three-miRNA Signature Research Articles

Identification and bioinformatic characterization of a serum miRNA signature for early detection of laryngeal squamous cell carcinoma

BackgroundThe growing understanding of cancer biology and the establishment of new treatment modalities has not yielded the expected results in terms of survival for Laryngeal Squamous Cell Cancer (LSCC). Early diagnosis, as well as prompt identification of patients with high risk of relapse would ensure greater chance of therapeutic success. However, this goal remains a challenge due to the absence of specific biomarkers for this neoplasm.MethodsSerum samples from 45 LSCC patients and 23 healthy donors were collected for miRNA expression profiling by TaqMan Array analysis. Additional 20 patients and 42 healthy volunteers were included for the validation set, reaching an equal number of clinical samples for each group. The potential diagnostic ability of the such identified three-miRNA signature was confirmed by ROC analysis. Moreover, each miRNA was analyzed for the possible correlation with HNSCC patients’ survival and TNM status by online databases Kaplan–Meier (KM) plotter and OncomiR. In silico analysis of common candidate targets and their network relevance to predict shared biological functions was finally performed by PANTHER and GeneMANIA software.ResultsWe characterized serum miRNA profile of LSCC patients identifying a novel molecular signature, including miR-223, miR-93 and miR-532, as circulating marker endowed with high selectivity and specificity. The oncogenic effect and the prognostic significance of each miRNA was investigated by bioinformatic analysis, denoting significant correlation with OS. To analyse the molecular basis underlying the pro-tumorigenic role of the signature, we focused on the simultaneously regulated gene targets—IL6ST, GTDC1, MAP1B, CPEB3, PRKACB, NFIB, PURB, ATP2B1, ZNF148, PSD3, TBC1D15, PURA, KLF12—found by prediction tools and deepened for their functional role by pathway enrichment analysis. The results showed the involvement of 7 different biological processes, among which inflammation, proliferation, migration, apoptosis and angiogenesis.ConclusionsIn conclusion, we have identified a possible miRNA signature for early LSCC diagnosis and we assumed that miR-93, miR-223 and miR-532 could orchestrate the regulation of multiple cancer-related processes. These findings encourage the possibility to deepen the molecular mechanisms underlying their oncogenic role, for the desirable development of novel therapeutic opportunities based on the use of short single-stranded oligonucleotides acting as non-coding RNA antagonists in cancer.

Blood-Based microRNA Biomarker Signature of Early-Stage Pancreatic Ductal Adenocarcinoma With Lead-Time Trajectory in Prediagnostic Samples

Clinically validated biomarker of pancreatic ductal adenocarcinoma (PDAC), carbohydrate antigen 19-9 (CA19-9), has limited sensitivity and specificity for early-stage disease. Circulating miRNAs in plasma associated with cancer relevant pathways were developed as early detection biomarkers. 2083 miRNAs in 15 μl of plasma from multicenter age-matched cohorts (N= 203: healthy controls, n= 46; pancreatitis controls, n= 36; diagnosed cases: n= 121) and a prediagnostic Prostate, Lung, Colorectal, and Ovarian age- and gender-matched cohort (N=96; controls, n= 48; prediagnosed cases, n= 48) were interrogated. A three-miRNA biomarker signature was developed for early-stage PDAC. The three-miRNA signature (let-7i-5p, miR-130a-3p and miR-221-3p) detected PDAC from healthy controls independently (area under the curve [AUC] of stage I, II, I-IV= 0.970, 0.975, 0.974) and in combination with CA19-9 (AUC of stage I, II, I-IV= 1.000, 0.992, 0.995). It also discriminated chronic pancreatitis (AUC of stage I, II, I-IV= 0.932, 0.931, 0.929), improving performance of CA19-9 alone (AUC of stage I, II, I-IV= 0.763, 0.701, 0.735) in combination (AUC of stage I, II, I-IV= 0.971, 0.943, 0.951). Blinded validation in prediagnostic Prostate, Lung, Colorectal, and Ovarian cohort revealed lead-time trajectory increase in AUC from 0.702 to 0.729 to 0.757 at twelve-, six-, and three-months before PDAC diagnosis, respectively. The signature also helped stratification of patients with different circulating tumor DNA and imaging subtypes. Plasma miRNAs associated with oncogenic pathways may serve as PDAC early detection biomarkers.

