Chronic Disruption of the Late Cholesterol Synthesis Leads to Female-Prevalent Liver Cancer

Knockouts of CYP51A1, DHCR24, or SC5D from cholesterol synthesis reveal pathways modulated by sterol intermediates

Sterols from cholesterol synthesis are crucial for cholesterol production, but also have individual roles difficult to assess in vivo due to essentiality of cholesterol. We developed HepG2 cell models with knockouts (KOs) for three enzymes of cholesterol synthesis, each accumulating specific sterols. Surprisingly, KOs of CYP51, DHCR24, and SC5D shared only 9% of differentially expressed genes. The most striking was the phenotype of CYP51 KO with highly elevated lanosterol and 24,25-dihydrolanosterol, significant increase in G2+M phase and enhanced cancer and cell cycle pathways. Comparisons with mouse liver Cyp51 KO data suggest 24,25-dihydrolanosterol activates similar cell proliferation pathways, possibly via elevated LEF1 and WNT/NFKB signaling. In contrast, SC5D and DHCR24 KO cells with elevated lathosterol or desmosterol proliferated slowly, with downregulated E2F, mitosis, and enriched HNF1A. These findings demonstrate that increase of lanosterol and 24,25-dihydrolanosterol, but not other sterols, promotes cell proliferation in hepatocytes.

Association between blood lipid levels and the risk of liver cancer: a systematic review and meta-analysis

PURPOSE

The association between blood lipid levels and the risk of developing liver cancer remains a subject of ongoing debate. To elucidate this association, we conducted a meta-analysis by systematically incorporating data from all relevant prospective cohort studies.

METHODS

We conducted a systematic search of the PubMed, Embase, Web of Science, and Cochrane Library databases covering studies published from database inception through July 2023. This study included prospective cohort studies related to lipid profiles (e.g., total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels) that reported hazard ratios (HRs) or relative risks (RRs) with corresponding 95% confidence intervals (95% CIs) to investigate their association with the risk of liver cancer. During the analysis process, we used fixed-effects or random-effects models based on the level of heterogeneity among the studies and obtained pooled risk ratios using these models. To ensure the robustness and reliability of the study findings, we also conducted sensitivity analyses and publication bias analyses.

RESULTS

After conducting a systematic search, 12 studies were identified from a total of 11,904 articles and were included in the meta-analysis. These studies included a combined population of 10,765,221 participants, among whom 31,055 cases of liver cancer were reported. The analysis revealed that the pooled HR for the serum TC concentration (highest versus lowest) was 0.45 (95% CI = 0.35-0.58, I2 = 78%). For TGs, the HR was 0.67 (95% CI = 0.46-0.96, I2 = 86%), while for HDL-C, the HR was 0.72 (95% CI = 0.58-0.90, I2 = 65%). The HR for LDL-C was 0.51 (95% CI = 0.23-1.13, I2 = 93%).

CONCLUSION

The findings of this study indicate that serum TC, TG, and HDL-C levels are negatively associated with liver cancer risk, suggesting that higher concentrations of these lipids are associated with a reduced risk of liver cancer. However, no significant association has been found between LDL-C levels and liver cancer risk.

Inhibiting cholesterol de novo synthesis promotes hepatocellular carcinoma progression by upregulating prostaglandin E synthase 2-mediated arachidonic acid metabolism under high fatty acid conditions

Inhibition of cholesterol de novo synthesis (DNS) by statins has controversial effects on the treatment of hepatocellular carcinoma (HCC). High fatty acid conditions have been reported to limit the effect of statins on metabolism diseases. Whether high fatty acid conditions interfere with the effect of statins on HCC remains unclear. Here, we reported that inhibiting cholesterol DNS with atorvastatin promoted the oncogenic capabilities of diethylnitrosamine (DEN) in mice fed high fatty acid diets (HFD). The combined analysis of metabolomics and transcriptomics revealed that arachidonic acid (AA) metabolism was the most significant changed pathway between mice with and without atorvastatin treatment. In vitro, in the presence of AA precursor linoleic acid (LA), atorvastatin promoted the proliferation and migration ability of HCC cell lines. However, in the absence of LA, these phenomena disappeared. TCGA and tissue microarray examination revealed that prostaglandin e synthase 2 (PTGES2), a key enzyme in AA metabolism, was associated with the poor outcome of HCC patients. Overexpression of PTGES2 promoted the proliferation and migration of HCC cell lines, and knockdown of PTGES2 inhibited the proliferation and migration of cells. Additionally, atorvastatin upregulated PTGES2 expression by enhancing Sterol-regulatory element binding protein 2 (SREBP2)-mediated transcription. Knockdown of PTGES2 reversed the proliferation and migration ability enhanced by atorvastatin. Overall, our study reveals that a high fatty acid background is one of the possible conditions limiting the application of statins in HCC, under which statins promote the progression of HCC by enhancing SREBP2-mediated PTGES2 transcription.

