Construct Gene Co-expression Networks Research Articles

Large-scale RNAseq analysis reveals new insights into the key genes and regulatory networks of anthocyanin biosynthesis during development and stress in cassava

Anthocyanins play key roles in plant development and response to various biotic and abiotic stresses including drought, cold, and pathogens. However, the key genes and regulatory networks relevant to anthocyanin biosynthesis remain largely elusive in cassava (Manihot esculenta L.), an important starchy root crop in tropical and sub-tropical regions. In this study, 465 RNAseq data were gathered to construct gene coexpression networks in cassava, resulting in a total of 30 coexpression modules (M1-M30). The associations of module-tissue or module-stress/development were systematically analyzed. Meanwhile, the hub genes and molecular roles were extensively dissected for each module, respectively. Notably, module M5 was significantly enriched in phenylpropanoid and flavonoid metabolisms, and several crucial genes participating in anthocyanin biosynthesis were included, suggesting a major role of M5 in anthocyanin biosynthesis in cassava. Coexpression analyses further revealed that phenylalanine ammonia-lyase 1 (PAL1), anthocyanidin synthase (ANS), and flavanone 3-hydroxylase (F3H) were the hub genes with most connections to others. Four transcription factors and three cis-regulatory motifs which might involve in anthocyanin biosynthesis regulation were also explored. Finally, a conserved coexpression network, covering the entire pathway of anthocyanin biosynthesis, was revealed among cassava, Arabidopsis thaliana (L.), and rice (Oryza sativa L.). The results provide valuable information to the key genes and regulatory networks of anthocyanin biosynthesis, and also lay a useful foundation for the genetic improvement in cassava.

A novel tailored immune gene pairs signature for overall survival prediction in lower-grade gliomas

Lower-grade gliomas (LGGs) have a good prognosis with a wide range of overall survival (OS) outcomes. An accurate prognostic system can better predict survival time. An RNA-Sequencing (RNA-seq) prognostic signature showed a better predictive power than clinical predictor models. A signature constructed using gene pairs can transcend changes from biological heterogeneity, technical biases, and different measurement platforms. RNA-seq coupled with corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Immune-related gene pairs (IRGPs) were used to establish a prognostic signature through univariate and multivariate Cox proportional hazards regression. Weighted gene co-expression network analysis (WGCNA) was used to evaluate module eigengenes correlating with immune cell infiltration and to construct gene co-expression networks. Samples in the training and testing cohorts were dichotomized into high- and low-risk groups. Risk score was identified as an independent predictor, and exhibited a closed relationship with prognosis. WGCNA presented a gene set that was positively correlated with age, WHO grade, isocitrate dehydrogenase (IDH) mutation status, 1p/19 codeletion, risk score, and immune cell infiltrations (CD4 T cells, B cells, dendritic cells, and macrophages). A nomogram comprising of age, WHO grade, 1p/19q codeletion, and three gene pairs (BIRC5|SSTR2, BMP2|TNFRSF12A, and NRG3|TGFB2) was established as a tool for predicting OS. The IPGPs signature, which is associated with immune cell infiltration, is a novel tailored tool for individual-level prediction.

Whitefly Network Analysis Reveals Gene Modules Involved in Host Plant Selection, Development and Evolution.

Whiteflies are Hemipterans that typically feed on the undersides of plant leaves. They cause severe damage by direct feeding as well as transmitting plant viruses to a wide range of plants. However, it remains largely unknown which genes play a key role in development and host selection. In this study, weighted gene co-expression network analysis was applied to construct gene co-expression networks in whitefly. Nineteen gene co-expression modules were detected from 15560 expressed genes of whitefly. Combined with the transcriptome data of salivary glands and midgut, we identified three gene co-expression modules related to host plant selection. These three modules contain genes related to host-plant recognition, such as detoxification genes, chemosensory genes and some salivary gland-associated genes. Results of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses elucidated the following pathways involved in these modules: lysosome, metabolic and detoxification pathways. The modules related to the development contain two co-expression modules; moreover, the genes were annotated to the development of chitin-based cuticle. This analysis provides a basis for future functional analysis of genes involved in host-plant recognition.

