scholarly journals Comprehensive Analysis of a ceRNA Network Reveals Potential Prognostic lncRNAs Involved in Progression of Bladder Cancer

2021 ◽  
Author(s):  
Dehua Ou ◽  
Zesong Wu ◽  
Peilin Shen ◽  
Yingkai Hong ◽  
Si Chen ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Score ◽  
Cox Model ◽  
Expression Profiles ◽  
Training Group ◽  
Survival Prediction ◽  
Competing Endogenous Rna ◽  
Aberrant Expression ◽  
Cerna Network ◽  
Risk Patients

Abstract Background: The aberrant expression of long non-coding RNAs (lncRNAs) has attracted more and more attention in the biological field of bladder cancer(BC). We aim to construct a competing endogenous RNA(ceRNA) network reveals potential prognostic lncRNAs involved in progression of BC.Results: Expression profiles of messenger RNA(mRNA), micro RNA(miRNA) and lncRNA of 397 BC samples and 19 non-tumor tissues were downloaded from The Cancer Genome Atlas(TCGA) database. Patients with BC were randomly divided into training group (n=198) and validation group (n=199). Then, 130 lncRNAs, 159 miRNAs and 2,048 mRNAs were identified as differentially expressed genes (DEGs, |logFC|>1, FDR<0.01) related to BC progression. Nextly, we constructed an BC associated deregulated competing endogenous RNA(ceRNA) network with 70 lncRNAs, 30 miRNAs, and 62 mRNAs involved in. Subsequently, a seven-lncRNA signature was constructed by establishing a LASSO Cox model with 13 lncRNAs associated with survival from the ceRNA network. This signature can well distinguish high-risk patients from low-risk patients in training group and verification group. Furthermore, we combined the risk score model with other clinical fictures to estimate the ability of survival prediction. The result suggested that the risk score can be selected as an independent prognostic factor for overall survival(OS) rate. Conclusion: In this study, we construct a ceRNA network related to progression of BC and established an seven-lncRNA signature, which was a candidate prognostic biomarker for prognostic prediction of BC patients .

scholarly journals Application of an Autophagy-Related Gene Prognostic Risk Model Based on TCGA Database in Cervical Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Huadi Shi ◽  
Fulan Zhong ◽  
Xiaoqiong Yi ◽  
Zhenyi Shi ◽  
Feiyan Ou ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Survival Prediction ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Model Based

Background: Autophagy plays an important role in the development of cancer. However, the prognostic value of autophagy-related genes (ARGs) in cervical cancer (CC) is unclear. The purpose of this study is to construct a survival model for predicting the prognosis of CC patients based on ARG signature.Methods: ARGs were obtained from the Human Autophagy Database and Molecular Signatures Database. The expression profiles of ARGs and clinical data were downloaded from the TCGA database. Differential expression analysis of CC tissues and normal tissues was performed using R software to screen out ARGs with an aberrant expression. Univariate Cox, Lasso, and multivariate Cox regression analyses were used to construct a prognostic model which was validated by using the test set and the entire set. We also performed an independent prognostic analysis of risk score and some clinicopathological factors of CC. Finally, a clinical practical nomogram was established to predict individual survival probability.Results: Compared with normal tissues, there were 63 ARGs with an aberrant expression in CC tissues. A risk model based on 3 ARGs was finally obtained by Lasso and Cox regression analysis. Patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both train set and validation set. The ROC curve validated its good performance in survival prediction, suggesting that this model has a certain extent sensitivity and specificity. Multivariate Cox analysis showed that the risk score was an independent prognostic factor. Finally, we mapped a nomogram to predict 1-, 3-, and 5-year survival for CC patients. The calibration curves indicated that the model was reliable.Conclusion: A risk prediction model based on CHMP4C, FOXO1, and RRAGB was successfully constructed, which could effectively predict the prognosis of CC patients. This model can provide a reference for CC patients to make precise treatment strategy.

