Wide-Targeted Metabolome Analysis Identifies Potential Biomarkers for Prognosis Prediction of Epithelial Ovarian Cancer.
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ABSTRACT:
SUBMITTER: Hishinuma E
PROVIDER: S-EPMC8309959 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Epithelial ovarian cancer (EOC) is one of the malignancies in women, which has the highest mortality. However, the microlevel mechanism has not been discussed in detail. The expression profiles GSE27651, GSE38666, GSE40595, and GSE66957 including 188 tumor and 52 nontumor samples were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were filtered using R software, and we performed functional analysis using the clusterProfiler. Cytoscape software, the molecular complex detection plugin and database STRING analyzed DEGs to construct protein-protein interaction network. We identified 116 DEGs including 81 upregulated and 35 downregulated DEGs. Functional analysis revealed that they were significantly enriched in the extracellular region and biosynthesis of amino acids. We next identified four bioactive compounds (vorinostat, LY-294002,trichostatin A, and tanespimycin) based on ConnectivityMap. Then 114 nodes were obtained in protein-protein interaction. The three most relevant modules were detected. In addition, according to degree ≥ 10, 14 core genes including FOXM1, CXCR4, KPNA2, NANOG, UBE2C, KIF11, ZWINT, CDCA5, DLGAP5, KIF15, MCM2, MELK, SPP1, and TRIP13 were identified. Kaplan-Meier analysis, Oncomine, and Gene Expression Profiling Interactive Analysis showed that overexpression of FOXM1, SPP1, UBE2C, KIF11, ZWINT, CDCA5, UBE2C, and KIF15 was related to bad prognosis of EOC patients. CDCA5, FOXM1, KIF15, MCM2, and ZWINT were associated with stage. Receiver operating characteristic (ROC) curve showed that messenger RNA levels of these five genes exhibited better diagnostic efficiency for normal and tumor tissues. The Human Protein Atlas database was performed. The protein levels of these five genes were significantly higher in tumor tissues compared with normal tissues. Functional enrichment analysis suggested that all the hub genes played crucial roles in citrate cycle tricarboxylic acid cycle. Furthermore, the univariate and multivariate Cox proportional hazards regression showed that ZWINT was independent prognostic indictor among EOC patients. The genes and pathways discovered in the above studies may open a new direction for EOC treatment.
| S-EPMC6822059 | biostudies-literature
Project description:BackgroundThere is a significant role for peroxisome proliferator-activated receptors (PPARs) in the development of cancer. Nevertheless, the role of PPARs-related genes in ovarian cancer (OC) remains unclear.MethodsThe open-accessed data used for analysis were downloaded from The Cancer Genome Atlas database, which was analyzed using the R software.ResultsIn our study, we comprehensively investigated the PPAR target genes in OC, including their biological role. Meanwhile, a prognosis signature consisting of eight PPAR target genes was established, including apolipoprotein A-V, UDP glucuronosyltransferase 2 family, polypeptide B4, TSC22 domain family, member 1, growth hormone inducible transmembrane protein, renin, dedicator of cytokinesis 4, enoyl CoA hydratase 1, peroxisomal (ECH1), and angiopoietin-like 4, which showed a good prediction efficiency. A nomogram was constructed by combining the clinical feature and risk score. Immune infiltration and biological enrichment analysis were applied to investigate the difference between high- and low-risk patients. Immunotherapy analysis indicated that low-risk patients might respond better to immunotherapy. Drug sensitivity analysis indicated that high-risk patients might respond better to bleomycin, nilotinib, pazopanib, pyrimethamine, and vinorelbine, yet worse to cisplatin and gefitinib. Furthermore, the gene ECH1 was selected for further analysis.ConclusionsOur study identified a prognosis signature that could effectively indicates patients survival. Meanwhile, our study can provide the direction for future studies focused on the PPARs in OC.
| S-EPMC10195182 | biostudies-literature
Project description:DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.
| S-EPMC6380428 | biostudies-literature
Project description:BackgroundOvarian cancer is the second most common gynecologic cancer with high mortality rate and generally diagnosed in advanced stages. The 5-year disease-free survival is below 40%. MicroRNAs, subset of the non-coding RNA molecules, regulate the translation in post transcriptional level by binding to specific mRNAs to promote or degrade the target oncogenes or tumor suppressor genes. Abnormal expression of miRNAs were found in numerous human cancer, including ovarian cancer. Investigating the miRNAs derived from the peripheral blood samples can be used as a marker in the diagnose, treatment and prognosis of ovarian cancer. We aimed to find biological markers for early diagnosis of ovarian cancer by investigating BRCA1 gene mutation carrier monozygotic discordant twins and their high risk healthy family individual's miRNAs.MethodsThe study was conducted on monozygotic twins discordant for ovarian cancer, and the liquid biopsy exploration of miRNAs was performed on mononuclear cells that were isolated from the peripheral blood samples. The miRNA expression profile changes in the study were found by using microarray analysis. miRNA isolation procedure performed from the lymphocyte in accordance with the kit protocol. The presence and quality of the isolated miRNAs screened by electrophoresis. Raw data logarithmic analysis was studied by identifying the threshold, normalization, correlation, mean and median values. Target proteins were detected for each miRNA by using different algorithms.ResultsAfter the comparison of monozygotic discordant twins for epithelial ovarian carcinoma upregulation of the 4 miRNAs, miR-6131, miR-1305, miR-197-3p, miR-3651 and downregulation of 4 miRNAs, miR-3135b, miR-4430, miR-664b-5p, miR-766-3p were found statically significant.ConclusionsThe detected 99 miRNAs out of 2549 miRNAs might be used in the clinic as new biological indicators in the diagnosis and follow up of epithelial ovarian cancer with complementary studies. The miRNA expression profiles were identified to be statistically significant in the evaluation of ovarian cancer etiology, BRCA1 mutation status, and ovarian cancer risk in accordance with the obtained data. There is a need for validation of the miRNAs which were particularly detected between monozygotic twins and its association with ovarian cancer was emphasized in our study in wider cohorts including ovarian cancer patients, and healthy individuals.
