Identification of novel biomarkers of prostate cancer through integrated analysis.
Ontology highlight
ABSTRACT: Background
The current methods adopted to screen for prostate cancer (PCa) can sometimes be misleading and inaccurate. Moreover, for advanced stages of PCa, the current effect of treatment is not satisfactory for some patients. Accordingly, we aimed to identify new biomarkers for the diagnosis and prognosis of PCa.Methods
A series of bioinformatic tools were utilized to search for potential new biomarkers of PCa and analyze their functions, expression, clinical relevance, prognostic value, and underlying mechanisms.Results
Although ASPN was overexpressed in PCa, EDN3, PENK, MEIS2, IGF1, and CXCL12 were downregulated. The univariate Cox regression analysis showed that abnormally high expression of ASPN and low expression of other genes predicted worse prognosis. Moreover, the multivariate Cox regression analysis showed that ASPN, PENK, and MEIS2 were independently associated with the overall survival (OS) of patients, whereas other markers were not. The outcomes of gene ontology and gene set enrichment analysis showed that the expression levels of these genes might be associated with cell proliferation and infiltration of immune cells in PCa.Conclusions
We demonstrated that ASPN, EDN3, PENK, MEIS2, IGF1, and CXCL12 are possibly novel diagnostic indicators for PCa, whereas ASPN, PENK, and MEIS2 show appealing potential to predict the prognosis of this disease.
SUBMITTER: Zhang P
PROVIDER: S-EPMC8421833 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Liver cancer is a common malignant tumor with poor prognosis. Due to the lack of specific clinical manifestations at early stages, most patients are already at advanced stages of the disease by the time of diagnosis. Identification of novel biomarkers for liver cancer may thus enable earlier detection, improving outcome. MicroRNAs (miRNAs) are small endogenous noncoding RNAs of 18-22 nucleotides in length, which have a regulatory role in the expression of target proteins. Increased evidence suggests that miRNAs are abnormally expressed in a variety of cancer malignancies. Here, we combined RNA sequencing data and clinical information from The Cancer Genome Atlas Liver Hepatocellular Carcinoma database for weighted gene coexpression network analysis to identify potential miRNA prognostic biomarkers. We constructed nine coexpression modules, allowing us to identify that miR-105-5p, miR-767-5p, miR-1266-5p, miR-4746-5p, miR-500a-3p, miR-1180-3p and miR-139-5p are significantly associated with liver cancer prognosis. We found that these miRNAs exhibit significant association with prognosis of patients with liver cancer and confirmed the expression of these miRNAs in liver cancer tissues. Multivariate Cox regression analysis showed that miR-105-5p and miR-139-5p may be considered as independent factors. In summary, here we report that seven miRNAs have potential value as prognostic biomarkers of liver cancer.
| S-EPMC7609804 | biostudies-literature
Project description:Parkinson's disease (PD) is a quite common neurodegenerative disorder with a prevalence of approximately 1:800-1,000 in subjects over 60 years old. The aim of our study was to determine the candidate target genes in PD through meta-analysis of multiple gene expression arrays datasets and to further combine mRNA and miRNA expression analyses to identify more convincing biological targets and their regulatory factors. Six included datasets were obtained from the Gene Expression Omnibus database by systematical search, including five mRNA datasets (150 substantia nigra samples in total) and one miRNA dataset containing 32 peripheral blood samples. A chip meta-analysis of five microarray data was conducted by using the metaDE package and 94 differentially expressed (DE) mRNAs were comprehensively obtained. And 19 deregulated DE miRNAs were obtained through the analysis of one miRNAs dataset by Qlucore Omics Explorer software. An interaction network formed by DE mRNAs, DE miRNAs, and important pathways was discovered after we analyzed the functional enrichment, protein-protein interactions, and miRNA targetome prediction analysis. In conclusion, this study suggested that five significantly downregulated mRNAs (MAPK8, CDC42, NDUFS1, COX4I1, and SDHC) and three significantly downregulated miRNAs (miR-126-5p, miR-19-3p, and miR-29a-3p) were potentially useful diagnostic markers in clinic, and lipid metabolism (especially non-alcoholic fatty liver disease pathway) and mitochondrial dysregulation may be the keys to biochemically detectable molecular defects. However, the role of these new biomarkers and molecular mechanisms in PD requires further experiments in vivo and in vitro and further clinical evidence.
