Apolipoproteins: New players in cancers

Apolipoprotein H-based prognostic risk correlates with liver lipid metabolism disorder in patients with HBV-related hepatocellular carcinoma

Background

/Aim: Chronic hepatitis B patients often develop concomitant fatty liver disease, which is associated with increased risk of liver cirrhosis and hepatocarcinoma. Our previous studies have shown that apolipoprotein H (APOH) levels are gradually decreased in patients with chronic HBV infection at different stages of disease progression, and APOH deficiency disrupted hepatic lipid metabolism and caused fatty liver. We focus on the relationship between APOH and hepatocellular carcinoma (HCC) in the context of chronic HBV infection.

Methods and results

APOH was downregulated at the transcriptional level in HBV-related HCC patients from open-source human liver transcriptome databases, and relatively high expression of APOH might be a favourable prognostic marker in HCC. APOH downregulation was positively associated with tumour grade and HCC subtypes. The analysis result of CHCC-HBV database showed that APOH-associated differential expression genes (DEGs) enriched in lipid metabolic pathways and downregulated APOH correlated with macrophage, neutrophil and CD8 T cell infiltration levels. Next, in vitro experiments were performed and APOH gene was silenced in HepG2.2.15 cells, an HBV producing human HCC cells. Further transcriptomic assay and analysis revealed the DEGs were enriched in cholesterol metabolism. The subsequent RT-qPCR experiments identified that CYP7A1 expression was higher upregulated in APOH silencing HepG2.2.15 cells than vehicle control cells (p < 0.05). Finally, demographic data of patients with HBV-related HCC were enrolled, and serum APOH levels were analysed using ELISA. Serum APOH levels were significantly lower in patients with HCC than in healthy controls (p < 0.05), and positively correlated with triglyceride level in healthy controls (p < 0.05). In HBV-HCC patients, serum APOH levels were positively correlated with albumin levels and negatively correlated with alkaline phosphatase (ALP), total bilirubin, and INR levels (p < 0.05).

Conclusion

APOH downregulation disrupted liver lipid metabolism to potentially affect the overall survival in patients with HBV-related HCC.

Identification of phenomic data in the pathogenesis of cancers of the gastrointestinal (GI) tract in the UK biobank

Gastrointestinal (GI) cancers account for a significant incidence and mortality rates of cancers globally. Utilization of a phenomic data approach allows researchers to reveal the mechanisms and molecular pathogenesis of these conditions. We aimed to investigate the association between the phenomic features and GI cancers in a large cohort study. We included 502,369 subjects aged 37-73 years in the UK Biobank recruited since 2006, followed until the date of the first cancer diagnosis, date of death, or the end of follow-up on December 31st, 2016, whichever occurred first. Socio-demographic factors, blood chemistry, anthropometric measurements and lifestyle factors of participants collected at baseline assessment were analysed. Unvariable and multivariable logistic regression were conducted to determine the significant risk factors for the outcomes of interest, based on the odds ratio (OR) and 95% confidence intervals (CI). The analysis included a total of 441,141 participants, of which 7952 (1.8%) were incident GI cancer cases and 433,189 were healthy controls. A marker, cystatin C was associated with total and each gastrointestinal cancer (adjusted OR 2.43; 95% CI 2.23-2.64). In this cohort, compared to Asians, the Whites appeared to have a higher risk of developing gastrointestinal cancers. Several other factors were associated with distinct GI cancers. Cystatin C and race appear to be important features in GI cancers, suggesting some overlap in the molecular pathogenesis of GI cancers. Given the small proportion of Asians within the UK Biobank, the association between race and GI cancers requires further confirmation.

Proteomic analysis of holocarboxylase synthetase deficient-MDA-MB-231 breast cancer cells revealed the biochemical changes associated with cell death, impaired growth signaling, and metabolism

We have previously shown that the holocarboxylase synthetase (HLCS) is overexpressed in breast cancer tissue of patients, and silencing of its expression in triple-negative cancer cell line inhibits growth and migration. Here we investigated the global biochemical changes associated with HLCS knockdown in MDA-MB-231 cells to discern the pathways that involve HLCS. Proteomic analysis of two independent HLCS knockdown cell lines identified 347 differentially expressed proteins (DEPs) whose expression change > 2-fold (p < 0.05) relative to the control cell line. GO enrichment analysis showed that these DEPs were mainly associated with the cellular process such as cellular metabolic process, cellular response to stimulus, and cellular component organization or biogenesis, metabolic process, biological regulation, response to stimuli, localization, and signaling. Among the 347 identified DEPs, 64 proteins were commonly found in both HLCS knockdown clones, confirming their authenticity. Validation of some of these DEPs by Western blot analysis showed that plasminogen activator inhibitor type 2 (SerpinB2) and interstitial collagenase (MMP1) were approximately 90% decreased in HLCS knockdown cells, consistent with a 50%-60% decrease in invasion ability of knockdown cells. Notably, argininosuccinate synthase 1 (ASS1), one of the enzymes in the urea cycle, showed approximately a 10-fold increase in the knockdown cells, suggesting the crucial role of HLCS in supporting the urea cycle in the triple-negative cancer cell line. Collectively, our proteomic data provide biochemical insights into how suppression of HLCS expression perturbs global changes in cellular processes and metabolic pathways, impairing cell growth and invasion.

Modulation of the replication of positive-sense RNA viruses by the natural plant metabolite xanthohumol and its derivatives

The COVID-19 pandemic has highlighted the importance of identifying new potent antiviral agents. Nutrients as well as plant-derived substances are promising candidates because they are usually well tolerated by the human body and readily available in nature, and consequently mostly cheap to produce. A variety of antiviral effects have recently been described for the hop chalcone xanthohumol (XN), and to a lesser extent for its derivatives, making these hop compounds particularly attractive for further investigation. Noteworthy, mounting evidence indicated that XN can suppress a wide range of viruses belonging to several virus families, all of which share a common reproductive cycle. As a result, the purpose of this review is to summarize the most recent research on the antiviral properties of XN and its derivatives, with a particular emphasis on the positive-sense RNA viruses human hepatitis C virus (HCV), porcine reproductive and respiratory syndrome virus (PRRSV), and severe acute respiratory syndrome corona virus (SARS-CoV-2).

A large-scale targeted proteomics of serum and tissue shows the utility of classifying high grade and low grade meningioma tumors

BACKGROUND

Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective.

METHODS

A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins.

RESULTS

A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades.

CONCLUSIONS

The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.