Comprehensive analysis of ceRNA network related to lincRNA in glioblastoma and prediction of clinical prognosis

Abstract Background Long intergenic non-coding RNAs (lincRNAs) are capable of regulating several tumours, while competitive endogenous RNA (ceRNA) networks are of great significance in revealing the biological mechanism of tumours. Here, we aimed to study the ceRNA network of lincRNA in glioblastoma (GBM). Methods We obtained GBM and normal brain tissue samples from TCGA, GTEx, and GEO databases, and performed weighted gene co-expression network analysis and differential expression analysis on all lincRNA and mRNA data. Subsequently, we predicted the interaction between lincRNAs, miRNAs, and target mRNAs. Univariate and multivariate Cox regression analyses were performed on the mRNAs using CGGA data, and a Cox proportional hazards regression model was constructed. The ceRNA network was further screened by the DEmiRNA and mRNA of Cox model. Results A prognostic prediction model was constructed for patients with GBM. We assembled a ceRNA network consisting of 18 lincRNAs, 6 miRNAs, and 8 mRNAs. Gene Set Enrichment Analysis was carried out on four lincRNAs with obvious differential expressions and relatively few studies in GBM. Conclusion We identified four lincRNAs that have research value for GBM and obtained the ceRNA network. Our research is expected to facilitate in-depth understanding and study of the molecular mechanism of GBM, and provide new insights into targeted therapy and prognosis of the tumour.

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Comprehensive Analysis of lincRNA-Related ceRNA Network in Glioblastoma

10.21203/rs.3.rs-86624/v1 ◽

2020 ◽

Author(s):

Guangdong Liu ◽

Danian Liu ◽

Jingjing Huang ◽

Jianxin Li ◽

Chuang Wang ◽

...

Keyword(s):

Proportional Hazards ◽

Cox Regression ◽

Cox Model ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Cox Proportional Hazards ◽

Gene Set Enrichment Analysis ◽

Normal Brain ◽

Tissue Samples ◽

Cerna Network

Abstract BackgroundLong intergenic non-coding RNAs (lincRNAs) are capable of regulating several tumours, while competitive endogenous RNA (ceRNA) networks are of great significance in revealing the biological mechanism of tumours. Currently, there is a dearth of studies on the ceRNA network of lincRNAs in glioblastoma (GBM), which we aimed to assess in the present study. MethodsWe obtained GBM and normal brain tissue samples from TCGA, GTEx, and GEO databases, and performed weighted gene co-expression network analysis and differential expression analysis on all lincRNA and mRNA data. Subsequently, we predicted the interaction between lincRNAs, miRNAs, and target mRNAs. Univariate and multivariate Cox regression analyses were performed on the mRNAs using CGGA data, and a Cox proportional hazards regression model was constructed. ResultsAccording to the Cox model, we assembled a ceRNA network consisting of 23 lincRNAs, 14 miRNAs, and 13 mRNAs. Gene Set Enrichment Analysis was carried out on four lincRNAs with obvious differential expressions and relatively few studies in GBM. ConclusionWe identified four lincRNAs that have the most research values for GBM and obtained the ceRNA network. Our research is expected to facilitate in-depth understanding and study of the molecular mechanism of GBM, and provide new insights into targeted therapy and prognosis of the tumour.

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Construction of a ceRNA network in glioma and analysis of its clinical significance

BMC Genomics ◽

10.1186/s12864-021-08035-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Guangdong Liu ◽

Haihong Li ◽

Wenyang Ji ◽

Haidong Gong ◽

Yan Jiang ◽

...