521P Deciphering a three-miRNA signature as a prognostic biomarker in glioma patients: Correlation with DFS and OS

521P Deciphering a three-miRNA signature as a prognostic biomarker in glioma patients: Correlation with DFS and OS

A serum miRNAs signature for early diagnosis of bladder cancer

Background Bladder cancer accounts for the most common type of urologic malignancy and presents high recurrence rate after surgical resection and adjuvant intravesical therapy. We aim to search for an early diagnostic biomarker in serum for bladder cancer in this study. Methods The expression profiles of miRNAs in serum samples of 112 bladder cancer patients and 112 healthy controls were detected with real-time polymerase chain reaction (RT-qPCR). Receiver operating characteristic (ROC) curve and area under curve (AUC) analysis were performed to assess the diagnostic efficiency of miRNAs. Stepwise logic regression analysis was used to construct a diagnostic signature with highest sensitivity and specificity. Bioinformatics analysis was applied to explore the potential biological functions and mechanisms of candidate miRNAs. Results Five miRNAs including miR-451a, miR-381-3p, miR-223-3p, miR-142-5p and miR-27b-3p were found differentially expressed in serum samples of bladder patients and healthy subjects. The diagnostic signature was constructed with miR-27b-3p, miR-381-3p and miR-451a. AUC of the three-miRNA signature was 0.894 (0.837–0.936, p < 0.001). The sensitivity and specificity of this signature were 86.90% and 77.38%, respectively, indicating that this signature has a good ability to diagnose bladder cancer. Conclusion The three-miRNA signature we constructed has favorable diagnostic capacity and may be a promising non-invasive biomarker in the early diagnosis of bladder cancer. KEY MESSAGES There is still no clinical utilization of serum miRNAs in the early detection of bladder cancer. We screened and constructed a three-miRNA signature with the sensitivity of 86.90% and specificity of 77.38% which may be a promising non-invasive biomarker in the early diagnosis of bladder cancer.

Downregulated miR-367-3p, miR-548aq-5p, and miR-4710 in Human Whole Blood: Potential Biomarkers for Breast Cancer With Axillary Lymph Node Metastasis

BackgroundIncreasing studies have shown that microRNAs (miRNAs) have great diagnostic value in cancer. Axillary lymph node metastasis (ALNM) is closely related to the prognosis of breast cancer. However, it remains unknown whether miRNAs in whole blood could be promising biomarkers in breast cancer ALNM. MethodsAn miRNA microarray was used to screen potential differentially expressed miRNA candidates in whole blood of three breast cancer patients with ALNM and three without ALNM. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect candidate differentially expressed miRNAs in the whole blood of 109 breast cancer patients. Furthermore, bioinformatics analysis was carried to predict the potential targets and enriched pathway of miRNAs. ResultsQRT-PCR validated the fact that miR-367-3p, miR-548aq-5p and miR-4710 are downregulated in breast cancer with ALNM compared to it without ALNM. Receiver operating characteristic (ROC) curve analysis revealed that miR-367-3p, miR-548aq-5p and miR-4710 have good diagnostic values. Notably, the three-miRNA signature showed better predictive value, with an area under ROC curve (AUC) of 0.7414. Bioinformatics analysis revealed that the miRNAs could participate in a complex network and thus be involved in cancer-related pathways. ConclusionsOur findings support the potential of miR-367-3p, miR-548aq-5p and miR-4710 and the three-miRNA signature as biomarkers for breast cancer with ALNM.

