A seven-long noncoding RNA signature predicts overall survival for patients with early stage non-small cell lung cancer

Lin, Ting; Fu, Yunong; Zhang, Xing; Gu, Jingxian; Ma, Xiaojie; Miao, Runchen; Xiang, Xiaohong; Niu, Wenquan; Qu, Kai; Liu, Chang; Wu, Qifei

doi:10.18632/aging.101550

Cited by 47 publications

(51 citation statements)

References 27 publications

(29 reference statements)

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“…For example, Meng Zhou et al [31] analyzed the lncRNA-expression pro les of 603 patients from 3 independent NSCLC cohorts in the GEO database and developed a risk-score model based on the expression of 8 lncRNAs, which were signi cantly associated with OS in patients with NSCLC. Lin et al [10]. identi ed a 7-lncRNA signature for predicting the OS of patients with NSCLC after combining lncRNA pro les from 4 GEO datasets and validated the signature in 2 independent datasets (TCGA and GSE31210).…”

Section: Discussionmentioning

confidence: 99%

“…Genomic abnormalities such as DNA mutations, copynumber variations, DNA methylation, and gene expression have been investigated for their usefulness in identifying prognostic biomarkers in patients with NSCLC. Microarray-and RNA-sequencing (RNA-seq)based high-throughput technologies have enabled simultaneous analysis of hundreds or thousands of genes and their relationships with clinical features, including the survival of patients with cancer, which has led to the discovery of many novel biomarkers (single genes or signatures) for diagnosis, prognosis, and targeted therapy in patients with NSCLC [9,10]. However, only a few molecular biomarkers have been evaluated in clinical practice (mainly as therapeutic targets) [11] because most of the biomarkers show low accuracy (low sensitivity and/or speci city) [12] or need to be further con rmed with a larger population in an independent validation study [13].…”

Section: Introductionmentioning

confidence: 99%

Identification of a 4-lncRNA Signature Predicting Prognosis of Patients with Non-Small Cell Lung Cancer: a Multicenter Study in China

Wang

Long

et al. 2020

Preprint

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Abstract Background: Previous findings have indicated that the tumor, nodes, and metastases (TNM) staging system is sub-optimal in terms of predicting survival outcomes in patients with non-small lung carcinoma (NSCLC).Thus, the aims to identify a long non-coding RNA (lncRNA) signature for predicting survival in patients with NSCLC and to provide additional prognostic information in combination with the TNM system.Methods: Patients with NSCLC were recruited from a hospital and divided into a discovery cohort (n=194) and validation cohort (n=172), and detected using a custom lncRNA microarray. Lung tissues obtained from patients at a different hospital (n = 73, independent validation cohort) were examined via qRT-PCR. Differentially expressed lncRNAs were determined with the Significance Analysis of Microarrays program and used to identify those associated with survival in the discovery cohort. These prognostic lncRNAs were employed to construct a prognostic signature with a risk-score method. Then, the utility of the prognostic signature was confirmed using the validation cohort and the independent cohort. Results: In the discovery cohort, we identified 305 lncRNAs that were differentially expressed between the NSCLC tissues and matched, adjacent normal lung tissues, of which 15 are associated with survival; a 4-lncRNA prognostic signature was identified from the 15 survival lncRNAs, which was significantly correlated with survivals of NSCLC patients. This signature was further validated in the validation cohort and independent cohort. Moreover, multivariate Cox analysis demonstrates that the 4-LncRNA signature is an independent survival predictor. Then we established a new risk-score model by combining 4-lncRNA signature and TNM stage. The receiver operating characteristics (ROC) curve indicates that the prognostic value of the combined model is significantly higher than that of the TNM stage alone, in all the cohorts. Conclusions: In this study, we identified a 4-lncRNA signature that can potentially serve as a powerful prognosis biomarker and can provide additional survival information to the traditional TNM staging system.

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“…A risk score of patients was calculated based on the Equation. Risk score = Exp1 * Coe1 + Exp2 * Coe2 + Exp3 + Coe3 + ……Expi * Coei . In this Equation, the Exp are the expression value of lncRNAs and Coe are their corresponding coefficients from the multivariable Cox regression analysis.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive analysis of five long noncoding RNAs expression as competing endogenous RNAs in regulating hepatoma carcinoma

