Identification and Validation of a Five-lncRNA Signature Related to Glioma Using Bioinformatics Analysis

Abstract BackgroundTo accurately predict the prognosis of glioma patients. Methods and ResultsA total of 541 samples from the TCGA cohort and 181 observations from the CGGA database were included in our study. By weighted gene co-expression network analysis (WGCNA), 14 long non-coding RNAs (lncRNAs) associated with glioma grade were identified. Using univariate and multivariate Cox analysis Five lncRNAs (CYTOR, MIR155HG, LINC00641, AC120036.4 and PWAR6) were selected to develop the prognostic signature. The Kaplan-Meier curve depicted that the patients in high risk group had poor prognosis in both cohorts. The areas under the receiver operating characteristic curve of the signature in predicting the survival of glioma patients at 1, 3, and 5 years were 0.84, 0.92, and 0.90 in the CGGA cohort and 0.8, 0.85 and 0.77 in the TCGA set. Multivariate Cox analysis demonstrated that the five-lncRNA signature was an independent prognostic indicator in both sets (HR = 2.002, p < 0.001; HR = 1.243, p = 0.007, respectively). A nomogram including the lncRNAs signature and clinical covariates was constructed and demonstrated high predictive accuracy in predicting 1-, 3- and 5-year survival probability of glioma patients. ConclusionWe established a five-lncRNA signature as a potentially reliable tool for survival prediction of glioma patients.

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Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis

BMC Cancer ◽

10.1186/s12885-021-07972-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Chunyu Zhang ◽

Haitao Liu ◽

Pengfei Xu ◽

Yinqiu Tan ◽

Yang Xu ◽

...

Keyword(s):

Network Analysis ◽

Cox Regression ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Prognostic Indicator ◽

High Risk Group ◽

Survival Prediction ◽

Prognostic Signature ◽

Who Grade ◽

Cox Analysis

Abstract Background To accurately predict the prognosis of glioma patients. Methods A total of 541 samples from the TCGA cohort, 181 observations from the CGGA database and 91 samples from our cohort were included in our study. Long non-coding RNAs (LncRNAs) associated with glioma WHO grade were evaluated by weighted gene co-expression network analysis (WGCNA). Five lncRNA features were selected out to construct prognostic signatures based on the Cox regression model. Results By weighted gene co-expression network analysis (WGCNA), 14 lncRNAs related to glioma grade were identified. Using univariate and multivariate Cox analysis, five lncRNAs (CYTOR, MIR155HG, LINC00641, AC120036.4 and PWAR6) were selected to develop the prognostic signature. The Kaplan-Meier curve depicted that the patients in high risk group had poor prognosis in all cohorts. The areas under the receiver operating characteristic curve of the signature in predicting the survival of glioma patients at 1, 3, and 5 years were 0.84, 0.92, 0.90 in the CGGA cohort; 0.8, 0.85 and 0.77 in the TCGA set and 0.72, 0.90 and 0.86 in our own cohort. Multivariate Cox analysis demonstrated that the five-lncRNA signature was an independent prognostic indicator in the three sets (CGGA set: HR = 2.002, p < 0.001; TCGA set: HR = 1.243, p = 0.007; Our cohort: HR = 4.457, p = 0.008, respectively). A nomogram including the lncRNAs signature and clinical covariates was constructed and demonstrated high predictive accuracy in predicting 1-, 3- and 5-year survival probability of glioma patients. Conclusion We established a five-lncRNA signature as a potentially reliable tool for survival prediction of glioma patients.

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H19- and hsa-miR-338-3p-mediated NRP1 expression is an independent predictor of poor prognosis in glioblastoma

PLoS ONE ◽

10.1371/journal.pone.0260103 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0260103

Author(s):

Yong Liu ◽

Yuelin Liu ◽

Yong Gao ◽

Lei Wang ◽

Hengliang Shi ◽

...

