Construction and validation of prognostic risk model for patients with hepatocellular carcinoma based on bioinformatics analysis

HU Wenting; MIAO Xiaye; YANG Bingyin; YE Bicheng

doi:10.7619/jcmp.20213658

Journal of Clinical Medicine in Practice > 2022 > 26(4): 119-126. > DOI: 10.7619/jcmp.20213658

HU Wenting, MIAO Xiaye, YANG Bingyin, YE Bicheng. Construction and validation of prognostic risk model for patients with hepatocellular carcinoma based on bioinformatics analysis[J]. Journal of Clinical Medicine in Practice, 2022, 26(4): 119-126. DOI: 10.7619/jcmp.20213658

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Construction and validation of prognostic risk model for patients with hepatocellular carcinoma based on bioinformatics analysis

1.
Huaian Hospital Affiliated to Xuzhou Medical University, Huaian, Jiangsu, 223001
2.
Medical College of Yangzhou University, Yangzhou, Jiangsu, 225002
3.
School of Medicine of Yangzhou Polytechnic College, Yangzhou, Jiangsu, 225002

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Received Date: September 10, 2021
Available Online: March 21, 2022
Published Date: February 27, 2022

Graphical Abstract

Abstract

Abstract

Objective To construct a prognostic risk model for clinical treatment of hepatocellular carcinoma (HCC) based on public databases.
Methods The mRNA expression data and clinical information of HCC and adjacent normal tissues were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Differentially expressed genes (DEGs) related to overall survival (OS) were screened in the TCGA cohort, 2 or 3 mRNAs were selected to form a combination, and Cox risk proportional regression model was constructed for all combinations. The optimal gene combination was determined by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and external validation based on ICGC cohort was carried out. The patients were divided into high-risk group and low-risk group according to the median risk score of TCGA cohort, gene set enrichment analysis (GSEA) was performed, and the relative half-inhibitory concentrations (IC₅₀) of sorafenib, mitomycin, etoposide, adriamycin, paclitaxel and cisplatin in HCC patients were predicted by pRRophetic R software package.
Results For this prognostic risk model, the AUC of the ROC curve for predicting 1-, 3- and 5-year OS in the TCGA cohort were 0.786, 0.713 and 0.699, respectively, and the AUC of the ROC curve for predicting 1-, 3- and 4-year OS in the ICGC cohort were 0.719, 0.709 and 0.766, respectively. GSEA revealed that cell cycle related pathways were activated and bile acid metabolism was inhibited in the high-risk group. The IC₅₀ of sorafenib in the low-risk group was significantly lower than that in the high-risk group, while the IC₅₀ of cell cycle related chemotherapy drugs in the low-risk group was significantly higher than that in the high-risk group (P < 0.05).
Conclusion This study establishes and verifies the prognostic risk model for HCC, and provides a reference for the formulation of individualized diagnosis and treatment plan for HCC patients.

