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. 2011 Apr 28;44(3):291–299. doi: 10.1111/j.1365-2184.2011.00752.x

Cyclin‐dependent kinase inhibitor 3 (CDKN3) novel cell cycle computational network between human non‐malignancy associated hepatitis/cirrhosis and hepatocellular carcinoma (HCC) transformation

L Wang 1, L Sun 1, J Huang 1, M Jiang 2
PMCID: PMC6496776  PMID: 21535270

Abstract

The relationship of cyclin‐dependent kinase inhibitor 3 (CDKN3) with tumours has previously been presented in a number of publications. However, the molecular network and interpretation of CDKN3 through the cell cycle between non‐malignancy associated hepatitis/cirrhosis and hepatocellular carcinoma (HCC) have remained to be elucidated. Here, we have constructed and analysed significant high expression gene CDKN3 activated and inhibited cell cycle networks from 25 HCC versus 25 non‐malignancy associated hepatitis/cirrhosis patients (viral infection HCV or HBV) in GEO Dataset GSE10140‐10141, by combination of a gene regulatory network inference method based on linear programming, and decomposition procedure using CapitalBio MAS 3.0 software, based on integration of public databases including Gene Ontology, KEGG, BioCarta, GenMapp, Intact, UniGene, OMIM, and others. Comparing the same and differently activated and inhibited CDKN3 networks with GO analysis, between non‐malignancy associated hepatitis/cirrhosis and HCC, our results suggest a CDKN3 cell cycle network (i) with stronger DNA replication and with weaker ubiquitin‐dependent protein catabolism as common characteristics in both non‐malignancy associated hepatitis/cirrhosis and HCC; (ii) with more cell division and weaker mitotic G2 checkpoint in non‐malignancy associated hepatitis/cirrhosis; (iii) with stronger cell cycle and weaker cytokinesis, as a result forming multinucleate cells in HCC. Thus, it is useful to identify CDKN3 cell cycle networks for comprehension of molecular mechanism between non‐malignancy associated hepatitis/cirrhosis and HCC transformation.

Introduction

Cyclin‐dependent kinase inhibitor 3 (CDKN3) is one of the genes our group has identified as highly expressed in 25 HCC versus 25 non‐malignancy associated hepatitis/cirrhosis cases. We data‐mined CDKN3 roles in cell components, molecular functions, biological processes, KEGG, GenMAPP, BioCarta and disease, from CapitalBio MAS 3.0 software (CapitalBio Corporation, Beijing, China). CDKN3 cell component is localized in perinuclear regions, molecular function is comprised of protein serine/threonine phosphatase activity, protein tyrosine phosphatase activity, protein binding, protein tyrosine • serine • threonine phosphatase activity and hydrolase activity, biological process includes regulation of cyclin‐dependent protein kinase activity, G1/S transition after mitosis in the cell cycle, cell cycle in general, cell cycle arrest, negative regulation of cell proliferation, dephosphorylation (GO (http://www.geneontology.org)). CDKN3 is relative to hs_1‐tissue‐endocrine_and_cns, regulation of protein kinase activity, regulation of transferase activity, protein amino acid dephosphorylation, negative regulation of cell proliferation and regulation of cell proliferation (GenMAPP (http://www.genmapp.org/)). Studies into CDKN3 relationships in tumours has already been presented in a number of papers (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13). However, distinct molecular networks and interpretation concerning CDKN3 in the cell cycle during non‐malignancy associated hepatitis/cirrhosis and hepatocellular carcinoma (HCC) transformation have remained to be elucidated.

HCC is one of the most common causes of cancer‐related death the world over. Thus, to develop novel drugs for treatment of HCC has become a challenge for biologists. Here, we have constructed and analysed significantly higher expression of the gene CDKN3 activated and inhibited in cell cycle networks from 25 HCC versus 25 non‐malignancy associated hepatitis/cirrhosis patients (viral infection HCV or HBV) in GEO Dataset GSE10140‐10141, by combination of gene regulatory network inference methods based on linear programming and decomposition procedures, with the CapitalBio MAS 3.0 software, based on integration of public databases including Gene Ontology, KEGG, BioCarta, GenMapp, Intact, UniGene, OMIM, and more.

Mechanisms that inhibit a signal are as important as mechanisms that initiate it. The GRNInfer tool (14) identifies molecular activation and inhibition relationships based on a novel mathematic method called Gene Network Reconstruction tool (GNR), using linear programming and a decomposition procedure for inferring gene networks. The method theoretically ensures derivation of the most consistent network structures with respect to all the datasets, thereby not only significantly alleviating the problem of data scarcity but also remarkably improving reconstruction reliability.