Diagnostic implication of a circulating serum-based three-microRNA signature in hepatocellular carcinoma.

Owing to the diagnostic dilemma, the prognosis of hepatocellular carcinoma (HCC) remains impoverished, contributing to the globally high mortality rate. Currently, HCC diagnosis depends on the combination of imaging modalities and the measurement of serum alpha-fetoprotein (AFP) levels. Nevertheless, these conventional modalities exhibit poor performance in detecting HCC at early stages. Thus, there is a pressing need to identify novel circulating biomarkers to promote diagnostic accuracy and surveillance. Circulating miRNAs are emerging as promising diagnostic tools in screening various cancers, including HCC. However, because of heterogenous and, at times, contradictory reports, the universality of miRNAs in clinical settings remains elusive. Consequently, we proposed to explore the diagnostic potential of ten miRNAs selected on a candidate-based approach in HCC diagnosis. The expression of ten candidate miRNAs (Let-7a, miR-15a, miR-26a, miR-124, miR-126, miR-155, miR-219, miR-221, miR-222, and miR-340) was investigated in serum and tissue of 66 subjects, including 33 HCC patients and 33 healthy controls (HC), by rt-PCR. Receiver operating characteristic curve (ROC) analysis was used to determine the diagnostic accuracy of the prospective serum miRNA panel. To anticipate the potential biological roles of a three-miRNA signature, the target genes were evaluated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway. The serum and tissue expression of miRNAs (Let-7a, miR-26a, miR-124, miR-155, miR-221, miR-222, and miR-340) were differentially expressed in HCC patients (p < 0.05). The ROC analysis revealed promising diagnostic performance of Let-7a (AUC = 0.801), miR-221 (AUC = 0.786), and miR-2 (AUC = 0.758) in discriminating HCC from HC. Furthermore, in a logistic regression equation, we identified a three-miRNA panel (Let-7a, miR-221, and miR-222; AUC = 0.932) with improved diagnostic efficiency in differentiating HCC from HC. Remarkably, the combination of AFP and a three-miRNA panel offered a higher accuracy of HCC diagnosis (AUC = 0.961) than AFP alone. The functional enrichment analysis demonstrated that target genes may contribute to pathways associated with HCC and cell-cycle regulation, indicating possible crosstalk of miRNAs with HCC development. To conclude, the combined classifier of a three-miRNA panel and AFP could be indispensable circulating biomarkers for HCC diagnosis. Furthermore, targeting predicted genes may provide new therapeutic clues for the treatment of aggressive HCC.

Detection of SARS-CoV-2 infection by microRNA profiling of the upper respiratory tract.

Host biomarkers are increasingly being considered as tools for improved COVID-19 detection and prognosis. We recently profiled circulating host-encoded microRNA (miRNAs) during SARS-CoV-2 infection, revealing a signature that classified COVID-19 cases with 99.9% accuracy. Here we sought to develop a signature suited for clinical application by analyzing specimens collected using minimally invasive procedures. Eight miRNAs displayed altered expression in anterior nasal tissues from COVID-19 patients, with miR-142-3p, a negative regulator of interleukin-6 (IL-6) production, the most strongly upregulated. Supervised machine learning analysis revealed that a three-miRNA signature (miR-30c-2-3p, miR-628-3p and miR-93-5p) independently classifies COVID-19 cases with 100% accuracy. This study further defines the host miRNA response to SARS-CoV-2 infection and identifies candidate biomarkers for improved COVID-19 detection.