Differential Expression Profiles and Functional Analysis of Long Non-coding RNAs in Children With Dilated Cardiomyopathy.

Aim: To evaluate the expression profile of long non-coding RNAs (lncRNAs) in different left ventricular function of dilated cardiomyopathy (DCM) in children and explore their possible functions.Methods: The lncRNA microarray experiment was used to determine the differential expression profile of lncRNA in three children with DCM and three healthy volunteers. The functional analysis and the construction of the lncRNA-mRNA interaction network were carried out to study the biological functions. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis was used to verify the microarray data.Results: There were 369 up-regulated lncRNAs identified in the DCM patients (fold change >2, P < 0.05), and 505 down-regulated lncRNAs. Based on target gene prediction and co-expression network construction, 9 differentially expressed lncRNAs were selected for the PCR to verify the accuracy of the microarray data, of which 5 were up-regulated and 4 were down-regulated, and finally proved that 7 of them were consistent with the trend of microarray data results. Four of these lncRNAs had significant differences between the patients with poor cardiac function and patients with improved left ventricle function.Conclusion: LncRNAs may play an important role in pediatric DCM and may provide a new perspective for the pathogenesis, diagnosis, and treatment of this disease.

Robust gene coexpression networks using signed distance correlation.

MotivationEven within well-studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes/proteins, using a network of gene coexpression data that includes functional annotations. However, the lack of trustworthy functional annotations can impede the validation of such networks. Hence, there is a need for a principled method to construct gene coexpression networks that capture biological information and are structurally stable even in the absence of functional information.ResultsWe introduce the concept of signed distance correlation as a measure of dependency between two variables, and apply it to generate gene coexpression networks. Distance correlation offers a more intuitive approach to network construction than commonly used methods, such as Pearson correlation and mutual information. We propose a framework to generate self-consistent networks using signed distance correlation purely from gene expression data, with no additional information. We analyse data from three different organisms to illustrate how networks generated with our method are more stable and capture more biological information compared to networks obtained from Pearson correlation or mutual information.Availability and implementationCode is available online (https://github.com/javier-pardodiaz/sdcorGCN). Supplementary information Supplementary data are available at Bioinformatics online.

Comparative Analysis of Core and Accessory Genes in Coexpression Network.

Prokaryotes harbor a various proportion of accessory genes in their genomes. The integration of accessory functions with the core regulation network is critical for environmental adaptation, particularly considering a theoretically unlimited number of niches on the earth for microorganisms. Comparative genomics can reveal a co-occurrence pattern between a subset of accessory genes (or variations in core genes) and an adaptation trait, while comparative transcriptomics can further uncover whether a coordinated regulation of gene expression is involved. In this chapter, we introduce a protocol for weighted gene coexpression network construction by using well-developed open source tools, and a further application of such a network in comparative analysis of bacterial core and accessory genes.

Co-expression network analysis of protein phosphatase 2A (PP2A) genes with stress-responsive genes in Arabidopsis thaliana reveals 13 key regulators

Abiotic and biotic stresses adversely affect plant growth and development and eventually result in less yield and threaten food security worldwide. In plants, several studies have been carried out to understand molecular responses to abiotic and biotic stresses. However, the complete circuitry of stress-responsive genes that plants utilise in response to those environmental stresses are still unknown. The protein phosphatase 2A (PP2A) gene has been known to have a crucial role in abiotic and biotic stresses; but how it regulates the stress response in plants is still not known completely. In this study, we constructed gene co-expression networks of PP2A genes with stress-responsive gene datasets from cold, drought, heat, osmotic, genotoxic, salt, and wounding stresses to unveil their relationships with the PP2A under different conditions of stress. The graph analysis identified 13 hub genes and several influential genes based on closeness centrality score (CCS). Our findings also revealed the count of unique genes present in different settings of stresses and subunits. We also formed clusters of influential genes based on the stress, CCS, and co-expression value. Analysis of cis-regulatory elements (CREs), recurring in promoters of these genes was also performed. Our study has led to the identification of 16 conserved CREs.

