Perilipin 5 deletion protects against nonalcoholic fatty liver disease and hepatocellular carcinoma by modulating lipid metabolism and inflammatory responses.

The molecular mechanisms underlying the transition from nonalcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC) are incompletely understood. During the development of NAFLD, Perilipin 5 (PLIN5) can regulate lipid metabolism by suppressing lipolysis and preventing lipotoxicity. Other reports suggest that the lack of PLIN5 decreases hepatic injury, indicating a protective role in NAFLD pathology. To better understand the role of PLIN5 in liver disease, we established mouse models of NAFLD and NAFLD-induced HCC, in which wild-type and Plin5 null mice were exposed to a single dose of acetone or 7,12-dimethylbenz[a]anthracene (DMBA) in acetone, followed by a 30-week high-fat diet supplemented with glucose/fructose. In the NAFLD model, RNA-seq revealed significant changes in genes related to lipid metabolism and immune response. At the intermediate level, pathways such as AMP-activated protein kinase (AMPK), signal transducer and activator of transcription 3 (STAT3), c-Jun N-terminal kinase (JNK), and protein kinase B (AKT) were blunted in Plin5-deficient mice (Plin5-/-) compared to wild-type mice (WT). In the NAFLD-HCC model, only WT mice developed liver tumors, while Plin5-/- mice were resistant to tumorigenesis. Furthermore, only 32 differentially expressed genes associated with NALFD progession were identified in Plin5 null mice. The markers of mitochondrial function and immune response, such as the peroxisome proliferator-activated receptor-γ, coactivator 1-α (PGC-1α) and phosphorylated STAT3, were decreased. Lipidomic analysis revealed differential levels of some sphingomyelins between WT and Plin5-/- mice. Interestingly, these changes were not detected in the HCC model, indicating a possible shift in the metabolism of sphingomelins during carcinogenesis.

Machine learning for pan-cancer classification based on RNA sequencing data.

Despite recent improvements in cancer diagnostics, 2%-5% of all malignancies are still cancers of unknown primary (CUP), for which the tissue-of-origin (TOO) cannot be determined at the time of presentation. Since the primary site of cancer leads to the choice of optimal treatment, CUP patients pose a significant clinical challenge with limited treatment options. Data produced by large-scale cancer genomics initiatives, which aim to determine the genomic, epigenomic, and transcriptomic characteristics of a large number of individual patients of multiple cancer types, have led to the introduction of various methods that use machine learning to predict the TOO of cancer patients. In this review, we assess the reproducibility, interpretability, and robustness of results obtained by 20 recent studies that utilize different machine learning methods for TOO prediction based on RNA sequencing data, including their reported performance on independent data sets and identification of important features. Our review investigates the strengths and weaknesses of different methods, checks the correspondence of their results, and identifies potential issues with datasets used for model training and testing, assessing their potential usefulness in a clinical setting and suggesting future improvements.

Adherence to Mediterranean Diet in Croatia: Lessons Learned Today for a Brighter Tomorrow.

Non-communicable diseases (NCD) and lifestyle, particularly diet, have a close relationship. Based on the recent statistics, Croatian men and women lead in European overweight lists, which implies pessimistic prognosis in terms of incidence and prevalence of NCDs in the future. One of the possible solutions to overcome weight problems is turn to traditional balanced and sustainable diets, such as the Mediterranean diet. In this study, we assessed adherence towards Mediterranean diet using a validated questionnaire in an online survey and associated adherence scores with several demographic and anthropometric data. Based on the results of a validated Mediterranean Diet Adherence Screener (N = 3326), we assessed the adherence score to be 7.6 ± 2.5. The score tended to depend on sex, residence, age, education, income, and body mass index (BMI); indeed, women, residents of a coastal part of the country, older volunteers, those possessing a higher education degree, those with higher income, and those with lower BMI were associated with higher scores. As income was one of the significant findings related to higher adherence scores, we developed a dietary plan complying with Mediterranean diet principles that, on average, costed less than the average traditional balanced diet menu. Taken together, this study brought new findings regarding target groups who need to be encouraged to make lifestyle changes, and highlighted the first steps on how to make them.

The Great Majority of Homologous Recombination Repair-Deficient Tumors Are Accounted for by Established Causes.

Background: Gene-agnostic genomic biomarkers were recently developed to identify homologous recombination deficiency (HRD) tumors that are likely to respond to treatment with PARP inhibitors. Two machine-learning algorithms that predict HRD status, CHORD, and HRDetect, utilize various HRD-associated features extracted from whole-genome sequencing (WGS) data and show high sensitivity in detecting patients with BRCA1/2 bi-allelic inactivation in all cancer types. When using only DNA mutation data for the detection of potential causes of HRD, both HRDetect and CHORD find that 30-40% of cases that have been classified as HRD are due to unknown causes. Here, we examined the impact of tumor-specific thresholds and measurement of promoter methylation of BRCA1 and RAD51C on unexplained proportions of HRD cases across various tumor types. Methods: We gathered published CHORD and HRDetect probability scores for 828 samples from breast, ovarian, and pancreatic cancer from previous studies, as well as evidence of their biallelic inactivation (by either DNA alterations or promoter methylation) in HR-related genes. ROC curve analysis evaluated the performance of each classifier in specific cancer. Tenfold nested cross-validation was used to find the optimal threshold values of HRDetect and CHORD for classifying HR-deficient samples within each cancer type. Results: With the universal threshold, HRDetect has higher sensitivity in the detection of biallelic inactivation in BRCA1/2 than CHORD and resulted in a higher proportion of unexplained cases. When promoter methylation was excluded, in ovarian carcinoma, the proportion of unexplained cases increased from 26.8 to 48.8% for HRDetect and from 14.7 to 41.2% for CHORD. A similar increase was observed in breast cancer. Applying cancer-type-specific thresholds led to similar sensitivity and specificity for both methods. The cancer-type-specific thresholds for HRDetect reduced the number of unexplained cases from 21 to 12.3% without reducing the 96% sensitivity to known events. For CHORD, unexplained cases were reduced from 10 to 9% while sensitivity increased from 85.3 to 93.9%. Conclusion: These results suggest that WGS-based HRD classifiers should be adjusted for tumor types. When applied, only ∼10% of breast, ovarian, and pancreas cancer cases are not explained by known events in our dataset.