scholarly journals Long Non-coding RNA Expression Profiling Identifies a Four-Long Non-coding RNA Prognostic Signature for Isocitrate Dehydrogenase Mutant Glioma

2020 ◽  
Vol 11 ◽  
Author(s):  
Yusheng Chen ◽  
Yang Guo ◽  
Hang Chen ◽  
Fengjin Ma
Keyword(s):  
Isocitrate Dehydrogenase ◽  
Microarray Dataset ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Regulation Mechanism ◽  
Non Coding Rna ◽  
Clinicopathologic Factors ◽  
Cox Analysis ◽  
Long Non Coding Rna ◽  
Genome Atlas

Background: Isocitrate dehydrogenase (IDH) mutant is one of the most robust and important genetic aberrations in glioma. However, the underlying regulation mechanism of long non-coding RNA (lncRNA) in IDH mutant glioma has not been systematically portrayed.Methods:In this work, 775 IDH mutant glioma samples with transcriptome data, including 167 samples from the Chinese Glioma Genome Atlas (CGGA) RNAseq dataset, 390 samples from The Cancer Genome Atlas (TCGA) dataset, 79 samples from GSE16011 dataset, and 139 samples from CGGA microarray dataset, were enrolled. R language and GraphPad Prism software were applied for the statistical analysis and graphical work.Results: By comparing the differentially lncRNA genes between IDH mutant and IDH wild-type glioma samples, a four-lncRNA (JAG1, PVT1, H19, and HAR1A) signature was identified in IDH mutant glioma patients. The signature model was established based on the expression level and the regression coefficient of the four lncRNA genes. IDH mutant glioma samples could be successfully stratified into low-risk and high-risk groups in CGGA RNAseq, TCGA, GSE16011, and CGGA microarray databases. Meanwhile, multivariate Cox analysis showed that the four-lncRNA signature was an independent prognostic biomarker after adjusting for other clinicopathologic factors. Moreover, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that the immune response and cellular metabolism were significantly associated with the four-lncRNA risk signature.Conclusion: Taken together, the four-lncRNA risk signature was identified as a novel prognostic marker for IDH mutant glioma patients and may potentially lead to improvements in the lives of glioma patients.

scholarly journals Autophagy‐related Long Non-coding RNA Signature Associated with Prognosis of Lung Adenocarcinoma

2021 ◽  
Author(s):  
Guofei Zhang ◽  
Jiayi Shen ◽  
Zipu Yu ◽  
Gang Shen ◽  
Chengxiao Liang
Keyword(s):  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Microarray Dataset ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Non Coding Rna ◽  
Cancer Genome Atlas ◽  
Non Coding Rnas ◽  
Long Non Coding Rna

Abstract BackgroundEvidence suggests that long non-coding RNAs (lncRNAs) are involved in various cancers. Here, we developed and evaluated an autophagy-related prognostic lncRNA signature for lung adenocarcinoma (LUAD). ResultsUsing a publicly available microarray dataset from The Cancer Genome Atlas, we analyzed the lncRNA expression profile in a cohort of 439 LUAD patients. The lncRNA-mRNA co-expression network along with univariate and multivariate Cox regression analyses were used to determine 15 autophagy-related lncRNA signatures that were significantly correlated with patient overall survival. Autophagy-related lncRNA signatures stratified patients into high- and low-risk groups with significantly different survival (hazard ratio = 3.256, 95% confidence interval = 2.858–4.101, P < 0.001). The lncRNA signature was further confirmed in other independent datasets. Moreover, the lncRNA signature had prognostic value independent of routine clinical factors. Functional analysis indicated that autophagy-related lncRNA signatures may be involved in LUAD via known autophagy-related pathways. ConclusionsThis newly identified autophagy-related lncRNA signature is a more powerful prognostic tool than the clinicopathological factors routinely used to predict patient survival, and can provide further insights into the molecular mechanisms underlying LUAD.

scholarly journals Prognostic value of an immune long non-coding RNA signature in liver hepatocellular carcinoma

2019 ◽  
Author(s):  
rui kong ◽  
Nan Wang ◽  
Wei Han ◽  
Yuejuan Zheng ◽  
Jie Lu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Kaplan Meier ◽  
Non Coding Rna ◽  
Liver Hepatocellular Carcinoma ◽  
Long Non Coding Rna

