Open Access

Identification of a long non‑coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma

  • Authors:
    • Xiaolin Yu
    • Yanxia Zhang
  • View Affiliations

  • Published online on: February 17, 2020     https://doi.org/10.3892/ol.2020.11400
  • Pages: 2793-2800
  • Copyright: © Yu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Long non‑coding RNAs (lncRNAs) have a number of functions in various cellular processes and are potential prognostic factors for lung adenocarcinoma (LUAD). A gene risk model could provide novel evidence to improve the prediction of overall outcomes and provide more potential biomarkers. The present study aimed improve a previously published method of gene signature construction to make it more robust and accurate. The lncRNA expression profiles from 594 patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database and samples were divided into high‑ and low‑risk groups based on median risk scores calculated using a prognosis‑related risk score formula. Univariate Cox regression, least absolute shrinkage and selection operator algorithm and multivariate Cox regression were performed to construct a gene signature based on the differentially expressed lncRNAs in patients with LUAD. The robustness and accuracy of the present model was assessed using area under the calculated curves (AUC) and Kaplan‑Meier (K‑M) survival analysis of the high‑ and low‑risk cohorts. Potential biomarkers associated with survival status were then identified using K‑M survival analysis and potential biomarker functions were predicted using enrichment analysis of co‑expressed mRNAs. The gene signature constructed contained 44 lncRNAs. The AUCs for 3‑ and 5‑year survival with the model were 0.836 and 0.818, respectively, of a time‑dependent receiver operator characteristic curve. Moreover, lncRNAs AC124804.1 and MIR34AHG were identified using K‑M survival analysis and the potential function of these two lncRNAs was predicted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment. The present lncRNA model provides novel insight which may improve prediction of prognosis for patients with LUAD and identify potentially novel biomarkers for the diagnosis.

Introduction

Lung cancer is the most common type of malignant tumor and was the leading cause of cancer-associated death in the USA in 2019 (1). Lung cancer can be categorized into two main histological subtypes, non-small-cell lung cancer (NSCLC) and small-cell lung cancer, accounting for ~85% and ~15% of cases, respectively (2,3). Lung adenocarcinoma (LUAD) is one of the major subtypes of NSCLC with a low 5-year survival rate of ~20% in the USA (4,5). It is currently understood that two of the driver oncogenes involved in tumorigenesis and progression of LUAD are epidermal growth factor receptor and anaplastic lymphoma kinase (68). These biomarkers can be used for diagnosis and targeted in the treatment of LUAD; however, prognosis remains unsatisfactory due to the underlying molecular heterogeneity and diverse etiology of LUAD. Therefore, it is important that novel valuable biomarkers, which have associations with prognosis in LUAD are identified.

Long non-coding RNA (lncRNA) is a type of non-coding RNA which is >200 nucleotides in length (9). Previous research has demonstrated that lncRNAs serve diverse functions in cellular processes and are involved in tumorigenesis, progression and metastasis (10,11). However, only a few lncRNAs involved in the development of LUAD have been identified, including histocompatibility leukocyte antigen complex P5, chromatin-associated RNA 10 and metastasis-associated lung adenocarcinoma transcript 1 (1214). The underlying molecular mechanisms for the functions of numerous lncRNAs remains unclear, to the best of our knowledge, but it is hypothesized that lncRNAs may have valuable clinical applications in the future.

Previous studies have described several novel lncRNAs with potential use as biomarkers differentially expressed in human LUAD tissue (15,16) and further investigation into these lncRNAs may elude their prognostic value. Integrated prognostic analysis of lncRNAs combined with overall outcomes, pathological stages and other clinical parameters may improve the prediction of overall survival time of patients with LUAD.

In the present study, the construction method for a previous lncRNA gene signature was amended (17,18), aiming to improve its accuracy. Based on lncRNA expression profiling, a prognostic model was constructed using a comprehensive approach involving the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression. Model robustness and accuracy were assessed using area under the calculated curve (AUC) and Kaplan-Meier (K-M) survival analyses in high- and low-risk groups. Finally, potential biomarkers associated with survival status were identified using K-M survival analysis and possible functions of these biomarkers were predicted using enrichment analysis of co-expressed mRNAs.

