scholarly journals A long non-coding RNA signature predicts survival for glioblastoma as prognostic biomarkers

2020 ◽  
Author(s):  
Zhenzhe Li ◽  
Zhonghua Lv ◽  
Lei Yu ◽  
Sibin Zhang ◽  
Yingjie Wang ◽  
...  
Keyword(s):  
Noncoding Rna ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Differential Analysis ◽  
Cox Regression Analysis ◽  
Non Coding Rna ◽  
Cancer Genome Atlas ◽  
The Central Nervous System ◽  
Long Non Coding Rna

Abstract Background: Glioblastoma (GBM) is one of the most fatal tumors in the central nervous system. Its prognosis is very poor. There is increasing evidence that long noncoding RNA (lncRNA) participates in the biological process of glioblastoma. Nevertheless, the role of lncRNA in predicting the prognosis of GBM is still uncertain. Methods: In this study, using RNA-Seq and clinical follow-up data of GBM patients from The Cancer Genome Atlas (TCGA), we performed differential analysis of lncRNA, univariable and multivariable Cox regression analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Ontology (GO) analysis.Results: We identified four lncRNAs closely interrelated with survival and prognosis of GBM patients. This lncRNA signature was effective in both the training set and the testing set, and it was independent to clinical factors.Conclusions: Our data suggested that the four lncRNAs could be used as promising biomarkers for predicting prognosis in GBM patients.

scholarly journals Identification of lnc RNAs Related to Prognosis of Patients With Colorectal Cancer

2020 ◽  
Vol 19 ◽  
pp. 153303382096212
Author(s):  
Yuqi Sun ◽  
Peng Peng ◽  
Lanlan He ◽  
Xueren Gao
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
High Expression ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Score Model ◽  
Prognostic Prediction

The purpose of this study was to identify long noncoding RNAs (lncRNAs) related to prognosis of patients with colorectal cancer (CRC) and develop a prognostic prediction model for CRC. Transcriptome data and survival information of CRC patients were downloaded from The Cancer Genome Atlas. The differentially expressed lncRNAs (DElncRNAs) between CRC and normal colorectal tissues were identified by the edgeR package. The association of DElncRNAs expression with prognosis of CRC patients was analyzed by the survival package. A nomogram predicting 3- and 5- year overall survival of CRC patients was drawn by the rms package. A total of 1046 DElncRNAs were identified, including 271 down-regulated and 775 up-regulated lncRNAs in CRC. Multivariate Cox regression analysis showed 10 lncRNAs related to the prognosis of CRC patients. Thereinto high expression of AC004009.1, LHX1-DT, ELFN1-AS1, AL136307.1, AC087379.2, RBAKDN and AC078820.1 was associated with poorer prognosis of CRC patients. High expression of LINC01055, AL590483.1 and AC008514.1 was associated with better prognosis of CRC patients. Furthermore, the risk score model developed based on the 10 lncRNAs could effectively predict overall survival of CRC patients. In conclusion, 10 prognostic biomarkers for CRC were identified, which would be helpful to understand the role of lncRNAs in CRC progression.

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.

A long non-coding RNA-based signature predicts early recurrence in hepatocellular carcinoma

2021 ◽  
pp. 1-10
Author(s):  
Shuai He ◽  
Jin-Feng Li ◽  
Hao Tian ◽  
Ye Sang ◽  
Xiao-Jing Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Curative Resection ◽  
Risk Model ◽  
Cox Regression ◽  
Early Recurrence ◽  
Support Vector ◽  
Term Survival ◽  
Cox Regression Analysis ◽  
Non Coding Rna ◽  
Long Non Coding Rna

BACKGROUND: Early recurrence is the main obstacle for long-term survival of hepatocellular carcinoma (HCC) patients after curative resection. OBJECTIVE: We aimed to develop a long non-coding RNA (lncRNA) based signature to predict early recurrence. METHODS: Using bioinformatics analysis and quantitative reverse transcription PCR (RT-qPCR), we screened for lncRNA candidates that were abnormally expressed in HCC. The expression levels of candidate lncRNAs were analyzed in HCC tissues from 160 patients who underwent curative resection, and a risk model for the prediction of recurrence within 1 year (early recurrence) of HCCs was constructed with linear support vector machine (SVM). RESULTS: A lncRNA-based classifier (Clnc), which contained nine differentially expressed lncRNAs including AF339810, AK026286, BC020899, HEIH, HULC, MALAT1, PVT1, uc003fpg, and ZFAS1 was constructed. In the test set, this classifier reliably predicted early recurrence (AUC, 0.675; sensitivity, 72.0%; specificity, 63.1%) with an odds ratio of 4.390 (95% CI, 2.120–9.090). Clnc showed higher accuracy than traditional clinical features, including tumor size, portal vein tumor thrombus (PVTT) in predicting early recurrence (AUC, 0.675 vs 0.523 vs 0.541), and had much higher sensitivity than Barcelona Clinical Liver Cancer (BCLC; 72.0% vs 50.0%), albeit their AUCs were comparable (0.675 vs 0.678). Moreover, combining Clnc with BCLC significantly increased the AUC, compared with Clnc or BCLC alone in predicting early recurrence (all P< 0.05). Finally, logistic and Cox regression analysis suggested that Clnc was an independent prognostic factor and associated with the early recurrence and recurrence-free survival of HCC patients after resection, respectively (all P= 0.001). CONCLUSIONS: Our lncRNA-based classifier Clnc can predict early recurrence of patients undergoing surgical resection of HCC.

