Astrocytic Insulin Signaling Couples Brain Glucose Uptake with Nutrient Availability.

We report that astrocytic insulin signaling co-regulates hypothalamic glucose sensing and systemic glucose metabolism. Postnatal ablation of insulin receptors (IRs) in glial fibrillary acidic protein (GFAP)-expressing cells affects hypothalamic astrocyte morphology, mitochondrial function, and circuit connectivity. Accordingly, astrocytic IR ablation reduces glucose-induced activation of hypothalamic pro-opio-melanocortin (POMC) neurons and impairs physiological responses to changes in glucose availability. Hypothalamus-specific knockout of astrocytic IRs, as well as postnatal ablation by targeting glutamate aspartate transporter (GLAST)-expressing cells, replicates such alterations. A normal response to altering directly CNS glucose levels in mice lacking astrocytic IRs indicates a role in glucose transport across the blood-brain barrier (BBB). This was confirmed in vivo in GFAP-IR KO mice by using positron emission tomography and glucose monitoring in cerebral spinal fluid. We conclude that insulin signaling in hypothalamic astrocytes co-controls CNS glucose sensing and systemic glucose metabolism via regulation of glucose uptake across the BBB.

The obesity-linked human lncRNA AATBC stimulates mitochondrial function in adipocytes.

Adipocytes are critical regulators of metabolism and energy balance. While white adipocyte dysfunction is a hallmark of obesity-associated disorders, thermogenic adipocytes are linked to cardiometabolic health. As adipocytes dynamically adapt to environmental cues by functionally switching between white and thermogenic phenotypes, a molecular understanding of this plasticity could help improving metabolism. Here, we show that the lncRNA Apoptosis associated transcript in bladder cancer (AATBC) is a human-specific regulator of adipocyte plasticity. Comparing transcriptional profiles of human adipose tissues and cultured adipocytes we discovered that AATBC was enriched in thermogenic conditions. Using primary and immortalized human adipocytes we found that AATBC enhanced the thermogenic phenotype, which was linked to increased respiration and a more fragmented mitochondrial network. Expression of AATBC in adipose tissue of mice led to lower plasma leptin levels. Interestingly, this association was also present in human subjects, as AATBC in adipose tissue was inversely correlated with plasma leptin levels, BMI, and other measures of metabolic health. In conclusion, AATBC is a novel obesity-linked regulator of adipocyte plasticity and mitochondrial function in humans.

Unraveling phenotypic variance in metabolic syndrome through multi-omics.

Complex multi-omics effects drive the clustering of cardiometabolic risk factors, underscoring the imperative to comprehend how individual and combined omics shape phenotypic variation. Our study partitions phenotypic variance in metabolic syndrome (MetS), blood glucose (GLU), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and blood pressure through genome, transcriptome, metabolome, and exposome (i.e., lifestyle exposome) analyses. Our analysis included a cohort of 62,822 unrelated individuals with white British ancestry, sourced from the UK biobank. We employed linear mixed models to partition phenotypic variance using the restricted maximum likelihood (REML) method, implemented in MTG2 (v2.22). We initiated the analysis by individually modeling omics, followed by subsequent integration of pairwise omics in a joint model that also accounted for the covariance and interaction between omics layers. Finally, we estimated the correlations of various omics effects between the phenotypes using bivariate REML. Significant proportions of the MetS variance were attributed to distinct data sources: genome (9.47%), transcriptome (4.24%), metabolome (14.34%), and exposome (3.77%). The phenotypic variances explained by the genome, transcriptome, metabolome, and exposome ranged from 3.28% for GLU to 25.35% for HDL-C, 0% for GLU to 19.34% for HDL-C, 4.29% for systolic blood pressure (SBP) to 35.75% for TG, and 0.89% for GLU to 10.17% for HDL-C, respectively. Significant correlations were found between genomic and transcriptomic effects for TG and HDL-C. Furthermore, significant interaction effects between omics data were detected for both MetS and its components. Interestingly, significant correlation of omics effect between the phenotypes was found. This study underscores omics' roles, interaction effects, and random-effects covariance in unveiling phenotypic variation in multi-omics domains.

Pan-cancer characterization of ncRNA synergistic competition uncovers potential carcinogenic biomarkers.

