The histological grade/stage of tumor is widely acknowledged as an important clinical prognostic factor for cancer progression. Recent experimental studies have explored the following two topics at the molecular level: (1) whether or not gene expression levels vary by different degrees among different tumor grades/stages, and (2) whether some well-defined modules could distinguish one grade/stage from another. In this article, using breast cancer as an example, we investigated this topic and identified grade/stage-related active modules under the framework of a weighted network integrated from a human protein interaction network and a transcriptional regulatory network. Our results enabled us to draw the conclusion that the gene expression profile could provide more clues about tumor grade, but reveals less evidence about tumor stage. In addition, we found that our modular biomarker method had additional advantages in identifying some tumor grade/stage-related genes with slightly altered expression. According to our case study, the framework we introduced could be used for other cancers to identify their modules during grading or staging.
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References
1.
AllredDC, MohsinSK, FuquaSA. 2001. Histological and biological evolution of human premalignant breast disease. Endocr Relat Cancer, 8:47–61.
2.
BaskinY, YigitbasiT. 2010. Clinical proteomics of breast cancer. Curr Genomics, 11:528–536.
3.
BombonatiA, SgroiDC. 2010. The molecular pathology of breast cancer progression. J Pathol, 223:307–317.
4.
ChenL, WangH, ZhangLet al.2010. Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks. BMC Bioinformatics, 11:392.
5.
ChoiJA, LeeJW, KimHet al.2010. Pro-survival of estrogen receptor-negative breast cancer cells is regulated by a BLT2-reactive oxygen species-linked signaling pathway. Carcinogenesis, 31:543–551.
6.
ChuangHY, LeeE, LiuYT, LeeD, IdekerT. 2007. Network-based classification of breast cancer metastasis. Mol Syst Biol, 3:140.
7.
ColakR, MoserF, ChuJS, SchonhuthA, ChenN, EsterM. 2010. Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks. PLoS One, 5:e13348.
8.
DennisGJr, ShermanBT, HosackDAet al.2003. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol, 4:P3.
9.
HamritaB, Ben NasrH, GabboujS, BouaouinaN, ChouchaneL, ChahedK. 2010. Apolipoprotein A1 −75 G/A and +83 C/T polymorphisms: susceptibility and prognostic implications in breast cancer. Mol Biol Rep, 38:1637–1643.
10.
HanCZ, DuLL, JingJXet al.2008. Associations among lipids, leptin, and leptin receptor gene Gin223Arg polymorphisms and breast cancer in China. Biol Trace Elem Res, 126:38–48.
11.
HempelN, YeH, AbessiB, MianB, MelendezJA. 2009. Altered redox status accompanies progression to metastatic human bladder cancer. Free Radic Biol Med, 46:42–50.
12.
Hernandez-VargasH, OuzounovaM, Le Calvez-KelmFet al.2011. Methylome analysis reveals Jak-STAT pathway deregulation in putative breast cancer stem cells. Epigenetics, 6:428–439.
13.
Huang daW, ShermanBT, LempickiRA. 2009. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 4:44–57.
14.
IgnatiadisM, SotiriouC. 2008. Understanding the molecular basis of histologic grade. Pathobiology, 75:104–111.
15.
JemalA, SiegelR, WardEet al.2008. Cancer statistics, 2008. CA Cancer J Clin, 58:71–96.
16.
JerussJS, MittendorfEA, TuckerSLet al.2008. Staging of breast cancer in the neoadjuvant setting. Cancer Res, 68:6477–6481.
17.
KempkensteffenC, HinzS, ChristophFet al.2007. Expression parameters of the inhibitors of apoptosis cIAP1 and cIAP2 in renal cell carcinomas and their prognostic relevance. Int J Cancer, 120:1081–1086.
18.
KidokoroT, TanikawaC, FurukawaY, KatagiriT, NakamuraY, MatsudaK. 2008. CDC20, a potential cancer therapeutic target, is negatively regulated by p53. Oncogene, 27:1562–1571.
19.
KlaholzBP, MitschlerA, MorasD. 2000. Structural basis for isotype selectivity of the human retinoic acid nuclear receptor. J Mol Biol, 302:155–170.
20.
KoyamaH, HibiT, IsogaiZet al.2007. Hyperproduction of hyaluronan in neu-induced mammary tumor accelerates angiogenesis through stromal cell recruitment: possible involvement of versican/PG-M. Am J Pathol, 170:1086–1099.
21.
LapointeJ, LiC, HigginsJPet al.2004. Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci USA, 101:811–816.
22.
