Identification of an 8-miRNA signature as a potential prognostic biomarker for glioma
ABSTRACT: Background Glioma is the most common form of primary malignant intracranial tumor. Methods In the current study, miRNA matrix were obtained from the Chinese Glioma Genome Atlas (CGGA), and then univariate Cox regression analysis and Lasso regression analysis were utilized to select candidate miRNAs and multivariate Cox regression analysis was applied to establish a miRNA signature for predicting overall survival (OS) of glioma. The signature was assessed with the area under the curve (AUC) of the receiver operating characteristic curve (ROC) and validated by data from Gene Expression Omnibus (GEO). Results Eight miRNAs (miR-1246, miR-148a, miR-150, miR-196a, miR-338-3p, miR-342-5p, miR-548h and miR-645) were included in the miRNA signature. The AUC of ROC analysis for 1- and 3-year OS in the CGGA dataset was 0.747 and 0.905, respectively. In the GEO dataset, The AUC for 1- and 3-year was 0.736 and 0.809, respectively. The AUC in both the CGGA and GEO datasets was similar to that based on WHO 2007 classification (0.736 and 0.799) and WHO 2016 classification (0.663 and 0.807). Additionally, Kaplan–Meier plot revealed that high-risk score patients had a poorer clinical outcome. Multivariate Cox regression analysis suggested that the miRNA signature was an independent prognosis-related factor [HR: 6.579, 95% CI [1.227?35.268], p = 0.028]. Conclusion On the whole, in the present study, based on eight miRNAs, a novel prognostic signature was developed for predicting the 1- and 3- year survival rate in glioma. The results may be conducive to predict the precise prognosis of glioma and to elucidate the underlying molecular mechanisms. However, further experimental researches of miRNAs are needed to validate the findings of this study.
Project description:BACKGROUND Osteosarcoma, the most common solid malignancy, has high incidence and mortality rates. We constructed a miRNA-based signature that can be used to assess the prognosis of osteosarcoma patients. MATERIAL AND METHODS The miRNA profile was derived from the Gene Expression Omnibus (GEO) website, with matched clinical records. The miRNA-based overall survival (OS)-predicting signature was established by LASSO Cox regression analysis. Receiver operating characteristic (ROC) curve and Kaplan-Meier (K-M) analyses were performed to examine the stability and discriminatory ability of the OS-predicting signatures. Pathway enrichment analyses were performed to uncover potential mechanisms. RESULTS Three miRNAs (miR-153, miR-212, and miR-591) independently related to the OS were extracted to build a risk score formula. The ROC curve and K-M analyses revealed good discrimination ability of the OS signature for osteosarcoma patients in both the training cohort (P=0.00015, AUC=0.962) and the validation cohort (P=0.0065, AUC=0.793). As shown in multivariate analysis, the classifier showed favorable predictive accuracy similar to the recurrence status to be an independent risk factor for osteosarcoma. Furthermore, the nomogram showed a synergistic effect by combining the clinicopathological features with our classifier. Also, the enrichment analyses of the target genes may contribute to improved treatment of osteosarcoma. CONCLUSIONS The 3-miRNA-based classifier serves as an effective prognosis-predicting signature for osteosarcoma patients.
Project description:Glioma is the most common central nervous system tumor and associated with poor prognosis. Identifying effective diagnostic biomarkers for glioma is particularly important in order to guide optimizing treatment. MicroRNAs (miRNAs) have drawn much attention because of their diagnostic value in diverse cancers, including glioma. We summarized studies to identify the potential diagnostic values of miRNAs in glioma patients. We included articles reporting miRNAs for differentiation of glioma patients from controls. We calculated sensitivities, specificities, and area under the curves (AUC) of individual miRNA and miRNA panels. We found that overall sensitivity, specificity, and AUC of miRNAs in diagnosis of glioma were 85% (95% confidence interval [CI]: 0.81-0.89), 90% (95% CI 0.85-0.93), and 93% (95% CI 0.91-0.95), respectively. Meta-regression analysis showed that the detection of miRNAs expression in cerebrospinal fluid (CSF) and brain tissue largely improved the diagnostic accuracy. Likewise, panels of multiple miRNAs could enhance the pooled sensitivity. Moreover, AUC of miR-21 was 0.88, with 86% sensitivity and 94% specificity. This study demonstrated that miRNAs could function as potential diagnosis markers in glioma. Detection of miRNAs in CSF and brain tissue displays high accuracy in the diagnosis of glioma.