Identifying Breast Cancer-induced Gene Perturbations and its Application in Guiding Drug Repurposing

Background:Breast cancer is a complex disease with high prevalence in women, the molecular mechanisms of which are still unclear at present. Most transcriptomic studies on breast cancer focus on differential expression of each gene between tumor and the adjacent normal tissues, while the other perturbations induced by breast cancer including the gene regulation variations, the changes of gene modules and the pathways, which might be critical to the diagnosis, treatment and prognosis of breast cancer are more or less ignored.Objective:We presented a complete process to study breast cancer from multiple perspectives, including differential expression analysis, constructing gene co-expression networks, modular differential connectivity analysis, differential gene connectivity analysis, gene function enrichment analysis key driver analysis. In addition, we prioritized the related anti-cancer drugs based on enrichment analysis between differential expression genes and drug perturbation signatures.Methods:The RNA expression profiles of 1109 breast cancer tissue and 113 non-tumor tissues were downloaded from The Cancer Genome Atlas (TCGA) database. Differential expression of RNAs was identified using the “DESeq2” bioconductor package in R, and gene co-expression networks were constructed using the weighted gene co-expression network analysis (WGCNA). To compare the module changes and gene co-expression variations between tumor and the adjacent normal tissues, modular differential connectivity (MDC) analysis and differential gene connectivity analysis (DGCA) were performed.Results:Top differential genes like MMP11 and COL10A1 were known to be associated with breast cancer. And we found 23 modules in the tumor network had significantly different co-expression patterns. The top differential modules were enriched in Goterms related to breast cancer like MHC protein complex, leukocyte activation, regulation of defense response and so on. In addition, key genes like UBE2T driving the top differential modules were significantly correlated with the patients’ survival. Finally, we predicted some potential breast cancer drugs, such as Eribulin, Taxane, Cisplatin and Oxaliplatin.Conclusion:As an indication, this framework might be useful in understanding the molecular pathogenesis of diseases like breast cancer and inferring useful drugs for personalized medication.

RNA-Seq-Based Breast Cancer Subtypes Classification Using Machine Learning Approaches.

Background Breast invasive carcinoma (BRCA) is not a single disease as each subtype has a distinct morphology structure. Although several computational methods have been proposed to conduct breast cancer subtype identification, the specific interaction mechanisms of genes involved in the subtypes are still incomplete. To identify and explore the corresponding interaction mechanisms of genes for each subtype of breast cancer can impose an important impact on the personalized treatment for different patients. Methods We integrate the biological importance of genes from the gene regulatory networks to the differential expression analysis and then obtain the weighted differentially expressed genes (weighted DEGs). A gene with a high weight means it regulates more target genes and thus holds more biological importance. Besides, we constructed gene coexpression networks for control and experiment groups, and the significantly differentially interacting structures encouraged us to design the corresponding Gene Ontology (GO) enrichment based on gene coexpression networks (GOEGCN). The GOEGCN considers the two-side distinction analysis between gene coexpression networks for control and experiment groups. The method allows us to study how the modulated coexpressed gene couples impact biological functions at a GO level. Results We modeled the binary classification with weighted DEGs for each subtype. The binary classifier could make a good prediction for an unseen sample, and the experimental results validated the effectiveness of our proposed approaches. The novel enriched GO terms based on GOEGCN for control and experiment groups of each subtype explain the specific biological function changes according to the two-side distinction of coexpression network structures to some extent. Conclusion The weighted DEGs contain biological importance derived from the gene regulatory network. Based on the weighted DEGs, five binary classifiers were learned and showed good performance concerning the “Sensitivity,” “Specificity,” “Accuracy,” “F1,” and “AUC” metrics. The GOEGCN with weighted DEGs for control and experiment groups presented a novel GO enrichment analysis results and the novel enriched GO terms would further unveil the changes of specific biological functions among all the BRCA subtypes to some extent. The R code in this research is available at https://github.com/yxchspring/GOEGCN_BRCA_Subtypes.

Differential Expression Analysis Revealing CLCA1 to Be a Prognostic and Diagnostic Biomarker for Colorectal Cancer.

