A novel prognostic nomogram based on 5 long non‑coding RNAs in clear cell renal cell carcinoma

Wang,Sheng; Chai,Kequn; Chen,Jiabin

doi:10.3892/ol.2019.11009

A novel prognostic nomogram based on 5 long non‑coding RNAs in clear cell renal cell carcinoma

Authors:
- Sheng Wang
- Kequn Chai
- Jiabin Chen
View Affiliations

Affiliations: The Second Clinical Medical College, Zhejiang Chinese Medicine University, Hangzhou, Zhejiang 310053, P.R. China, Department of Oncology, Tongde Hospital of Zhejiang, Hangzhou, Zhejiang 310012, P.R. China
Published online on: October 18, 2019 https://doi.org/10.3892/ol.2019.11009
Pages: 6605-6613
Copyright: © Wang 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: )

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

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common and invasive histological subtype of all kidney malignancies, with high levels of incidence and mortality. In the present study, long non‑coding (lnc)RNA expression profiles of patients with ccRCC from The Cancer Genome Atlas database were comprehensively analyzed to identify differentially expressed lncRNAs (DElncRNAs). The patients with ccRCC were then divided into training and validation cohorts. Univariate and LASSO regression analyses were performed to select the most significant survival‑associated candidate DElncRNAs in the training cohort. Multivariate Cox regression analysis was then performed to develop a risk score formula and a prognostic nomogram for predicting 3‑ and 5‑year overall survival (OS). The accuracies of the nomogram predictions were evaluated by determining the area under the receiver operating characteristic curve (AUC) and a calibration plot. Finally, functional enrichment analysis and protein‑protein interaction network prediction were implemented to predict the functions and molecular mechanisms of the candidate DElncRNAs in ccRCC. A total of 1,553 DElncRNAs were identified, and 5 candidate DElncRNAs (AC026992.2, AC245041.2, LINC00524, LINC01956 and LINC02080) were included in the nomogram. The AUC values for 3‑ and 5‑year overall survival in the training cohort were 0.768 and 0.814, respectively, which were increased compared with that based on the clinical index (0.760 and 0.694, respectively). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that the 521 mRNAs highly associated with 5 DElncRNAs were primarily involved in 17 terms and 25 pathways, respectively. Based on the 5 DElncRNAs, a novel and convenient prognostic nomogram for predicting 3‑ and 5‑year OS for patients with ccRCC was developed. The results of the present study may be conducive to the development of a precise predictive tool for the prognosis of ccRCC and may provide information regarding the molecular mechanisms of ccRCC. However, additional experimental in vitro and in vivo studies investigating lncRNAs may be required.

