A five‑gene signature to predict the overall survival time of patients with esophageal squamous cell carcinoma

He,Wenwu; Yan,Qunlun; Fu,Liangmin; Han,Yongtao

doi:10.3892/ol.2019.10449

A five‑gene signature to predict the overall survival time of patients with esophageal squamous cell carcinoma

Authors:
- Wenwu He
- Qunlun Yan
- Liangmin Fu
- Yongtao Han
View Affiliations

Affiliations: Department of Thoracic Surgery, Sichuan Cancer Hospital and Research Institute, Chengdu, Sichuan 610041, P.R. China, Department of Clinical Medicine, North Sichuan Medical College, Nanchong, Sichuan 637007, P.R. China
Published online on: June 7, 2019 https://doi.org/10.3892/ol.2019.10449
Pages: 1381-1387
Copyright: © He 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

Esophageal squamous cell carcinoma (ESCC) is one of the six most commonly diagnosed tumor types in the Chinese population. Gene expression profiles help to predict the prognosis of patients with ESCC. Disease recurrence as the survival endpoint has been analyzed in the majority of previous studies; therefore, the aim of the present study was to construct a robust gene signature in order to determine the overall survival (OS) of patients with ESCC. The gene expression and clinical data of patients with ESCC were downloaded from The Cancer Genome Atlas (TCGA) database. Of the selected data (172 samples from surviving patients), 72 samples were randomly selected as modeling data, and verification was conducted using the entire dataset. Data from the Gene Expression Omnibus was analyzed simultaneously, and a venn diagram was constructed to determine the intersection between these two sets of results; a total of 97 genes were found to be associated with OS. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that these genes were primarily associated with specific pathways (Homo sapiens), including DNA replication, protein processing in endoplasmic reticulum and influenza A. A five‑gene signature was identified with a robust likelihood‑based survival modeling approach. Using regression coefficient modeling, a prognostic model consisting of the C‑X‑C motif chemokine ligand 8, DNA damage inducible transcript 3, RAB27A, member RAS oncogene family, replication factor C subunit 2 and elongation factor for RNA polymerase II 2 genes was constructed and validated. Based on these results, patients were subdivided into high and low‑risk groups. Compared with the high‑risk group, the OS time of patients in the low‑risk group was significantly increased. Furthermore, it was determined that the five genes were all differentially expressed in ESCC tissues compared with normal tissues, indicating the potential role of these genes in ESCC initiation and progression. In another independent cohort, this five‑gene signature was further confirmed and was considered as an independent prognostic biomarker for OS prediction in patients with ESCC. In conclusion, the OS of patients with ESCC may be predicted using this five‑gene signature, which may be useful in identifying patients with high‑risk ESCC.