| S-EPMC7453540 | biostudies-literature
Project description:Rapid and reliable diagnosis of prostate cancer (PCa) is highly desirable as current used methods lack specificity. In addition, identification of PCa biomarkers that can classify patients into high- and low-risk groups for disease progression at early stage will improve treatment decision-making. Here, we describe a set of protein-combination panels in urinary extracellular vesicles (EVs), defined by targeted proteomics and immunoblotting techniques that improve early non-invasive detection and stratification of PCa patients.We report a two-protein combination in urinary EVs that classifies benign and PCa patients (ADSV-TGM4), and a combination of five proteins able to significantly distinguish between high- and low-grade PCa patients (CD63-GLPK5-SPHM-PSA-PAPP). Proteins composing the panels were validated by immunohistochemistry assays in tissue microarrays (TMAs) confirming a strong link between the urinary EVs proteome and alterations in PCa tissues. Moreover, ADSV and TGM4 abundance yielded a high diagnostic potential in tissue and promising TGM4 prognostic power. These results suggest that the proteins identified in urinary EVs distinguishing high- and low grade PCa are a reflection of histological changes that may be a consequence of their functional involvement in PCa development. In conclusion, our study resulted in the identification of protein-combination panels present in urinary EVs that exhibit high sensitivity and specificity for PCa detection and patient stratification. Moreover, our study highlights the potential of targeted proteomic approaches-such as selected reaction monitoring (SRM)-as diagnostic assay for liquid biopsies via urinary EVs to improve diagnosis and prognosis of suspected PCa patients.
| S-EPMC5354884 | biostudies-literature
Project description:BackgroundEpithelial ovarian cancer (EOC) is a heterogeneous disease, which has been recently classified into four molecular subtypes, of which the mesenchymal subtype exhibited the worst prognosis. We aimed to identify a microRNA- (miRNA-) based signature by incorporating the molecular modalities involved in the mesenchymal subtype for risk stratification, which would allow the identification of patients who might benefit from more rigorous treatments.MethodWe characterized the regulatory mechanisms underlying the mesenchymal subtype using network analyses integrating gene and miRNA expression profiles from The Cancer Genome Atlas (TCGA) cohort to identify a miRNA signature for prognosis prediction.ResultsWe identified four miRNAs as the master regulators of the mesenchymal subtype and developed a risk score model. The 4-miRNA signature significantly predicted overall survival (OS) and progression-free survival (PFS) in discovery (p=0.004 and p=0.04) and two independent public datasets (GSE73582: OS, HR: 2.26 (1.26-4.05), p=0.005, PFS, HR: 2.03 (1.34-3.09), p<0.001; GSE25204: OS, HR: 3.07 (1.73-5.46), p<0.001, PFS, HR: 2.59 (1.72-3.88), p<0.001). Moreover, in multivariate analyses, the miRNA signature maintained as an independent prognostic predictor and achieved superior efficiency compared to the currently used clinical factors.ConclusionsIn conclusion, our network analysis identified a 4-miRNA signature which has prognostic value superior to currently reported clinical covariates. This signature warrants further testing and validation for use in clinical practice.
| S-EPMC6582839 | biostudies-literature
Project description:Ovarian cancer is one of the most common causes of death in women as survival is highly dependent on the stage of the disease. Ovarian cancer is typically diagnosed in the late stage due to the fact that in the early phases is mostly asymptomatic. Genomic instability is one of the hallmarks of ovarian cancer. While ovarian cancer is stratified into different clinical subtypes, there still exists extensive genetic and progressive diversity within each subtype. Early detection of the disorder is one of the most important steps that facilitate a favorable prognosis and a good response to medical therapy for the patients. In targeted therapies, individual patients are treated by agents targeting the changes in tumor cells that help them grow, divide and spread. Currently, in gynecological malignancies, potential therapeutic targets include tumor-intrinsic signaling pathways, angiogenesis, homologous-recombination deficiency, hormone receptors, and immunologic factors. Ovarian cancer is usually diagnosed in the final stages, partially due to the absence of an effective screening strategy, although, over the times, numerous biomarkers have been studied and used to assess the status, progression, and efficacy of the drug therapy in this type of disorder.
