Advances in Clinical and Experimental Medicine

Title abbreviation: Adv Clin Exp Med
JCR Impact Factor (IF) – 1.736
5-Year Impact Factor – 2.135
Index Copernicus  – 166.39
MEiN – 70 pts

ISSN 1899–5276 (print)
ISSN 2451-2680 (online)
Periodicity – monthly

Download original text (EN)

Advances in Clinical and Experimental Medicine

Ahead of print

doi: 10.17219/acem/150256

Publication type: original article

Language: English

Download citation:

  • BIBTEX (JabRef, Mendeley)
  • RIS (Papers, Reference Manager, RefWorks, Zotero)

Cite as:


Xu C, Qi X. Development and validation of a 4-lncRNA combined prediction model for patients with hepatocellular carcinoma [published online as ahead of print on August 1, 2022]. Adv Clin Exp Med. 2022. doi:10.17219/acem/150256

Development and validation of a 4-lncRNA combined prediction model for patients with hepatocellular carcinoma

Cui Xu1,B,D, Xiangxiu Qi1,A,F

1 Department of General Surgery, ShengJing Hospital of China Medical University, Shenyang, China

Abstract

Background. Hepatocellular carcinoma (HCC) is one of the most common and lethal cancers worldwide. Therefore, it is necessary to develop and validate a novel prognostic model for HCC patients.
Objectives. To establish an innovative and valuable prediction model of long non-coding RNAs (lncRNAs) for HCC.
Material and Methods. Transcriptome and clinical data from The Cancer Genome Atlas (TCGA) were analyzed globally using bioinformatic approaches. We used Cox and least absolute shrinkage and selection operator (LASSO) regression analyses to screen for prognostic lncRNAs, while receiver operating characteristic (ROC) and Kaplan–Meier curve analyses were used to evaluate the effectiveness of the models. Clinical data from our center were used as a validation set.
Results. In the training set, a prediction model was established based on the expression of AP000844.2, LINC00942, SRGAP3-AS2, and AC010280.2 Hepatocellular carcinoma patients were divided into 2 groups (high-risk group and low-risk group) according to their risk score, and differences in survival were compared between the groups. The clinical data from our center served as a validation set to re-evaluate the effectiveness of the predictive model. The model had an excellent performance. The area under the curve (AUC) of 3-year survival was 0.771, while for 5-year survival it was 0.741, and the concordance index (C-index) was 0.756 (standard error (SE) = 0.023, 95% confidence interval (95% CI) = 0.620–0.891).
Conclusion. The 4-lncRNA combination model is critically important in evaluating the prognosis of HCC. It is an effective independent prognostic factor, although prospective, multi-center studies are needed to validate our findings.

Key words

prognosis, survival analysis, hepatocellular carcinoma, least absolute shrinkage and selection operator

References (28)