Here, we have compared the same and different, activated and inhibited, CDKN3 networks with GO analysis, between non‐malignancy associated hepatitis/cirrhosis and HCC. Our results suggest CDKN3 cell cycle network (i) with stronger DNA replication and weaker ubiquitin‐dependent protein catabolism, as common characteristics in both non‐malignancy associated hepatitis/cirrhosis and HCC; (ii) with the stronger cell division and the weaker G2 phase checkpoint in non‐malignancy associated hepatitis/cirrhosis; (iii) with stronger ell cycle and the weaker cytokinesis, as a result forming multinucleate cells in HCC. Thus, it is useful to be able to identify the CDKN3 cell cycle network for understanding molecular mechanisms between non‐malignancy associated hepatitis/cirrhosis and HCC transformation.

Materials and methods

Microarray data

We used microarrays containing 6144 genes from 25 non‐malignancy associated hepatitis/cirrhosis and 25 HCC patients, in the same GEO Dataset GSE10140‐10141 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10140, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10141) (15) and we pre‐processed raw microarray data as log 2.

Gene selection algorithms

Potential HCC molecular markers were identified using significant analysis of microarrays (SAM), which is a statistical technique for finding significant genes in a set of microarray experiments. Input to SAM comprised gene expression measurements from a set of microarray experiments, as well as a response variable from each experiment. The response variable might be a grouping such as untreated, treated, groups and so on. SAM computes a statistic d i for each gene i, and measures strength of relationship between gene expression and response variable. It uses repeated permutations of data to determine whether expression of any genes is significantly related to the response. Cut‐off for significance is determined by a tuning parameter delta (chosen by the user) based on the false positive rate. We normalized data by log 2, selected two classes, paired and minimum fold change ≥2, and chose significant (high expression genes of HCC compared to non‐malignancy associated hepatitis/cirrhosis) genes under the false‐discovery rate and q‐value being 0%. q‐value [invented by John Storey (16)] is similar to the well‐known P‐value, but adapted to multiple‐testing situations.

Molecule annotation system

Molecule Annotation System (MAS) is a web‐based software toolkit for whole data mining and function annotation solution, to extract & analyse biological molecule relationships from public database. MAS uses relational databases of biological networks created from millions of individually modelled relationships between genes, proteins, complexes, cells, disease and tissues. MAS allows view on our data, integrated in biological networks according to different kinds of biological context. This unique feature results from multiple lines of evidence, integrated in MASCORE. MAS helps comprehension of relationships of gene expression data through the given molecular symbols list, and provides thorough, unbiased and visible results.

Primary databases of MAS integrated various well‐known biological resources including Genbank, EMBL, SwissProt, Gene Ontology, KEGG, BioCarta, GenMapp, mirBase, EPD, HPRD, MIND, BIND, Intact, TRANSFAC, UniGene, dbSNP, OMIM, InterPro, HUGO, MGI and RGD. MAS offers various query entries and graphical results. The system represents an alternative approach to mining biological signification for high‐throughput array data (17).

Network establishment of candidate genes

The entire network was constructed using GRNInfer (42) and GVedit tools (http://www.graphviz.org/About.php). GRNInfer is a novel mathematical method called GNR based on linear programming and a decomposition procedure for inferring gene networks. The method theoretically ensures derivation of the most consistent network structure with respect to all of the datasets, thereby not only significantly alleviating the problem of data scarcity but also remarkably improving reconstruction reliability. Equation (1) represents all of the possible networks for the same dataset.

graphic file with name CPR-44-291-e001.jpg (1)

where J = (J ij)n×n = ∂f (x)/ ∂x is an n × n Jacobian matrix or connectivity matrix, X = (x(t 1), …, x(t m)), A = (a(t 1), …, a(t m) and X′ = (x′(t 1), …, x′(t m)) are all n × m matrices with x′i(t j) = [x i(t j + 1) − x i(t j)]/[t j +1 − t j] for i = 1, …, n; j = 1, …, m. X(t) = (x 1(t), …, x n(t))T∈R n, a = (a 1, …, a n)T∈R n, x i(t) is the expression level (mRNA concentrations) of gene i at time instance t. y = (y ij) is an n × n matrix, where y ij is zero if e j ≠ 0 and is otherwise an arbitrary scalar coefficient. ∧−1 = diag (1/e i) and 1/e is set to be zero if e i = 0. U is a unitary m × n matrix of left eigenvectors, ∧ = diag (e 1, …, e n) is a diagonal n × n matrix containing the n eigenvalues and V T is the transpose of a unitary n × n matrix of right eigenvectors. We established network based on the fold change ≥2 distinguished genes and selected parameters as lambda 0.0 because we used one dataset and tried several thresholds 1, 0.5, 0.1, 0.001, 0.0001, 0.000001, 0.0000001, 0.00000001, 0.000000001. Lambda was a positive parameter, which balanced the matching and sparsity terms in the objective function. Using different thresholds, we could predict various networks with different edge density.