Colorectal cancer (CRC) is a common malignant tumor of the digestive tract and lacks specific diagnostic markers. In this study, we utilized 10 public datasets from the NCBI Gene Expression Omnibus (NCBI-GEO) database to identify a set of significantly differentially expressed genes (DEGs) between tumor and control samples and WGCNA (Weighted Gene Co-Expression Network Analysis) to construct gene co-expression networks incorporating the DEGs from The Cancer Genome Atlas (TCGA) and then identify genes shared between the GEO datasets and key modules. Then, these genes were screened via MCC to identify 20 hub genes. We utilized regression analyses to develop a prognostic model and utilized the random forest method to validate. All hub genes had good diagnostic value for CRC, but only CLCA1 was related to prognosis. Thus, we explored the potential biological value of CLCA1. The results of gene set enrichment analysis (GSEA) and immune infiltration analysis showed that CLCA1 was closely related to tumor metabolism and immune invasion of CRC. These analysis results revealed that CLCA1 may be a candidate diagnostic and prognostic biomarker for CRC.

LSTrAP-Crowd: prediction of novel components of bacterial ribosomes with crowd-sourced analysis of RNA sequencing data

BackgroundBacterial resistance to antibiotics is a growing health problem that is projected to cause more deaths than cancer by 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the structurally conserved bacterial ribosomes, factors involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. Here, we use a bioinformatics approach to identify novel components of protein synthesis.ResultsIn order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data.ConclusionsWe identified genes related to protein synthesis in common bacterial pathogens and thus provide a resource of potential antibiotic development targets for experimental validation. The data can be used to explore additional vulnerabilities of bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowd-sourced.

NetExtractor: Extracting a Cerebellar Tissue Gene Regulatory Network Using Differentially Expressed High Mutual Information Binary RNA Profiles.

Bigenic expression relationships are conventionally defined based on metrics such as Pearson or Spearman correlation that cannot typically detect latent, non-linear dependencies or require the relationship to be monotonic. Further, the combination of intrinsic and extrinsic noise as well as embedded relationships between sample sub-populations reduces the probability of extracting biologically relevant edges during the construction of gene co-expression networks (GCNs). In this report, we address these problems via our NetExtractor algorithm. NetExtractor examines all pairwise gene expression profiles first with Gaussian mixture models (GMMs) to identify sample sub-populations followed by mutual information (MI) analysis that is capable of detecting non-linear differential bigenic expression relationships. We applied NetExtractor to brain tissue RNA profiles from the Genotype-Tissue Expression (GTEx) project to obtain a brain tissue specific gene expression relationship network centered on cerebellar and cerebellar hemisphere enriched edges. We leveraged the PsychENCODE pre-frontal cortex (PFC) gene regulatory network (GRN) to construct a cerebellar cortex (cerebellar) GRN associated with transcriptionally active regions in cerebellar tissue. Thus, we demonstrate the utility of our NetExtractor approach to detect biologically relevant and novel non-linear binary gene relationships.

A sparse Bayesian factor model for the construction of gene co-expression networks from single-cell RNA sequencing count data

BackgroundGene co-expression networks (GCNs) are powerful tools that enable biologists to examine associations between genes during different biological processes. With the advancement of new technologies, such as single-cell RNA sequencing (scRNA-seq), there is a need for developing novel network methods appropriate for new types of data.ResultsWe present a novel sparse Bayesian factor model to explore the network structure associated with genes in scRNA-seq data. Latent factors impact the gene expression values for each cell and provide flexibility to account for common features of scRNA-seq: high proportions of zero values, increased cell-to-cell variability, and overdispersion due to abnormally large expression counts. From our model, we construct a GCN by analyzing the positive and negative associations of the factors that are shared between each pair of genes.ConclusionsSimulation studies demonstrate that our methodology has high power in identifying gene-gene associations while maintaining a nominal false discovery rate. In real data analyses, our model identifies more known and predicted protein-protein interactions than other competing network models.

