Identification of the miRNA signature associated with survival in patients with ovarian cancer.
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ABSTRACT:
SUBMITTER: Sathipati SY
PROVIDER: S-EPMC8148489 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundCutaneous melanoma (CM) is the deadliest form of skin cancer. Numerous studies have revealed that microRNAs (miRNAs) are expressed abnormally in melanoma tissues. Our work aimed to assess multiple miRNAs using bioinformatic analysis in order to predict the prognoses of cutaneous melanoma patients.MethodsThe microarray dataset GSE35579 was downloaded from the Gene Expression Omnibus (GEO) database to detect the differential expression of miRNAs (DEMs), including 41 melanoma (primary and metastatic) tissues and 11 benign nevi. Clinical information and miRNA sequencing data of cutaneous melanoma tissues were downloaded from the Cancer Genome Atlas database (TCGA) to assess the prognostic values of DEMs. Additionally, the target genes of DEMs were anticipated using miRanda, miRmap, TargetScan, and PicTar. Finally, functional analysis was performed using selected target genes on the Annotation, Visualization and Integrated Discovery (DAVID) website.ResultsAfter performing bioinformatic analysis, a total of 185 DEMs were identified: 80 upregulated miRNAs and 105 downregulated miRNAs. A five-miRNA (miR-25, miR-204, miR-211, miR-510, miR-513c) signature was discovered to be a potential significant prognostic biomarker of cutaneous melanoma when using the Kaplan-Meier survival method (P = 0.001). Univariate and multivariate Cox regression analyses showed that the five-miRNA signature could be an independent prognostic marker (HR = 0.605, P = 0.006) in cutaneous melanoma patients. Biological pathway analysis indicated that the target genes may be involved in PI3K-Akt pathways, ubiquitin-mediated proteolysis, and focal adhesion.ConclusionThe identified five-miRNA signature may serve as a prognostic biomarker, or as a potential therapeutic target, in cutaneous melanoma patients.
| S-EPMC6814066 | biostudies-literature
Project description:BackgroundOvarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV.MethodsThe expression profiles of glycolysis-related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets.ResultsA gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high-grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3- and 5-year survival, respectively. Similar results were found in the test sets, and the AUCs of 3-, 5-year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed.ConclusionOur study established a nine-GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.
| S-EPMC8607265 | biostudies-literature
Project description:Ovarian cancer (OvCa) is an intractable gynecological malignancy due to the high recurrence rate. Several molecular biomarkers have been previously screened for early identifying patients with a high recurrence risk and poor prognosis. However, all the known studies focused on a single type of RNAs, not integrating various types. This study was to construct a new multi-RNA-based model to predict the recurrence and prognosis for OvCa patients by using the messenger RNA (mRNA, including long noncoding RNA (lncRNA)) and microRNA (miRNA) sequencing data of The Cancer Genome Atlas database. After univariate Cox regression and least absolute shrinkage and selection operator analyses, a multi-RNA-based signature (2 miRNAs: hsa-miR-508, hsa-miR-506; 1 lncRNA: TM4SF1-AS1; 11 mRNAs: MAGI3, SLAMF7, GLI2, PDK1, ARID3A, PLEKHG4B, TNFAIP8L3, C1QTNF3, NDUFAF1, CH25H, TMEM129) was generated and used to establish a risk score model. The high- and low-risk patients classified by the median risk score exhibited significantly different recurrence risks (89% versus 61%, p < 0.001) and survival time (the area under the receiver operating characteristic curve (AUC) = 0.901 for 5-year disease-free survival (DFS)). This risk model was independent of other clinical features and superior to pathologic staging for DFS prediction (AUC, 0.906 versus 0.524; C-index, 0.633 versus 0.510). Furthermore, some new interaction axes were revealed to explain the possible functions of these RNAs (competing endogenous RNA: TM4SF1-AS1-miR-186-STEAP2, LINC00536-miR-508-STEAP2, LINC00475-miR-506-TMEM129; coexpression: LINC00598-PLEKHG4B). In conclusion, this multi-RNA-based risk model may be clinically useful to stratify OvCa patients with different recurrence risks and survival outcomes and included RNAs may be potential therapeutic targets.
