Open Access

A seven‑miRNA expression‑based prognostic signature and its corresponding potential competing endogenous RNA network in early pancreatic cancer

  • Authors:
    • Xue Bai
    • Donglan Lu
    • Yan Lin
    • Yufeng Lv
    • Liusheng He
  • View Affiliations

  • Published online on: July 3, 2019     https://doi.org/10.3892/etm.2019.7728
  • Pages: 1601-1608
  • Copyright: © Bai et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The present study aimed to establish a microRNA (miRNA/miR) signature to predict the prognosis of patients with pancreatic cancer (PC) at the early stage and to investigate the involvement of competing endogenous RNAs (ceRNAs) in PC. Using mature miRNA expression profiles from The Cancer Genome Atlas, differentially expressed miRNAs in tissues derived from patients exhibiting early PC and tissues from healthy individuals were compared. The least absolute shrinkage and selection operator regression method was used to construct a miRNA‑based signature for predicting prognosis. The miRNet tool, gene set enrichment analysis (GSEA) and the LncRNADisease database were utilized to explore the mechanistic involvement of ceRNAs. A total of seven downregulated miRNAs in PC (miR‑424‑5p, miR‑139‑5p, miR‑5586‑5p, miR‑126‑3p, miR‑3613‑5p, miR‑454‑3p and miR‑1271‑5p) were selected to generate a signature. Based on this seven‑miRNA signature, it was possible to stratify patients with PC into low‑ and high‑risk groups. The overall survival of the low‑risk group was significantly longer than that of the high‑risk group (P<0.001). The seven‑miRNA signature was able to predict the 2‑year‑survival rate of patients with early PC with an area under the curve of 0.750. Furthermore, as opposed to routine clinicopathological features, this seven‑miRNA signature was an independent prognostic factor according to multivariate Cox regression analysis. GSEA indicated that the extracellular matrix receptor interaction pathway and the transforming growth factor‑β signaling pathway were enriched in the high‑risk group. A ceRNA network of the seven‑miR signature was constructed. In conclusion, the present study provided a seven‑miRNA signature, according to which patients with early PC may be divided into high‑ and low‑risk groups. The ceRNA network of the prognostic signature was preliminarily explored.

References

1 

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA and Jemal A: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 68:394–424. 2018. View Article : Google Scholar : PubMed/NCBI

2 

Rahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM and Matrisian LM: Projecting cancer incidence and deaths to 2030: The unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 74:2913–2921. 2014. View Article : Google Scholar : PubMed/NCBI

3 

Wagner M, Redaelli C, Lietz M, Seiler CA, Friess H and Buchler MW: Curative resection is the single most important factor determining outcome in patients with pancreatic adenocarcinoma. Br J Surg. 91:586–594. 2004. View Article : Google Scholar : PubMed/NCBI

4 

Yadav D and Lowenfels AB: The epidemiology of pancreatitis and pancreatic cancer. Gastroenterology. 144:1252–1261. 2013. View Article : Google Scholar : PubMed/NCBI

5 

Beger HG, Thorab FC, Liu Z, Harada N and Rau BM: Pathogenesis and treatment of neoplastic diseases of the papilla of Vater: Kausch-Whipple procedure with lymph node dissection in cancer of the papilla of Vater. J Hepatobiliary Pancreat Surg. 11:232–238. 2004. View Article : Google Scholar : PubMed/NCBI

6 

Bartel DP: MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell. 116:281–297. 2004. View Article : Google Scholar : PubMed/NCBI

7 

Schultz NA, Andersen KK, Roslind A, Willenbrock H, Wøjdemann M and Johansen JS: Prognostic microRNAs in cancer tissue from patients operated for pancreatic cancer-five microRNAs in a prognostic index. World J Surg. 36:2699–2707. 2012. View Article : Google Scholar : PubMed/NCBI