Project description:Periodontitis progression is accompanied by irreversible alveolar bone absorption and leads to tooth loss. Early diagnosis is important for tooth stability and periodontal tissue preservation. However, there is no recognized miRNA diagnostic signature with convincing sensitivity and specificity for periodontitis. In this study, we obtained miRNA array expression profiles of periodontitis from the Gene Expression Omnibus (GEO) database. After screening for differentially expressed miRNAs, the least absolute shrinkage and selection operator (LASSO) method was performed to identify and construct a 17-miRNA-based diagnostic signature (hsa-miR-3917, hsa-mir-4271, hsa-miR-3156, hsa-miR-3141, hsa-miR-1246, hsa-miR-125a-5p, hsa-miR-671-5p, hcmv-mir-UL70, hsa-miR-650, hsa-miR-497-3p, hsa-miR-145-3p, hsa-miR-141-3p, hsa-miR-210-3p, hsa-miR-204-3p, hsa-miR-203a-5p, hsa-miR-99a-3p, and hsa-miR-30a-3p). Periodontal tissue samples with higher risk scores were more likely to show symptoms of periodontitis. Then, the receiver operating characteristic (ROC) curves were used to assess the diagnostic value of the miRNA signature, which indicated that the optimum cutoff value in periodontitis diagnosis was 0.5056 with an area under the ROC curve (AUC) of 0.996, a sensitivity of 97.3%, a specificity of 100.0% in the training cohort; in the testing cohort, the corresponding values were as follows: an AUC of 0.998, a sensitivity of 97.9%, and a specificity of 91.7%. We next evaluated the efficacy of the signature in differentiating disease subtype and affected range. Furthermore, we conducted functional enrichment analysis of the 17 miRNA-targeted mRNAs, including the regulation of mTOR activity and cell autophagy, Th1/Th2 cell balance and immunoregulation, cell apoptosis, and so on. In summary, our study identified and validated a 17-miRNA diagnostic signature with convincing AUC, sensitivity, and specificity for periodontitis.
Project description:GBM tissues are comprised of not only tumor cells but also tumor-associated nontumor cells, such as stromal cells and immune cells, which dilute the purity of glioma cells and function in glioma biology. However, the roles of miRNAs in modulating glioma purity are not clariﬁed. In total, 838 glioma samples with transcriptome data, including 537 RNAseq data from TCGA project and 301 microarray data from Chinese Glioma Genome Atlas (CGGA project), were recruited into our investigation. Tumor purity, molecular subtypes and IDH status were also available. R language was employed as the main tool for statistical analysis and graphical work. Screening miRNA profiling and paired TCGA samples' transcriptome data demonstrates that miR-17-5p expression harbors the most significant positive correlation with glioma purity among all miRNAs. CXCL14 shows robust negative correlation with miR-17-5p expression in TCGA and CGGA dataset. miR-17-5p directly targets CXCL14 and functions as a tumor-suppressor of GBM. CXCL14 showed lower expression in proneural subtype and may contribute as a potential marker for proneural subtype in glioma. Genes markedly correlated with CXCL14 are involved in essential functions associated with anti-tumor immune process. CXCL14 has a strong correlation with immune(T cells, Monocytic lineage and Neutrophils) and Fibroblasts within glioma environment. miR-17-5p and CXCL14 exhibited predictive values for high-grade glioma(HGG) patients: Higher miR-17-5p indicated significantly longer survival while lower CXCL14 indicated longer survival. Our results highlight the importance of the miR-17-5p-CXCL14 axis in regulating key steps of anti-tumor immune process and may serve as potential targets of immune treatments for gliomas.