Abstract 995: Integrative genomic characterization and CRISPR-mediated gene editing studies identify IRAKs as novel therapeutic targets for inflammation-driven prostate tumorigenesis

Abstract A major drawback to accurate diagnosis and treatment of inflammation-driven prostate cancer (PCa) progression is our limited understanding of the molecular mechanisms underlying aberrant inflammation signaling in PCa patients. Since asymptomatic chronic inflammation is hard to diagnose, we do not have adequate knowledge of how specific genes within the inflammatory cascade drive PCa aggressiveness. Hence, an integrative genomic approach may provide us with effective prognostic and diagnostic tools to manage inflammation-driven PCa progression. The aim of this study was to characterize inflammation-associated hub genes as predictors of PCa progression and investigate the effect of aberrant signaling of interleukin-1 receptor-associated kinases (IRAKs) on prostate tumorigenesis. WES and RNA-seq datasets from a total of 726 PCa patients, stratified into subtypes: primary PCa (PPC; n=493), metastatic PCa (MPC; n=150) and the castration-resistant PCa (CRPC; n=52) and neuroendocrine PCa (NEPC; n=30) from 3 cohort studies were acquired from cBioPortal. A bioinformatics pipeline was designed to analyze the datasets. Weighted gene co-expression network analysis (WGCNA) was used to construct gene co-expression networks and identify inflammation hub genes associated with PCa progression. The top inflammation hub genes, such as ILR1, TLRs, IRAK1-4, and NF-kB were validated. IRAK1-4 hub genes were found to be differentially expressed and genetically altered in NEPC and CRPC (&amp;gt;40%) compared to PPC and MPC (&amp;lt;20%) patients. GISTIC and MutSigCV were used to identify somatic mutations and copy-number alterations (CNAs). IRAK1 was found to have the highest CNA in CRPC and NEPC compared to MPC and PPC patients. Interestingly, no mutation was identified in CRPC and NEPC patients. The co-occurrence of IRAK1/4 CNVs and mRNA with AR, PI3K and AURKA signaling pathways suggest a role in PCa progression. To mechanistically investigate the role of IRAK1/4 signaling on PCa progression, CRISPR-mediated knockdown of IRAK1/4 was performed in PCa cell lines (LNCaP, PC3, and NCI-H660). mRNA and protein expression analyses were performed to determine changes in the levels of AR, PI3K/AKT, and AURKA between IRAK1/4-/- and IRAK1/4+/+ cell lines. Inhibition of IRAK1/4 significantly altered AR, PI3K, and AURKA signaling, leading to a reduction in PCa viability and metastatic potential. To investigate the effect of aberrant IRAK1/4 signaling on chemoresistance induction, IRAK+/+ PCa cells were treated with either TLR agonists or IRAK1/4 inhibitors and then treated with different anticancer drugs, such as Enzalutamide, Docetaxel, and Alisertib (AURKA inhibitor) dose-dependently. IRAK1/4-/- cells were more susceptible to anticancer drug treatment compared to IRAK1+/+ cells. The combination treatments were shown to be synergistic, thereby suppressing inflammation-induced chemoresistance. In conclusion, we have identified IRAK1/4 as a potential target for treating inflammation-driven prostate cancer progression. Citation Format: Saheed Oluwasina Oseni, Mirjana Pavlovic, James Hartmann, James Kumi-Diaka. Integrative genomic characterization and CRISPR-mediated gene editing studies identify IRAKs as novel therapeutic targets for inflammation-driven prostate tumorigenesis [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 995.

Using mRNAsi to identify prognostic-related genes in endometrial carcinoma based on WGCNA