8 

Yu L, Xiang L, Feng J, Li B, Zhou Z, Li J, Lin Y, Lv Y, Zou D, Lei Z and Zhang J: miRNA-21 and miRNA-223 expression signature as a predictor for lymph node metastasis, distant metastasis and survival in kidney renal clear cell carcinoma. J Cancer. 9:3651–3659. 2018. View Article : Google Scholar : PubMed/NCBI

9 

Lin Y, Lv Y, Liang R, Yuan C and Zhang J, He D, Zheng X and Zhang J: Four-miRNA signature as a prognostic tool for lung adenocarcinoma. Onco Targets Ther. 11:29–36. 2018. View Article : Google Scholar : PubMed/NCBI

10 

Wiemer EA: The role of microRNAs in cancer: No small matter. Eur J Cancer. 43:1529–1544. 2007. View Article : Google Scholar : PubMed/NCBI

11 

Tay Y, Rinn J and Pandolfi PP: The multilayered complexity of ceRNA crosstalk and competition. Nature. 505:344–352. 2014. View Article : Google Scholar : PubMed/NCBI

12 

Bak RO and Mikkelsen JG: miRNA sponges: Soaking up miRNAs for regulation of gene expression. Wiley Interdiscip Rev RNA. 5:317–333. 2014. View Article : Google Scholar : PubMed/NCBI

13 

Salmena L, Poliseno L, Tay Y, Kats L and Pandolfi PP: A ceRNA hypothesis: The Rosetta Stone of a hidden RNA language? Cell. 146:353–358. 2011. View Article : Google Scholar : PubMed/NCBI

14 

Duell EJ, Lujan-Barroso L, Sala N, Deitz McElyea S, Overvad K, Tjonneland A, Olsen A, Weiderpass E, Busund LT, Moi L, et al: Plasma microRNAs as biomarkers of pancreatic cancer risk in a prospective cohort study. Int J Cancer. 141:905–915. 2017. View Article : Google Scholar : PubMed/NCBI

15 

Namkung J, Kwon W, Choi Y, Yi SG, Han S, Kang MJ, Kim SW, Park T and Jang JY: Molecular subtypes of pancreatic cancer based on miRNA expression profiles have independent prognostic value. J Gastroenterol Hepatol. 31:1160–1167. 2016. View Article : Google Scholar : PubMed/NCBI

16 

Lee KH, Lee JK, Choi DW, Do IG, Sohn I, Jang KT, Jung SH, Heo JS, Choi SH and Lee KT: Postoperative prognosis prediction of pancreatic cancer with seven microRNAs. Pancreas. 44:764–768. 2015. View Article : Google Scholar : PubMed/NCBI

17 

Goldman M, Craft B, Hastie M, Repečka K, Kamath A, McDade F, Rogers D, Brooks AN, Zhu J and Haussler D: The UCSC Xena platform for public and private cancer genomics data visualization and interpretation. bioRxiv. 3264702019.

18 

Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, Meyer L, Gress DM, Byrd DR and Winchester DP: The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more ‘personalized’ approach to cancer staging. CA Cancer J Clin. 67:93–99. 2017. View Article : Google Scholar : PubMed/NCBI

19 

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

20 

Xie M, Lv Y, Liu Z, Zhang J, Liang C, Liao X, Liang R, Lin Y and Li Y: Identification and validation of a four-miRNA (miRNA-21-5p, miRNA-9-5p, miR-149-5p, and miRNA-30b-5p) prognosis signature in clear cell renal cell carcinoma. Cancer Manag Res. 10:5759–5766. 2018. View Article : Google Scholar : PubMed/NCBI

21 

Wu TT, Chen YF, Hastie T, Sobel E and Lange K: Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics. 25:714–721. 2009. View Article : Google Scholar : PubMed/NCBI

22 

Tibshirani R: The lasso method for variable selection in the Cox model. Stat Med. 16:385–395. 1997. View Article : Google Scholar : PubMed/NCBI