The emerging role of PPAR-alpha in breast cancer

Breast cancer has been confirmed to have lipid disorders in the tumour microenvironment. Peroxisome proliferator-activated receptor alpha (PPARα) is a ligand-activated transcriptional factor that belongs to the family of nuclear receptors. PPARα regulates the expression of genes involved in fatty acid homeostasis and is a major regulator of lipid metabolism. Because of its effects on lipid metabolism, an increasing number of studies have investigated the relationship of PPARα with breast cancer. PPARα has been shown to impact the cell cycle and apoptosis in normal cells and tumoral cells through regulating genes of the lipogenic pathway, fatty acid oxidation, fatty acid activation, and uptake of exogenous fatty acids. Besides, PPARα is involved in the regulation of the tumour microenvironment (anti-inflammation and inhibition of angiogenesis) by modulating different signal pathways such as NF-κB and PI3K/AKT/mTOR. Some synthetic PPARα ligands are used in adjuvant therapy for breast cancer. PPARα agonists are reported to reduce the side effects of chemotherapy and endocrine therapy. In addition, PPARα agonists enhance the curative effects of targeted therapy and radiation therapy. Interestingly, with the emerging role of immunotherapy, attention has been focused on the tumour microenvironment. The dual functions of PPARα agonists in immunotherapy need further research. This review aims to consolidate the operations of PPARα in lipid-related and other ways, as well as discuss the current and potential applications of PPARα agonists in tackling breast cancer.

Circular RNA hsa_circ_0062682 Binds to YBX1 and Promotes Oncogenesis in Hepatocellular Carcinoma

Circular RNAs (circRNAs) have been shown to play an important role in the pathogenesis of hepatocellular carcinoma (HCC). By implementing available transcriptomic analyses of HCC patients, we identified an upregulated circRNA hsa_circ_0062682. Stable perturbations of hsa_circ_0062682 in Huh-7 and SNU-449 cell lines influenced colony formation, migration, cell proliferation, sorafenib sensitivity, and additionally induced morphological changes in cell lines, indicating an important role of hsa_circ_0062682 in oncogenesis. Pathway enrichment analysis and gene set enrichment analysis of the transcriptome data from hsa_circ_0062682 knockdown explained the observed phenotypes and exposed transcription factors E2F1, Sp1, HIF-1α, and NFκB1 as potential downstream targets. Biotinylated oligonucleotide pulldown combined with proteomic analyses identified protein interaction partners of which YBX1, a known oncogene, was confirmed by RNA immunoprecipitation. Furthermore, we discovered a complex cell-type-specific phenotype in response to the oncogenic potential of hsa_circ_0062682. This finding is in line with different classes of HCC tumours, and more studies are needed to shed a light on the molecular complexity of liver cancer.

Matching mouse models to specific human liver disease states by comparative functional genomics of mouse and human datasets

Omics has broadened our view of transcriptional and gene regulatory networks of multifactorial diseases, such as metabolism associated liver disease and its advanced stages including hepatocellular carcinoma. Identifying liver disease biomarkers and potential treatment targets makes use of experimental models, e.g. genetically engineered mice, which show molecular features of human pathologies but are experimentally tractable. We compared gene expression profiling data from human to our studies on transgenic mice with hepatocyte deletion of Cyp51 from cholesterol synthesis with the aim of identifying the human liver disease state best matched by the Cyp51 knockout model. Gene Expression Omnibus was used to identify relevant human datasets. We identified enriched and deregulated genes, pathways and transcription factors of mouse and human disease samples. Analysis showed a closer match of the Cyp51 knockout to the female patient samples. Importantly, CYP51 was depleted in both mouse and female human data. Among the enriched genes were the oxysterol-binding protein-related protein 3 (OSBPL3), which was enriched in all datasets, and the collagen gene COL1A2, which was enriched in both the mouse and one human dataset. KEGG and Reactome analyses revealed the most enriched pathway to be ECM-receptor interaction. Numerous transcription factors were differentially expressed in mice of both sexes and in the human female dataset, while depleted HNF4α and RXRα:PPARα-isoform1 were a hallmark in all cases. Our analysis exposed novel potential biomarkers, which may provide new avenues towards more personalized approaches and different targets in females and males. The analysis was only possible because of availability of open data resources and tools and broadly consistent annotation.

