Expression and prognostic value of long non-coding RNA H19 in glioma via integrated bioinformatics analyses

Xiao, Yi; Zhu, Zipeng; Jian-xiong, LI; Yao, Jie; Jiang, Haitao; Ran, Ran; Li, Xueyuan; Li, Zhiqiang

doi:10.18632/aging.102819

Cited by 45 publications

(40 citation statements)

References 50 publications

(46 reference statements)

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“…Studies by Deng et al showed that prognostic models based on immune-related genes have been developed and proved to have good predictive performance, which provided theoretical ideas for the immunotherapy of LGG (20). Studies show that many lncNRAs had important effects on the immune microenvironment of gliomas (12), for example, lncRNA H19 could affect the level of immune infiltration of gliomas, as a result of affecting the prognosis of patients (21). In view of the important role of immune-related lncRNAs, Shen et al applied ssGSEA to identify immune-related lncRNAs associated with the prognosis of breast cancer, and constructed a risk scoring model to predict the OS of patients with breast cancer (14).…”

Section: Discussionmentioning

confidence: 99%

Identification and Verification on Prognostic Index of Lower-Grade Glioma Immune-Related LncRNAs

Wen

Wang

et al. 2020

Front. Oncol.

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Previous studies have shown that the prognosis of patients with lower-grade glioma (LGG) is closely related to the infiltration of immune cells and the expression of long non-coding RNAs (lncRNAs). In this paper, we applied single-sample gene set enrichment analysis (ssGSEA) algorithm to evaluate the expression level of immune genes from tumor tissues in The Cancer Genome Atlas (TCGA) database, and divided patients into the high immune group and the low immune group, which were separately analyzed for differential expression. Venn analysis was taken to select 36 immune-related lncRNAs. To construct a prognostic model of LGG based on immune-related lncRNAs, we divided patients into a training set and a verification set at a ratio of 2:1. Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to select 11 immune-related lncRNAs associated with the prognosis of LGG, and based on these selected lncRNAs, the risk scoring model was constructed. Through Kaplan-Meier analysis, the overall survival (OS) of patients in the high-risk group was significantly lower than that of the low-risk group. Then, established a nomogram including age, gender, neoplasm histologic grade, and risk score. Meanwhile, the predictive performance of the model was evaluated by calculating the C-index, drawing the calibration chart, the clinical decision curve as well as the Receiver Operating Characteristic (ROC) curve. Similar results were obtained by utilizing the validation data to verify the above consequences. Based on the TIMER database, the correlation analysis showed that the 11 immune-related lncRNAs risk score of LGG were in connection with the infiltration of the subtypes of immune cells. Subsequently, we performed enrichment analysis, whose results showed that these immune-related lncRNAs played important roles in the progress of LGG. In conclusion, these 11 immune-related lncRNAs have the potential to predict the prognosis of patients with LGG, which may play a key role in the development of LGG.

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Section: Discussionmentioning

confidence: 99%

Identification and Verification on Prognostic Index of Lower-Grade Glioma Immune-Related LncRNAs

Wen

Wang

et al. 2020

Front. Oncol.

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“…GSEA analysis was carried out as previously reported[ 22 ] to evaluate whether a significant set of genes are associated with the APEX2 difference in LIHC. The GO Ontology was selected as the gene sets database to confirm the cellular component and biological process.…”

Section: Methodsmentioning

confidence: 99%

Identification of APEX2 as an oncogene in liver cancer

Ru¹,

Zhu²,

Hu³

et al. 2020

WJCC

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BACKGROUND DNA damage is one of the critical contributors to the occurrence and development of some cancers. APEX1 and APEX2 are the most important molecules in the DNA damage, and APEX1 has been identified as a diagnostic and prognostic biomarker in liver hepatocellular carcinoma (LIHC). However, the expression of APEX2 and its functional mechanisms in LIHC are still unclear. AIM To examine the expression of APEX2 and the potential mechanism network in LIHC. METHODS We conducted a pan-cancer analysis of the expression of APEX1 and APEX2 using the interactive TIMER tool. GEO datasets, including GSE14520, GSE22058, and GSE64041, were used to compare the APEX2 expression level in tumor tissues and adjacent non-tumor tissues. Then, we calculated the 5-year survival rate according to the web-based Kaplan-Meier analysis. We included the TCGA liver cancer database in GSEA analysis based on the high and low APEX2 expression, showing the potential mechanisms of APEX2 in LIHC. After that, we conducted Pearson correlation analysis using GEPIA2. Next, we performed quantitative polymerase chain reaction (qPCR) assay to examine the APEX2 levels in normal liver cell line LO2 and several liver cancer cell lines, including HepG2, Huh7, SMMC7721, and HCCLM3. APEX2 in HCCLM3 cells was knocked down using small interfering RNA. The role of APEX2 in cell viability was confirmed using CCK-8. Dual-luciferase reporter assay was performed to examine the promoter activity of CCNB1 and MYC . RESULTS APEX1 and APEX2 are both highly expressed in the tumor tissues of BLCA, BRCA, CHOL, COAD, ESCA, HNSC, LIHC, LUAD, LUSC, READ, and STAD. APEX2 overexpression in LIHC was validated using GSE14520, GSE22058, and GSE64041 datasets. The survival analysis showed that LIHC patients with high expression of APEX2 had a lower overall survival rate, even in the AJCC T1 patients. High level of APEX2 could indicate a lower overall survival rate in patients with or without viral hepatitis. The GSEA analysis identified that kinetochore and spindle microtubules are the two main cellular components of APEX2 in GO Ontology. APEX2 was also positively associated with molecular function regulation of chromosome segregation and DNA replication. The results of KEGG analysis indicated that APEX2 expression was positively correlated with cell cycle pathway and pro-oncogenic MYC signaling. Pearson correlation analysis showed that APEX2 had a significant positive correlation with CCNB1 and MYC. APEX2 level was higher in liver cancer cell lines than in normal liver LO2 cells. Small interfering RNA could knock down the APEX2 expression in HCCLM3 cells. Knockdown of APEX2 resulted in a decrease in the viability of HCCLM3 cells as well as the expression and promoter activity of CCNB1 and MYC . CONCLUSION APEX2 i...

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“…Original gene expression data were measured as fragments per kilobase of transcript per million mapped reads (FPKM) and log2-transformed. Furthermore, the RNA expression profiles were normalized with the R package “sva” to remove batch effects, as previously reported ( Xiao et al, 2020 ; Zhang et al, 2020b ). Then, the “limma” package in R was applied, and genes with an average count value lower than 1 were all excluded from further analyses.…”

Section: Methodsmentioning

confidence: 99%

Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

et al. 2020

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BackgroundIntratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.MethodsWe compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients’ gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.ResultsA total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.ConclusionOur study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.

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Expression and prognostic value of long non-coding RNA H19 in glioma via integrated bioinformatics analyses

Cited by 45 publications

References 50 publications

Identification and Verification on Prognostic Index of Lower-Grade Glioma Immune-Related LncRNAs

Identification and Verification on Prognostic Index of Lower-Grade Glioma Immune-Related LncRNAs

Identification of APEX2 as an oncogene in liver cancer

Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

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