Biomedical subjects

Momiao Xiong

Publications and source records attributed to Momiao Xiong.

At least 19 recordsLinked to original sources

PubMed2006

Test for interaction between two unlinked loci.

Despite the growing consensus on the importance of testing gene-gene interactions in genetic studies of complex diseases, the effect of gene-gene interactions has often been defined as a deviance from genetic additive effects, which is essentially treated as a residual term in genetic analysis and leads to low power in detecting the presence of interacting effects. To what extent the definition of gene-gene interaction at population level reflects the genes' biochemical or physiological interaction remains a mystery. In this article, we introduce a novel definition and a new measure of gene-gene interaction between two unlinked loci (or genes). We developed a general theory for studying linkage disequilibrium (LD) patterns in disease population under two-locus disease models. The properties of using the LD measure in a disease population as a function of the measure of gene-gene interaction between two unlinked loci were also investigated. We examined how interaction between two loci creates LD in a disease population and showed that the mathematical formulation of the new definition for gene-gene interaction between two loci was similar to that of the LD between two loci. This finding motived us to develop an LD-based statistic to detect gene-gene interaction between two unlinked loci. The null distribution and type I error rates of the LD-based statistic for testing gene-gene interaction were validated using extensive simulation studies. We found that the new test statistic was more powerful than the traditional logistic regression under three two-locus disease models and demonstrated that the power of the test statistic depends on the measure of gene-gene interaction. We also investigated the impact of using tagging SNPs for testing interaction on the power to detect interaction between two unlinked loci. Finally, to evaluate the performance of our new method, we applied the LD-based statistic to two published data sets. Our results showed that the P values of the LD-based statistic were smaller than those obtained by other approaches, including logistic regression models.

Alleles↗

PubMed2006

Nonlinear tests for genomewide association studies.

As millions of single-nucleotide polymorphisms (SNPs) have been identified and high-throughput genotyping technologies have been rapidly developed, large-scale genomewide association studies are soon within reach. However, since a genomewide association study involves a large number of SNPs it is therefore nearly impossible to ensure a genomewide significance level of 0.05 using the available statistics, although the multiple-test problems can be alleviated, but not sufficiently, by the use of tagging SNPs. One strategy to circumvent the multiple-test problem associated with genome-wide association tests is to develop novel test statistics with high power. In this report, we introduce several nonlinear tests, which are based on nonlinear transformation of allele or haplotype frequencies. We investigate the power of the nonlinear test statistics and demonstrate that under certain conditions, some nonlinear test statistics have much higher power than the standard chi2-test statistic. Type I error rates of the nonlinear tests are validated using simulation studies. We also show that a class of similarity measure-based test statistics is based on the quadratic function of allele or haplotype frequencies, and thus they belong to nonlinear tests. To evaluate their performance, the nonlinear test statistics are also applied to three real data sets. Our study shows that nonlinear test statistics have great potential in association studies of complex diseases.

Acyltransferases↗

PubMed2005

A systems biology approach to genetic studies of complex diseases.

Revealing mechanisms underlying complex diseases poses great challenges to biologists. The traditional linkage and linkage disequilibrium analysis that have been successful in the identification of genes responsible for Mendelian traits, however, have not led to similar success in discovering genes influencing the development of complex diseases. Emerging functional genomic and proteomic ('omic') resources and technologies provide great opportunities to develop new methods for systematic identification of genes underlying complex diseases. In this report, we propose a systems biology approach, which integrates omic data, to find genes responsible for complex diseases. This approach consists of five steps: (1) generate a set of candidate genes using gene-gene interaction data sets; (2) reconstruct a genetic network with the set of candidate genes from gene expression data; (3) identify differentially regulated genes between normal and abnormal samples in the network; (4) validate regulatory relationship between the genes in the network by perturbing the network using RNAi and monitoring the response using RT-PCR; and (5) genotype the differentially regulated genes and test their association with the diseases by direct association studies. To prove the concept in principle, the proposed approach is applied to genetic studies of the autoimmune disease scleroderma or systemic sclerosis.

Genomics↗

PubMed2005

An entropy-based statistic for genomewide association studies.

