Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact.

BACKGROUND: Cancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine. RESULTS: In the present work, we propose a method for identifying driver genes using a Generalized Linear Regression Model (GLM) with Shrinkage and double-Weighted strategies based on Functional Impact, which is named GSW-FI. Firstly, an estimating model is proposed for assessing the background functional impacts of genes based on GLM, utilizing gene features as predictors. Secondly, the shrinkage and double-weighted strategies as two revising approaches are integrated to ensure the rationality of the identified driver genes. Lastly, a statistical method of hypothesis testing is designed to identify driver genes by leveraging the estimated background function impacts. Experimental results conducted on 31 The Cancer Genome Altas datasets demonstrate that GSW-FI outperforms ten other prediction methods in terms of the overlap fraction with well-known databases and consensus predictions among different methods. CONCLUSIONS: GSW-FI presents a novel approach that efficiently identifies driver genes with functional impact mutations using computational methods, thereby advancing the development of precision medicine for cancer.

Mechanism study of oleanolic acid derivative, K73-03, inducing cell apoptosis in hepatocellular carcinoma.

Oleanolic acid (3ß-hydroxyolean-12-en-28-oic acid, OA) is a kind of pentacyclic triterpene, which widely distributes in nature. OA possesses a powerful anti-cancer effect; however, its low solubility limits its bioavailability and application. In this study, a new OA derivative, K73-03, was used to determine its effect on liver cancer cells and detailed molecular mechanisms. Here, we show that K73-03 may lead to the disorder of mitochondria in HepG2 cells, leading to excessive ROS production and apoptosis in cells. Meanwhile, K73-03 could induce cell apoptosis by inhibiting JAK2/STAT3 pathway and NF-κB/P65 pathway. Collectively, this study may provide a preliminary basis for further cancer treatment of hepatocellular carcinoma.

Combined-task deep network based on LassoNet feature selection for predicting the comorbidities of acute coronary syndrome.

Acute coronary syndrome (ACS) is a multifaceted cardiovascular condition frequently accompanied by multiple comorbidities, which can have significant implications for patient outcomes and treatment approaches. Precisely predicting these comorbidities is crucial for providing personalized care and making well-informed clinical decisions. However, there is a shortage of research investigating the identification of risk factors associated with ACS comorbidities and accurately predicting their likelihood of occurrence beyond heart failure. In this study, an approach called Combined-task Deep Network based on LassoNet feature selection (CDNL) is presented for predicting ACS comorbidities, including hypertension, diabetes, hyperlipidemia, and heart failure. In order to identify crucial biomarkers associated with ACS comorbidities, the proposed framework first incorporates LassoNet, which extends Lasso regression to the deep network by adding a skip (residual) layer. Additionally, a correlation score calculation method across tasks is introduced based on measuring the overlap of identified biomarkers and their assigned importance. This method enables the development of an optimal combined-task prediction model for each ACS comorbidity, addressing the challenge of limited representations in traditional multi-task learning. Our evaluation, conducted through a meticulous cross-sectional study at a tertiary hospital in China, involved a dataset of 2941 samples with 42 clinical features. The results demonstrate that CDNL facilitates the identification of significant biomarkers and achieves an average improvement in AUC of 4.93% and 8.58% compared to deep learning multi-layer neural network (DNN) and SVM, respectively. Additionally, it shows an average improvement of 2.64% and 1.92% compared to two state-of-the-art multi-task models.

Protective effects of phosphocreatine on human vascular endothelial cells against hydrogen peroxide-induced apoptosis and in the hyperlipidemic rat model.