LereboursF, VacherS, AndrieuC, EspieM, MartyM, LidereauR, BiecheI. 2008. NF-kappa B genes have a major role in inflammatory breast cancer. BMC Cancer, 8:41.
23.
LiJ, LenferinkAE, DengYet al.2010. Identification of high-quality cancer prognostic markers and metastasis network modules. Nat Commun, 1:34.
24.
LiuY, MayoMW, NagjiASet al.2012. Phosphorylation of RelA/p65 promotes DNMT-1 recruitment to chromatin and represses transcription of the tumor metastasis suppressor gene BRMS1. Oncogene, 31:1143–1154.
25.
MaXJ, SalungaR, TuggleJTet al.2003. Gene expression profiles of human breast cancer progression. Proc Natl Acad Sci USA, 100:5974–5979.
26.
MatysV, FrickeE, GeffersRet al.2003. TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res, 31:374–378.
27.
MesterJ, RedeuilhG. 2008. Proliferation of breast cancer cells: regulation, mediators, targets for therapy. Anticancer Agents Med Chem, 8:872–885.
28.
MichalakP. 2008. Coexpression, coregulation, and cofunctionality of neighboring genes in eukaryotic genomes. Genomics, 91:243–248.
29.
MolloyAP, MartinFT, DwyerRMet al.2009. Mesenchymal stem cell secretion of chemokines during differentiation into osteoblasts, and their potential role in mediating interactions with breast cancer cells. Int J Cancer, 124:326–332.
30.
PengX, YunD, ChristovK. 2004. Breast cancer progression in MCF10A series of cell lines is associated with alterations in retinoic acid and retinoid X receptors and with differential response to retinoids. Int J Oncol, 25:961–971.
31.
PeriS, NavarroJD, KristiansenTZet al.2004. Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res, 32:D497–D501.
32.
PerouCM, SorlieT, EisenMBet al.2000. Molecular portraits of human breast tumours. Nature, 406:747–752.
33.
PurmannA, ToedlingJ, SchuelerMet al.2007. Genomic organization of transcriptomes in mammals: Coregulation and cofunctionality. Genomics, 89:580–587.
34.
RakhaEA, El-SayedME, LeeAHet al.2008. Prognostic significance of Nottingham histologic grade in invasive breast carcinoma. J Clin Oncol, 26:3153–3158.
35.
RakhaEA, Reis-FilhoJS, BaehnerFet al.2010. Breast cancer prognostic classification in the molecular era: the role of histological grade. Breast Cancer Res, 12:207.
36.
RansohoffDF. 2004. Rules of evidence for cancer molecular-marker discovery and validation. Nat Rev Cancer, 4:309–314.
37.
SchlotterCM, VogtU, AllgayerH, BrandtB. 2008. Molecular targeted therapies for breast cancer treatment. Breast Cancer Res, 10:211.
38.
SegalE, FriedmanN, KaminskiN, RegevA, KollerD. 2005. From signatures to models: understanding cancer using microarrays. Nat Genet, 37,Suppl:S38–S45.
39.
SimonR, RadmacherMD, DobbinK, McShaneLM. 2003. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst, 95:14–18.
40.
UdabageL, BrownleeGR, WalthamMet al.2005. Antisense-mediated suppression of hyaluronan synthase 2 inhibits the tumorigenesis and progression of breast cancer. Cancer Res, 65:6139–6150.
41.
VaclavicekA, BermejoJL, SchmutzlerRKet al.2007. Polymorphisms in the Janus kinase 2 (JAK)/signal transducer and activator of transcription (STAT) genes: putative association of the STAT gene region with familial breast cancer. Endocr Relat Cancer, 14:267–277.
42.
Van't VeerLJ, DaiH, van de VijverMJet al.2002. Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415:530–536.
43.
VegranF, BoidotR, BonnetainF, CadouotM, ChevrierS, Lizard-NacolS. 2011. Apoptosis gene signature of Survivin and its splice variant expression in breast carcinoma. Endocr Relat Cancer, 18:783–792.
44.
WangL, ZhangB, WolfingerRD, ChenX. 2008. An integrated approach for the analysis of biological pathways using mixed models. PLoS Genet, 4:e1000115.
45.
WangY, KlijnJG, ZhangYet al.2005. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet, 365:671–679.
46.
WennmalmK, BerghJ. 2011. A simple method for assigning genomic grade to individual breast tumours. BMC Cancer, 11:306.
47.
ZhangJ, WarrenMA, ShoemakerSF, IpMM. 2007. NFkappaB1/p50 is not required for tumor necrosis factor-stimulated growth of primary mammary epithelial cells: implications for NFkappaB2/p52 and RelB. Endocrinology, 148:268–278.
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