Results

Identification of HCC novel molecular markers

We normalized data by log 2, selected two classes paired and minimum fold change ≥2, and chose the significant (high expression genes of HCC compared non‐malignancy associated hepatitis/cirrhosis) genes under false‐discovery rate and q‐value being 0%. We obtained 225 significant high expression molecules (fold change ≥2) from 6144 genes of 25 HCC versus 25 non‐malignancy associated hepatitis/cirrhosis in the same GEO Dataset GSE10140‐10141 containing NEK2, NUSAP1, CAD, DLGAP5, LCN2, SFRP4, RRM2, HIST1H3H, TAGLN2, MYBL2, TK1, PRCC, E2F1, ACTN2, etc. as shown in Table 1.

Table 1.

 Abbreviations of 225 significant high expression genes (fold≥2) of HCC compared with human non‐malignancy associated hepatitis/cirrhosis

Gene Gene Gene Gene Gene Gene Gene Gene Gene Gene
ACTG2 CAMK1 CHST1 DKK1 GJA5 KCNQ3 MAN2A1 MYBL2 PAGE4 RBCK1
ACTN2 CBX5 CIAO1 DLGAP5 GML KCTD2 MAOA MYCN PCOLCE2 RBM34
ADAMDEC1 CCL20 CKS1B E2F1 GNAZ KIAA0101 MAP2 MYH6 PHLDA2 REG1A
AFP CCNA2 CLIC1 ECT2 GNG10 KIAA0513 MAP2K6 MYOM1 PIGC REG3A
AKR1B10 CCNB1 CNTNAP2 EFNA1 GPC3 KIAA0859 MAP4K4 NAT9 PLA2G1B RFC4
ALDH3A1 CCNB2 CORO2A EIF1AX GPSM2 KLHL35 MAPK3 NCAPH PLK4 RIMS3
ALK CCNE2 CPD ELAVL3 GRM1 KLRC3 MAPT NEK2 POLD1 RNF185
AMELY CD34 CRYGA ENAH HIST1H2AD KPNA2 MCM2 NFKBIB PPP1R12B RNF2
ARHGDIG CDC2 CSPG4 EPHA4 HIST1H2AG LAPTM4B MCM4 NINJ2 PRCC ROBO1
B4GALNT2 CDC20 CST6 ESM1 HIST1H2BJ LCN2 MCM7 NKX2‐5 PRKCG RRM2
BAP1 CDC6 CSTB ESPL1 HIST1H3H LEF1 MDK NOTCH3 PRKG2 RRP1B
BCAT1 CDH13 CSTF2 EYA1 HMGB2 LGALS3 MELK NOTCH4 PROK1 S100P
BIRC5 CDKN2C CTHRC1 F13A1 HOMER2 LLGL2 METAP2 NQO1 PRSS1 SBF1
BLVRA CDKN3 CYP17A1 FGF9 HOXA5 LOX MKRN3 NR5A1 PSMC3IP SCGB1D2
BRCA1 CEBPA CYP21A2 FKBP1B HOXD4 LTBP2 MMP11 NRXN3 PTH2R SCML2
BUB1B CELSR2 CYP27B1 FOLR1 IGF2BP3 LTBP3 MMP9 NTN1 PTHLH SEMA3B
C4orf8 CENPF CYP51A1 FOXM1 IRF5 LYPD3 MRPL49 NUP62 PTTG1 SERPINB2
C9orf127 CHAF1A DDX10 GALK1 ISG20 MAGI1 MS4A1 NUSAP1 PVRL2 SFRP4
CAD CHL1 DDX11 GAS7 ITGA2 ZNF43 MS4A2 OCRL RAB3B SFTPA2B
ZIC2 CHRNA4 DFFB GDPD5 KATNB1 ZWINT MUTYH ORC1L RABGGTA SLC16A3
ORC6L TPSD1 TRAF2 TRIP13 TSHB TSTA3 TUBG1 UNG VDR XRCC2
TOP2A TPST2 TRIM26 TROAP TSR1 TTK UBE2C VCAN WDR1 YWHAE
SLC4A3 SORT1 SPINK1 SQLE ST6GALNAC2 STX1A SULT1C4 TAGLN2 TBL3 TK1
SLC6A12 SOX2 SPON2 SSTR5 STMN1 SULT1C2 SYN2 TANK TCAP TNFRSF9
TP53I11
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Candidate novel activated and inhibited genes of CDKN3 upstream and downstream network in human non‐malignancy associated hepatitis/cirrhosis and HCC by GRNInfer

We assayed GRNInfer at several thresholds 1, 0.5, 0.1, 0.001, 0.0001, 0.000001, 0.0000001, 0.00000001, 0.000000001 and finally, selected threshold 0.000000001 as its results covered CDKN3 pathway by CapitalBio MAS 3.0 software, from the published data. We identified candidate genes of CDKN3 upstream and downstream networks from our constructed total network between non‐malignancy associated hepatitis/cirrhosis and HCC by GRNInfer separately, as shown in Table 2.