A novel three-microRNA signature for predicting survival in patients with nasopharyngeal carcinoma

Background/purposeNasopharyngeal carcinoma (NPC) is a malignant neoplasm of the head and neck. This study aims to use integrated bioinformatics technologies to develop a predictive miRNA-signature correlated with the prognosis of NPC. Materials and methodsInitially, the differentially expressed miRNAs (DEMs) in NPC were identified, and then DEMs related to the prognosis of NPC were further screened. Subsequently, the relatively important DEMs identified by random forest algorithm were used to construct a predictive signature by multivariate COX regression analysis. Moreover, PCA, Kaplan–Meier analysis, time-dependent ROC analysis, and univariate and multivariate COX regression analysis were performed to evaluate the ability of the signature in risk identification and prognosis prediction in NPC. ResultsHsa-miR-29c, hsa-miR-30e and hsa-miR-93 were selected from DEMs to construct a signature, and their abnormal expression was significantly associated with poor prognosis of NPC. The average AUC values of 1- to 5-year OS, DFS and DMFS predicted by the signature were all above 0.7, and showed better clinical independence than other indexes. In addition, 295 differentially expressed mRNAs could be used as potential target genes of the 3 DEMs. Among them, 56 differentially expressed mRNAs were related to PFS. GO and KEGG enrichment analysis indicated that the poor prognosis of NPC was related to the abnormality of chromosomes, cytokines, and chemokines. ConclusionWe constructed a three-miRNA signature with good independent performance in predicting the prognosis for NPC. This study may lay the foundation for exploring new therapeutic targets and improving survival outcomes in NPC patients.

Establishing a three-miRNA signature as a prognostic model for colorectal cancer through bioinformatics analysis.

Background: Identification of more promising microRNAs (miRNAs) are being extensively studied with respect to colorectal cancer (CRC), since CRC is the leading cause of cancer deaths and most common malignant tumors worldwide. A series of colon cancer (CCa) samples from The Cancer Genome Atlas (TCGA) were analyzed to provide a new perspective into this field.Methods: The expression of miRNAs, mRNAs and the clinical data of 437 CRC patients were downloaded from the TCGA database. The survival-related differentially expressed miRNAs (sDMIRs) and mRNAs were detected by COX regression analysis. The high-risk group and low-risk group were separated by the median risk score of the risk score model. The potential clinical characteristics of these sDMIRs were analyzed by R software. The potential molecular mechanisms of these sDMIRs were explored by computational biology. The expression levels of three sDMIRs were explored by qPCR in CRC samples.Results: Three DMIRs (hsa-miR-21-3p, hsa-miR-194-3p and hsa-miR-891a-5p) correlated with the most remarkable prognostic values of CRC patients were selected to establish the risk score model (RSM) by univariate and multivariate COX regression analysis and the survival probability of the low-risk group was longer than that in the high-risk group. We detected the target genes of three sDMIRs and the potential molecular mechanisms of these sDMIRs. We further verified the high expression levels of hsa-miR-21-3p and hsa-miR-194-3p were associated with the early T-stages, while hsa-miR-891a-5p illustrated the reversed result.Conclusion: Our study demonstrated three sDMIRs with significantly clinical values illustrated the potential predicting values in the prognosis of CRC patients. Our results may provide a new perspective for the diagnostic methods and treatment strategies in CRC patients.

Altered microRNA expression in COVID-19 patients enables identification of SARS-CoV-2 infection.

The host response to SARS-CoV-2 infection provide insights into both viral pathogenesis and patient management. The host-encoded microRNA (miRNA) response to SARS-CoV-2 infection, however, remains poorly defined. Here we profiled circulating miRNAs from ten COVID-19 patients sampled longitudinally and ten age and gender matched healthy donors. We observed 55 miRNAs that were altered in COVID-19 patients during early-stage disease, with the inflammatory miR-31-5p the most strongly upregulated. Supervised machine learning analysis revealed that a three-miRNA signature (miR-423-5p, miR-23a-3p and miR-195-5p) independently classified COVID-19 cases with an accuracy of 99.9%. In a ferret COVID-19 model, the three-miRNA signature again detected SARS-CoV-2 infection with 99.7% accuracy, and distinguished SARS-CoV-2 infection from influenza A (H1N1) infection and healthy controls with 95% accuracy. Distinct miRNA profiles were also observed in COVID-19 patients requiring oxygenation. This study demonstrates that SARS-CoV-2 infection induces a robust host miRNA response that could improve COVID-19 detection and patient management.