View Figures

View References

1	Yang K, Lu XF, Luo PC and Zhang J: Identification of six potentially long noncoding RNAs as biomarkers involved competitive endogenous RNA in clear cell renal cell carcinoma. Biomed Res Int. 2018:93034862018. View Article : Google Scholar : PubMed/NCBI
2	Sanchez DJ and Simon MC: Genetic and metabolic hallmarks of clear cell renal cell carcinoma. Biochim Biophys Acta Rev Cancer. 1870:23–31. 2018. View Article : Google Scholar : PubMed/NCBI
3	Xing T and He H: Epigenomics of clear cell renal cell carcinoma: Mechanisms and potential use in molecular pathology. Chin J Cancer Res. 28:80–91. 2016.PubMed/NCBI
4	Godlewski J, Kiezun J, Krazinski BE, Kozielec Z, Wierzbicki PM and Kmiec Z: The immunoexpression of YAP1 and LATS1 proteins in clear cell renal cell carcinoma: Impact on Patients' survival. Biomed Res Int. 2018:26536232018. View Article : Google Scholar : PubMed/NCBI
5	Atkins MB and Tannir NM: Current and emerging therapies for first-line treatment of metastatic clear cell renal cell carcinoma. Cancer Treat Rev. 70:127–137. 2018. View Article : Google Scholar : PubMed/NCBI
6	Cao Q, Ruan H, Wang K, Song Z, Bao L, Xu T, Xiao H, Wang C, Cheng G, Tong J, et al: Overexpression of PLIN2 is a prognostic marker and attenuates tumor progression in clear cell renal cell carcinoma. Int J Oncol. 53:137–147. 2018.PubMed/NCBI
7	Cui N, Liu J, Xia H and Xu D: LncRNA SNHG20 contributes to cell proliferation and invasion by upregulating ZFX expression sponging miR-495-3p in gastric cancer. J Cell Biochem. 120:3114–3123. 2019. View Article : Google Scholar : PubMed/NCBI
8	Dong D, Mu Z, Wei N, Sun M, Wang W, Xin N, Shao Y and Zhao C: Long non-coding RNA ZFAS1 promotes proliferation and metastasis of clear cell renal cell carcinoma via targeting miR-10a/SKA1 pathway. Biomed Pharmacother. 111:917–925. 2019. View Article : Google Scholar : PubMed/NCBI
9	Ning L, Li Z, Wei D, Chen H and Yang C: LncRNA, NEAT1 is a prognosis biomarker and regulates cancer progression via epithelial-mesenchymal transition in clear cell renal cell carcinoma. Cancer Biomark. 19:75–83. 2017. View Article : Google Scholar : PubMed/NCBI
10	Yang T, Zhou H, Liu P, Yan L, Yao W, Chen K, Zeng J, Li H, Hu J, Xu H and Ye Z: lncRNA PVT1 and its splicing variant function as competing endogenous RNA to regulate clear cell renal cell carcinoma progression. Oncotarget. 8:85353–85367. 2017.PubMed/NCBI
11	Casuscelli J, Weinhold N, Gundem G, Wang L, Zabor EC, Drill E, Wang PI, Nanjangud GJ, Redzematovic A, Nargund AM, et al: Genomic landscape and evolution of metastatic chromophobe renal cell carcinoma. JCI Insight. 2(pii): 926882017. View Article : Google Scholar : PubMed/NCBI
12	Feng Y, Shen Y, Chen H, Wang X, Zhang R, Peng Y, Lei X, Liu T, Liu J, Gu L, et al: Expression profile analysis of long non-coding RNA in acute myeloid leukemia by microarray and bioinformatics. Cancer Sci. 109:340–353. 2018. View Article : Google Scholar : PubMed/NCBI
13	Wang C, Yang C, Wang W, Xia B, Li K, Sun F and Hou Y: Prognostic nomogram for cervical cancer after surgery from SEER database. J Cancer. 9:3923–3928. 2018. View Article : Google Scholar : PubMed/NCBI
14	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
15	Zhu Q, Sun Y, Zhou Q, He Q and Qian H: Identification of key genes and pathways by bioinformatics analysis with TCGA RNA sequencing data in hepatocellular carcinoma. Mol Clin Oncol. 9:597–606. 2018.PubMed/NCBI
16	Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, et al: STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental dataset. Nucleic Acids Res. 47:D607–D613. 2019. View Article : Google Scholar : PubMed/NCBI
17	Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK and Varambally S: UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 19:649–658. 2017. View Article : Google Scholar : PubMed/NCBI
