Integrative analysis of miRNA and mRNA expression profiles reveals a novel mRNA/miRNA signature to improve risk classification for patients with gastric cancer

Yin,Xiang; Zhang,Fumin; Guo,Zhongwu; Kong,Weiyuan; Wang,Yuanyuan

doi:10.3892/ol.2019.10536

Integrative analysis of miRNA and mRNA expression profiles reveals a novel mRNA/miRNA signature to improve risk classification for patients with gastric cancer

Authors:
- Xiang Yin
- Fumin Zhang
- Zhongwu Guo
- Weiyuan Kong
- Yuanyuan Wang
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Affiliations: Department of Minimally Invasive Tumor Surgery, Daqing Oilfield General Hospital, Daqing, Heilongjiang 163453, P.R. China, Department of Gastroenterology, Daqing Long Nan Hospital, Daqing, Heilongjiang 163453, P.R. China
Published online on: June 27, 2019 https://doi.org/10.3892/ol.2019.10536
Pages: 2330-2339
Copyright: © Yin et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Gastric cancer (GC) is one of the most common types of malignant cancer and is associated with poor prognosis. Although the prognosis of patients with GC is associated with grade, stage and lymph node metastases, these traditional clinical features are inadequate to predict the outcome of GC. Therefore, there has been an increased focus on identifying novel molecular biomarkers for early diagnosis and prognosis, in order to improve outcomes in GC. In the present study, an integrative analysis of microRNA (miRNA) expression profiles, mRNA expression profiles and clinical characteristics was performed in a large cohort of patients with GC in order to identify an integrative prognostic model for improving postoperative risk classification. An integrative mRNA/miRNA signature (IMMIS), comprised of three miRNAs and one mRNA, was identified from a large number of differentially expressed miRNAs and mRNAs using univariate and multivariate Cox regression analysis. The prognostic value of the IMMIS was validated in the discovery cohort, testing cohort and The Cancer Genome Atlas (TCGA) cohort. The present results suggested that the identified signature had a reliable predictive performance and could classify the patients into high‑ and low‑risk groups with significantly different overall survival times. In the discovery cohort, the hazard ratio (HR) was 2.805 with a 95% CI=1.722‑4.567 (P<0.001). The median overall survival time as 1.49 vs. 3.85 years. In the testing cohort, the HR was 1.625 with a 95% CI=1.004‑2.638 (P=0.039) and the median overall survival time was 2.17 vs. 4.62 years. In the TCGA cohort, the HR was 2.139 with a 95% CI=1.519‑3.012 (P<0.001) and the median overall survival time was 1.53 vs. 4.62 years. The IMMIS constituted a reliable independent prognostic factor compared with clinical covariates, including age, sex, grade and stage, as indicated by multivariate and stratified analyses. Furthermore, comparative analysis revealed that the predictive value of the IMMIS was superior to the mRNA‑based signature alone. The present results suggested the potential value of the IMMIS as a promising novel biomarker for improving the clinical management of patients with GC.