Materials and methods

Datasets

RNA sequencing data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database (cancer.gov/tcga), including data for 535 LUAD tissue samples and 59 control adjacent normal tissues. The LUAD group consisted of 249 males and 286 females with an age range of 33–88 years and a median age of 66 years, whereas the control group consisted of 25 males and 34 females with an age range of 42–86 years and a median age of 66 years. LUAD patients with incomplete survival data were excluded, therefore 500 samples were used for analysis.

Identification of differentially expressed lncRNAs

All analyses were performed using R software (version 3.5.3; r-project.org/) and R packages by R studio version 1.1.463 (19,20). Raw lncRNA expression profiles were constructed using an expression matrix and normalized using the edgeR package (version 3.22.5) (21) and the lncRNAs differentially expressed between LUAD and control samples were identified. lncRNAs which met the threshold of |log2[fold change (FC)]|≥2 and false discovery rate (FDR) <0.05 were screened out for subsequent analysis.

Definition of the gene-related prognostic model

Univariate Cox, LASSO and multivariate Cox regression analyses were performed to identify the association between survival time and lncRNA expression using the glmnet (version 2.0.18) (22) and survival (version 2.44.1.1) (23) packages. A univariate Cox model was established to assess the correlation between overall survival (OS) and lncRNA expression levels. P<0.05 was considered to indicate a statistically significant difference. lncRNAs meeting this criterion were included in LASSO regression analysis, which was to further select prognostic genes and to avoid the over-fitting of the signature model. After selection using the minimum λ value, which represented the optimal number of variables in this model, a further refined group of lncRNAs was included in a multivariate Cox analysis to determine the independent contribution of each lncRNA to prognosis and hazard ratios (HR) and 95% confidence intervals (CI) were calculated. The Cox regression coefficient (β) and expression levels of lncRNAs were used to calculate prognosis-related risk scores according to the formula: Σ (explncRNAn × βlncRNAn). Samples were further divided into high- and low-risk groups using the median risk score.

Evaluation of the risk model and identification of potential biomarkers

Time-dependent receiver operator characteristic (ROC) curves were plotted to evaluate performance of the prognostic model. K-M survival analyses were performed to predict survival times for the high and low risk groups and to calculate AUCs value for the prognostic model. Multivariate Cox analysis results were used to identify potential biomarkers. A threshold of P<0.05 was used to identify eligible lncRNAs for survival and K-M curve analyses and to predict the OS associated with each lncRNA. Subsequently, potential biomarkers were identified if this P<0.05 criterion from the survival curves was met.

Functional enrichment analysis

The biological function of the lncRNAs with significant P-values for OS in the comparison between the high and low risk groups was predicted using their co-expressed protein-coding mRNAs. The mRNAs that correlated with prognostic lncRNAs were identified using Pearson correlation analysis. An absolute correlation coefficient value >0.4 was considered significant. The Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Tool (version 6.8) (24,25) was used to perform the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis.

Results

lncRNAs are differentially expressed between LUAD and control samples

A total of 1,684 differentially expressed lncRNAs (Table SI) were identified, meeting the conditions of |log2 (FC)|≥2 and FDR <0.05. Of these, 1,499 lncRNAs were upregulated and 185 lncRNAs were downregulated (Fig. 1). The lncRNAs which had significant differential expression levels between the LUAD and control tissue groups were used for subsequent prognostic analysis.

Construction of the prognostic model

Univariate Cox regression was used to analyze the correlations between differentially expressed lncRNA profiles and the OS of patients with LUAD. A total of 107 lncRNAs were significantly correlated with patient OS (P<0.05) and these lncRNAs were used in a LASSO regression analysis (Table SII). Overall, 44 key lncRNAs were selected when the λ value was at a minimum (Fig. 2A and B). Finally, a multivariate Cox regression analysis was performed to obtain HR values and 95% CIs for each key lncRNA (Fig. 3). Samples were classified into high- and low-risk groups according to respective median risk scores (Fig. 4A and B).

