A three‑mRNA status risk score has greater predictive ability compared with a lncRNA‑based risk score for predicting prognosis in patients with hepatocellular carcinoma

Zhang,Wenxia; Zhang,Wenxia; Fu,Qiang; Fu,Qiang; Yao,Kanyu; Yao,Kanyu

doi:10.3892/ol.2020.11911

A three‑mRNA status risk score has greater predictive ability compared with a lncRNA‑based risk score for predicting prognosis in patients with hepatocellular carcinoma

Authors:
- Wenxia Zhang
- Qiang Fu
- Kanyu Yao
View Affiliations

Affiliations: Department of Hepatobiliary Surgery, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia 010050, P.R. China, Department of General Surgery, Erenhot Hospital, Erenhot, Inner Mongolia 011100, P.R. China, Department of Emergency Surgery, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia 010050, P.R China
Published online on: July 24, 2020 https://doi.org/10.3892/ol.2020.11911
Article Number: 48
Copyright: © Zhang 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

Hepatocellular carcinoma (HCC) represents the fifth most common cause of cancer‑associated mortality in men, and the seventh in women, worldwide. The aim of the present study was to identify a reliable and robust RNA‑based risk score for the survival prediction of patients with hepatocellular carcinoma (HCC). Gene expression data from HCC and healthy control samples were obtained from The Cancer Genome Atlas to screen differentially expressed mRNAs and long non‑coding RNAs (lncRNAs). Univariate and multivariate Cox proportional‑hazards regression models and the LASSO algorithm for the Cox proportional‑hazards model (LASSO Cox‑PH model) were used to identify the prognostic mRNAs and lncRNAs among differentially expressed mRNAs (DEMs) and differentially expressed lncRNAs (DELs), respectively. Prognostic risk scores were generated based on the expression level or status of the prognostic lncRNAs and mRNAs, and the predictive abilities of these RNAs in TCGA and validation datasets were compared. Functional enrichment analyses were also performed. The results revealed a total of 154 downregulated and 625 upregulated mRNAs and 18 upregulated lncRNAs between tumor and control samples in TCGA dataset. A three‑mRNA and a five‑lncRNA expression signatures were identified using the LASSO Cox‑PH model. Three‑mRNA and five‑lncRNA expression and status risk scores were generated. Using likelihood ratio P‑values and area under the curve values from TCGA and the validation datasets, the three‑mRNA status risk score was more accurate compared with the other risk scores in predicting the mortality of patients with HCC. The three identified mRNAs, including hepatitis A virus cellular receptor 1, MYCN proto‑oncogene BHLH transcription factor and stratifin, were associated with the cell cycle and oocyte maturation pathways. Therefore, a three‑mRNA status risk score may be valuable and robust for risk stratification of patients with HCC. The three‑mRNA status risk score exhibited greater prognostic value compared with the lncRNA‑based risk score.

