A novel hypergraph model for identifying and prioritizing personalized drivers in cancer.

Cancer development is driven by an accumulation of a small number of driver genetic mutations that confer the selective growth advantage to the cell, while most passenger mutations do not contribute to tumor progression. The identification of these driver genes responsible for tumorigenesis is a crucial step in designing effective cancer treatments. Although many computational methods have been developed with this purpose, the majority of existing methods solely provided a single driver gene list for the entire cohort of patients, ignoring the high heterogeneity of driver events across patients. It remains challenging to identify the personalized driver genes. Here, we propose a novel method (PDRWH), which aims to prioritize the mutated genes of a single patient based on their impact on the abnormal expression of downstream genes across a group of patients who share the co-mutation genes and similar gene expression profiles. The wide experimental results on 16 cancer datasets from TCGA showed that PDRWH excels in identifying known general driver genes and tumor-specific drivers. In the comparative testing across five cancer types, PDRWH outperformed existing individual-level methods as well as cohort-level methods. Our results also demonstrated that PDRWH could identify both common and rare drivers. The personalized driver profiles could improve tumor stratification, providing new insights into understanding tumor heterogeneity and taking a further step toward personalized treatment. We also validated one of our predicted novel personalized driver genes on tumor cell proliferation by vitro cell-based assays, the promoting effect of the high expression of Low-density lipoprotein receptor-related protein 1 (LRP1) on tumor cell proliferation.

Activation of the IL-6/STAT3 pathway contributes to the pathogenesis of membranous nephropathy and is a target for Mahuang Fuzi and Shenzhuo Decoction (MFSD) to repair podocyte damage.

BACKGROUND: Primary membranous nephropathy (PMN) is an autoimmune glomerular disease. IL-6 is a potential therapeutic target for PMN. Previous clinical studies have demonstrated the effectiveness of Mahuang Fuzi and Shenzhuo Decoction (MFSD) in treating membranous nephropathy. However, the mechanism of action of MFSD remains unclear. METHODS: Serum IL-6 levels were measured in patients with PMN and healthy subjects. The passive Heymann nephritis (PHN) rat model was established, and high and low doses of MFSD were used for intervention to observe the repair effect of MFSD on renal pathological changes and podocyte injury. RNA-seq was used to screen the possible targets of MFSD, and the effect of MFSD targeting IL-6/STAT3 was further verified by combining the experimental results. Finally, the efficacy of tocilizumab in PHN rats was observed. RESULTS: Serum IL-6 levels were significantly higher in PMN patients than in healthy subjects. These levels significantly decreased in patients in remission after MFSD treatment. MFSD treatment improved laboratory indicators in PHN rats, as well as glomerular filtration barrier damage and podocyte marker protein expression. Renal transcriptome changes showed that MFSD-targeted differential genes were enriched in JAK/STAT and cytokine-related pathways. MFSD inhibits the IL6/STAT3 pathway in podocytes. Additionally, MFSD significantly reduced serum levels of IL-6 and other cytokines in PHN rats. However, treatment of PHN with tocilizumab did not achieve the expected effect. CONCLUSION: The IL-6/STAT3 signaling pathway is activated in podocytes of experimental membranous nephropathy. MFSD alleviates podocyte damage by inhibiting the IL-6/STAT3 pathway.

Efficacy and safety of Mahuang Fuzi and Shenzhuo Decoction for treatment of primary membranous nephropathy: a multicenter prospective trial.