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7–30. doi:10.3322/caac.21590
  2. Tsuchiya N. Biomarkers for the early diagnosis of hepatocellular carcinoma. World J Gastroenterol. 2015;21(37):10573–10583. doi:10.3748/wjg.v21.i37.10573
  3. Pillai A, Ahn J, Kulik L. Integrating genomics into clinical practice in hepatocellular carcinoma: The challenges ahead. Am J Gastroenterol. 2020;115(12):1960–1969. doi:10.14309/ajg.0000000000000843
  4. Trevisan França de Lima L, Broszczak D, Zhang X, Bridle K, Crawford D, Punyadeera C. The use of minimally invasive biomarkers for the diagnosis and prognosis of hepatocellular carcinoma. Biochim Biophys Acta Rev Cancer. 2020;1874(2):188451. doi:10.1016/j.bbcan.2020.188451
  5. Singal AG, Lok AS, Feng Z, Kanwal F, Parikh ND. Conceptual model for the hepatocellular carcinoma screening continuum: Current status and research agenda. Clin Gastroenterol Hepatol. 2022;20(1):9–18. doi:10.1016/j.cgh.2020.09.036
  6. Xiao Y, Hu J, Yin W. Systematic identification of non-coding RNAs. In: Li X, Xu J, Xiao Y, Ning S, Zhang Y, eds. Non-Coding RNAs in Complex Diseases. Vol 1094. Advances in Experimental Medicine and Biology. Singapore, Singapore: Springer Singapore; 2018:9–18. doi:10.1007/978-981-13-0719-5_2
  7. Delaunay S, Frye M. RNA modifications regulating cell fate in cancer. Nat Cell Biol. 2019;21(5):552–559. doi:10.1038/s41556-019-0319-0
  8. Evans JR, Feng FY, Chinnaiyan AM. The bright side of dark matter: lncRNAs in cancer. J Clin Invest. 2016;126(8):2775–2782. doi:10.1172/JCI84421
  9. Chu C, Lei X, Li Y, et al. High expression of miR-222-3p in children with Mycoplasma pneumoniae pneumonia. Ital J Pediatr. 2019;45(1):163. doi:10.1186/s13052-019-0750-7
  10. Qian X, Zhao J, Yeung PY, Zhang QC, Kwok CK. Revealing lncRNA structures and interactions by sequencing-based approaches. Trends Biochem Sci. 2019;44(1):33–52. doi:10.1016/j.tibs.2018.09.012
  11. Peng WX, Koirala P, Mo YY. LncRNA-mediated regulation of cell signaling in cancer. Oncogene. 2017;36(41):5661–5667. doi:10.1038/onc.2017.184
  12. Xie C, Li SY, Fang JH, Zhu Y, Yang JE. Functional long non-coding RNAs in hepatocellular carcinoma. Cancer Lett. 2021;500:281–291. doi:10.1016/j.canlet.2020.10.042
  13. Zhang DY, Sun QC, Zou XJ, et al. Long noncoding RNA UPK1A-AS1 indicates poor prognosis of hepatocellular carcinoma and promotes cell proliferation through interaction with EZH2. J Exp Clin Cancer Res. 2020;39(1):229. doi:10.1186/s13046-020-01748-y
  14. Yi T, Luo H, Qin F, et al. LncRNA LL22NC03-N14H11.1 promoted hepatocellular carcinoma progression through activating MAPK pathway to induce mitochondrial fission. Cell Death Dis. 2020;11(10):832. doi:10.1038/s41419-020-2584-z
  15. Gao H, Li L, Xiao M, et al. Elevated DKK1 expression is an independent unfavorable prognostic indicator of survival in head and neck squamous cell carcinoma. Cancer Manag Res. 2018;10:5083–5089. doi:10.2147/CMAR.S177043
  16. Wu Q, Pi L, Le Trinh T, et al. A novel vaccine targeting glypican-3 as a treatment for hepatocellular carcinoma. Mol Ther. 2017;25(10):2299–2308. doi:10.1016/j.ymthe.2017.08.005
  17. Sahu A, Singhal U, Chinnaiyan AM. Long noncoding RNAs in cancer: From function to translation. Trends Cancer. 2015;1(2):93–109. doi:10.1016/j.trecan.2015.08.010
  18. Zeng YL, Guo ZY, Su HZ, Zhong FD, Jiang KQ, Yuan GD. Diagnostic and prognostic value of lncRNA cancer susceptibility candidate 9 in hepatocellular carcinoma. World J Gastroenterol. 2019;25(48):6902–6915. doi:10.3748/wjg.v25.i48.6902
  19. Sun T, Wu Z, Wang X, et al. LNC942 promoting METTL14-mediated m6A methylation in breast cancer cell proliferation and progression. Oncogene. 2020;39(31):5358–5372. doi:10.1038/s41388-020-1338-9
  20. Silva IMW, Rosenfeld J, Antoniuk SA, Raskin S, Sotomaior VS. A 1.5Mb terminal deletion of 12p associated with autism spectrum disorder. Gene. 2014;542(1):83–86. doi:10.1016/j.gene.2014.02.058
  21. Li W, Chen QF, Huang T, Wu P, Shen L, Huang ZL. Identification and validation of a prognostic lncRNA signature for hepatocellular carcinoma. Front Oncol. 2020;10:780. doi:10.3389/fonc.2020.00780
  22. Liu S, Wang W, Zhao Y, Liang K, Huang Y. Identification of potential key genes for pathogenesis and prognosis in prostate cancer by integrated analysis of gene expression profiles and the cancer genome atlas. Front Oncol. 2020;10:809. doi:10.3389/fonc.2020.00809
  23. Huang ZL, Li W, Chen QF, Wu PH, Shen LJ. Eight key long non-coding RNAs predict hepatitis virus positive hepatocellular carcinoma as prognostic targets. World J Gastrointest Oncol. 2019;11(11):983–997. doi:10.4251/wjgo.v11.i11.983
  24. Yang Z, Li H, Wang Z, et al. Microarray expression profile of long non-coding RNAs in human lung adenocarcinoma: lncRNA expression in LAD. Thorac Cancer. 2018;9(10):1312–1322. doi:10.1111/1759-7714.12845
  25. Santosa F, Symes WW. Linear inversion of band-limited reflection seismograms. SIAM J Sci Stat Comput. 1986;7(4):1307–1330. doi:10.1137/0907087
  26. Tibshirani R. Regression shrinkage and selection via the lasso: A retrospective. J Royal Statist Soc B Statist Methodol. 2011;73(3):273–282. doi:10.1111/j.1467-9868.2011.00771.x
  27. Tibshirani R. The Lasso method for variable selection in the Cox model. Statist Med. 1997;16(4):385–395. doi:10.1002/(SICI)1097-0258 (19970228)16:4<385::AID-SIM380>3.0.CO;2-3
  28. Liu Y, Wu W, Hong S, et al. Lasso proteins: Modular design, cellular synthesis, and topological transformation. Angew Chem Int Ed. 2020;59(43):19153–19161. doi:10.1002/anie.202006727