Down-regulation of microRNA152 is associated with the diagnosis and prognosis of patients with osteosarcoma.
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Potential values of microRNA152 (miR-152) as a serum diagnostic and prognostic biomarker have not been determined in human osteosarcoma. By detecting the expression of miR-152 among 80 osteosarcoma patients, 20 periostitis patients and 20 healthy individuals using qRT-PCR, we aimed to explore the clinical significance of miR-152 in osteosarcoma patients. The expression of miR-152 was significantly decreased in patients with osteosarcoma compared to patients with periostitis (P<0.01) and healthy controls (P<0.01). The relationship between clinicopathologic characteristics and miR-152 was analyzed by chi-square test. The outcome indicated that miR-152 might be linked with the development of osteosarcoma. Moreover, the receiver operating characteristic (ROC) curve was performed to estimate the diagnostic value of miR-152. The result demonstrated that miR-152 might be a promising diagnostic marker of osteosarcoma with an AUC of 0.956, combing with 92.5% specificity and 96.2% sensitivity. The relationship between miR-152 and overall survival of osteosarcoma patients was analyzed by Kaplan-Meier curve and log rank test. As a result, the survival time of patients with low miR-152 expression was significantly shorter than those with high miR-152 expression (P<0.001). Then Cox regression analysis was used to estimate the prognostic value of miR-152 in osteosarcoma. The outcomes showed that low miR-152 expression (P=0.004) might be a potential independent prognostic marker for osteosarcoma patients. These findings suggested that down-regulation of miR-152 could be considered as a predictor for diagnosis and prognosis of osteosarcoma patients.Cite
Selecting patients for early clinical trials is a challenging process and clinicians lack sufficient tools to predict overall survival (OS). Circulating cell-free DNA (cfDNA) has recently been shown to be a promising prognostic biomarker. The aim of this study was to investigate whether baseline cfDNA measurement could improve the prognostic information of the Royal Marsden Hospital (RMH) score. Solid tumour patients referred for phase I trials were included in the Copenhagen Personalized Oncology (CoPPO) programme. Baseline characteristics were collected prospectively, including the RMH prognostic score, Eastern Cooperative Oncology Group (ECOG) performance status and concentration of cfDNA per millilitre plasma. Cox proportional hazards model was used to assess the prognostic value of baseline variables. Plasma cfDNA concentration was quantifiable in 302 patients out of a total of 419 included in the study period of 2 years and 5 months. The RMH score was confirmed to be associated with OS. Cell-free DNA was shown to be an independent prognostic marker of OS and improved the risk model, including RMH, performance status and age. Furthermore, both plasma cfDNA concentration and RMH score were associated with treatment allocation (p < 0.00001). Our model based on RMH score, age, ECOG performance status and cfDNA improved prediction of OS and constitutes a clinically valuable tool when selecting patients for early clinical trials. An interactive version of the prognostic model is published on http://bit.ly/phase1survival .
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This study was conducted in order to establish a long non-coding RNA (lncRNA)-based model for predicting overall survival (OS) in patients with lung adenocarcinoma (LUAD).Original RNA-seq data of LUAD samples were extracted from The Cancer Genome Atlas (TCGA) database. Univariate Cox survival analysis was performed to select lncRNAs associated with OS. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox analysis were performed for building an OS-associated lncRNA prognostic model. Moreover, receiver operating characteristic (ROC) curves were generated to assess predictive values of the hub lncRNAs. Consequently, qRT-PCR was conducted to validate its prognostic value. The potential roles of these lncRNAs in immunotherapy and anti-angiogenic therapy were also investigated.The lncRNA-associated risk score of OS (LARSO) was established based on the LASSO coefficient of six individual lncRNAs, including CTD-2124B20.2, CTD-2168K21.1, DEPDC1-AS1, RP1-290I10.3, RP11-454K7.3, and RP11-95M5.1. Kaplan-Meier analysis revealed that LUAD patients with higher LARSO values had a shorter OS. Furthermore, a new risk score (NRS), including LARSO, stage, and N stage, could better predict the prognosis of LUAD patients compared with LARSO alone. Evaluation of the prognostic model in our cohort demonstrated that patients with higher scores had a worse prognosis. In addition, correlation analysis between these six lncRNAs and immune checkpoints or anti-angiogenic targets suggested that LUAD patients with high LARSO might not be sensitive to immunotherapy or anti-angiogenic therapy.This robust six-lncRNA prognostic signature may be used as a novel and powerful prognostic biomarker for lung adenocarcinoma.