BACKGROUND: This study aims to undertake a comprehensive assessment of the effectiveness and safety profile of Mahuang Fuzi and Shenzhuo Decoction (MFSD) in the management of primary membranous nephropathy (PMN), within the context of a prospective clinical investigation. METHODS: A multicenter, open-label clinical trial was executed on patients diagnosed with PMN. These individuals were subjected to MFSD therapy for a duration of at least 24 months, with primary outcome of clinical remission rates. The Cox regression analysis was employed to discern the pertinent risk factors exerting influence on the efficacy of MFSD treatment, with scrupulous monitoring of any adverse events. RESULTS: The study comprised 198 participants in total. Following 24 months of treatment, the remission rate was 58.6% (116/198). Among the subgroup of 130 participants subjected to a 36-month follow-up, the remission rate reached 70% (91/130). Subgroup analysis revealed that neither a history of immunosuppressive therapy (HIST) nor an age threshold of ≥60 years exhibited a statistically significant impact on the remission rate at the 24-month mark (p > .05). Multivariate Cox regression analyses elucidated HIST, nephrotic syndrome, or mass proteinuria, and a high-risk classification as noteworthy risk factors in the context of MFSD treatment. Remarkably, no fatalities resulting from side effects were documented throughout the study's duration. CONCLUSIONS: This trial establishes the efficacy of MFSD as a treatment modality for membranous nephropathy. MFSD demonstrates a favorable side effect profile, and remission rates are consistent across patients, irrespective of HIST and age categories.

Experimental models for elderly patients with membranous nephropathy: Application and advancements.

Membranous nephropathy (MN) occurs predominantly in middle-aged and elderly individuals and ranks among the most prevalent etiologies of elderly nephrotic syndrome. As an autoimmune glomerular disorder characterized by glomerular basement membrane thickening and immune complex deposition, conventional MN animal models, including the Heymann nephritis rat model and the c-BSA mouse model, have laid a foundation for MN pathogenesis research. However, differences in target antigens between rodents and humans have impeded this work. In recent years, researchers have created antigen-specific MN animal models, primarily centered on PLA2R1 and THSD7A, employing diverse techniques that provide innovative in vivo research platforms for MN. Furthermore, significant advancements have been made in the development of in vitro podocyte models relevant to MN. This review compiles recent antigen-specific MN animal models and podocyte models, elucidates their immune responses and pathological characteristics, and offers insights into the future of MN experimental model development. Our aim is to provide a comprehensive resource for research into the pathogenesis of MN and the development of targeted therapies for older patients with MN to prolong lifespan and improve quality of life.

MaxCLK: discovery of cancer driver genes via maximal clique and information entropy of modules.

MOTIVATION: Cancer is caused by the accumulation of somatic mutations in multiple pathways, in which driver mutations are typically of the properties of high coverage and high exclusivity in patients. Identifying cancer driver genes has a pivotal role in understanding the mechanisms of oncogenesis and treatment. RESULTS: Here, we introduced MaxCLK, an algorithm for identifying cancer driver genes, which was developed by an integrated analysis of somatic mutation data and protein-protein interaction (PPI) networks and further improved by an information entropy index. Tested on pancancer and single cancers, MaxCLK outperformed other existing methods with higher accuracy. About pancancer, we predicted 154 driver genes and 787 driver modules. The analysis of co-occurrence and exclusivity between modules and pathways reveals the correlation of their combinations. Overall, our study has deepened the understanding of driver mechanism in PPI topology and found novel driver genes. AVAILABILITY AND IMPLEMENTATION: The source codes for MaxCLK are freely available at https://github.com/ShandongUniversityMasterMa/MaxCLK-main.

DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph.

BACKGROUND: Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data. A critical challenge in cancer genomics is identification of a few cancer driver genes whose mutations cause tumor growth. However, the majority of existing computational approaches underuse the co-occurrence mutation information of the individuals, which are deemed to be important in tumorigenesis and tumor progression, resulting in high rate of false positive. RESULTS: To make full use of co-mutation information, we present a random walk algorithm referred to as DriverRWH on a weighted gene mutation hypergraph model, using somatic mutation data and molecular interaction network data to prioritize candidate driver genes. Applied to tumor samples of different cancer types from The Cancer Genome Atlas, DriverRWH shows significantly better performance than state-of-art prioritization methods in terms of the area under the curve scores and the cumulative number of known driver genes recovered in top-ranked candidate genes. Besides, DriverRWH discovers several potential drivers, which are enriched in cancer-related pathways. DriverRWH recovers approximately 50% known driver genes in the top 30 ranked candidate genes for more than half of the cancer types. In addition, DriverRWH is also highly robust to perturbations in the mutation data and gene functional network data. CONCLUSION: DriverRWH is effective among various cancer types in prioritizes cancer driver genes and provides considerable improvement over other tools with a better balance of precision and sensitivity. It can be a useful tool for detecting potential driver genes and facilitate targeted cancer therapies.