AimsCancer Stem Cells (CSCs) refers to heterogeneous tumor cells retaining the abilities of self-renewal and differentiation. This study used mRNAsi, which is an index to describe the similarity between tumor cells and CSCs, to define genes involved in endometrial carcinoma. Materials and methodsThe mRNA expression profiles of 552 tumor samples and 23 non-tumor samples were calculated for differentially expressed genes. WGCNA was utilized to construct gene co-expression networks and classify screened genes into different modules. Univariate and multivariate Cox regression models were performed to identify and construct the prognostic model. Time-dependent receiver operating characteristic (ROC), Kaplan–Meier curve, multivariate Cox regression analysis, and nomogram were used to assess the prognostic capacity of the six-gene signature. The screened genes were further validated by GEO (GSE17025) and qRT-PCR in EC tissues. Key findings2573 upregulated and 1890 downregulated genes were identified. A total of 35 genes in the turquoise module were identified as key genes. With multivariate analysis, six genes (DEPDC1, FAM83D, NCAPH, SPC25, TPX2, and TTK) up-regulated in endometrial carcinoma were identified, and their higher expression was associated with a higher stage/age/grade. Moreover, ROC and Kaplan-Meier plots indicated these genes had a high prognostic value for EC. A nomogram was constructed for clinical use. In addition, we explored the pathogenesis involving six genes. The results showed that these genes may become pathogenic as their copy numbers changes and methylation level reduces. Finally, GSEA revealed these genes had a close association with cell cycle, etc. SignificanceThese findings may provide new insights into the treatment of diseases.

Gene Co-expression Is Distance-Dependent in Breast Cancer.

Breast carcinomas are characterized by anomalous gene regulatory programs. As is well-known, gene expression programs are able to shape phenotypes. Hence, the understanding of gene co-expression may shed light on the underlying mechanisms behind the transcriptional regulatory programs affecting tumor development and evolution. For instance, in breast cancer, there is a clear loss of inter-chromosomal (trans-) co-expression, compared with healthy tissue. At the same time cis- (intra-chromosomal) interactions are favored in breast tumors. In order to have a deeper understanding of regulatory phenomena in cancer, here, we constructed Gene Co-expression Networks by using TCGA-derived RNA-seq whole-genome samples corresponding to the four breast cancer molecular subtypes, as well as healthy tissue. We quantify the cis-/trans- co-expression imbalance in all phenotypes. Additionally, we measured the association between co-expression and physical distance between genes, and characterized the ratio of intra/inter-cytoband interactions per phenotype. We confirmed loss of trans- co-expression in all molecular subtypes. We also observed that gene cis- co-expression decays abruptly with distance in all tumors in contrast with healthy tissue. We observed co-expressed gene hotspots, that tend to be connected at cytoband regions, and coincide accurately with already known copy number altered regions, such as Chr17q12, or Chr8q24.3 for all subtypes. Our methodology recovered different alterations already reported for specific breast cancer subtypes, showing how co-expression network approaches might help to capture distinct events that modify the cell regulatory program.

TDAview: an online visualization tool for topological data analysis

TDAview is an online tool for topological data analysis (TDA) and visualization. It implements the Mapper algorithm for TDA and provides extensive graph visualization options. TDAview is a user-friendly tool that allows biologists and clinicians without programming knowledge to harness the power of TDA. TDAview supports an analysis and visualization mode in which a Mapper graph is constructed based on user-specified parameters, followed by graph visualization. It can also be used in a visualization only mode in which TDAview is used for visualizing the data properties of a Mapper graph generated using other open-source software. The graph visualization options allow data exploration by graphical display of metadata variable values for nodes and edges, as well as the generation of publishable figures. TDAview can handle large datasets, with tens of thousands of data points, and thus has a wide range of applications for high-dimensional data, including the construction of topology-based gene co-expression networks. TDAview is a free online tool available at https://voineagulab.github.io/TDAview/. The source code, usage documentation and example data are available at TDAview GitHub repository: https://github.com/Voineagulab/TDAview.

An Integrative Framework of Heterogeneous Genomic Data for Cancer Dynamic Modules Based on Matrix Decomposition.

Cancer progression is dynamic, and tracking dynamic modules is promising for cancer diagnosis and therapy. Accumulated genomic data provide us an opportunity to investigate the underlying mechanisms of cancers. However, as far as we know, no algorithm has been designed for dynamic modules by integrating heterogeneous omics data. To address this issue, we propose an integrative framework for dynamic module detection based on regularized nonnegative matrix factorization method (DrNMF) by integrating the gene expression and protein interaction network. To remove the heterogeneity of genomic data, we divide the samples of expression profiles into groups to construct gene co-expression networks. To characterize the dynamics of modules, the temporal smoothness framework is adopted, in which the gene co-expression network at the previous stage and protein interaction network are incorporated into the objective function of DrNMF via regularization. The experimental results demonstrate that DrNMF is superior to state-of-the-art methods in terms of accuracy. For breast cancer data, the obtained dynamic modules are more enriched by the known pathways, and can be used to predict the stages of cancers and survival time of patients. The proposed model and algorithm provide an effective integrative analysis of heterogeneous genomic data for cancer progression.