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Molecular Mechanisms ◽

Cox Regression ◽

Differential Expression Analysis ◽

Gene Set Enrichment Analysis ◽

Normal Brain ◽

Central Nervous System Tumor ◽

Cox Regression Analysis ◽

Cerna Network

Abstract Background Glioma is the most common central nervous system tumor with a poor survival rate and prognosis. Previous studies have found that long non-coding RNA (lncRNA) and competitive endogenous RNA (ceRNA) play important roles in regulating various tumor mechanisms. We obtained RNA-Seq data of glioma and normal brain tissue samples from TCGA and GTEx databases and extracted the lncRNA and mRNA expression data. Further, we analyzed these data using weighted gene co-expression network analysis and differential expression analysis, respectively. Differential expression analysis was also carried out on the mRNA data from the GEO database. Further, we predicted the interactions between lncRNA, miRNA, and targeted mRNA. Using the CGGA data to perform univariate and multivariate Cox regression analysis on mRNA. Results We constructed a Cox proportional hazard regression model containing four mRNAs and performed immune infiltration analysis. Moreover, we also constructed a ceRNA network including 21 lncRNAs, two miRNAs, and four mRNAs, and identified seven lncRNAs related to survival that have not been previously studied in gliomas. Through the gene set enrichment analysis, we found four lncRNAs that may have a significant role in tumors and should be explored further in the context of gliomas. Conclusions In short, we identified four lncRNAs with research value for gliomas, constructed a ceRNA network in gliomas, and developed a prognostic prediction model. Our research enhances our understanding of the molecular mechanisms underlying gliomas, providing new insights for developing targeted therapies and efficiently evaluating the prognosis of gliomas.

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Integrated Analysis of lncRNA-Associated ceRNA Network Identifies Two lncRNA Signatures as a Prognostic Biomarker in Gastric Cancer

Disease Markers ◽

10.1155/2021/8886897 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Shuyan Zhang ◽

Shanshan Li ◽

Jian-Lin Guo ◽

Ningyi Li ◽

Cai-Ning Zhang ◽

...

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

Proportional Hazards ◽

Cox Regression ◽

Cox Proportional Hazards ◽

Integrated Analysis ◽

Survival Prediction ◽

Survival Prognosis ◽

Cox Regression Analysis ◽

Cerna Network

Background. Gastric cancer (GC) is a malignant tumour that originates in the gastric mucosal epithelium and is associated with high mortality rates worldwide. Long noncoding RNAs (lncRNAs) have been identified to play an important role in the development of various tumours, including GC. Yet, lncRNA biomarkers in a competing endogenous RNA network (ceRNA network) that are used to predict survival prognosis remain lacking. The aim of this study was to construct a ceRNA network and identify the lncRNA signature as prognostic factors for survival prediction. Methods. The lncRNAs with overall survival significance were used to construct the ceRNA network. Function enrichment, protein-protein interaction, and cluster analysis were performed for dysregulated mRNAs. Multivariate Cox proportional hazards regression was performed to screen the potential prognostic lncRNAs. RT-qPCR was used to measure the relative expression levels of lncRNAs in cell lines. CCK8 assay was used to assess the proliferation of GC cells transfected with sh-lncRNAs. Results. Differentially expressed genes were identified including 585 lncRNAs, 144 miRNAs, and 2794 mRNAs. The ceRNA network was constructed using 35 DElncRNAs associated with overall survival of GC patients. Functional analysis revealed that these dysregulated mRNAs were enriched in cancer-related pathways, including TGF-beta, Rap 1, calcium, and the cGMP-PKG signalling pathway. A multivariate Cox regression analysis and cumulative risk score suggested that two of those lncRNAs (LINC01644 and LINC01697) had significant prognostic value. Furthermore, the results indicate that LINC01644 and LINC01697 were upregulated in GC cells. Knockdown of LINC01644 or LINC01697 suppressed the proliferation of GC cells. Conclusions. The authors identified 2-lncRNA signature in ceRNA regulatory network as prognostic biomarkers for the prediction of GC patient survival and revealed that silencing LINC01644 or LINC01697 inhibited the proliferation of GC cells.

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Improved survival for patients with AIDS-related Kaposi's sarcoma.