scholarly journals Integrated Analysis of a Competing Endogenous RNA Network Reveals a Prognostic lncRNA Signature in Bladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Mou Peng ◽  
Xu Cheng ◽  
Wei Xiong ◽  
Lu Yi ◽  
Yinhuai Wang
Keyword(s):  
Bladder Cancer ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Competing Endogenous Rna ◽  
Predictive Tool ◽  
Aberrant Expression ◽  
Cerna Network ◽  
Rna Interaction

Long non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs) to regulate mRNA expression through sponging microRNA in tumorigenesis and progression. However, following the discovery of new RNA interaction, the differentially expressed RNAs and ceRNA regulatory network are required to update. Our study comprehensively analyzed the differentially expressed RNA and corresponding ceRNA network and thus constructed a potentially predictive tool for prognosis. “DESeq2” was used to perform differential expression analysis. Two hundred and six differentially expressed (DE) lncRNAs, 222 DE miRNAs, and 2,463 DE mRNAs were found in this study. The lncRNA-mRNA interactions in the miRcode database and the miRNA-mRNA interactions in the starBase, miRcode, and mirTarBase databases were searched, and a competing endogenous RNA (ceRNA) network with 186 nodes and 836 interactions was subsequently constructed. Aberrant expression patterns of lncRNA NR2F1-AS1 and lncRNA AC010168.2 were evaluated in two datasets (GSE89006, GSE31684), and real-time polymerase chain reaction was also performed to validate the expression pattern. Furthermore, we found that these two lncRNAs were independent prognostic biomarkers to generate a prognostic lncRNA signature by univariate and multivariate Cox analyses. According to the lncRNA signature, patients in the high-risk group were associated with a poor prognosis and validated by an external dataset. A novel genomic-clinicopathologic nomogram to improve prognosis prediction of bladder cancer was further plotted and calibrated. Our study deepens the understanding of the regulatory ceRNA network and provides an easy-to-do genomic-clinicopathological nomogram to predict the prognosis in patients with bladder cancer.

scholarly journals The characteristics of circRNA as competing endogenous RNA in pathogenesis of acute myeloid leukemia

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Siyuan Zhang
Keyword(s):  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
The Novel ◽  
Competing Endogenous Rna ◽  
Regulatory Modules ◽  
Cerna Network ◽  
Regulatory Module ◽  
Acute Myeloid

Abstract Background As one of the novel molecules, circRNA has been identified closely involved in the pathogenesis of many diseases. However, the function of circRNA in acute myeloid leukemia (AML) still remains unknown. Methods In the current study, the RNA expression profiles were obtained from Gene Expression Omnibus (GEO) datasets. The differentially expressed RNAs were identified using R software and the competing endogenous RNA (ceRNA) network was constructed using Cytoscape. Functional and pathway enrichment analyses were performed to identify the candidate circRNA-mediated aberrant signaling pathways. The hub genes were identified by MCODE and CytoHubba plugins of Cytoscape, and then a subnetwork regulatory module was established. Results A total of 27 circRNA-miRNA pairs and 208 miRNA-mRNA pairs, including 12 circRNAs, 24 miRNAs and 112 mRNAs were included in the ceRNA network. Subsequently, a subnetwork, including 4 circRNAs, 5 miRNAs and 6 mRNAs, was established based on related circRNA-miRNA-mRNA regulatory modules. Conclusions In summary, this work analyzes the characteristics of circRNA as competing endogenous RNA in AML pathogenesis, which would provide hints for developing novel prognostic, diagnostic and therapeutic strategy for AML.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied. Methods Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazard models were used to investigate the association between ARG expression profiles and patient prognosis. Results Twenty ARGs were significantly associated with the overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3-, and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians. Conclusion The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Construction and Comprehensive Analysis of Dysregulated Long Noncoding RNA-Associated Competing Endogenous RNA Network in Moyamoya Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xuefeng Gu ◽  
Dongyang Jiang ◽  
Yue Yang ◽  
Peng Zhang ◽  
Guoqing Wan ◽  
...  
Keyword(s):  
Moyamoya Disease ◽  
Cerebral Artery ◽  
Regulatory Network ◽  
Noncoding Rna ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Cerna Network ◽  
And Control ◽  
Control Samples