Keyword(s):

Operating Characteristic ◽

Independent Predictor ◽

Clinical Diagnostics ◽

Competing Endogenous Rna ◽

Kaplan Meier ◽

Competing Endogenous Rna Network ◽

Non Coding Rnas ◽

Patient Prognosis ◽

Cox Analysis ◽

Improve Patient

Glioblastoma multiforme (GBM) is the most common and also the most invasive brain cancer. GBM progression is rapid and its prognosis is poor. Therefore, finding molecular targets in GBM is a critical goal that could also play important roles in clinical diagnostics and treatments to improve patient prognosis. We jointly analyzed the GSE103227, GSE103229, and TCGA databases for differentially expressed RNA species, obtaining 52 long non-coding RNAs (lncRNAs), 31 microRNAs (miRNAs), and 186 mRNAs, which were used to build a competing endogenous RNA network. Kaplan–Meier and receiver operating characteristic (ROC) analyses revealed five survival-related lncRNAs: H19, LINC01574, LINC01614, RNF144A-AS1, and OSMR-AS1. With multiple optimization mRNAs, we found the H19-hsa-miR-338-3P-NRP1 regulatory pathway. Additionally, we noted high NRP1 expression in GBM patients, and Kaplan–Meier and ROC analyses showed that NRP1 expression was associated with GBM prognosis. Cox analysis indicated that NRP1 is an independent prognostic factor in GBM patients. In conclusion, H19 and hsa-miR-338-3P regulate NRP1 expression, and this pathway plays an important role in GBM.

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Soluble SIGLEC5: A New Prognosis Marker in Colorectal Cancer Patients

Cancers ◽

10.3390/cancers13153896 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3896

Author(s):

Karla Montalbán-Hernández ◽

Ramón Cantero-Cid ◽

Roberto Lozano-Rodríguez ◽

Alejandro Pascual-Iglesias ◽

José Avendaño-Ortiz ◽

...

Keyword(s):

Colorectal Cancer ◽

Operating Characteristic ◽

Prognostic Markers ◽

Characteristic Curve ◽

Area Under The Curve ◽

Significant Heterogeneity ◽

Kaplan Meier ◽

Prognosis Marker ◽

Colorectal Cancer Patients ◽

Healthy Participants

Colorectal cancer (CRC) is the second most deadly and third most commonly diagnosed cancer worldwide. There is significant heterogeneity among patients with CRC, which hinders the search for a standard approach for the detection of this disease. Therefore, the identification of robust prognostic markers for patients with CRC represents an urgent clinical need. In search of such biomarkers, a total of 114 patients with colorectal cancer and 67 healthy participants were studied. Soluble SIGLEC5 (sSIGLEC5) levels were higher in plasma from patients with CRC compared with healthy volunteers. Additionally, sSIGLEC5 levels were higher in exitus than in survivors, and the receiver operating characteristic curve analysis revealed sSIGLEC5 to be an exitus predictor (area under the curve 0.853; cut-off > 412.6 ng/mL) in these patients. A Kaplan–Meier analysis showed that patients with high levels of sSIGLEC5 had significantly shorter overall survival (hazard ratio 15.68; 95% CI 4.571–53.81; p ≤ 0.0001) than those with lower sSIGLEC5 levels. Our study suggests that sSIGLEC5 is a soluble prognosis marker and exitus predictor in CRC.

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Performance of Platelet Mass Index as a Marker of Severity for Sepsis and Septic Shock in Children

Journal of Pediatric Intensive Care ◽

10.1055/s-0041-1731434 ◽

2021 ◽

Author(s):

Madhuradhar Chegondi ◽

Niranjan Vijayakumar ◽

Ramya Deepthi Billa ◽

Aditya Badheka ◽

Oliver Karam

Keyword(s):

Septic Shock ◽

Operating Characteristic ◽

Characteristic Curve ◽

Pediatric Intensive Care ◽

Prognostic Indicator ◽

Clinical Value ◽

Operating Characteristic Curve ◽

Pediatric Sepsis ◽

Sepsis And Septic Shock ◽

Platelet Mass

AbstractPlatelet mass index (PMI) as a prognostic indicator in pediatric sepsis is not reported. In this retrospective observational study, we evaluated PMI's performance as a prognostic indicator in children aged younger than 18 years with sepsis and septic shock in relationship with survival. Over 5 years, we collected data of 122 children admitted to our pediatric intensive care unit (PICU). PMI accuracy was assessed with sensitivity and specificity and its discrimination was assessed using the area under the receiver operating characteristic curve (AUC). The median PMI values on days 1 and 3 of PICU admission were lower among nonsurvivors. On day 1 of PICU admission, a cutoff PMI value of 1,450 fL/nL resulted in a sensitivity of 72% and a specificity of 69%, and the AUC was 0.70 (95% confidence interval [CI]: 0.55–0.86). Similarly, on day 3, a cutoff of 900 fL/nL resulted in a sensitivity of 71% and a specificity of 70%, and the AUC was 0.76 (95% CI: 0.59–0.92). Our exploratory study suggests that low PMI in children with septic shock is associated with increased mortality. Considering the PMI's fair performance, further studies have to assess its clinical value.