Table 2.

 Candidate upstream and downstream genes of CDKN3 networks in human non‐malignancy associated hepatitis/cirrhosis and HCC transformation by GRNInfer respectively. control: human non‐malignancy associated hepatitis/cirrhosis, experiment: HCC patients

Control Experiment
Upstream
NUSAP1, DLG7, LCN2, SFRP4, RRM2, TROAP, HIST1H3H, TAGLN2, MYBL2, TK1, PRCC, E2F1, BRCA1, CENPF, SCML2, BIRC5, TOP2A, SPINK1, MDK, GNG10, FOXM1, TRAF2, SPON2, RAB3B, ECT2, KIAA0859, PIGC, TTK, CDC20, GPSM2, PLK4, TSTA3, VDR, MCM2, CIAO1, XRCC2, MELK, MAP4K4, CCNE2, AMELY, CDC6, SEMA3B, TUBG1, KIAA0513, CSTF2, TNFRSF9, IGF2BP3, NCAPH, YWHAE, SBF1, PVRL2, ORC1L, EIF1AX, ZWINT, GDPD5, GRM1, ARHGDIG, MCM4, BUB1B, ACTN2, RBCK1, DDX10, C9orf127, PTTG1, ACTG2, CLIC1, RNF185, CCNA2, RBM34, LTBP2, SORT1, ZIC2, CDH13, GPC3, ST6GALNAC, CYP21A2, CEBPA, PRKCG, HIST1H2AG, UBE2C, CCNB2, CST6, NAT9, MYH6, HOXD4, CAMK1, CNTNAP2, MUTYH, PSMC3IP, SFTPA2, ALK, CDC2, CD34, TP53I11, CHST1, NOTCH3, HMGB2, BLVRA, LGALS3, MKRN3, CHAF1A, KPNA2, ITGA2, CELSR2, SLC16A3, GNAZ, KCTD2, MAPK3, GJA5, RRP1B, BAP1, CAD, HIST1H2BJ, REG3A, ESM1, WDR1, TRIP13, GML, CKS1B, ROBO1, ORC6L, FGF9, SYN2, MYCN, AFP, CCL20, LEF1, SERPINB2, IRF5, BCAT1, PRKG2, REG1A, TPST2, S100P, NR5A1, HIST1H2AD, MMP9, PTHLH, SQLE, PHLDA2, CSPG4, CCNB1, CRYGA, STX1A, UNG, SULT1C2, MYOM1, MAOA, ZNF43, FKBP1B, HOXA5, GAS7, CYP51A1, EYA1, PRSS1, CHRNA4, KATNB1, MAN2A1, OCRL, ALDH3A1, F13A1, VCAN, STMN1, FLJ33790, EFNA1, PLA2G1B, KCNQ3, PAGE4, TCAP, CDKN2C, NQO1, CYP17A1, KIAA0101, TSHB, KLRC3, ESPL1, MRPL49, RIMS3, NKX2‐5, PTHR2, NTN1, CTHRC1, TBL3, MMP11, PROK1, TSR1, ADAMDEC1, MS4A2, SCGB1D2, PCOLCE2, NUP62 NINJ2 LAPTM4B DMN RABGGTA SOX2 SSTR5 NRXN3 CDKN3 NEK2, NUSAP1, DLG7, LCN2, SFRP4, RRM2, TROAP, HIST1H3H, TAGLN2, MYBL2, TK1, PRCC, E2F1, BRCA1, CENPF, SCML2, TOP2A, SPINK1, MDK, GNG10, FOXM1, TRAF2, SPON2, RAB3B, ECT2, ENAH, SLC4A3, TTK, CDC20, GPSM2, PLK4, TSTA3, MAPT, VDR, MCM2, MELK, MAP4K4, CCNE2, MAP2, AMELY, SEMA3B, TUBG1, CSTF2, CPD, TNFRSF9, IGF2BP3, NCAPH, YWHAE, SBF1, PVRL2, EIF1AX, ZWINT, GDPD5, GRM1, ARHGDIG, BUB1B, GALK1, ACTN2, RBCK1, DDX10, C9orf127, PTTG1, ACTG2, CLIC1, RNF185, CCNA2, RBM34, LTBP2, SORT1, ZIC2, CDH13, GPC3, CYP21A2, CEBPA, PRKCG, HIST1H2AG, UBE2C, CCNB2, CST6, NAT9, MYH6, SFTPA2, ALK, CDC2, CD34, NFKBIB, TP53I11, NOTCH3, HMGB2, FOLR1, BLVRA, LGALS3, RFC4, MKRN3, CHAF1A, ITGA2, EPHA4, CELSR2, SLC16A3, GNAZ, MAPK3, TPSD1, GJA5, BAP1, CAD, HIST1H2BJ, PPP1R12B, REG3A, ESM1, WDR1, LYPD3, GML, CKS1B, ROBO1, FGF9, MYCN, ELAVL3, AFP, CCL20, CBX5, LEF1, IRF5, PRKG2, REG1A, C4orf8, TPST2, S100P, NR5A1, MAP2K6, CHL1, HIST1H2AD, MMP9, PTHLH, SQLE, PHLDA2, CSPG4, CCNB1, CRYGA, STX1A, SULT1C2, MYOM1, MAOA, ZNF43, GAS7, EYA1, PRSS1, CHRNA4, KATNB1, MAN2A1, OCRL, ALDH3A1, F13A1, STMN1, EFNA1, PLA2G1B, KCNQ3, PAGE4, TCAP, CDKN2C, NQO1, CYP17A1, KIAA0101, TSHB, KLRC3, DKK1, RIMS3, NKX2‐5, PTHR2, NTN1, TBL3, MMP11, PROK1, TSR1, ADAMDEC1, PCOLCE2, NUP62, NINJ2, LAPTM4B, RABGGTA, SOX2, SSTR5, NRXN3, CDKN3
Downstream