Abstract C010: The wiring between genes and short noncoding RNAs in cancer depends on race/ethnicity and sex

Abstract Short noncoding RNAs (ncRNAs) have been attracting increasing interest as regulators of messenger RNA (mRNA) abundance. Such regulatory molecules can have influence the abundance of mRNAs both positively and negatively. Two important categories of short ncRNAs are microRNAs (miRNAs) and their isoforms (isomiRs), and the tRNA-derived fragments (tRFs). We previously showed that their “wiring” with mRNA abundance strongly depends on the race/ethnicity in triple-negative breast cancer. Here, we focused on two cancers, bladder urothelial carcinoma (BLCA) and stomach adenocarcinoma (STAD). Men and women have been known to exhibit different incidence rates of BLCA, as well as differences in invasion, and overall aggressiveness. Analogous observations hold true when comparing White and Asian individuals in the case of STAD. We mined a total of 615 primary tumors, 337 from STAD and 278 from BLCA patients. After stringent abundance filtering, we identified 1,478 distinct isomiRs and 355 distinct tRFs. We found that the abundance profiles of these molecules exhibited strong tissue dependencies. We constructed gene co-expression networks for the healthy and disease states. Mining the correlations of isomiRs/tRFs with mRNAs, we found that core cancer pathways are wired with small ncRNAs. We carried out a large-scale computational analysis of the correlation patterns to dissect the dependencies on race/ethnicity and sex in STAD and BLCA, respectively. We found that core pathways are differentially wired in each case. For example, in BLCA, cell cycle genes are linked with several tRFs in one sex but not in the other. While the contribution of isomiRs in sex disparities in BLCA is limited, we find that isomiRs exhibit strong sex-independent associations with immune system responses and extracellular matrix and cell adhesion. In the case of STAD, we observed several instances of differential-by-race/ethnicity correlations of tRFs with genes involved in cell signaling, like ITPA and PICK1 and numerous cell adhesion genes differentially wired with isomiRs. Our data provide a detailed perspective of the structure and hubs of the gene expression networks in BLCA and STAD as well as their sex- and race/ethnicity-specific components. The identification and discovery of context-specific potential regulators have considerable ramifications for precision medicine and can be leveraged to facilitate the design of better-targeted treatments that are tuned to the patient's sex and race. Citation Format: Aristeidis G. Telonis, Isidore Rigoutsos. The wiring between genes and short noncoding RNAs in cancer depends on race/ethnicity and sex [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr C010.

Recommendations for the analysis of gene expression data to identify intrinsic differences between similar tissues

Identifying genes involved in functional differences between similar tissues from expression profiles is challenging, because the expected differences in expression levels are small. To exemplify this challenge, we studied the expression profiles of two skeletal muscles, deltoid and biceps, in healthy individuals. We provide a series of guides and recommendations for the analysis of this type of studies. These include how to account for batch effects and inter-individual differences to optimize the detection of gene signatures associated with tissue function. We provide guidance on the selection of optimal settings for constructing gene co-expression networks through parameter sweeps of settings and calculation of the overlap with an established knowledge network. Our main recommendation is to use a combination of the data-driven approaches, such as differential gene expression analysis and gene co-expression network analysis, and hypothesis-driven approaches, such as gene set connectivity analysis. Accordingly, we detected differences in metabolic gene expression between deltoid and biceps that were supported by both data- and hypothesis-driven approaches. Finally, we provide a bioinformatic framework that support the biological interpretation of expression profiles from related tissues from this combination of approaches, which is available at github.com/tabbassidaloii/AnalysisFrameworkSimilarTissues.