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Nomogram Based on Cytokines for Cardiovascular Diseases in Xinjiang Kazakhs

Mediators of Inflammation ◽

10.1155/2019/4756295 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8

Author(s):

Lei Mao ◽

Xianghui Zhang ◽

Yunhua Hu ◽

Xinping Wang ◽

Yanpeng Song ◽

...

Keyword(s):

Cardiovascular Diseases ◽

Operating Characteristic ◽

Validation Cohort ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Baseline Survey ◽

Cvd Risk ◽

Cytokine Levels ◽

Study Population ◽

The Town

Background. This study involved the development of a predictive 5-year morbidity nomogram for cardiovascular diseases (CVD) in Xinjiang Kazakhs based on cytokine levels. Methods. The nomogram was based on a baseline survey of the town of Nalati in the Kazakh Autonomous Prefecture of Xinjiang from 2009 to 2013. By 2016, we had monitored 1508 people for a median time of 5.17 years and identified CVD events in the study population by collecting case information from local hospitals. The study population was divided into the training (n=1005) and validation cohorts (n=503) in a 2 : 1 ratio. The area under the receiver operating characteristic curve (AUC) was used to verify the predictive accuracy of the nomogram. The result was assessed in a validation cohort. Results. At the end of the study, the incidence of CVD in Xinjiang Kazakhs was found to be 11.28%. We developed a new nomogram to predict the 5-year incidence of CVD based on age, interleukin-6 (IL-6), and adiponectin (APN) levels, diastolic blood pressure, and dyslipidemia. The AUC for the predictive accuracy of the nomogram was 0.836 (95% confidence interval: 0.802–0.869), which was higher than that for IL-6 and APN. These results were supported by validation studies. Conclusions. The nomogram model can more directly assess the risk of CVD in Kazakhs and can be used for CVD risk assessment.

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A Novel Predictive Model Associated With Osteosarcoma Metastasis

10.21203/rs.3.rs-597836/v1 ◽

2021 ◽

Author(s):

Han Zhang ◽

Guanhong Chen ◽

Xiajie Lyu ◽

Tao Li ◽

Rong Chun ◽

...

Keyword(s):

Operating Characteristic ◽

Cox Regression ◽

High Reliability ◽

Characteristic Curve ◽

Predictive Ability ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Metastasis Model ◽

Non Coding Rnas ◽

Novel Model

Abstract Background: Long non-coding RNAs (lncRNAs) have diverse roles in modulating gene expression on both transcriptional and translational aspects, whereas its role in the metastasis of osteosarcoma (OS) is unclear.Method: Expression and clinical data were downloaded from TARGET datasets. The OS metastasis model was established by seven lncRNAs screened by univariate cox regression, lasso regression and multivariate cox regression analysis. The area under receiver operating characteristic curve (AUC) values were used to evaluate the models.Results: The predictive ability of this model is extraordinary (1 year: AUC = 0.92, 95% Cl = 0.83–1.01; 3 years: AUC = 0.87, 95% Cl = 0.79–0.96; 5 years: AUC = 0.86, 95% Cl = 0.76–0.96). Patients in high group had poor survival compared to low group (p < 0.0001). “NOTCH_SIGNALING”, and “WNT_BETA_CATENIN_SIGNALING” were enriched via the GSEA analysis and dendritic cells resting were associated with the AL512422.1, AL357507.1 and AC006033.2 (p < 0.05).Conclusion: We constructed a novel model with high reliability and accuracy to predict the metastasis of OS patients based on seven prognosis-related lncRNAs.