Evaluation of the prognostic model

K-M survival curves for OS outcomes were performed according to median risk score values, demonstrating that the predicted survival time of the high-risk group was significantly shorter compared with the low risk group (P< 0.001; Fig. 5). The AUCs for 3- and 5-year survivals were 0.836 and 0.818, respectively, of a time-dependent ROC curve demonstrating that the risk score model had stable performance (Fig. 6).

Identification of potential biomarkers

Multivariate Cox regression showed that lncRNAs with P<0.05 were correlated with survival status (Fig. 3). K-M survival curves enabled screening out of two lncRNAs, AC124804.1 and MIR34AHG, with significantly different predicted survival times between the high- and low-risk score groups (Fig. 7A and B). These two lncRNAs had the greatest correlation with OS time and may have potential as prognostic biomarkers for patients with LUAD.

Functional enrichment

To investigate the underlying molecular mechanisms of the functions of AC124804.1 and MIR34AHG, the co-expressed mRNAs of these lncRNAs were identified using Pearson's correlation analysis. For MIR34AHG, no mRNAs met the screening condition of a correlation coefficient value of >0.4. For AC124804.1, a total of 818 protein coding mRNAs were identified for further functional enrichment. Overall, four GO terms and four KEGG pathways had confirmed association with AC124804.1. The 818 co-expressed mRNAs were primarily clustered in the poly(A) RNA binding and protein binding of molecular functions, and to the mitochondrion and nucleoplasm cellular components (P<0.05; Fig. 8A). KEGG pathway enrichment analysis demonstrated that these mRNAs were primarily associated with Proteasome, Alzheimer's disease, SNARE interactions in vesicular transport and Peroxisome pathways (Fig. 8B).

Discussion

LUAD is a large global health challenge (1,26) and our knowledge of its underlying mechanisms may be improved by preclinical next-generation sequencing bioinformatics technology (27,28). However, despite advances in the diagnosis and treatment of LUAD, the OS time of patients with LUAD remains poor (29). Recent studies have identified molecular biomarkers throughout the various clinical stages and pathological tissue types (30,31). However, patients with the same clinical and pathological features at diagnosis often have a completely different final prognoses (32). Molecular gene signatures associated with prognosis may aid the identification of novel molecular biomarkers for use in diagnosis and treatment of LUAD in combination with patients' genetic profiles and clinical parameters (17,18). Recent studies pertaining to LUAD have investigated gene signatures based on lncRNA data and the selected lncRNAs may be important risk factors and have potential predictive power for patient survival (17,18). However, due to complex underlying mechanisms, the biological functions of the majority of lncRNAs have not been resolved (33). The prognostic lncRNA signature constructed in the present study, demonstrating correlation with clinical features of LUAD, may be aid the screening out of potential lncRNAs for further investigation.

To improve the accuracy and robustness of the present model compared with previous studies (15,17,18), a different approach was taken to data preprocessing and construction of the model. First, the present study utilized larger sample sizes compared with previous studies (15,17,18) resulting from differences in inclusion criteria, including a total of 535 LUAD tissues and 59 adjacent normal tissues (controls). Second, the present study used different methods of data preprocessing and normalization. For example, previous studies using some online tools for analysis, whereas the present study performed analysis using edgeR package in R software, which could analyze the latest raw counts of RNA-seq data of TCGA. Third, the present group selected variables using univariate Cox analysis and LASSO regression, which were not employed in previous studies. In addition, the eligible lncRNAs in the present study were confirmed using P-values of multivariate Cox regression and survival analyses.

In the present study, lncRNA profiles were used to construct the prognostic model, using data from tissue samples and clinical information of 500 patients held in the TCGA database. A total of 1,684 differentially expressed lncRNAs were investigated as potential biomarkers using univariate Cox regression to screen out lncRNAs that were significantly associated with the clinical characteristics. The results of univariate Cox regression were further filtered using the LASSO algorithm, which weakens collinearity of the risk model and enables the most influential variables to be selected (34,35). This approach is suitable for dealing with datasets containing a large volume of variables, as occurs in genomics (36). The final 44 lncRNAs identified using LASSO regression were then analyzed using multivariate Cox regression. The correlation of each lncRNA with survival of patients with LUAD was presented as an HR value. Patients were divided into high and low risk groups according to the median risk scores which demonstrated that 3 and 5-year survival times were significantly different between the high and low risk score groups. These data combined with the AUC score results confirmed the accuracy and robustness of this model for predicting prognosis of patient with LUAD.

