Identification of a 5‑lncRNA signature‑based risk scoring system for survival prediction in colorectal cancer

Gu,Liqiang; Yu,Jun; Wang,Qing; Xu,Bin; Ji,Liechen; Yu,Lin; Zhang,Xipeng; Cai,Hui

doi:10.3892/mmr.2018.8963

Identification of a 5‑lncRNA signature‑based risk scoring system for survival prediction in colorectal cancer

Authors:
- Liqiang Gu
- Jun Yu
- Qing Wang
- Bin Xu
- Liechen Ji
- Lin Yu
- Xipeng Zhang
- Hui Cai
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Affiliations: Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin 300121, P.R. China, Department of General Surgery, Changhai Hospital, Second Military Medical University, Shanghai 200433, P.R. China
Published online on: May 3, 2018 https://doi.org/10.3892/mmr.2018.8963
Pages: 279-291
Copyright: © Gu 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 investigate potential prognostic long noncoding RNAs (lncRNAs) associated with colorectal cancer (CRC). An mRNA‑seq dataset obtained from The Cancer Genome Atlas was employed to identify the differentially expressed lncRNAs (DELs) between CRC patients with good and poor prognoses. Subsequently, univariate and multivariate Cox regression analyses were conducted to analyze the prognosis‑associated lncRNAs among all DELs. In addition, a risk scoring system was developed according to the expression levels of the prognostic lncRNAs, which was then applied to a training set and an independent testing set. Furthermore, the co‑expressed genes of prognostic lncRNAs were screened using a Multi‑Experiment Matrix online tool for construction of lncRNA‑gene networks. Finally, Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology (GO) function enrichment analyses were performed on genes in the lncRNA‑gene networks using KOBAS, GOATOOLS and ClusterProfiler. The present study identified 82 DELs, of which long intergenic nonprotein coding RNA 2159, RP11‑452L6.6, RP11‑894P9.1 and RP11‑69M1.6, and whey acidic protein four‑disulfide core domain 21 (WFDC21P) were reported to be independently associated with the prognosis of patients with CRC. A 5‑lncRNA signature‑based risk scoring system was developed, which may be used to classify patients into low‑ and high‑risk groups with significantly different recurrence‑free survival times in the training and testing sets (P<0.05). Co‑expressed genes of WFDC21P or RP11‑69M1.6 were utilized to construct the lncRNA‑gene networks. Genes in the networks were significantly enriched in ‘tight junction’, ‘focal adhesion’ and ‘regulation of actin cytoskeleton’ pathways, and numerous GO terms associated with ‘reactive oxygen species metabolism’ and ‘nitric oxide metabolism’. The present study proposed a 5‑lncRNA signature‑based risk scoring system for predicting the prognosis of patients with CRC, and revealed the associated signaling pathways and biological processes. The results of the present study may help improve prognostic evaluation in clinical practice.