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1	Siegel RL, Miller KD and Jemal A: Cancer statistics, 2016. CA Cancer J Clin. 66:7–30. 2016. View Article : Google Scholar : PubMed/NCBI
2	Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ and He J: Cancer statistics in China, 2015. CA Cancer J Clin. 66:115–132. 2016. View Article : Google Scholar : PubMed/NCBI
3	Hu B, El Hajj N, Sittler S, Lammert N, Barnes R and Meloni-Ehrig A: Gastric cancer: Classification, histology and application of molecular pathology. J Gastrointest Oncol. 3:251–261. 2012.PubMed/NCBI
4	Orditura M, Galizia G, Sforza V, Gambardella V, Fabozzi A, Laterza MM, Andreozzi F, Ventriglia J, Savastano B, Mabilia A, et al: Treatment of gastric cancer. World J Gastroenterol. 20:1635–1649. 2014. View Article : Google Scholar : PubMed/NCBI
5	Chen CN, Lin JJ, Chen JJ, Lee PH, Yang CY, Kuo ML, Chang KJ and Hsieh FJ: Gene expression profile predicts patient survival of gastric cancer after surgical resection. J Clin Oncol. 23:7286–7295. 2005. View Article : Google Scholar : PubMed/NCBI
6	Cho JY, Lim JY, Cheong JH, Park YY, Yoon SL, Kim SM, Kim SB, Kim H, Hong SW, Park YN, et al: Gene expression signature-based prognostic risk score in gastric cancer. Clin Cancer Res. 17:1850–1857. 2011. View Article : Google Scholar : PubMed/NCBI
7	Ding B, Gao X, Li H, Liu L and Hao X: A novel microRNA signature predicts survival in stomach adenocarcinoma. Oncotarget. 8:28144–28153. 2017.PubMed/NCBI
8	Xiong Y, Wang R, Peng L, You W, Wei J, Zhang S, Wu X, Guo J, Xu J, Lv Z and Fu Z: An integrated lncRNA, microRNA and mRNA signature to improve prognosis prediction of colorectal cancer. Oncotarget. 8:85463–85478. 2017. View Article : Google Scholar : PubMed/NCBI
9	Volinia S and Croce CM: Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer. Proc Natl Acad Sci USA. 110:7413–7417. 2013. View Article : Google Scholar : PubMed/NCBI
10	Bing Z, Tian J, Zhang J, Li X, Wang X and Yang K: An integrative model of miRNA and mRNA expression signature for patients of breast invasive carcinoma with radiotherapy prognosis. Cancer Biother Radiopharm. 31:253–260. 2016. View Article : Google Scholar : PubMed/NCBI
11	Love MI, Huber W and Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15:5502014. View Article : Google Scholar : PubMed/NCBI
12	Sun J, Chen X, Wang Z, Guo M, Shi H, Wang X, Cheng L and Zhou M: A potential prognostic long non-coding RNA signature to predict metastasis-free survival of breast cancer patients. Sci Rep. 5:165532015. View Article : Google Scholar : PubMed/NCBI
13	Sun J, Cheng L, Shi H, Zhang Z, Zhao H, Wang Z and Zhou M: A potential panel of six-long non-coding RNA signature to improve survival prediction of diffuse large-B-cell lymphoma. Sci Rep. 6:278422016. View Article : Google Scholar : PubMed/NCBI
14	Zhou M, Guo M, He D, Wang X, Cui Y, Yang H, Hao D and Sun J: A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer. J Transl Med. 13:2312015. View Article : Google Scholar : PubMed/NCBI
15	Zhou M, Xu W, Yue X, Zhao H, Wang Z, Shi H, Cheng L and Sun J: Relapse-related long non-coding RNA signature to improve prognosis prediction of lung adenocarcinoma. Oncotarget. 7:29720–29738. 2016.PubMed/NCBI
16	Zhou M, Zhang Z, Zhao H, Bao S and Sun J: A novel lncRNA-focus expression signature for survival prediction in endometrial carcinoma. BMC Cancer. 18:392018. View Article : Google Scholar : PubMed/NCBI
17	Zhou M, Zhang Z, Zhao H, Bao S, Cheng L and Sun J: An immune-related six-lncRNA signature to improve prognosis prediction of glioblastoma multiforme. Mol Neurobiol. 55:3684–3697. 2018.PubMed/NCBI
18	Zhou M, Zhong L, Xu W, Sun Y, Zhang Z, Zhao H, Yang L and Sun J: Discovery of potential prognostic long non-coding RNA biomarkers for predicting the risk of tumor recurrence of breast cancer patients. Sci Rep. 6:310382016. View Article : Google Scholar : PubMed/NCBI
19	R Core Team. R, . A language and environment for statistical computing. R Foundation for Statistical Computing; Vienna: 2012, http://www.R-project.org/
20	Liu JY, Peng CW, Yang XJ, Huang CQ and Li Y: The prognosis role of AJCC/UICC 8th edition staging system in gastric cancer, a retrospective analysis. Am J Transl Res. 10:2922018.PubMed/NCBI
