DCGL v2.0: an R package for unveiling differential regulation from differential co-expression

A Novel Strategy to Identify Prognosis-Relevant Gene Sets in Cancers

Molecular prognosis markers hold promise for improved prediction of patient survival, and a pathway or gene set may add mechanistic interpretation to their prognostic prediction power. In this study, we demonstrated a novel strategy to identify prognosis-relevant gene sets in cancers. Our study consists of a first round of gene-level analyses and a second round of gene-set-level analyses, in which the Composite Gene Expression Score critically summarizes a surrogate expression value at gene set level and a permutation procedure is exerted to assess prognostic significance of gene sets. An optional differential coexpression module is appended to the two phases of survival analyses to corroborate and refine prognostic gene sets. Our strategy was demonstrated in 33 cancer types across 32,234 gene sets. We found oncogenic gene sets accounted for an increased proportion among the final gene sets, and genes involved in DNA replication and DNA repair have ubiquitous prognositic value for multiple cancer types. In summary, we carried out the largest gene set based prognosis study to date. Compared to previous similar studies, our approach offered multiple improvements in design and methodology implementation. Functionally relevant gene sets of ubiquitous prognostic significance in multiple cancer types were identified.

The dynamic alteration of transcriptional regulation by crucial TFs during tumorigenesis of gastric cancer

Background

The mechanisms of Gastric cancer (GC) initiation and progression are complicated, at least partly owing to the dynamic changes of gene regulation during carcinogenesis. Thus, investigations on the changes in regulatory networks can improve the understanding of cancer development and provide novel insights into the molecular mechanisms of cancer.

Methods

Differential co-expression analysis (DCEA), differential gene regulation network (GRN) modeling and differential regulation analysis (DRA) were integrated to detect differential transcriptional regulation events between gastric normal mucosa and cancer samples based on GSE54129 dataset. Cytological experiments and IHC staining assays were used to validate the dynamic changes of CREB1 regulated targets in different stages.

Results

A total of 1955 differentially regulated genes (DRGs) were identified and prioritized in a quantitative way. Among the top 1% DRGs, 14 out of 19 genes have been reported to be GC relevant. The four transcription factors (TFs) among the top 1% DRGs, including CREB1, BPTF, GATA6 and CEBPA, were regarded as crucial TFs relevant to GC progression. The differentially regulated links (DRLs) around the four crucial TFs were then prioritized to generate testable hypotheses on the differential regulation mechanisms of gastric carcinogenesis. To validate the dynamic alterations of gene regulation patterns of crucial TFs during GC progression, we took CREB1 as an example to screen its differentially regulated targets by using cytological and IHC staining assays. Eventually, TCEAL2 and MBNL1 were proved to be differentially regulated by CREB1 during tumorigenesis of gastric cancer.

Conclusions

By combining differential networking information and molecular cell experiments verification, testable hypotheses on the regulation mechanisms of GC around the core TFs and their top ranked DRLs were generated. Since TCEAL2 and MBNL1 have been reported to be potential therapeutic targets in SCLC and breast cancer respectively, their translation values in GC are worthy of further investigation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-022-00468-7.

Identification and Validation of Key Genes of Differential Correlations in Gastric Cancer

Background: Gastric cancer (GC) is aggressive cancer with a poor prognosis. Previously bulk transcriptome analysis was utilized to identify key genes correlated with the development, progression and prognosis of GC. However, due to the complexity of the genetic mutations, there is still an urgent need to recognize core genes in the regulatory network of GC.

Methods: Gene expression profiles (GSE66229) were retrieved from the GEO database. Weighted correlation network analysis (WGCNA) was employed to identify gene modules mostly correlated with GC carcinogenesis. R package ‘DiffCorr’ was applied to identify differentially correlated gene pairs in tumor and normal tissues. Cytoscape was adopted to construct and visualize the gene regulatory network.

Results: A total of 15 modules were detected in WGCNA analysis, among which three modules were significantly correlated with GC. Then genes in these modules were analyzed separately by “DiffCorr”. Multiple differentially correlated gene pairs were recognized and the network was visualized by the software Cytoscape. Moreover, GEMIN5 and PFDN2, which were rarely discussed in GC, were identified as key genes in the regulatory network and the differential expression was validated by real-time qPCR, WB and IHC in cell lines and GC patient tissues.

Conclusions: Our research has shed light on the carcinogenesis mechanism by revealing differentially correlated gene pairs during transition from normal to tumor. We believe the application of this network-based algorithm holds great potential in inferring relationships and detecting candidate biomarkers.

PPARα and PPARγ activation is associated with pleural mesothelioma invasion but therapeutic inhibition is ineffective

Mesothelioma is a cancer that typically originates in the pleura of the lungs. It rapidly invades the surrounding tissues, causing pain and shortness of breath. We compared cell lines injected either subcutaneously or intrapleurally and found that only the latter resulted in invasive and rapid growth. Pleural tumors displayed a transcriptional signature consistent with increased activity of nuclear receptors PPARα and PPARγ and with an increased abundance of endogenous PPAR-activating ligands. We found that chemical probe GW6471 is a potent, dual PPARα/γ antagonist with anti-invasive and anti-proliferative activity in vitro. However, administration of GW6471 at doses that provided sustained plasma exposure levels sufficient for inhibition of PPARα/γ transcriptional activity did not result in significant anti-mesothelioma activity in mice. Lastly, we demonstrate that the in vitro anti-tumor effect of GW6471 is off-target. We conclude that dual PPARα/γ antagonism alone is not a viable treatment modality for mesothelioma.

Identification of Key Genes in 'Luang Pratahn', Thai Salt-Tolerant Rice, Based on Time-Course Data and Weighted Co-expression Networks

Salinity is an important environmental factor causing a negative effect on rice production. To prevent salinity effects on rice yields, genetic diversity concerning salt tolerance must be evaluated. In this study, we investigated the salinity responses of rice (Oryza sativa) to determine the critical genes. The transcriptomes of 'Luang Pratahn' rice, a local Thai rice variety with high salt tolerance, were used as a model for analyzing and identifying the key genes responsible for salt-stress tolerance. Based on 3' Tag-Seq data from the time course of salt-stress treatment, weighted gene co-expression network analysis was used to identify key genes in gene modules. We obtained 1,386 significantly differentially expressed genes in eight modules. Among them, six modules indicated a significant correlation within 6, 12, or 48h after salt stress. Functional and pathway enrichment analysis was performed on the co-expressed genes of interesting modules to reveal which genes were mainly enriched within important functions for salt-stress responses. To identify the key genes in salt-stress responses, we considered the two-state co-expression networks, normal growth conditions, and salt stress to investigate which genes were less important in a normal situation but gained more impact under stress. We identified key genes for the response to biotic and abiotic stimuli and tolerance to salt stress. Thus, these novel genes may play important roles in salinity tolerance and serve as potential biomarkers to improve salt tolerance cultivars.

Identification of HIV-1-specific cascaded microRNA-mRNA regulatory relationships by parallel mRNA and microRNA expression profiling with AIDS patients after antiviral treatment

BACKGROUND

The pathogenesis of human immunodeficiency virus 1 (HIV-1) infection is so complex that have not been clearly defined, despite intensive efforts have been made by many researchers. MicroRNA (miRNA) as regulation factor in various human diseases may influence the course of HIV-1 infection by targeting mRNAs. Thus, studies combining transcription of posttranscriptional miRNA regulation are required.

METHODS

With the purpose of identifying cascaded miRNA-mRNA regulatory relationships related to HIV infection in gene level, the parallel miRNA, and mRNA expression profiles were analyzed to select differential expressed miRNAs and mRNAs. Then, miRNA-mRNA interactions were predicted using 3 data sources and Pearson correlation coefficient was calculated based on the gene expression level for accuracy improvement. Furthermore, the calculation of the regulatory impact factors was conducted to reveal crucial regulators in HIV-1 infection. To give further insight into these transcription factor (TF) regulators, the differentially co-expression analysis was conducted to identify differentially co-expressed links and differential co-expressed genes and the co-expression gene modules were identified using a threshold-based hierarchical clustering method, then modules were combined into a miRNA-TF-mRNA network.

RESULTS

A total of 69,126 differentially co-expressed links and 626 differential co-expressed genes were identified. Functional enrichment analysis indicated that these co-expressed genes were significantly involved in immune response and apoptosis. Moreover, according to regulatory impact factors, 5 most influential TFs and miRNA in HIV-1 infection were identified and miRNA-TF-mRNA regulatory networks were built during the computing process.