Non-coding RNAs (ncRNAs) act as important modulators of gene expression and they have been confirmed to play critical roles in the physiology and development of malignant tumors. Understanding the synergism of multiple ncRNAs in competing endogenous RNA (ceRNA) regulation can provide important insights into the mechanisms of malignant tumors caused by ncRNA regulation. In this work, we present a framework, SCOM, for identifying ncRNA synergistic competition. We systematically construct the landscape of ncRNA synergistic competition across 31 malignant tumors, and reveal that malignant tumors tend to share hub ncRNAs rather than the ncRNA interactions involved in the synergistic competition. In addition, the synergistic competition ncRNAs (i.e. ncRNAs involved in the synergistic competition) are likely to be involved in drug resistance, contribute to distinguishing molecular subtypes of malignant tumors, and participate in immune regulation. Furthermore, SCOM can help to infer ncRNA synergistic competition across malignant tumors and uncover potential diagnostic and prognostic biomarkers of malignant tumors. Altogether, the SCOM framework (https://github.com/zhangjunpeng411/SCOM/) and the resulting web-based database SCOMdb (https://comblab.cn/SCOMdb/) serve as a useful resource for exploring ncRNA regulation and to accelerate the identification of carcinogenic biomarkers.

STING-driven interferon signaling triggers metabolic alterations in pancreas cancer cells visualized by [18F]FLT PET imaging.

Type I interferons (IFNs) are critical effectors of emerging cancer immunotherapies designed to activate pattern recognition receptors (PRRs). A challenge in the clinical translation of these agents is the lack of noninvasive pharmacodynamic biomarkers that indicate increased intratumoral IFN signaling following PRR activation. Positron emission tomography (PET) imaging enables the visualization of tissue metabolic activity, but whether IFN signaling-induced alterations in tumor cell metabolism can be detected using PET has not been investigated. We found that IFN signaling augments pancreatic ductal adenocarcinoma (PDAC) cell nucleotide metabolism via transcriptional induction of metabolism-associated genes including thymidine phosphorylase (TYMP). TYMP catalyzes the first step in the catabolism of thymidine, which competitively inhibits intratumoral accumulation of the nucleoside analog PET probe 3'-deoxy-3'-[18F]fluorothymidine ([18F]FLT). Accordingly, IFN treatment up-regulates cancer cell [18F]FLT uptake in the presence of thymidine, and this effect is dependent upon TYMP expression. In vivo, genetic activation of stimulator of interferon genes (STING), a PRR highly expressed in PDAC, enhances the [18F]FLT avidity of xenograft tumors. Additionally, small molecule STING agonists trigger IFN signaling-dependent TYMP expression in PDAC cells and increase tumor [18F]FLT uptake in vivo following systemic treatment. These findings indicate that [18F]FLT accumulation in tumors is sensitive to IFN signaling and that [18F]FLT PET may serve as a pharmacodynamic biomarker for STING agonist-based therapies in PDAC and possibly other malignancies characterized by elevated STING expression.

NAD+ depletion by type I interferon signaling sensitizes pancreatic cancer cells to NAMPT inhibition.

Emerging evidence suggests that intratumoral interferon (IFN) signaling can trigger targetable vulnerabilities. A hallmark of pancreatic ductal adenocarcinoma (PDAC) is its extensively reprogrammed metabolic network, in which nicotinamide adenine dinucleotide (NAD) and its reduced form, NADH, are critical cofactors. Here, we show that IFN signaling, present in a subset of PDAC tumors, substantially lowers NAD(H) levels through up-regulating the expression of NAD-consuming enzymes PARP9, PARP10, and PARP14. Their individual contributions to this mechanism in PDAC have not been previously delineated. Nicotinamide phosphoribosyltransferase (NAMPT) is the rate-limiting enzyme in the NAD salvage pathway, a dominant source of NAD in cancer cells. We found that IFN-induced NAD consumption increased dependence upon NAMPT for its role in recycling NAM to salvage NAD pools, thus sensitizing PDAC cells to pharmacologic NAMPT inhibition. Their combination decreased PDAC cell proliferation and invasion in vitro and suppressed orthotopic tumor growth and liver metastases in vivo.

Targeting deoxycytidine kinase improves symptoms in mouse models of multiple sclerosis.

