The molecular mechanism of tumorigenesis of the prevalent cancer hepatocellular carcinoma (HCC) is unclear. In this study, through weighted gene coexpression network analysis, a coexpression network was constructed by selecting the top 25% most variant genes in the dataset GSE62232. The average linkage hierarchical clustering identified 24 modules, and among them, the pink module associated with prognosis of HCC was screened. Five gene candidates (PCNA, RFC4, PTTG1, H2AFZ, and RRM1) with a common network in the module were screened after the protein–protein interaction network complex was combined with the coexpression network. After progression and survival analysis, all candidates were identified as real core genes. According to the Human Protein Atlas and the Oncomine database, these genes were dysregulated in HCC samples. The receiver operating characteristic curve proved that the expression levels of the core genes had high diagnostic efficacy. The results of gene set enrichment analysis and functional enrichment analysis demonstrated the importance of the cell cycle-related pathways in HCC progression and prognosis. In conclusion, the five real core genes and cell cycle-related pathways identified in this study could greatly improve the knowledge about HCC progression and contribute to HCC treatment.
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References
1.
AkitaH., ZhengZ., TakedaY., KimC., KittakaN., KobayashiS., et al. (2009). Significance of RRM1 and ERCC1 expression in resectable pancreatic adenocarcinoma. Oncogene, 28, 2903–2909.
2.
AraiM., KondohN., ImazekiN., HadaA., HatsuseK., MatsubaraO., et al. (2009). The knockdown of endogenous replication factor C4 decreases the growth and enhances the chemosensitivity of hepatocellular carcinoma cells. Liver Int, 29, 55–62.
3.
BotiaJ.A., VandrovcovaJ., ForaboscoP., GuelfiS., D'SaK., HardyJ., et al. (2017). An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks. BMC Syst Biol 11,47.
4.
ChenW., ZhengR., BaadeP.D., ZhangS., ZengH., BrayF., et al. (2016). Cancer statistics in China, 2015. CA Cancer J Clin, 66, 115–132.
5.
ChenY., HuangY., ChenD.M., WuC., LengQ.P., WangW.Y., et al. (2018). RRM1 expression and the clinicopathological characteristics of patients with non-small cell lung cancer treated with gemcitabine. Onco Targets Ther, 11, 5579–5589.
6.
Cho-RokJ., YooJ., JangY.J., KimS., ChuI.S., YeomY.I., et al. (2006). Adenovirus-mediated transfer of siRNA against PTTG1 inhibits liver cancer cell growth in vitro and in vivo. Hepatology, 43, 1042–1052.
7.
DunicanD.S., McWilliamP., TigheO., Parle-McDermottA., and CrokeD.T. (2002). Gene expression differences between the microsatellite instability (MIN) and chromosomal instability (CIN) phenotypes in colorectal cancer revealed by high-density cDNA array hybridization. Oncogene, 21, 3253–3257.
8.
FornerA., ReigM., and BruixJ. (2018). Hepatocellular carcinoma. Lancet, 391, 1301–1314.
9.
FujiiT., NomotoS., KoshikawaK., YatabeY., TeshigawaraO., MoriT., et al. (2006). Overexpression of pituitary tumor transforming gene 1 in HCC is associated with angiogenesis and poor prognosis. Hepatology, 43, 1267–1275.
10.
HoY.J., LinY.M., HuangY.C., ShiB., YehK.T., GongZ., et al. (2017). Prognostic significance of high YY1AP1 and PCNA expression in colon adenocarcinoma. Biochem Biophys Res Commun, 494, 173–180.
11.
HuaS., KallenC.B., DharR., BaqueroM.T., MasonC.E., RussellB.A., et al. (2008). Genomic analysis of estrogen cascade reveals histone variant H2A.Z associated with breast cancer progression. Mol Syst Biol 4,188.
12.
JohnsonA., YaoN.Y., BowmanG.D., KuriyanJ., and O'DonnellM. (2006). The replication factor C clamp loader requires arginine finger sensors to drive DNA binding and proliferating cell nuclear antigen loading. J Biol Chem, 281, 35531–35543.