| S-EPMC6016006 | biostudies-literature
Project description:Aim:To identify new biomarkers of prostate cancer (PCa) for the diagnosis and prediction of clinical outcomes. Materials and Methods:Existing microarray data of PCa tissues in the Oncomine database were analyzed and candidate differentially expressed genes (DEGs) that may be novel and noninvasive biomarkers were obtained. On this basis, plasma mRNA was extracted from PCa patients and healthy donors. Furthermore, plasma mRNA expression of DEGs was evaluated by qRT-PCR. Finally, the diagnostic power of the biomarkers was evaluated in comparison to the clinical characteristics of the patients. Results:In this study, the top five significantly overexpressed mRNA (AMACR, PPP1R14b, PCA3, DLX1, and RPL22L1) and the top five significantly underexpressed mRNA (DUOX1, EFS, GSTP1, S100A16, and NCRNA00087) were selected for further validation in PCa patients and healthy donors by qRT-PCR. The results showed that AMACR, DLX1, PCA3, DUOX1, and GSTP1 mRNA were stably amplified in plasma. Additionally, DLX1, PCA3, DUOX1, and GSTP1 mRNA expression was significantly different between PCa circulating free mRNA samples and healthy donors. These mRNAs may be useful biomarkers for PCa diagnosis. Conclusion:Analysis of the expression of genes in the Oncomine database showed that DLX1, PCA3, and DUOX1 expressions have a cancer specific pattern in PCa. Collectively, DLX1, PCA3, and DUOX1 may be useful candidate biomarkers for PCa diagnosis.
| S-EPMC6974148 | biostudies-literature
Project description:Background Small cell lung cancer (SCLC) is an aggressive and recalcitrant cancer. In recent years, studies focused on the abnormal expression of microRNA which has proven valuable in terms of prognosis, diagnosis and treatment in SCLC. To address the limitations of independent studies data, a meta-analysis seems necessary for further exploration of microRNA as biological target and regulatory factor in SCLC. Methods We performed comprehensive literature retrieval in GEO database and EBI ArrayExpress database. The microRNA expression data was extracted from 4 related researches (GSE15008, GSE74190, GSE19945, GSE77380), which was obtained from GEO database. In each included study, the R. Affymetrix Expression Console’s Limma package and RMA algorithms were used to screen for raw data for gene chip quality control, standardization, log2 conversion and differential expression of the gene chip, respectively. Significant microRNA meta-signatures were identified by Robust Rank Aggregation method. Subsequently, gene ontology (GO) enrichment analysis and pathway analysis were performed using bioinformatics tools. Results We found a significant microRNA meta-signature of six up-regulated (hsa-miR-182-5p, hsa-miR-96-5p, hsa-miR-7-5p, hsa-miR-301b-3p, hsa-miR-130b-3p, hsa-miR-210-3p) and four down-regulated (hsa-miR-126-3p, hsa-miR-451a, hsa-miR-145-5p, hsa-miR-486-5p) microRNA s in meta-analysis approaches. GO analysis showed that target gene of meta-signatures microRNA was mainly enriched in endosome, chordate embryonic development and transforming growth factor beta receptor. The related functional gene of microRNA meta signature synergistically targeting SCLC signaling pathway was confirmed by enrichment analysis. In particular, neurotrophin and TGF-beta signaling pathway play the most important roles in the pathway network. Conclusions Our study identified 10 highly significant and consistently dysregulated microRNA s from 4 datasets, which offering convincing molecular targets and regulatory factors in future research of SCLC.