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1	Miller KD, Siegel RL, Lin CC, Mariotto AB, Kramer JL, Rowland JH, Stein KD, Alteri R and Jemal A: Cancer treatment and survivorship statistics, 2016. CA Cancer J Clin. 66:271–289. 2016. View Article : Google Scholar : PubMed/NCBI
2	Brenner H, Kloor M and Pox CP: Colorectal cancer. Lancet. 383:1490–1502. 2014. View Article : Google Scholar : PubMed/NCBI
3	García Sánchez J: Colonoscopic polypectomy and long-term prevention of colorectal cancer deaths. Rev Clin Esp. 212:4082012.(In Spanish).
4	Li X, Wu Z, Fu X and Han W: lncRNAs: Insights into their function and mechanics in underlying disorders. Mutat Res Rev Mutat Res. 762:1–21. 2014. View Article : Google Scholar : PubMed/NCBI
5	Schmitt AM and Chang HY: Long noncoding RNAs in cancer pathways. Cancer Cell. 29:452–463. 2016. View Article : Google Scholar : PubMed/NCBI
6	Huarte M: The emerging role of lncRNAs in cancer. Nat Med. 21:1253–1261. 2015. View Article : Google Scholar : PubMed/NCBI
7	Zheng HT, Shi DB, Wang YW, Li XX, Xu Y, Tripathi P, Gu WL, Cai GX and Cai SJ: High expression of lncRNA MALAT1 suggests a biomarker of poor prognosis in colorectal cancer. Int J Clin Exp Pathol. 7:3174–3181. 2014.PubMed/NCBI
8	Ge X, Chen Y, Liao X, Liu D, Li F, Ruan H and Jia W: Overexpression of long noncoding RNA PCAT-1 is a novel biomarker of poor prognosis in patients with colorectal cancer. Med Oncol. 30:5882013. View Article : Google Scholar : PubMed/NCBI
9	Qi P, Xu MD, Ni SJ, Huang D, Wei P, Tan C, Zhou XY and Du X: Low expression of LOC285194 is associated with poor prognosis in colorectal cancer. J Transl Med. 11:1222013. View Article : Google Scholar : PubMed/NCBI
10	Ozawa T, Matsuyama T, Toiyama Y, Takahashi N, Ishikawa T, Uetake H, Yamada Y, Kusunoki M, Calin G and Goel A: CCAT1 and CCAT2 long noncoding RNAs, located within the 8q.24.21 ‘gene desert’, serve as important prognostic biomarkers in colorectal cancer. Ann Oncol. 28:1882–1888. 2017. View Article : Google Scholar : PubMed/NCBI
11	Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S, et al: GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22:1760–1774. 2012. View Article : Google Scholar : PubMed/NCBI
12	Sillar B and Plint CW: The price of a false-negative result of mammography and an overenthusiastic lay press. Med J Aust. 151:4181989.PubMed/NCBI
13	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
14	McCarthy DJ, Chen Y and Smyth GK: Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40:4288–4297. 2012. View Article : Google Scholar : PubMed/NCBI
15	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
16	Wang P, Wang Y, Bo H, Zou X and Mao JH: A novel gene expression-based prognostic scoring system to predict survival in gastric cancer. Oncotarget. 7:55343–55351. 2016.PubMed/NCBI
17	Zhou M, Wang X, Shi H, Cheng L, Wang Z, Zhao H, Yang L and Sun J: Characterization of long non-coding RNA-associated ceRNA network to reveal potential prognostic lncRNA biomarkers in human ovarian cancer. Oncotarget. 7:12598–12611. 2016.PubMed/NCBI
18	Kolde R, Laur S, Adler P and Vilo J: Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics. 28:573–580. 2012. View Article : Google Scholar : PubMed/NCBI
19	Adler P, Kolde R, Kull M, Tkachenko A, Peterson H, Reimand J and Vilo J: Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods. Genome Biol. 10:R1392009. View Article : Google Scholar : PubMed/NCBI
20	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
21	Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al: The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45:D362–D368. 2017. View Article : Google Scholar : PubMed/NCBI
22	Xie C, Mao X, Huang J, Ding Y, Wu J, Dong S, Kong L, Gao G, Li CY and Wei L: KOBAS 2.0: A web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 39:W316–W322. 2011. View Article : Google Scholar : PubMed/NCBI
23	Yu G, Wang LG, Han Y and He QY: clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS. 16:284–287. 2012. View Article : Google Scholar : PubMed/NCBI
24	Xu MD, Qi P and Du X: Long non-coding RNAs in colorectal cancer: Implications for pathogenesis and clinical application. Mod Pathol. 27:1310–1320. 2014. View Article : Google Scholar : PubMed/NCBI
25	Wang P, Xue Y, Han Y, Lin L, Wu C, Xu S, Jiang Z, Xu J, Liu Q and Cao X: The STAT3-binding long noncoding RNA lnc-DC controls human dendritic cell differentiation. Science. 344:310–313. 2014. View Article : Google Scholar : PubMed/NCBI
26	Li Q and Liu Q: Noncoding RNAs in Cancer Immunology. Adv Exp Med Biol. 927:2432016. View Article : Google Scholar : PubMed/NCBI
27	Wu GC, Li J, Leng RX, Li XP, Li XM, Wang DG, Pan HF and Ye DQ: Identification of long non-coding RNAs GAS5, linc0597 and lnc-DC in plasma as novel biomarkers for systemic lupus erythematosus. Oncotarget. 8:23650–23663. 2017.PubMed/NCBI
28	Zhang W, Zhou Y and Ding Y: Lnc-DC mediates the over-maturation of decidual dendritic cells and induces the increase in Th1 cells in preeclampsia. Am J Reprod Immunol. 77:2017. View Article : Google Scholar
29	Di Salvo TG, Guo Y, Su YR, Clark T, Brittain E, Absi T, Maltais S and Hemnes A: Right ventricular long noncoding RNA expression in human heart failure. Pulm Circ. 5:135–161. 2015. View Article : Google Scholar : PubMed/NCBI
30	Roxburgh CS, Mcmillan DC, Richards CH, Atwan M, Anderson JH, Harvey T, Horgan PG and Foulis AK: The clinical utility of the combination of T stage and venous invasion to predict survival in patients undergoing surgery for colorectal cancer. Ann Surg. 259:1156–1165. 2014. View Article : Google Scholar : PubMed/NCBI
31	Berger AC, Sigurdson ER, Levoyer T, Hanlon A, Mayer RJ, Macdonald JS, Catalano PJ and Haller DG: Colon cancer survival is associated with decreasing ratio of metastatic to examined lymph nodes. J Clin Oncol. 23:8706–8712. 2005. View Article : Google Scholar : PubMed/NCBI
32	Martin TA: The role of tight junctions in cancer metastasis. Semin Cell Dev Biol. 36:224–231. 2014. View Article : Google Scholar : PubMed/NCBI
33	Eke I and Cordes N: Focal adhesion signaling and therapy resistance in cancer. Seminars in Cancer Biol. 31:65–75. 2015. View Article : Google Scholar
34	Olson MF and Sahai E: The actin cytoskeleton in cancer cell motility. Clin Exp Metastasis. 26:273–287. 2009. View Article : Google Scholar : PubMed/NCBI
35	Costa A, Scholerdahirel A and Mechta-Grigoriou F: The role of reactive oxygen species and metabolism on cancer cells and their microenvironment. Semin Cancer Biol. 25:23–32. 2014. View Article : Google Scholar : PubMed/NCBI
36	Colin DJ, Limagne E, Ragot K, Lizard G, Ghiringhelli F, Solary É, Chauffert B, Latruffe N and Delmas D: The role of reactive oxygen species and subsequent DNA-damage response in the emergence of resistance towards resveratrol in colon cancer models. Cell Death Dis. 5:e15332014. View Article : Google Scholar : PubMed/NCBI
37	Chang CF, Diers AR and Hogg N: Cancer cell metabolism and the modulating effects of nitric oxide. Free Radic Biol Med. 79:324–336. 2015. View Article : Google Scholar : PubMed/NCBI
38	Wink DA, Vodovotz Y, Laval J, Laval F, Dewhirst MW and Mitchell JB: The multifaceted roles of nitric oxide in cancer. Carcinogenesis. 19:711–721. 1998. View Article : Google Scholar : PubMed/NCBI