View Figures

View References

1	McGuire S: World Cancer Report 2014. Geneva, Switzerland: World Health Organization, International Agency for Research on Cancer, WHO Press, 2015. Adv Nutr. 7:418–419. 2016. View Article : Google Scholar : PubMed/NCBI
2	Enzinger PC and Mayer RJ: Esophageal cancer. N Engl J Med. 349:2241–2252. 2003. View Article : Google Scholar : PubMed/NCBI
3	Luo J, Wang W, Tang Y, Zhou D, Gao Y, Zhang Q, Zhou X, Zhu H, Xing L and Yu J: mRNA and methylation profiling of radioresistant esophageal cancer cells: The involvement of Sall2 in acquired aggressive phenotypes. J Cancer. 8:646–656. 2017. View Article : Google Scholar : PubMed/NCBI
4	Zhang W, Pang Q, Yan C, Wang Q, Yang J, Yu S, Liu X, Yuan Z, Wang P and Xiao Z: Induction of PD-L1 expression by epidermal growth factor receptor-mediated signaling in esophageal squamous cell carcinoma. Onco Targets Ther. 10:763–771. 2017. View Article : Google Scholar : PubMed/NCBI
5	Zhang W, Yan S, Liu M, Zhang G, Yang S, He S, Bai J, Quan L, Zhu H, Dong Y and Xu N: Beta-catenin/TCF pathway plays a vital role in selenium induced-growth inhibition and apoptosis in esophageal squamous cell carcinoma (ESCC) cells. Cancer Lett. 296:113–122. 2010. View Article : Google Scholar : PubMed/NCBI
6	Xiang J, Zang W, Che J, Chen K and Hang J: Regulation network analysis in the esophageal squamous cell carcinoma. Eur Rev Med Pharmacol Sci. 16:2051–2056. 2012.PubMed/NCBI
7	Ashktorab H, Kupfer SS, Brim H and Carethers JM: Racial disparity in gastrointestinal cancer risk. Gastroenterology. 153:910–923. 2017. View Article : Google Scholar : PubMed/NCBI
8	Quackenbush J: Microarray analysis and tumor classification. N Engl J Med. 354:2463–2472. 2006. View Article : Google Scholar : PubMed/NCBI
9	Wang L, Wang C, Wang J, Huang X and Cheng Y: A novel systemic immune-inflammation index predicts survival and quality of life of patients after curative resection for esophageal squamous cell carcinoma. J Cancer Res Clin Oncol. 143:2077–2086. 2017. View Article : Google Scholar : PubMed/NCBI
10	Salmaninejad A, Zamani MR, Pourvahedi M, Golchehre Z, Hosseini Bereshneh A and Rezaei N: Cancer/testis antigens: Expression, regulation, tumor invasion, and use in immunotherapy of cancers. Immunol Invest. 45:619–640. 2016. View Article : Google Scholar : PubMed/NCBI
11	Lin ZW, Gu J, Liu RH, Liu XM, Xu FK, Zhao GY, Lu CL and Ge D: Genome-wide screening and co-expression network analysis identify recurrence-specific biomarkers of esophageal squamous cell carcinoma. Tumour Biol. 35:10959–10968. 2014. View Article : Google Scholar : PubMed/NCBI
12	Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M and Edgar R: NCBI GEO: Mining tens of millions of expression profiles-database and tools update. Nucleic Acids Res 35 (Database Issue). D760–D765. 2007. View Article : Google Scholar
13	James G, Witten D, Hastie T and Tibshirani R: An introduction to statistical learning with applications in R. Springer-Verlag New York; 2013
14	Cho HJ, Ami Y, Kim S, Kang J, Seung-Mo H and Kang: Robust likelihood-based survival modeling with microarray data. J Statist Software. 29:1–16. 2008.
15	Eisen MB, Spellman PT, Brown PO and Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 95:14863–14868. 1998. View Article : Google Scholar : PubMed/NCBI
16	Therneau T: A package for survival analysis in S. R package version 2.37–7. 2014.
17	Simon R, Lam A, Li MC, Ngan M, Menenzes S and Zhao Y: Analysis of gene expression data using BRB-ArrayTools. Cancer Inform. 3:11–17. 2007. View Article : Google Scholar : PubMed/NCBI
18	Ogata H, Goto S, Sato K, Fujibuchi W, Bono H and Kanehisa M: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 27:29–34. 1999. View Article : Google Scholar : PubMed/NCBI
19	Wang J, Vasaikar S, Shi Z, Greer M and Zhang B: WebGestalt 2017: A more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res 45 (W1). W130–W137. 2017. View Article : Google Scholar