Multiple sclerosis (MS) is an autoimmune disease driven by lymphocyte activation against myelin autoantigens in the central nervous system leading to demyelination and neurodegeneration. The deoxyribonucleoside salvage pathway with the rate-limiting enzyme deoxycytidine kinase (dCK) captures extracellular deoxyribonucleosides for use in intracellular deoxyribonucleotide metabolism. Previous studies have shown that deoxyribonucleoside salvage activity is enriched in lymphocytes and required for early lymphocyte development. However, specific roles for the deoxyribonucleoside salvage pathway and dCK in autoimmune diseases such as MS are unknown. Here we demonstrate that dCK activity is necessary for the development of clinical symptoms in the MOG35-55 and MOG1-125 experimental autoimmune encephalomyelitis (EAE) mouse models of MS. During EAE disease, deoxyribonucleoside salvage activity is elevated in the spleen and lymph nodes. Targeting dCK with the small molecule dCK inhibitor TRE-515 limits disease severity when treatments are started at disease induction or when symptoms first appear. EAE mice treated with TRE-515 have significantly fewer infiltrating leukocytes in the spinal cord, and TRE-515 blocks activation-induced B and T cell proliferation and MOG35-55 -specific T cell expansion without affecting innate immune cells or naïve T and B cell populations. Our results demonstrate that targeting dCK limits symptoms in EAE mice and suggest that dCK activity is required for MOG35-55 -specific lymphocyte activation-induced proliferation.

The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge.

MOTIVATION: Predicting cell locations is important since with the understanding of cell locations, we may estimate the function of cells and their integration with the spatial environment. Thus, the DREAM challenge on single-cell transcriptomics required participants to predict the locations of single cells in the Drosophila embryo using single-cell transcriptomic data. RESULTS: We have developed over 50 pipelines by combining different ways of preprocessing the RNA-seq data, selecting the genes, predicting the cell locations and validating predicted cell locations, resulting in the winning methods which were ranked second in sub-challenge 1, first in sub-challenge 2 and third in sub-challenge 3. In this paper, we present an R package, SCTCwhatateam, which includes all the methods we developed and the Shiny web application to facilitate the research on single-cell spatial reconstruction. All the data and the example use cases are available in the Supplementary data.

Diet triggers specific responses of hypothalamic astrocytes in time and region dependent manner.

Hypothalamic astrocytes are particularly affected by energy-dense food consumption. How the anatomical location of these glial cells and their spatial molecular distribution in the arcuate nucleus of the hypothalamus (ARC) determine the cellular response to a high caloric diet remains unclear. In this study, we investigated their distinctive molecular responses following exposure to a high-fat high-sugar (HFHS) diet, specifically in the ARC. Using RNA sequencing and proteomics, we showed that astrocytes have a distinct transcriptomic and proteomic profile dependent on their anatomical location, with a major proteomic reprogramming in hypothalamic astrocytes. By ARC single-cell sequencing, we observed that a HFHS diet dictates time- and cell- specific transcriptomic responses, revealing that astrocytes have the most distinct regulatory pattern compared to other cell types. Lastly, we topographically and molecularly characterized astrocytes expressing glial fibrillary acidic protein and/or aldehyde dehydrogenase 1 family member L1 in the ARC, of which the abundance was significantly increased, as well as the alteration in their spatial and molecular profiles, with a HFHS diet. Together, our results provide a detailed multi-omics view on the spatial and temporal changes of astrocytes particularly in the ARC during different time points of adaptation to a high calorie diet.

NIBNA: a network-based node importance approach for identifying breast cancer drivers.

MOTIVATION: Identifying meaningful cancer driver genes in a cohort of tumors is a challenging task in cancer genomics. Although existing studies have identified known cancer drivers, most of them focus on detecting coding drivers with mutations. It is acknowledged that non-coding drivers can regulate driver mutations to promote cancer growth. In this work, we propose a novel node importance-based network analysis (NIBNA) framework to detect coding and non-coding cancer drivers. We hypothesize that cancer drivers are crucial to the formation of community structures in cancer network, and removing them from the network greatly perturbs the network structure thereby critically affecting the functioning of the network. NIBNA detects cancer drivers using a three-step process: first, a condition-specific network is built by incorporating gene expression data and gene networks; second, the community structures in the network are estimated; and third, a centrality-based metric is applied to compute node importance. RESULTS: We apply NIBNA to the BRCA dataset, and it outperforms existing state-of-art methods in detecting coding cancer drivers. NIBNA also predicts 265 miRNA drivers, and majority of these drivers have been validated in literature. Further we apply NIBNA to detect cancer subtype-specific drivers, and several predicted drivers have been validated to be associated with cancer subtypes. Lastly, we evaluate NIBNA's performance in detecting epithelial-mesenchymal transition drivers, and we confirmed 8 coding and 13 miRNA drivers in the list of known genes. AVAILABILITY AND IMPLEMENTATION: The source code can be accessed at https://github.com/mandarsc/NIBNA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GraphDTA: predicting drug-target binding affinity with graph neural networks.