13.
KandothC., McLellanM.D., VandinF., YeK., NiuB., LuC., et al. (2013). Mutational landscape and significance across 12 major cancer types. Nature, 502, 333–339.
14.
KumarA., and PurohitR. (2014). Use of long-term molecular dynamics simulation in predicting cancer associated SNPs. PLoS Comput Biol 10,e1003318.
15.
LangfelderP., and HorvathS. (2008). WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9,559.
16.
LiN., DengW., MaJ., WeiB., GuoK., ShenW., et al. (2015a). Prognostic evaluation of Nanog, Oct4, Sox2, PCNA, Ki67 and E-cadherin expression in gastric cancer. Med Oncol 32,433.
17.
LiX., LiuX., CuiD., WuX., and QianR. (2015b). Clinical significance of nucleostemin and proliferating cell nuclear antigen protein expression in non-small cell lung cancer. J BUON, 20, 1088–1093.
18.
LiuS., YangT.B., NanY.L., LiA.H., PanD.X., XuY., et al. (2017). Genetic variants of cell cycle pathway genes predict disease-free survival of hepatocellular carcinoma. Cancer Med, 6, 1512–1522.
19.
LiuX., GaoH., ZhangJ., and XueD. (2019). FAM83D is associated with gender, AJCC stage, overall survival and disease-free survival in hepatocellular carcinoma. Biosci Rep, 39, BSR20181640.
20.
LiuX., XuZ., HouC., WangM., ChenX., LinQ., et al. (2016). Inhibition of hepatitis B virus replication by targeting ribonucleotide reductase M2 protein. Biochem Pharmacol, 103, 118–128.
21.
MaL., LiuJ., LiuL., DuanG., WangQ., XuY., et al. (2014). Overexpression of the transcription factor MEF2D in hepatocellular carcinoma sustains malignant character by suppressing G2-M transition genes. Cancer Res, 74, 1452–1462.
22.
MaS., YangJ., LiJ., and SongJ. (2016). The clinical utility of the proliferating cell nuclear antigen expression in patients with hepatocellular carcinoma. Tumour Biol, 37, 7405–7412.
23.
MalumbresM., and BarbacidM. (2009). Cell cycle, CDKs and cancer: a changing paradigm. Nat Rev Cancer, 9, 153–166.
24.
MiaoR., LuoH., ZhouH., LiG., BuD., YangX., et al. (2014). Identification of prognostic biomarkers in hepatitis B virus-related hepatocellular carcinoma and stratification by integrative multi-omics analysis. J Hepatol, 61, 840–849.
25.
QianJ., ChenY., HuY., DengY., LiuY., LiG., et al. (2018). Arabidopsis replication factor C4 is critical for DNA replication during the mitotic cell cycle. Plant J, 94, 288–303.
26.
QiuZ.Q., and ZhaoK. (2014). Expression of ERCC1, RRM1 and LRP in non-small cell lung cancers and their influence on chemotherapeutic efficacy of gemcitabine concomitant with nedaplatin. Asian Pac J Cancer Prev, 15, 7303–7307.
27.
RangasamyD., GreavesI., and TremethickD.J. (2004). RNA interference demonstrates a novel role for H2A.Z in chromosome segregation. Nat Struct Mol Biol, 11, 650–655.
28.
RavaszE., SomeraA.L., MongruD.A., OltvaiZ.N., and BarabasiA.L. (2002). Hierarchical organization of modularity in metabolic networks. Science, 297, 1551–1555.
29.
RenQ., and JinB. (2017). The clinical value and biological function of PTTG1 in colorectal cancer. Biomed Pharmacother, 89, 108–115.
30.
SchulzeK., ImbeaudS., LetouzeE., AlexandrovL.B., CalderaroJ., RebouissouS., et al. (2015). Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat Genet, 47, 505–511.
31.
SheynkmanG.M., ShortreedM.R., FreyB.L., ScalfM., and SmithL.M. (2014). Large-scale mass spectrometric detection of variant peptides resulting from nonsynonymous nucleotide differences. J Proteome Res, 13, 228–240.