View Figures

View References

1	Siegel RL, Miller KD and Jemal A: Cancer statistics, 2017. CA Cancer J Clin. 67:7–30. 2017. View Article : Google Scholar : PubMed/NCBI
2	Mittal S and El-Serag HB: Epidemiology of hepatocellular carcinoma: Consider the population. J Clin Gastroenterol. 47 (Suppl):S2–S6. 2013. View Article : Google Scholar : PubMed/NCBI
3	Wallace MC, David P, Jeffrey GP and Adams LA: The evolving epidemiology of hepatocellular carcinoma: A global perspective. Expert Rev Gastroenterol Hepatol. 9:765–779. 2015. View Article : Google Scholar : PubMed/NCBI
4	Yang Y, Chen L, Gu J, Zhang H, Yuan J, Lian Q, Lv G, Wang S, Wu Y, Yang YT, et al: Recurrently deregulated lncRNAs in hepatocellular carcinoma. Nat Commun. 8:144212017. View Article : Google Scholar : PubMed/NCBI
5	Huo X, Han S, Wu G, Latchoumanin O, Zhou G, Hebbard L, George J and Qiao L: Dysregulated long noncoding RNAs (lncRNAs) in hepatocellular carcinoma: Implications for tumorigenesis, disease progression, and liver cancer stem cells. Mol Cancer. 16:1652017. View Article : Google Scholar : PubMed/NCBI
6	Gu JX, Zhang X, Miao RC, Xiang XH, Fu YN, Zhang JY, Liu C and Qu K: Six-long non-coding RNA signature predicts recurrence-free survival in hepatocellular carcinoma. World J Gastroenterol. 25:220–232. 2019. View Article : Google Scholar : PubMed/NCBI
7	Wang Z, Wu Q, Feng S, Zhao Y and Tao C: Identification of four prognostic LncRNAs for survival prediction of patients with hepatocellular carcinoma. PeerJ. 5:e3742017. View Article : Google Scholar
8	Yan J, Zhou C, Guo K, Li Q and Wang Z: A novel seven-lncRNA signature for prognosis prediction in hepatocellular carcinoma. J Cell Biochem. 120:213–223. 2019. View Article : Google Scholar : PubMed/NCBI
9	Li B, Feng W, Luo O, Xu T, Cao Y, Wu H, Yu D and Ding Y: Development and validation of a three-gene prognostic signature for patients with hepatocellular carcinoma. Sci Rep. 7:55172017. View Article : Google Scholar : PubMed/NCBI
10	Shi YM, Li YY, Lin JY, Zheng L, Zhu YM and Huang J: The discovery of a novel eight-mRNA-lncRNA signature predicting survival of hepatocellular carcinoma patients. J Cell Biochem. 2018.(Online ahead of print).
11	Tomczak K, Czerwińska P and Wiznerowicz M: The cancer genome Atlas (TCGA): An immeasurable source of knowledge. Contemp Oncol (Pozn). 19:A68–A77. 2015.PubMed/NCBI
12	Kim SM, Leem SH, Chu IS, Park YY, Kim SC, Kim SB, Park ES, Lim JY, Heo J, Kim YJ, et al: Sixty-five gene-based risk score classifier predicts overall survival in hepatocellular carcinoma. Hepatology. 55:1443–1452. 2012. View Article : Google Scholar : PubMed/NCBI
13	Chaudhary K, Poirion OB, Lu L and Garmire LX: Deep learning based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res. 24:1248–1259. 2018. View Article : Google Scholar : PubMed/NCBI
14	Li S, Chen X, Liu X, Yu Y, Pan H, Haak R, Schmidt J, Ziebolz D and Schmalz G: Complex integrated analysis of lncRNAs-miRNAs-mRNAs in oral squamous cell carcinoma. Oral Oncol. 73:1–9. 2017. View Article : Google Scholar : PubMed/NCBI
15	Liu JY, Yao J, Li XM, Song YC, Wang XQ, Li YJ, Yan B and Jiang Q: Pathogenic role of lncRNA-MALAT1 in endothelial cell dysfunction in diabetes mellitus. Cell Death Dis. 5:e15062014. View Article : Google Scholar : PubMed/NCBI
16	Wang P, Wang Y, Hang B, Zou X and Mao JH: A novel gene expression-based prognostic scoring system to predict survival in gastric cancer. Oncotarget. 7:55343–55351. 2016. View Article : Google Scholar : PubMed/NCBI
17	Goeman JJ: L1 penalized estimation in the Cox proportional hazards model. Biom J. 52:70–84. 2010.PubMed/NCBI
18	Anevski D: Functional central limit theorems for the Nelson-Aalen and Kaplan-Meier estimators for dependent stationary data. Statistics Probability Lett. 124:83–91. 2017. View Article : Google Scholar
19	Zhi S, Liu Y, Li Z and Zhang J: A weighted likelihood ratio test-based chart for monitoring process mean and variability. J Statistical Computation Simulation. 88:1–22. 2018.
20	Obuchowski NA and Bullen JA: Receiver operating characteristic (ROC) curves: Review of methods with applications in diagnostic medicine. Phy Med Biol. 63:07TR012018. View Article : Google Scholar
21	Khaksar E, Askarishahi M, Hekmatimoghaddam S and Vahedian-Ardakani H: Cox regression and parametric models comparison of how they determine factors influencing survival of patients with non-small cell lung carcinoma. Asian Pac J Cancer Prev. 18:3389–3393. 2017.PubMed/NCBI
22	Eng KH, Emily S and Kayla M: On representing the prognostic value of continuous gene expression biomarkers with the restricted mean survival curve. Oncotarget. 6:36308–36318. 2015. View Article : Google Scholar : PubMed/NCBI