CONCLUSIONS

In our study, a set of integrated methods was generated to identify important regulators and miRNA-TF-mRNA interactions. Parallel profiling analysis of the miRNAs and mRNAs expression of HIV/acquired immunodeficiency syndrome (AIDS) patients after antiretroviral therapy indicated that some regulators have wide impact on gene regulation and that these regulatory elements may bear significant implications on the underlying molecular mechanism and pathogenesis of AIDS occurrence.

Metabolome and transcriptome profiling of Theobroma cacao provides insights into the molecular basis of pod color variation

The Theobroma cacao presents a wide diversity in pod color among different cultivars. Although flavonoid biosynthesis has been studied in many plants, molecular mechanisms governing the diversity of coloration in cacao pods are largely unknown. The flavonoid metabolite profiles and flavonoid biosynthetic gene expression in the pod exocarps of light green pod 'TAS 410' (GW), green pod 'TAS 166' (GF), and mauve pod 'TAS 168' (PF) were determined. Changes in flavonoid metabolites, particularly the anthocyanins (cyanidin 3-O-galactoside, cyanidin 3-O-glucoside, and cyanidin O-syringic acid) were significantly up-accumulated in the mauve phenotype (PF) compared to the light green or green phenotypes, endowing the pod color change from light green or green to mauve. Consistently, the PF phenotype showed different expression patterns of flavonoid biosynthetic structural genes in comparison with GW/GF phenotypes. The expression level of LAR and ANR in GW/GF was significantly higher than PF, while the expression level of UFGT in GW/GF was lower than PF. These genes likely generated more anthocyanins in the exocarps samples of PF than that of GW/GF. Simultaneously, colorless flavan-3-ols (catechin, epicatechin and proanthocyanidin) content in the exocarp samples of PF was lower than GW/GF. Additionally, MYB (gene18079) and bHLH (gene5045 and gene21575) may participate in the regulation of the pod color. This study sheds light on the molecular basis of cacao pod color variation, which will contribute to breeding cacao varieties with enhanced flavonoid profiles for nutritional applications.

IRF7-Associated Immunophenotypes Have Dichotomous Responses to Virus/Allergen Coexposure and OM-85-Induced Reprogramming

High risk for virus-induced asthma exacerbations in children is associated with an IRF7lo immunophenotype, but the underlying mechanisms are unclear. Here, we applied a Systems Biology approach to an animal model comprising rat strains manifesting high (BN) versus low susceptibility (PVG) to experimental asthma, induced by virus/allergen coexposure, to elucidate the mechanism(s)-of-action of the high-risk asthma immunophenotype. We also investigated potential risk mitigation via pretreatment with the immune training agent OM-85. Virus/allergen coexposure in low-risk PVG rats resulted in rapid and transient airways inflammation alongside IRF7 gene network formation. In contrast, responses in high-risk BN rats were characterized by severe airways eosinophilia and exaggerated proinflammatory responses that failed to resolve, and complete absence of IRF7 gene networks. OM-85 had more profound effects in high-risk BN rats, inducing immune-related gene expression changes in lung at baseline and reducing exaggerated airway inflammatory responses to virus/allergen coexposure. In low-risk PVG rats, OM-85 boosted IRF7 gene networks in the lung but did not alter baseline gene expression or cellular influx. Distinct IRF7-associated asthma risk immunophenotypes have dichotomous responses to virus/allergen coexposure and respond differentially to OM-85 pretreatment. Extrapolating to humans, our findings suggest that the beneficial effects OM-85 pretreatment may preferentially target those in high-risk subgroups.

Comparative Genomics Identifies Putative Interspecies Mechanisms Underlying Crbn-Sall4-Linked Thalidomide Embryopathy

The identification of thalidomide–Cereblon-induced SALL4 degradation has brought new understanding for thalidomide embryopathy (TE) differences across species. Some questions, however, regarding species variability, still remain. The aim of this study was to detect sequence divergences between species, affected or not by TE, and to evaluate the regulated gene co-expression in a murine model. Here, we performed a comparative analysis of proteins experimentally established as affected by thalidomide exposure, evaluating 14 species. The comparative analysis, regarding synteny, neighborhood, and protein conservation, was performed in 42 selected genes. Differential co-expression analysis was performed, using a publicly available assay, GSE61306, which evaluated mouse embryonic stem cells (mESC) exposed to thalidomide. The comparative analyses evidenced 20 genes in the upstream neighborhood of NOS3, which are different between the species who develop, or not, the classic TE phenotype. Considering protein sequence alignments, RECQL4, SALL4, CDH5, KDR, and NOS2 proteins had the biggest number of variants reported in unaffected species. In co-expression analysis, Crbn was a gene identified as a driver of the co-expression of other genes implicated in genetic, non-teratogenic, limb reduction defects (LRD), such as Tbx5, Esco2, Recql4, and Sall4; Crbn and Sall4 were shown to have a moderate co-expression correlation, which is affected after thalidomide exposure. Hence, even though the classic TE phenotype is not identified in mice, a deregulatory Crbn-induced mechanism is suggested in this animal. Functional studies are necessary, especially evaluating the genes responsible for LRD syndromes and their interaction with thalidomide–Cereblon.

Rewired Pathways and Disrupted Pathway Crosstalk in Schizophrenia Transcriptomes by Multiple Differential Coexpression Methods

Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found “Clostridium neurotoxicity” and “signaling events mediated by focal adhesion kinase” had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes.

Transcriptome regulation of carotenoids in five flesh-colored watermelons (Citrullus lanatus)

BACKGROUND

Fruit flesh color in watermelon (Citrullus lanatus) is a great index for evaluating the appearance quality and a key contributor influencing consumers' preferences. But the molecular mechanism of this intricate trait remains largely unknown. Here, the carotenoids and transcriptome dynamics during the fruit development of cultivated watermelon with five different flesh colors were analyzed.

RESULTS

A total of 13 carotenoids and 16,781 differentially expressed genes (DEGs), including 1295 transcription factors (TFs), were detected in five watermelon genotypes during the fruit development. The comprehensive accumulation patterns of carotenoids were closely related to flesh color. A number of potential structural genes and transcription factors were found to be associated with the carotenoid biosynthesis pathway using comparative transcriptome analysis. The differentially expressed genes were divided into six subclusters and distributed in different GO terms and metabolic pathways. Furthermore, we performed weighted gene co-expression network analysis and predicted the hub genes in six main modules determining carotenoid contents. Cla018406 (a chaperone protein dnaJ-like protein) may be a candidate gene for β-carotene accumulation and highly expressed in orange flesh-colored fruit. Cla007686 (a zinc finger CCCH domain-containing protein) was highly expressed in the red flesh-colored watermelon, maybe a key regulator of lycopene accumulation. Cla003760 (membrane protein) and Cla021635 (photosystem I reaction center subunit II) were predicted to be the hub genes and may play an essential role in yellow flesh formation.

CONCLUSIONS

The composition and contents of carotenoids in five watermelon genotypes vary greatly. A series of candidate genes were revealed through combined analysis of metabolites and transcriptome. These results provide an important data resource for dissecting candidate genes and molecular basis governing flesh color formation in watermelon fruit.

Pan-Cancer Analysis of Head-to-Head Gene Pairs in Terms of Transcriptional Activity, Co-expression and Regulation

Background

Head-to-Head (H2H) gene pairs are regulated by bidirectional promoters and divergently transcribed from opposite DNA strands with transcription start sites (TSSs) separated within 1 kb. H2H organization is ancient and conserved, and H2H pairs tend to exhibit similar expression patterns. Although some H2H genes have been reported to be associated with disease and cancer, there is a lack of systematic studies on H2H organization in the scenario of cancer development.

Methods

Human H2H gene pairs were identified based on GENCODE hg19 and the functional relevance of H2H pairs was explored through function enrichment and semantic similarity analysis. To investigate the association between H2H organization and carcinogenesis, pan-cancer differential analysis of H2H genes about transcriptional activity, co-expression and transcriptional regulation by transcription factors and enhancers were performed based on data from The Cancer Genome Atlas. Cox proportional hazards regression model and log-rank test were used to determine the prognostic powers of H2H pairs.