Journal of Clinical Oncology ◽

10.1200/jco.1994.12.9.1910 ◽

1994 ◽

Vol 12 (9) ◽

pp. 1910-1916 ◽

Cited By ~ 32

Author(s):

S A Miles ◽

H Wang ◽

R Elashoff ◽

R T Mitsuyasu

Keyword(s):

Prognostic Factors ◽

Kaposi's Sarcoma ◽

Proportional Hazards ◽

Cox Regression ◽

Cox Model ◽

Medical Center ◽

Kaposi’S Sarcoma ◽

Cell Number ◽

Cox Proportional Hazards ◽

Cd4 Cell

PURPOSE We retrospectively analyzed all patients with AIDS-related Kaposi's sarcoma (AIDS-KS) seen at one large California medical center to delineate factors that may have contributed to a relative decline in survival. METHODS Potential prognostic factors were analyzed individually, using the Cox proportional hazards regression model, for their association with survival. After a stepwise Cox regression procedure was applied to those factors that showed a significant effect on survival, a subset of factors that best predicted survival was identified. We then quantified the effect of the year of diagnosis on survival using a univariate Cox model. Next, we combined the year of diagnosis with the subset of prognostic factors previously identified into the Cox model to examine survival after adjustment for the prognostic factors. Survival distribution was estimated by the Kaplan-Meier method, and the 95% confidence interval for the median survival was computed using the modified reflected method. RESULTS In 688 patients, we identified four baseline variables that best predicted survival: CD4 cell number, hematocrit, number of KS lesions, and body mass index (BMI). Adjusted for these predictive factors, there was a significant improvement in survival for patients with AIDS-KS over the last 6 years. CONCLUSION Contrary to prior reports, survival has increased for patients with AIDS-KS. The apparent increase in observed mortality is most likely due to a decline in the CD4 cell number at presentation.

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Identification of Potential Key Genes Associated with the Pathogenesis and Prognosis of Glioblastoma Based on Integrated Bioinformatics Analysis

10.21203/rs.3.rs-20089/v1 ◽

2020 ◽

Author(s):

Youbing Tu ◽

Sijia Zhou ◽

Hui Zhang ◽

Jing Lv ◽

Dengfeng Ding ◽

...

Keyword(s):

Proportional Hazards ◽

Proportional Hazards Model ◽

Cox Proportional Hazards ◽

Cox Proportional Hazards Model ◽

Prognostic Biomarkers ◽

Integrated Analysis ◽

Normal Brain ◽

Tissue Samples ◽

Targeting Therapy ◽

Key Genes

Abstract Background: Despite striking advances in multimodality management, the low survival rate of Glioblastoma (GBM) patients has not been significantly improved and identifying novel diagnostic and prognostic biomarkers is urgently demanded. The present study aimed to identify potential key genes associated with the pathogenesis and prognosis of GBM.Methods: Differentially expressed genes between GBM and normal brain tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Key genes related to the pathogenesis and prognosis of GBM were identified by employing protein–protein interaction network and Cox proportional hazards model analyses.Results: We identified nine hub genes (TP53, FN1, EGFR, MYC, RRM2, EZH2, FOXM1, CD44 and MMP2) which might be closely associated with the pathogenesis of GBM. A prognostic gene signature consisted of RAB33B, KIAA1199, TEK, EVC, SOD2, CXCR4, hCG_40738, CHD9, GCSH, SUHW1, RPS6KA5, PDCD4, ZG16, KCNG1, DECR1, PPCS, SERPINF1, TMSB10, NAT10, HIC2, PIR and OR2W1 was constructed with a good performance in predicting overall survivals (OS).Conclusions: The findings of present study would provide certain reference for further predicting the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of GBM.

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Hierarchical Neural Regression Models for Customer Churn Prediction

Journal of Engineering ◽

10.1155/2013/543940 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Golshan Mohammadi ◽

Reza Tavakkoli-Moghaddam ◽

Mehrdad Mohammadi

Keyword(s):