Efficient genotyping methods and the availability of a large collection of single-nucleotide polymorphisms provide valuable tools for genetic studies of human disease. The standard chi2 statistic for case-control studies, which uses a linear function of allele frequencies, has limited power when the number of marker loci is large. We introduce a novel test statistic for genetic association studies that uses Shannon entropy and a nonlinear function of allele frequencies to amplify the differences in allele and haplotype frequencies to maintain statistical power with large numbers of marker loci. We investigate the relationship between the entropy-based test statistic and the standard chi2 statistic and show that, in most cases, the power of the entropy-based statistic is greater than that of the standard chi2 statistic. The distribution of the entropy-based statistic and the type I error rates are validated using simulation studies. Finally, we apply the new entropy-based test statistic to two real data sets, one for the COMT gene and schizophrenia and one for the MMP-2 gene and esophageal carcinoma, to evaluate the performance of the new method for genetic association studies. The results show that the entropy-based statistic obtained smaller P values than did the standard chi2 statistic.

Entropy↗

PubMed2005

Monozygotic twins clinically discordant for scleroderma show concordance for fibroblast gene expression profiles.

OBJECTIVE: Fewer than 5% of monozygotic (MZ) and dizygotic (DZ) twin pairs are clinically concordant for systemic sclerosis (SSc), yet the majority of MZ twins are concordant for antinuclear antibodies. To discover genetic versus nongenetic molecular pathways important to the pathogenesis of SSc, we compared global gene expression patterns in twins discordant for SSc. METHODS: Total RNA from dermal fibroblasts of 15 discordant twin pairs (10 MZ and 5 DZ) and 5 normal controls were used in microarray analysis. Aberrantly expressed genes were confirmed using quantitative real-time reverse transcriptase-polymerase chain reaction. RESULTS: Lesional and nonlesional fibroblasts from SSc patients showed no significant differences in gene expression, while SSc patients had gene profiles that were significantly different from those of unaffected DZ twins and normal controls. Unaffected MZ twins, however, were not significantly different from SSc patients. Unsupervised hierarchical clustering segregated the fibroblast samples as originating from 2 major groups. Group A contained 5 discordant MZ twin pairs, 3 affected MZ twins, and 3 affected DZ twins. Group B contained all 5 normal population controls, all 5 healthy DZ twins, 2 discordant MZ twins, and 2 discordant DZ twin pairs. Normal fibroblasts incubated with serum from an SSc-affected patient or with serum from her unaffected MZ twin sister developed the increased expression of COL1A2, SPARC, and CTGF typically seen in SSc fibroblasts. CONCLUSION: These results demonstrate that dermal fibroblasts from SSc patients and from 40-50% of their genetically identical but clinically unaffected MZ twins exhibit a similar gene expression pattern which can be induced in normal fibroblasts by sera from both. Thus, a stronger genetic predisposition to SSc (than can be detected clinically) is apparent at the molecular level in skin fibroblasts.

Adult↗

PubMed2005

High resolution T association tests of complex diseases based on family data.

This paper proposes family based Hotelling's T(2) tests for high resolution linkage disequilibrium (LD) mapping or association studies of complex diseases. Assume that genotype data of multiple markers or haplotype blocks are available for a sample of nuclear families, in which some offspring are affected. Paired Hotelling's T(2) test statistics are proposed for a high resolution association study using parents as controls for affected offspring, based on two coding methods: haplotype/allele coding and genotype coding. The paired Hotelling's T(2) tests take not only the correlation between the haplotype blocks or markers into account, but also take the correlation within each parent-offspring pair into account. The method extends two sample Hotelling's T(2) test statistics for population case control association studies, which are not valid for family data due to correlation of genetic data among family members. The validity of the proposed method is justified by rigorous mathematical and statistical proof under the large sample theory. The non-centrality parameter approximations of the test statistics are calculated for power and sample size calculations. From power comparison and type I error calculations, it is shown that the test statistic based on haplotype/allele coding is advantageous over the test statistic of genotype coding. Analysis using multiple markers may provide higher power than single marker analysis. If only one marker is utilized the power of the test statistic based on haplotype/allele coding is nearly identical to that of 1-TDT. Moreover, a permutation procedure is provided for data analysis. The method is applied to data from a German asthma family study. The results based on the paired Hotelling's T(2) statistic tests confirm the previous findings. However, the paired Hotelling's T(2) tests produce much smaller P-values than those of the previous study. The permutation tests produce similar results to those of the previous study; moreover, additional marker combinations are shown to be significant by permutation tests. The proposed paired Hotelling's T(2) statistic tests are potentially powerful in mapping complex diseases. A SAS Macro, Hotel_fam.sas, has been written to implement the method for data analysis.