Prediction of Grade Reclassification of Prostate Cancer Patients on Active Surveillance through the Combination of a Three-miRNA Signature and Selected Clinical Variables.

Simple SummaryActive surveillance (AS) has evolved as an alternative to radical treatment for potentially indolent prostate cancer. However, current selection criteria for entering AS are suboptimal, and a significant percentage of patients discontinue AS because of disease reclassification. Hence, there is an unmet need for novel biomarkers for the accurate identification of high-risk PCa and the unequivocal classification of indolent disease. Circulating biomarkers, including microRNAs identified through liquid biopsies, represent a valuable approach to improve on currently available clinicopathological risk-stratification tools. In an attempt to identify specific microRNA signatures as potential circulating biomarkers, the authors performed an unprecedented analysis of the global microRNA profile in plasma samples from AS patients and identified and validated a three-microRNA signature able to predict patient reclassification. The addition of the three-microRNA signature was able to improve the performance of currently available clinicopathological variables, thus showing potential for the refinement of AS patients’ selection.Active surveillance (AS) has evolved as a strategy alternative to radical treatments for very low risk and low-risk prostate cancer (PCa). However, current criteria for selecting AS patients are still suboptimal. Here, we performed an unprecedented analysis of the circulating miRNome to investigate whether specific miRNAs associated with disease reclassification can provide risk refinement to standard clinicopathological features for improving patient selection. The global miRNA expression profiles were assessed in plasma samples prospectively collected at baseline from 386 patients on AS included in three independent mono-institutional cohorts (training, testing and validation sets). A three-miRNA signature (miR-511-5p, miR-598-3p and miR-199a-5p) was found to predict reclassification in all patient cohorts (training set: AUC 0.74, 95% CI 0.60–0.87, testing set: AUC 0.65, 95% CI 0.51–0.80, validation set: AUC 0.68, 95% CI 0.56–0.80). Importantly, the addition of the three-miRNA signature improved the performance of the clinical model including clinicopathological variables only (AUC 0.70, 95% CI 0.61–0.78 vs. 0.76, 95% CI 0.68–0.84). Overall, we trained, tested and validated a three-miRNA signature which, combined with selected clinicopathological variables, may represent a promising biomarker to improve on currently available clinicopathological risk stratification tools for a better selection of truly indolent PCa patients suitable for AS.

Construction and validation of a three-microRNA signature as prognostic biomarker in patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC), a common type of primary liver cancer, is one of the most aggressive malignant tumors worldwide. Although overall survival (OS) rates for HCC has significantly improved in recent years, however, the exact predictive value of microRNA (miRNA) for the prognosis of HCC has not yet been recognized. Here, we aimed to identify potential prognostic miRNAs involved in HCC by bioinformatics analysis and validated expression levels through quantitative polymerase chain reaction (qPCR) and GEO database. The RNA expression profiles and corresponding clinical information of HCC were available from The Cancer Genome Atlas (TCGA) datasets. Differentially expression and standardization analysis of miRNAs, Kaplan-Meier curve and time dependent ROC curve were performed by using R tools. Differentially expressed miRNAs (DEmiRNAs) and clinical parameters involved in the OS of HCC were confirmed by Cox regression models. And functional enrichment analysis was used to establish functions of the targeted genes of DEmiRNAs. A total of 300 DEmiRNAs were significantly related with HCC, of which 40 were down-regulated and 260 were up-regulated. A total of 344 patients with DEmiRNAs, status, overall survival (OS) time were randomized into training group (172) and test group (172). Multivariate Cox regression analyses revealed that 3 miRNA (hsa-miR-139-3p, hsa-miR-760, hsa-miR-7-5p) had independent prognostic significance for the OS of HCC in both training and test group. Moreover, according to Kaplan Meier analysis, the OS of HCC patients with high-risk score was shorter in validation and entire series. The time dependent ROC curve demonstrated high accuracy of the signature for OS. Besides, target genes of three miRNAs were analyzed by functional enrichment analysis and 20 genes associated with OS were verified by using Kaplan-Meier method. Compared with normal and benign group, the relative expression level of hsa-miR-139-3p was significantly decreased, while hsa-miR-7-5p and hsa-miR-760 were distinctly increased in the plasma of HCC patients. The same results were observed in the independent cohort. Collectively, our research suggested that three-miRNA signature could serve as an independent prognostic indicator for HCC patients.