scholarly journals rs3200401 MALAT1 GENE POLYMORPHISM IS NOT RELATED TO AGE OF KIDNEY CANCER ONSET

2020 ◽  
Vol 20 (2) ◽  
pp. 119-123
Author(s):  
A.D. Volkohon ◽  
V. Yu. Harbuzova ◽  
O.V. Ataman
Keyword(s):  
Renal Cell Carcinoma ◽  
Gene Polymorphism ◽  
Cell Carcinoma ◽  
Kidney Cancer ◽  
Renal Cell ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Today, the long non-coding RNA MALAT1 is considered to be one of the major RNAs involved in the emergence and metastasizing of various malignant tumours. Recent experiments have shown that MALAT1 plays an important role in the onset and progression of kidney cancer as well. It was found that cancer patient survival depends on the level of MALAT1 gene expression. The aim of the study was to investigate the possible association between rs3200401 MALAT1 gene polymorphism and age of kidney cancer onset among Ukrainian patients. Materials and methods. The venous blood of 101 patients with clear cell renal cell carcinoma (42 women and 59 men) was used for study. Determination of MALAT1 gene rs320040 polymorphism was performed by the Real-Time polymerase chain reaction method using TaqManSNP Assay C_3246069_10 components. Statistical analysis of the data obtained was performed using SPSS (version 17.0). To test the possible association between rs3200401 genotypes and the age of kidney cancer onset Kaplan-Meier and Cox regression techniques were used. P value < 0.05 was considered as statistically significant. Results. The obtained results of MALAT1 gene rs3200401 polymorphic site genotyping revealed that 71 (70.3%) patients with renal cell carcinoma had CC genotype, 29 (28.7%) – CT, 1 (1%) – TT genotype. Survival analysis by Kaplan-Meier method showed that life expectancy until the tumour occurrence was not related to rs3200401 locus (log rank P = 0.449 – for codominant model; log rank P = 0.847 – for dominant model). The results of Cox regression analysis also showed no link between MALAT gene rs3200401-site and risk of renal cell carcinoma development (P > 0.05). No statistically significant results were found after adjustment for sex, body mass index, metastasis, smoking and drinking habits (P > 0.05). Conclusions. The rs3200401 gene polymorphism of long non-coding RNA MALAT1 is not associated with the age of kidney cancer onset in Ukrainian population.

scholarly journals A Six-lncRNA Signature for Immunophenotype Prediction of Glioblastoma Multiforme

2021 ◽  
Vol 11 ◽  
Author(s):  
Ming Gao ◽  
Xinzhuang Wang ◽  
Dayong Han ◽  
Enzhou Lu ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Immune Cell ◽  
Area Under The Curve ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Non Coding Rna ◽  
Cancer Genome Atlas ◽  
The Central Nervous System

Glioblastoma multiforme (GBM) is the most aggressive primary tumor of the central nervous system. As biomedicine advances, the researcher has found the development of GBM is closely related to immunity. In this study, we evaluated the GBM tumor immunoreactivity and defined the Immune-High (IH) and Immune-Low (IL) immunophenotypes using transcriptome data from 144 tumors profiled by The Cancer Genome Atlas (TCGA) project based on the single-sample gene set enrichment analysis (ssGSEA) of five immune expression signatures (IFN-γ response, macrophages, lymphocyte infiltration, TGF-β response, and wound healing). Next, we identified six immunophenotype-related long non-coding RNA biomarkers (im-lncRNAs, USP30-AS1, HCP5, PSMB8-AS1, AL133264.2, LINC01684, and LINC01506) by employing a machine learning computational framework combining minimum redundancy maximum relevance algorithm (mRMR) and random forest model. Moreover, the expression level of identified im-lncRNAs was converted into an im-lncScore using the normalized principal component analysis. The im-lncScore showed a promising performance for distinguishing the GBM immunophenotypes with an area under the curve (AUC) of 0.928. Furthermore, the im-lncRNAs were also closely associated with the levels of tumor immune cell infiltration in GBM. In summary, the im-lncRNA signature had important clinical implications for tumor immunophenotyping and guiding immunotherapy in glioblastoma patients in future.

scholarly journals The Prognostic Value of m6A-related LncRNAs in Patients with HNSCC

2021 ◽  
Author(s):  
Liu-qing Zhou ◽  
Jie-yu Zhou ◽  
Yao Hu
Keyword(s):  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