Asthma↗

PubMed2004

cDNA microarrays reveal distinct gene expression clusters in idiopathic inflammatory myopathies.

BACKGROUND: Polymyositis (PM) and dermatomyositis (DM) are complex human diseases of autoimmune origin which cause progressive skeletal muscle weakness in adults and children. Histopathological studies of affected muscles suggest that cytotoxic T cell-induced injury predominates in PM, while humoral immune mechanisms seem more likely in DM. Early expression of MHC class I molecules on myocytes is a striking feature of both disorders. This study was to use gene microarray analysis of muscle biopsies from PM and DM patients to obtain a comprehensive view of molecules participating in disease pathogenesis. MATERIAL/METHODS: Muscle biopsies from 10 patients (6 PM and 4 DM) and 5 controls were selected for gene expression profiles. Microarray filters containing 4000 known human genes were used to hybridize with RNAs from muscle biopsies. Real-time RT-PCR assays were used to confirmed selected genes that showed changes in expression levels in PM and DM from microarray data. The t-statistic was used to measure any differences in the amount of gene expression in the normal versus myositis biopsies. RESULTS: Forty genes clustering into two major groups showed significantly altered expression levels in PM/DM compared to normal biopsies. One cluster of over-expressed genes was primarily of immune origin and included HLA-class I and II, interferon-inducible proteins, natural killer (NK) protein, immunoglobulins and complement. The other cluster of under-expressed genes were of muscle origin, especially those involved in the fast twitch response. PM and DM gene profiles appeared similar. CONCLUSIONS: PM and DM muscle biopsies show similar gene expression profiles despite reported differences in histopathological changes. Altered gene expressions of immune regulation and myofibrillar proteins are prominent in muscle biopsies of myositis.

Adult↗

PubMed2004

Network-based regulatory pathways analysis.

MOTIVATION: A useful approach to unraveling and understanding complex biological networks is to decompose networks into basic functional and structural units. Recent application of convex analysis to metabolic networks leads to the development of network-based metabolic pathway analysis and the decomposition of metabolic networks into metabolic extreme pathways that are true functional units of metabolic systems. Metabolic extreme pathways are derived from limited knowledge of the metabolic networks, but provide an integrated predictive description of metabolic networks. It is important to extend the concept of network-based metabolic pathways to genetic networks and develop mathematical procedures for network-based regulatory pathway analysis. RESULTS: We have established Kirchhoff's first law in genetic networks and introduced a concept of gene flows using matrix decomposition method. The Kirchhoff's first law provides the theoretical foundations for mathematical framework for development defining network-based regulatory pathways, and applying convex analysis in decomposing the genetic networks into regulatory extreme pathways. We presented a new approach to characterize the extreme pathway and developed a new algorithm for identifying a set of extreme pathways. Convex analysis and extreme pathway structure provide a unified framework for functional and structural analysis of metabolic and genetic networks, which will increase our ability to analyze, interpret and predict the function of metabolic and genetic networks. The proposed models for network-based regulatory pathway analysis have been applied to apoptosis regulatory network.

Algorithms↗

PubMed2004

Discovery of Ca2+-relevant and differentiation-associated genes downregulated in esophageal squamous cell carcinoma using cDNA microarray.

To identify genes that are differentially expressed in human esophageal squamous cell carcinoma (ESCC), we have developed a cDNA microarray representing 34 176 clones to analyse gene expression profiles in ESCC. A total of 77 genes (including 31 novel genes) were downregulated, and 15 genes (including one novel gene) were upregulated in cancer tissues compared with their normal counterparts. Immunohistochemistry and Northern blot analysis were carried out to verify the cDNA microarray results. It was revealed that genes involved in squamous cell differentiation were coordinately downregulated, including annexin I, small proline-rich proteins (SPRRs), calcium-binding S100 proteins (S100A8, S100A9), transglutaminase (TGM3), cytokeratins (KRT4, KRT13), gut-enriched Krupple-like factor (GKLF) and cystatin A. Interestingly, most of the downregulated genes encoded Ca(2+)-binding or -modulating proteins that constitute the cell envelope (CE). Moreover, genes associated with invasion or proliferation were upregulated, including genes such as fibronectin, secreted protein acidic and rich in cystein (SPARC), cathepsin B and KRT17. Functional analysis of the alteration in the expression of GKLF suggested that GKLF might be able to regulate the expression of SPRR1A, SPRR2A and KRT4 in ESCC. This study provides new insights into the role of squamous cell differentiation-associated genes in ESCC initiation and progression.

