Identification of an oncogenic network with prognostic and therapeutic value in prostate cancer
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ABSTRACT:
SUBMITTER: Fiorella Magani
PROVIDER: S-SCDT-86105_2_1532693665_jats | biostudies-other |
REPOSITORIES: biostudies-other
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Project description: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.
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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.
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Project description:Cluster of differentiation (CD) antigens are cell surface markers used to differentiate haematopoietic cell types. These antigens are present in various malignancies and are reportedly linked to patient prognosis; however, they have not been implemented as prostate cancer progression markers. Here, we aimed to assess the impact of genetic variation in haematopoietic cell CD markers on clinical outcomes in patients with prostate cancer. An association study of 458 patients with prostate cancer was conducted to identify single-nucleotide polymorphisms in 11 candidate CD marker genes associated with biochemical recurrence (BCR) after radical prostatectomy. Identified predictors were further evaluated in an additional cohort of 185 patients. Joint population analyses showed that CD1B rs3181082 is associated with BCR (adjusted hazard ratio 1.42, 95% confidence interval 1.09-1.85, p = 0.010). In addition, rs3181082 overlapped with predicted transcriptional regulatory elements and affected CD1B expression. Furthermore, low CD1B expression correlated with poorer BCR-free survival. Our results indicated that CD1B rs3181082 confers prostate cancer progression and may help improve clinical prognostic stratification.
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Project description:Metastatic prostate cancer (mPC) has a poor prognosis, and new treatment strategies are currently being offered for patients in clinical practice, but mPC is still incurable. A considerable proportion of patients with mPC harbor homologous recombination repair (HRR) mutations, which may be more sensitive to poly (ADP-ribose) polymerase inhibitors (PARPis). We retrospectively included genomic and clinical data from 147 patients with mPC from a single clinical center, with a total of 102 circulating tumor DNA (ctDNA) samples and 60 tissue samples. The frequency of genomic mutations was analyzed and compared with that in Western cohorts. Cox analysis was used to assess progression-free survival (PFS) and prognostic factors related to prostate-specific antigen (PSA) after standard systemic therapy for mPC. The most frequently mutated gene in the HRR pathway was CDK12 (18.3%), followed by ATM (13.7%) and BRCA2 (13.0%). The remaining common ones were TP53 (31.3%), PTEN (12.2%), and PIK3CA (11.5%). The frequency of BRCA2 mutation was close to that of the SU2C-PCF cohort (13.3%), but the frequency of CDK12, ATM, and PIK3CA mutations was significantly higher than that in the SU2C-PCF cohort: 4.7%, 7.3%, and 5.3%, respectively. CDK12 mutation were less responsive to androgen receptor signaling inhibitors (ARSIs), docetaxel, and PARPi. BRCA2 mutation helps predict PARPi efficacy. Additionally, androgen receptor (AR)-amplified patients do not respond well to ARSIs, and PTEN mutation are associated with poorer docetaxel response. These findings support the genetic profiling of patients with mPC after diagnosis to guide treatment stratification to customize personalized treatment.
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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.
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Project description:BackgroundIncreasing evidence has been confirmed that small nucleolar RNAs (SnoRNAs) play critical roles in tumorigenesis and exhibit prognostic value in clinical practice. However, there is short of systematic research on SnoRNAs in ovarian cancer (OV).Material/methods379 OV patients with RNA-Seq and clinical parameters from TCGA database and 5 paired clinical OV tissues were embedded in our study. Cox regression analysis was used to identify prognostic SnoRNAs and construct prediction model. SNORic database was adopted to examine the copy number variation of SnoRNAs. ROC curves and KM plot curves were applied to validate the prognostic model. Besides, the model was validated in 5 paired clinical tissues by real-time PCR, H&E staining and immunohistochemistry.ResultsA prognostic model was constructed on the basis of SnoRNAs in OV patients. Patients with higher RiskScore had poor clinicopathological parameters, including higher age, larger tumor size, advanced stage and with tumor status. KM plot analysis confirmed that patients with higher RiskScore had poorer prognosis in subgroup of age, tumor size, and stage. 7 of 9 SnoRNAs in the prognostic model had positive correlation with their host genes. Moreover, 5 of 9 SnoRNAs in the prognostic model correlated with their CNVs, and SNORD105B had the strongest correction with its CNVs. ROC curve showed that the RiskScore had excellent specificity and accuracy. Further, results of H&E staining and immunohistochemistry of Ki67, P53 and P16 confirmed that patients with higher RiskScore are more malignant.ConclusionsIn summary, we identified a nine-SnoRNAs signature as an independent indicator to predict prognosis of OV, providing a prospective prognostic biomarker and potential therapeutic targets for ovarian cancer.