Phosphocreatine (PCr) has been shown to have a cardio-protective effect during cardiopulmonary resuscitation (CPR). However, little is known about its impact on atherosclerosis. In this study, we first evaluated the pharmacological effects of PCr on antioxidative defenses and mitochondrial protection against hydrogen peroxide (H2O2) induced human umbilical vascular endothelial cells (HUVECs) damage. Then we investigated the hypolipidemic and antioxidative effects of PCr on hyperlipidemic rat model. Via in vitro studies, H2O2 significantly reduced cell viability and increased apoptosis rate of HUVECs, while pretreatment with PCr abolished its apoptotic effect. PCr could reduce the generation of ROS induced by H2O2. Moreover, PCr could increase the activity of SOD and the content of NO, as well as decrease the activity of LDH and the content of MDA. PCr could also antagonize H2O2-induced up-regulation of Bax, cleaved-caspase3, cleaved-caspase9, and H2O2-induced down-regulation of Bcl-2 and p-Akt/Akt ratio. In addition, PCr reduced U937 cells' adhesion to H2O2-stimulated HUVECs. Via in vivo study, PCr could decrease MDA, TC, TG and LDL-C levels in hyperlipidemic rats. Finally, different-concentration PCr could increase the leaching of TC, HDL, and TG from fresh human atherosclerotic plaques. In conclusion, PCr could suppress H2O2-induced apoptosis in HUVECs and reduce hyperlipidemia through inhibiting ROS generation and modulating dysfunctional mitochondrial system, which might be an effective new therapeutic strategy to further prevent atherosclerosis.

Predicting anticancer drug sensitivity on distributed data sources using federated deep learning.

Drug sensitivity prediction plays a crucial role in precision cancer therapy. Collaboration among medical institutions can lead to better performance in drug sensitivity prediction. However, patient privacy and data protection regulation remain a severe impediment to centralized prediction studies. For the first time, we proposed a federated drug sensitivity prediction model with high generalization, combining distributed data sources while protecting private data. Cell lines are first classified into three categories using the waterfall method. Focal loss for solving class imbalance is then embedded into the horizontal federated deep learning framework, i.e., HFDL-fl is presented. Applying HFDL-fl to homogeneous and heterogeneous data, we obtained HFDL-Cross and HFDL-Within. Our comprehensive experiments demonstrated that (i) collaboration by HFDL-fl outperforms private model on local data, (ii) focal loss function can effectively improve model performance to classify cell lines in sensitive and resistant categories, and (iii) HFDL-fl is not significantly affected by data heterogeneity. To summarize, HFDL-fl provides a valuable solution to break down the barriers between medical institutions for privacy-preserving drug sensitivity prediction and therefore facilitates the development of cancer precision medicine and other privacy-related biomedical research.

Procyanidins: A promising anti-diabetic agent with potential benefits on glucose metabolism and diabetes complications.

Diabetes mellitus (DM) is a complex disease with alarming worldwide health implications and high mortality rates, largely due to its complications such as cardiovascular disease, nephropathy, neuropathy, and retinopathy. Recent research has shown that procyanidins (PC), a type of flavonoid, have strong antioxidant and free radical elimination effects, and may be useful in improving glucose metabolism, enhancing pancreatic islet cell activity, and decreasing the prevalence of DM complications. This review article presents a systematic search for peer-reviewed articles on the use of PC in the treatment of DM, without any language restrictions. The article also discusses the potential for PC to sensitise DM medications and improve their efficacy. Recent in vivo and in vitro studies have demonstrated promising results in improving the biological activity and bioavailability of PC for the treatment of DM. The article concludes by highlighting the potential for novel materials and targeted drug delivery methods to enhance the pharmacokinetics and bioactivity of PC, leading to the creation of safer and more effective anti-DM medications in the future.

Fisetin's Promising Antitumor Effects: Uncovering Mechanisms and Targeting for Future Therapies.