NEK2, NUSAP1, DLG7, LCN2, SFRP4, RRM2, TROAP, TAGLN2, MYBL2, TK1, PRCC, E2F1, BRCA1, CENPF, SCML2, BIRC5, TOP2A, SPINK1, MDK, GNG10, FOXM1, TRAF2, SPON2, RAB3B, ECT2, KIAA0859, ENAH, SLC4A3, TTK, CDC20, GPSM2, PLK4, TSTA3, MAPT, VDR, MCM2, CIAO1, XRCC2, MELK, MAP4K4, CCNE2, MAP2, AMELY, CDC6, SEMA3B, TUBG1, KIAA0513, CSTF2, CPD, TNFRSF9, IGF2BP3, NCAPH, YWHAE, SBF1, PVRL2, ORC1L, EIF1AX, ZWINT, GDPD5, GRM1, ARHGDIG, MCM4, LLGL2, BUB1B, GALK1, ACTN2, RBCK1, DDX10, C9orf127, PTTG1, ACTG2, CLIC1, RNF185, CCNA2, RBM34, LTBP2, SORT1, ZIC2, CDH13, GPC3, ST6GALNAC, CYP21A2, AKR1B10, CEBPA, PRKCG, HIST1H2AG, UBE2C, CCNB2, CST6, NAT9, MYH6, HOXD4, CAMK1, CNTNAP2, MUTYH, SFTPA2, ALK, CDC2, CD34, NFKBIB, TP53I11, CHST1, NOTCH3, HMGB2, FOLR1, BLVRA, LGALS3, RFC4, MKRN3, CHAF1A, KPNA2, ITGA2, EPHA4, CELSR2, SLC16A3, GNAZ, KCTD2, MAPK3, TPSD1, GJA5, RRP1B, BAP1, CAD, HIST1H2BJ, PPP1R12B, REG3A, ESM1, LYPD3, TRIP13, GML, CKS1B, ROBO1, ORC6L, FGF9, SYN2, MYCN, ELAVL3, AFP, CCL20, CBX5, LEF1, SERPINB2, IRF5, ISG20, BCAT1, SLC6A12, REG1A, C4orf8, TPST2, S100P, NR5A1, MAP2K6, CHL1, HIST1H2AD, MMP9, CORO2A, SQLE, PHLDA2, CSPG4, CCNB1, CRYGA, STX1A, UNG, SULT1C2, MYOM1, MAOA, ZNF43, FKBP1B, HOXA5, GAS7, CYP51A1, EYA1, PRSS1, CHRNA4, MAN2A1, OCRL, ALDH3A1, F13A1, VCAN, STMN1, FLJ33790, EFNA1, PLA2G1B, KCNQ3, PAGE4, TCAP, CDKN2C, NQO1, CYP17A1, KIAA0101, TSHB, KLRC3, DKK1, ESPL1, MRPL49, NKX2‐5, PTHR2, NTN1, CTHRC1, TBL3, MMP11, PROK1, TSR1, ADAMDEC1, MS4A2, SCGB1D2, PCOLCE2, NUP62, NINJ2, LAPTM4B, DMN, RABGGTA, LOX, SOX2, SSTR5, NRXN3, CDKN3 NEK2, NUSAP1, DLG7, LCN2, SFRP4, RRM2, HIST1H3H, TAGLN2, MYBL2, TK1, PRCC, E2F1, BRCA1, CENPF, SCML2, BIRC5, TOP2A, SPINK1, MDK, GNG10, FOXM1, RAB3B, ECT2, KIAA0859, PIGC, SLC4A3, TTK, CDC20, GPSM2, PLK4, TSTA3, MAPT, VDR, MCM2, CIAO1, XRCC2, MELK, MAP4K4, CCNE2, MAP2, AMELY, CDC6, SEMA3B, TUBG1, KIAA0513, CSTF2, CPD, TNFRSF9, IGF2BP3, NCAPH, YWHAE, SBF1, PVRL2, ORC1L, EIF1AX, GDPD5, GRM1, ARHGDIG, MCM4, LLGL2, BUB1B, GALK1, ACTN2, RBCK1, DDX10, C9orf127, PTTG1, ACTG2, CLIC1, RNF185, CCNA2, RBM34, LTBP2, SORT1, CDH13, GPC3, ST6GALNAC, CYP21A2, AKR1B10, PRKCG, HIST1H2AG, UBE2C, CCNB2, CST6, NAT9, MYH6, HOXD4, CAMK1, CNTNAP2, MUTYH, PSMC3IP, ALK, CDC2, CD34, NFKBIB, TP53I11, NOTCH3, HMGB2, FOLR1, BLVRA, LGALS3, RFC4, MKRN3, CHAF1A, KPNA2, ITGA2, EPHA4, CELSR2, SLC16A3, GNAZ, KCTD2, MAPK3, TPSD1, GJA5, RRP1B, CAD, HIST1H2BJ, PPP1R12B, REG3A, ESM1, WDR1, LYPD3, TRIP13, GML, CKS1B, ROBO1, ORC6L, SYN2, MYCN, ELAVL3, AFP, CCL20, CBX5, LEF1, SERPINB2, IRF5, ISG20, BCAT1, PRKG2, REG1A, C4orf8, TPST2, S100P, NR5A1, MAP2K6, CHL1, HIST1H2AD, MMP9, CORO2A, PTHLH, SQLE, PHLDA2, CSPG4, CCNB1, CRYGA, STX1A, UNG, SULT1C2, MYOM1, MAOA, FKBP1B, HOXA5, GAS7, CYP51A1, EYA1, PRSS1, CHRNA4, KATNB1, MAN2A1, OCRL, ALDH3A1, F13A1, VCAN, STMN1, FLJ33790, EFNA1, PLA2G1B, KCNQ3, PAGE4, TCAP, CDKN2C, NQO1, CYP17A1, KIAA0101, TSHB, KLRC3, DKK1, ESPL1, MRPL49, RIMS3, NKX2‐ 5, PTHR2, NTN1, CTHRC1, TBL3, MMP11, PROK1, TSR1, ADAMDEC1, MS4A2, SCGB1D2, PCOLCE2, NUP62, NINJ2, LAPTM4B, DMN, RABGGTA, LOX, SOX2, SSTR5, NRXN3, CDKN3
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Discussion