Abstract Background: In recent years, long non-coding RNAs (lncRNAs) are emerging as crucial regulators in the immunological process of liver hepatocellular carcinoma (LIHC). Increasing studies have found that some lncRNAs could be used as a diagnostic or therapeutic target for clinical management, but little research has investigated the role of immune-related lncRNA in tumor prognosis. In this study, we aimed to develop an immune lncRNA signature for the precise diagnosis and prognosis of liver hepatocellular carcinoma. Methods: Gene expression profiles of LIHC samples obtained from TCGA were screened for immune-related genes using two reference gene sets. The optimal immune-related lncRNA signature was built via correlational analysis, univariate and multivariate cox analysis. Then the Kaplan-Meier plot, ROC curve, clinical analysis, gene set enrichment analysis, and principal component analysis were carried out to evaluate the capability of immune lncRNA signature as a prognostic indicator. Results: Six long non-coding RNA MSC−AS1, AC009005.1, AL117336.3, AL031985.3, AL365203.2, AC099850.3 were identified via correlation analysis and cox regression analysis considering their interactions with immune genes. Next, tumor samples were separated into two risk groups by the signature with different clinical outcomes. Stratification analysis showed the prognostic ability of this signature acted as an independent factor. The AUC value of ROC curve was 0.779. The Kaplan-Meier method was used in survival analysis and results showed a statistical difference between the two risk groups. The predictive performance of this signature was validated by principal component analysis (PCA). Data from gene set enrichment analysis (GSEA) further unveiled several potential biological processes of these biomarkers may involve in. Conclusion: In summary, the study demonstrated the potential role of the six-lncRNA signature served as an independent prognostic factor for LIHC patients.

Immune and clinical features of CD96 expression in glioma by large-scale analysis

2020 ◽  
Author(s):  
Qiang Zhang ◽  
Hua Zhong ◽  
Yinchun Fan ◽  
Qian Liu ◽  
Jiancheng Song ◽  
...  
Keyword(s):  
Immune Response ◽  
T Cells ◽  
Large Scale ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Immune Functions ◽  
Clinical Prognosis ◽  
Regulatory Pathways ◽  
Cox Analysis ◽  
Genome Atlas

Abstract Background: Immune checkpoints target regulatory pathways in T cells which enhance antitumor immune responses and elicit durable clinical responses . As a novel immune checkpoint, CD96 is an attractive key target for cancer immunotherapy. However, there is no integrative investigation of CD96 in glioma. Our study explored the relationship between CD96 expression and clinical prognosis in glioma. Methods: A total of 1,024 RNA and clinical data were enrolled in this study, including 325 samples from the Chinese Glioma Genome Atlas (CGGA) database and 699 samples from The Cancer Genome Atlas (TCGA) dataset. R language was used to perform statistical analysis and draw figures. Results: CD96 had a consistently positive relationship with glioblastoma and highly enriched in IDH-wildtype and mesenchymal subtype glioma. GO enrichment and GSVA analyses suggested that CD96 was more involved in immune functions, especially related to T cell-mediated immune response in glioma. Subsequent immune infiltration analysis manifes ted that CD96 was positively correlated with infiltrating levels of CD4+ T and CD8+ T cells, macrophages , neutrophils, and DCs in GBM and LGG. Additionally, CD96 was tightly associated with other immune checkpoints including PD-1 , CTLA-4 , TIGIT , and TIM-3 . Univariate and multivariate Cox analysis demonstrated that CD96 acts as an independent indicator of poor prognosis in glioma. Conclusion: CD96 expression was increased in malignant phenotype and negatively associated with overall survival (OS) in glioma. CD96 also showed a positive correlation with other immune checkpoints, immune response, and inflammatory activity. Our findings indicate that CD96 is a promising clinical target for further immunotherapeutic in glioma patients.

scholarly journals Long non-coding RNA SNHG15 in various cancers: a meta and bioinformatic analysis