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References

[1]	VILLANUEVA A. Hepatocellular carcinoma[J]. N Engl J Med, 2019, 380(15): 1450-1462. doi: 10.1056/NEJMra1713263
[2]	YANG J D, HAINAUT P, GORES G J, et al. A global view of hepatocellular carcinoma: trends, risk, prevention and management[J]. Nat Rev Gastroenterol Hepatol, 2019, 16(10): 589-604. doi: 10.1038/s41575-019-0186-y
[3]	TORRE L A, BRAY F, SIEGEL R L, et al. Global cancer statistics, 2012[J]. CA Cancer J Clin, 2015, 65(2): 87-108. doi: 10.3322/caac.21262
[4]	BROWN Z J, GRETEN T F, HEINRICH B. Adjuvant treatment of hepatocellular carcinoma: prospect of immunotherapy[J]. Hepatology, 2019, 70(4): 1437-1442. doi: 10.1002/hep.30633
[5]	HUANG W T, SKANDERUP A J, LEE C G. Advances in genomic hepatocellular carcinoma research[J]. Gigascience, 2018, 7(11): 1-22. http://www.onacademic.com/detail/journal_1000041617495499_6d02.html
[6]	DOMINGUEZ D A, WANG X W. Impact of next-generation sequencing on outcomes in hepatocellular carcinoma: how precise are we really[J]. J Hepatocell Carcinoma, 2020, 7: 33-37. doi: 10.2147/JHC.S217948
[7]	CARUSO S, O'BRIEN D R, CLEARY S P, et al. Genetics of hepatocellular carcinoma: approaches to explore molecular diversity[J]. Hepatology, 2021, 73(Suppl 1): 14-26. http://www.researchgate.net/publication/348322949_Genetics_of_Hepatocellular_Carcinoma_Approaches_to_Explore_Molecular_Diversity
[8]	GEELEHER P, COX N, HUANG R S. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels[J]. PLoS One, 2014, 9(9): e107468. doi: 10.1371/journal.pone.0107468
[9]	PAN Y S, CHEN H R, YU J. Biomarkers in hepatocellular carcinoma: current status and future perspectives[J]. Biomedicines, 2020, 8(12): 576. doi: 10.3390/biomedicines8120576
[10]	OURA K, MORISHITA A, MASAKI T. Molecular and functional roles of microRNAs in the progression of hepatocellular carcinoma-A review[J]. Int J Mol Sci, 2020, 21(21): 8362. doi: 10.3390/ijms21218362
[11]	KIM E, VIATOUR P. Hepatocellular carcinoma: old friends and new tricks[J]. Exp Mol Med, 2020, 52(12): 1898-1907. doi: 10.1038/s12276-020-00527-1
[12]	MUINAO T, DEKA BORUAH H P, PAL M. Multi-biomarker panel signature as the key to diagnosis of ovarian cancer[J]. Heliyon, 2019, 5(12): e02826. doi: 10.1016/j.heliyon.2019.e02826
[13]	FOUNTZILAS C, KAKLAMANI V G. Multi-gene panel testing in breast cancer management[J]. Cancer Treat Res, 2018, 173: 121-140.
[14]	YANG Z C, ZI Q, XU K, et al. Development of a macrophages-related 4-gene signature and nomogram for the overall survival prediction of hepatocellular carcinoma based on WGCNA and LASSO algorithm[J]. Int Immunopharmacol, 2021, 90: 107238. doi: 10.1016/j.intimp.2020.107238
[15]	LIU G M, XIE W X, ZHANG C Y, et al. Identification of a four-gene metabolic signature predicting overall survival for hepatocellular carcinoma[J]. J Cell Physiol, 2020, 235(2): 1624-1636. doi: 10.1002/jcp.29081
[16]	LIN P, HE R Q, DANG Y W, et al. An autophagy-related gene expression signature for survival prediction in multiple cohorts of hepatocellular carcinoma patients[J]. Oncotarget, 2018, 9(25): 17368-17395. doi: 10.18632/oncotarget.24089
[17]	LI G X, XU W Q, ZHANG L, et al. Development and validation of a CIMP-associated prognostic model for hepatocellular carcinoma[J]. EBioMedicine, 2019, 47: 128-141. doi: 10.1016/j.ebiom.2019.08.064
[18]	YU J, WU X L, LV M, et al. A model for predicting prognosis in patients with esophageal squamous cell carcinoma based on joint representation learning[J]. Oncol Lett, 2020, 20(6): 387. http://www.ncbi.nlm.nih.gov/pubmed/33193847
