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Identification of an oncogenic network with prognostic and therapeutic value in prostate cancer.

ABSTRACT: Identifying critical pathways governing disease progression is essential for accurate prognosis and effective therapy. We developed a broadly applicable and novel systems-level gene discovery strategy. This approach focused on constitutively active androgen receptor (AR) splice variant-driven pathways as representative of an intractable mechanism of prostate cancer (PC) therapeutic resistance. We performed a meta-analysis of human prostate samples using weighted gene co-expression network analysis combined with experimental AR variant transcriptome analyses. An AR variant-driven gene module that is upregulated during human PC progression was identified. We filtered this module by identifying genes that functionally interacted with AR variants using a high-throughput synthetic genetic array screen in Schizosaccharomyces pombe This strategy identified seven AR variant-regulated genes that also enhance AR activity and drive cancer progression. Expression of the seven genes predicted poor disease-free survival in large independent PC patient cohorts. Pharmacologic inhibition of interacting members of the gene set potently and synergistically decreased PC cell proliferation. This unbiased and novel gene discovery strategy identified a clinically relevant, oncogenic, interacting gene hub with strong prognostic and therapeutic potential in PC.

SUBMITTER: Magani F

PROVIDER: S-EPMC6684952 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Identification of an oncogenic network with prognostic and therapeutic value in prostate cancer.

Magani Fiorella F Bray Eric R ER Martinez Maria J MJ Zhao Ning N Copello Valeria A VA Heidman Laine L Peacock Stephanie O SO Wiley David J DJ D'Urso Gennaro G Burnstein Kerry L KL

Molecular systems biology 20180814 8

Identifying critical pathways governing disease progression is essential for accurate prognosis and effective therapy. We developed a broadly applicable and novel systems-level gene discovery strategy. This approach focused on constitutively active androgen receptor (AR) splice variant-driven pathways as representative of an intractable mechanism of prostate cancer (PC) therapeutic resistance. We performed a meta-analysis of human prostate samples using weighted gene co-expression network analys ...[more]

PMID: 30108134

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Project description:Prostate is an immune-competent organ normally populated by inflammatory cells. Prostatic inflammation origin can be multi-factorial and there are some emerging evidences on its possible role as a factor involved in prostate cancer (PC) pathogenesis and progression. This review critically analyzes the role of inflammation as a prognostic factor for progression and aggressiveness of PC. We verified the last 10 years literature data on the association between inflammation and PC aggressiveness, or PC response to therapies. Several studies tried to correlate different inflammatory factors with the aggressiveness and metastatization of PC; all data sustain the role of inflammation in PC progression but they also produce confusion to identify a reliable clinical prognostic marker. Data on patients submitted to radical prostatectomy (RP) showed that cases with marked intraprostatic tissue inflammation are associated with higher rate of biochemical progression; systemic inflammation markers appear to have a significant prognostic value. Analyzing data on patients submitted to radiotherapy (RT) emerges a significant association between high neuthrophil to lymphocyte ratio (NLR) and decreased progression free survival and overall survival; also plateled to lymphocyte ratio (PLR) and C-reactive protein (CRP) have been proposed as significant prognostic factors for progression and overall survival. In patients submitted to androgen deprivation therapy (ADT), inflammation may drive castration resistant PC (CRPC) development by activation of STAT3 in PC cells. NLR has been proposed as independent predictor of overall survival in CRPC submitted to chemotherapy. Most of data are focused on markers related to systemic inflammation such as NLR and CRP, more than specifically to chronic prostatic inflammation. The suggestion is that these inflammatory parameters, also if not specific for prostatic inflammation and possibly influenced by several factors other than PC, can integrate with established prognostic factors.

Project description:BackgroundThe tumor microenvironment (TME) has recently been proven to play a crucial role in the development and prognosis of tumors. However, the current knowledge on the potential of the TME in prostate cancer (PCa) remains scarce.PurposeThis study aims to elucidate the value of TME-related genes for PCa prognosis by integrative bioinformatics analysis.Materials and methodsWe downloaded the immune and stromal scores of PCa samples via the ESTIMATE and correlated these scores to clinicopathological characteristics and recurrence-free survival (RFS) of patients. Based on these scores, the TME-related differentially expressed genes were identified for functional enrichment analysis. Cox regression analyses were performed to identify prognostic genes and establish a predictive risk model. Moreover, gene set enrichment analysis (GSEA) was performed to evaluate the relationship between risk score and immune pathway.ResultsThe stromal and immune scores were associated with clinicopathological characteristics and RFS in PCa patients. In total, 238 intersecting differentially expressed genes were identified. Functional enrichment analysis further revealed that these genes dramatically participated in the immune-related pathways. The immune-related risk model was built with C-type lectin domain containing 7A (CLEC7A) and collagen type XI alpha 1 chain (COL11A1) using Cox regression analyses. Kaplan-Meier survival analysis showed that the expression levels of CLEC7A and COL11A1 were significantly associated with the RFS. Further, the RFS time in high-risk group was significantly shorter than that in low-risk group. The areas under the curve for the risk model in predicting 3- and 5-year RFS rates were 0.694 and 0.731, respectively. GSEA suggested that immunosuppression existed in high-risk PCa patients.ConclusionCLEC7A and COL11A1 were selected to build a predictive risk model, which may help clinicians to assess the prognosis of PCa patients and select appropriate targets for immunotherapy.

Project description:IntroductionCervical cancer has high mortality, high recurrence and poor prognosis. Although prognostic biomarkers such as clinicopathological features have been proposed, their accuracy and precision are far from satisfactory. Therefore, novel biomarkers are urgently needed for disease surveillance, prognosis prediction and treatment selection.MaterialsDifferentially expressed genes (DEGs) between cervical cancer and normal tissues from three microarray datasets extracted from the Gene Expression Omnibus platform were identified and screened. Based on these DEGs, a six-gene prognostic signature was constructed using cervical squamous cell carcinoma and endocervical adenocarcinoma data from The Cancer Genome Atlas. Next, the molecular functions and related pathways of the six genes were investigated through gene set enrichment analysis and co-expression analysis. Additionally, immunophenoscore analysis and the QuartataWeb Server were employed to explore the therapeutic value of the six-gene signature.ResultsWe discovered 178 overlapping DEGs in three microarray datasets and established a six-gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) prognostic signature with stable and excellent performance in predicting overall survival in different subgroups. Intriguingly, the six-gene signature was closely associated with the immune response and tumour immune microenvironment. The six-gene signature might be used for predicting response to immune checkpoint inhibitors (ICIs) and the six genes may serve as new drug targets for cervical cancer.ConclusionOur study established a novel six-gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) signature that was closely associated with the immune response and tumour immune microenvironment. The six-gene signature was indicative of aggressive features of cervical cancer and therefore might serve as a promising biomarker for predicting not only overall survival but also ICI treatment effectiveness. Moreover, three genes (UPP1, ISG20 and GLTP) within the six-gene signature have the potential to become novel drug targets.

Dataset Information

Identification of an oncogenic network with prognostic and therapeutic value in prostate cancer.

Publications

Identification of an oncogenic network with prognostic and therapeutic value in prostate cancer.

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