Results

In the present study, we first updated H2H genes from 1,447 to 3,150 pairs, from which the peak group with TSS distance of 1–100 was observed as expected in our previous work. It was found that housekeeping genes, mitochondrial-functional associated genes and cancer genes tend to be organized in H2H arrangement. Pan-cancer analysis indicates that H2H genes are transcriptionally active than random genes in both normal and cancer tissues, but H2H pairs display higher correlation in cancer than in normal. Particularly, housekeeping H2H pairs are differentially correlated much more significantly than non-housekeeping H2H pairs are. Some of differentially correlated H2H pairs were found to be associated with prognosis. The alteration of TF similarity seems to contribute to differential co-expression of H2H pairs during carcinogenesis; meanwhile remote enhancers also at least partly explain the differential co-expression and co-regulation of H2H pairs.

Conclusion

H2H pairs tend to show much stronger positive expression correlation in cancer than in normal due to differential regulation of bidirectional promoters. The study provides insights into the significance of H2H organization in carcinogenesis and the underlying dysfunctional regulation mechanisms. Those differentially correlated H2H pairs associated with survival have the potential to be prognostic biomarkers and therapeutic targets for cancer.

Isolation and Functional Analysis of Genes Involved in Polyacylated Anthocyanin Biosynthesis in Blue Senecio cruentus

Polyacylated anthocyanins with multiple glycosyl and aromatic acyl groups tend to make flowers display bright and stable blue colours. However, there are few studies on the isolation and functional characterization of genes involved in the polyacylated anthocyanin biosynthesis mechanism, which limits the molecular breeding of truly blue flowers. Senecio cruentus is an important potted ornamental plant, and its blue flowers contain 3',7-polyacylated delphinidin-type anthocyanins that are not reported in any other plants, suggesting that it harbours abundant gene resources for the molecular breeding of blue flowers. In this study, using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) analysis of blue, carmine and white colours of cineraria cultivars "Venezia" (named VeB, VeC, and VeW, respectively), we found that 3',7-polyacylated anthocyanin, cinerarin, was the main pigment component that determined the blue colour of ray florets of cineraria. Based on the transcriptome sequencing and differential gene expression (DEG) analysis combined with RT- and qRT-PCR, we found two genes encoding uridine diphosphate glycosyltransferase, named ScUGT1 and ScUGT4; two genes encoding acyl-glucoside-dependent glucosyltransferases which belong to glycoside hydrolase family 1 (GH1), named ScAGGT11 and ScAGGT12; one gene encoding serine carboxypeptidase-like acyltransferase ScSCPL2; and two MYB transcriptional factor genes ScMYB2 and ScMYB4, that were specifically highly expressed in the ray florets of VeB, which indicated that these genes may be involved in cinerarin biosynthesis. The function of ScSCPL2 was analysed by virus-induced gene silencing (VIGS) in cineraria leaves combined with HPLC-MS/MS. ScSCPL2 mainly participated in the 3' and 7-position acylation of cinerarin. These results will provide new insight into the molecular basis of the polyacylated anthocyanin biosynthesis mechanism in higher plants and are of great significance for blue flower molecular breeding of ornamental plants.

Analysis of master transcription factors related to Parkinson's disease through the gene transcription regulatory network

INTRODUCTION

This study investigated the relationships between differentially co-expressed gene pairs or links (DCLs) and transcription factors (TFs) in the gene transcription regulatory network (GTRN) to clarify the molecular mechanisms underlying the pathogenesis of Parkinson's disease (PD).

MATERIAL AND METHODS

Microarray dataset GSE7621 from Gene Expression Omnibus (GEO) was used to identify differentially expressed genes (DEGs) and perform Gene Ontology (GO) enrichment analysis. Differentially co-expressed genes (DCGs) and DCLs were identified by the DCGL package in R soft-ware. DCLs that were potentially related to the regulation mechanisms, and corresponding TFs, were identified using the DR sort function in the DCGL V2.0 package. The GTRN was constructed with these DCLs-TFs, and visualized with Cytoscape software.

RESULTS

A total of 131 DEGs, including 77 up-regulated DEGs and 54 down-regulated DEGs, were identified, which were mainly enriched for plasma membrane, cell activities, and metabolism. We found that ICAM1-LTBP and CTHRC1-UTP3 might alter gene regulation relationships in PD. The GTRN was constructed with DCLs-TFs, including 348 nodes (118 TFs and 230 DCGs) and 1045 DCLs. These TFs (AHR, SP1, PAX5, etc.) could regulate many target genes (e.g. ICAM1 and LTBP) in the GTRN of PD.

CONCLUSIONS

ICAM1 and LTBP may play a role in PD symptom development and pathology, and might be regulated by important TFs (AHR, SP1, PAX5, etc.) identified in the GTRN of PD. These findings may help elucidate the molecular mechanisms underlying PD and find a novel drug target for this disease.

Identifying Key Genes for Nasopharyngeal Carcinoma by Prioritized Consensus Differentially Expressed Genes Caused by Aberrant Methylation

Background: Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus (EBV)-associated epithelial malignancy. Large-scale genetics or epigenetics studies of NPC have been relatively scarce and sporadic, and there are no effective targeted drugs for NPC. Integrative analysis of multiple different omics profiles has been proved to be an effective approach to shed new light on cancer.

Methods: We developed a pipeline to aggregate consensus differentially expressed genes (DEGs) from multiple expression datasets from different platforms. Integrated bioinformatics analysis of DNA methylation and gene expression was used to prioritize key genes in NPC. We explored the biological and clinical importance of key genes, combining differential co-expression analysis, network analysis of protein-protein and microRNA (miRNA)-target interactions, and pan-cancer survival analysis.

Results: We obtained 668 upregulated and 594 downregulated consensus DEGs, which enriched in the PI3K-AKT, NF-κB and immune-related pathways. In NPC, 98% of 3364 differentially methylated sites were hypermethylated. Actively expressed EBV gene EBNA1 was positively correlated with over-expressed genes coding DNA methyltransferase and Polycomb group proteins, suggesting that EBV infection may have an important role in the hypermethylation of NPC. Through integrated analysis of DNA methylation and mRNA and miRNA expression profiles, we prioritized 56 hypermethylated downregulated genes, including 7 tumor suppressor genes, and constructed a miRNA-target regulation network consisting of 12 hypermethylated miRNAs and 25 upregulated oncogenes. The promoter hypermethylation of PRKCB causing its downregulation was validated by experimental results and higher PRKCB expression was associated with longer overall survival in head-neck squamous cell carcinoma, suggesting the potential of PRKCB as a promising disease biomarker for NPC.

Conclusions: Our integrative analysis provides reliable key genes for candidate biomarkers for diagnosis and prognosis in NPC. Based on the combined evidence of promoter hypermethylation, expression up-regulation, and association with overall survival, genes such as SCUBE2, PRKCB, IKZF1, MAP4K1, and GATA6 could be promising novel diagnostic biomarkers, and miRNAs including MIR150, MIR152, and MIR34 could be candidate prognosis biomarkers.

Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression

Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes’ mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.

Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA)

Biological and medical sciences are increasingly acknowledging the significance of gene co-expression-networks for investigating complex-systems, phenotypes or diseases. Typically, complex phenotypes are investigated under varying conditions. While approaches for comparing nodes and links in two networks exist, almost no methods for the comparison of multiple networks are available and—to best of our knowledge—no comparative method allows for whole transcriptomic network analysis. However, it is the aim of many studies to compare networks of different conditions, for example, tissues, diseases, treatments, time points, or species. Here we present a method for the systematic comparison of an unlimited number of networks, with unlimited number of transcripts: Co-expression Differential Network Analysis (CoDiNA). In particular, CoDiNA detects links and nodes that are common, specific or different among the networks. We developed a statistical framework to normalize between these different categories of common or changed network links and nodes, resulting in a comprehensive network analysis method, more sophisticated than simply comparing the presence or absence of network nodes. Applying CoDiNA to a neurogenesis study we identified candidate genes involved in neuronal differentiation. We experimentally validated one candidate, demonstrating that its overexpression resulted in a significant disturbance in the underlying gene regulatory network of neurogenesis. Using clinical studies, we compared whole transcriptome co-expression networks from individuals with or without HIV and active tuberculosis (TB) and detected signature genes specific to HIV. Furthermore, analyzing multiple cancer transcription factor (TF) networks, we identified common and distinct features for particular cancer types. These CoDiNA applications demonstrate the successful detection of genes associated with specific phenotypes. Moreover, CoDiNA can also be used for comparing other types of undirected networks, for example, metabolic, protein-protein interaction, ecological and psychometric networks. CoDiNA is publicly available as an R package in CRAN (https://CRAN.R-project.org/package=CoDiNA).