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Abstract Introduction: In recent years, it has been found that the expression of 17 centromere proteins (CENPs) is closely related to malignant tumors. This study intends to investigate the prognostic value of CENPs in breast cancer (BC). Methods: A total of 800 BC patients were included from the TCGA database. The Cox proportional regression models was used to develop a CENPs-related prognostic signature. Furthermore, the mRNA expression and overall survival (OS) of CENPP (centromere protein P) in BC patients with different clinicopathological featureswas analyzed via GEPIA, bcGenExMiner v4.4 and Kaplan-Meier plotter. Finally, the nomogram was established based on the independent predictors recognized by multivariate Cox regression analysis and further validated by receiver-operating characteristic (ROC) curves and calibration plots internally and externally. Results: The result shown that age, Her2 status, pathologic_T stage, pathologic_M stage and CENPP expression with independent prognostic values for BC. CENPP was overexpressed in BC tissues and CENPP high expression was associated with better OS. We then established a nomogram based on those independent predictors, and the calibration curve demonstrated good fitness of the nomogram for OS prediction. In the training set, the AUC of 3−year and 5−year survival were 0.757 and 0.797, respectively. In the validation set, the AUC of 3−year and 5−year survival were 0.727 and 0.71. Conclusion: Our study showed that CENPP is a novel prognostic factor for patients with BC, and the established nomogram can provide valuable information on prognostic prediction for patients with BC.
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The aim of this investigation was to examine the potential usefulness of long non-coding RNA UCA1 (UCA1) as a biomarker for diagnosis and prognosis in osteosarcoma.The expression level of UCA1 was determined using TaqMan real-time PCR in human osteosarcoma tissues and patients' sera. Next, we investigated to clarify the relationship of UCA1 with clinicopathological features. The receiver operating characteristic (ROC) curve was performed to estimate the diagnostic value of UCA1. Finally, the prognosis of patients with osteosarcoma was assessed by Kaplan-Meier method and Cox proportional hazards model.We observed that UCA1 was significantly increased in osteosarcoma tissue compared with normal bone tissue (p<0.001) and the serum expression of UCA1 was significantly higher in patients with osteosarcoma than that in healthy controls (p<0.01). Up-regulation of UCA1 was correlated with clinical stage (p=0.001) and metastasis (p=0.007). Furthermore, serum UCA1 levels were observed to be robust in differentiating osteosarcoma patients from control subjects [area under the curve (AUC) = 0.831; 95% confidence interval (CI)= 0.746 to 0.916]. Kaplan-Meier analysis showed that that high UCA1 expression level was associated with poorer overall survival (p<0.001) and disease-free survival (p<0.001). Finally, Cox regression analyses showed that UCA1 expression might be an independent prognostic parameter to predict poor prognosis.Our study firstly showed that UCA1 could be a specific and noninvasive candidate biomarker for the diagnosis and prognosis of UCA1.
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Abstract Background Osteosarcoma is a highly malignant and common bone tumour with an aggressive disease course and a poor prognosis. Previous studies have demonstrated the relationship between long noncoding RNAs (lncRNAs) and tumorigenesis, metastasis, and progression. Methods We utilized a large cohort from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database osteosarcoma project to identify potential lncRNAs related to the overall survival of patients with osteosarcoma by using univariate and multivariate Cox proportional hazards regression analyses. Kaplan–Meier curves were generated to evaluate the overall survival difference between patients in the high-risk group and the low-risk group. A time-dependent receiver operating characteristic curve (ROC) was employed, and the area under the curve (AUC) of ROC was measured to assess the sensitivity and specificity of the multi-lncRNA signature. Results Five lncRNAs (RP11-128N14.5, RP11-231|13.2, RP5-894D12.4, LAMA5-AS1, RP11-346L1.2) were identified, and a five-lncRNA signature was constructed. The AUC for predicting 5-year survival was 0.745, which suggested good performance of the five-lncRNA signature. In addition, functional enrichment analysis of the five-lncRNA-correlated protein-coding genes (PCGs) was performed to show the biological function of the five lncRNAs. Additionally, PPI network suggested RTP1 is a potential biomarker that regulates the prognosis of osteosarcoma. Conclusions We developed a five-lncRNA signature as a potential prognostic indicator for osteosarcoma.