Project description:There are currently no highly sensitive and specific minimally invasive biomarkers for detection of early-stage breast cancer. MicroRNAs (miRNAs) are present in the circulation and may be unique biomarkers for early diagnosis of human cancers. The aim of this study was to investigate the differential expression of miRNAs in the serum of breast cancer patients and healthy controls.Global miRNA analysis was performed on serum from 48 patients with ER-positive early-stage breast cancer obtained at diagnosis (24 lymph node-positive and 24 lymph node-negative) and 24 age-matched healthy controls using LNA-based quantitative real-time PCR (qRT-PCR). A signature of miRNAs was subsequently validated in an independent set of 111 serum samples from 60 patients with early-stage breast cancer and 51 healthy controls and further tested for reproducibility in 3 independent data sets from the GEO Database.A multivariable signature consisting of 9 miRNAs (miR-15a, miR-18a, miR-107, miR-133a, miR-139-5p, miR-143, miR-145, miR-365, miR-425) was identified that provided considerable discrimination between breast cancer patients and healthy controls. Further, the ability of the 9 miRNA signature to stratify samples from breast cancer patients and healthy controls was confirmed in the validation set (p = 0.012) with a corresponding AUC = 0.665 in the ROC-curve analysis. No association between miRNA expression and tumor grade, tumor size, menopausal- or lymph node status was observed. The signature was also successfully validated in a previously published independent data set of circulating miRNAs in early-stage breast cancer (p = 0.024).We present herein a 9 miRNA signature capable of discriminating between ER-positive breast cancer and healthy controls. Using a specific algorithm based on the 9 miRNA signature, the risk for future individuals can be predicted. Since microRNAs are highly stable in blood components, this signature might be useful in the development of a blood-based multi-marker test to improve early detection of breast cancer. Such a test could potentially be used as a screening tool to identify individuals who would benefit from further diagnostic assessment.
Project description:Rationale: Triple-negative breast cancer (TNBC), which has the highest recurrence rate and shortest survival time of all breast cancers, is in urgent need of a risk assessment method to determine an accurate treatment course. Recently, miRNA expression patterns have been identified as potential biomarkers for diagnosis, prognosis, and personalized therapy. Here, we investigate a combination of candidate miRNAs as a clinically applicable signature that can precisely predict relapse in TNBC patients after surgery. Methods: Four total cohorts of training (TCGA_TNBC and GEOD-40525) and validation (GSE40049 and GSE19783) datasets were analyzed with logistic regression and Gaussian mixture analyses. We established a miRNA signature risk model and identified an 8-miRNA signature for the prediction of TNBC relapse. Results: The miRNA signature risk model identified ten candidate miRNAs in the training set. By combining 8 of the 10 miRNAs (miR-139-5p, miR-10b-5p, miR-486-5p, miR-455-3p, miR-107, miR-146b-5p, miR-324-5p and miR-20a-5p), an accurate predictive model of relapse in TNBC patients was established and was highly correlated with prognosis (AUC of 0.80). Subsequently, this 8-miRNA signature prognosticated relapse in the two validation sets with AUCs of 0.89 and 0.90. Conclusion: The 8-miRNA signature predictive model may help clinicians provide a prognosis for TNBC patients with a high risk of recurrence after surgery and provide further personalized treatment to decrease the chance of relapse.
Project description:The levels of expression of O6-methylguanine-DNA methyltransferase (MGMT) are relevant in predicting the response to the alkylating chemotherapy in patients affected by glioblastoma. MGMT promoter methylation and the published MGMT regulating microRNAs (miRNAs) do not completely explain the expression pattern of MGMT in clinical glioblastoma specimens. Here we used a genome-wide microarray-based approach to identify MGMT regulating miRNAs. Our screen unveiled three novel MGMT regulating miRNAs, miR-127-3p, miR-409-3p, and miR-124-3p, in addition to the previously identified miR-181d-5p. Transfection of these three novel miRNAs into the T98G glioblastoma cell line suppressed MGMT mRNA and protein expression. However, their MGMT- suppressive effects are 30-50% relative that seen with miR-181d-5p transfection. In silico analyses of The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) revealed that miR-181d-5p is the only miRNA that consistently exhibited inverse correlation with MGMT mRNA expression. However, statistical models incorporating both miR-181d-5p and miR-409-3p expression better predict MGMT expression relative to models involving either miRNA alone. Our results confirmed miR-181d-5p as the key MGMT-regulating miRNA. Other MGMT regulating miRNAs, including the miR-409-3p identified in this report, modify the effect of miR-181d-5p on MGMT expression. MGMT expression is, thus, regulated by cooperative interaction between key MGMT-regulating miRNAs.