SUMMARY: The development of new drugs is costly, time consuming and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug-target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task. However, these models represent the drugs as strings, which is not a natural way to represent molecules. We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug-target affinity. We show that graph neural networks not only predict drug-target affinity better than non-deep learning models, but also outperform competing deep learning methods. Our results confirm that deep learning models are appropriate for drug-target binding affinity prediction, and that representing drugs as graphs can lead to further improvements. AVAILABILITY OF IMPLEMENTATION: The proposed models are implemented in Python. Related data, pre-trained models and source code are publicly available at https://github.com/thinng/GraphDTA. All scripts and data needed to reproduce the post hoc statistical analysis are available from https://doi.org/10.5281/zenodo.3603523. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A pseudotemporal causality approach to identifying miRNA-mRNA interactions during biological processes.

MOTIVATION: microRNAs (miRNAs) are important gene regulators and they are involved in many biological processes, including cancer progression. Therefore, correctly identifying miRNA-mRNA interactions is a crucial task. To this end, a huge number of computational methods has been developed, but they mainly use the data at one snapshot and ignore the dynamics of a biological process. The recent development of single cell data and the booming of the exploration of cell trajectories using 'pseudotime' concept have inspired us to develop a pseudotime-based method to infer the miRNA-mRNA relationships characterizing a biological process by taking into account the temporal aspect of the process. RESULTS: We have developed a novel approach, called pseudotime causality, to find the causal relationships between miRNAs and mRNAs during a biological process. We have applied the proposed method to both single cell and bulk sequencing datasets for Epithelia to Mesenchymal Transition, a key process in cancer metastasis. The evaluation results show that our method significantly outperforms existing methods in finding miRNA-mRNA interactions in both single cell and bulk data. The results suggest that utilizing the pseudotemporal information from the data helps reveal the gene regulation in a biological process much better than using the static information. AVAILABILITY AND IMPLEMENTATION: R scripts and datasets can be found at https://github.com/AndresMCB/PTC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

pDriver: a novel method for unravelling personalized coding and miRNA cancer drivers.

MOTIVATION: Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalized methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level. RESULTS: We propose the novel method, pDriver, to discover personalized cancer drivers. pDriver includes two stages: (i) constructing gene networks for each cancer patient and (ii) discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyze the predicted personalized drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer. AVAILABILITY AND IMPLEMENTATION: pDriver is available at https://github.com/pvvhoang/pDriver. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Lysosome inhibition sensitizes pancreatic cancer to replication stress by aspartate depletion.

Functional lysosomes mediate autophagy and macropinocytosis for nutrient acquisition. Pancreatic ductal adenocarcinoma (PDAC) tumors exhibit high basal lysosomal activity, and inhibition of lysosome function suppresses PDAC cell proliferation and tumor growth. However, the codependencies induced by lysosomal inhibition in PDAC have not been systematically explored. We performed a comprehensive pharmacological inhibition screen of the protein kinome and found that replication stress response (RSR) inhibitors were synthetically lethal with chloroquine (CQ) in PDAC cells. CQ treatment reduced de novo nucleotide biosynthesis and induced replication stress. We found that CQ treatment caused mitochondrial dysfunction and depletion of aspartate, an essential precursor for de novo nucleotide synthesis, as an underlying mechanism. Supplementation with aspartate partially rescued the phenotypes induced by CQ. The synergy of CQ and the RSR inhibitor VE-822 was comprehensively validated in both 2D and 3D cultures of PDAC cell lines, a heterotypic spheroid culture with cancer-associated fibroblasts, and in vivo xenograft and syngeneic PDAC mouse models. These results indicate a codependency on functional lysosomes and RSR in PDAC and support the translational potential of the combination of CQ and RSR inhibitors.

Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis.