Impact of aging on primary liver cancer: epidemiology, pathogenesis and therapeutics

Aging involves progressive physiological and metabolic reprogramming to adapt to gradual deterioration of organs and functions. This includes mechanisms of defense against pre-malignant transformations. Thus, certain tumors are more prone to appear in elderly patients. This is the case of the two most frequent types of primary liver cancer, i.e., hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). Accordingly, aging hallmarks, such as genomic instability, telomere attrition, epigenetic alterations, altered proteostasis, mitochondrial dysfunction, cellular senescence, exhaustion of stem cell niches, impaired intracellular communication, and deregulated nutrient sensing can play an important role in liver carcinogenesis in the elders. In addition, increased liver fragility determines a worse response to risk factors, which more frequently affect the aged population. This, together with the difficulty to carry out an early detection of HCC and iCCA, accounts for the late diagnosis of these tumors, which usually occurs in patients with approximately 60 and 70 years, respectively. Furthermore, there has been a considerable controversy on what treatment should be used in the management of HCC and iCCA in elderly patients. The consensus reached by numerous studies that have investigated the feasibility and safety of different curative and palliative therapeutic approaches in elders with liver tumors is that advanced age itself is not a contraindication for specific treatments, although the frequent presence of comorbidities in these individuals should be taken into consideration for their management.

Sex-dependent dynamics of metabolism in primary mouse hepatocytes

The liver is one of the most sexually dimorphic organs. The hepatic metabolic pathways that are subject to sexual dimorphism include xenobiotic, amino acid and lipid metabolism. Non-alcoholic fatty liver disease and hepatocellular carcinoma are among diseases with sex-dependent prevalence, progression and outcome. Although male and female livers differ in their abilities to metabolize foreign compounds, including drugs, sex-dependent treatment and pharmacological dynamics are rarely applied in all relevant cases. Therefore, it is important to consider hepatic sexual dimorphism when developing new treatment strategies and to understand the underlying mechanisms in model systems. We isolated primary hepatocytes from male and female C57BL6/N mice and examined the sex-dependent transcriptome, proteome and extracellular metabolome parameters in the course of culturing them for 96 h. The sex-specific gene expression of the general xenobiotic pathway altered and the female-specific expression of Cyp2b13 and Cyp2b9 was significantly reduced during culture. Sex-dependent differences of several signaling pathways increased, including genes related to serotonin and melatonin degradation. Furthermore, the ratios of male and female gene expression were inversed for other pathways, such as amino acid degradation, beta-oxidation, androgen signaling and hepatic steatosis. Because the primary hepatocytes were cultivated without the influence of known regulators of sexual dimorphism, these results suggest currently unknown modulatory mechanisms of sexual dimorphism in vitro. The large sex-dependent differences in the regulation and dynamics of drug metabolism observed during cultivation can have an immense influence on the evaluation of pharmacodynamic processes when conducting initial preclinical trials to investigate potential new drugs.

Guided extraction of genome-scale metabolic models for the integration and analysis of omics data

Omics data can be integrated into a reference model using various model extraction methods (MEMs) to yield context-specific genome-scale metabolic models (GEMs). How to chose the appropriate MEM, thresholding rule and threshold remains a challenge. We integrated mouse transcriptomic data from a Cyp51 knockout mice diet experiment (GSE58271) using five MEMs (GIMME, iMAT, FASTCORE, INIT an tINIT) in a combination with a recently published mouse GEM iMM1865. Except for INIT and tINIT, the size of extracted models varied with the MEM used (t-test: p-value < 0.001). The Jaccard index of iMAT models ranged from 0.27 to 1.0. Out of the three factors under study in the experiment (diet, gender and genotype), gender explained most of the variability ( > 90%) in PC1 for FASTCORE. In iMAT, each of the three factors explained less than 40% of the variability within PC1, PC2 and PC3. Among all the MEMs, FASTCORE captured the most of the true variability in the data by clustering samples by gender. Our results show that for the efficient use of MEMs in the context of omics data integration and analysis, one should apply various MEMs, thresholding rules, and thresholding values to select the MEM and its configuration that best captures the true variability in the data. This selection can be guided by the methodology as proposed and used in this paper. Moreover, we describe certain approaches that can be used to analyse the results obtained with the selected MEM and to put these results in a biological context.