Network Based Multi-Omics Analyses to Reveal Mechanisms and Biomarkers of Complex Diseases

Editors

Xiaoping Liu

Hangzhou Institute for Advanced Study, University of Chinese Academy of Science

Yunjie Zhao

Central China Normal University

Yanan Zhu

University of Oxford

Ben-gong Zhang

Wuhan Textile University

Impact

Research on tumor immunity of the 14-ARlncRNA signature. (A,B) KEGG (A) and GO (B) function enrichment analyses reveal that several immune pathways and immune function were enriched in different groups, suggesting the ARlncRNA signature may be associated with tumor immunity. (C,D) The scatter diagram (C) and column diagram (D) show that high-risk patients exhibit significantly higher TMB. (E,F) The survival curves indicate that patients with a combination of low-risk and high TMB showed a greater prognosis.

Original Research

08 December 2021

Identification of an Autophagy-Related lncRNA Prognostic Signature and Related Tumor Immunity Research in Lung Adenocarcinoma

Hang Chen

, 9 more and

Guodong Xu

Autophagy is closely associated with the tumor immune microenvironment (TIME) and prognosis of patients with lung adenocarcinoma (LUAD). In the present study, we established a signature on the basis of long noncoding RNAs (lncRNAs) related to autophagy (ARlncRNAs) to investigate the TIME and survival of patients with LUAD. We selected ARlncRNAs associated with prognosis to construct a model and divided each sample into different groups on the basis of risk score. The ARlncRNA signature could be recognized as an independent prognostic factor for patients with LUAD, and patients in the low-risk group had a greater survival advantage. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analysis suggested that several immune functions and pathways were enriched in different groups. A high-risk score correlated significantly negatively with high abundance of immune cells and stromal cells around the tumor and high tumor mutational burden. Low-risk patients had a higher PD-1, CTLA-4, and HAVCR2 expression and had a better efficacy of immune checkpoint inhibitors, including PD-1/CTLA-4 inhibitor. A reliable signature on the basis of ARlncRNAs was constructed to explore the TIME and prognosis of patients with LUAD, which could provide valuable information for individualized LUAD treatment.

2,762 views

14 citations

Method performance comparisons with partially known prior information. The simulated protein network graph includes 1000 (p) nodes with fixed sample size (n = 300). The density of the simulated graph is around 4%. The prior information is a subset of the target true network. A variety of subset percentages of the prior network were simulated as shown in X-axis. We estimated the true network topology by using two weighted graphical Lasso (wGlasso_2015 and wGlasso_2017), Netgsa, and the proposed AhGlasso methods. The F1 score (A) and MCC (B) were calculated based on the estimated network and true network. For each simulation setting, the simulations were repeated 5 times. The lines represent the mean scores for the simulated sample size and the error bars represent the standard error of the mean for each method.

Original Research

27 January 2022

An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning

Yonghua Zhuang

, 4 more and

Katerina Kechris

3,662 views

5 citations

Original Research

28 October 2021

Identifying miRNA-mRNA Networks Associated With COPD Phenotypes

Yonghua Zhuang

, 2 more and

Katerina Kechris

Chronic obstructive pulmonary disease (COPD) is characterized by expiratory airflow limitation and symptoms such as shortness of breath. Although many studies have demonstrated dysregulated microRNA (miRNA) and gene (mRNA) expression in the pathogenesis of COPD, how miRNAs and mRNAs systematically interact and contribute to COPD development is still not clear. To gain a deeper understanding of the gene regulatory network underlying COPD pathogenesis, we used Sparse Multiple Canonical Correlation Network (SmCCNet) to integrate whole blood miRNA and RNA-sequencing data from 404 participants in the COPDGene study to identify novel miRNA–mRNA networks associated with COPD-related phenotypes including lung function and emphysema. We hypothesized that phenotype-directed interpretable miRNA–mRNA networks from SmCCNet would assist in the discovery of novel biomarkers that traditional single biomarker discovery methods (such as differential expression) might fail to discover. Additionally, we investigated whether adjusting -omics and clinical phenotypes data for covariates prior to integration would increase the statistical power for network identification. Our study demonstrated that partial covariate adjustment for age, sex, race, and CT scanner model (in the quantitative emphysema networks) improved network identification when compared with no covariate adjustment. However, further adjustment for current smoking status and relative white blood cell (WBC) proportions sometimes weakened the power for identifying lung function and emphysema networks, a phenomenon which may be due to the correlation of smoking status and WBC counts with the COPD-related phenotypes. With partial covariate adjustment, we found six miRNA–mRNA networks associated with COPD-related phenotypes. One network consists of 2 miRNAs and 28 mRNAs which had a 0.33 correlation (p = 5.40E-12) to forced expiratory volume in 1 s (FEV₁) percent predicted. We also found a network of 5 miRNAs and 81 mRNAs that had a 0.45 correlation (p = 8.80E-22) to percent emphysema. The miRNA–mRNA networks associated with COPD traits provide a systems view of COPD pathogenesis and complements biomarker identification with individual miRNA or mRNA expression data.

4,233 views

17 citations

The predicted accuracy of each model for classification on Diabetes dataset (A), Arrhythmia dataset (B), Epilepsy dataset 2 (C) and Breast cancer dataset (D).

