Key pathways involved in prostate cancer based on gene set enrichment analysis and meta analysis

Regulation of anti-tumorigenic pathways by the combinatory treatment of calcitriol and TGF-β in PC-3 and DU145 cells

Calcitriol and transforming growth factors beta (TGF-β) are involved in several biological pathways such as cell proliferation, differentiation, migration and invasion. Their cellular effects could be similar or opposite depending on the genetic target, cell type and context. Despite the reported association of calcitriol deficiency and disruption of the TGF-β pathway in prostate cancer and the well-known independent effects of calcitriol and TGF-βs on cancer cells, there is limited information regarding the cellular effects of calcitriol and TGF-β in combination. In this study, we in vitro analyze the combinatory effects of calcitriol and TGF-β on cell growth and apoptosis using PC-3 and DU145 human prostate cancer cell lines. Using high-throughput microarray profiling of PC-3 cells upon independent and combinatory treatments, we identified distinct transcriptional landscapes of each intervention, with a higher effect established by the combinatorial treatment, following by TGF-β1 and later by calcitriol. A set of genes and enriched pathways converge among the treatments, mainly between the combinatory scheme and TGF-β1, but the majority were treatment-specific. Of note, CYP24A1, IGFBP3, CDKN1A, NOX4 and UBE2D3 were significantly up-regulated upon the combinatorial treatment whereas CCNA1, members of the CT45A and APOBEC3 family were down-regulated. By public RNA signatures, we were able to confirm the regulation by the co-treatment over cell proliferation and cell cycle. We finally investigated the possible clinical impact of genes modulated by the combinatorial treatment using benchmark prostate cancer data. This comprehensive analysis reveals that the combinatory treatment impairs cell growth without affecting apoptosis and their combinatory actions might synergize and improved their individual effects to reprogram prostate cancer signaling.

POM121 overexpression is related to a poor prognosis in colorectal cancer

Background: Nuclear pore membrane protein 121 (POM121) plays a crucial role in nucleocytoplasmic transport, but its significance in tumorigenesis and the progression of colorectal cancer (CRC) remains unknown. The aim of this study was to evaluate the relationship between POM121 and CRC.Methods: POM121 expression in colorectal tissues was analyzed at both the gene and protein levels. We investigated the connection between POM121 expression and clinicopathological features, as well as overall survival. A gene set enrichment analysis (GSEA) was performed, and a protein-protein interaction (PPI) network was constructed to determine the mechanism of POM121 in CRC.Results: CRC tissues displayed a striking increase in POM121 expression compared with colonitis and pericarcinomatous mucosa tissues (66.61% vs 24.36% vs 24.11%, respectively, p < 0.0167). POM121 overexpression was significantly associated with lymph node metastasis, distant metastasis, TNM stage, venous invasion, perineural invasion, preoperative CEA and CA19-9 levels, and Ki67 expression. CRC patients with high POM121 levels tended to have poor overall survival rates. POM121 may participate in the regulation of the cell cycle and DNA repair in CRC.Conclusions: Our results suggest that POM121 has the potential to serve as a novel prognostic biomarker in CRC patients.

A novel microRNA signature predicts survival in stomach adenocarcinoma

Recent microRNA (miRNA) expression profiling studies suggest the clinical use of miRNAs as potential prognostic biomarkers in various malignancies. In this study, aiming to identify microRNAs with prognostic value for overall survival (OS) in stomach adenocarcinoma (STAD) patients, we analyzed the miRNA expression profiles and the associated clinical characteristics in 380 STAD samples from The Cancer Genome Atlas (TCGA) dataset. An eight-miRNA signature for predicting OS in STAD patients was identified and self-validated by survival analysis and semi-supervised principal components method. We developed a linear prognostic model composed of these miRNAs to divide patients into high- and low-risk groups according to the calculated prognostic scores. Kaplan-Meier analysis demonstrated that patients in the high-risk group had worse OS compared with patients in the low-risk group. Notably, this miRNA prognostic model showed prognostic significance to the STAD patients in early stages and the chemo-resistant patients, who would potentially benefit from additional medical interventions. Finally, this eight-miRNA signature is an independent prognostic biomarker and demonstrates a good predictive performance for 5-year survival. Thus, this signature may serve as a novel biomarker for predicting survival as well as chemotherapy response in STAD patients.