| S-EPMC7044477 | biostudies-literature
Project description:Lung adenocarcinoma is a multifactorial disease. MicroRNA (miRNA) expression profiles are extensively used for discovering potential theranostic biomarkers of lung cancer. This work proposes an optimized support vector regression (SVR) method called SVR-LUAD to simultaneously identify a set of miRNAs referred to the miRNA signature for estimating the survival time of lung adenocarcinoma patients using their miRNA expression profiles. SVR-LUAD uses an inheritable bi-objective combinatorial genetic algorithm to identify a small set of informative miRNAs cooperating with SVR by maximizing estimation accuracy. SVR-LUAD identified 18 out of 332 miRNAs using 10-fold cross-validation and achieved a correlation coefficient of 0.88 ± 0.01 and mean absolute error of 0.56 ± 0.03 year between real and estimated survival time. SVR-LUAD performs well compared to some well-recognized regression methods. The miRNA signature consists of the 18 miRNAs which strongly correlates with lung adenocarcinoma: hsa-let-7f-1, hsa-miR-16-1, hsa-miR-152, hsa-miR-217, hsa-miR-18a, hsa-miR-193b, hsa-miR-3136, hsa-let-7g, hsa-miR-155, hsa-miR-3199-1, hsa-miR-219-2, hsa-miR-1254, hsa-miR-1291, hsa-miR-192, hsa-miR-3653, hsa-miR-3934, hsa-miR-342, and hsa-miR-141. Gene ontology annotation and pathway analysis of the miRNA signature revealed its biological significance in cancer and cellular pathways. This miRNA signature could aid in the development of novel therapeutic approaches to the treatment of lung adenocarcinoma.
| S-EPMC5548864 | biostudies-literature
Project description:PurposeWe aimed to develop molecular classifier that can predict lymphatic invasion and their clinical significance in epithelial ovarian cancer (EOC) patients.Materials and methodsWe analyzed gene expression (mRNA, methylated DNA) in data from The Cancer Genome Atlas. To identify molecular signatures for lymphatic invasion, we found differentially expressed genes. The performance of classifier was validated by receiver operating characteristics analysis, logistic regression, linear discriminant analysis (LDA), and support vector machine (SVM). We assessed prognostic role of classifier using random survival forest (RSF) model and pathway deregulation score (PDS). For external validation,we analyzed microarray data from 26 EOC samples of Samsung Medical Center and curatedOvarianData database.ResultsWe identified 21 mRNAs, and seven methylated DNAs from primary EOC tissues that predicted lymphatic invasion and created prognostic models. The classifier predicted lymphatic invasion well, which was validated by logistic regression, LDA, and SVM algorithm (C-index of 0.90, 0.71, and 0.74 for mRNA and C-index of 0.64, 0.68, and 0.69 for DNA methylation). Using RSF model, incorporating molecular data with clinical variables improved prediction of progression-free survival compared with using only clinical variables (p < 0.001 and p=0.008). Similarly, PDS enabled us to classify patients into high-risk and low-risk group, which resulted in survival difference in mRNA profiles (log-rank p-value=0.011). In external validation, gene signature was well correlated with prediction of lymphatic invasion and patients' survival.ConclusionMolecular signature model predicting lymphatic invasion was well performed and also associated with survival of EOC patients.
| S-EPMC5912145 | biostudies-literature
Project description:Identification of patients who likely will or will not benefit from cytotoxic chemotherapy through the use of biomarkers could greatly improve clinical management by better defining appropriate treatment options for patients. microRNAs may be potentially useful biomarkers that help guide individualized therapy for cancer because microRNA expression is dysregulated in cancer. In order to identify miRNA signatures for gastric cancer and for predicting clinical resistance to cisplatin/fluorouracil (CF) chemotherapy, a comprehensive miRNA microarray analysis was performed using endoscopic biopsy samples.Biopsy samples were collected prior to chemotherapy from 90 gastric cancer patients treated with CF and from 34 healthy volunteers. At the time of disease progression, post-treatment samples were additionally collected from 8 clinical responders. miRNA expression was determined using a custom-designed Agilent microarray. In order to identify a miRNA signature for chemotherapy resistance, we correlated miRNA expression levels with the time to progression (TTP) of disease after CF therapy.A miRNA signature distinguishing gastric cancer from normal stomach epithelium was identified. 30 miRNAs were significantly inversely correlated with TTP whereas 28 miRNAs were significantly positively correlated with TTP of 82 cancer patients (P<0.05). Prominent among the upregulated miRNAs associated with chemosensitivity were miRNAs known to regulate apoptosis, including let-7g, miR-342, miR-16, miR-181, miR-1, and miR-34. When this 58-miRNA predictor was applied to a separate set of pre- and post-treatment tumor samples from the 8 clinical responders, all of the 8 pre-treatment samples were correctly predicted as low-risk, whereas samples from the post-treatment tumors that developed chemoresistance were predicted to be in the high-risk category by the 58 miRNA signature, suggesting that selection for the expression of these miRNAs occurred as chemoresistance arose.We have identified 1) a miRNA expression signature that distinguishes gastric cancer from normal stomach epithelium from healthy volunteers, and 2) a chemoreresistance miRNA expression signature that is correlated with TTP after CF therapy. The chemoresistance miRNA expression signature includes several miRNAs previously shown to regulate apoptosis in vitro, and warrants further validation.