23 

Lin Z, Cai YJ, Chen RC, Chen BC, Zhao L, Xu SH, Wang XD, Song M, Wu JM, Wang YQ, et al: A microRNA expression profile for vascular invasion can predict overall survival in hepatocellular carcinoma. Clin Chim Acta. 469:171–179. 2017. View Article : Google Scholar : PubMed/NCBI

24 

Heagerty PJ, Lumley T and Pepe MS: Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics. 56:337–344. 2000. View Article : Google Scholar : PubMed/NCBI

25 

Fan Y, Siklenka K, Arora SK, Ribeiro P, Kimmins S and Xia J: miRNet-dissecting miRNA-target interactions and functional associations through network-based visual analysis. Nucleic Acids Res. 44:W135–W141. 2016. View Article : Google Scholar : PubMed/NCBI

26 

Demchak B, Hull T, Reich M, Liefeld T, Smoot M, Ideker T and Mesirov JP: Cytoscape: The network visualization tool for GenomeSpace workflows. F1000Res. 3:1512014. View Article : Google Scholar : PubMed/NCBI

27 

Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES and Mesirov JP: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 102:15545–15550. 2005. View Article : Google Scholar : PubMed/NCBI

28 

Chen G, Wang Z, Wang D, Qiu C, Liu M, Chen X, Zhang Q, Yan G and Cui Q: LncRNADisease: A database for long-non-coding RNA-associated diseases. Nucleic Acids Res. 41:D983–D986. 2013. View Article : Google Scholar : PubMed/NCBI

29 

Tavano F, di Mola FF, Piepoli A, Panza A, Copetti M, Burbaci FP, Latiano T, Pellegrini F, Maiello E, Andriulli A and di Sebastiano P: Changes in miR-143 and miR-21 expression and clinicopathological correlations in pancreatic cancers. Pancreas. 41:1280–1284. 2012. View Article : Google Scholar : PubMed/NCBI

30 

Giovannetti E, van der Velde A, Funel N, Vasile E, Perrone V, Leon LG, De Lio N, Avan A, Caponi S, Pollina LE, et al: High-throughput microRNA (miRNAs) arrays unravel the prognostic role of MiR-211 in pancreatic cancer. PLoS One. 7:e491452012. View Article : Google Scholar : PubMed/NCBI

31 

Gurbuz N and Ozpolat B: MicroRNA-based targeted therapeutics in pancreatic cancer. Anticancer Res. 39:529–532. 2019. View Article : Google Scholar : PubMed/NCBI

32 

Chiaravalli M, Reni M and O'Reilly EM: Pancreatic ductal adenocarcinoma: State-of-the-art 2017 and new therapeutic strategies. Cancer Treat Rev. 60:32–43. 2017. View Article : Google Scholar : PubMed/NCBI

33 

Liang L, Wei DM, Li JJ, Luo DZ, Chen G, Dang YW and Cai XY: Prognostic microRNAs and their potential molecular mechanism in pancreatic cancer: A study based on The Cancer Genome Atlas and bioinformatics investigation. Mol Med Rep. 17:939–951. 2018.PubMed/NCBI

34 

Yu Y, Feng X and Cang S: A two-microRNA signature as a diagnostic and prognostic marker of pancreatic adenocarcinoma. Cancer Manag Res. 10:1507–1515. 2018. View Article : Google Scholar : PubMed/NCBI

35 

Zhou X, Huang Z, Xu L, Zhu M, Zhang L, Zhang H, Wang X, Li H, Zhu W, Shu Y and Liu P: A panel of 13-miRNA signature as a potential biomarker for predicting survival in pancreatic cancer. Oncotarget. 7:69616–69624. 2016.PubMed/NCBI

36 

Dou D, Yang S, Lin Y and Zhang J: An eight-miRNA signature expression-based risk scoring system for prediction of survival in pancreatic adenocarcinoma. Cancer Biomark. 23:79–93. 2018. View Article : Google Scholar : PubMed/NCBI