A multivariate Cox regression forest plot identified lncRNAs with significant P-values as potential prognostic biomarkers for LUAD. The respective survival times correlated with these lncRNAs were calculated prospectively using K-M survival analysis, identifying candidate prognostic factors. According to the prognostic signature constructed in the present study, the expression levels of AC124804.1 and MIR34AHG were significantly correlated with differential clinical outcomes and predicted OS times of the two risk groups. These two lncRNAs may have value as novel biomarkers for estimating prognosis of patients with LUAD. To the best of our knowledge, there is no published work describing the relationships between these lncRNAs and LUAD to date. To explore the potential functions of AC124804.1 and MIR34AHG, the co-expressed protein-coding mRNAs of these lncRNAs were investigated using a Pearson correlation coefficient test followed by GO and KEGG functional enrichment analyses. These analyses demonstrated that the potential underlying molecular mechanisms of AC124804.1 function were pathways involving proteasome, peroxisome and SNARE interactions in vesicular transport, which have confirmed roles in tumorigenesis and progression in previous studies (3739). MIR34AHG is the host gene of miR-34a and consequently may have biological functions associated with the miR-34 family, which may have suppressive functions in LUAD as previously demonstrated (40,41). The relationship between these two lncRNAs and LUAD should be studied further.

There are several limitations to the present study. First, the data for the lncRNA signatures were from the TCGA database and these samples primarily originated from Caucasians and African Americans, limiting extrapolation of the results of the present study to other ethnic groups. Second, the present study used a database mining design without validation in fresh samples and prospective experimental studies. Third, the clinical parameters included only OS time, with histological subtypes and other risk factors for LUAD were excluded from the analysis. The primary purpose of the present study was to identify novel biomarkers for the diagnosis and treatment of LUAD. The lncRNA prognosis signature should be further investigated incorporating more specific clinical characteristics to fully understand the associations involved.

In conclusion, a robust lncRNA signature that could stratify and predict survival time of patients with LUAD was constructed. Furthermore, Cox regression was used to identify two novel survival associated lncRNA biomarkers and provided potential targets for diagnostic and therapeutic application. The biological functions of the two lncRNAs identified in the present study need confirmation in further experimental studies.

Supplementary Material

Supporting Data

Acknowledgements

Not applicable.

Funding

The present study was funded by The National Natural Science Foundation of China (grant no. 81603577).

Availability of data and materials

The datasets used and/or analyzed during the present study available from the corresponding author on reasonable request.

Authors' contributions

YZ conceived and designed the study. XY conducted the analysis and wrote the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Glossary

Abbreviations

Abbreviations:

AUC

area under the curve

CI

confidence interval

FDR

false discovery rate

GO

Gene Ontology

HR

hazard ratio

KEGG

Kyoto Encyclopedia of Genes and Genomes

K-M analysis

Kaplan-Meier analysis

LASSO

least absolute shrinkage and selection operator

lncRNA

long non-coding RNA

LUAD

lung adenocarcinoma

NSCLC

non-small-cell lung cancer

OS

overall survival

ROC

receive operator characteristic

TCGA

The Cancer Genome Atlas.