32.
SrihariS., KalimuthoM., LalS., SinglaJ., PatelD., SimpsonP.T., et al. (2016). Understanding the functional impact of copy number alterations in breast cancer using a network modeling approach. Mol Biosyst, 12, 963–972.
33.
StoimenovI., and HelledayT. (2009). PCNA on the crossroad of cancer. Biochem Soc Trans, 37, 605–613.
34.
SuG., MorrisJ.H., DemchakB., and BaderG.D. (2014). Biological network exploration with Cytoscape 3. Curr Protoc Bioinformatics, 47, 8–13.
35.
SungW.K., ZhengH., LiS., ChenR., LiuX., LiY., et al. (2012). Genome-wide survey of recurrent HBV integration in hepatocellular carcinoma. Nat Genet, 44, 765–769.
36.
TangJ., KongD., CuiQ., WangK., ZhangD., GongY., et al. (2018). Prognostic genes of breast cancer identified by gene co-expression network analysis. Front Oncol 8,374.
37.
TongY., TanY., ZhouC., and MelmedS. (2007). Pituitary tumor transforming gene interacts with Sp1 to modulate G1/S cell phase transition. Oncogene, 26, 5596–5605.
38.
TothC., SukosdF., ValicsekE., HerpelE., SchirmacherP., RennerM., et al. (2017). Expression of ERCC1, RRM1, TUBB3 in correlation with apoptosis repressor ARC, DNA mismatch repair proteins and p53 in liver metastasis of colorectal cancer. Int J Mol Med, 40, 1457–1465.
39.
VillanuevaA., HoshidaY., BattistonC., TovarV., SiaD., AlsinetC., et al. (2011). Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma. Gastroenterology, 140, 1501–1512.
40.
VogelsteinB., PapadopoulosN., VelculescuV.E., ZhouS., DiazL.J., and KinzlerK.W. (2013). Cancer genome landscapes. Science, 339, 1546–1558.
41.
WangS.C. (2014). PCNA: a silent housekeeper or a potential therapeutic target?. Trends Pharmacol Sci, 35, 178–186.
42.
WangZ., WeiW., SunC.K., ChuaM.S., and SoS. (2015). Suppressing the CDC37 cochaperone in hepatocellular carcinoma cells inhibits cell cycle progression and cell growth. Liver International, 35, 1403–1415.
43.
XiangJ., FangL., LuoY., YangZ., LiaoY., CuiJ., et al. (2014). Levels of human replication factor C4, a clamp loader, correlate with tumor progression and predict the prognosis for colorectal cancer. J Transl Med, 12, 320.
44.
YangH.D., KimP.J., EunJ.W., ShenQ., KimH.S., ShinW.C., et al. (2016). Oncogenic potential of histone-variant H2A.Z.1 and its regulatory role in cell cycle and epithelial-mesenchymal transition in liver cancer. Oncotarget, 7, 11412–11423.
45.
YeC., TaoR., CaoQ., ZhuD., WangY., WangJ., et al. (2016). Whole-genome DNA methylation and hydroxymethylation profiling for HBV-related hepatocellular carcinoma. Int J Oncol, 49, 589–602.
46.
YueS.Q., YangY.L., DouK.F., and LiK.Z. (2003). Expression of PCNA and CD44mRNA in colorectal cancer with venous invasion and its relationship to liver metastasis. World J Gastroenterol, 9, 2863–2865.
47.
ZhangZ., JinB., JinY., HuangS., NiuX., MaoZ., et al. (2017). PTTG1, A novel androgen responsive gene is required for androgen-induced prostate cancer cell growth and invasion. Exp Cell Res, 350, 1–8.
48.
ZhengY., GuoJ., ZhouJ., LuJ., ChenQ., ZhangC., et al. (2015). FoxM1 transactivates PTTG1 and promotes colorectal cancer cell migration and invasion. BMC Med Genomics 8,49.
49.
ZhengY., LiuY., ZhaoS., ZhengZ., ShenC., AnL., et al. (2018). Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma. Cancer Manag Res, 10, 6079–6096.
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