Dataset Information

Transcriptome Network Analysis Identifies CXCL13-CXCR5 Signaling Modules in the Prostate Tumor Immune Microenvironment.

ABSTRACT: The tumor immune microenvironment (TIME) consists of multiple cell types that contribute to the heterogeneity and complexity of prostate cancer (PCa). In this study, we sought to understand the gene-expression signature of patients with primary prostate tumors by investigating the co-expression profiles of patient samples and their corresponding clinical outcomes, in particular "disease-free months" and "disease reoccurrence". We tested the hypothesis that the CXCL13-CXCR5 axis is co-expressed with factors supporting TIME and PCa progression. Gene expression counts, with clinical attributes from PCa patients, were acquired from TCGA. Profiles of PCa patients were used to identify key drivers that influence or regulate CXCL13-CXCR5 signaling. Weighted gene co-expression network analysis (WGCNA) was applied to identify co-expression patterns among CXCL13-CXCR5, associated genes, and key genetic drivers within the CXCL13-CXCR5 signaling pathway. The processing of downloaded data files began with quality checks using NOISeq, followed by WGCNA. Our results confirmed the quality of the TCGA transcriptome data, identified 12 co-expression networks, and demonstrated that CXCL13, CXCR5 and associated genes are members of signaling networks (modules) associated with G protein coupled receptor (GPCR) responsiveness, invasion/migration, immune checkpoint, and innate immunity. We also identified top canonical pathways and upstream regulators associated with CXCL13-CXCR5 expression and function.

SUBMITTER: Ohandjo AQ

PROVIDER: S-EPMC6802083 | biostudies-other | 2019 Oct

REPOSITORIES: biostudies-other

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Transcriptome Network Analysis Identifies CXCL13-CXCR5 Signaling Modules in the Prostate Tumor Immune Microenvironment.

Ohandjo Adaugo Q AQ Liu Zongzhi Z Dammer Eric B EB Dill Courtney D CD Griffen Tiara L TL Carey Kaylin M KM Hinton Denise E DE Meller Robert R Lillard James W JW

Scientific reports 20191018 1

The tumor immune microenvironment (TIME) consists of multiple cell types that contribute to the heterogeneity and complexity of prostate cancer (PCa). In this study, we sought to understand the gene-expression signature of patients with primary prostate tumors by investigating the co-expression profiles of patient samples and their corresponding clinical outcomes, in particular "disease-free months" and "disease reoccurrence". We tested the hypothesis that the CXCL13-CXCR5 axis is co-expressed w ...[more]

PMID: 31628349

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Project description:BACKGROUND: Plants respond to diverse environmental cues including microbial perturbations by coordinated regulation of thousands of genes. These intricate transcriptional regulatory interactions depend on the recognition of specific promoter sequences by regulatory transcription factors. The combinatorial and cooperative action of multiple transcription factors defines a regulatory network that enables plant cells to respond to distinct biological signals. The identification of immune-related modules in large-scale transcriptional regulatory networks can reveal the mechanisms by which exposure to a pathogen elicits a precise phenotypic immune response. RESULTS: We have generated a large-scale immune co-expression network using a comprehensive set of Arabidopsis thaliana (hereafter Arabidopsis) transcriptomic data, which consists of a wide spectrum of immune responses to pathogens or pathogen-mimicking stimuli treatments. We employed both linear and non-linear models to generate Arabidopsis immune co-expression regulatory (AICR) network. We computed network topological properties and ascertained that this newly constructed immune network is densely connected, possesses hubs, exhibits high modularity, and displays hallmarks of a "real" biological network. We partitioned the network and identified 156 novel modules related to immune functions. Gene Ontology (GO) enrichment analyses provided insight into the key biological processes involved in determining finely tuned immune responses. We also developed novel software called OCCEAN (One Click Cis-regulatory Elements ANalysis) to discover statistically enriched promoter elements in the upstream regulatory regions of Arabidopsis at a whole genome level. We demonstrated that OCCEAN exhibits higher precision than the existing promoter element discovery tools. In light of known and newly discovered cis-regulatory elements, we evaluated biological significance of two key immune-related functional modules and proposed mechanism(s) to explain how large sets of diverse GO genes coherently function to mount effective immune responses. CONCLUSIONS: We used a network-based, top-down approach to discover immune-related modules from transcriptomic data in Arabidopsis. Detailed analyses of these functional modules reveal new insight into the topological properties of immune co-expression networks and a comprehensive understanding of multifaceted plant defense responses. We present evidence that our newly developed software, OCCEAN, could become a popular tool for the Arabidopsis research community as well as potentially expand to analyze other eukaryotic genomes.