Expression correlation attenuates within and between key signaling pathways in chronic kidney disease

Background

Compared to the conventional differential expression approach, differential coexpression analysis represents a different yet complementary perspective into diseased transcriptomes. In particular, global loss of transcriptome correlation was previously observed in aging mice, and a most recent study found genetic and environmental perturbations on human subjects tended to cause universal attenuation of transcriptome coherence. While methodological progresses surrounding differential coexpression have helped with research on several human diseases, there has not been an investigation of coexpression disruptions in chronic kidney disease (CKD) yet.

Methods

RNA-seq was performed on total RNAs of kidney tissue samples from 140 CKD patients. A combination of differential coexpression methods were employed to analyze the transcriptome transition in CKD from the early, mild phase to the late, severe kidney damage phase.

Results

We discovered a global expression correlation attenuation in CKD progression, with pathway Regulation of nuclear SMAD2/3 signaling demonstrating the most remarkable intra-pathway correlation rewiring. Moreover, the pathway Signaling events mediated by focal adhesion kinase displayed significantly weakened crosstalk with seven pathways, including Regulation of nuclear SMAD2/3 signaling. Well-known relevant genes, such as ACTN4, were characterized with widespread correlation disassociation with partners from a wide array of signaling pathways.

Conclusions

Altogether, our analysis reported a global expression correlation attenuation within and between key signaling pathways in chronic kidney disease, and presented a list of vanishing hub genes and disrupted correlations within and between key signaling pathways, illuminating on the pathophysiological mechanisms of CKD progression.

Comparative transcriptome analysis of Parkinson's disease and Hutchinson-Gilford progeria syndrome reveals shared susceptible cellular network processes

BACKGROUND

Parkinson's Disease (PD) and Hutchinson-Gilford Progeria Syndrome (HGPS) are two heterogeneous disorders, which both display molecular and clinical alterations associated with the aging process. However, similarities and differences between molecular changes in these two disorders have not yet been investigated systematically at the level of individual biomolecules and shared molecular network alterations.

METHODS

Here, we perform a comparative meta-analysis and network analysis of human transcriptomics data from case-control studies for both diseases to investigate common susceptibility genes and sub-networks in PD and HGPS. Alzheimer's disease (AD) and primary melanoma (PM) were included as controls to confirm that the identified overlapping susceptibility genes for PD and HGPS are non-generic.

RESULTS

We find statistically significant, overlapping genes and cellular processes with significant alterations in both diseases. Interestingly, the majority of these shared affected genes display changes with opposite directionality, indicating that shared susceptible cellular processes undergo different mechanistic changes in PD and HGPS. A complementary regulatory network analysis also reveals that the altered genes in PD and HGPS both contain targets controlled by the upstream regulator CDC5L.

CONCLUSIONS

Overall, our analyses reveal a significant overlap of affected cellular processes and molecular sub-networks in PD and HGPS, including changes in aging-related processes that may reflect key susceptibility factors associated with age-related risk for PD.

Gene dysregulation analysis builds a mechanistic signature for prognosis and therapeutic benefit in colorectal cancer

The implementation of cancer precision medicine requires biomarkers or signatures for predicting prognosis and therapeutic benefits. Most of current efforts in this field are paying much more attention to predictive accuracy than to molecular mechanistic interpretability. Mechanism-driven strategy has recently emerged, aiming to build signatures with both predictive power and explanatory power. Driven by this strategy, we developed a robust gene dysregulation analysis framework with machine learning algorithms, which is capable of exploring gene dysregulations underlying carcinogenesis from high-dimensional data with cooperativity and synergy between regulators and several other transcriptional regulation rules taken into consideration. We then applied the framework to a colorectal cancer (CRC) cohort from The Cancer Genome Atlas. The identified CRC-related dysregulations significantly covered known carcinogenic processes and exhibited good prognostic effect. By choosing dysregulations with greedy strategy, we built a four-dysregulation (4-DysReg) signature, which has the capability of predicting prognosis and adjuvant chemotherapy benefit. 4-DysReg has the potential to explain carcinogenesis in terms of dysfunctional transcriptional regulation. These results demonstrate that our gene dysregulation analysis framework could be used to develop predictive signature with mechanistic interpretability for cancer precision medicine, and furthermore, elucidate the mechanisms of carcinogenesis.

Prognostic prediction of carcinoma by a differential-regulatory-network-embedded deep neural network

The accurate prognostic prediction is essential for precise diagnosis and treatment of carcinoma. In addition to clinical survival prediction method, many computational methods based on transcriptomic data have been proposed to build the prediction models and study the prognosis of cancer patients. We propose a differential-regulatory-network-embedded deep neural network (DRE-DNN) method by integrating differential regulatory analysis based on gene co-expression network and deep neural network (DNN) method. From three public hepatocellular carcinoma (HCC) datasets, we derive differential regulatory network and embed regulatory information into DNN. By employing 1869 differential regulatory genes and survival data, we apply DRE-DNN to build a prediction model. We compare our method with the one which has all gene features in normal DNN, and results show that our method has better generalization ability and accuracy. We modify the normal DNN and develop an efficient method to predict prognosis of HCC from gene expression data. Our method decreases the inconsistence caused by the overfitting problem when the training sample size is small. DRE-DNN is also extendable for prognostic prediction of other cancers.

(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices

Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.

Excessive supply of glucose elicits an NF-κB2-dependent glycolysis in lactating goat mammary glands

During lactation, an improper glucose supply often threatens mammary gland (MG) health. However, information is limited on the metabolic trajectories and molecules that regulate lactating MGs with an excessive glucose supply. Based on the network analysis of transcriptome and microRNAs, we found that the oversupply of glucose-induced severe glucose metabolic disorders in MGs of lactating goats, shifting lactose synthesis to acute fermentative glycolysis which caused increased flux of glucose metabolism into lactate. Moreover, NF-κB2 played a key role in regulating glycolysis, exhibiting a metabolic shift when MGs had an excessive supply of glucose. In primary mammary epithelial cells, fermentative glycolysis, and intracellular concentration of reactive oxygen species (ROS) were reduced by ganoderic acid A through blocking NF-κB2, while activation of NF-κB2 with phorbol myristate acetate (PMA) upregulated fermentative glycolysis and increased cellular ROS accumulation under excessive glucose. Thus, we established an NF-κB2-targeting method to reform the metabolic shift toward glycolysis caused by glucose oversupply by integrating NF-κB2 blockade and intracellular ROS scavenging.

Enhanced identification of significant regulators of gene expression

Background

Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of specific regulators, normally transcription factors. This study explores alternative measures of correlation, using the Fisher and Sobolev metrics, and an extended set of regulators, including epigenetic regulators and long non-coding RNAs (lncRNAs). Data on prostate cancer have been used to explore the effect of these modifications.

Results

A tool for computation of RIF scores with alternative correlation measures and extended sets of regulators was developed and tested on gene expression data for prostate cancer. The study showed that the Fisher and Sobolev metrics lead to improved identification of well-documented regulators of gene expression in prostate cancer, and the sets of identified key regulators showed improved overlap with previously defined gene sets of relevance to cancer. The extended set of regulators lead to identification of several interesting candidates for further studies, including lncRNAs. Several key processes were identified as important, including spindle assembly and the epithelial-mesenchymal transition (EMT).

Conclusions

The study has shown that using alternative metrics of correlation can improve the performance of tools based on correlation of gene expression in genomic data. The Fisher and Sobolev metrics should be considered also in other correlation-based applications.

Identification of lncRNA-associated differential subnetworks in oesophageal squamous cell carcinoma by differential co-expression analysis

Differential expression analysis has led to the identification of important biomarkers in oesophageal squamous cell carcinoma (ESCC). Despite enormous contributions, it has not harnessed the full potential of gene expression data, such as interactions among genes. Differential co-expression analysis has emerged as an effective tool that complements differential expression analysis to provide better insight of dysregulated mechanisms and indicate key driver genes. Here, we analysed the differential co-expression of lncRNAs and protein-coding genes (PCGs) between normal oesophageal tissue and ESCC tissues, and constructed a lncRNA-PCG differential co-expression network (DCN). DCN was characterized as a scale-free, small-world network with modular organization. Focusing on lncRNAs, a total of 107 differential lncRNA-PCG subnetworks were identified from the DCN by integrating both differential expression and differential co-expression. These differential subnetworks provide a valuable source for revealing lncRNA functions and the associated dysfunctional regulatory networks in ESCC. Their consistent discrimination suggests that they may have important roles in ESCC and could serve as robust subnetwork biomarkers. In addition, two tumour suppressor genes (AL121899.1 and ELMO2), identified in the core modules, were validated by functional experiments. The proposed method can be easily used to investigate differential subnetworks of other molecules in other cancers.