Project description:Purpose:Globally, colorectal cancer (CRC) is one of the most common cancers with high mortality. Although CRC patients in stages I-II are curable after surgical resection, due to the lack of sensitive and specific biomarkers, many patients are in the advanced stages when diagnosed. This study aimed to investigate whether circulating miRNAs in plasma could act as biomarkers for early CRC diagnosis. Patients and methods:All healthy subjects and patients were from Nanjing First Hospital. We first selected 2 differential miRNAs by integrated analysis of 4 Gene Expression Omnibus (GEO) data sets and The Cancer Genome Atlas (TCGA) database. Next, the expression of these 2 miRNAs in tissue and plasma samples were examined through quantitative real-time polymerase chain reaction. Training phase and validation phase were designed to investigate the diagnostic utility of these differential miRNAs using receiver operating characteristic (ROC) curve analysis. Results:After integrated analysis of 4 GEO and TCGA databases, upregulated miR-182 and miR-20a were selected to further investigate their diagnostic potential for CRC. We discovered that miR-182 and miR-20a were upregulated in CRC tissue and plasma and that circulating miR-182 and miR-20a in the plasma of CRC patients were tumor derived. The area under the ROC curve (AUC) of circulating miR-182 was 0.929 (95% CI 0.875-0.983) in the training phase and 0.891 (95% CI 0.821-0.961) in the validation phase. The AUC of circulating miR-20a expression was 0.801 (95% CI 0.695-0.906) in the training phase and 0.736 (95% CI 0.631-0.842) in the validation phase. Conclusion:Circulating miR-182 is a novel potential biomarker for early CRC diagnosis.
Project description:Since brain tissue is not readily accessible, a new focus in search of biomarkers for schizophrenia is blood-based expression profiling of non-protein coding genes such as microRNAs (miRNAs), which regulate gene expression by inhibiting the translation of messenger RNAs. This study aimed to identify potential miRNA signature for schizophrenia by comparing genome-wide miRNA expression profiles in patients with schizophrenia vs. healthy controls. A genome-wide miRNA expression profiling was performed using a Taqman array of 365 human miRNAs in the mononuclear leukocytes of a learning set of 30 cases and 30 controls. The discriminating performance of potential biomarkers was validated in an independent testing set of 60 cases and 30 controls. The expression levels of the miRNA signature were then evaluated for their correlation with the patients' clinical symptoms, neurocognitive performances, and neurophysiological functions. A seven-miRNA signature (hsa-miR-34a, miR-449a, miR-564, miR-432, miR-548d, miR-572 and miR-652) was derived from a supervised classification with internal cross-validation, with an area under the curve (AUC) of receiver operating characteristics of 93%. The putative signature was then validated in the testing set, with an AUC of 85%. Among these miRNAs, miR-34a was differentially expressed between cases and controls in both the learning (P?=?0.005) and the testing set (P?=?0.002). These miRNAs were differentially correlated with patients' negative symptoms, neurocognitive performance scores, and event-related potentials. The results indicated that the mononuclear leukocyte-based miRNA profiling is a feasible way to identify biomarkers for schizophrenia, and the seven-miRNA signature warrants further investigation.
Project description:Background:Cutaneous 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. Methods:The 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. Results:After 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. Conclusion:The identified five-miRNA signature may serve as a prognostic biomarker, or as a potential therapeutic target, in cutaneous melanoma patients.