Project description:Immunotherapy is less effective in patients with epidermal growth factor receptor (EGFR) mutant non-small-cell lung cancer (NSCLC). Lower programmed cell death-ligand 1 (PD-L1) expression and tumor mutation burden (TMB) are reported to be the underlying mechanism. Being another important factor to affect the efficacy of immunotherapy, tumor microenvironment (TME) characteristics of this subgroup of NSCLC are not comprehensively understood up to date. Hence, we initiated this study to describe the specific TME of EGFR-mutant lung adenocarcinoma (LUAD) from cellular compositional and functional perspectives to better understand the immune landscape of this most common subtype of NSCLC. We used single-cell transcriptome sequencing and multiplex immunohistochemistry to investigate the immune microenvironment of EGFR-mutant and EGFR wild-type LUADs and determined the efficacy of immunotherapy. We analyzed single cells from nine treatment-naïve samples and compared them to three post-immunotherapy samples previously reported from single cell perspective using bioinformatics methods. We found that EGFR-mutant malignant epithelial cells had similar characteristics to the epithelial cells in non-responders. EGFR-mutant LUAD lacked CD8+ tissue-resident memory (TRM) cells, which could promote tertiary lymphoid structure generation by secreting CXCL13. In addition, other cell types, including tumor-associated macrophages and cancer-associated fibroblasts, which are capable of recruiting, retaining, and expanding CD8+ TRM cells in the TME, were also deficient in EGFR-mutant LUAD. Furthermore, EGFR-mutant LUAD had significantly less crosstalk between T cells and other cell types via programmed cell death-1 (PD-1) and PD-L1 or other immune checkpoints compared with EGFR wild-type LUAD. Our findings provide a comprehensive understanding of the immune landscape of EGFR-mutant LUAD at the single-cell level. Based on the results, many cellular components might have negative impact on the specific TME of EGFR-mutant LUAD through influencing CD8+ TRM. Lack of CD8+ TRM might be a key factor responsible for the suppressive TME of EGFR-mutant LUAD.

Project description:BackgroundGastric cancer (GC) still represents the third leading cause of cancer-related death worldwide. Peritoneal relapse (PR) is the most frequent metastasis occurring among patients with advanced gastric cancer. Increasingly more evidence have clarified the tumor immune microenvironment (TIME) may predict survival and have clinical significance in GC. However, tumor-transcriptomics based immune signatures derived from immune profiling have not been established for predicting the peritoneal recurrence of the advanced GC.MethodsIn this study, we depict the immune landscape of GC by using transcriptome profiling and clinical characteristics retrieved from GSE62254 of Gene Expression Omnibus (GEO). Immune cell infiltration score was evaluated via single-sample gene set enrichment (ssGSEA) analysis algorithm. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was used to select the valuable immune cells and construct the final model for the prediction of PR. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to check the accuracy of PRIs. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to explore the molecular pathways associated with PRIs.ResultsA peritoneal recurrence related immune score (PRIs) with 10 immune cells was constructed. Compared to the low-PRIs group, the high-PRIs group had a greater risk. The upregulation of the focal adhesion signaling was observed in the high-PRIs subtype by GSEA and KEGG. Multivariate analysis found that both in the internal training cohort and the internal validation cohort, PRIs was a stable and independent predictor for PR. A nomogram that integrated clinicopathological features and PRIs to predict peritoneal relapse was constructed. Subgroup analysis indicated that the PRIs could obviously distinguish peritoneal recurrence in different molecular subtypes, pathological stages and Lauren subtypes, in which PRIs of Epithelial-Mesenchymal Transitions (EMT) subtype, III-IV stage and diffuse subtype are higher respectively.ConclusionOverall, we performed a comprehensive evaluation of the immune landscape of GC and constructed a predictive PR model based on the immune cell infiltration. The PRIs represents novel promising feature of predicting peritoneal recurrence of GC and sheds light on the improvement of the personalized management of GC patients after surgery.

Dataset Information

Transcriptome Network Analysis Identifies CXCL13-CXCR5 Signaling Modules in the Prostate Tumor Immune Microenvironment.

Publications

Transcriptome Network Analysis Identifies CXCL13-CXCR5 Signaling Modules in the Prostate Tumor Immune Microenvironment.

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