The Ameliorative Effect of Mahuang Fuzi and Shenzhuo Decoction on Membranous Nephropathy of Rodent Model is Associated With Autophagy and Wnt/ß-Catenin Pathway.

The increased incidence of membranous nephropathy (MN) has made it the most common pathological type of primary nephrotic syndrome in adults in China. According to the theory of Traditional Chinese Medicine (TCM), Mahuang Fuzi (Chinese ephedra and Radix Aconiti Lateralis Preparata) and Shenzhuo Decoction (MFSD) could be used to treat such diseases. We treated patients of MN with MFSD, and observed comparable efficacy to glucocorticoid and/or immunosuppressants. In this study, we observed the therapeutic effect of MFSD on the rat model of passive Heymann nephritis (PHN), a classical MN model. Our results showed that MFSD treatment significantly reduced urinary protein level and podocyte injury in PHN rats, and correspondingly improved renal pathology, with the improvement effect on MN comparable to that of Cyclosporine A (CsA) alone. To explore the potential therapeutical mechanism of MFSD, the main chemical components of MFSD were determined by High-performance liquid chromatography-mass spectrometry (HPLC-MS). There were about 30 active components of MFSD. Next, based on network pharmacology methods, we screened related targets of MSFD on MN, which provided a preliminary understanding of the MFSD bioactive compounds. The clustering analysis showed that its active site might be in the autophagy-related protein and Wnt/ß-catenin pathway, which was related to podocyte injury. Finally, we observed an improvement in renal autophagy and a down-regulation of the Wnt/ß-catenin pathway after MSFD treatment in a PHN rat model. According to this study, autophagy and Wnt/ß-catenin pathway may be potential targets for MFSD in the treatment of MN.

CStreet: a computed Cell State trajectory inference method for time-series single-cell RNA sequencing data.

MOTIVATION: The increasing amount of time-series single-cell RNA sequencing (scRNA-seq) data raises the key issue of connecting cell states (i.e. cell clusters or cell types) to obtain the continuous temporal dynamics of transcription, which can highlight the unified biological mechanisms involved in cell state transitions. However, most existing trajectory methods are specifically designed for individual cells, so they can hardly meet the needs of accurately inferring the trajectory topology of the cell state, which usually contains cells assigned to different branches. RESULTS: Here, we present CStreet, a computed Cell State trajectory inference method for time-series scRNA-seq data. It uses time-series information to construct the k-nearest neighbor connections between cells within each time point and between adjacent time points. Then, CStreet estimates the connection probabilities of the cell states and visualizes the trajectory, which may include multiple starting points and paths, using a force-directed graph. By comparing the performance of CStreet with that of six commonly used cell state trajectory reconstruction methods on simulated data and real data, we demonstrate the high accuracy and high tolerance of CStreet. AVAILABILITY AND IMPLEMENTATION: CStreet is written in Python and freely available on the web at https://github.com/TongjiZhanglab/CStreet and https://doi.org/10.5281/zenodo.4483205. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Helper T Cells in Idiopathic Membranous Nephropathy.

