Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Cancer Cell International
Published in: Cancer Cell International 1/2019

Open Access 01-12-2019 | Primary research

A six-gene prognostic model predicts overall survival in bladder cancer patients

Authors: Liwei Wang, Jiazhong Shi, Yaqin Huang, Sha Liu, Jingqi Zhang, Hua Ding, Jin Yang, Zhiwen Chen

Published in: Cancer Cell International | Issue 1/2019

Login to get access

Abstract

Background

The fatality and recurrence rates of bladder cancer (BC) have progressively increased. DNA methylation is an influential regulator associated with gene transcription in the pathogenesis of BC. We describe a comprehensive epigenetic study performed to analyse DNA methylation-driven genes in BC.

Methods

Data related to DNA methylation, the gene transcriptome and survival in BC were downloaded from The Cancer Genome Atlas (TCGA). MethylMix was used to detect BC-specific hyper-/hypo-methylated genes. Metascape was used to carry out gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was conducted to identify the characteristic dimension decrease and distinguish prognosis-related methylation-driven genes. Subsequently, we developed a six-gene risk evaluation model and a novel prognosis-related nomogram to predict overall survival (OS). A survival analysis was carried out to explore the individual prognostic significance of the six genes.

Results

In total, 167 methylation-driven genes were identified. Based on the LASSO Cox regression, six genes, i.e., ARHGDIB, LINC00526, IDH2, ARL14, GSTM2, and LURAP1, were selected for the development of a risk evaluation model. The Kaplan–Meier curve indicated that patients in the low-risk group had considerably better OS (P = 1.679e−05). The area under the curve (AUC) of this model was 0.698 at 3 years of OS. The verification performed in subgroups demonstrated the validity of the model. Then, we designed an OS-associated nomogram that included the risk score and clinical factors. The concordance index of the nomogram was 0.694. The methylation levels of IDH2 and ARL14 were appreciably related to the survival results. In addition, the methylation and gene expression-matched survival analysis revealed that ARHGDIB and ARL14 could be used as independent prognostic indicators. Among the six genes, 6 methylation sites in ARHGDIB, 3 in GSTM2, 1 in ARL14, 2 in LINC00526 and 2 in LURAP1 were meaningfully associated with BC prognosis. In addition, several abnormal methylated sites were identified as linked to gene expression.