BACKGROUND: Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced. However, it is not clear whether using miRNA and/or lncRNA expression data helps improve the performance of gene expression based subtyping and prognosis methods, and this raises challenges as to how and when to use these data and methods in practice. RESULTS: In this paper, we conduct a comparative study of 35 methods, including 12 breast cancer subtyping methods and 23 breast cancer prognosis methods, on a collection of 19 independent breast cancer datasets. We aim to uncover the roles of miRNAs and lncRNAs in breast cancer subtyping and prognosis from the systematic comparison. In addition, we created an R package, CancerSubtypesPrognosis, including all the 35 methods to facilitate the reproducibility of the methods and streamline the evaluation. CONCLUSIONS: The experimental results show that integrating miRNA expression data helps improve the performance of the mRNA-based cancer subtyping methods. However, miRNA signatures are not as good as mRNA signatures for breast cancer prognosis. In general, lncRNA expression data does not help improve the mRNA-based methods in both cancer subtyping and cancer prognosis. These results suggest that the prognostic roles of miRNA/lncRNA signatures in the improvement of breast cancer prognosis needs to be further verified.

Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data.

BACKGROUND: Existing computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation. RESULTS: In this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to combine single-cell miRNA-mRNA co-sequencing data and putative miRNA-mRNA binding information to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks for understanding miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. The comparison results indicate that CSmiR is effective in predicting cell-specific miRNA targets. Finally, through exploring cell-cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells and helps to understand cell-cell crosstalk. CONCLUSIONS: To the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.

Dietary fat exacerbates postprandial hypothalamic inflammation involving glial fibrillary acidic protein-positive cells and microglia in male mice.

In humans, obesity is associated with brain inflammation, glial reactivity, and immune cells infiltration. Studies in rodents have shown that glial reactivity occurs within 24 hr of high-fat diet (HFD) consumption, long before obesity development, and takes place mainly in the hypothalamus (HT), a crucial brain structure for controlling body weight. Here, we sought to characterize the postprandial HT inflammatory response to 1, 3, and 6 hr of exposure to either a standard diet (SD) or HFD. HFD exposure increased gene expression of astrocyte and microglial markers (glial fibrillary acidic protein [GFAP] and Iba1, respectively) compared to SD-treated mice and induced morphological modifications of microglial cells in HT. This remodeling was associated with higher expression of inflammatory genes and differential regulation of hypothalamic neuropeptides involved in energy balance regulation. DREADD and PLX5622 technologies, used to modulate GFAP-positive or microglial cells activity, respectively, showed that both glial cell types are involved in hypothalamic postprandial inflammation, with their own specific kinetics and reactiveness to ingested foods. Thus, recurrent exacerbated postprandial inflammation in the brain might promote obesity and needs to be characterized to address this worldwide crisis.

Inferring and analyzing module-specific lncRNA-mRNA causal regulatory networks in human cancer.

It is known that noncoding RNAs (ncRNAs) cover ∼98% of the transcriptome, but do not encode proteins. Among ncRNAs, long noncoding RNAs (lncRNAs) are a large and diverse class of RNA molecules, and are thought to be a gold mine of potential oncogenes, anti-oncogenes and new biomarkers. Although only a minority of lncRNAs is functionally characterized, it is clear that they are important regulators to modulate gene expression and involve in many biological functions. To reveal the functions and regulatory mechanisms of lncRNAs, it is vital to understand how lncRNAs regulate their target genes for implementing specific biological functions. In this article, we review the computational methods for inferring lncRNA-mRNA interactions and the third-party databases of storing lncRNA-mRNA regulatory relationships. We have found that the existing methods are based on statistical correlations between the gene expression levels of lncRNAs and mRNAs, and may not reveal gene regulatory relationships which are causal relationships. Moreover, these methods do not consider the modularity of lncRNA-mRNA regulatory networks, and thus, the networks identified are not module-specific. To address the above two issues, we propose a novel method, MSLCRN, to infer and analyze module-specific lncRNA-mRNA causal regulatory networks. We have applied it into glioblastoma multiforme, lung squamous cell carcinoma, ovarian cancer and prostate cancer, respectively. The experimental results show that MSLCRN, as an expression-based method, could be a useful complementary method to study lncRNA regulations.

DriverGroup: a novel method for identifying driver gene groups.

MOTIVATION: Identifying cancer driver genes is a key task in cancer informatics. Most existing methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesize that there are driver gene groups that work in concert to regulate cancer, and we develop a novel computational method to detect those driver gene groups. RESULTS: We develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (i) constructing the gene network, (ii) discovering critical nodes of the constructed network and (iii) identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup. AVAILABILITY AND IMPLEMENTATION: DriverGroup is available at https://github.com/pvvhoang/DriverGroup. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A novel single-cell based method for breast cancer prognosis.

Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.