18	Martínez-Salamanca JI, Huang WC, Millán I, Bertini R, Bianco FJ, Carballido JA, Ciancio G, Hernández C, Herranz F, Haferkamp A, et al: Prognostic impact of the 2009 UICC/AJCC TNM staging system for renal cell carcinoma with venous extension. Eur Urol. 59:120–127. 2011. View Article : Google Scholar : PubMed/NCBI
19	Vecchio SJD and Ellis RJ: Cabozantinib for the management of metastatic clear cell renal cell carcinoma. J Kidney Cancer VHL. 5:1–5. 2018. View Article : Google Scholar : PubMed/NCBI
20	Majer W, Kluzek K, Bluyssen H and Wesoły J: Potential approaches and recent advances in biomarker discovery in clear-cell renal cell carcinoma. J Cancer. 6:1105–1113. 2015. View Article : Google Scholar : PubMed/NCBI
21	Zhang L, Yin W, Yao L, Li X, Fang D, Ren D, Zhang Z, Fan Y, He Q, Ci W, et al: Growth pattern of clear cell renal cell carcinoma in patients with delayed surgical intervention: Fast growth rate correlates with high grade and may result in poor prognosis. Biomed Res Int. 2015:5981342015.PubMed/NCBI
22	Qiao F, Li N and Li W: Integrative bioinformatics analysis reveals potential long non-coding RNA biomarkers and analysis of function in Non-smoking females with lung cancer. Med Sci Monit. 24:5771–5778. 2018. View Article : Google Scholar : PubMed/NCBI
23	Yan J, Zhou C, Guo K, Li Q and Wang Z: A novel seven-lncRNA signature for prognosis prediction in hepatocellular carcinoma. J Cell Biochem. 120:213–223. 2019. View Article : Google Scholar : PubMed/NCBI
24	Shi D, Qu Q, Chang Q, Wang Y, Gui Y and Dong D: A five-long non-coding RNA signature to improve prognosis prediction of clear cell renal cell carcinoma. Oncotarget. 8:58699–58708. 2017.PubMed/NCBI
25	Qu L, Wang ZL, Chen Q, Li YM, He HW, Hsieh JJ, Xue S, Wu ZJ, Liu B, Tang H, et al: Prognostic value of a long Non-coding RNA signature in localized clear cell renal cell carcinoma. Eur Urol. 74:756–763. 2018. View Article : Google Scholar : PubMed/NCBI
26	Taciak B, Pruszynska I, Kiraga L, Bialasek M and Krol M: Wnt signaling pathway in development and cancer. J Physiol Pharmacol. 69:185–196. 2018.
27	Huang JL, Liao Y, Qiu MX, Li J and An Y: Long non-coding RNA CCAT2 promotes cell proliferation and invasion through regulating Wnt/β-catenin signaling pathway in clear cell renal cell carcinoma. Tumour Biol. 39:10104283177113142017. View Article : Google Scholar : PubMed/NCBI
28	Tian T, Li X and Zhang J: mTOR signaling in cancer and mTOR inhibitors in solid tumor targeting therapy. Int J Mol Sci. 20(pii): E7552019. View Article : Google Scholar : PubMed/NCBI
29	Liu G, Zhao X, Zhou J, Cheng X, Ye Z and Ji Z: LncRNA TP73-AS1 promotes cell proliferation and inhibits cell apoptosis in clear cell renal cell carcinoma through repressing KISS1 expression and inactivation of PI3K/Akt/mTOR signaling pathway. Cell Physiol Biochem. 48:371–384. 2018. View Article : Google Scholar : PubMed/NCBI
30	Li Y and Burridge K: Cell-cycle-dependent regulation of cell adhesions: Adhering to the schedule: Three papers reveal unexpected properties of adhesion structures as cells progress through the cell cycle. Bioessays. 41:e18001652019. View Article : Google Scholar : PubMed/NCBI
31	Hongo F, Takaha N, Oishi M, Ueda T, Nakamura T, Naitoh Y, Naya Y, Kamoi K, Okihara K, Matsushima T, et al: CDK1 and CDK2 activity is a strong predictor of renal cell carcinoma recurrence. Urol Oncol. 32:1240–1246. 2014. View Article : Google Scholar : PubMed/NCBI
32	Li Y, Quan J, Chen F, Pan X, Zhuang C, Xiong T, Zhuang C, Li J, Huang X, Ye J, et al: MiR-31-5p acts as a tumor suppressor in renal cell carcinoma by targeting cyclin-dependent kinase 1 (CDK1). Biomed Pharmacother. 111:517–526. 2019. View Article : Google Scholar : PubMed/NCBI