Background. Moyamoya disease (MMD) is a rare cerebrovascular disease characterized by chronic progressive stenosis or occlusion of the bilateral internal carotid artery (ICA), the anterior cerebral artery (ACA), and the middle cerebral artery (MCA). MMD is secondary to the formation of an abnormal vascular network at the base of the skull. However, the etiology and pathogenesis of MMD remain poorly understood. Methods. A competing endogenous RNA (ceRNA) network was constructed by analyzing sample-matched messenger RNA (mRNA), long non-coding RNA (lncRNA), and microRNA (miRNA) expression profiles from MMD patients and control samples. Then, a protein-protein interaction (PPI) network was constructed to identify crucial genes associated with MMD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were employed with the DAVID database to investigate the underlying functions of differentially expressed mRNAs (DEmRNAs) involved in the ceRNA network. CMap was used to identify potential small drug molecules. Results. A total of 94 miRNAs, 3649 lncRNAs, and 2294 mRNAs were differentially expressed between MMD patients and control samples. A synergistic ceRNA lncRNA-miRNA-mRNA regulatory network was constructed. Core regulatory miRNAs (miR-107 and miR-423-5p) and key mRNAs (STAT5B, FOSL2, CEBPB, and CXCL16) involved in the ceRNA network were identified. GO and KEGG analyses indicated that the DEmRNAs were involved in the regulation of the immune system and inflammation in MMD. Finally, two potential small molecule drugs, CAY-10415 and indirubin, were identified by CMap as candidate drugs for treating MMD. Conclusions. The present study used bioinformatics analysis of candidate RNAs to identify a series of clearly altered miRNAs, lncRNAs, and mRNAs involved in MMD. Furthermore, a ceRNA lncRNA-miRNA-mRNA regulatory network was constructed, which provides insights into the novel molecular pathogenesis of MMD, thus giving promising clues for clinical therapy.

scholarly journals Development of a tRNA-Derived Small RNA Prognostic Panel and Their Potential Functions in Osteosarcoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhenming Tang ◽  
Shuhui Zhang ◽  
Zhougui Ling
Keyword(s):  
Small Rna ◽  
Target Genes ◽  
Cox Model ◽  
Expression Profiles ◽  
Survival Rates ◽  
Low Risk ◽  
Validation Dataset ◽  
Therapeutic Outcomes ◽  
Risk Patients ◽  
Geo Database

BackgroundTherapeutic outcomes of osteosarcoma treatment have not significantly improved in several decades. Therefore, strong prognostic biomarkers are urgently needed.MethodsWe first extracted the tRNA-derived small RNA (tsRNA) expression profiles of osteosarcoma from the GEO database. Then, we performed a unique module analysis and use the LASSO-Cox model to select survival-associated tsRNAs. Model effectiveness was further verified using an independent validation dataset. Target genes with selected tsRNAs were predicted using RNAhybrid.ResultsA LASSO-Cox model was established to select six prognostic tsRNA biomarkers: tRF-33-6SXMSL73VL4YDN, tRF-32-6SXMSL73VL4YK, tRF-32-M1M3WD8S746D2, tRF-35-RPM830MMUKLY5Z, tRF-33-K768WP9N1EWJDW, and tRF-32-MIF91SS2P46I3. We developed a prognostic panel for osteosarcoma patients concerning their overall survival by high-low risk. Patients with a low-risk profile had improved survival rates in training and validation dataset.ConclusionsThe suggested prognostic panel can be utilized as a reliable biomarker to predict osteosarcoma patient survival rates.

scholarly journals Identification and Clinical Validation of Genes Signatures With Grade and Survival in Head and Neck Carcinomas