Lou

Dong

et al. 2019

Cancer Med

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Liver cancer is the most common cancer and is the epitome of a recalcitrant cancer. Increasing evidence shown that long noncoding RNAs (lncRNA) were associated with cancer‐related death and could function as a competing endogenous RNA (ceRNA). To explore regulatory roles and potential prognostic biomarkers of lncRNA for liver cancer, RNA‐sequencing expression data were downloaded from the TCGA database and GEO database. A total of 357 patients were randomly divided into a discovery group and a validation group, of which 313 patients can obtain clinical data. In discovery phrase, 58 lncRNAs, 16 miRNAs, and 34 mRNAs were screened to construct the ceRNA network based on 252 patients employed from discovery group. Univariate and multivariate Cox hazard regression analysis model revealed that five lncRNAs (AATK‐AS1, C10orf91, LINC00162, LINC00200, and LINC00501) from 58 lncRNAs were formulated to predict the overall survival (OS). We used the value of gene expression and regression coefficients to construct a risk score based on the five lncRNAs. Next, we validated our model in the GSE116174 dataset (n = 64) and the validation group (n = 94) from TCGA database. Subgroup analysis suggest that the five lncRNAs played critical parts in early stage in cancer from both discovery and validation groups. The five lncRNAs were also found to be associated with immune cells infiltration including CD4+ memory activated, NK cells activated and mast cells activated, then the results were also validated according to the validation group. Furthermore, KEGG pathway enrichment analysis showed that these nine coexpressed modules using the method of WGCNA, and many of these pathways are associated with the development and progression of disease. At last, the transcription factor binding sites (TFBS) of the five lncRNAs were predicted, which help us to understand the potential mechanism that the TFBS adjusted the ceRNA network. In summary, the ceRNA regulatory network may contribute to a better understanding of liver cancer mechanism and provide potential prognostic biomarkers and therapeutic targets.

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“…HCC patients can be assigned to high-risk or low-risk groups according to the median risk as the critical point. Overall survival differences between highrisk and low-risk groups were assessed using the Kaplan-Meier method and compared using the logrank test [18].…”

Section: Validation Of Prognostic Model and Survival Analysismentioning

confidence: 99%

Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

Liu

Song

Zhu

et al. 2020

Preprint

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Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

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“…Each subtype of NSCLC demonstrates different molecular profiles and different drug response [8,9]. Although, gene alterations [10,11], mRNA expression signature [12,13], microRNA profiles [14,15], long non-coding RNAs [16,17], immune signature [18] and tumor microenvironment [19] are used for the prognosis of patients with NSCLC, more candidate biomarkers are needed to be identified.…”

Section: Introductionmentioning

confidence: 99%

Integrated analysis reveals the prognostic significance of pyrimidine metabolic rate limiting enzymes in patients with lung adenocarcinoma

Wang

et al. 2020

Preprint

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Background: Reprogramming of metabolism is illustrated in many types of cancer and is associated with the clinical outcomes of cancer patients. However, the prognostic significance of pyrimidine metabolism signaling pathway in patients with lung adenocarcinoma (LUAD) is unclear.Methods: Using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets, we determined the prognostic significance of pyrimidine metabolic rate limiting enzymes in patients with lung cancer. The enrichment of pyrimidine metabolism signaling pathway was analyzed by gene set enrichment analysis (GSEA). The expression profiles of pyrimidine metabolic rate limiting enzymes were identified from Affymetrix gene expression data and RNA-seq data. The Kaplan-Meier Plotter was used to identify the prognostic significance of the pyrimidine metabolic rate limiting enzymes. Spearman’s correlation and multivariate cox regression were used to determine the correlation efficiency of pyrimidine metabolic rate limiting enzymes.Results: The pyrimidine metabolism signaling pathway is significantly enriched in LUAD tissues, but not in lung squamous cell carcinoma (LUSC) tissues. Compared with normal lung tissues, the pyrimidine metabolic rate limiting enzymes are highly expressed in lung tumor tissues. Furthermore, the high expression levels of pyrimidine metabolic rate limiting enzymes are associated with unfavorable prognosis in patients with LUAD. Moreover, we demonstrate that the hypo-DNA methylation, DNA amplification and TP53 mutation are contributing to the high expression levels of pyrimidine metabolic rate limiting enzymes in cancer cells. Finally, we reveal the significant connections between the pyrimidine metabolic rate limiting enzymes. Comprehensively, the pyrimidine metabolic rate limiting enzymes are highly expressed in patients with bladder cancer, breast cancer, colon cancer, liver cancer and stomach cancer. And the high expression levels of pyrimidine metabolic rate limiting enzymes are associated with unfavorable prognosis in patients with liver cancer. Conclusions: Pyrimidine metabolism signaling pathway is associated with the development of lung adenocarcinoma. Pyrimidine metabolic rate limiting enzymes CAD, DTYMK, RRM1, RRM2, TK1, TYMS, UCK2, NR5C2 and TK2 have significant prognostic effects in patients with lung adenocarcinoma.

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A seven-long noncoding RNA signature predicts overall survival for patients with early stage non-small cell lung cancer

Cited by 47 publications

References 27 publications

Identification of a 4-lncRNA Signature Predicting Prognosis of Patients with Non-Small Cell Lung Cancer: a Multicenter Study in China

Comprehensive analysis of five long noncoding RNAs expression as competing endogenous RNAs in regulating hepatoma carcinoma

Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

Integrated analysis reveals the prognostic significance of pyrimidine metabolic rate limiting enzymes in patients with lung adenocarcinoma

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