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Abstract Background Although the outcome of breast cancer patients has been improved by advances in early detection, diagnosis and treatment. Due to the heterogeneity of the disease, prognostic assessment still faces challenges. The accumulated data indicate that there is a clear correlation between the tumor immune microenvironment and clinical outcomes. Objective Construct immune-related gene pairs to evaluate the prognosis of breast cancer and patient survival rate. Methods From the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, the Gene expression profiles and clinical data of breast cancer samples were downloaded. TCGA cohort were further divided into a training set (n = 764) and internal validation sets (n = 325). The GEO cohort was analyzed as an external validation cohort (n = 327). In the training set, differently expressed immune-relevant genes (IRGs) were screened firstly, and they were used to construct immune-relevant gene pairs (IRGPs). Then, the prognostic IRGPs were identified via univariate Cox regression analysis. Finally, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to constituted the IRGP prognostic signature. Kaplan-Meier (KM) survival curves, receiver operating characteristic (ROC) curve analysis, univariate and multivariate Cox regression analysis were used to estimate the predictive value of the IRGP prognostic signature. And the IRGP prognostic signature was validated in the internal validation cohort and external validation cohort. We used gene set enrichment analysis (GSEA) to elucidate the biological functions of the IRGP prognostic signature. Results A total of 474 differently expressed IRGs and 2942 prognostic IRGPs were identified. Finally, we generated a IRGP prognostic signature consisting of 33 IRGPs. Subsequently, the 33 IRGPs grouped BRCA patients into high- and low-risk groups. Kaplan-Meier curves shown a significantly different overall survival in risk groups. Time-dependent ROC curves indicated that the IRGP prognostic signature possessed a high specificity and sensitivity in all the sets. Univariate and multivariate Cox regression analysis showed a statistical significance for the prognostic value of IRGP prognostic signature and the IRGP prognostic signature was a strong independent risk factor. The functional enrichment analysis indicated that low IRGP value was correlated with biological processes related to immune. Immune cell infiltration analysis indicated a significant difference in percentage of M2 macrophages between high- and low-risk groups. Conclusion The 33-IRGPs prognostic signature was developed to provide new insights for the identification of high-risk breast cancer and the evaluation of the possibility of immunotherapy in personalized breast cancer treatment.
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Abstract Background: Iron metabolism-related genes have shown good predictive value for the prognosis of many solid tumours. However, iron metabolism-related genes have not been reported as prognostic biomarkers in bladder urothelial carcinoma.Methods: In this study, gene expression data and clinical data were downloaded from The Cancer Genome Atlas database. Differential gene expression analysis, univariate Cox regression analysis and the least absolute shrinkage and selection operator regression algorithm were used to screen prognostic iron metabolism-related genes and to construct a risk scoring model. Kaplan-Meier survival plots and receiver operating characteristic curve analysis were used to evaluate the prognostic performance of the risk scoring model in the TCGA-BLCA cohort. In addition, a nomogram model with the risk score was established, and its predictive performance was verified by receiver operating characteristic analysis and calibration plot analysis in the TCGA-BLCA cohort. Gene set enrichment analysis identified pathways and biological processes that may be enriched in the high-risk group. Finally, immune infiltration analysis was used to explore the characteristics of the tumour microenvironment related to the risk score. Results: We identified 14 iron metabolism-related genes with prognostic value and constructed a risk scoring model. Receiver operating characteristic analysis showed that the risk scoring model can accurately predict the 1-year, 3-year, and 5-year overall survival of BLCA patients in the TCGA-BLCA cohort. Kaplan-Meier analysis showed that the overall survival of the high-risk group was significantly lower than that of the low-risk group (P<0.001). The nomogram model effectively predicted the overall survival of BLCA patients in the TCGA-BLCA cohort. Gene set enrichment analysis indicated that iron metabolism-related genes may be involved in biological processes such as developmental processes, the cell cycle, mitosis, the RHO GTPase response, DNA repair, and extracellular matrix regulation. Immune infiltration analysis showed that the level of immune cell infiltration in the high-risk group was high, and the risk score was positively correlated with infiltrating immune cells. Conclusions: Our prognostic model based on iron metabolism-related genes in BLCA could help the prognostic assessment of BLCA patients and provide potential targets for BLCA inhibition.