Genetic association analysis of the RTK/ERK pathway with aggressive prostate cancer highlights the potential role of CCND2 in disease progression

The RTK/ERK signaling pathway has been implicated in prostate cancer progression. However, the genetic relevance of this pathway to aggressive prostate cancer at the SNP level remains undefined. Here we performed a SNP and gene-based association analysis of the RTK/ERK pathway with aggressive prostate cancer in a cohort comprising 956 aggressive and 347 non-aggressive cases. We identified several loci including rs3217869/CCND2 within the pathway shown to be significantly associated with aggressive prostate cancer. Our functional analysis revealed a statistically significant relationship between rs3217869 risk genotype and decreased CCND2 expression levels in a collection of 119 prostate cancer patient samples. Reduced expression of CCND2 promoted cell proliferation and its overexpression inhibited cell growth of prostate cancer. Strikingly, CCND2 downregulation was consistently observed in the advanced prostate cancer in 18 available clinical data sets with a total amount of 1,095 prostate samples. Furthermore, the lower expression levels of CCND2 markedly correlated with prostate tumor progression to high Gleason score and elevated PSA levels, and served as an independent predictor of biochemical relapse and overall survival in a large cohort of prostate cancer patients. Together, we have identified an association of genetic variants and genes in the RTK/ERK pathway with prostate cancer aggressiveness, and highlighted the potential importance of CCND2 in prostate cancer susceptibility and tumor progression to metastasis.

CAFÉ-Map: Context Aware Feature Mapping for mining high dimensional biomedical data

Feature selection and ranking is of great importance in the analysis of biomedical data. In addition to reducing the number of features used in classification or other machine learning tasks, it allows us to extract meaningful biological and medical information from a machine learning model. Most existing approaches in this domain do not directly model the fact that the relative importance of features can be different in different regions of the feature space. In this work, we present a context aware feature ranking algorithm called CAFÉ-Map. CAFÉ-Map is a locally linear feature ranking framework that allows recognition of important features in any given region of the feature space or for any individual example. This allows for simultaneous classification and feature ranking in an interpretable manner. We have benchmarked CAFÉ-Map on a number of toy and real world biomedical data sets. Our comparative study with a number of published methods shows that CAFÉ-Map achieves better accuracies on these data sets. The top ranking features obtained through CAFÉ-Map in a gene profiling study correlate very well with the importance of different genes reported in the literature. Furthermore, CAFÉ-Map provides a more in-depth analysis of feature ranking at the level of individual examples.

AVAILABILITY

CAFÉ-Map Python code is available at: http://faculty.pieas.edu.pk/fayyaz/software.html#cafemap . The CAFÉ-Map package supports parallelization and sparse data and provides example scripts for classification. This code can be used to reconstruct the results given in this paper.

Identification of key target genes and pathways in laryngeal carcinoma

The purpose of the present study was to screen the key genes associated with laryngeal carcinoma and to investigate the molecular mechanism of laryngeal carcinoma progression. The gene expression profile of GSE10935 [Gene Expression Omnibus (GEO) accession number], including 12 specimens from laryngeal papillomas and 12 specimens from normal laryngeal epithelia controls, was downloaded from the GEO database. Differentially expressed genes (DEGs) were screened in laryngeal papillomas compared with normal controls using Limma package in R language, followed by Gene Ontology (GO) enrichment analysis and pathway enrichment analysis. Furthermore, the protein-protein interaction (PPI) network of DEGs was constructed using Cytoscape software and modules were analyzed using MCODE plugin from the PPI network. Furthermore, significant biological pathway regions (sub-pathway) were identified by using iSubpathwayMiner analysis. A total of 67 DEGs were identified, including 27 up-regulated genes and 40 down-regulated genes and they were involved in different GO terms and pathways. PPI network analysis revealed that Ras association (RalGDS/AF-6) domain family member 1 (RASSF1) was a hub protein. The sub-pathway analysis identified 9 significantly enriched sub-pathways, including glycolysis/gluconeogenesis and nitrogen metabolism. Genes such as phosphoglycerate kinase 1 (PGK1), carbonic anhydrase II (CA2), and carbonic anhydrase XII (CA12) whose node degrees were >10 were identified in the disease risk sub-pathway. Genes in the sub-pathway, such as RASSF1, PGK1, CA2 and CA12 were presumed to serve critical roles in laryngeal carcinoma. The present study identified DEGs and their sub-pathways in the disease, which may serve as potential targets for treatment of laryngeal carcinoma.

