gene modules
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Abstract Objectives: This study was anchored on the state of local immune-infiltration in the endometrium, which acts as critical factors affecting embryonic implantation, and aimed at establishing novel approaches to assess endometrial receptivity for patients with IVF failure.Methods: Immune-infiltration levels in the GSE58144 dataset (n=115) from GEO were analyzed by digital deconvolution and validated by immunofluorescence (n=30), illustrating that dysregulation of the ratio of Mf1 to Mf2 is an important factor contributing to implantation failure. Then, modules most associated with M1/M2 macrophages (Mfs) and their hub genes were then selected by weighted gene co-expression network and univariate analyses, then validated by GSE5099 macrophage dataset, qPCR analysis (n=16), and western blot. It revealed that closely related gene modules dominated three biological processes in macrophages: antigen presentation, interleukin−1−mediated signalling pathway, and phagosome acidification, respectively. Their hub genes were significantly altered in patients and related with ribosomal, lysosome, and proteasomal pathways. Finally, the artificial neural network (ANN) and nomogram models were established from hub genes, of which efficacy was compared and validated in the GSE165004 dataset (n=72). Models established by the selected hub genes exhibited excellent predictive values in both datasets, and ANN performed best with an accuracy of 98.3% and an AUC of 0.975 (95% CI 0.945-1). Conclusions: Macrophages, proven to be essential for endometrial receptivity, were regulated by gene modules dominating antigen presentation, interleukin−1−mediated signalling pathway, and phagosome acidification. Selected hub genes can effectively assess endometrial dysfunction receptivity for IVF outcomes by the ANN approach.

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.

Salmonella enterica Typhimurium is a serious pathogen that is involved in human nontyphoidal infections. Tackling Typhimurium infections is difficult due to the species' dynamic adaptation to its environment, which is dictated by a complex transcriptional regulatory network (TRN). While traditional biomolecular methods provide characterizations of specific regulators, it is laborious to construct the global TRN structure from this bottom-up approach. Here, we used a machine learning technique to understand the transcriptional signatures of S. enterica Typhimurium from the top down, as a whole and in individual strains. Furthermore, we conducted cross-strain comparison of 6 strains in serovar Typhimurium to investigate similarities and differences in their TRNs with pan-genomic analysis. By decomposing all the publicly available RNA-Seq data of Typhimurium with independent component analysis (ICA), we obtained over 400 independently modulated sets of genes, called iModulons. Through analysis of these iModulons, we 1) discover three transport iModulons linked to antibiotic resistance, 2) describe concerted responses to cationic antimicrobial peptides (CAMPs), 3) uncover evidence towards new regulons, and 4) identify two iModulons linked to bile responses in strain ST4/74. We extend this analysis across the pan-genome to show that strain-specific iModulons 5) reveal different genetic signatures in pathogenicity islands that explain phenotypes and 6) capture the activity of different phages in the studied strains. Using all high-quality publicly-available RNA-Seq data to date, we present a comprehensive, data-driven Typhimurium TRN. It is conceivable that with more high-quality datasets from more strains, the approach used in this study will continue to guide our investigation in understanding the pan-transcriptome of Typhimurium. Interactive dashboards for all gene modules in this project are available at https://imodulondb.org/ to enable browsing for interested researchers.

Breast cancer (BC) is the most common tumor in women, and the molecular mechanism underlying its pathogenesis remains unclear. In this study, we aimed to investigate gene modules related to the phenotypes of BC, and identify representative candidate biomarkers for clinical prognosis of BC patients. Using weighted gene co-expression network analysis, we here identified NPY5R as a hub gene in BC. We further found that NPY5R was frequently downregulated in BC tissues compared with adjacent tumor-matched control tissues, due to its aberrant promoter CpG methylation which was confirmed by methylation analysis and treatment with demethylation agent. Higher expression of NPY5R was closely associated with better prognosis for BC patients. Gene set enrichment analysis showed that transcriptome signatures concerning apoptosis and cell cycle were critically enriched in specimens with elevated NPY5R. Ectopic expression of NPY5R significantly curbed breast tumor cell growth, induced cell apoptosis and G2/M arrest. Moreover, NPY5R also promoted the sensitivity of BC cells to doxorubicin. Mechanistically, we found that NPY5R restricted STAT3 signaling pathway activation through interacting with IL6, which may be responsible for the antitumor activity of NPY5R. Collectively, our findings indicate that NPY5R functions as a tumor suppressor but was frequently downregulated in BC.

