potential roles of costimulatory molecule genes (CMGs) in HCC remain unclear. Our study is aimed to develop a costimulatory molecule-related gene signature that could evaluate the prognosis of HCC patients.

METHODS

Based on The Cancer Gene Atlas (TCGA) database, univariate Cox regression analysis was applied in CMGs to identify prognosis-related CMGs. Consensus clustering analysis was performed to stratify HCC patients into different subtypes and compared them in OS. Subsequently, the LASSO Cox regression analysis was performed to construct the CMGs-related prognostic signature and Kaplan-Meier survival curves as well as ROC curve were used to validate the predictive capability. Then we explored the correlations of the risk signature with tumor-infiltrating immune cells, tumor mutation burden (TMB) and response to immunotherapy. The expression levels of prognosis-related CMGs were validated based on qRT-PCR and Human Protein Atlas (HPA) databases.

RESULTS

All HCC patients were classified into two clusters based on 11 CMGs with prognosis values and cluster 2 correlated with a poorer prognosis. Next, a prognostic signature of six CMGs was constructed, which was an independent risk factor for HCC patients. Patients with low-risk score were associated with better prognosis. The correlation analysis showed that the risk signature could predict the infiltration of immune cells and immune status of the immune microenvironment in HCC. The qRT-PCR and immunohistochemical results indicated six CMGs with differential expression in HCC tissues and normal tissues.

CONCLUSION

In conclusion, our CMGs-related risk signature could be used as a prediction tool in survival assessment and immunotherapy for HCC patients.