21	Chia NY and Tan P: Molecular classification of gastric cancer. Ann Oncol. 27:763–769. 2016. View Article : Google Scholar : PubMed/NCBI
22	Jiang HB, Yang TJ, Lu P and Ma YJ: Gene expression profiling of gastric cancer. Eur Rev Med Pharmacol Sci. 18:2109–2115. 2014.PubMed/NCBI
23	Marimuthu A, Jacob HK, Jakharia A, Subbannayya Y, Keerthikumar S, Kashyap MK, Goel R, Balakrishnan L, Dwivedi S, Pathare S, et al: Gene expression profiling of gastric cancer. J Proteomics Bioinform. 4:74–82. 2011. View Article : Google Scholar : PubMed/NCBI
24	Takeno A, Takemasa I, Doki Y, Yamasaki M, Miyata H, Takiguchi S, Fujiwara Y, Matsubara K and Monden M: Integrative approach for differentially overexpressed genes in gastric cancer by combining large-scale gene expression profiling and network analysis. Br J Cancer. 99:1307–1315. 2008. View Article : Google Scholar : PubMed/NCBI
25	Ishimoto T, Baba H, Izumi D, Sugihara H, Kurashige J, Iwatsuki M and Tan P: Current perspectives toward the identification of key players in gastric cancer microRNA dysregulation. Int J Cancer. 138:1337–1349. 2016. View Article : Google Scholar : PubMed/NCBI
26	Pan HW, Li SC and Tsai KW: MicroRNA dysregulation in gastric cancer. Curr Pharm Des. 19:1273–1284. 2013. View Article : Google Scholar : PubMed/NCBI
27	Wu WK, Lee CW, Cho CH, Fan D, Wu K, Yu J and Sung JJ: MicroRNA dysregulation in gastric cancer: A new player enters the game. Oncogene. 29:5761–5771. 2010. View Article : Google Scholar : PubMed/NCBI
28	Zhou M, Hu L, Zhang Z, Wu N, Sun J and Su J: Recurrence-associated long non-coding RNA signature for determining the risk of recurrence in patients with colon cancer. Mol Ther Nucleic Acids. 12:518–529. 2018. View Article : Google Scholar : PubMed/NCBI
29	Zhou M, Zhao H, Wang X, Sun J and Su J: Analysis of long noncoding RNAs highlights region-specific altered expression patterns and diagnostic roles in Alzheimer's disease. Brief Bioinform:. 20:598–608. 2019. View Article : Google Scholar : PubMed/NCBI
30	Guo L, Peng Y, Meng Y, Liu Y, Yang S, Jin H and Li Q: Expression profiles analysis reveals an integrated miRNA-lncRNA signature to predict survival in ovarian cancer patients with wild-type BRCA1/2. Oncotarget. 8:68483–68492. 2017.PubMed/NCBI
31	Shen J, Zhang Y, Yu H, Shen B, Liang Y, Jin R, Liu X, Shi L and Cai X: Role of DUSP1/MKP1 in tumorigenesis, tumor progression and therapy. Cancer Med. 5:2061–2068. 2016. View Article : Google Scholar : PubMed/NCBI
32	Teng F, Xu Z, Chen J, Zheng G, Zheng G, Lv H, Wang Y, Wang L and Cheng X: DUSP1 induces apatinib resistance by activating the MAPK pathway in gastric cancer. Oncol Rep. 40:1203–1222. 2018.PubMed/NCBI
33	Zhou M, Zhao H, Xu W, Bao S, Cheng L and Sun J: Discovery and validation of immune-associated long non-coding RNA biomarkers associated with clinically molecular subtype and prognosis in diffuse large B cell lymphoma. Mol Cancer. 16:162017. View Article : Google Scholar : PubMed/NCBI
34	Kilic MO, Aynekin B, Kara A, Icen D and Demircan K: Differentially regulated ADAMTS1, 8, and 18 in gastric adenocarcinoma. Bratisl Lek Listy. 118:71–76. 2017.PubMed/NCBI
35	Kim N, Baek SW, Ryu H, Choi YS, Song IC, Yun HJ, Jo DY, Kim SY and Lee HJ: Atypical chemokine receptor ACKR3 expression is associated with aggressive behavior and poor prognosis in gastric cancer. Cancer Res. 76 (Suppl 14):39472016.
36	Tian GA, Zhu CC, Zhang XX, Zhu L, Yang XM, Jiang SH, Li RK, Tu L, Wang Y, Zhuang C, et al: CCBE1 promotes GIST development through enhancing angiogenesis and mediating resistance to imatinib. Sci Rep. 6:310712016. View Article : Google Scholar : PubMed/NCBI
37	Cheng Z, Wang HZ, Li X, Wu Z, Han Y, Li Y, Chen G, Xie X, Huang Y, Du Z and Zhou Y: MicroRNA-184 inhibits cell proliferation and invasion, and specifically targets TNFAIP2 in Glioma. J Exp Clin Cancer Res. 34:272015. View Article : Google Scholar : PubMed/NCBI
38	Wu G, Liu J, Wu Z, Wu X and Yao X: MicroRNA-184 inhibits cell proliferation and metastasis in human colorectal cancer by directly targeting IGF-1R. Oncol Lett. 14:3215–3222. 2017. View Article : Google Scholar : PubMed/NCBI