Abstract Background When it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. The nature of module detection is the use of unsupervised clustering approaches and algorithms. Those methods are advanced undoubtedly, but the selection of a certain clustering method for sample- and gene-clustering tasks is separate, in which the latter task is often more complicated. Results This study presented an R-package, Overlapping CoExpressed gene Module (oCEM), armed with the decomposition methods to solve the challenges above. We also developed a novel auxiliary statistical approach to select the optimal number of principal components using a permutation procedure. We showed that oCEM outperformed state-of-the-art techniques in the ability to detect biologically relevant modules additionally. Conclusions oCEM helped non-technical users easily perform complicated statistical analyses and then gain robust results. oCEM and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/oCEM.

Colorectal cancer (CRC) is a common, multifactorial disease. While observational studies have identified an association between lower vitamin D and higher CRC risk, supplementation trials have been inconclusive and the mechanisms by which vitamin D may modulate CRC risk are not well understood. We sought to perform a weighted gene co-expression network analysis (WGCNA) to identify modules present after vitamin D supplementation (when plasma vitamin D level was sufficient) which were absent before supplementation, and then to identify influential genes in those modules. The transcriptome from normal rectal mucosa biopsies of 49 individuals free from CRC were assessed before and after 12 weeks of 3200IU/day vitamin D (Fultium-D3) supplementation using paired-end total RNAseq. While the effects on expression patterns following vitamin D supplementation were subtle, WGCNA identified highly correlated genes forming gene modules. Four of the 17 modules identified in the post-vitamin D network were not preserved in the pre-vitamin D network, shedding new light on the biochemical impact of supplementation. These modules were enriched for GO terms related to the immune system, hormone metabolism, cell growth and RNA metabolism. Across the four treatment-associated modules, 51 hub genes were identified, with enrichment of 40 different transcription factor motifs in promoter regions of those genes, including VDR:RXR. Six of the hub genes were nominally differentially expressed in studies of vitamin D effects on adult normal mucosa organoids: LCN2, HLA-C, AIF1L, PTPRU, PDE4B and IFI6. By taking a gene-correlation network approach, we have described vitamin D induced changes to gene modules in normal human rectal epithelium in vivo, the target tissue from which CRC develops.

Aims/hypothesis: The purpose of this study is to manually and semi-automatically curate a database and develop an R package that will act as a comprehensive resource to understand how biological processes are dysregulated due to interactions with environmental factors. Methods: We followed a two-step process to achieve the objectives of this study. First, we conducted a systematic review of the existing gene expression datasets to identify the integrated genomic and environmental factors used in available studies. This enabled us to curate a comprehensive genomic-environmental database for four key environmental factors (smoking, diet, infections and toxic chemicals) associated with various autoimmune and chronic conditions. Second, we developed a statistical analysis package that allows users to understand the relationships between differentially expressed genes and environmental factors under different disease conditions. Results: The initial database search run on the Gene Expression Omnibus (GEO) and the Molecular Signature Database (MSigDB) retrieved a total of 90,018 articles. After title and abstract screening against pre-set criteria, a total of 186 studies were selected. From those, 243 individual sets of genes, or gene modules, were obtained. We then curated a database containing four environmental factors, namely cigarette smoking, diet, infections and toxic chemicals, along with a total of 25789 genes that had an association with one or more of these factors. In 6 case studies, the database and statistical analysis package were then tested with lists of differentially expressed genes obtained from the published literature related to type 1 diabetes, rheumatoid arthritis, small cell lung cancer, cobalt exposure, COVID-19 and smoking. On testing, we uncovered statistically enriched biological processes, which could help us understand the pathways associated with environmental factors and gene modules. Conclusions: A novel curated database and software tool is provided as an R Package. Users can enter a list of genes to discover associated environmental factors under various disease conditions.