Data Mining ◽

Hierarchical Models ◽

Proportional Hazards ◽

Cox Regression ◽

Cox Model ◽

Cox Proportional Hazards ◽

Churn Prediction ◽

Data Mining Techniques ◽

Customer Churn ◽

Customer Churn Prediction

As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN), self-organizing maps (SOM), alpha-cut fuzzyc-means (α-FCM), and Cox proportional hazards regression model. The hierarchical models are ANN + ANN + Cox, SOM + ANN + Cox, andα-FCM + ANN + Cox. In particular, the first component of the models aims to cluster data in two churner and nonchurner groups and also filter out unrepresentative data or outliers. Then, the clustered data as the outputs are used to assign customers to churner and nonchurner groups by the second technique. Finally, the correctly classified data are used to create Cox proportional hazards model. To evaluate the performance of the hierarchical models, an Iranian mobile dataset is considered. The experimental results show that the hierarchical models outperform the single Cox regression baseline model in terms of prediction accuracy, Types I and II errors, RMSE, and MAD metrics. In addition, theα-FCM + ANN + Cox model significantly performs better than the two other hierarchical models.

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The Construction and Analysis of ceRNA Network and Immune Infiltration in Kidney Renal Clear Cell Carcinoma

10.21203/rs.3.rs-484087/v1 ◽

2021 ◽

Author(s):

Lugang Deng ◽

Zhi Qu ◽

Peixi Wang ◽

Nan Liu

Keyword(s):

Immune Cells ◽

Proportional Hazards ◽

Immune Cell ◽

Clear Cell ◽

Cell Types ◽

Renal Clear Cell Carcinoma ◽

Cox Proportional Hazards ◽

Tissue Samples ◽

Cerna Network ◽

Proportional Hazards Regression

Abstract Purpose Kidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patients’ quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear. Methods We constructed a ceRNA network associated with KIRC by analyzing the long noncoding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from the Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through “CIBERSORT”. Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employed to assess the discrimination and accuracy of the nomograms. Consequently, co-expression analysis was carried out to explore the relationship between each prognostic gene in a Cox proportional hazards regression model of ceRNA and each survival-related immune cell in a Cox proportional hazards regression model of immune cell types to reveal the potential regulatory mechanism. Results We established a ceRNA network consisting of 12 lncRNAs, 25 miRNAs and 136 mRNAs. Two nomograms containing seven prognostic genes and two immune cells, respectively, were successfully constructed. Both ROC [Area Under Curves (AUCs) of 1, 3 and 5-year survival in the nomogram based on ceRNA network: 0.779, 0.747 and 0.772; AUCs of 1, 3 and 5-year survivals in nomogram based on immune cells: 0.603, 0.642 and 0.607] and calibration curves indicated good accuracy and clinical application value of both models. Through co-correlation analysis between ceRNA and immune cells, we found both LINC00894 and KIAA1324 were positively correlated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells. Conclusions Based on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.

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A Novel Glycolysis-Related Gene Signature that can Predict the Prognosis of Glioblastoma Patients

10.21203/rs.3.rs-36552/v1 ◽

2020 ◽

Author(s):

Chaocai Zhang ◽

Minjie Wang ◽

Fenghu Ji ◽

Yizhong Peng ◽

Jiannong Zhao ◽

...

Keyword(s):

Cancer Progression ◽

Proportional Hazards ◽

Cox Regression ◽

Treatment Options ◽

Gene Signature ◽

Cox Proportional Hazards ◽

Gene Set Enrichment Analysis ◽

Related Gene ◽

Poor Overall Survival ◽

Cox Regression Analysis

Abstract Background: Glioblastoma (GBM) is one of the most common primary intracranial malignancies, with limited treatment options and poor overall survival (OS). Metabolic changes in GBM have attracted wide attention in recent years, and one of the main metabolic features of cancer cells is the high level of glycolysis. Therefore, it is necessary to identify novel biomarkers associated with glycolysis in GBM. Methods: In this study, we performed gene set enrichment analysis and profiled four glycolysis-related gene sets, which revealed 327 genes associated with biological processes. Univariate and multivariate Cox regression analyses were performed to identify genes for constructing a risk signature, and we identified ten mRNAs (B4GALT7, CHST12, G6PC2, GALE, IL13RA1, LDHB, SPAG4, STC1, TGFBI and TPBG) in the Cox proportional hazards regression model for GBM. Results: Based on this gene signature, we divided patients into high-risk (with poor outcomes) and low-risk (with better outcomes) subgroups. Multivariate Cox regression analysis showed that the prognostic power of this ten-gene signature is independent of clinical variables. Furthermore, we validated this model in two other GBM databases (Chinese Glioma Genome Atlas (CGGA) and REMBRANDT). In the functional analysis, the risk signature was associated with almost every step of cancer progression, such as adhesion, proliferation, angiogenesis, drug resistance and even an immune-suppressed microenvironment. Conclusion: The 10 glycolysis-related gene risk signature could serve as an independent prognostic factor for GBM patients and might be valuable for the clinical management of GBM patients.