Idiopathic membranous nephropathy (IMN) is an autoimmune disease in which the immune system produces an antibody response to its own antigens due to impaired immune tolerance. Although antibodies are derived from plasma cells differentiated by B cells, the T-B cells also contribute a lot to the immune system. In particular, the subsets of helper T (Th) cells, including the dominant subsets such as Th2, Th17, and follicular helper T (Tfh) cells and the inferior subsets such as regulatory T (Treg) cells, shape the immune imbalance of IMN and promote the incidence and development of autoimmune responses. After reviewing the physiological knowledge of various subpopulations of Th cells and combining the existing studies on Th cells in IMN, the role model of Th cells in IMN was explained in this review. Finally, the existing clinical treatment regimens for IMN were reviewed, and the importance of the therapy for Th cells was highlighted.

Protein-Protein Interaction Prediction Based on Spectral Radius and General Regression Neural Network.

Protein-protein interaction (PPI) not only plays a critical role in cell life activities, but also plays an important role in discovering the mechanism of biological activity, protein function, and disease states. Developing computational methods is of great significance for PPIs prediction since experimental methods are time-consuming and laborious. In this paper, we proposed a PPI prediction algorithm called GRNN-PPI only using the amino acid sequence information based on general regression neural network and two feature extraction methods. Specifically, we designed a new feature extraction method named Mutation Spectral Radius (MSR) to extract evolutionary information by the BLOSUM62 matrix. Meanwhile, we integrated another feature extraction method, autocorrelation description, which can completely extract information on physicochemical properties and protein sequences. The principal component analysis was applied to eliminate noise, and the general regression neural network was adopted as a classifier. The prediction accuracy of the yeast, human, and Helicobacter pylori1 (H. pylori1) data sets were 97.47%, 99.63%, and 99.97%, respectively. In addition, we also conducted experiments on two important PPI networks and six independent data sets. All results were significantly higher than some state-of-the-art methods used for comparison, showing that our method is feasible and robust.

Acute and chronic infection of H. pylori caused the difference in apoptosis of gastric epithelial cells.

Helicobacter pylori (H. pylori) is one of the most important pathogenic bacteria associated with various gastrointestinal diseases. At present, its apoptotic or antiapoptotic mechanism on gastric epithelial cells remains unknown and needs further illustrated. In this study, acute infection model (H. pylori and GES-1 cells were co-cultured for 24 h at a multiplicity of infection MOI of 100:1) and chronic infection model (GES-1 cells were infected repeatedly every 24 h at a multiplicity of infection MOI of 100:1 for approximately 8 weeks) were established, respectively. the chronic H. pylori infected GES-1 cells underwent a typically morphological change and Western Blot results showed that there was slight decrease in expression of E-cadherin, and obvious increase in expression of Vimentin. Apoptosis of these two models were analyzed by flow cytometry compared with the control cells, meanwhile, apoptosis associated markers (Bcl-xL, Bcl-2, Bax, etc) were detected by Western blot, additional in clinical H. pylori-positive gastric cancer tissues. Results showed that compared with the control cells, acute infection of H. pylori significantly accelerated the apoptosis of GES-1, increased the expression of Bax and Cleaved caspase-3, down-regulated expression of Bcl-xL and Bcl-2. Moreover, an opposite result was found in chronic infection of model and clinical gastric cancer tissues, and enhanced expression of NF-κB p65. Taken together, these findings suggest that H. pylori infection plays differential effects on apoptosis of gastric epithelial cells.

QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion.

Studies have shown that microRNAs (miRNAs) are closely associated with many human diseases, but we have not yet fully understand the role and potential molecular mechanisms of miRNAs in the process of disease development. However, ordinary biological experiments often require higher costs, and computational methods can be used to quickly and effectively predict the potential miRNA-disease association effect at a lower cost, and can be used as a useful reference for experimental methods. For miRNA-disease association prediction, we have proposed a new method called Matrix completion algorithm based on q-kernel information (QIMCMDA). We use fivefold cross-validation and leave-one-out cross-validation to prove the effectiveness of QIMCMDA. LOOCV shows that AUC can reach 0.9235, and its performance is significantly better than other commonly used technologies. In addition, we applied QIMCMDA to case studies of three human diseases, and the results show that our method performs well in inferring potential interaction between miRNAs and diseases. It is expected that QIMCMDA will become an excellent supplement in the field of biomedical research in the future.