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Caizhi Chen ◽  
Yeqian Feng ◽  
Jingjing Wang ◽  
Ye Liang ◽  
Wen Zou
Keyword(s):  
Publication Bias ◽  
Clinical Features ◽  
Tumour Progression ◽  
Meta Analysis ◽  
Disease Free Survival ◽  
Host Gene ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Abstract Background The snoRNA host gene SNHG15 produces a long non-coding RNA (lncRNA) with a short half-life and has been reported to be dysregulated in multiple cancers and has recently been found to be correlated with tumour progression. Therefore, this meta-analysis was performed to evaluate the generalised prognostic role of small nucleolar RNA host gene 15 (SNHG15) in malignancies, based on variable data from different studies. Methods Four public databases were used to identify eligible studies. The association between prognostic indicators and clinical features was extracted and pooled to estimate the hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs). Publication bias was measured using Begg’s test and Egger’s test, and the stability of pooled results were measured using sensitivity analysis. Additionally, an online database based on The Cancer Genome Atlas (TCGA) was screened to further validate our results. Ultimately, we predicted the molecular regulation of SNHG15 based on the public databases. Results In total, 11 studies including 1087 patients were ultimately enrolled in our meta-analysis. We found that SNHG15 overexpression was associated with worse overall survival (OS) and disease-free survival (DFS), and this was validated in the Gene Expression Profiling Interactive Analysis (GEPIA) cohort. Moreover, increased SNHG15 expression suggested advanced TNM stage and LNM, but was not associated with age, gender, or tumour size. No publication bias or instability of the results was observed. SNHG15 was significantly upregulated in seven cancers and elevated expression of SNHG15 indicated shorter OS and DFS in five malignancies based on the validation using the GEPIA cohort. Further functional prediction indicated that SNHG15 may participate in some cancer-related pathways. Conclusions Upregulation of lncRNA SNHG15 was notably associated with worse prognosis and clinical features, suggesting that SNHG15 might serve as a novel prognostic factor in various cancers.

scholarly journals Correlation of Long Non-coding RNA LncRNA-FA2H-2 With Inflammatory Markers in the Peripheral Blood of Patients With Coronary Heart Disease

2021 ◽  
Vol 8 ◽  
Author(s):  
Fengxia Guo ◽  
Yanhua Sha ◽  
Bing Hu ◽  
Gang Li
Keyword(s):  
Cell Adhesion Molecule ◽  
Density Lipoprotein ◽  
Risk Groups ◽  
Gensini Score ◽  
Moderate Risk ◽  
Comparative Analyses ◽  
Non Coding Rna ◽  
Tnf Α ◽  
Cell Adhesion Molecule 1 ◽  
Long Non Coding Rna

