scholarly journals Systematic analysis and prediction model construction of alternative splicing events in hepatocellular carcinoma: a study on the basis of large-scale spliceseq data from The Cancer Genome Atlas

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8245 ◽  
Author(s):  
Lingpeng Yang ◽  
Yang He ◽  
Zifei Zhang ◽  
Wentao Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Gene Network ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Systematic Analysis ◽  
Cox Regression Analysis ◽  
Regulation Network ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Growing evidence showed that alternative splicing (AS) event is significantly related to tumor occurrence and progress. This study was performed to make a systematic analysis of AS events and constructed a robust prediction model of hepatocellular carcinoma (HCC). The clinical information and the genes expression profile data of 335 HCC patients were collected from The Cancer Genome Atlas (TCGA). Information of seven types AS events were collected from the TCGA SpliceSeq database. Overall survival (OS) related AS events and splicing factors (SFs) were identified using univariate Cox regression analysis. The corresponding genes of OS-related AS events were sent for gene network analysis and functional enrichment analysis. Optimal OS-related AS events were selected by LASSO regression to construct prediction model using multivariate Cox regression analysis. Prognostic value of the prediction models were assessed by receiver operating characteristic (ROC) curve and KaplanMeir survival analysis. The relationship between the Percent Spliced In (PSI) value of OS-related AS events and SFs expression were analyzed using Spearman correlation analysis. And the regulation network was generated by Cytoscape. A total of 34,163 AS events were identified, which consist of 3,482 OS-related AS events. UBB, UBE2D3, SF3A1 were the hub genes in the gene network of the top 800 OS-related AS events. The area under the curve (AUC) of the final prediction model based on seven types OS-related AS events was 0.878, 0.843, 0.821 in 1, 3, 5 years, respectively. Upon multivariate analysis, risk score (All) served as the risk factor to independently predict OS for HCC patients. SFs HNRNPH3 and HNRNPL were overexpressed in tumor samples and were signifcantly associated with the OS of HCC patients. The regulation network showed prominent correlation between the expression of SFs and OS-related AS events in HCC patients. The final prediction model performs well in predicting the prognosis of HCC patients. And the findings in this study improve our understanding of the association between AS events and HCC.

Identification of Prognostic miRNAs Targeting EZH2 in Hepatocellular Carcinoma Using The Cancer Genome Atlas Database

2021 ◽  
Author(s):  
Tianyu Lin ◽  
Xinli Guo ◽  
Qian Du ◽  
Wei Liu ◽  
Xin Zhong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Pathway Network ◽  
Cancer Genome Atlas ◽  
And Control ◽  
Genome Atlas ◽  
Control Samples

Abstract Background: Enhancer of zeste homolog 2 (EZH2) gene have a prognostic role in hepatocellular carcinoma (HCC). This study aimed to identify the prognostic microRNAs (miRNAs) targeting EZH2 in HCC. Methods and Results: We downloaded the gene and miRNA RNA-seq data from The Cancer Genome Atlas (TCGA) database. Differences in EZH2 expression between tumor and control samples and those between tumors with different clinical variables were analyzed using the Mann-Whitney U test. Association of EZH2 expression with prognosis in HCC patients was detected using Cox regression analysis. We also identified miRNAs targeting EZH2 with negative correlations, compared the miRNA expression profiles between tumor and control tissues, and identified pathways and protein-protein interaction pairs related to EZH2. The miRNA-EZH2-pathway network was constructed accordingly. EZH2 was significantly upregulated in HCC tumors compared with control samples (p<0.0001) and in tumors with advanced T classifications (3/4 vs. 1/2, p=0.0039) and stages (III/IV vs. I/II, p=0.0028). The Cox regression analysis showed that TCGA HCC patients who had high EZH2 expression levels showed a short survival time (HR=1.677, 95% CI 1.316-2.137; p<0.0001). Among miRNAs targeting EZH2, seven miRNAs, including hsa-let-7c-5p, were negatively correlated with EZH2 expression and were significantly downregulated in HCC tumor samples compared with controls (p<0.0001). The miRNA-EZH2-pathway network included seven downregulated miRNAs and four pathways, including hsa00310: Lysine degradation. Hsa-let-7c-5p was associated with prognosis in HCC (HR=0.849 95% CI 0.739-0.975; p=0.021). Conclusions: EZH2-hsa-let-7c-5p has a significant association with HCC prognosis and the mechanism worth investigating.

scholarly journals Identification and Validation of a Prognostic Prediction Model of m6A Regulator-Related LncRNAs in Hepatocellular Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Chen Jin ◽  
Rui Li ◽  
Tuo Deng ◽  
Jialiang Li ◽  
Yan Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Risk Score ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Vital Role ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Prediction Ability ◽  
Cox Regression Analysis