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The Construction and Analysis of ceRNA Network and Immune Infiltration in Kidney Renal Clear Cell Carcinoma

10.1101/2021.02.07.21251289 ◽

2021 ◽

Author(s):

Lugang Deng ◽

Zhi Qu ◽

Peixi Wang ◽

Nan Liu

Keyword(s):

Immune Cells ◽

Proportional Hazards ◽

Immune Cell ◽

Clear Cell ◽

Cell Types ◽

Renal Clear Cell Carcinoma ◽

Cox Proportional Hazards ◽

Tissue Samples ◽

Cerna Network ◽

Proportional Hazards Regression

AbstractBackgroundKidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patients’ quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear.MethodsWe constructed a ceRNA network associated with KIRC by analyzing the long noncoding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from the Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through “The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts”. Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employed to assess the discrimination and accuracy of the nomograms. Consequently, co-expression analysis was carried out to explore the relationship between each prognostic gene in a Cox proportional hazards regression model of ceRNA and each survival-related immune cell in a Cox proportional hazards regression model of immune cell types to reveal the potential regulatory mechanism.ResultsWe established a ceRNA network consisting of 12 lncRNAs, 25 miRNAs and 136 mRNAs. Two nomograms containing seven prognostic genes and two immune cells, respectively, were successfully constructed. Both ROC [Area Under Curves (AUCs) of 1, 3 and 5-year survival in the nomogram based on ceRNA network: 0.779, 0.747 and 0.772; AUCs of 1, 3 and 5-year survivals in nomogram based on immune cells: 0.603, 0.642 and 0.607] and calibration curves indicated good accuracy and clinical application value of both models. Through co-correlation analysis between ceRNA and immune cells, we found both LINC00894 and KIAA1324 were positively correlated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells.ConclusionsBased on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.

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The Construction and Analysis of ceRNA Network and Immune Infiltration in Kidney Renal Clear Cell Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.667610 ◽

2021 ◽

Vol 12 ◽

Author(s):

Lugang Deng ◽

Peixi Wang ◽

Zhi Qu ◽

Nan Liu

Keyword(s):

Immune Cells ◽

Proportional Hazards ◽

Immune Cell ◽

Clear Cell ◽

Cell Types ◽

Renal Clear Cell Carcinoma ◽

Cox Proportional Hazards ◽

Tissue Samples ◽

Cerna Network ◽

Proportional Hazards Regression

Background: Kidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patient’s quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear.Methods: We constructed a ceRNA network associated with KIRC by analyzing the long non-coding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from The Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through “The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts.” Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employed to assess the discrimination and accuracy of the nomograms. Consequently, co-expression analysis was carried out to explore the relationship between each prognostic gene in a Cox proportional hazards regression model of ceRNA and each survival-related immune cell in a Cox proportional hazards regression model of immune cell types to reveal the potential regulatory mechanism.Results: We established a ceRNA network consisting of 12 lncRNAs, 25 miRNAs and 136 mRNAs. Two nomograms containing seven prognostic genes and two immune cells, respectively, were successfully constructed. Both ROC [area under curves (AUCs) of 1, 3, and 5-year survival in the nomogram based on ceRNA network: 0.779, 0.747, and 0.772; AUCs of 1, 3, and 5-year survivals in nomogram based on immune cells: 0.603, 0.642, and 0.607] and calibration curves indicated good accuracy and clinical application value of both models. Through co-correlation analysis between ceRNA and immune cells, we found both LINC00894 and KIAA1324 were positively correlated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells.Conclusion: Based on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.

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