References

1 

Siegel RL, Miller KD and Jemal A: Cancer statistics, 2019. CA Cancer J Clin. 69:7–34. 2019. View Article : Google Scholar : PubMed/NCBI

2 

Simó M, Vaquero L, Ripollés P, Gurtubay-Antolin A, Jové J, Navarro A, Cardenal F, Bruna J and Rodríguez-Fornells A: Longitudinal brain changes associated with prophylactic cranial irradiation in lung cancer. J Thorac Oncol. 11:475–486. 2016. View Article : Google Scholar : PubMed/NCBI

3 

Shahabi S, Kumaran V, Castillo J, Cong Z, Nandagopal G, Mullen DJ, Alvarado A, Correa MR, Saizan A, Goel R, et al: LINC00261 is an epigenetically regulated tumor suppressor essential for activation of the DNA damage response. Cancer Res. 79:3050–3062. 2019. View Article : Google Scholar : PubMed/NCBI

4 

Lu GS, Li M, Xu CX and Wang D: APE1 stimulates EGFR-TKI resistance by activating Akt signaling through a redox-dependent mechanism in lung adenocarcinoma. Cell Death Dis. 9:11112018. View Article : Google Scholar : PubMed/NCBI

5 

Macheleidt IF, Dalvi PS, Lim SY, Meemboor S, Meder L, Käsgen O, Müller M, Kleemann K, Wang L, Nürnberg P, et al: Preclinical studies reveal that LSD1 inhibition results in tumor growth arrest in lung adenocarcinoma independently of driver mutations. Mol Oncol. 12:1965–1979. 2018. View Article : Google Scholar : PubMed/NCBI

6 

Hung MS, Lung JH, Lin YC, Fang YH, Huang SY, Jiang YY, Hsieh MJ and Tsai YH: Comparative analysis of two methods for the detection of EGFR mutations in plasma circulating tumor DNA from lung adenocarcinoma patients. Cancers (Basel). 11:8032019. View Article : Google Scholar

7 

Li S, Choi YL, Gong Z, Liu X, Lira M, Kan Z, Oh E, Wang J, Ting JC, Ye X, et al: Comprehensive characterization of oncogenic drivers in Asian lung adenocarcinoma. J Thorac Oncol. 11:2129–2140. 2016. View Article : Google Scholar : PubMed/NCBI

8 

Wang X, Min S, Liu H, Wu N, Liu X, Wang T, Li W, Shen Y, Wang H, Qian Z, et al: Nf1 loss promotes Kras-driven lung adenocarcinoma and results in Psat1-mediated glutamate dependence. EMBO Mol Med. 11:e98562019. View Article : Google Scholar : PubMed/NCBI

9 

Zhang L, Meng X, Zhu XW, Yang DC, Chen R, Jiang Y and Xu T: Long non-coding RNAs in Oral squamous cell carcinoma: Biologic function, mechanisms and clinical implications. Mol Cancer. 18:1022019. View Article : Google Scholar : PubMed/NCBI

10 

Yan Y, Xu Z, Li Z, Sun L and Gong Z: An insight into the increasing role of lncRNAs in the pathogenesis of gliomas. Front Mol Neurosci. 10:532017. View Article : Google Scholar : PubMed/NCBI

11 

Xu Z, Yan Y, Qian L and Gong Z: Long non-coding RNAs act as regulators of cell autophagy in diseases (Review). Oncol Rep. 37:1359–1366. 2017. View Article : Google Scholar : PubMed/NCBI

12 

Jiang L, Wang R, Fang L, Ge X, Chen L, Zhou M, Zhou Y, Xiong W, Hu Y, Tang X, et al: HCP5 is a SMAD3-responsive long non-coding RNA that promotes lung adenocarcinoma metastasis via miR-203/SNAI axis. Theranostics. 9:2460–2474. 2019. View Article : Google Scholar : PubMed/NCBI

13 

Ge X, Li GY, Jiang L, Jia L, Zhang Z, Li X, Wang R, Zhou M, Zhou Y, Zeng Z, et al: Long noncoding RNA CAR10 promotes lung adenocarcinoma metastasis via miR-203/30/SNAI axis. Oncogene. 38:3061–3076. 2019. View Article : Google Scholar : PubMed/NCBI

14 

Chen J, Liu X, Xu Y, Zhang K, Huang J, Pan B, Chen D, Cui S, Song H, Wang R, et al: TFAP2C-activated MALAT1 modulates the chemoresistance of docetaxel-resistant lung adenocarcinoma cells. Mol Ther Nucleic Acids. 14:567–582. 2019. View Article : Google Scholar : PubMed/NCBI