Our aim here was to compare and analyse novel CDKN3 cell cycle networks between non‐malignancy associated hepatitis/cirrhosis and HCC transformation for potential novel markers for prognosis and therapy of HCC. On the basis of our previous published novel molecular network constructions and functional comparison from different databases presented in our papers (18, 19, 20, 21, 22, 23, 24, 25, 26, 27), we constructed and analysed significant higher expression gene CDKN3 activated and inhibited cell cycle network from 25 HCC versus 25 non‐malignancy associated hepatitis/cirrhosis patients (viral infection HCV or HBV) in GEO Dataset GSE10140‐10141 by a combination of gene regulatory network inference methods based on linear programming and decomposition procedure with the CapitalBio MAS 3.0 software, based on integration of public databases including Gene Ontology, KEGG, BioCarta, GenMapp, Intact, UniGene, OMIM, and more. We identified some same and other different novel activated and inhibited upstream and downstream genes of CDKN3’s cell cycle module between non‐malignancy associated hepatitis/cirrhosis and HCC, on the condition that our CDKN3 network covered CDKN3 pathways and matched cell cycle enrichment analysis by the CapitalBio MAS 3.0 software, from published data.

To confirm our prediction for covering published data, we setup CDKN3 interaction and pathways in HCC using CapitalBio MAS 3.0 software for standard comparison with our CDKN3 network. We identified CDKN3 interaction and pathways from our total established network by inputting 225 significant high expression genes (fold change ≥2) to the CapitalBio MAS 3.0 software based on integration of public databases including Gene Ontology, KEGG, BioCarta, GenMapp, Intact, UniGene, OMIM, and more. CDKN3 interaction molecules and genes included CDC2, CDK3, CDK2, CEBPA, CDC25A, CDKN3, MS4A3, CDC28_YEAST. CDKN3 pathway molecules consisted of SBF1, EYA1, MCM2, MCM7, UBE2C, CHAF1A, CKS1B, SPINK1, MAPT, CYP21A2, REG3A, REG1A, STX1A, PRSS1, PLA2G1B, CYP17A1, TSHB, RIMS3, CCNE2, TNFRSF9, BAP1, GML, PTHLH, SSTR5, TTK, CIAO1, DDX11, BRCA1, BUB1B, CCNA2, CDC6, CDKN2C. By comparison of similarities, we observed that our high expression molecules in HCC did not contain CDKN3 interaction proteins, whereas they completely covered CDKN3 pathway proteins (28).