Background Cancer remains a critical global health challenge and a leading cause of mortality. Flavonoids found in fruits and vegetables have gained attention for their potential anti-cancer properties. Fisetin, abundantly present in strawberries, apples, onions, and other plant sources, has emerged as a promising candidate for cancer prevention. Epidemiological studies linking a diet rich in these foods to lower cancer risk have sparked extensive research on fisetin's efficacy. Objective This review aims to comprehensively explore the molecular mechanisms of fisetin's anticancer properties and investigate its potential synergistic effects with other anticancer drugs. Furthermore, the review examines the therapeutic and preventive effects of fisetin against various cancers. Methods A systematic analysis of the available scientific literature was conducted, including research articles, clinical trials, and review papers related to fisetin's anticancer properties. Reputable databases were searched, and selected studies were critically evaluated to extract essential information on fisetin's mechanisms of action and its interactions with other anticancer drugs. Results Preclinical trials have demonstrated that fisetin inhibits cancer cell growth through mechanisms such as cell cycle alteration, induction of apoptosis, and activation of the autophagy signaling pathway. Additionally, fisetin reduces reactive oxygen species levels, contributing to its overall anticancer potential. Investigation of its synergistic effects with other anticancer drugs suggests potential for combination therapies. Conclusion Fisetin, a bioactive flavonoid abundant in fruits and vegetables, exhibits promising anticancer properties through multiple mechanisms of action. Preclinical trials provide a foundation for further exploration in human clinical trials. Understanding fisetin's molecular mechanisms is vital for developing novel, safe, and effective cancer prevention and treatment strategies. The potential synergy with other anticancer drugs opens new avenues for combination therapies, enhancing cancer management approaches and global health outcomes.

DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods.

Although computational methods for driver gene identification have progressed rapidly, it is far from the goal of obtaining widely recognized driver genes for all cancer types. The driver gene lists predicted by these methods often lack consistency and stability across different studies or datasets. In addition to analytical performance, some tools may require further improvement regarding operability and system compatibility. Here, we developed a user-friendly R package (DriverGenePathway) integrating MutSigCV and statistical methods to identify cancer driver genes and pathways. The theoretical basis of the MutSigCV program is elaborated and integrated into DriverGenePathway, such as mutation categories discovery based on information entropy. Five methods of hypothesis testing, including the beta-binomial test, Fisher combined p-value test, likelihood ratio test, convolution test, and projection test, are used to identify the minimal core driver genes. Moreover, de novo methods, which can effectively overcome mutational heterogeneity, are introduced to identify driver pathways. Herein, we describe the computational structure and statistical fundamentals of the DriverGenePathway pipeline and demonstrate its performance using eight types of cancer from TCGA. DriverGenePathway correctly confirms many expected driver genes with high overlap with the Cancer Gene Census list and driver pathways associated with cancer development. The DriverGenePathway R package is freely available on GitHub: https://github.com/bioinformatics-xu/DriverGenePathway.

RNA-Seq analysis of obese Pdha1fl/flLyz2-Cre mice induced by a high-fat diet.

Pyruvate dehydrogenase complex (PDH) is an important complex of three enzymes that transforms pyruvate into acetyl-CoA, subsequently entering the tricarboxylic acid (TCA) cycle to produce ATP and electron donors. As a key regulator of energy and metabolic homeostasis, PDH is considered a potential therapeutic target of many diseases. On the other hand, the relationship between PDH and obesity is not clear. In this study, peripheral blood of Pdha1fl/flLyz2-Cre and C57BL/6 mice fed a high-fat diet (HFD) was collected and subjected to extensive transcriptome sequencing. Differentially expressed genes (DEGs) were identified. Enrichment of functions and signaling pathways analyses were performed based on Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the genes selected from RNA sequencing (RNA-seq). Eventually, we found that Pdha1fl/flLyz2-Cre mice were more susceptible to HFD-induced obesity. A total of 302 up-regulated genes and 30 down-regulated genes were screened that were differentially expressed between Pdha1fl/flLyz2-Cre mice fed the HFD and the control groups. Furthermore, we verified that significant transcriptional changes in the genes Sgstm1, Ncoa4, Rraga, Slc3a2, Usp15, Gabarapl2, Wipi1, Sh3glb1, Mtmr3, and Cd36 were consistent with the results obtained from RNA-seq analysis. In summary, this study preliminarily established that there is a close relationship between Pdha1 and obesity and revealed the possible downstream pathways and target genes involved, laying a good foundation for the further study of Pdha1 function in the future.

The role of non-coding RNA network in atherosclerosis.