MicroRNA hsa-mir-3923 serves as a diagnostic and prognostic biomarker for gastric carcinoma

Gastric carcinoma (GC) refers to a common digestive system disease that exhibits a very high incidence. MicroRNA hsa-mir-3923 belongs to a type of miRNA, of which the function has been merely investigated in breast, pancreatic cancers and pre-neoplasic stages of gastric cancer. It has not been studied or reported in gastric carcinoma, so the relationship between gastric hsa-mir-3923 expression and the clinics feature and pathology of GC cases was examined. This study employed data mining for analyzing gastric carcinoma data in The Cancer Genome Atlas database. A Chi squared test was performed for assessing the relations of hsa-mir-3923 expression with clinics-related and pathology-regulated variables. This study conducted the assessment of the role of hsa-mir-3923 in prognostic process using Kaplan-Meier curves, Receiver operating characteristic (ROC) analysis and proportional hazards model (Cox) study. With the use of Gene Expression Omnibus, this study carried out gene set enrichment analysis (GSEA). In the meantime, the common miRNA database was compared to predict potential target genes; as revealed by co-expression analysis, a regulatory network probably existed, containing hsa-mir-3923. For the analysis of the most tightly associated cytological behavior and pathway in GC, this study adopted the databases for Annotation, Visualization and Integrated Discovery (David) and KO-Based Annotation System (KOBAS). Cytoscape, R and STRING were employed for mapping probable regulatory networks displaying relations to hsa-mir-3923. Lastly, we obtained 69 genes most tightly associated with hsa-mir-3923 and described their relationship with Circos plot. As revealed from the results, hsa-mir-3923 displayed up-regulation in gastric carcinoma, and it displayed associations with vital status, N stage and histologic grade when being expressed. The predicted results of miRNA target genes suggested that there may be a close relationship between 66 genes and hsa-mir-3923 in gastric cancer. As indicated from co-expression data, a small regulating network of 4 genes probably existed. Our results elucidated that hsa-mir-3923 high-expression reveals poor prognosis of GC patients.

MdMYB8 is associated with flavonol biosynthesis via the activation of the MdFLS promoter in the fruits of Malus crabapple

Flavonols are polyphenolic compounds that play important roles in plant stress resistance and development. They are also valuable components of the human diet. The Malus crabapple cultivar 'Flame' provides an excellent model for studying flavonol biosynthesis due to the high flavonol content of its fruit peel. To obtain a more detailed understanding of the flavonol regulatory network involved in fruit development, the transcriptomes of the fruit of the Malus cv. 'Flame' from five continuous developmental stages were analyzed using RNA sequencing. A flavonol-related gene module was identified through weighted gene coexpression network analysis (WGCNA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that phytohormones are involved in regulating flavonol biosynthesis during fruit development. A putative transcription factor, MdMYB8, was selected for further study through hub gene correlation network analysis and yeast one-hybrid assays. Stable overexpression or RNAi knockdown of MdMYB8 in transgenic 'Orin' apple calli resulted in a higher or lower flavonol content, respectively, suggesting that MdMYB8 is a regulator of flavonol biosynthesis. This transcriptome analysis provides valuable data for future studies of flavonol synthesis and regulation.

Sex biased expression and co-expression networks in development, using the hymenopteran Nasonia vitripennis

Sexual dimorphism requires regulation of gene expression in developing organisms. These developmental differences are caused by differential expression of genes and isoforms. The effect of expressing a gene is also influenced by which other genes are simultaneously expressed (functional interactions). However, few studies have described how these processes change across development. We compare the dynamics of differential expression, isoform switching and functional interactions in the sexual development of the model parasitoid wasp Nasonia vitripennis, a system that permits genome wide analysis of sex bias from early embryos to adults. We find relatively little sex-bias in embryos and larvae at the gene level, but several sub-networks show sex-biased functional interactions in early developmental stages. These networks provide new candidates for hymenopteran sex determination, including histone modification. In contrast, sex-bias in pupae and adults is driven by the differential expression of genes. We observe sex-biased isoform switching consistently across development, but mostly in genes that are already differentially expressed. Finally, we discover that sex-biased networks are enriched by genes specific to the Nasonia clade, and that those genes possess the topological properties of key regulators. These findings suggest that regulators in sex-biased networks evolve more rapidly than regulators of other developmental networks.

Parallel Bud Mutation Sequencing Reveals that Fruit Sugar and Acid Metabolism Potentially Influence Stress in Malus

Apple sugar and acid are the most important traits of apple fruit. Bud sport cultivars can provide abundant research materials for functional gene studies in apple. In this study, using bud sport materials with a rather different sugar and acid flavor, i.e., "Jonathan" and "Sweet Jonathan", we profiled the whole genome variations and transcriptional regulatory network during fruit developmental stages using whole genome sequencing and RNA-sequencing. Variation analysis identified 4,198,955 SNPs, 319,494 InDels, and 32,434 SVs between the two cultivars. In total, 4313 differentially expressed genes among all of the d 44,399 genes expressed were identified between the two cultivars during fruit development, and functional analysis revealed stress response and signal transduction related genes were enriched. Using 24,047 genes with a more variable expression value, we constructed 28 co-expression modules by weighted correlation network analysis. Deciphering of 14 co-expression modules associated with sugar or acid accumulation during fruit development revealed the hub genes associated with sugar and acid metabolism, e.g., MdDSP4, MdINVE, and MdSTP7. Furthermore, exploration of the intra network of the co-expression module indicated the close relationship between sugar and acid metabolism or sugar and stress. Motif-based sequence analysis of the 17 differentially expressed ATP-binding cassette transporter genes and Yeast one-hybrid assay identified and confirmed a transcription factor, MdBPC6, regulating the ATP-binding cassette (ABC) transporter genes and potentially participating in the apple fruit development or stress response. Collectively, all of the results demonstrated the use of parallel bud mutation sequencing and identified hub genes, and inferred regulatory relationships providing new information about apple fruit sugar and acid accumulation or stress response.

Differential network analysis depicts regulatory mechanisms for hepatocellular carcinoma from diverse backgrounds

Aim: To elucidate the integrative combinational gene regulatory network landscape of hepatocellular carcinoma (HCC) molecular carcinogenesis from diverse background. Materials & methods: Modified gene regulatory network analysis was used to prioritize differentially regulated genes and links. Integrative comparisons using bioinformatics methods were applied to identify potential critical molecules and pathways in HCC with different backgrounds. Results: E2F1 with its surrounding regulatory links were identified to play different key roles in the HCC risk factor dysregulation mechanisms. Hsa-mir-19a was identified as showed different effects in the three HCC differential regulation networks, and showed vital regulatory role in HBV-related HCC. Conclusion: We describe in detail the regulatory networks involved in HCC with different backgrounds. E2F1 may serve as a universal target for HCC treatment.