2020 ◽  
Author(s):  
Wei Ma ◽  
Qing Cao ◽  
Wandong She
Keyword(s):  
High Risk ◽  
Head And Neck ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Low Risk ◽  
Low Grade ◽  
High Grade ◽  
Head And Neck Carcinomas ◽  
Risk Patients

Abstract Background: The mechanism of transition from low-grade to high-grade head and neck carcinomas (HNC) still remains unclear. The aim of this study was to explore the genes expression profiles that drive malignancy from low to high-grade HNC, as well as analyze their correlations with the survival.Methods: Gene expressions and clinical data of HNC were downloaded from the Gene Expression Omnibus (GEO) repository. The significantly differential genes (SDGs) between low and high-grade HNC were screened by GEO2R and R software. Bioinformatics functions of SDGs were investigated by the enrichment analyses. Univariate and multivariate cox regressions were performed to identify prognostic SDGs of progression free survival (PFS) and disease specific survival (DSS). ROC curve was established to evaluate the ability to predict the prognosis. Then, the correlations between SDGs and clinical features were evaluated. The genes were experimentally validated by RT-PCR in clinical specimens’ tissues at last.Results: Thirty-five SDGs were identified in 47 low-grade and 30 high-grade HNC samples. Enrichment analysis showed these SDGs were mainly enriched in the DNA repair pathway and the regulation of I−kappaB kinase/NF−kappaB signaling pathway. Cox regression analyses showed that CXCL14, SLC44A1 and UBD were significantly associated with DSS, and PPP2R2C and SLC44A1 were associated with PFS. Patients at a high-risk or low-risk for prognosis were established based on genes signatures. High-risk patients had significantly shorter DSS and PFS than low-risk patients (P=0.033, 0.010 respectively). Multivariate cox regression showed HPV (P=0.033), lymph node status (P=0.032) and residual status (P＜0.044) were independent risk factors for PFS. ROC curves showed the risk score had better efficacy to predict survival both for DSS and PFS (AUC=0.858, 0.901 respectively). In addition, we found UBD, PPP2R2C and risk score were significantly associated with HPV status (all P＜0.05). The experiment results showed CXCL14 and SLC44A1 were significantly overexpressed in the HNC grade I/II tissues and the UBD were overexpressed in the HNC grade III/IV tissues.Conclusions: Our results suggested that SDGs had different expression profiles between the low-grade and high-grade HNC, and these genes may serve as prognostic biomarker to predict the survival.

scholarly journals Identification of the Pyroptosis-Related Gene Signature for Overall Survival Prediction in Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Shuang Liu ◽  
Ruonan Shao ◽  
Xiaoyun Bu ◽  
Yujie Xu ◽  
Ming Shi
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Multivariable Analysis ◽  
Low Risk ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Incidence And Mortality ◽  
Risk Patients

Hepatocellular carcinoma (HCC) is the second most lethal malignant tumor worldwide, with an increasing incidence and mortality. Due to general resistance to antitumor drugs, only limited therapies are currently available for advanced HCC patients, leading to a poor prognosis with a 5-year survival rate less than 20%. Pyroptosis is a type of inflammation-related programmed cell death and may become a new potential target for cancer therapy. However, the function and prognostic value of pyroptosis-related genes (PRGs) in HCC remain unknown. Here, we identified a total of 58 PRGs reported before and conducted a six-PRG signature via the LASSO regression method in the GEO training cohort, and model efficacy was further validated in an external dataset. The HCC patients can be classified into two subgroups based on the median risk score. High-risk patients have significantly shorter overall survival (OS) than low-risk patients in both training and validation cohorts. Multivariable analysis indicated that the risk score was an independent prognostic factor for OS of HCC patients. Functional enrichment analysis and immune infiltration evaluation suggested that immune status was more activated in the low-risk group. In summary, PRGs can be a prediction factor for prognosis of HCC patients and targeting pyroptosis is a potential therapeutic alternative in HCC.

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