Conclusion

We discovered differential methylation in BC patients with better and worse survival and provided a risk evaluation model by merging six gene markers with clinical characteristics.
Appendix
Available only for authorised users
Literature
1.
Sanli O, Dobruch J, Knowles MA, Burger M, Alemozaffar M, Nielsen ME, Lotan Y. Bladder cancer. Nat Rev Dis Primers. 2017;3:17022.PubMedCrossRef
2.
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66(1):7–30.
3.
Pietzak EJ, Bagrodia A, Cha EK, Drill EN, Iyer G, Isharwal S, Ostrovnaya I, Baez P, Li Q, Berger MF, et al. Next-generation sequencing of nonmuscle invasive bladder cancer reveals potential biomarkers and rational therapeutic targets. Eur Urol. 2017;72(6):952–9.PubMedPubMedCentralCrossRef
4.
5.
Robertson AG, Kim J, Al-Ahmadie H, Bellmunt J, Guo G, Cherniack AD, Hinoue T, Laird PW, Hoadley KA, Akbani R, et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell. 2017;171(3):540–56.PubMedPubMedCentralCrossRef
6.
Chung W, Bondaruk J, Jelinek J, Lotan Y, Liang S, Czerniak B, Issa JP. Detection of bladder cancer using novel DNA methylation biomarkers in urine sediments. Cancer Epidemiol Biomark Prev. 2011;20(7):1483–91.CrossRef
7.
Dudziec E, Goepel JR, Catto JW. Global epigenetic profiling in bladder cancer. Epigenomics. 2011;3(1):35–45.PubMedCrossRef
8.
Dulaimi E, Uzzo RG, Greenberg RE, Al-Saleem T, Cairns P. Detection of bladder cancer in urine by a tumor suppressor gene hypermethylation panel. Clin Cancer Res. 2004;10(6):1887–93.PubMedCrossRef
9.
Casadio V, Molinari C, Calistri D, Tebaldi M, Gunelli R, Serra L, Falcini F, Zingaretti C, Silvestrini R, Amadori D, et al. DNA Methylation profiles as predictors of recurrence in non muscle invasive bladder cancer: an MS-MLPA approach. J Exp Clin Cancer Res. 2013;32:94.PubMedPubMedCentralCrossRef
10.
Shoshany O, Mano R, Margel D, Baniel J, Yossepowitch O. Presence of detrusor muscle in bladder tumor specimens—predictors and effect on outcome as a measure of resection quality. Urol Oncol. 2014;32(1):17–40.CrossRef
11.
12.
Xiong J, Bing Z, Guo S. Observed survival interval: a supplement to TCGA pan-cancer clinical data resource. Cancers (Basel). 2019;11(3):280.CrossRef
13.
14.
Gao C, Zhuang J, Li H, Liu C, Zhou C, Liu L, Sun C. Exploration of methylation-driven genes for monitoring and prognosis of patients with lung adenocarcinoma. Cancer Cell Int. 2018;18:194.PubMedPubMedCentralCrossRef
15.
Lu T, Chen D, Wang Y, Sun X, Li S, Miao S, Wo Y, Dong Y, Leng X, Du W, et al. Identification of DNA methylation-driven genes in esophageal squamous cell carcinoma: a study based on The Cancer Genome Atlas. Cancer Cell Int. 2019;19:52.PubMedPubMedCentralCrossRef
16.
17.
18.
19.
Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, Chanda SK. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523.PubMedPubMedCentralCrossRef
20.
Tang Z, Liu Z, Li R, Yang X, Cui X, Wang S, Yu D, Li H, Dong E, Tian J. Identifying the white matter impairments among ART-naive HIV patients: a multivariate pattern analysis of DTI data. Eur Radiol. 2017;27(10):4153–62.PubMedCrossRef
21.
Ren S, Huang S, Ye J, Qian X. Safe feature screening for generalized LASSO. IEEE Trans Pattern Anal Mach Intell. 2018;40(12):2992–3006.PubMedCrossRef
22.
23.
24.
25.
Casadevall D, Kilian AY, Bellmunt J. The prognostic role of epigenetic dysregulation in bladder cancer: a systematic review. Cancer Treat Rev. 2017;61:82–93.PubMedCrossRef
26.
Koch A, Joosten SC, Feng Z, de Ruijter TC, Draht MX, Melotte V, Smits KM, Veeck J, Herman JG, Van Neste L, et al. Analysis of DNA methylation in cancer: location revisited. Nat Rev Clin Oncol. 2018;15(7):459–66.PubMedCrossRef
27.
Andresen K, Boberg KM, Vedeld HM, Honne H, Jebsen P, Hektoen M, Wadsworth CA, Clausen OP, Lundin KE, Paulsen V, et al. Four DNA methylation biomarkers in biliary brush samples accurately identify the presence of cholangiocarcinoma. Hepatology. 2015;61(5):1651–9.PubMedPubMedCentralCrossRef
28.
Kandimalla R, van Tilborg AA, Zwarthoff EC. DNA methylation-based biomarkers in bladder cancer. Nat Rev Urol. 2013;10(6):327–35.PubMedCrossRef
29.
Goessl C, Muller M, Straub B, Miller K. DNA alterations in body fluids as molecular tumor markers for urological malignancies. Eur Urol. 2002;41(6):668–76.PubMedCrossRef
30.
Mehta D, Rahman A, Malik AB. Protein kinase C-alpha signals rho-guanine nucleotide dissociation inhibitor phosphorylation and rho activation and regulates the endothelial cell barrier function. J Biol Chem. 2001;276(25):22614–20.PubMedCrossRef
31.
32.
Zeller C, Dai W, Steele NL, Siddiq A, Walley AJ, Wilhelm-Benartzi CS, Rizzo S, van der Zee A, Plumb JA, Brown R. Candidate DNA methylation drivers of acquired cisplatin resistance in ovarian cancer identified by methylome and expression profiling. Oncogene. 2012;31(42):4567–76.PubMedCrossRef