15 

Salavaty A, Rezvani Z and Najafi A: Survival analysis and functional annotation of long non-coding RNAs in lung adenocarcinoma. J Cell Mol Med. 23:5600–5617. 2019. View Article : Google Scholar : PubMed/NCBI

16 

Serghiou S, Kyriakopoulou A and Ioannidis JP: Long noncoding RNAs as novel predictors of survival in human cancer: A systematic review and meta-analysis. Mol Cancer. 15:502016. View Article : Google Scholar : PubMed/NCBI

17 

Shi X, Tan H, Le X, Xian H, Li X, Huang K, Luo VY, Liu Y, Wu Z, Mo H, et al: An expression signature model to predict lung adenocarcinoma-specific survival. Cancer Manag Res. 10:3717–3732. 2018. View Article : Google Scholar : PubMed/NCBI

18 

Sui J, Yang S, Liu T, Wu W, Xu S, Yin L, Pu Y, Zhang X, Zhang Y, Shen B, et al: Molecular characterization of lung adenocarcinoma: A potential four-long noncoding RNA prognostic signature. J Cell Biochem. 120:705–714. 2019. View Article : Google Scholar : PubMed/NCBI

19 

R Core Team (2012), . R: A language and environment for statistical computing. R Foundation for Statistical Computing; Vienna: http://www.R-project.org/

20 

RStudio Team (2015), . RStudio: Integrated Development for R. RStudio, Inc.; Boston, MA: http://www.rstudio.com/

21 

Robinson MD, McCarthy DJ and Smyth GK: edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 26:139–140. 2010. View Article : Google Scholar : PubMed/NCBI

22 

Friedman J, Hastie T and Tibshirani R: Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 33:1–22. 2010. View Article : Google Scholar : PubMed/NCBI

23 

Therneau TM and Grambsch PM: Modeling Survival Data: Extending the Cox Model. Statistics for Biology and Health. Springer Science and Business Media; New York, NY: pp. 39–76. 2000, View Article : Google Scholar

24 

Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4:32003. View Article : Google Scholar

25 

Yan Y, Xu Z, Qian L, Zeng S, Zhou Y, Chen X, Wei J and Gong Z: Identification of CAV1 and DCN as potential predictive biomarkers for lung adenocarcinoma. Am J Physiol Lung Cell Mol Physiol. 316:L630–L643. 2019. View Article : Google Scholar : PubMed/NCBI

26 

Sivakumar S, San Lucas FA, Jakubek YA, McDowell TL, Lang W, Kallsen N, Peyton S, Davies GE, Fukuoka J, Yatabe Y, et al: Genomic landscape of allelic imbalance in premalignant atypical adenomatous hyperplasias of the lung. EBioMedicine. 42:296–303. 2019. View Article : Google Scholar : PubMed/NCBI

27 

Yu C, Tian F, Liu J, Su M, Wu M, Zhu X and Qian W: Circular RNA cMras inhibits lung adenocarcinoma progression via modulating miR-567/PTPRG regulatory pathway. Cell Prolif. 52:e126102019. View Article : Google Scholar : PubMed/NCBI

28 

Kerdidani D, Chouvardas P, Arjo AR, Giopanou I, Ntaliarda G, Guo YA, Tsikitis M, Kazamias G, Potaris K, Stathopoulos GT, et al: Wnt1 silences chemokine genes in dendritic cells and induces adaptive immune resistance in lung adenocarcinoma. Nat Commun. 10:14052019. View Article : Google Scholar : PubMed/NCBI

29 

Misono S, Seki N, Mizuno K, Yamada Y, Uchida A, Sanada H, Moriya S, Kikkawa N, Kumamoto T, Suetsugu T, et al: Molecular pathogenesis of gene regulation by the miR-150 duplex: miR-150-3p regulates TNS4 in lung adenocarcinoma. Cancers (Basel). 11:6012019. View Article : Google Scholar

30 

Abdel-Aziz A, Ahmed RA and Ibrahiem AT: Expression of pRb, Ki67 and HER 2/neu in gastric carcinomas: Relation to different histopathological grades and stages. Ann Diagn Pathol. 30:1–7. 2017. View Article : Google Scholar : PubMed/NCBI