Abstract BackgroundIgA nephropathy (IgAN), which has been reported as the most prevalent glomerulonephritis globally, is the major contributor to end-stage renal illness. This bioinformatics study aimed to explore glomeruli-tubulointerstitial crosstalk genes and dysregulated pathways relating to the pathogenesis of IgAN. MethodsThe microarray datasets from the Gene Expression Omnibus (GEO) database were searched. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) of both glomeruli and tubulointerstitial were conducted individually. The co-expression gene modules of tubulointerstitial and glomeruli were compared via gene function enrichment analysis. Subsequently, the crosstalk co-expression network was constructed via the STRING database and key genes were mined from the crosstalk network. Results583 DEGs and eight modules were identified in glomeruli samples, while 272 DEGs and four modules were in tubulointerstitial samples. There were 119 overlapping DEGs of the two groups. Among the distinctive modules, four modules in glomeruli and one module in tubulointerstitial were positively associated with IgAN. While four modules in glomeruli and two modules in tubulointerstitial were negatively associated with IgAN. The top ten key genes screened by CytoHubba were ITGAM, ALB, TYROBP, ITGB2, CYBB, HCK, CSF1R, LAPTM5, FN1and CTSS. The above genes were all validated using another two datasets, and all of the key genes demonstrated possible diagnostic significance. Conclusionshe crosstalk genes confirmed in this study may provide novel insight into the pathogenesis of IgAN. Immune-related pathways are associated with both glomerular and tubulointerstitial injuries in IgAN. The glomerulotubular crosstalk might perform a role in the pathogenesis of IgAN.

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is characterized by intensive stroma involvement and heterogeneity. Pancreatic cancer cells interplay with surrounding tumor micro-environment (TME), leading to exacerbated tumorigenesis, dismal prognosis and tenacious therapy resistance. Herein, we aim to ascertain a gene-network indicative of vicious features of TME, then find a vulnerability for pancreatic cancer. Methods Single cell RNA sequencing data was processed by Seurat package, retrieving the cell component marker genes (CCMGs). Correlation networks/modules of CCMGs were determined by WGCNA algorithm in a combined PDAC mRNA expression dataset. The gene modules that statistically associate with prognosis were chosen for classifying TME subgroups, constructing neural network and designing the risk score system. Cell-cell communication analysis was achieved by NATMI software. The tumor suppressive effect of ITGA2 inhibitor was evaluated in vivo by using a Kras G12D -driven murine pancreatic cancer model.Results WGCNA analysis categorized cell component marker genes into eight co-expression networks. From gene modules with the maximum and minimum hazard ratio, we stratify PDAC samples based on TME gene patterns, resulting in two main TME subclasses with contrasting survival periods. Furthermore, we generated a neural network model and a risk score model which robustly predict prognosis and therapeutic outcomes. The hub genes in both gene modules were also gathered for functional enrichment analysis, elucidating a crucial role of cell communication-mediating integrins in TME associated PDAC malignancy. To perform a confirmatory experiment underpinning the significance of hub gene targeting, the mice with spontaneously developed pancreatic cancer were orally treated with an integrin inhibitor. The in vivo assays unraveled that pharmacologically inhibiting ITGA2 counteracts cancer-promoting micro-environment, and ameliorates pancreatic lesions. Conclusions By recapitulating gene-network across various cell types, we exploited novel PDAC prognosis-predicting strategies. Medically interfering ITGA2, a key factor guiding cellular reciprocal interaction, attenuated tumor development. These findings may open new avenue about PDAC targeting therapy.