Original Research

13 October 2021

SS-RNN: A Strengthened Skip Algorithm for Data Classification Based on Recurrent Neural Networks

Wenjie Cao

, 2 more and

Bengong Zhang

3,116 views

2 citations

Original Research

26 July 2021

ARG2, MAP4K5 and TSTA3 as Diagnostic Markers of Steroid-Induced Osteonecrosis of the Femoral Head and Their Correlation With Immune Infiltration

Rongguo Yu

, 6 more and

Yiyuan Zhang

Data preprocessing and DEGs screening. (A,B) Box plots of the expression profiles: Before normalization (blue) and after normalization (red); (C) volcano map of DEGs; red represents up-regulated differential genes, black represents no significant difference genes, and green represents down-regulated differential genes; (D) heat map of top 30 up-regulated and down-regulated genes from all samples; red indicates higher gene expression and green indicates lower gene expression.

Background: The diagnosis for steroid-induced osteonecrosis of the femoral head (SONFH) is hard to achieve at the early stage, which results in patients receiving ineffective treatment options and a poor prognosis for most cases. The present study aimed to find potential diagnostic markers of SONFH and analyze the effect exerted by infiltration of immune cells in this pathology.

Materials and Methods: R software was adopted for identifying differentially expressed genes (DEGs) and conducting functional investigation based on the microarray dataset. Then we combined SVM-RFE, WGCNA, LASSO logistic regression, and random forest (RF) algorithms for screening the diagnostic markers of SONFH and further verification by qRT-PCR. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. CIBERSORT was then adopted for assessing the infiltration of immune cells and the relationship of infiltration-related immune cells and diagnostic markers.

Results: We identified 383 DEGs overall. This study found ARG2, MAP4K5, and TSTA3 (AUC = 0.980) to be diagnostic markers of SONFH. The results of qRT-PCR showed a statistically significant difference in all markers. Analysis of infiltration of immune cells indicated that neutrophils, activated dendritic cells and memory B cells were likely to show the relationship with SONFH occurrence and progress. Additionally, all diagnostic markers had different degrees of correlation with T cell follicular helper, neutrophils, memory B cells, and activated dendritic cells.

Conclusion: ARG2, MAP4K5, and TSTA3 are potential diagnostic genes for SONFH, and infiltration of immune cells may critically impact SONFH occurrence and progression.

4,090 views

18 citations

$Housekeeping genes and housekeeping interactions. (A) The heatmap of score between heart failure with preserved ejection fraction (HFpEF) and genes of housekeeping genes and housekeeping interactions. (B) The binding site and interactions between fostamatinib and FKBP 5. (C) The binding site and interactions between fostamatinib and GDA. (D) The binding site and interactions between fostamatinib and MLYCD. (E) The binding site and interactions between fostamatinib and PRKCD. (F) The binding site and interactions between fostamatinib and SERPINE 1.$

Original Research

08 July 2021

Multiple-Tissue and Multilevel Analysis on Differentially Expressed Genes and Differentially Correlated Gene Pairs for HFpEF

Guofeng Zhou

, 6 more and

Xiao Li

3,577 views

1 citations

Original Research

15 July 2021

Identification of HCC-Related Genes Based on Differential Partial Correlation Network

Yuyao Gao

, 4 more and

Xiaoping Liu

2,635 views

3 citations

Original Research

11 May 2021

Predicting the Key Genes Involved in Aortic Valve Calcification Through Integrated Bioinformatics Analysis

Dinghui Wang

, 5 more and

Qiang She

Background: Valvular heart disease is obtaining growing attention in the cardiovascular field and it is believed that calcific aortic valve disease (CAVD) is the most common valvular heart disease (VHD) in the world. CAVD does not have a fully effective treatment to delay its progression and the specific molecular mechanism of aortic valve calcification remains unclear.

Materials and Methods: We obtained the gene expression datasets GSE12644 and GSE51472 from the public comprehensive free database GEO. Then, a series of bioinformatics methods, such as GO and KEGG analysis, STING online tool, Cytoscape software, were used to identify differentially expressed genes in CAVD and healthy controls, construct a PPI network, and then identify key genes. In addition, immune infiltration analysis was used via CIBERSORT to observe the expression of various immune cells in CAVD.

Results: A total of 144 differential expression genes were identified in the CAVD samples in comparison with the control samples, including 49 up-regulated genes and 95 down-regulated genes. GO analysis of DEGs were most observably enriched in the immune response, signal transduction, inflammatory response, proteolysis, innate immune response, and apoptotic process. The KEGG analysis revealed that the enrichment of DEGs in CAVD were remarkably observed in the chemokine signaling pathway, cytokine-cytokine receptor interaction, and PI3K-Akt signaling pathway. Chemokines CXCL13, CCL19, CCL8, CXCL8, CXCL16, MMP9, CCL18, CXCL5, VCAM1, and PPBP were identified as the hub genes of CAVD. It was macrophages that accounted for the maximal proportion among these immune cells. The expression of macrophages M0, B cells memory, and Plasma cells were higher in the CAVD valves than in healthy valves, however, the expression of B cells naïve, NK cells activated, and macrophages M2 were lower.

Conclusion: We detected that chemokines CXCL13, CXCL8, CXCL16, and CXCL5, and CCL19, CCL8, and CCL18 are the most important markers of aortic valve disease. The regulatory macrophages M0, plasma cells, B cells memory, B cells naïve, NK cells activated, and macrophages M2 are probably related to the occurrence and the advancement of aortic valve stenosis. These identified chemokines and these immune cells may interact with a subtle adjustment relationship in the development of calcification in CAVD.

6,110 views

14 citations

Open for submission