20	Wu C, Irizarry R, MacDonald J and Gentry J: gcrma: Background adjustment using sequence information. 2005.
21	Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43:e472015. View Article : Google Scholar : PubMed/NCBI
22	Wu H, Liu C, Xu M, Guo M, Xu S and Xie M: Prognostic value of the number of negative lymph nodes in esophageal carcinoma without lymphatic metastasis. Thorac Cancer. 9:1129–1135. 2018. View Article : Google Scholar : PubMed/NCBI
23	Yildirim BA, Torun N, Guler OC and Onal C: Prognostic value of metabolic tumor volume and total lesion glycolysis in esophageal carcinoma patients treated with definitive chemoradiotherapy. Nucl Med Commun. 39:553–563. 2018. View Article : Google Scholar : PubMed/NCBI
24	Li JC, Zhao YH, Wang XY, Yang Y, Pan DL, Qiu ZD, Su Y and Pan JJ: Clinical significance of the expression of EGFR signaling pathway-related proteins in esophageal squamous cell carcinoma. Tumour Biol. 35:651–657. 2014. View Article : Google Scholar : PubMed/NCBI
25	Qin YR, Qiao JJ, Chan TH, Zhu YH, Li FF, Liu H, Fei J, Li Y, Guan XY and Chen L: Adenosine-to-inosine RNA editing mediated by ADARs in esophageal squamous cell carcinoma. Cancer Res. 74:840–851. 2014. View Article : Google Scholar : PubMed/NCBI
26	Zhang J, Chen Z, Tang Z, Huang J, Hu X and He J: RNA editing is induced by type I interferon in esophageal squamous cell carcinoma. Tumour Biol. 39:10104283177085462017. View Article : Google Scholar : PubMed/NCBI
27	Sakai M, Sohda M, Miyazaki T, Suzuki S, Sano A, Tanaka N, Inose T, Nakajima M, Kato H and Kuwano H: Significance of karyopherin-{alpha} 2 (KPNA2) expression in esophageal squamous cell carcinoma. Anticancer Res. 30:851–856. 2010.PubMed/NCBI
28	Yuan Y, Xue L and Fan F: Screening of differentially expressed genes related to esophageal squamous cell carcinoma and functional analysis with DNA microarrays. Int J Oncol. 44:1163–1170. 2014. View Article : Google Scholar : PubMed/NCBI
29	Anastas JN and Moon RT: WNT signalling pathways as therapeutic targets in cancer. Nat Rev Cancer. 13:11–26. 2013. View Article : Google Scholar : PubMed/NCBI
30	McBride WH, Iwamoto KS, Syljuasen R, Pervan M and Pajonk F: The role of the ubiquitin/proteasome system in cellular responses to radiation. Oncogene. 22:5755–5773. 2003. View Article : Google Scholar : PubMed/NCBI
31	Wang BY, Lin PY, Wu SC, Chen HS, Hsu PK, Shih CS, Liu CY, Liu CC and Chen YL: Comparison of pathologic stage in patients receiving esophagectomy with and without preoperative chemoradiation therapy for esophageal SCC. J Natl Compr Canc Netw. 12:1697–1705. 2014. View Article : Google Scholar : PubMed/NCBI
32	Li L, Zhao L, Lin B, Su H, Su M, Xie D, Jin X and Xie C: Adjuvant Therapeutic modalities following three-field lymph node dissection for stage II/III esophageal squamous cell carcinoma. J Cancer. 8:2051–2059. 2017. View Article : Google Scholar : PubMed/NCBI
33	Zhang L, Li W, Lyu X, Song Y, Mao Y, Wang S and Huang J: Adjuvant chemotherapy with paclitaxel and cisplatin in lymph node-positive thoracic esophageal squamous cell carcinoma. Chin J Cancer Res. 29:149–155. 2017. View Article : Google Scholar : PubMed/NCBI
34	Tong Q, Wang XL, Li SB, Yang GL, Jin S, Gao ZY and Liu XB: Combined detection of IL-6 and IL-8 is beneficial to the diagnosis of early stage esophageal squamous cell cancer: A preliminary study based on the screening of serum markers using protein chips. Onco Targets Ther. 11:5777–5787. 2018. View Article : Google Scholar : PubMed/NCBI
35	Kunizaki M, Hamasaki K, Wakata K, Tobinaga S, Sumida Y, Hidaka S, Yasutake T, Miyazaki T, Matsumoto K, Yamasaki T, et al: Clinical value of serum p53 antibody in the diagnosis and prognosis of esophageal squamous cell carcinoma. Anticancer Res. 38:1807–1813. 2018.PubMed/NCBI