A novel microRNA-based signature predicts prognosis among nasopharyngeal cancer patients

Nasopharyngeal cancer is one of the most common malignant tumors in the head and neck. Identification of promising miRNA biomarkers might benefit a lot to the detection of nasopharyngeal carcinoma. A three-miRNA signature (has-miR-142-3p, has-miR-29c, and has-miR-30e) was obviously associated with the overall survival of nasopharyngeal carcinoma patients. The model has better clinical independence and has better clinical prediction effect when combined with clinical characteristics. Our results revealed that a three-miRNA signature was a potential novel prognostic biomarker for nasopharyngeal carcinoma.

148P A three-miRNA signature as promising prognostic biomarker for breast cancer

148P A three-miRNA signature as promising prognostic biomarker for breast cancer

Identification of a three-miRNA signature as a novel prognostic model for papillary renal cell carcinoma

BackgroundPapillary renal cell carcinoma (pRCC) accounting for near 20% of renal cell carcinoma is the second most common histological subtype. MiRNAs have been demonstrated to played significant roles on predicting prognosis of patients with tumors. An appropriate and comprehensive miRNAs analysis based on a great deal of pRCC samples from The Cancer Genome Atlas (TCGA) will provide perspective in this field.MethodsWe integrated the expression of mRNAs, miRNAs and the relevant clinical data of 321 pRCC patients recorded in the TCGA database. The survival-related differential expressed miRNAs (sDEmiRs) were estimated by COX regression analysis. The high-risk group and the low-risk group were separated by the median risk score of the risk score model (RSM) based on three screened sDEmiRs. The target genes, underlying molecular mechanisms of these sDEmiRs were explored by computational biology. The expression levels of the three sDEmiRs and their correlations with clinicopathological parameters were further validated by qPCR.ResultsBased on univariate COX analysis (P < 0.001), eighteen differential expressed miRNAs (DEmiRs) were remarkably related with the overall survival (OS) of pRCC patients. Three sDEmiRs with the most significant prognostic values (miR-34a-5p, miR-410-3p and miR-6720-3p) were employed to establish the RSM which was certified as an independent prognosis factor and closely correlated with OS. In the verification of clinical samples, the overexpression of miR-410-3p and miR-6720-3p were detected to be associated with the advanced T-stages, while miR-34a-5p showed the reversed results.ConclusionThe study developed a RSM based on the identified sDEmiRs with significant prognosis prediction values for pRCC patients. The results pave the avenue for establishing and optimizing a reliable and referable risk assessing model and provide novel insight into the researches of biomarkers and clinical treatment strategies.

A 3-MicroRNA Signature Identified From Serum Predicts Clinical Outcome of the Locally Advanced Gastric Cancer.