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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.
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Project description:BackgroundN6-methyladenosine (m6A) is the most prevalent RNA modification. While the role of m6A in prostate cancer remains unknown. We aim to measure the effects of m6A methylation regulatory genes during the development and progression of prostate cancer.MethodsWe collected transcriptome information and gene-level alteration data from The Cancer Genome Atlas datasets. The log-rank test and Cox regression model were used to examine the prognosis value of m6A methylation regulatory genes of prostate cancer.ResultsWe discovered that most of m6A methylation regulators were highly expressed in aggressive prostate cancer. Univariable and multivariable Cox regression results showed that the expression of Insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3), Heterogeneous nuclear ribonucleoproteins A2/B1 (HNRNPA2B1) and N6-adenosine-methyltransferase non-catalytic subunit (METTL14) and copy number variant of AlkB Homolog 5 (ALKBH5) were considerably associated with a recurrence-free survival of prostate cancer. Furthermore, a high level of m6A methylation in mRNA promotes the progression of prostate cancer via regulating subcellular protein localization.ConclusionPatients with a high level of mRNA methylation resulted from overexpression of reader proteins and methyltransferase complexes had poor survival benefits through influencing protein subcellular location in prostate cancer.
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Project description:There is growing evidence that phagocytosis regulatory factors (PRFs) play important roles in tumor progression, and therefore, identifying and characterizing these factors is crucial for understanding the mechanisms of cellular phagocytosis in tumorigenesis. Our research aimed to comprehensively characterize PRFs in prostate adenocarcinoma (PRAD) and to screen and determine important PRFs in PRAD which may help to inform tumor prognostic and therapeutic signatures based on these key PRFs. Here, we first systematically described the expression of PRFs in PRAD and evaluated their expression patterns and their prognostic value. We then analyzed prognostic phagocytic factors by Cox and Lasso analysis and constructed a phagocytic factor-mediated risk score. We then divided the samples into two groups with significant differences in overall survival (OS) based on the risk score. Then, we performed correlation analysis between the risk score and clinical features, immune infiltration levels, immune characteristics, immune checkpoint expression, IC50 of several classical sensitive drugs, and immunotherapy efficacy. Finally, the Human Protein Atlas (HPA) database was used to determine the protein expression of 18 PRF characteristic genes. The aforementioned results confirmed that multilayer alterations of PRFs were associated with the prognosis of patients with PRAD and the degree of macrophage infiltration. These findings may provide us with potential new therapies for PRAD.
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Project description:BackgroundEndometrial cancer poses a serious threat to women's health worldwide, and its pathogenesis, although actively explored, is not fully understood. DLGAP5 is a recently identified cell cycle-regulation gene not reported in endometrial cancer. This study was aiming to analyze the role of DLGAP5 in tumorigenesis and development and to investigate its prognostic significance of patients with endometrial cancer.MethodologyMicroarray datasets (GSE17025, GSE39099 and GSE63678) from the GEO database were used for comparative analysis, and their intersection was obtained by applying the Venn diagram, and DLGAP5 was selected as the target gene. Next, transcriptome data (n = 578) was downloaded from TCGA-UCEC to analyze the mRNA expression profile of DLGAP5. Then, immunohistochemical data provided by HPA were used to identify the different protein expression levels of DLGAP5 in tumor tissues and normal tissues. Subsequently, the prognostic meaning of DLGAP5 in patients with endometrial cancer was explored based on survival data from TCGA-UCEC (n = 541). Finally, the reliability of DLGAP5 expression was verified by RT-qPCR.ResultsTranscriptome data from TCGA-UCEC, immunohistochemical data from HPA, and RT-qPCR results from clinical samples were used for triple validation to confirm that the expression of DLGAP5 in endometrial cancer tissues was significantly higher than that in normal endometrial tissues. Kaplan-Meier survival analysis announced that the expression level of DLGAP5 was negatively correlated with the overall survival of patients with endometrial cancer.ConclusionsDLGAP5 is a potential oncogene with cell cycle regulation, and its overexpression can predict the poor prognosis of patients with endometrial cancer. As a candidate target for the diagnosis and treatment of endometrial cancer, it is worthwhile to make further study to reveal the carcinogenicity of DLGAP5 and the mechanism of its resistance of organisms.
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