To establish candidate novel genes concerned CDKN3 upstream and downstream network covering CDKN3 pathways, we assayed GRNInfer in several thresholds 1, 0.5, 0.1, 0.001, 0.0001, 0.000001, 0.0000001, 0.00000001, 0.000000001. Finally, we selected threshold 0.000000001 and set up candidate genes of the CDKN3 network between non‐malignancy associated hepatitis/cirrhosis and HCC by GRNInfer (Table 2). Our GRNInfer finding was verified covering CDKN3 pathways using the CapitalBio MAS 3.0 software from published data.

To construct novel CDKN3 cell cycle modules between non‐malignancy associated hepatitis/cirrhosis and HCC respectively by GRNInfer, our CDKN3 network needed to match cell cycle enrichment analysis. We identified cell cycle enrichment from our total established enrichment results by inputting 225 significant high expression genes (fold change ≥2) to the CapitalBio MAS 3.0 software based on integration of public databases including Gene Ontology, KEGG, BioCarta, GenMapp, Intact, UniGene, OMIM, and more. We data‐mined molecules and genes of cell cycle enrichment including NEK2, NUSAP1, CDKN3, DLGAP5, BIRC5, CDC20, MCM2, MCM7, CCNE2, CDC6, ZWINT, LLGL2, PTTG1, CCNA2, UBE2C, CCNB2, CDC2, CHAF1A, MAPK3, CKS1B, DDX11, CCNB1, KATNB1, CDKN2C. On the basis of cell cycle enrichment, we predicted novel candidate activated and inhibited upstream and downstream networks of CDKN3 cell cycle components between non‐malignancy associated hepatitis/cirrhosis and HCC by GRNInfer separately, as shown in 1, 2. Study into ion binding relationship in tumour cells is presented previously in many papers (28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46). However, implications concerning CDKN3 cell cycle networks between non‐malignancy associated hepatitis/cirrhosis and HCC transformation remained to be elucidated. We further compared and interpreted candidate molecules of CDKN3 cell cycle modules between non‐malignancy associated hepatitis/cirrhosis and HCC transformation considering activation and inhibition relationships (29).

Figure 1.

Figure 1

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  Candidate activated and inhibited upstream and downstream network of CDKN3 in the cell cycle in non‐malignancy associated hepatitis/cirrhosis by GRNInfer. Arrowhead represents activation relationship, empty circle represents inhibition relationship.

Figure 2.

Figure 2

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  Candidate activated and inhibited upstream and downstream network of CDKN3 in the cell cycle in HCC by GRNInfer. Arrowhead represents activation relationship, empty circle represents inhibition relationship.

First, we identified the same novel molecules of CDKN3 cell cycle modules in both non‐malignancy associated hepatitis/cirrhosis and HCC. The same upstream NUSAP1, CCNA2, UBE2C, MAPK3 activated CDKN3, and CDC20, PTTG1, CDC2 inhibited CDKN3. The same downstream CDKN3 activated MCM2, CHAF1A, MAPK3 and inhibited CDC20, LLGL2, PTTG1, UBE2C, CCNB2, CCNB1 in both non‐malignancy associated hepatitis/cirrhosis and HCC. To interpret molecular mechanisms of CDKN3 cell cycle modules between non‐malignancy associated hepatitis/cirrhosis and HCC transformation, we analysed GO of the same activated and inhibited molecules. For example, activated MCM2 between non‐malignancy associated hepatitis/cirrhosis and an HCC biological process is relevant to DNA replication, DNA unwinding during replication, DNA replication initiation, nucleosome assembly, transcription, regulation of transcription, DNA‐dependency and cell cycle progression. Inhibited CDC20 between non‐malignancy associated hepatitis/cirrhosis and HCC biological processes is involved in ubiquitin‐dependent protein catabolism, cell cycle progression, mitosis, anaphase‐promoting complex‐dependent proteasomal ubiquitin‐dependent protein catabolism, cell division, negative regulation of ubiquitin ligase activity during mitotic cell cycle and positive regulation of ubiquitin ligase activity during mitosis of the cell cycle. Compared to the same and different activated and inhibited CDKN3 networks with GO analysis between non‐malignancy associated hepatitis/cirrhosis and HCC, our results suggested that the same molecular network of CDKN3 cell cycle modules maybe stronger DNA replication and weaker ubiquitin‐dependent protein catabolism, as common characteristics in both non‐malignancy associated hepatitis/cirrhosis and HCC.