Hepatocellular carcinoma (HCC) is a highly invasive malignancy prone to recurrence, and patients with HCC have a low 5-year survival rate. Long non-coding RNAs (lncRNAs) play a vital role in the occurrence and development of HCC. N6-methyladenosine methylation (m6A) is the most common modification influencing cancer development. Here, we used the transcriptome of m6A regulators and lncRNAs, along with the complete corresponding clinical HCC patient information obtained from The Cancer Genome Atlas (TCGA), to explore the role of m6A regulator-related lncRNA (m6ARlnc) as a prognostic biomarker in patients with HCC. The prognostic m6ARlnc was selected using Pearson correlation and univariate Cox regression analyses. Moreover, three clusters were obtained via consensus clustering analysis and further investigated for differences in immune infiltration, immune microenvironment, and prognosis. Subsequently, nine m6ARlncs were identified with Lasso-Cox regression analysis to construct the prognostic signature m6A-9LPS for patients with HCC in the training cohort (n = 226). Based on m6A-9LPS, the risk score for each case was calculated. Patients were then divided into high- and low-risk subgroups based on the cutoff value set by the X-tile software. m6A-9LPS showed a strong prognosis prediction ability in the validation cohort (n = 116), the whole cohort (n = 342), and even clinicopathological stratified survival analysis. Combining the risk score and clinical characteristics, we established a nomogram for predicting the overall survival (OS) of patients. To further understand the mechanism underlying the m6A-9LPS-based classification of prognosis differences, KEGG and GO enrichment analyses, competitive endogenous RNA (ceRNA) network, chemotherapeutic agent sensibility, and immune checkpoint expression level were assessed. Taken together, m6A-9LPS could be used as a precise prediction model for the prognosis of patients with HCC, which will help in individualized treatment of HCC.

Eight-gene prognostic signature associated with hypoxia and ferroptosis for gastric cancer with general applicability

Epigenomics ◽  
2021 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aims: To investigate the prognostic significance of hypoxia- and ferroptosis-related genes for gastric cancer (GC). Materials & methods: We extracted data on 259 hypoxia- and ferroptosis-related genes from The Cancer Genome Atlas and identified the differentially expressed genes between normal (n = 32) and tumor (n = 375) tissues. A risk score was established by univariate Cox regression analysis and LASSO penalized Cox regression analysis. Results: The risk score contained eight genes showed good performance in predicting overall survival and relapse-free survival in GC patients in both the training cohort (The Cancer Genome Atlas, n = 350) and the testing cohorts (GSE84437, n = 431; GSE62254, n = 300; GSE15459, n = 191; GSE26253, n = 432). Conclusion: The eight-gene signature may help to the improve the prognostic risk classification of GC.

scholarly journals Systematic Analysis of the Functions and Prognostic Values of Rna Binding Protein in Head and Neck Squamous Cell Carcinoma

2020 ◽  
Author(s):  
song jukun ◽  
Feng Liu ◽  
Bo Liu ◽  
Xianlin Cheng ◽  
Xinhai Yin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Head And Neck ◽  
Clinical Decision Making ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Clinical Decision ◽  
The Cancer Genome Atlas ◽  
Systematic Analysis ◽  
Cox Regression Analysis

Abstract Background: Dysregulation of RNA-binding proteins (RBPs) playsan important role in controlling processes in cancer development.However, the function of RBPs has not been thoroughly and systematically documented in head and neck cancer.We aim to explore the role of RPB in the pathogenesis of HNSC.Methods: We obtained HNSC gene expression data and corresponding clinical information from The Cancer Genome Atlas (TCGA) and the GEO databases, andidentified aberrantly expressed RBPs between tumors and normal tissues.Meanwhile, we performed a series of bioinformatics to explore the function and prognostic value of these RBPs.Results: A total of 249 abnormally expressed RBPs were identified, including 101 down-regulated RBPs and 148 up-regulated RBPs.Using least absolute shrinkage and selection operator (LASSO) and univariate Cox regression analysis, the fifteen RPBs were identified as hub genes. With the fifteen RPBS, the prognostic prediction model was constructed.Further analysis showed that the high-risk score of the patients expressed a better survival outcome. The prediction model was validated in another HNSC dataset, and similar findings were observed. Conclusions: Our results provide novel insights into the pathogenesis of HNSC. The fifteen RBP gene signature exhibited the predictive value of moderate HNSC prognosis, and have potential application value in clinical decision-making and individualized treatment.

scholarly journals Interferon-lambda (IFNL) germline variations and their significance for HCC and PDAC progression: an analysis of The Cancer Genome Atlas (TCGA) data

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Henriette Huschka ◽  
Sabine Mihm
Keyword(s):  
Disease Progression ◽  
Cox Regression ◽  
Genetic Variations ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Disease Outcome ◽  
Ductal Adenocarcinoma ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Abstract Background Hepatocellular carcinoma (HCC) and pancreatic ductal adenocarcinoma (PDAC) are malignancies with a leading lethality. With reference to interferons (IFNs) known to mediate antitumor activities, this study investigated the relationship between germline genetic variations in type III IFN genes and cancer disease progression from The Cancer Genome Atlas (TCGA) data. The genetic variations under study tag a gain-or-loss-of-function dinucleotide polymorphism within the IFNL4 gene, rs368234815 [TT/ΔG]. Methods The entirety of the TCGA sequencing data was used to assess genotypes of 187 patients with HCC and of 162 patients with PDAC matched for ethnicity. Stratified for IFNL genotypes, both cohorts were subjected to time-to-event analyses according to Kaplan-Meier with regard to the length of the specific progression free interval (PFI) and the overall survival (OS) time as two clinical endpoints for disease progression. Results Logrank analysis revealed a significant relationship between IFNL genotypes and disease outcome for PDAC. This relationship was not found for HCC. A multiple Cox regression analysis employing patients’ age, tumor grade and tumor stage as further covariates proved IFNL genotypes to be independent predictors for PDAC disease outcome. Conclusion This repository-based approach unveiled clinical evidence suggestive for an impact of IFNL germline variations for PDAC progression with an IFNL haplotype predisposing for IFNL4 expression being favorable.

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