LncRNA ROR1‑AS1 high expression and its prognostic significance in liver cancer

Hepatocellular carcinoma (HCC) is a common disease of the digestive system with no curative treatments. Long noncoding RNA tyrosine protein kinase transmembrane receptor 1 antisense RNA 1 (lncRNA ROR1-AS1) is an lncRNA whose functions have been predicted in human diseases; however, its important role in cancer has been probed only in mantle cell lymphoma, not in HCC. Therefore, the present study aimed to elucidate the prognostic significance of lncRNA ROR1-AS1 in HCC. The Cancer Genome Atlas Liver Hepatocellular Carcinoma was used to analyze the expression of ROR1-AS1 in liver cancer. χ² tests were performed to evaluate associations between clinical characteristics and ROR1-AS1 expression. The role of ROR1-AS1 in HCC prognosis was assessed using Kaplan-Meier curves and proportional hazards model (Cox) analysis. Gene set enrichment analysis was performed by using a Gene Expression Omnibus dataset. At the same time, Multi Experiment Matrix was used to predict genes that may be co-expressed with ROR1-AS1. The Database for Annotation, Visualization and Integrated Discovery and KO-Based Annotation System were used to analyze the most closely associated cytological behaviors and pathways in HCC. Then, the genes in the three databases were integrated to screen mRNAs, microRNAs and lncRNAs that had co-expression relationships with ROR1-AS1. Cytoscape, Search Tool for the Retrieval of Interacting Genes/Proteins and Molecular Evolutionary Genetics Analysis were used to map potential regulatory networks and developmental relationships associated with ROR1-AS1. Finally, 12 genes most closely associated with ROR1-AS1 were identified, and their relationship was described using a Circos plot. The results showed that ROR1-AS1 was upregulated in HCC, and its expression was related to clinical stage, T stage and N stage. Furthermore, Kaplan-Meier curves and Cox analysis indicated that high expression of ROR1-AS1 was associated with poor prognosis, and that ROR1-AS1 was an independent risk factor for HCC. Co-expression data suggested that there may be a large regulatory network of 45 genes with indirect associations with ROR1-AS1, a small regulatory network of 15 genes with direct or indirect regulatory relationships, and a special regulatory network containing 12 genes directly associated with ROR1-AS1. The present findings indicated that high expression of ROR1-AS1 suggests poor prognosis in patients with HCC.

REGGAE: a novel approach for the identification of key transcriptional regulators

Motivation

Transcriptional regulators play a major role in most biological processes. Alterations in their activities are associated with a variety of diseases and in particular with tumor development and progression. Hence, it is important to assess the effects of deregulated regulators on pathological processes.

Results

Here, we present REGulator-Gene Association Enrichment (REGGAE), a novel method for the identification of key transcriptional regulators that have a significant effect on the expression of a given set of genes, e.g. genes that are differentially expressed between two sample groups. REGGAE uses a Kolmogorov-Smirnov-like test statistic that implicitly combines associations between regulators and their target genes with an enrichment approach to prioritize the influence of transcriptional regulators. We evaluated our method in two different application scenarios, which demonstrate that REGGAE is well suited for uncovering the influence of transcriptional regulators and is a valuable tool for the elucidation of complex regulatory mechanisms.

Availability and implementation

REGGAE is freely available at https://regulatortrail.bioinf.uni-sb.de.

Supplementary information

Supplementary data are available at Bioinformatics online.

Comparison of Illumina 450K and EPIC arrays in placental DNA methylation

Illumina HumanMethylation450 BeadChip (450K) has been commonly used to investigate DNA methylation in human tissues. Recently, it has been replaced by Illumina HumanMethylationEPIC BeadChip (EPIC) covering over 850,000 CpGs distributed genome-wide. Many consortia have now datasets coming from both arrays and aspire to analyze the two together. The placenta shows a high number of intermediate methylation levels and is often investigated for obstetric/birth outcomes, and potentially for long-term programming in offspring. We performed a systematic comparison between the two arrays using 108 duplicate placental samples from Gen3G birth cohort. We find that placenta shows a high per-sample correlation between the arrays, and higher median correlations at individual CpGs than those reported for blood. We identify 26,340 probes with absolute difference in per cent methylation >10%. We conclude that EPIC and 450K placental data can be combined, and we provide two lists of CpGs that should be excluded to avoid misleading results.

Identification of new regulators through transcriptome analysis that regulate anthocyanin biosynthesis in apple leaves at low temperatures

Anthocyanin pigments play many roles in plants, including providing protection against biotic and abiotic stresses. To identify new regulatory genes in apple (Malus domestica) that may be involved in regulating low temperature induced anthocyanin biosynthesis, we performed RNA-seq analysis of leaves from the ‘Gala’ apple cultivar following exposure to a low temperature (16 °C). A visible red color appeared on the upper leaves and the anthocyanin content increased significantly after the low temperature treatment. Genes from the flavonoid biosynthesis pathway were significantly enriched among the differentially expressed genes, and the expression of several transcription factors was shown by WGCNA (weighted gene co-expression network analysis) to correlate with anthocyanin accumulation, including members of the MYB, MADS, WRKY, WD40, Zinc Finger and HB-ZIP families. Three MYB transcription factors (MdMYB12, MdMYB22 and MdMYB114), which had several CBF/DREB response elements in their promoters, were significantly induced by low temperature exposure and their expression also correlated highly with anthocyanin accumulation. We hypothesize that they may act as regulators of anthocyanin biosynthesis and be regulated by CBF/DREB transcription factors in apple leaves under low temperature conditions. The analyses presented here provide insights into the molecular mechanisms underlying anthocyanin accumulation during low temperature exposure.

Differential Coexpression Network Analysis for Gene Expression Data

Gene expression profiling by microarray has been used to uncover molecular variations in many areas. The traditional analysis method to gene expression profiling just focuses on the individual genes, and the interactions among genes are ignored, while genes play their roles not by isolations but by interactions with each other. Consequently, gene-to-gene coexpression analysis emerged as a powerful approach to solve the above problems. Then complementary to the conventional differential expression analysis, the differential coexpression analysis can identify gene markers from the systematic level. There are three aspects for differential coexpression network analysis including the network global topological comparison, differential coexpression module identification, and differential coexpression genes and gene pairs identification. To date, the coexpression network and differential coexpression analysis are widely used in a variety of areas in response to environmental stresses, genetic differences, or disease changes. In this chapter, we reviewed the existing methods for differential coexpression network analysis and discussed the applications to cancer research.

Differential Connectivity in Colorectal Cancer Gene Expression Network

BACKGROUND

Colorectal cancer (CRC) is one of the challenging types of cancers; thus, exploring effective biomarkers related to colorectal could lead to significant progresses toward the treatment of this disease.

METHODS

In the present study, CRC gene expression datasets have been reanalyzed. Mutual differentially expressed genes across 294 normal mucosa and adjacent tumoral samples were then utilized in order to build two independent transcriptional regulatory networks. By analyzing the networks topologically, genes with differential global connectivity related to cancer state were determined for which the potential transcriptional regulators including transcription factors were identified.

RESULTS

The majority of differentially connected genes (DCGs) were up-regulated in colorectal transcriptome experiments. Moreover, a number of these genes have been experimentally validated as cancer or CRC-associated genes. The DCGs, including GART, TGFB1, ITGA2, SLC16A5, SOX9, and MMP7, were investigated across 12 cancer types. Functional enrichment analysis followed by detailed data mining exhibited that these candidate genes could be related to CRC by mediating in metastatic cascade in addition to shared pathways with 12 cancer types by triggering the inflammatory events.

DISCUSSION

Our study uncovered correlated alterations in gene expression related to CRC susceptibility and progression that the potent candidate biomarkers could provide a link to disease.

Transcription Factor Binding Site Enrichment Analysis in Co-Expression Modules in Celiac Disease

The aim of this study was to construct celiac co-expression patterns at a whole genome level and to identify transcription factors (TFs) that could drive the gliadin-related changes in coordination of gene expression observed in celiac disease (CD). Differential co-expression modules were identified in the acute and chronic responses to gliadin using expression data from a previous microarray study in duodenal biopsies. Transcription factor binding site (TFBS) and Gene Ontology (GO) annotation enrichment analyses were performed in differentially co-expressed genes (DCGs) and selection of candidate regulators was performed. Expression of candidates was measured in clinical samples and the activation of the TFs was further characterized in C2BBe1 cells upon gliadin challenge. Enrichment analyses of the DCGs identified 10 TFs and five were selected for further investigation. Expression changes related to active CD were detected in four TFs, as well as in several of their in silico predicted targets. The activation of TFs was further characterized in C2BBe1 cells upon gliadin challenge, and an increase in nuclear translocation of CAMP Responsive Element Binding Protein 1 (CREB1) and IFN regulatory factor-1 (IRF1) in response to gliadin was observed. Using transcriptome-wide co-expression analyses we are able to propose novel genes involved in CD pathogenesis that respond upon gliadin stimulation, also in non-celiac models.