Secondly, we identified the different novel molecules of CDKN3 cell cycle modules between non‐malignancy associated hepatitis/cirrhosis compared to HCC. The different upstream BIRC5, MCM2, CKS1B, KATNB1, CDKN2C activated CDKN3, and CCNE2, CDC6, ZWINT, CCNB2, CHAF1A, CCNB1 inhibited CDKN3; different downstream CDKN3 activated NEK2, BIRC5, CCNE2, CDC6, ZWINT, CDKN2C and inhibited NUSAP1, CCNA2, CDC2, CKS1B in non‐malignancy associated hepatitis/cirrhosis. To further interpret molecular mechanisms of CDKN3 in cell cycle modules of non‐malignancy associated hepatitis/cirrhosis, we analysed GO of the different activated and inhibited molecules from non‐malignancy associated hepatitis/cirrhosis. For example, activated NEK2 in the non‐malignancy associated hepatitis/cirrhosis biological process is relevant to cell cycle progression, regulation of mitosis, meiosis, protein amino acid autophosphorylation, centrosome separation and cell division. Inhibited CCNA2 in the HCC biological process is involved in cell cycle progression, mitosis, mitotic G2 checkpoint, positive regulation of transcription, cell division. Compared to the same and different activated & inhibited CDKN3 networks with GO analysis, our results demonstrated different molecular networks of CDKN3 cell cycle modules, stronger cell division and the weaker mitotic G2 checkpoint as the only characteristics in non‐malignancy associated hepatitis/cirrhosis.

Thirdly, we identified the different novel molecules of CDKN3 cell cycle modules in HCC compared to non‐malignancy associated hepatitis/cirrhosis. The different upstream NEK2, CCNE2, ZWINT, CCNB2, CHAF1A, CCNB1 activated CDKN3, and MCM2, CKS1B, KATNB1, CDKN2C inhibited CDKN3; different downstream CDKN3 activated NUSAP1, CCNA2, CDC2, CKS1B and inhibited NEK2, BIRC5, CCNE2, CDC6, KATNB1, CDKN2C in HCC. To interpret further molecular mechanisms of the CDKN3 cell cycle module in HCC, we analysed GO of the different activated and inhibited molecules from HCC. For example, activated NUSAP1 in the HCC biological process is relevant to cytokinesis after mitosis, cell cycle progression, mitotic chromosome condensation, establishment of mitotic spindle localization, positive regulation of mitosis and cell division progression. Inhibited BIRC5 in the HCC biological process is involved in G2/M transition of during the cell cycle, cytokinesis, apoptosis, anti‐apoptosis, cell cycle progression, mitosis, protein complex localization, positive regulation of exit from mitosis, spindle checkpoint, negative regulation of caspase activity, positive regulation of progression through mitosis of the cell cycle and establishment of chromosome localization. Compared to the same and different activated and inhibited CDKN3 networks with GO analysis, our results indicate different molecular network of CDKN3 cell cycle modules, stronger ell cycle progression and weaker cytokinesis, and as a result formation of multinucleate cells in HCC.

In conclusion, our high expression molecules of HCC did not contain CDKN3 interaction proteins, whereas they completely covered CDKN3 pathway proteins by comparison of similarities. We identified same and different novel activated and inhibited upstream and downstream genes related to CDKN3 in the cell cycle module between non‐malignancy associated hepatitis/cirrhosis and HCC, on condition that our CDKN3 network covered CDKN3 pathways and matched cell cycle enrichment analysis by the CapitalBio MAS 3.0 software from published data. Comparing same and different activated & inhibited CDKN3 networks with GO analysis between non‐malignancy associated hepatitis/cirrhosis and HCC, our results suggested CDKN3 cell cycle networks (1) with stronger DNA replication and weaker ubiquitin‐dependent protein catabolism as common characteristics in both non‐malignancy associated hepatitis/cirrhosis and HCC; (2) with stronger cell division and the weaker mitotic G2 checkpoint in non‐malignancy associated hepatitis/cirrhosis and (3) with stronger cell cycle and weaker cytokinesis ‐ and as a result formation of multinucleate cells in HCC. Thus, it is most useful to identify CDKN3 cell cycle networks for understanding of molecular mechanisms between non‐malignancy associated hepatitis/cirrhosis and HCC transformation.

Acknowledgements

This work was supported by the National Natural Science Foundation in China (No.60871100). The Returned Overseas Chinese Scholars for Scientific research Foundation of State Education Ministry. Significant Science and Technology Project for new transgenic biological Species (2009ZX08012‐001B). State Key Lab of Pattern Recognition Open Foundation.

All authors contributed equally to this study.

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