[19]	YANG Y J, WANG C Y, WEI N D, et al. Identification of prognostic chromatin-remodeling genes in clear cell renal cell carcinoma[J]. Aging (Albany NY), 2020, 12(24): 25614-25642.
[20]	HONG W F, LIANG L, GU Y J, et al. Immune-related lncRNA to construct novel signature and predict the immune landscape of human hepatocellular carcinoma[J]. Mol Ther Nucleic Acids, 2020, 22: 937-947. doi: 10.1016/j.omtn.2020.10.002
[21]	陈懿, 李雪, 林文雅, 等. 自噬基因预测肝癌患者长期生存及通路分析[J]. 医学研究杂志, 2021, 50(1): 137-141. https://www.cnki.com.cn/Article/CJFDTOTAL-YXYZ202101031.htm
[22]	段万里, 任伟, 邓骞, 等. 基于TCGA数据库的肾癌自噬相关基因预后模型的建立与应用[J]. 现代泌尿外科杂志, 2020, 25(10): 870-875, 889. doi: 10.3969/j.issn.1009-8291.2020.10.003
[23]	TABUSE M, OHTA S, OHASHI Y, et al. Functional analysis of HOXD9 in human gliomas and glioma cancer stem cells[J]. Mol Cancer, 2011, 10: 60. doi: 10.1186/1476-4598-10-60
[24]	LONG J Y, ZHANG L, WAN X S, et al. A four-gene-based prognostic model predicts overall survival in patients with hepatocellular carcinoma[J]. J Cell Mol Med, 2018, 22(12): 5928-5938. doi: 10.1111/jcmm.13863
[25]	LV X P, LI L L, LV L, et al. HOXD9 promotes epithelial-mesenchymal transition and cancer metastasis by ZEB1 regulation in hepatocellular carcinoma[J]. J Exp Clin Cancer Res, 2015, 34: 133. doi: 10.1186/s13046-015-0245-3
[26]	CHRISTIANSEN A, DYRSKJOT L. The functional role of the novel biomarker karyopherin α 2 (KPNA2) in cancer[J]. Cancer Lett, 2013, 331(1): 18-23. doi: 10.1016/j.canlet.2012.12.013
[27]	GUO X G, WANG Z H, ZHANG J N, et al. Upregulated KPNA2 promotes hepatocellular carcinoma progression and indicates prognostic significance across human cancer types[J]. Acta Biochim Biophys Sin (Shanghai), 2019, 51(3): 285-292. doi: 10.1093/abbs/gmz003
[28]	JIANG P, TANG Y Q, HE L, et al. Aberrant expression of nuclear KPNA2 is correlated with early recurrence and poor prognosis in patients with small hepatocellular carcinoma after hepatectomy[J]. Med Oncol, 2014, 31(8): 1-7.
[29]	KONIROVA J, OLTOVA J, CORLETT A, et al. Modulated DISP3/PTCHD2 expression influences neural stem cell fate decisions[J]. Sci Rep, 2017, 7: 41597. doi: 10.1038/srep41597
[30]	XIE G X, WANG X N, HUANG F J, et al. Dysregulated hepatic bile acids collaboratively promote liver carcinogenesis[J]. Int J Cancer, 2016, 139(8): 1764-1775. doi: 10.1002/ijc.30219
[31]	CHEN W B, OU M L, TANG D E, et al. Identification and validation of immune-related gene prognostic signature for hepatocellular carcinoma[J]. J Immunol Res, 2020, 2020: 5494858. http://qikan.cqvip.com/Qikan/Article/Detail?id=7106408960
[32]	STACEY D, KAZLAUSKAS A. Regulation of Ras signaling by the cell cycle[J]. Curr Opin Genet Dev, 2002, 12(1): 44-46. doi: 10.1016/S0959-437X(01)00262-3
[33]	MATSUDA Y. Molecular mechanism underlying the functional loss of cyclindependent kinase inhibitors p16 and p27 in hepatocellular carcinoma[J]. World J Gastroenterol, 2008, 14(11): 1734-1740. doi: 10.3748/wjg.14.1734
[34]	MATSUDA Y, ICHIDA T. p16 and p27 are functionally correlated during the progress of hepatocarcinogenesis[J]. Med Mol Morphol, 2006, 39(4): 169-175. doi: 10.1007/s00795-006-0339-2
[35]	GREENBAUM L E. Cell cycle regulation and hepatocarcinogenesis[J]. Cancer Biol Ther, 2004, 3(12): 1200-1207. doi: 10.4161/cbt.3.12.1392

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Construction and validation of prognostic risk model for patients with hepatocellular carcinoma based on bioinformatics analysis