Atherosclerosis is the primary culprit of cardiovascular and cerebrovascular diseases. Also, atherogenesis and the development of atherosclerosis involve endothelial cells, monocytes/macrophages, smooth myocytes, and others. Increasingly, studies have found that non-coding RNA (ncRNA) which can regulate apoptosis, pyroptosis, autophagy, proliferation, and monocyte migration participates in atherogenesis and progress of atherosclerosis by the above. The ncRNA networks may be essential in regulating the complicated process of atherosclerosis. Accordingly, this review delves into the regulatory roles of ncRNA, which were introduced previously. The answer above is particularly crucial to explain further the regulatory mechanism of ncRNA in cardiovascular disorders. Furthermore, we discuss the possibility and related research of ncRNAs as a biomarker and therapeutic target for the prevention, diagnosis, and treatment of atherosclerosis.

Rac3, but not Rac1, promotes ox-LDL induced endothelial dysfunction by downregulating autophagy.

The endothelial dysfunction induced by oxidized low-density lipoprotein (ox-LDL) plays an important role in the pathogenesis of atherosclerosis, which can lead to oxidative stress and inflammation. The role of autophagy in the process of atherosclerosis has drawn increasing attention. The human umbilical vein endothelial cells (HUVECs), whose Ras-related C3 botulinum toxin substrate 1 (Rac1) and Rac3 was knockdown, were used to detect whether the possible molecular mechanisms of Rac1 and Rac3 for anti-inflammatory in endothelial cells was effected by downregulation of autophagy. The HUVECs were incubated with ox-LDL. The inflammatory factors and autophagy proteins were evaluated to ascertain and compare the effect of Rac1 and Rac3 on autophagy. Then, 3-methyladenine (3-MA) as an inhibiter of autophagy was used to detect whether the effect of Rac1 and Rac3 was related to autophagy. ox-LDL-induced cell dysfunction in HUVECs was determined by testing the formation of foam cells, the expression of nuclear factor (NF)-κB and nucleotide-binding oligomerization domain (NOD)-like receptor protein 3 and NF-κB p65 and other inflammatory factors, the release of reactive oxygen species by oxidative stress and the dysfunction of the cytomembrane. And ApoE-/- mice on a high-fat diet were used as an animal model to detect the effect of Rac1 and Rac3 in vivo. The results showed that when Rac1 and Rac3 were decreased in HUVECs, the cell dysfunction caused by ox-LDL was inhibited. If 3-MA was used to inhibit autophagy in Rac1 and Rac3 knockdown cells, the injury induced by ox-LDL on the cells was recovered. These results indicated that the effect of Rac1 and Rac3 was combined with ox-LDL, which was related to inhibition of autophagy. The effect of Rac3 was more significant than that of Rac1.

Expression of Platelet Collagen Receptor Glycoprotein VI in Coronary Heart Disease: Interaction between Platelets and Endothelial Cells.

BACKGROUND: 1) To investigate the expression of platelet collagen receptor glycoprotein VI (GPVI) on the surface of platelets in patients with coronary heart disease (CHD). 2) To investigate the expression of intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1) on the surface of endothelial cells after exposure to subjects' platelets. METHODS: Platelets were extracted from blood samples of 97 subjects including 43 with acute coronary syndrome (ACS), 21 with stable angina pectoris (SAP), and 33 serving as controls in which CHD was excluded. Using flow cytometry, we measured the expression of GPVI and platelet degranulation markers CD62P and CD63. In addition we quantified expression of endothelial cell ICAM-1 and VCAM-1. RESULTS: A higher expression of platelet GPVI was observed in ACS compared with SAP and control groups. The expression level of platelet GPVI significantly correlated with CD62P and CD63 in the CHD population (r = 0.713 and 0.615, both p < 0.01). Significantly higher ICAM-1 expression was observed in endothelial cells after interaction with platelets from the ACS population. In the CHD subjects, the surface expression of platelet GPVI correlated with the level of ICAM-1 in endothelial cells (r = 0.371, p < 0.05). CONCLUSIONS: Platelet GPVI associated with platelet activation and endothelial inflammation is a vital index for predicting CHD occurrence and severity.