| S-EPMC8235073 | biostudies-literature
Project description:Ovarian cancer is the second most dangerous gynecologic cancer with a high mortality rate. The classification of gene expression data from high-dimensional and small-sample gene expression data is a challenging task. The discovery of miRNAs, a small non-coding RNA with 18-25 nucleotides in length that regulates gene expression, has revealed the existence of a new array for regulation of genes and has been reported as playing a serious role in cancer. By using LASSO and Elastic Net as embedded algorithms of feature selection techniques, the present study identified 10 miRNAs that were regulated in ovarian serum cancer samples compared to non-cancer samples in public available dataset GSE106817: hsa-miR-5100, hsa-miR-6800-5p, hsa-miR-1233-5p, hsa-miR-4532, hsa-miR-4783-3p, hsa-miR-4787-3p, hsa-miR-1228-5p, hsa-miR-1290, hsa-miR-3184-5p, and hsa-miR-320b. Further, we implemented state-of-the-art machine learning classifiers, such as logistic regression, random forest, artificial neural network, XGBoost, and decision trees to build clinical prediction models. Next, the diagnostic performance of these models with identified miRNAs was evaluated in the internal (GSE106817) and external validation dataset (GSE113486) by ROC analysis. The results showed that first four prediction models consistently yielded an AUC of 100%. Our findings provide significant evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.
| S-EPMC8656459 | biostudies-literature
Project description:BackgroundOvarian cancer (OC) is considered the most lethal gynecological cancer, of which more than 65% cases are diagnosed in advanced stages, requiring platinum-based neoadjuvant chemotherapy (NACT).MethodsA prospective-longitudinal study was conducted among women with advanced epithelial ovarian cancer (AEOC), III and IV stages, and treated with NACT, at the National Cancer Institute - Mexico, from July 2017 to July 2018. Serum samples were obtained for quantification of CA125 and HE4 using ELISA at the first and in each of the three NACT cycles. The therapeutic response was evaluated through standard tomography. We determined whether CA125 and HE4, alone or in combination, were associated with TR to NACT during follow up.Results53 patients aged 38 to 79 years were included, 92.4% presented papillary serous subtype OC. Higher serum HE4 levels were observed in patients with non-tomographic response (6.89 vs 5.19 pmol/mL; p = 0.031), specially during the second (p = 0.039) and third cycle of NACT (p = 0.031). Multivariate-adjusted models showed an association between HE4 levels and TR, from the second treatment cycle (p = 0.042) to the third cycle (p = 0.033). Changes from baseline HE4 levels during the first cycle was negative associated with TR. No associations were found between CA125 and TR.ConclusionsSerum HE4 levels were independently associated with TR among patients with AOEC treated with NACT, also a reduction between baseline HE4 and first chemotherapy levels was also independently associated with the TR. These findings might be relevant for predicting a lack of response to treatment.
| S-EPMC8287739 | biostudies-literature
Project description:Tuberculous meningitis (TBM) is the most common and severe form of central nervous system tuberculosis. Due to the non-specific clinical presentation and lack of efficient diagnosis methods, it is difficult to discriminate TBM from other frequent types of meningitis, especially viral meningitis (VM). In order to identify the potential biomarkers for discriminating TBM and VM and to reveal the different pathophysiological processes between TBM and VM, a genome-wide miRNA screening of PBMCs from TBM, VM, and healthy controls (HCs) using microarray assay was performed (12 samples). Twenty-eight differentially expressed miRNAs were identified between TBM and VM, and 11 differentially expressed miRNAs were identified between TBM and HCs. The 6 overlapping miRNAs detected in both TBM vs. VM and TBM vs. HCs were verified by qPCR analysis and showed a 100% consistent expression patterns with that in microarray test. Statistically significant differences of 4 miRNAs (miR-126-3p, miR-130a-3p, miR-151a-3p, and miR-199a-5p) were further confirmed in TBM compared with VM and HCs in independent PBMCs sample set (n = 96, P < 0.01). Three of which were also showed significantly different between TBM and VM in CSF samples (n = 70, P < 0.05). The receiver operating characteristic curve (ROC) analysis showed that the area under the ROC curve (AUC) of these 4 miRNAs in PBMCs were more than 0.70 in discriminating TBM from VM. Combination of these 4 miRNAs could achieve better discriminative capacity [AUC = 0.893 (0.788-0.957)], with a sensitivity of 90.6% (75.0-98.0%), and a specificity of 86.7% (69.3-96.2%). Additional validation was performed to evaluate the diagnostic panel in another independent sample set (n = 49), which yielded a sensitivity of 81.8% (9/11), and specificity of 90.0% (9/10) in distinguishing TBM and VM, and a sensitivity of 81.8% (9/11), and a specificity of 84.6% (11/13) in discriminating TBM from other non-TBM patients. This study uncovered the miRNA profiles of TBM and VM patients, which can facilitate better understanding of the pathogenesis involved in these two diseases and identified 4 novel miRNAs in distinguishing TBM and VM.
| S-EPMC6749153 | biostudies-literature