31 

Ren Y, Zhao S, Jiang D, Feng X, Zhang Y, Wei Z, Wang Z, Zhang W, Zhou QF, Li Y, et al: Proteomic biomarkers for lung cancer progression. Biomarkers Med. 12:205–215. 2018. View Article : Google Scholar

32 

Wang P, Jin M, Sun CH, Yang L, Li YS, Wang X, Sun YN, Tian LL and Liu M: A three-lncRNA expression signature predicts survival in head and neck squamous cell carcinoma (HNSCC). Biosci Rep. 38:BSR201815282018. View Article : Google Scholar : PubMed/NCBI

33 

Peng W, He D, Shan B, Wang J, Shi W, Zhao W, Peng Z, Luo Q, Duan M, Li B, et al: LINC81507 act as a competing endogenous RNA of miR-199b-5p to facilitate NSCLC proliferation and metastasis via regulating the CAV1/STAT3 pathway. Cell Death Dis. 10:5332019. View Article : Google Scholar : PubMed/NCBI

34 

Machado AD, Gómez LM, Marchioni DM, Dos Anjos FS, Molina MD, Lotufo PA, Benseñor IJ and Titan SM: Association between dietary intake and coronary artery calcification in non-dialysis chronic kidney disease: The PROGREDIR Study. Nutrients. 10:3722018. View Article : Google Scholar

35 

Kwon Y, Lee T, Lang A and Burnette D: Assessment on latitudinal tree species richness using environmental factors in the southeastern United States. PeerJ. 7:e67812019. View Article : Google Scholar : PubMed/NCBI

36 

Waldmann P, Ferenčaković M, Mészáros G, Khayatzadeh N, Curik I and Sölkner J: AUTALASSO: An automatic adaptive LASSO for genome-wide prediction. BMC Bioinformatics. 20:1672019. View Article : Google Scholar : PubMed/NCBI

37 

Follo C, Vidoni C, Morani F, Ferraresi A, Seca C and Isidoro C: Amino acid response by Halofuginone in cancer cells triggers autophagy through proteasome degradation of mTOR. Cell Commun Signal. 17:392019. View Article : Google Scholar : PubMed/NCBI

38 

Kaur S, Nag A, Gangenahalli G and Sharma K: Peroxisome proliferator activated receptor gamma sensitizes non-small cell lung carcinoma to gamma irradiation induced apoptosis. Front Genet. 10:5542019. View Article : Google Scholar : PubMed/NCBI

39 

Wang YS, Tzeng HT, Tsai CH, Cheng HC, Lai WW, Liu HS and Wang YC: VAMP8, a vesicle-SNARE required for RAB37-mediated exocytosis, possesses a tumor metastasis suppressor function. Cancer Lett. 437:79–88. 2018. View Article : Google Scholar : PubMed/NCBI

40 

Yun MR, Lim SM, Kim SK, Choi HM, Pyo KH, Kim SK, Lee JM, Lee YW, Choi JW, Kim HR, et al: Enhancer remodeling and MicroRNA alterations are associated with acquired resistance to ALK inhibitors. Cancer Res. 78:3350–3362. 2018.PubMed/NCBI

41 

Zhao K, Cheng J, Chen B, Liu Q, Xu D and Zhang Y: Circulating microRNA-34 family low expression correlates with poor prognosis in patients with non-small cell lung cancer. J Thorac Dis. 9:3735–3746. 2017. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

April-2020
Volume 19 Issue 4

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Yu X and Yu X: Identification of a long non‑coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma. Oncol Lett 19: 2793-2800, 2020
APA
Yu, X., & Yu, X. (2020). Identification of a long non‑coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma. Oncology Letters, 19, 2793-2800. https://doi.org/10.3892/ol.2020.11400
MLA
Yu, X., Zhang, Y."Identification of a long non‑coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma". Oncology Letters 19.4 (2020): 2793-2800.
Chicago
Yu, X., Zhang, Y."Identification of a long non‑coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma". Oncology Letters 19, no. 4 (2020): 2793-2800. https://doi.org/10.3892/ol.2020.11400