Objective: To characterize the expression of long non-coding RNA LncRNA-FA2H-2 in coronary heart disease (CHD) and its correlation with inflammatory markers.Methods: From December 2018 to December 2020, 316 patients at Henan Provincial People's Hospital who complained of chest tightness or chest pain and had coronary angiography to clarify their coronary artery conditions for definitive diagnoses were selected as the study subjects. Plasma was collected to detect white blood cells (WBCs), total cholesterol (TG), triglyceride cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1 (ApoA1), and C-reactive protein (CRP) levels. Tumor necrosis factor (TNF-α), monocyte chemotactic protein 1 (MCP-1), vascular cell adhesion molecule-1 (VCAM-1), intercellular cell adhesion molecule-1 (ICAM-1), and interleukin-6 (IL-6) levels were also measured using ELISA. The expression levels of lncRNA-FA2H-2 were measured using quantitative real-time PCR. The data obtained were analyzed by independent sample t-tests, rank sum tests, regression analyses, Pearson's or Spearman's correlation analyses, and receiver operating characteristic curves.Results: (1) Compared with the control group, the differences in age, sex, diabetes, smoking, drinking, body mass index (BMI), WBC, TC, and LDL-C in CHD were not statistically significant, while the differences in hypertension, TG, HDL-C, ApoA1, and CRP were statistically significant. (2) In the grouping of coronary lesion branches, patients with age, sex, hypertension, diabetes, smoking, drinking, BMI, WBC, TC, LDL-C, HDL-C, and ApoA1 differences were not statistically significant, but TG and CRP differences were statistically significant. (3) The relative expressions of TNF-α, MCP-1, VCAM-1, ICAM-1, and IL-6 were significantly upregulated in the CHD group (P &lt; 0.001). (4) The results showed that the relative levels of TNF-α, MCP-1, VCAM-1, ICAM-1, and IL-6 between the two comparative analyses (high risk, moderate risk, and low risk groups) were statistically significant. In addition, positive correlations were found between the Gensini score and TNF-α, MCP-1, VCAM-1, ICAM-1, and IL-6 in CHD patients. (5) LncRNA-FA2H-2 relative expression in the CHD group was significantly downregulated (P &lt; 0.001). (6) The differences in the expression levels of LncRNA-FA2H-2 were statistically significant between the two comparative analyses (P &lt; 0.01), except between the 2-branch lesion and 3-branch lesion groups. (7) LncRNA-FA2H-2 was not associated with age, sex, hypertension, diabetes, smoking, drinking, BMI, WBC, TG, TC, LDL-C, HDL-C, and ApoA1 (P &gt; 0.05). (8) A correlation was found between LncRNA-FA2H-2 and MCP-1, and VCAM-1, ICAM-1, IL-6, and Gensini. (9) The results indicated that the relative levels of LncRNA-FA2H-2 between the two comparative analyses (high risk, moderate risk, and low risk groups) were statistically significant. A negative correlation was found between the Gensini score and LncRNA-FA2H-2. (10) ROC curve analyses of TNF-α, MCP-1, VCAM-1, ICAM-1, and IL-6 in CHD showed the area under the curve (AUC) = 0.832 (0.77, 0.893) with a cut-off value of 290.5, a sensitivity of 73%, and a specificity of 64%; AUC = 0.731 (0.653, 0.809) with a cut-off value of 396 and with a sensitivity of 59% and specificity of 79%; AUC = 0.822 (0.757, 0.887) with a cut-off value of 264 and with a sensitivity of 72% and specificity of 83%; AUC = 0.794 (0.715, 0.874) with a cut-off value of 201.5 and with a sensitivity of 75% and specificity of 65%; AUC = 0.760 (0.685, 0.834) with a cut-off value of 328 and with a sensitivity of 55% and specificity of 90%. (11) ROC curve analysis of LncRNA-FA2H-2 in CHD patients showed AUC = 0.834 (0.688, 0.85) with a cut-off value of 3.155 and with a sensitivity of 85% and specificity of 82%. (12) Logistic analyses showed that TNF-α, MCP-1, VCAM-1, IL-6, and LncRNA-FA2H-2 were independent risk factors for CHD.Conclusions: The expression of LncRNA-FA2H-2 was reduced and inversely correlated with inflammation-related factors in CHD patients. LncRNA-FA2H-2 may have potential as an inflammatory marker for risk assessment of CHD development.

scholarly journals Long non-coding RNA SNHG15 in various cancers: a meta and bioinformatic analysis

2020 ◽  
Author(s):  
Caizhi Chen ◽  
Yeqian Feng ◽  
Jingjing Wang ◽  
Ye Liang ◽  
Wen Zou
Keyword(s):  
Publication Bias ◽  
Clinical Features ◽  
Tumour Progression ◽  
Meta Analysis ◽  
Disease Free Survival ◽  
Host Gene ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Abstract Background: The snoRNA host gene SNHG15 produces a long non‐coding RNA (lncRNA) with a short half-life and has been reported to be dysregulated in multiple cancers and has recently been found to be correlated with tumour progression. Therefore, this meta-analysis was performed to evaluate the generalised prognostic role of small nucleolar RNA host gene 15 (SNHG15) in malignancies, based on variable data from different studies. Methods: Four public databases were used to identify eligible studies. The association between prognostic indicators and clinical features was extracted and pooled to estimate the hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs). Publication bias was measured using Begg’s test and Egger’s test, and the stability of pooled results were measured using sensitivity analysis. Additionally, an online database based on The Cancer Genome Atlas (TCGA) was screened to further validate our results. Ultimately, we predicted the molecular regulation of SNHG15 based on the public databases. Results: In total, 11 studies including 1,087 patients were ultimately enrolled in our meta-analysis. We found that SNHG15 overexpression was associated with worse overall survival (OS) and disease-free survival (DFS), and this was validated in the Gene Expression Profiling Interactive Analysis (GEPIA) cohort. Moreover, increased SNHG15 expression suggested advanced TNM stage and LNM, but was not associated with age, gender, or tumour size. No publication bias or instability of the results was observed. SNHG15 was significantly upregulated in seven cancers and elevated expression of SNHG15 indicated shorter OS and DFS in five malignancies based on the validation using the GEPIA cohort. Further functional prediction indicated that SNHG15 may participate in some cancer-related pathways. Conclusions: Upregulation of lncRNA SNHG15 was notably associated with worse prognosis and clinical features, suggesting that SNHG15 might serve as a novel prognostic factor in various cancers.

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