A DNA methylation state transition model reveals the programmed epigenetic heterogeneity in human pre-implantation embryos.

BACKGROUND: During mammalian early embryogenesis, expression and epigenetic heterogeneity emerge before the first cell fate determination, but the programs causing such determinate heterogeneity are largely unexplored. RESULTS: Here, we present MethylTransition, a novel DNA methylation state transition model, for characterizing methylation changes during one or a few cell cycles at single-cell resolution. MethylTransition involves the creation of a transition matrix comprising three parameters that represent the probabilities of DNA methylation-modifying activities in order to link the methylation states before and after a cell cycle. We apply MethylTransition to single-cell DNA methylome data from human pre-implantation embryogenesis and elucidate that the DNA methylation heterogeneity that emerges at promoters during this process is largely an intrinsic output of a program with unique probabilities of DNA methylation-modifying activities. Moreover, we experimentally validate the effect of the initial DNA methylation on expression heterogeneity in pre-implantation mouse embryos. CONCLUSIONS: Our study reveals the programmed DNA methylation heterogeneity during human pre-implantation embryogenesis through a novel mathematical model and provides valuable clues for identifying the driving factors of the first cell fate determination during this process.

Caregiver perspectives on a smart home-based socially assistive robot for individuals with Alzheimer's disease and related dementia.

Purpose: Innovative assistive technology can address aging-in-place and caregiving needs of individuals with Alzheimer's disease and related dementia (ADRD). The purpose of this study was to beta-test a novel socially assistive robot (SAR) with a cohort of ADRD caregivers and gather their perspectives on its potential integration in the home context.Methods: The SAR involved a programmable research robot linked with commercially available Internet of things sensors to receive and respond to care recipient's behaviour. Eight caregivers observed the SAR perform two care protocols concerning the care recipient's daily routine and home safety, and then participated in a focus group and phone interview. The researchers used grounded theory and the Unified Theory of Acceptance and Use of Technology as a framework to gather and analyse the data.Results: The caregivers' asserted the potential of the SAR to relieve care burden and envisioned it as a next-generation technology for caregivers. Adoption of the SAR, as an identified theme, was subject to the SAR's navigability, care recipient engagement, adaptability, humanoid features, and interface design. In contrast, barriers leading to potential rejection were technological complexity, system failure, exasperation of burden, and failure to address digital divide.Conclusion: From a broader outlook, success of SARs as a home-health technology for ADRD is reliant on the timing of their integration, commercial viability, funding provisions, and their bonding with the care recipient. Long-term research in the home settings is required to verify the usability and impact of SARs in mediating aging-in-place of individuals with ADRD.IMPLICATIONS FOR REHABILITATIONSocially assistive robots (SARs), an emerging domain of assistive technology, are projected to have a crucial role in supporting aging-in-place of individuals with Alzheimer's disease and related dementia (ADRD).Caregivers of individuals with ADRD who observed and interacted with a novel SAR asserted their acceptance of the technology as well as its scope and feasibility for the upcoming generation of caregivers.Navigability, care recipient engagement, adaptability, humanoid features, and interface design were stated to be critical factors for SAR's acceptance by caregiver and care recipient dyads.In contrast, technological complexity, system failure, exasperation of burden, and failure to address digital divide are detrimental to SAR's adoption.Several design and implementation requirements must be considered towards the full-scale development and deployment of the SARs in the home context.

Estimating and accounting for tumor purity in the analysis of DNA methylation data from cancer studies.