| S-EPMC8797432 | biostudies-literature
Project description:Background: Therapy-related neuroendocrine prostate cancer (NEPC) is a lethal castration-resistant prostate cancer (CRPC) subtype that, at present, lacks well-characterized molecular biomarkers. The clinical diagnosis of this disease is dependent on biopsy and histological assessment: methods that are experience-based and easily misdiagnosed due to tumor heterogeneity. The development of robust diagnostic tools for NEPC may assist clinicians in making medical decisions on the choice of continuing anti-androgen receptor therapy or switching to platinum-based chemotherapy. Methods: Gene expression profiles and clinical characteristics data of 208 samples of metastatic CRPC, including castration-resistant prostate adenocarcinoma (CRPC-adeno) and castration-resistant neuroendocrine prostate adenocarcinoma (CRPC-NE), were obtained from the prad_su2c_2019 dataset. Weighted Gene Co-expression Network Analysis (WGCNA) was subsequently used to construct a free-scale gene co-expression network to study the interrelationship between the potential modules and clinical features of metastatic prostate adenocarcinoma and to identify hub genes in the modules. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to build a model to predict the clinical characteristics of CRPC-NE. The findings were then verified in the nepc_wcm_2016 dataset. Results: A total of 51 co-expression modules were successfully constructed using WGCNA, of which three co-expression modules were found to be significantly associated with the neuroendocrine features and the NEPC score. In total, four novel genes, including NPTX1, PCSK1, ASXL3, and TRIM9, were all significantly upregulated in NEPC compared with the adenocarcinoma samples, and these genes were all associated with the neuroactive ligand receptor interaction pathway. Next, the expression levels of these four genes were used to construct an NEPC diagnosis model, which was successfully able to distinguish CRPC-NE from CRPC-adeno samples in both the training and the validation cohorts. Moreover, the values of the area under the receiver operating characteristic (AUC) were 0.995 and 0.833 for the training and validation cohorts, respectively. Conclusion: The present study identified four specific novel biomarkers for therapy-related NEPC, and these biomarkers may serve as an effective tool for the diagnosis of NEPC, thereby meriting further study.
| S-EPMC8503838 | biostudies-literature
Project description:BACKGROUND:Ischemic Stroke (IS) is the most common neurological emergency disease and has become the second most frequent cause of death after coronary artery disease in 2015. Owing to its high fatality rate and narrow therapeutic time window, early identification and prevention of potential stroke is becoming increasingly important. METHODS:We used meta-analysis and bioinformatics mining to explore disease-related pathways and regulatory networks after combining messengerRNA (mRNA) and miRNA expression analyses. The purpose of our study was to screen for candidate target genes and microRNA(miRNA) for early diagnosis of potential stroke. RESULTS:Five datasets were collected from the Gene Expression Omnibus (GEO) database by systematical retrieval, which contained three mRNA datasets (102 peripheral blood samples in total) and two miRNA dataset (59 peripheral blood samples). Approximately 221 different expression(DE) mRNAs (155 upregulated and 66 downregulated mRNAs) and 185 DE miRNAs were obtained using the metaDE package and GEO2R tools. Further functional enrichments of DE-mRNA, DE-miRNA and protein-protein interaction (PPI) were performed and visualized using Cytoscape. CONCLUSION:Our study identified six core mRNAs and two regulated miRNAs in the pathogenesis of stroke, and we elaborated the intrinsic role of systemic lupus erythematosus (SLE) and atypical infections in stroke, which may aid in the development of precision medicine for treating ischemic stroke. However, the role of these novel biomarkers and the underlying molecular mechanisms in IS require further fundamental experiments and further clinical evidence.
| S-EPMC7106706 | biostudies-literature
Project description:The aim of this study was to explore colorectal tumor-associated macrophage (TAM) biomarkers for early diagnosis and surveillance of colorectal cancer (CRC). We used bioinformatic methods to screen array expression data of CRC-related macrophages (GEO: GSE29214) to detect the differentially expressed genes (DEGs) between CRC-related macrophages and normal control cells. We found 431 DEGs in TAMs compared with the control group; 399 were up-regulated and 32 were down-regulated. A functional enrichment analysis showed that the DEGs were involved in positive regulation of the ERK1 and ERK2 cascade, cell activation involved in the immune response, cytokine-mediated signaling pathway, and receptor activity, all of which were significantly enriched. We constructed a protein-protein interaction (PPI) network to identify hub genes. We identified 10 hub genes: ITGB2, ITGAM, C3AR1, PTAFR, C3, CYBB, FCER1G, PLAU, STOM, and GPR84, in the PPI network. We verified the results using array expression data of peripheral blood mononuclear cells (GEO: GSE47756). The results showed that the expression trends of CYBB, PLAU, and STOM were consistent with those found in the GSE29214 dataset. Further verification with The Cancer Genome Atlas and Human Protein Atlas showed that the high expression of PLAU in TAMs was statistically significant (P<0.05). We concluded that PLAU may be a biomarker of CRC-associated macrophages and may have prognostic and predictive significance for clinical utility in CRC management.