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The aim of the present study was to identify potential prognostic microRNA (miRNA) biomarkers for hepatocellular carcinoma (HCC) prognosis prediction based on a dataset from The Cancer Genome Atlas (TCGA).A miRNA sequencing dataset and corresponding clinical parameters of HCC were obtained from TCGA. Genome-wide univariate Cox regression analysis was used to screen prognostic differentially expressed miRNAs (DEMs), and multivariable Cox regression analysis was used for prognostic signature construction. Comprehensive survival analysis was performed to evaluate the prognostic value of the prognostic signature.Five miRNAs were regarded as prognostic DEMs and used for prognostic signature construction. The five-DEM prognostic signature performed well in prognosis prediction (adjusted P < 0.0001, adjusted hazard ratio = 2.249, 95% confidence interval =1.491-3.394), and time-dependent receiver-operating characteristic (ROC) analysis showed an area under the curve (AUC) of 0.765, 0.745, 0.725, and 0.687 for 1-, 2-, 3-, and 5-year HCC overall survival (OS) prediction, respectively. Comprehensive survival analysis of the prognostic signature suggests that the risk score model could serve as an independent factor of HCC and perform better in prognosis prediction than other traditional clinical indicators. Functional assessment of the target genes of hsa-mir-139 and hsa-mir-5003 indicates that they were significantly enriched in multiple biological processes and pathways, including cell proliferation and cell migration regulation, pathways in cancer, and the cyclic adenosine monophosphate (cAMP) signaling pathway.Our study indicates that the novel miRNA expression signature may be a potential prognostic biomarker for HCC patients.
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The identification of early diagnostic and prognostic biomarkers in oral squamous cell carcinoma (OSCC) is an unmet clinical need. We hypothesized that extracellular vesicles miR-210 expression (EV-miR-210) could be a potential biomarker for OSCC diagnosis and follow-up.The expression of EV-miR-210 was measured in the plasma of OSCC patients (n = 30) and compared to that of controls (n = 14).The median EV-miR-210 expression was significantly higher in OSCC patients compared to controls who had often, undetectable levels (p < 0.0001). We performed receiver operating characteristic (ROC) analysis for discriminating OSCC cases from controls. EV-miR-210 yielded an area under the curve (AUC) of 0.9513 with sensitivity 92.3% and specificity 86.6%. Kaplan-Meier curves indicated that high EV-miR-210 expression was associated with worse 3 years' survival (p < 0.05). Cox regression hazard model indicated that high EV-miR-210, G2, and G3 grading and pathological nodal status (pN)>1 were independent predictors of worse survival in OSCC patients.These preliminary data suggest that EV-mir-210 may be a novel diagnostic and prognostic biomarker in OSCC.
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The aberrant expressions of lncRNAs have been frequently demonstrated to be closely associated with the prognosis of patients in many cancer types including hepatocellular carcinoma (HCC). Integration of these lncRNAs might provide accurate evaluation of HCC. Therefore, this study aims to develop a novel prognostic evaluation model based on the expression of lncRNAs to predict the survival of HCC patients, postoperatively.RNA sequencing (RNA-seq) analysis was performed for 61 HCC patients (training cohort) to screen prognosis-associated lncRNAs with univariate Cox regression and Log rank test analyses. Multivariate Cox regression analysis was then applied to establish the final model, which was further verified in a validation cohort (n=191). Moreover, performance of the mode was assessed with time-dependent receiver operating characteristic curve (tdROC), Harrell's c-index, and Gönen & Heller's K.After a serial statistical computation, a novel risk scoring model consisting of four lncRNAs and TNM staging was established, which could successfully divide the HCC patients into low-risk and high-risk groups with significantly different OS and RFS in both training and validation cohorts. tdROC analysis showed that this model achieved a high performance in predicting OS and 2-year RFS in both cohorts. Gene Set Enrichment Analysis revealed that HCC tumor tissues with high-risk score have stronger capacities in immune escape and resistance to treatment.We successfully established a novel prognostic evaluation model, which exhibited reliable capacity in predicting the OS and early recurrence of HCC patients with relatively higher accuracy.
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