We present a set of statistical methods for the analysis of DNA methylation microarray data, which account for tumor purity. These methods are an extension of our previously developed method for purity estimation; our updated method is flexible, efficient, and does not require data from reference samples or matched normal controls. We also present a method for incorporating purity information for differential methylation analysis. In addition, we propose a control-free differential methylation calling method when normal controls are not available. Extensive analyses of TCGA data demonstrate that our methods provide accurate results. All methods are implemented in InfiniumPurify.

Tumor purity and differential methylation in cancer epigenomics.

DNA methylation is an epigenetic modification of DNA molecule that plays a vital role in gene expression regulation. It is not only involved in many basic biological processes, but also considered an important factor for tumorigenesis and other human diseases. Study of DNA methylation has been an active field in cancer epigenomics research. With the advances of high-throughput technologies and the accumulation of enormous amount of data, method development for analyzing these data has gained tremendous interests in the fields of computational biology and bioinformatics. In this review, we systematically summarize the recent developments of computational methods and software tools in high-throughput methylation data analysis with focus on two aspects: differential methylation analysis and tumor purity estimation in cancer studies.

Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model.

The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.

Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection.

BACKGROUND: An enduring challenge in personalized medicine is to select right drug for individual patients. Testing drugs on patients in large clinical trials is one way to assess their efficacy and toxicity, but it is impractical to test hundreds of drugs currently under development. Therefore the preclinical prediction model is highly expected as it enables prediction of drug response to hundreds of cell lines in parallel. METHODS: Recently, two large-scale pharmacogenomic studies screened multiple anticancer drugs on over 1000 cell lines in an effort to elucidate the response mechanism of anticancer drugs. To this aim, we here used gene expression features and drug sensitivity data in Cancer Cell Line Encyclopedia (CCLE) to build a predictor based on Support Vector Machine (SVM) and a recursive feature selection tool. Robustness of our model was validated by cross-validation and an independent dataset, the Cancer Genome Project (CGP). RESULTS: Our model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80% accuracy for 10 drugs, ≥75% accuracy for 19 drugs). Independent tests on eleven common drugs between CCLE and CGP achieved satisfactory performance for three of them, i.e., AZD6244, Erlotinib and PD-0325901, using expression levels of only twelve, six and seven genes, respectively. CONCLUSIONS: These results suggest that drug response could be effectively predicted from genomic features. Our model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine.

Predicting tumor purity from methylation microarray data.

MOTIVATION: In cancer genomics research, one important problem is that the solid tissue sample obtained from clinical settings is always a mixture of cancer and normal cells. The sample mixture brings complication in data analysis and results in biased findings if not correctly accounted for. Estimating tumor purity is of great interest, and a number of methods have been developed using gene expression, copy number variation or point mutation data. RESULTS: We discover that in cancer samples, the distributions of data from Illumina Infinium 450 k methylation microarray are highly correlated with tumor purities. We develop a simple but effective method to estimate purities from the microarray data. Analyses of the Cancer Genome Atlas lung cancer data demonstrate favorable performance of the proposed method. AVAILABILITY AND IMPLEMENTATION: The method is implemented in InfiniumPurify, which is freely available at https://bitbucket.org/zhengxiaoqi/infiniumpurify. CONTACT: xqzheng@shnu.edu.cn or hao.wu@emory.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DISIS: prediction of drug response through an iterative sure independence screening.

Prediction of drug response based on genomic alterations is an important task in the research of personalized medicine. Current elastic net model utilized a sure independence screening to select relevant genomic features with drug response, but it may neglect the combination effect of some marginally weak features. In this work, we applied an iterative sure independence screening scheme to select drug response relevant features from the Cancer Cell Line Encyclopedia (CCLE) dataset. For each drug in CCLE, we selected up to 40 features including gene expressions, mutation and copy number alterations of cancer-related genes, and some of them are significantly strong features but showing weak marginal correlation with drug response vector. Lasso regression based on the selected features showed that our prediction accuracies are higher than those by elastic net regression for most drugs.