37 

Pai P, Rachagani S, Are C and Batra SK: Prospects of miRNA-based therapy for pancreatic cancer. Curr Drug Targets. 14:1101–1109. 2013. View Article : Google Scholar : PubMed/NCBI

38 

Principe DR, DeCant B, Mascariñas E, Wayne EA, Diaz AM, Akagi N, Hwang R, Pasche B, Dawson DW, Fang D, et al: TGFβ signaling in the pancreatic tumor microenvironment promotes fibrosis and immune evasion to facilitate tumorigenesis. Cancer Res. 76:2525–2539. 2016. View Article : Google Scholar : PubMed/NCBI

39 

Wu K, Hu G, He X, Zhou P, Li J, He B and Sun W: MicroRNA-424-5p suppresses the expression of SOCS6 in pancreatic cancer. Pathol Oncol Res. 19:739–748. 2013. View Article : Google Scholar : PubMed/NCBI

40 

Ma J, Zhang J, Weng YC and Wang JC: EZH2-mediated microRNA-139-5p regulates epithelial-mesenchymal transition and lymph node metastasis of pancreatic cancer. Mol Cells. 41:868–880. 2018.PubMed/NCBI

41 

Ku JL, Yoon KA, Kim WH, Jang Y, Suh KS, Kim SW, Park YH and Park JG: Establishment and characterization of four human pancreatic carcinoma cell lines. Genetic alterations in the TGFBR2 gene but not in the MADH4 gene. Cell Tissue Res. 308:205–214. 2002. View Article : Google Scholar : PubMed/NCBI

42 

Kim K, Jutooru I, Chadalapaka G, Johnson G, Frank J, Burghardt R, Kim S and Safe S: HOTAIR is a negative prognostic factor and exhibits pro-oncogenic activity in pancreatic cancer. Oncogene. 32:1616–1625. 2013. View Article : Google Scholar : PubMed/NCBI

43 

Li D, Yang W, Zhang Y, Yang JY, Guan R, Xu D and Yang MQ: Genomic analyses based on pulmonary adenocarcinoma in situ reveal early lung cancer signature. BMC Med Genomics. 11 (Suppl 5):S1062018. View Article : Google Scholar

44 

Gao X, Chen Y, Chen M, Wang S, Wen X and Zhang S: Identification of key candidate genes and biological pathways in bladder cancer. PeerJ. 6:e60362018. View Article : Google Scholar : PubMed/NCBI

45 

Yeh MH, Tzeng YJ, Fu TY, You JJ, Chang HT, Ger LP and Tsai KW: Extracellular matrix-receptor interaction signaling genes associated with inferior breast cancer survival. Anticancer Res. 38:4593–4605. 2018. View Article : Google Scholar : PubMed/NCBI

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September 2019
Volume 18 Issue 3

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APA
Bai, X., Lu, D., Lin, Y., Lv, Y., & He, L. (2019). A seven‑miRNA expression‑based prognostic signature and its corresponding potential competing endogenous RNA network in early pancreatic cancer. Experimental and Therapeutic Medicine, 18, 1601-1608. https://doi.org/10.3892/etm.2019.7728
MLA
Bai, X., Lu, D., Lin, Y., Lv, Y., He, L."A seven‑miRNA expression‑based prognostic signature and its corresponding potential competing endogenous RNA network in early pancreatic cancer". Experimental and Therapeutic Medicine 18.3 (2019): 1601-1608.
Chicago
Bai, X., Lu, D., Lin, Y., Lv, Y., He, L."A seven‑miRNA expression‑based prognostic signature and its corresponding potential competing endogenous RNA network in early pancreatic cancer". Experimental and Therapeutic Medicine 18, no. 3 (2019): 1601-1608. https://doi.org/10.3892/etm.2019.7728