The Use of RNA Sequencing and Correlation Network Analysis to Study Potential Regulators of Crabapple Leaf Color Transformation

Anthocyanins are plant pigments that contribute to the color of leaves, flowers and fruits, and that are beneficial to human health in the form of dietary antioxidants. The study of a transformable crabapple cultivar, 'India magic', which has red buds and green mature leaves, using mRNA profiling of four leaf developmental stages, allowed us to characterize molecular mechanisms regulating red color formation in early leaf development and the subsequent rapid down-regulation of anthocyanin biosynthesis. This analysis of differential gene expression during leaf development revealed that ethylene signaling-responsive genes are up-regulated during leaf pigmentation. Genes in the ethylene response factor (ERF), SPL, NAC, WRKY and MADS-box transcription factor (TF) families were identified in two weighted gene co-expression network analysis (WGCNA) modules as having a close relationship to anthocyanin accumulation. Analyses of network hub genes indicated that SPL TFs are located in central positions within anthocyanin-related modules. Furthermore, cis-motif and yeast one-hybrid assays suggested that several anthocyanin biosynthetic or regulatory genes are potential targets of SPL8 and SPL13B. Transient silencing of these two genes confirmed that they play a role in co-ordinating anthocyanin biosynthesis and crabapple leaf development. We present a high-resolution method for identifying regulatory modules associated with leaf pigmentation, which provides a platform for functional genomic studies of anthocyanin biosynthesis.

Differential networking meta-analysis of gastric cancer across Asian and American racial groups

Background

Gastric Carcinoma is one of the most lethal cancer around the world, and is also the most common cancers in Eastern Asia. A lot of differentially expressed genes have been detected as being associated with Gastric Carcinoma (GC) progression, however, little is known about the underlying dysfunctional regulation mechanisms. To address this problem, we previously developed a differential networking approach that is characterized by involving differential coexpression analysis (DCEA), stage-specific gene regulatory network (GRN) modelling and differential regulation networking (DRN) analysis.

Result

In order to implement differential networking meta-analysis, we developed a novel framework which integrated the following steps. Considering the complexity and diversity of gastric carcinogenesis, we first collected three datasets (GSE54129, GSE24375 and TCGA-STAD) for Chinese, Korean and American, and aimed to investigate the common dysregulation mechanisms of gastric carcinogenesis across racial groups. Then, we constructed conditional GRNs for gastric cancer corresponding to normal and carcinoma, and prioritized differentially regulated genes (DRGs) and gene links (DRLs) from three datasets separately by using our previously developed differential networking method. Based on our integrated differential regulation information from three datasets and prior knowledge (e.g., transcription factor (TF)-target regulatory relationships and known signaling pathways), we eventually generated testable hypotheses on the regulation mechanisms of two genes, XBP1 and GIF, out of 16 common cross-racial DRGs in gastric carcinogenesis.

Conclusion

The current cross-racial integrative study from the viewpoint of differential regulation networking provided useful clues for understanding the common dysfunctional regulation mechanisms of gastric cancer progression and discovering new universal drug targets or biomarkers for gastric cancer.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0564-z) contains supplementary material, which is available to authorized users.

Systemic bioinformatics analysis of skeletal muscle gene expression profiles of sepsis

Sepsis is a type of systemic inflammatory response syndrome with high morbidity and mortality. Skeletal muscle dysfunction is one of the major complications of sepsis that may also influence the outcome of sepsis. The aim of the present study was to explore and identify potential mechanisms and therapeutic targets of sepsis. Systemic bioinformatics analysis of skeletal muscle gene expression profiles from the Gene Expression Omnibus was performed. Differentially expressed genes (DEGs) in samples from patients with sepsis and control samples were screened out using the limma package. Differential co-expression and coregulation (DCE and DCR, respectively) analysis was performed based on the Differential Co-expression Analysis package to identify differences in gene co-expression and coregulation patterns between the control and sepsis groups. Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways of DEGs were identified using the Database for Annotation, Visualization and Integrated Discovery, and inflammatory, cancer and skeletal muscle development-associated biological processes and pathways were identified. DCE and DCR analysis revealed several potential therapeutic targets for sepsis, including genes and transcription factors. The results of the present study may provide a basis for the development of novel therapeutic targets and treatment methods for sepsis.

Construction of differential mRNA-lncRNA crosstalk networks based on ceRNA hypothesis uncover key roles of lncRNAs implicated in esophageal squamous cell carcinoma

Increasing evidence has indicated that lncRNAs acting as competing endogenous RNAs (ceRNAs) play crucial roles in tumorigenesis, metastasis and diagnosis of cancer. However, the function of lncRNAs as ceRNAs involved in esophageal squamous cell carcinoma (ESCC) is still largely unknown. In this study, clinical implications of two intrinsic subtypes of ESCC were identified based on expression profiles of lncRNA and mRNA. ESCC subtype-specific differential co-expression networks between mRNAs and lncRNAs were constructed to reveal dynamic changes of their crosstalks mediated by miRNAs during tumorigenesis. Several well-known cancer-associated lncRNAs as the hubs of the two networks were firstly proposed in ESCC. Based on the ceRNA mechanism, we illustrated that the"loss" of miR-186-mediated PVT1-mRNA and miR-26b-mediated LINC00240-mRNA crosstalks were related to the two ESCC subtypes respectively. In addition, crosstalks between LINC00152 and EGFR, LINC00240 and LOX gene family were identified, which were associated with the function of "response to wounding" and "extracellular matrix-receptor interaction". Furthermore, functional cooperation of multiple lncRNAs was discovered in the two differential mRNA-lncRNA crosstalk networks. These together systematically uncovered the roles of lncRNAs as ceRNAs implicated in ESCC.

Exploring Potential Biomarkers Underlying Pathogenesis of Alzheimer's Disease by Differential Co-expression Analysis

BACKGROUND

Alzheimer's Disease (AD) is the most common form of dementia in the elderly. Due to the facts that biological causes of AD are complex in addition to increasing rates of AD worldwide, a deeper understanding of AD etiology is required for AD treatment and diagnosis.

METHODS

To identify molecular pathological alterations in AD brains, GSE36980 series containing microarray data samples from temporal cortex, frontal cortex and hippocampus were downloaded from Gene Expression Omnibus (GEO) database and valid gene symbols were subjected to building a gene co-expression network by a bioinformatics tool known as differential regulation from differential co-expression (DCGL) software package. Then, a network-driven integrative analysis was performed to find significant genes and underlying biological terms.

RESULTS

A total of 17088 unique genes were parsed into three independent differential co-expression networks. As a result, a small number of differentially co-regulated genes mostly in frontal and hippocampus lobs were detected as potential biomarkers related to AD brains. Ultimately differentially co-regulated genes were enriched in biological terms including response to lipid and fatty acid and pathways mainly signaling pathway such as G-protein signaling pathway and glutamate receptor groups II and III. By conducting co-expression analysis, our study identified multiple genes that may play an important role in the pathogenesis of AD.

CONCLUSION

The study aimed to provide a systematic understanding of the potential relationships among these genes and it is hoped that it could aid in AD biomarker discovery.

Exploring of the molecular mechanism of rhinitis via bioinformatics methods

The aim of this study was to analyze gene expression profiles for exploring the function and regulatory network of differentially expressed genes (DEGs) in pathogenesis of rhinitis by a bioinformatics method. The gene expression profile of GSE43523 was downloaded from the Gene Expression Omnibus database. The dataset contained 7 seasonal allergic rhinitis samples and 5 non-allergic normal samples. DEGs between rhinitis samples and normal samples were identified via the limma package of R. The webGestal database was used to identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DEGs. The differentially co-expressed pairs of the DEGs were identified via the DCGL package in R, and the differential co-expression network was constructed based on these pairs. A protein-protein interaction (PPI) network of the DEGs was constructed based on the Search Tool for the Retrieval of Interacting Genes database. A total of 263 DEGs were identified in rhinitis samples compared with normal samples, including 125 downregulated ones and 138 upregulated ones. The DEGs were enriched in 7 KEGG pathways. 308 differential co-expression gene pairs were obtained. A differential co-expression network was constructed, containing 212 nodes. In total, 148 PPI pairs of the DEGs were identified, and a PPI network was constructed based on these pairs. Bioinformatics methods could help us identify significant genes and pathways related to the pathogenesis of rhinitis. Steroid biosynthesis pathway and metabolic pathways might play important roles in the development of allergic rhinitis (AR). Genes such as CDC42 effector protein 5, solute carrier family 39 member A11 and PR/SET domain 10 might be also associated with the pathogenesis of AR, which provided references for the molecular mechanisms of AR.

