Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA

Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways.

A Cancer Gene Module Mining Method Based on Bio-Network of Multi-Omics Gene Groups

The initiation, promotion and progression of cancer are highly associated to the environment a human lives in as well as individual genetic factors. In view of the dangers to life and health caused by this abnormally complex systemic disease, many top scientific research institutions around the world have been actively carrying out research in order to discover the pathogenic mechanisms driving cancer occurrence and development. The emergence of high-throughput sequencing technology has greatly advanced oncology research and given rise to the revelation of important oncogenes and the interrelationship among them. Here, we have studied heterogeneous multi-level data within a context of integrated data, and scientifically introduced lncRNA omics data to construct multi-omics bio-network models, allowing the screening of key cancer-related gene groups. We propose a compactness clustering algorithm based on corrected cumulative rank scores, which uses the functional similarity between groups of genes as a distance measure to excavate key gene modules for abnormal regulation contained in gene groups through clustering. We also conducted a survival analysis using our results and found that our model could divide groups of different levels very well. The results also demonstrate that the integration of multi-omics biological data, key gene modules and their dysregulated gene groups can be discovered, which is crucial for cancer research.

Identification of Differentially Expressed Genes to Establish New Biomarker for Cancer Prediction

The goal of the human genome project is to integrate genetic information into different clinical therapies. To achieve this goal, different computational algorithms are devised for identifying the biomarker genes, cause of complex diseases. However, most of the methods developed so far using DNA microarray data lack in interpreting biological findings and are less accurate in disease prediction. In the paper, we propose two parameters risk_factor and confusion_factor to identify the biologically significant genes for cancer development. First, we evaluate risk_factor of each gene and the genes with nonzero risk_factor result misclassification of data, therefore removed. Next, we calculate confusion_factor of the remaining genes which determines confusion of a gene in prediction due to closeness of the samples in the cancer and normal classes. We apply nondominated sorting genetic algorithm (NSGA-II) to select the maximally uncorrelated differentially expressed genes in the cancer class with minimum confusion_factor. The proposed Gene Selection Explore (GSE) algorithm is compared to well established feature selection algorithms using 10 microarray data with respect to sensitivity, specificity, and accuracy. The identified genes appear in KEGG pathway and have several biological importance.

Network enhancement as a general method to denoise weighted biological networks

Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. However, biological networks are noisy due to the limitations of measurement technology and inherent natural variation, which can hamper discovery of network patterns and dynamics. We propose Network Enhancement (NE), a method for improving the signal-to-noise ratio of undirected, weighted networks. NE uses a doubly stochastic matrix operator that induces sparsity and provides a closed-form solution that increases spectral eigengap of the input network. As a result, NE removes weak edges, enhances real connections, and leads to better downstream performance. Experiments show that NE improves gene-function prediction by denoising tissue-specific interaction networks, alleviates interpretation of noisy Hi-C contact maps from the human genome, and boosts fine-grained identification accuracy of species. Our results indicate that NE is widely applicable for denoising biological networks.

Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer

Gene-expression data are often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. Pathway enrichment is, however, typically computed with DEGs rather than "upstream" nodes that are potentially causal of "downstream" changes. Here, we present graph-based models to predict causal targets from compound-microarray data. We test several approaches to traversing network topology, and show that a consensus minimum-rank score (SigNet) beat individual methods and could highly rank compound targets among all network nodes. In addition, larger, less canonical networks outperformed linear canonical interactions. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To further validate our approach, we used integrated data sets from the Cancer Genome Atlas to identify driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including the epidermal growth factor receptor 2-phosphatidylinositide 3-kinase-AKT-MAPK growth pathway andATR-p53-BRCA DNA damage pathway, in addition to unexpected pathways, such as TGF-WNT cytoskeleton remodeling, IL12-induced interferon gamma production, and TNFR-IAP (inhibitor of apoptosis) apoptosis; the latter was validated by pooled small hairpin RNA profiling in cancer cells. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer.