Background: Current staging systems are inadequate for evaluating the prognosis of patients with locally advanced gastric cancer (LAGC, stages II–III). Therefore, we developed a serum microRNA (miRNA) signature to facilitate individualized management of these patients.Methods: Using microarray analysis, we analyzed 12 serum specimens based on different prognoses (good survival group, n = 7; poor survival group, n = 5). We identified and confirmed differential expression of these miRNAs using quantitative reverse transcription PCR (qRT-PCR) of serum from 51 patients with LAGC. A three miRNA-based classifier was established as a training set by Cox proportional hazard regression and risk-score analysis. We validated the prognostic accuracy of this model in an internal validation cohort (Sun Yat-Sen University Cancer Center, SYSUCC validation cohort, n = 50) and an external independent cohort (Beijing Cancer Hospital, BJCH cohort, n = 67).Results: Three miRNAs were found to be associated with survival of LAGC (P < 0.001 for miR-132, P = 0.011 for miR-548a-3p, and P < 0.001 for miR-1826). A three-miRNA signature was developed for the training set, and a significant difference was found between the survival of low- and high-risk score patients (P < 0.01). The combination of the miRNA signature and tumor–node–metastasis (TNM) stage exhibited superior discrimination. Consistent results were obtained by further validation of the internal set and the BJCH set, which confirmed the predictive value of the model.Conclusions: We built an easy-to-use prognostic signature using three serum miRNAs as markers. Our miRNA signature may improve postoperative risk stratification and serve as a complement to the TNM staging system.

Identification of three miRNAs signature as a prognostic biomarker in breast cancer using bioinformatics analysis.

BackgroundAccumulating evidences indicated that some miRNAs are dysregulated in breast cancer and involved in cell growth, migration and invasion, differentiation, cell cycle arrest, apoptosis, and autophagy. Our study aims to identify a novel set of biomarkers for predicting the prognosis of breast cancer patients.MethodsWe downloaded clinical information and raw sequencing data from The Cancer Genome Atlas (TCGA) database. We selected samples with miRNA sequencing data and relevant clinical prognostic data for subsequent analysis. The association between miRNA and prognosis function was analyzed by Cox regression analysis. The potential biofunctions of target miRNAs were investigated through bioinformatic analysis.ResultsWe identified 84 differentially expressed miRNAs (DEmiRNAs), among them, 17 were downregulated and 67 were upregulated. We used Kaplan-Meier survival analysis to evaluate the prognostic value of three miRNAs (mir-105-1, mir-301b and mir-1258). We also found that the three-miRNA signature is independent prognostic factors for breast cancer by using Cox regression analysis. It might be participated in different signaling pathways associated with cancer by using functional enrichment analysis, including adherens junction, autophagy, and TGF-beta signaling pathway, ErbB signaling pathway, FoxO signaling pathway.ConclusionsTaken together, three-miRNA signature might be used as a potential predicting prognostic biomarker in breast cancer.

A Novel Three-miRNA Signature Identified Using Bioinformatics Predicts Survival in Esophageal Carcinoma.

Objective We identified differentially expressed microRNAs (DEMs) between esophageal carcinoma (ESCA) tissues and normal esophageal tissues. We then constructed a novel three-miRNA signature to predict the prognosis of ESCA patients using bioinformatics analysis. Materials and Methods. We combined two microarray profiling datasets from the Gene Expression Omnibus (GEO) database and RNA-seq datasets from the Cancer Genome Atlas (TCGA) database to analyze DEMs in ESCA. The clinical data from 168 ESCA patients were selected from the TCGA database to assess the prognostic role of the DEMs. The TargetScan, miRDB, miRWalk, and DIANA websites were used to predict the miRNA target genes. Functional enrichment analysis was conducted using the Database for Annotation, Visualization, and Integrated Discovery (David), and protein-protein interaction (PPI) networks were obtained using the Search Tool for the Retrieval of Interacting Genes database (STRING). Results With cut-off criteria of P < 0.05 and |log2FC| > 1.0, 33 overlapping DEMs, including 27 upregulated and 6 downregulated miRNAs, were identified from GEO microarray datasets and TCGA RNA-seq count datasets. The Kaplan–Meier survival analysis indicated that a three-miRNA signature (miR-1301-3p, miR-431-5p, and miR-769-5p) was significantly associated with the overall survival of ESCA patients. The results of univariate and multivariate Cox regression analysis showed that the three-miRNA signature was a potential prognostic factor in ESCA. Furthermore, the gene functional enrichment analysis revealed that the target genes of the three miRNAs participate in various cancer-related pathways, including viral carcinogenesis, forkhead box O (FoxO), vascular endothelial growth factor (VEGF), human epidermal growth factor receptor 2 (ErbB2), and mammalian target of rapamycin (mTOR) signaling pathways. In the PPI network, three target genes (MAPK1, RB1, and CLTC) with a high degree of connectivity were selected as hub genes. Conclusions Our results revealed that a three-miRNA signature (miR-1301-3p, miR-431-5p, and miR-769-5p) is a potential novel prognostic biomarker for ESCA.