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A Five-MicroRNA Signature Predicts the Prognosis in Nasopharyngeal Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.723362 ◽

2021 ◽

Vol 11 ◽

Author(s):

Shixiong Wu ◽

Cen Zhang ◽

Jing Xie ◽

Shuang Li ◽

Shuo Huang

Keyword(s):

Overall Survival ◽

High Risk ◽

Nasopharyngeal Carcinoma ◽

Receiver Operating Characteristic Curve ◽

Operating Characteristic ◽

Risk Group ◽

Characteristic Curve ◽

High Risk Group ◽

Prognostic Signature ◽

Operating Characteristic Curve

BackgroundThere is no effective prognostic signature that could predict the prognosis of nasopharyngeal carcinoma (NPC).MethodsWe constructed a prognostic signature based on five microRNAs using random forest and Least Absolute Shrinkage And Selection Operator (LASSO) algorithm on the GSE32960 cohort (N = 213). We verified its prognostic value using three independent external validation cohorts (GSE36682, N = 62; GSE70970, N = 246; and TCGA-HNSC, N = 523). Through principal component analysis, receiver operating characteristic curve analysis, and C-index calculation, we confirmed the predictive accuracy of this prognostic signature.ResultsWe calculated the risk score based on the LASSO algorithm and divided the patients into high- and low-risk groups according to the calculated optimal cutoff value. The patients in the high-risk group tended to have a worse prognosis outcome and chemotherapy response. The time-dependent receiver operating characteristic curve showed that the 1-year overall survival rate of the five-microRNA signature had an area under the curve of more than 0.83. A functional annotation analysis of the five-microRNA signature showed that the patients in the high-risk group were usually accompanied by activation of DNA repair and MYC-target pathways, while the patients in the low-risk group had higher immune-related pathway signals.ConclusionsWe constructed a five-microRNA prognostic signature, which could accurately predict the prognosis of nasopharyngeal carcinoma, and constructed a nomogram that could conveniently predict the overall survival of patients.

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A Novel Model Based on Genomic Instability-Associated Long Non-Coding RNAs for Predicting Prognosis and Response to Immunotherapy in Patients With Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.720013 ◽

2021 ◽

Vol 12 ◽

Author(s):

Guangxu Tu ◽

Weilin Peng ◽

Qidong Cai ◽

Zhenyu Zhao ◽

Xiong Peng ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Genomic Instability ◽

Prognostic Value ◽

Predictive Accuracy ◽

Expression Profiles ◽

Pearson Correlation ◽

Characteristic Curve ◽

Prognostic Index ◽

The Cancer Genome Atlas ◽

Non Coding Rnas

Background: Emerging scientific evidence has shown that long non-coding RNAs (lncRNAs) exert critical roles in genomic instability (GI), which is considered a hallmark of cancer. To date, the prognostic value of GI-associated lncRNAs (GI-lncRNAs) remains largely unexplored in lung adenocarcinoma (LUAC). The aims of this study were to identify GI-lncRNAs associated with the survival of LUAC patients, and to develop a novel GI-lncRNA-based prognostic model (GI-lncRNA model) for LUAC.Methods: Clinicopathological data of LUAC patients, and their expression profiles of lncRNAs and somatic mutations were obtained from The Cancer Genome Atlas database. Pearson correlation analysis was conducted to identify the co-expressed mRNAs of GI-lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted to determine the main biological function and molecular pathways of the differentially expressed GI-lncRNAs. Univariate and multivariate Cox proportional hazard regression analyses were performed to identify GI-lncRNAs significantly related to overall survival (OS) for construction of the GI-lncRNA model. Kaplan–Meier survival analysis and receiver operating characteristic curve analysis were performed to evaluate the predictive accuracy. The performance of the newly developed GI-lncRNA model was compared with the recently published lncRNA-based prognostic index models.Results: A total of 19 GI-lncRNAs were found to be significantly associated with OS, of which 9 were identified by multivariate analysis to construct the GI-lncRNA model. Notably, the GI-lncRNA model showed a prognostic value independent of key clinical characteristics. Further performance evaluation indicated that the area under the curve (AUC) of the GI-lncRNA model was 0.771, which was greater than that of the TP53 mutation status and three existing lncRNA-based models in predicting the prognosis of patients with LUAC. In addition, the GI-lncRNA model was highly correlated with programed death ligand 1 (PD-L1) expression and tumor mutational burden in immunotherapy for LUAC.Conclusion: The GI-lncRNA model was established and its performance was found to be superior to existing lncRNA-based models. As such, the GI-lncRNA model holds promise as a more accurate prognostic tool for the prediction of prognosis and response to immunotherapy in patients with LUAC.