| S-EPMC3287139 | biostudies-literature
Project description:Ovarian cancer (OvCa) is the leading cause of death among all gynecological malignancies, and recurrent OvCa is almost always incurable. In this study, we developed a signature based on long non-coding RNAs (lncRNAs) associated with OvCa recurrence to facilitate personalized OvCa therapy. lncRNA expression data were extracted from GSE9891 and GSE30161. LASSO (least absolute shrinkage and selection operator) penalized regression was used to identify an lncRNA-based signature using the GSE9891 training cohort. The signature was then validated in GSE9891 internal and GSE30161 external validation cohorts. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to explore the possible functions of identified lncRNAs. A six-lncRNA signature (RUNX1-IT1, MALAT1, H19, HOTAIRM1, LOC100190986 and AL132709.8) was identified in the training cohort and validated in internal and external validation cohorts using the LASSO method (P < 0.05). This signature was also independent of other clinical factors according to multivariate and sub-group analyses. The identified lncRNAs are involved in cancer-related biological processes and pathways. We selected a highly reliable signature based on six lncRNAs associated with OvCa recurrence. This six-lncRNA signature is a promising method to personalize ovarian cancer therapy and may improve patient quality of life quality according to patients' condition in the future.
| S-EPMC5429632 | biostudies-literature
Project description:Background Recent years, the global prevalence of breast cancer (BC) was still high and the underlying molecular mechanisms remained largely unknown. The investigation of prognosis-related biomarkers had become an urgent demand. Results In this study, gene expression profiles and clinical information of breast cancer patients were downloaded from the TCGA database. The differentially expressed genes (DEGs) were estimated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A risk score formula involving five novel prognostic associated biomarkers (EDN2, CLEC3B, SV2C, WT1, and MUC2) were then constructed by LASSO. The prognostic value of the risk model was further confirmed in the TCGA entire cohort and an independent external validation cohort. To explore the biological functions of the selected genes, in vitro assays were performed, indicating that these novel biomarkers could markedly influence breast cancer progression. Conclusions We established a predictive five-gene signature, which could be helpful for a personalized management in breast cancer patients.
| S-EPMC8428534 | biostudies-literature
Project description:Despite the existence of morphologically indistinguishable disease, patients with advanced ovarian tumors display a broad range of survival end points. We hypothesize that gene expression profiling can identify a prognostic signature accounting for these distinct clinical outcomes. To resolve survival-associated loci, gene expression profiling was completed for an extensive set of 185 (90 optimal/95 suboptimal) primary ovarian tumors using the Affymetrix human U133A microarray. Cox regression analysis identified probe sets associated with survival in optimally and suboptimally debulked tumor sets at a P value of <0.01. Leave-one-out cross-validation was applied to each tumor cohort and confirmed by a permutation test. External validation was conducted by applying the gene signature to a publicly available array database of expression profiles of advanced stage suboptimally debulked tumors. The prognostic signature successfully classified the tumors according to survival for suboptimally (P = 0.0179) but not optimally debulked (P = 0.144) patients. The suboptimal gene signature was validated using the independent set of tumors (odds ratio, 8.75; P = 0.0146). To elucidate signaling events amenable to therapeutic intervention in suboptimally debulked patients, pathway analysis was completed for the top 57 survival-associated probe sets. For suboptimally debulked patients, confirmation of the predictive gene signature supports the existence of a clinically relevant predictor, as well as the possibility of novel therapeutic opportunities. Ultimately, the prognostic classifier defined for suboptimally debulked tumors may aid in the classification and enhancement of patient outcome for this high-risk population.
| S-EPMC7039050 | biostudies-literature
Project description:Introduction: Ovarian cancer is a highly malignant cancer with a poor prognosis. At present, there is no accurate strategy for predicting the prognosis of ovarian cancer. A prognosis prediction signature associated with DNA repair genes in ovarian cancer was explored in this study. Methods: Gene expression profiles of ovarian cancer were downloaded from the GEO, UCSC, and TCGA databases. Cluster analysis, univariate analysis, and stepwise regression were used to identify DNA repair genes as potential targets and a prognostic signature for ovarian cancer survival prediction. The top genes were evaluated by immunohistochemical staining of ovarian cancer tissues, and external data were used to assess the signature. Results: A total of 28 DNA repair genes were identified as being significantly associated with overall survival (OS) among patients with ovarian cancer. The results showed that high expression of XPC and RECQL and low expression of DMC1 were associated with poor prognosis in ovarian cancer patients. The prognostic signature combining 14 DNA repair genes was able to separate ovarian cancer samples associated with different OS times and showed robust performance for predicting survival (Training set: p < 0.0001, AUC = 0.759; Testing set: p < 0.0001, AUC = 0.76). Conclusion: Our study identified 28 DNA repair genes related to the prognosis of ovarian cancer. Using some of these potential biomarkers, we constructed a prognostic signature to effectively stratify ovarian cancer patients with different OS rates, which may also serve as a potential therapeutic target in ovarian cancer.
| S-EPMC6751318 | biostudies-literature