Abstract Background: N6-methyladenosine (m6A) modifications play an essential role in tumorigenesis. m6A modifications are known to modulate RNAs, including mRNAs and lncRNAs. However, the prognostic role of m6A-related lncRNAs in head and neck squamous cell carcinoma (HNSCC) is poorly understood.Methods: Based on LASSO Cox regression, enrichment analysis, univariate and multivariate Cox regression analysis, a risk prognostic model, and consensus clustering analysis, we analyzed the 12 m6A-related lncRNAs in HNSCC samples data using the data from The Cancer Genome Atlas (TCGA) database.Results: We found twelve m6A-related lncRNAs in the training cohort and validated in all cohorts by Kaplan-Meier and Cox regression analyses, and revealing their independent prognostic value in HNSCC. Moreover, ROC analysis was conducted, confirming the strong predictive ability of this signature for HNSCC prognosis. GSEA and detailed immune infiltration analyses revealed specific pathways associated with m6A-related lncRNAs.Conclusions: In this study, a novel risk model including twelve genes (SAP30L-AS1, AC022098.1, LINC01475, AC090587.2, AC008115.3, AC015911.3, AL122035.2, AC010226.1, AL513190.1, ZNF32-AS1, AL035587.1 and AL031716.1) was built. It could accurately predict HNSCC prognosis and provide potential prediction outcome and new therapeutic target for HNSCC patients.

lncRNA and mRNA signature for prognosis prediction of glioblastoma

2020 ◽  
Vol 16 (13) ◽  
pp. 837-848 ◽  
Author(s):  
Guohong Liu ◽  
Yunbao Pan ◽  
Yueying Li ◽  
Haibo Xu
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Normal Brain ◽  
Multiple Gene ◽  
Cox Regression Analysis ◽  
R Language ◽  
Cancer Genome Atlas ◽  
Novel Biomarkers

Aims: We aimed to find out potential novel biomarkers for prognosis of glioblastoma (GBM). Materials & methods: We downloaded mRNA and lncRNA expression profiles of 169 GBM and five normal samples from The Cancer Genome Atlas and 129 normal brain samples from genotype-tissue expression. We use R language to perform the following analyses: differential RNA expression analysis of GBM samples using ‘edgeR’ package, survival analysis taking count of single or multiple gene expression level using ‘survival’ package, univariate and multivariate Cox regression analysis using Cox function plugged in ‘survival’ package. Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis were performed using FunRich tool online. Results and conclusion: We obtained differentially DEmRNAs and DElncRNAs in GBM samples. Most prognostically relevant mRNAs and lncRNAs were filtered out. ‘GPCR ligand binding’ and ‘Class A/1’ are found to be of great significance. In short, our study provides novel biomarkers for prognosis of GBM.

scholarly journals Gene signature to predict prognostic survival of hepatocellular carcinoma

Open Medicine ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. 135-150
Author(s):  
Li Li ◽  
Yundi Cao ◽  
YingRui Fan ◽  
Rong Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Noncoding Rna ◽  
Cox Regression ◽  
External Validation ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Hypergeometric Test ◽  
Sequencing Data ◽  
Cox Regression Analysis

Abstract Hepatocellular carcinoma (HCC) has a high incidence and poor prognosis and is the second most fatal cancer, and certain HCC patients also show high heterogeneity. This study developed a prognostic model for predicting clinical outcomes of HCC. RNA and microRNA (miRNA) sequencing data of HCC were obtained from the cancer genome atlas. RNA dysregulation between HCC tumors and adjacent normal liver tissues was examined by DESeq algorithms. Survival analysis was conducted to determine the basic prognostic indicators. We identified competing endogenous RNA (ceRNA) containing 15,364 pairs of mRNA–long noncoding RNA (lncRNA). An imbalanced ceRNA network comprising 8 miRNAs, 434 mRNAs, and 81 lncRNAs was developed using hypergeometric test. Functional analysis showed that these RNAs were closely associated with biosynthesis. Notably, 53 mRNAs showed a significant prognostic correlation. The least absolute shrinkage and selection operator’s feature selection detected four characteristic genes (SAPCD2, DKC1, CHRNA5, and UROD), based on which a four-gene independent prognostic signature for HCC was constructed using Cox regression analysis. The four-gene signature could stratify samples in the training, test, and external validation sets (p <0.01). Five-year survival area under ROC curve (AUC) in the training and validation sets was greater than 0.74. The current prognostic gene model exhibited a high stability and accuracy in predicting the overall survival (OS) of HCC patients.

Identification of an immune-related long noncoding RNA signature that predicts prognosis in breast cancer patients

2021 ◽  
Vol 15 (3) ◽  
pp. 167-180
Author(s):  
Na Li ◽  
Zubin Li ◽  
Xin Li ◽  
Bingjie Chen ◽  
Huibo Sun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Expression Profiles ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis

Aim: The purpose of this study was to identify an immune-related long noncoding RNA (lncRNA) signature that predicts the prognosis of breast cancer. Materials & methods: The expression profiles of breast cancer were downloaded from The Cancer Genome Atlas. Cox regression analysis was used to identify an immune-related lncRNA signature. Results: The five immune-related lncRNAs could be used to construct a breast cancer survival prognosis model. The receiver operating characteristic curve evaluation found that the accuracy of the model for predicting the 1-, 3- and 5-year prognosis of breast cancer was 0.688, 0.708 and 0.686. Conclusion: This signature may have an important clinical significance for improving predictive results and guiding the treatment of breast cancer patients.

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