| S-EPMC7847498 | biostudies-literature
Project description:BackgroundDetecting prostate cancer at a non-aggressive stage is the main goal of prostate cancer screening. DNA methylation has been widely used as biomarkers for cancer diagnosis and prognosis, however, with low clinical translation rate. By taking advantage of multi-cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we aimed to identify prostate cancer specific biomarkers which can separate between non-aggressive and aggressive prostate cancer based on DNA methylation patterns.ResultsWe performed a comparison analysis of DNA methylation status between normal prostate tissues and prostate adenocarcinoma (PRAD) samples at different Gleason stages. The candidate biomarkers were selected by excluding the biomarkers existing in multiple cancers (pan-cancer) and requiring significant difference between PRAD and other urinary samples. By least absolute shrinkage and selection operator (LASSO) selection, 8 biomarkers (cg04633600, cg05219445, cg05796128, cg10834205, cg16736826, cg23523811, cg23881697, cg24755931) were identified and in-silico validated by model constructions. First, all 8 biomarkers could separate PRAD at different stages (Gleason 6 vs. Gleason 3 + 4: AUC = 0.63; Gleason 6 vs. Gleason 4 + 3 and 8-10: AUC = 0.87). Second, 5 biomarkers (cg04633600, cg05796128, cg23523811, cg23881697, cg24755931) effectively detected PRAD from normal prostate tissues (AUC ranged from 0.88 to 0.92). Last, 6 biomarkers (cg04633600, cg05219445, cg05796128, cg23523811, cg23881697, cg24755931) completely distinguished PRAD with other urinary samples (AUC = 1).ConclusionsOur study identified and in-silico validated a panel of prostate cancer specific DNA methylation biomarkers with diagnosis value.
| S-EPMC8507340 | biostudies-literature
Project description:Epigenetic silencing mediated by CpG methylation is a common feature of many cancers. Characterizing aberrant DNA methylation changes associated with tumor progression may identify potential prognostic markers for prostate cancer (PCa). We treated two PCa cell lines, 22Rv1 and DU-145 with the demethylating agent 5-Aza 2'-deoxycitidine (DAC) and global methylation status was analyzed by performing methylation-sensitive restriction enzyme based differential methylation hybridization strategy followed by genome-wide CpG methylation array profiling. In addition, we examined gene expression changes using a custom microarray. Gene Set Enrichment Analysis (GSEA) identified the most significantly dysregulated pathways. In addition, we assessed methylation status of candidate genes that showed reduced CpG methylation and increased gene expression after DAC treatment, in Gleason score (GS) 8 vs. GS6 patients using three independent cohorts of patients; the publically available The Cancer Genome Atlas (TCGA) dataset, and two separate patient cohorts. Our analysis, by integrating methylation and gene expression in PCa cell lines, combined with patient tumor data, identified novel potential biomarkers for PCa patients. These markers may help elucidate the pathogenesis of PCa and represent potential prognostic markers for PCa patients.
| S-EPMC4202166 | biostudies-literature
Project description:Alzheimer's disease (AD) is an intractable and progressive neurodegenerative disorder that can lead to severe cognitive decline, impaired speech, short-term memory loss, and finally an inability to function in daily life. For patients, their families, and even all of society, AD can impart great emotional pressure and economic costs. Therefore, this study aimed to investigate potential diagnostic biomarkers of AD. Using the Gene Expression Omnibus (GEO) database, the expression profiles of genes were extracted from the GSE5281, GSE28146, and GSE48350 microarray datasets. Then, immune-related genes were identified by the intersections of differentially expressed genes (DEGs). Functional enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA), were performed. Subsequently, random forest models and least absolute shrinkage and selection operator regression were used to further screen hub genes, which were then validated using receiver operating characteristic (ROC) curve analysis. Finally, 153 total immune-related DEGs were identified in relation to AD. DO analysis of these immune-related DEGs showed that they were enriched in "lung disease," "reproductive system disease," and "atherosclerosis." Single GSEA of hub genes showed that they were particularly enriched in "oxidative phosphorylation." ROC analysis of AGAP3 yielded an area under the ROC curve of 0.878 for GSE5281, 0.727 for GSE28146, and 0.635 for GSE48350. Moreover, immune infiltration analysis demonstrated that AGAP3 was related to follicular helper T cells, naïve CD4 T cells, naïve B cells, memory B cells, macrophages M0, macrophages M1, macrophages M2, resting natural killer (NK) cells, activated NK cells, monocytes, neutrophils, eosinophils, and activated mast cells. These results indicate that identifying immune-related DEGs might enhance the current understanding of the development and prognosis of AD. Furthermore, AGAP3 not only plays a vital role in AD progression and diagnosis but could also serve as a valuable target for further research on AD.
| S-EPMC9468260 | biostudies-literature