RegulatorTrail: a web service for the identification of key transcriptional regulators

Transcriptional regulators such as transcription factors and chromatin modifiers play a central role in most biological processes. Alterations in their activities have been observed in many diseases, e.g. cancer. Hence, it is of utmost importance to evaluate and assess the effects of transcriptional regulators on natural and pathogenic processes. Here, we present RegulatorTrail, a web service that provides rich functionality for the identification and prioritization of key transcriptional regulators that have a strong impact on, e.g. pathological processes. RegulatorTrail offers eight methods that use regulator binding information in combination with transcriptomic or epigenomic data to infer the most influential regulators. Our web service not only provides an intuitive web interface, but also a well-documented RESTful API that allows for a straightforward integration into third-party workflows. The presented case studies highlight the capabilities of our web service and demonstrate its potential for the identification of influential regulators: we successfully identified regulators that might explain the increased malignancy in metastatic melanoma compared to primary tumors, as well as important regulators in macrophages. RegulatorTrail is freely accessible at: https://regulatortrail.bioinf.uni-sb.de/.

Comprehensive analysis of differential co-expression patterns reveal transcriptional dysregulation mechanism and identify novel prognostic lncRNAs in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies worldwide and occurs at a relatively high frequency in People's Republic of China. However, the molecular mechanism underlying ESCC is still unclear. In this study, the mRNA and long non-coding RNA (lncRNA) expression profiles of ESCC were downloaded from the Gene Expression Omnibus database, and then differential co-expression analysis was used to reveal the altered co-expression relationship of gene pairs in ESCC tumors. A total of 3,709 mRNAs and 923 lncRNAs were differentially co-expressed between normal and tumor tissues, and we found that most of the gene pairs lost associations in the tumor tissues. The differential regulatory networking approach deciphered that transcriptional dysregulation was ubiquitous in ESCC, and most of the differentially regulated links were modulated by 37 TFs. Our study also found that two novel lncRNAs (ADAMTS9-AS1 and AP000696.2) might be essential in the development of ectoderm and epithelial cells, which could significantly stratify ESCC patients into high-risk and low-risk groups, and were much better than traditional clinical tumor markers. Further inspection of two risk groups showed that the changes in TF-target regulation in the high-risk patients were significantly higher than those in the low-risk patients. In addition, four signal transduction-related DCmRNAs (ERBB3, ENSA, KCNK7, MFSD5), which were differentially co-expressed with the two lncRNAs, might also have the predictive capacity. Our findings will enhance the understanding of ESCC transcriptional dysregulation from a view of cross-link of lncRNA and mRNA, and the two-lncRNA combination may serve as a novel prognostic biomarker for clinical applications of ESCC.

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

The aim of the present study was to identify hub genes and signaling pathways associated with bladder cancer (BC) utilizing centrality analysis and pathway enrichment analysis. The differentially expressed genes (DEGs) were screened from the ArrayExpress database between normal subjects and BC patients. Co-expression networks of BC were constructed using differentially co-expressed genes and links, and hub genes were investigated by degree centrality analysis of co-expression networks in BC. The enriched signaling pathways were investigated by Kyoto Encyclopedia of Genes and Genomes database analysis based on the DEGs. The hub gene expression in BC tissues was validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting. A total of 329 DEGs were screened, including 147 upregulated and 182 downregulated genes. The co-expression network constructed between BC and normal controls consisted of 182 nodes and 434 edges, and the two genes in each gene pair were differentially co-expressed genes. Centrality analysis of co-expression networks suggested that the top 5 hub genes with high degree included lectin, galactoside-binding, soluble, 4 (LGALS4), protein tyrosine phosphatase, receptor type N2 (PTPRN2), transmembrane protease, serine 11E (TMPRSS11E), tripartite motif containing 31 (TRIM31) and potassium voltage-gated channel subfamily D member 3 (KCND3). Pathway analysis revealed that the 329 DEGs were significantly enriched in 5 terms (cell cycle, DNA replication, oocyte meiosis, p53 signaling pathway and peroxisome proliferator-activated receptor signaling pathway). According to RT-qPCR and western blot analysis, 4/5 hub genes were significantly expressed, including LGALS4, PTPRN2, TMPRSS11E, TRIM31; however, KCND3 was not significantly expressed. In the present study, 5 hub genes were successfully identified (LGALS4, PTPRN2, TMPRSS11E, TRIM31 and KCND3) and 5 biological pathways that may be underlying biomarkers for early diagnosis and treatment associated with bladder cancer were revealed.

Identification of Transcriptional Modules and Key Genes in Chickens Infected with Salmonella enterica Serovar Pullorum Using Integrated Coexpression Analyses

Salmonella enterica Pullorum is one of the leading causes of mortality in poultry. Understanding the molecular response in chickens in response to the infection by S. enterica is important in revealing the mechanisms of pathogenesis and disease progress. There have been studies on identifying genes associated with Salmonella infection by differential expression analysis, but the relationships among regulated genes have not been investigated. In this study, we employed weighted gene coexpression network analysis (WGCNA) and differential coexpression analysis (DCEA) to identify coexpression modules by exploring microarray data derived from chicken splenic tissues in response to the S. enterica infection. A total of 19 modules from 13,538 genes were associated with the Jak-STAT signaling pathway, the extracellular matrix, cytoskeleton organization, the regulation of the actin cytoskeleton, G-protein coupled receptor activity, Toll-like receptor signaling pathways, and immune system processes; among them, 14 differentially coexpressed modules (DCMs) and 2,856 differentially coexpressed genes (DCGs) were identified. The global expression of module genes between infected and uninfected chickens showed slight differences but considerable changes for global coexpression. Furthermore, DCGs were consistently linked to the hubs of the modules. These results will help prioritize candidate genes for future studies of Salmonella infection.

Differential regulation analysis reveals dysfunctional regulatory mechanism involving transcription factors and microRNAs in gastric carcinogenesis

Gastric cancer (GC) is one of the most incident malignancies in the world. Although lots of featured genes and microRNAs (miRNAs) have been identified to be associated with gastric carcinogenesis, underlying regulatory mechanisms still remain unclear. In order to explore the dysfunctional mechanisms of GC, we developed a novel approach to identify carcinogenesis relevant regulatory relationships, which is characterized by quantifying the difference of regulatory relationships between stages. Firstly, we applied the strategy of differential coexpression analysis (DCEA) to transcriptomic datasets including paired mRNA and miRNA of gastric samples to identify a set of genes/miRNAs related to gastric cancer progression. Based on these genes/miRNAs, we constructed conditional combinatorial gene regulatory networks (cGRNs) involving both transcription factors (TFs) and miRNAs. Enrichment of known cancer genes/miRNAs and predicted prognostic genes/miRNAs was observed in each cGRN. Then we designed a quantitative method to measure differential regulation level of every regulatory relationship between normal and cancer, and the known cancer genes/miRNAs proved to be ranked significantly higher. Meanwhile, we defined differentially regulated link (DRL) by combining differential regulation, differential expression and the regulation contribution of the regulator to the target. By integrating survival analysis and DRL identification, three master regulators TCF7L1, TCF4, and MEIS1 were identified and testable hypotheses of dysfunctional mechanisms underlying gastric carcinogenesis related to them were generated. The fine-tuning effects of miRNAs were also observed. We propose that this differential regulation network analysis framework is feasible to gain insights into dysregulated mechanisms underlying tumorigenesis and other phenotypic changes.

Protein coding gene CRNKL1 as a potential prognostic biomarker in esophageal adenocarcinoma

BACKGROUND

Esophageal adenocarcinoma (EAC) is one of the most aggressive gastroesophageal cancers. PTGS2, EGFR, ERBB2 and TP53 are the traditional EAC prognostic biomarkers, but they are still limited in their ability to effectively predict the overall survival.

OBJECTIVES

To identify an improved biomarker for predicting the prognosis of EAC by using the expression profile.

MATERIALS AND METHODS

Differential co-expression analysis and differential expression analysis were performed to identify the related genes of EAC. The 5-fold cross-validation was used to select a prognostic biomarker from the 532 EAC related genes.

RESULTS

CRNKL1 was identified as a prognostic biomarker to predict the survival of EAC patients. It could significantly stratify EAC patients into high-risk and low-risk groups and was much better than the traditional biomarkers. Furthermore, ROC curve also verified that CRNKL1 with the highest area under the curve (AUC), reaching a sensitivity of 83.33% and a specificity of 78.57%.

CONCLUSIONS

Our research proposed that CRNKL1 might be a novel prognostic biomarker with better predictive ability by comparing with the traditional biomarkers, which provided a preferable opportunity in the clinical applications of EAC.