Network topology reveals key cardiovascular disease genes

The structure of protein-protein interaction (PPI) networks has already been successfully used as a source of new biological information. Even though cardiovascular diseases (CVDs) are a major global cause of death, many CVD genes still await discovery. We explore ways to utilize the structure of the human PPI network to find important genes for CVDs that should be targeted by drugs. The hope is to use the properties of such important genes to predict new ones, which would in turn improve a choice of therapy. We propose a methodology that examines the PPI network wiring around genes involved in CVDs. We use the methodology to identify a subset of CVD-related genes that are statistically significantly enriched in drug targets and "driver genes." We seek such genes, since driver genes have been proposed to drive onset and progression of a disease. Our identified subset of CVD genes has a large overlap with the Core Diseasome, which has been postulated to be the key to disease formation and hence should be the primary object of therapeutic intervention. This indicates that our methodology identifies "key" genes responsible for CVDs. Thus, we use it to predict new CVD genes and we validate over 70% of our predictions in the literature. Finally, we show that our predicted genes are functionally similar to currently known CVD drug targets, which confirms a potential utility of our methodology towards improving therapy for CVDs.

Classification of time series gene expression in clinical studies via integration of biological network

The increasing availability of time series expression datasets, although promising, raises a number of new computational challenges. Accordingly, the development of suitable classification methods to make reliable and sound predictions is becoming a pressing issue. We propose, here, a new method to classify time series gene expression via integration of biological networks. We evaluated our approach on 2 different datasets and showed that the use of a hidden Markov model/Gaussian mixture models hybrid explores the time-dependence of the expression data, thereby leading to better prediction results. We demonstrated that the biclustering procedure identifies function-related genes as a whole, giving rise to high accordance in prognosis prediction across independent time series datasets. In addition, we showed that integration of biological networks into our method significantly improves prediction performance. Moreover, we compared our approach with several state-of-the-art algorithms and found that our method outperformed previous approaches with regard to various criteria. Finally, our approach achieved better prediction results on early-stage data, implying the potential of our method for practical prediction.

Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study

BACKGROUND

The use of biological molecular network information for diagnostic and prognostic purposes and elucidation of molecular disease mechanism is a key objective in systems biomedicine. The network of regulatory miRNA-target and functional protein interactions is a rich source of information to elucidate the function and the prognostic value of miRNAs in cancer. The objective of this study is to identify miRNAs that have high influence on target protein complexes in prostate cancer as a case study. This could provide biomarkers or therapeutic targets relevant for prostate cancer treatment.

RESULTS

Our findings demonstrate that a miRNA's functional role can be explained by its target protein connectivity within a physical and functional interaction network. To detect miRNAs with high influence on target protein modules, we integrated miRNA and mRNA expression profiles with a sequence based miRNA-target network and human functional and physical protein interactions (FPI). miRNAs with high influence on target protein complexes play a role in prostate cancer progression and are promising diagnostic or prognostic biomarkers. We uncovered several miRNA-regulated protein modules which were enriched in focal adhesion and prostate cancer genes. Several miRNAs such as miR-96, miR-182, and miR-143 demonstrated high influence on their target protein complexes and could explain most of the gene expression changes in our analyzed prostate cancer data set.

CONCLUSIONS

We describe a novel method to identify active miRNA-target modules relevant to prostate cancer progression and outcome. miRNAs with high influence on protein networks are valuable biomarkers that can be used in clinical investigations for prostate cancer treatment.

Genetic network and gene set enrichment analysis to identify biomarkers related to cigarette smoking and lung cancer

OBJECTIVES

Cigarette smoking is the most demonstrated risk factor for the development of lung cancer, while the related genetic mechanisms are still unclear.

METHODS

The preprocessed microarray expression dataset was downloaded from Gene Expression Omnibus database. Samples were classified according to the disease state, stage and smoking state. A new computational strategy was applied for the identification and biological interpretation of new candidate genes in lung cancer and smoking by coupling a network-based approach with gene set enrichment analysis.

MEASUREMENTS

Network analysis was performed by pair-wise comparison according to the disease states (tumor or normal), smoking states (current smokers or nonsmokers or former smokers), or the disease stage (stages I-IV). The most activated metabolic pathways were identified by gene set enrichment analysis.

RESULTS

Panels of top ranked gene candidates in smoking or cancer development were identified, including genes involved in cell proliferation and drug metabolism like cytochrome P450 and WW domain containing transcription regulator 1. Semaphorin 5A and protein phosphatase 1F are the common genes represented as major hubs in both the smoking and cancer related network. Six pathways, e.g. cell cycle, DNA replication, RNA transport, protein processing in endoplasmic reticulum, vascular smooth muscle contraction and endocytosis were commonly involved in smoking and lung cancer when comparing the top ten selected pathways.

CONCLUSION

New approach of bioinformatics for biomarker identification and validation can probe into deep genetic relationships between cigarette smoking and lung cancer. Our studies indicate that disease-specific network biomarkers, interaction between genes/proteins, or cross-talking of pathways provide more specific values for the development of precision therapies for lung.