Atherosclerosis is a common and complex disease, whose morbidity increased significantly. Here, an integrated approach was proposed to elucidate systematically the pathogenesis of atherosclerosis from a systems biology point of view. Two weighted human signaling networks were constructed based on atherosclerosis related gene expression data of stem cells. Then, 37 candidate Atherosclerosis-risk Modules were detected using four kinds of permutation tests. Five Atherosclerosis-risk Modules (three Absent Modules and two Emerging Modules) enriched in functions significantly associated with disease genes were identified and verified to be associated with the maintenance of normal biological process and the pathogenesis and development of atherosclerosis. Especially for Atherosclerosis-risk Emerging Module P96, it could distinguish between normal and disease samples by Supporting Vector Machine with the average expression value of the module as classification feature. These identified modules and their genes may act as potential atherosclerosis biomarkers. Our study would shed light on the signal transduction of atherosclerosis, and provide new insights to its pathogenesis from the perspective of stem cells.

Objective To explore the pathogenesis of T-cells in the formation and development of atherosclerosis.Methods Significant differential permutation test based on the human signaling network and the data of gene expression profile containing normal and atherosclerosis samples with RNA expressions from T cells.Candidate Atherosclerosis-related Modules were identified using ClusterOne.Atherosclerosis-related Modules were identified using functional enrichment analysis.Results Nineteen potential atherosclerosis genes which played central roles and have consistent functions in modules (five atherosclerosis-related modules) were identified.Conclusion Based on a human signaling network,the atherosclerosis related module identification approach could not only identify modules and potential atherosclerosis genes closely related to disease,but also provide a new perspective for the pathogenesis of T cells in atherosclerosis.

Ischemic cardiomyopathy (ICM) is an important cause of heart failure, yet no ICM disease genes were stored in any public databases. Mutations of genes provided by RNA-Seq data could set a foundation for a variety of biological processes. This also made it possible to elucidate the mechanism and identify potential genes for ICM. In this paper, an integrated co-expression network was constructed using univariate and bivariate canonical correlation analysis for RNA-Seq data of human ICM samples. Three ICM-related modules were recognized after comparing between Pearson correlation coefficients of ICM samples and normal controls. Furthermore, 32 ICM potential genes were identified from ICM-related modules considering protein-protein interactions. Most of these genes were verified to be involved in ICM and diseases caused it by OMIM and literature. Our study could provide a novel perspective for potential gene identification and the pathogenesis for ICM and other complex diseases.

Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. The complexity of cancer can be reduced to a small number of underlying principles like cancer hallmarks which could govern the transformation of normal cells to cancer. Besides, the growth and metastasis of cancer often relate to combined effects of long non-coding RNAs (lncRNAs). Here, we performed comprehensive analysis for lncRNA expression profiles and clinical data of six types of human cancer patients from The Cancer Genome Atlas (TCGA), and identified six risk pathways and twenty three lncRNAs. In addition, twenty three cancer risk lncRNAs which were closely related to the occurrence or development of cancer had a good classification performance for samples of testing datasets of six cancer datasets. More important, these lncRNAs were able to separate samples in the entire cancer dataset into high-risk group and low-risk group with significantly different overall survival (OS), which was further validated in ten validation datasets. In our study, the robust and effective cancer biomarkers were obtained from cancer datasets which had information of normaltumor samples. Overall, our research can provide a new perspective for the further study of clinical diagnosis and treatment of cancer.