Metabolomic profiling identifies biochemical pathways associated with castration-resistant prostate cancer

Despite recent developments in treatment strategies, castration-resistant prostate cancer (CRPC) is still the second leading cause of cancer-associated mortality among American men, the biological underpinnings of which are not well understood. To this end, we measured levels of 150 metabolites and examined the rate of utilization of 184 metabolites in metastatic androgen-dependent prostate cancer (AD) and CRPC cell lines using a combination of targeted mass spectrometry and metabolic phenotyping. Metabolic data were used to derive biochemical pathways that were enriched in CRPC, using Oncomine concept maps (OCM). The enriched pathways were then examined in-silico for their association with treatment failure (i.e., prostate specific antigen (PSA) recurrence or biochemical recurrence) using published clinically annotated gene expression data sets. Our results indicate that a total of 19 metabolites were altered in CRPC compared to AD cell lines. These altered metabolites mapped to a highly interconnected network of biochemical pathways that describe UDP glucuronosyltransferase (UGT) activity. We observed an association with time to treatment failure in an analysis employing genes restricted to this pathway in three independent gene expression data sets. In summary, our studies highlight the value of employing metabolomic strategies in cell lines to derive potentially clinically useful predictive tools.

RTK/ERK pathway under natural selection associated with prostate cancer

Prostate cancer (PCa) is a global disease causing large numbers of deaths every year. Recent studies have indicated the RTK/ERK pathway might be a key pathway in the development of PCa. However, the exact association and evolution-based mechanism remain unclear. This study was conducted by combining genotypic and phenotypic data from the Chinese Consortium for Prostate Cancer Genetics (ChinaPCa) with related databases such as the HapMap Project and Genevar. In this analysis, expression of quantitative trait loci (eQTLs) analysis, natural selection and gene-based pathway analysis were involved. The pathway analysis confirmed the positive relationship between PCa risk and several key genes. In addition, combined with the natural selection, it seems that 4 genes (EGFR, ERBB2, PTK2, and RAF1) with five SNPs (rs11238349, rs17172438, rs984654, rs11773818, and rs17172432) especially rs17172432, might be pivotal factors in the development of PCa. The results indicate that the RTK/ERK pathway under natural selection is a key link in PCa risk. The joint effect of the genes and loci with positive selection might be one reason for the development of PCa. Dealing with all the factors simultaneously might give insight into prevention and aid in predicting the success of potential therapies for PCa.

The RTK/ERK pathway is associated with prostate cancer risk on the SNP level: a pooled analysis of 41 sets of data from case-control studies

Prostate cancer (PCa) is a malignant disease influencing numerous men worldwide every year. However, the exact pathogenesis and the genes, environment, and other factors involved have not been explained clearly. Some studies have proposed that cell signaling pathways might play a key role in the development and progression of PCa. According to our previous study, the RTK/ERK pathway containing nearly 40 genes was associated with PCa risk. On the basis of these genes, we conducted a meta-analysis with our own Chinese Consortium for Prostate Cancer Genetics (ChinaPCa) study and available studies in the databases to describe the association between the pathway and PCa on the SNP level. The results suggested that rs4764695/IGF1 (recessive model: pooled OR=0.92, 95%CI=0.852-0.994, P=0.034; I(2)=0%, P=0.042; allele analysis: pooled OR=0.915, 95%CI=0.874-0.958, P=0; I(2)=0%, P=0.424; codominant model: OR=0.835, 95%CI=0.762-0.916, P=0; I(2)=0%, P=0.684) and rs1570360/VEGF (recessive model: OR=0.596, 95%CI=0.421-0.843, P=0.003; I(2)=23.9%, P=0.269; codominant model: OR=0.576, 95%CI=0.404-0.820, P=0.002; I(2)=49.1%, P=0.140) were significantly associated with PCa. In subgroup analysis, the relationship was also found in Caucasians for IGF1 (dominant model: OR=0.834, 95%CI=0.769-0.904, P=0; allele analysis: OR=0.908, 95%CI=0.863-0.955, P=0; AA vs CC: OR=0.829, 95%CI=0.750-0.916, P=0; AC vs CC: OR=0.837, 95%CI=0.768-0.912, P=0). In addition, in Asians (allele analysis: OR=0.21, 95%CI=0.168-0.262, P=0) and Caucasians (recessive model: OR=0.453, 95%CI: 0.240-0.855, P=0.015; codominant model: OR=0.464, 95%CI=0.240-0.898, P=0.023) for VEGF, the association was significant. The results indicated that rs4764695/IGF1 and rs1570360/VEGF might play a key role in the development and progression of PCa. On the SNP level, we suggest that the study gives us a new view of gene-pathway analysis and targeted therapy for PCa.