23	Wu J, Zhou L, Huang L, Gu J, Li S, Liu B, Feng J and Zhou Y: Nomogram integrating gene expression signatures with clinicopathological features to predict survival in operable NSCLC: A pooled analysis of 2164 patients. J Exp Clin Cancer Res. 36:42017. View Article : Google Scholar : PubMed/NCBI
24	Huang G, Zhao G, Xia J, Wei Y, Chen F, Chen J and Shi J: FGF2 and FAM201A affect the development of osteonecrosis of the femoral head after femoral neck fracture. Gene. 652:39–47. 2018. View Article : Google Scholar : PubMed/NCBI
25	Balogh J, Victor D III, Asham EH, Burroughs SG, Boktour M, Saharia A, Li X, Ghobrial RM and Monsour HP Jr: Hepatocellular carcinoma: A review. J Hepatocell Carcinoma. 3:41–53. 2016. View Article : Google Scholar : PubMed/NCBI
26	Telford EJ, Jiang WG and Martin TA: HAVcR-1 involvement in cancer progression. Histol Histopathol. 32:121–128. 2017.PubMed/NCBI
27	Liu L, Song Z, Zhao Y, Li C, Wei H, Ma J and Du Y: HAVCR1 expression might be a novel prognostic factor for gastric cancer. PLoS One. 13:e02064232018. View Article : Google Scholar : PubMed/NCBI
28	Lee JK, Phillips JW, Smith BA, Park JW, Stoyanova T, McCaffrey EF, Baertsch R, Sokolov A, Meyerowitz JG, Mathis C, et al: N-myc drives neuroendocrine prostate cancer initiated from human prostate epithelial cells. Cancer Cell. 29:536–547. 2016. View Article : Google Scholar : PubMed/NCBI
29	Rickman DS, Schulte JH and Eilers M: The expanding world of N-MYC-driven tumors. Cancer Discov. 8:150–163. 2018. View Article : Google Scholar : PubMed/NCBI
30	Lin CP, Liu CR, Lee CN, Chan TS and Liu HE: Targeting c-Myc as a novel approach for hepatocellular carcinoma. World J Hepatol. 2:16–20. 2010. View Article : Google Scholar : PubMed/NCBI
31	Schlaeger C, Longerich T, Schiller C, Bewerunge P, Mehrabi A, Toedt G, Kleeff J, Ehemann V, Eils R, Lichter P and Bernhard PS: Etiology-dependent molecular mechanisms in human hepatocarcinogenesis. Hepatology. 47:511–520. 2008. View Article : Google Scholar : PubMed/NCBI
32	Qin XY, Suzuki H, Honda M, Okada H, Kaneko S, Inoue I, Ebisui E, Hashimoto K, Carninci P, Kanki K, et al: Prevention of hepatocellular carcinoma by targeting MYCN-positive liver cancer stem cells with acyclic retinoid. Proc Natl Acad Sci USA. 115:4969–4974. 2018. View Article : Google Scholar : PubMed/NCBI
33	Wu YJ, Jan YJ, Ko BS, Liang SM and Liou JY: Involvement of 14-3-3 proteins in regulating tumor progression of hepatocellular carcinoma. Cancers (Basel). 7:1022–1036. 2015. View Article : Google Scholar : PubMed/NCBI
34	Zhang Y, Li Y, Lin C, Ding J, Liao G and Tang B: Aberrant upregulation of 14-3-3σ and EZH2 expression serves as an inferior prognostic biomarker for hepatocellular carcinoma. PLoS One. 9:e1072512014. View Article : Google Scholar : PubMed/NCBI
35	Liu CC, Jan YJ, Ko BS, Wu YM, Liang SM, Chen SC, Lee YM, Liu TA, Chang TC, Wang J, et al: 14-3-3σ induces heat shock protein 70 expression in hepatocellular carcinoma. BMC Cancer. 14:4252014. View Article : Google Scholar : PubMed/NCBI
36	Wang Y, Jiang T, Li Z, Lu L, Zhang R, Zhang D, Wang X and Tan J: Analysis of differentially co-expressed genes based on microarray data of hepatocellular carcinoma. Neoplasma. 64:216–221. 2017. View Article : Google Scholar : PubMed/NCBI
37	Hainaut P, Amadou A and Gormally E: Cancer prevention and control: Hepatocellular carcinoma. Ecancermedicalscience. 13:9492019. View Article : Google Scholar : PubMed/NCBI
38	Yeh YT, Chang CW, Wei RJ and Wang SN: Progesterone and related compounds in hepatocellular carcinoma: Basic and clinical aspects. Biomed Res Int. 2013:2905752013. View Article : Google Scholar : PubMed/NCBI
39	Ma WL, Hsu CL, Yeh CC, Wu MH, Huang CK, Jeng LB, Hung YC, Lin TY, Yeh S and Chang C: Hepatic androgen receptor suppresses hepatocellular carcinoma metastasis through modulation of cell migration and anoikis. Hepatology. 56:176–185. 2012. View Article : Google Scholar : PubMed/NCBI
40	Keng VW, Largaespada DA and Augusto V: Why men are at higher risk for hepatocellular carcinoma? J Hepatol. 57:453–454. 2012. View Article : Google Scholar : PubMed/NCBI
41	Chang WT, Hsieh BS, Cheng HL, Lee KT and Chang KL: Progesterone augments epirubicin-induced apoptosis in HA22T/VGH cells by increasing oxidative stress and upregulating Fas/FasL. J Surg Res. 188:432–441. 2014. View Article : Google Scholar : PubMed/NCBI
42	Chang WT, Cheng HL, Hsieh BS, Chiu CC, Lee KT and Chang KL: Progesterone increases apoptosis and inversely decreases autophagy in human hepatoma HA22T/VGH cells treated with epirubicin. ScientificWorldJournal. 2014:5671482014. View Article : Google Scholar : PubMed/NCBI