33.
Huang T, Yang J, Cai YD. Novel candidate key drivers in the integrative network of genes, microRNAs, methylations, and copy number variations in squamous cell lung carcinoma. Biomed Res Int. 2015;2015:358125.PubMedPubMedCentral
34.
Theodorescu D, Sapinoso LM, Conaway MR, Oxford G, Hampton GM, Frierson HJ. Reduced expression of metastasis suppressor RhoGDI2 is associated with decreased survival for patients with bladder cancer. Clin Cancer Res. 2004;10(11):3800–6.PubMedCrossRef
35.
Ma L, Xu G, Sotnikova A, Szczepanowski M, Giefing M, Krause K, Krams M, Siebert R, Jin J, Klapper W. Loss of expression of LyGDI (ARHGDIB), a rho GDP-dissociation inhibitor, in Hodgkin lymphoma. Br J Haematol. 2007;139(2):217–23.PubMedCrossRef
36.
Niu H, Li H, Xu C, He P. Expression profile of RhoGDI2 in lung cancers and role of RhoGDI2 in lung cancer metastasis. Oncol Rep. 2010;24(237):465.PubMed
37.
Yan J, Xu C, Li Y, Tang B, Xie S, Hong T, Zeng E. Long non-coding RNA LINC00526 represses glioma progression via forming a double negative feedback loop with AXL. J Cell Mol Med. 2019;23(8):5518–31.PubMedPubMedCentralCrossRef
38.
Paul P, van den Hoorn T, Jongsma ML, Bakker MJ, Hengeveld R, Janssen L, Cresswell P, Egan DA, van Ham M, Ten BA, et al. A genome-wide multidimensional RNAi screen reveals pathways controlling MHC class II antigen presentation. Cell. 2011;145(2):268–83.PubMedCrossRef
39.
Li JJ, Li R, Wang W, Zhang B, Song X, Zhang C, Gao Y, Liao Q, He Y, You S, et al. IDH2 is a novel diagnostic and prognostic serum biomarker for non-small-cell lung cancer. Mol Oncol. 2018;12(5):602–10.PubMedPubMedCentralCrossRef
40.
Li J, He Y, Tan Z, Lu J, Li L, Song X, Shi F, Xie L, You S, Luo X, et al. Wild-type IDH2 promotes the Warburg effect and tumor growth through HIF1alpha in lung cancer. Theranostics. 2018;8(15):4050–61.PubMedPubMedCentralCrossRef
41.
Marcucci G, Maharry K, Wu YZ, Radmacher MD, Mrozek K, Margeson D, Holland KB, Whitman SP, Becker H, Schwind S, et al. IDH1 and IDH2 gene mutations identify novel molecular subsets within de novo cytogenetically normal acute myeloid leukemia: a cancer and leukemia group B study. J Clin Oncol. 2010;28(14):2348–55.PubMedPubMedCentralCrossRef
42.
Li WL, Xiao MS, Zhang DF, Yu D, Yang RX, Li XY, Yao YG. Mutation and expression analysis of the IDH1, IDH2, DNMT3A, and MYD88 genes in colorectal cancer. Gene. 2014;546(2):263–70.PubMedCrossRef
43.
Lv Q, Xing S, Li Z, Li J, Gong P, Xu X, Chang L, Jin X, Gao F, Li W, et al. Altered expression levels of IDH2 are involved in the development of colon cancer. Exp Ther Med. 2012;4(5):801–6.PubMedPubMedCentralCrossRef
44.
45.
Kresovich JK, Gann PH, Erdal S, Chen HY, Argos M, Rauscher GH. Candidate gene DNA methylation associations with breast cancer characteristics and tumor progression. Epigenomics. 2018;10(4):367–78.PubMedPubMedCentralCrossRef
46.
Ashour N, Angulo JC, Andres G, Alelu R, Gonzalez-Corpas A, Toledo MV, Rodriguez-Barbero JM, Lopez JI, Sanchez-Chapado M, Ropero S. A DNA hypermethylation profile reveals new potential biomarkers for prostate cancer diagnosis and prognosis. Prostate. 2014;74(12):1171–82.PubMedCrossRef
Metadata
Title
A six-gene prognostic model predicts overall survival in bladder cancer patients
Authors
Liwei Wang
Jiazhong Shi
Yaqin Huang
Sha Liu
Jingqi Zhang
Hua Ding
Jin Yang
Zhiwen Chen
Publication date
01-12-2019
Publisher
BioMed Central
Published in
Cancer Cell International / Issue 1/2019
Electronic ISSN: 1475-2867
DOI
https://doi.org/10.1186/s12935-019-0950-7

Other articles of this Issue 1/2019

Cancer Cell International 1/2019 Go to the issue

Primary research

Combination of the natural product capsaicin and docetaxel synergistically kills human prostate cancer cells through the metabolic regulator AMP-activated kinase

Primary research

HOXB8 enhances the proliferation and metastasis of colorectal cancer cells by promoting EMT via STAT3 activation

Primary research

Identification of the xenograft and its ascendant sphere-forming cell line as belonging to EBV-induced lymphoma, and characterization of the status of sphere-forming cells

Primary research

TGF-β1 stimulates epithelial–mesenchymal transition and cancer-associated myoepithelial cell during the progression from in situ to invasive breast cancer

Primary research

Genetic variants in the circadian rhythm pathway as indicators of prostate cancer progression

Primary research

MicroRNA-96-3p promotes metastasis of papillary thyroid cancer through targeting SDHB
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine
Image Credits