Combined identification of three miRNAs in serum as effective diagnostic biomarkers for HNSCC.

Background: Head and neck squamous cell carcinoma (HNSCC) is a disastrous disease with substantial morbidity and mortality. This study aims to explore the effective diagnostic and prognostic biomarkers for HNSCC.Methods: MiRNA expression data and corresponding clinical information of HNSCC from The Cancer Genome Atlas (TCGA) database were analyzed comprehensively to identify the miRNAs with diagnostic and prognostic power. The predictive ability of different classifications was analyzed for the three-miRNA combinations. Diagnostic and prognostic value were then evaluated and verified in clinical patients.Findings: 128 differentially expressed miRNAs in HNSCC tissues were identified in the TCGA dataset, and 10 miRNAs were finally selected for further study. Classification analysis developed a three-miRNA signature of hsa-mir-383, hsa-mir-615, and hsa-mir-877 with the best diagnosis power, which was verified in validation patients. Survival analysis indicated that different expression levels of hsa-mir-383, rather than that of hsa-mir-615 or hsa-mir-877 led to significantly different survival rates in both cohorts. Furthermore, the multivariate Cox hazards analysis suggested that the microRNA signature yielded statistical significance to predict clinical outcome independently from other clinical variables in validation patients.Interpretation: A three-miRNA signature of hsa-mir-383, hsa-mir-615, and hsa-mir-877 may serve as an excellent diagnostic biomarker for HNSCC, and potential prognostic significance for HNSCC patients.Funding: This work was supported by the grants of the National Natural Science Foundation of China (81901021), Key Research and Development Program of Shandong (2019GSF108277), China postdoctoral Scinence Foundation Grant (2019M652380), Fundamental Research Funds of Shandong University (2018CJ047).

Three-microRNA expression signature predicts survival in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a specific type of breast cancer with poor overall survival (OS) time. Previous studies revealed that microRNAs (miRNAs/miRs) serve important roles in the pathogenesis, progression and prognosis of TNBC. The present study analyzed the miRNA expression and clinical data of patients with TNBC downloaded from The Cancer Genome Atlas. A total of 194 differentially expressed miRNAs were identified between TNBC and matched normal tissues using the cut-off criteria of P<0.05 and |log2 fold change|>2. Of these miRNAs, 65 were downregulated and 129 were upregulated. Using Kaplan-Meier survival analysis, a total of 77 miRNAs that were closely associated with OS time were identified (P<0.05). The intersection of the 77 miRNAs and 194 differentially expressed miRNAs revealed six miRNAs. Log-rank tests based on survival curves were performed and two miRNAs were eliminated. The prognostic value of the remaining four miRNAs was evaluated with a Cox proportional hazards model using multiple logistic regression with forward stepwise selection of variables. Three miRNAs (miR-21-3p, miR-659-5p and miR-200b-5p) were subsequently identified as independent risk factors associated with OS time in the model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed that the target genes of these three miRNAs were mainly involved in ‘cell protein metabolism’, ‘RNA transcriptional regulation’, ‘cell migration’, ‘MAPK signaling pathway’, ‘ErbB signaling pathway’, ‘prolactin signaling pathway’ and ‘adherens junctions’. Taken together, the results obtained in the present study suggested that the three-miRNA signature may serve as a prognostic biomarker for patients with TNBC.