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Validation Study of the Accuracy of a Postoperative Nomogram for Recurrence After Radical Prostatectomy for Localized Prostate Cancer

Journal of Clinical Oncology ◽

10.1200/jco.2002.20.4.951 ◽

2002 ◽

Vol 20 (4) ◽

pp. 951-956 ◽

Cited By ~ 37

Author(s):

Markus Graefen ◽

Pierre I. Karakiewicz ◽

Ilias Cagiannos ◽

Eric Klein ◽

Patrick A. Kupelian ◽

...

Keyword(s):

Prostate Cancer ◽

Radical Prostatectomy ◽

Operating Characteristic ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Extracapsular Extension ◽

Validation Data ◽

Data Set ◽

Time Period

PURPOSE: A postoperative nomogram for prostate cancer was developed at Baylor College of Medicine. This nomogram uses readily available clinical and pathologic variables to predict 7-year freedom from recurrence after radical prostatectomy. We evaluated the predictive accuracy of the nomogram when applied to patients of four international institutions. PATIENTS AND METHODS: Clinical and pathologic data of 2,908 patients were supplied for validation, and 2,465 complete records were used. Nomogram-predicted probabilities of 7-year freedom from recurrence were compared with actual follow-up in two ways. First, the area under the receiver operating characteristic curve (AUC) was calculated for all patients and stratified by the time period of surgery. Second, calibration of the nomogram was achieved by comparing the predicted freedom from recurrence with that of an ideal nomogram. For patients in whom the pathologic report does not distinguish between focal and established extracapsular extension (an input variable of the nomogram), two separate calculations were performed assuming one or the other. RESULTS: The overall AUC was 0.80 when applied to the validation data set, with individual institution AUCs ranging from 0.77 to 0.82. The predictive accuracy of the nomogram was apparently higher in patients who were operated on between 1997 and 2000 (AUC, 0.83) compared with those treated between 1987 and 1996 (AUC, 0.78). Nomogram predictions of 7-year freedom from recurrence were within 10% of an ideal nomogram. CONCLUSION: The postoperative Baylor nomogram was accurate when applied at international treatment institutions. Our results suggest that accurate predictions may be expected when using this nomogram across different patient populations.

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Histone Deacetylase-2 Serves as an Independent Prognostic Indicator in Osteosarcoma

10.21203/rs.3.rs-127806/v1 ◽

2020 ◽

Author(s):

Dawei Wang ◽

Youchen Ye ◽

Tingting Qu ◽

Zhifang Zhao ◽

Zenghui Gu ◽

...

Keyword(s):

Histone Deacetylase ◽

Expression Pattern ◽

Prognostic Value ◽

Cox Regression ◽

Prognostic Indicator ◽

Gene Set Enrichment Analysis ◽

Survival Prediction ◽

Cox Regression Analysis ◽

Histone Deacetylase 2 ◽

Kaplan Meier

Abstract Background Osteosarcoma is the most common primary malignant tumor of skeleton in adolescence. Histone deacetylase 2 (HDAC2), a member of class I histone deacetylase, is putatively involved in tumorigenesis of human malignancies. This study aimed to evaluate the expression pattern and prognostic value of HDAC2 in osteosarcoma.Methods Four datasets were obtained from the gene expression omnibus (GEO) database to explore the expression and prognostic value of HDAC2. Level 3 mRNA expression profiles and clinical data were obtained in The Cancer Genome Atlas (TCGA) for validation. Expression pattern of HDAC2 were illustrated in GSE16088, GSE36001 and GSE42352. The prognostic value of HDAC2 was evaluated and validated by Kaplan-Meier analyses, receiver operating characteristic (ROC), concordance index (C-index) and calibration curve in GSE21257 and TCGA. Multivariate Cox regression analysis, nomogram, and decision curve analysis (DCA) were performed to assess the prognosis predictive capability. Protein-protein interaction (PPI) and gene set enrichment analysis (GSEA) were applied to further understand the molecular network and regulatory mechanisms.Results HDAC2 expression was significantly increased in osteosarcoma tissues. High HDAC2 expression was associated with tumor metastasis and chemotherapy efficacy. Kaplan-Meier analysis demonstrated that high HDAC2 predicted worse overall survival. The ROC curve showed good performance in survival prediction. Cox regression demonstrated that HDAC2 could be an independent prognostic indicator. GSEA revealed patients with high HDAC2 expression were enriched with multiple ontological signatures.Conclusions Elevated expression of HDAC2 may identify an aggressive subgroup in osteosarcoma and serve as an independent prognostic indicator in these patients.

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