Calcium↗

PubMed2004

Increased plasma non-esterified fatty acids and platelet-activating factor acetylhydrolase are associated with susceptibility to atherosclerosis in mice.

Animal models provide vital tools to explicate the pathogenesis of atherosclerosis. Accordingly, we established two atherosclerosis-prone mice models: (i) mice lacking the LDL (low-density lipoprotein) receptor (LDLR) and the ability to edit apo (apolipoprotein) B mRNA (Apobec1; designated LDb : LDLR-/- Apobec1-/-), and (ii) mice with the LDb background, who also overexpressed human apoB100 (designated LTp : LDLR-/- Apobec1-/- ERhB+/+). Both LDb and LTp mice had markedly elevated levels of LDL and increased levels of NEFAs (non-esterified fatty acids) compared with C57BL/6 wild-type mice. However, fasting glucose and insulin levels in both animals were not different than those in C57BL/6 wild-type mice. It has been suggested that PAF-AH (platelet-activating factor acetylhydrolase) increases susceptibility to vascular disease. Both LDb and LTp mice had significantly higher PAF-AH mRNA levels compared with C57BL/6 wild-type mice. PAF-AH gene expression was also significantly influenced by age and sex. Interestingly, PAF-AH mRNA levels were significantly higher in both LTp male and female mice than in the LDb mice. This increased PAF-AH gene expression was associated with elevated plasma PAF-AH enzyme activities ( LTp > LDb > C57BL/6 ). Moreover, a greater proportion of PAF-AH activity was associated with the apoB-containing lipoproteins: 29% in LTp and 13% in LDb mice compared with C57BL/6 wild-type animals (6.7%). This may explain why LTp mice developed more atherosclerotic lesions than LDb mice by 8 months of age. In summary, increased plasma NEFAs, PAF-AH mRNA and enzyme activities are associated with accelerated atherogenesis in these animal models.

1-Alkyl-2-acetylglycerophosphocholine Esterase↗

PubMed2004

Identification of genetic networks.

In this report, we propose the use of structural equations as a tool for identifying and modeling genetic networks and genetic algorithms for searching the most likely genetic networks that best fit the data. After genetic networks are identified, it is fundamental to identify those networks influencing cell phenotypes. To accomplish this task we extend the concept of differential expression of the genes, widely used in gene expression data analysis, to genetic networks. We propose a definition for the differential expression of a genetic network and use the generalized T2 statistic to measure the ability of genetic networks to distinguish different phenotypes. However, describing the differential expression of genetic networks is not enough for understanding biological systems because differences in the expression of genetic networks do not directly reflect regulatory strength between gene activities. Therefore, in this report we also introduce the concept of differentially regulated genetic networks, which has the potential to assess changes of gene regulation in response to perturbation in the environment and may provide new insights into the mechanism of diseases and biological processes. We propose five novel statistics to measure the differences in regulation of genetic networks. To illustrate the concepts and methods for reconstruction of genetic networks and identification of association of genetic networks with function, we applied the proposed models and algorithms to three data sets.

Algorithms↗

PubMed2003

A mathematical programming approach for gene selection and tissue classification.

MOTIVATION: Extracting useful information from expression levels of thousands of genes generated with microarray technology needs a variety of analytical techniques. Mathematical programming approaches for classification analysis outperform parametric methods when the data depart from assumptions underlying these methods. Therefore, a mathematical programming approach is developed for gene selection and tissue classification using gene expression profiles. RESULTS: A new mixed integer programming model is formulated for this purpose. The mixed integer programming model simultaneously selects genes and constructs a classification model to classify two groups of tissue samples as accurately as possible. Very encouraging results were obtained with two data sets from the literature as examples. These results show that the mathematical programming approach can rival or outperform traditional classification methods.

Algorithms↗

PubMed2003

SamCluster: an integrated scheme for automatic discovery of sample classes using gene expression profile.

MOTIVATION: Feature (gene) selection can dramatically improve the accuracy of gene expression profile based sample class prediction. Many statistical methods for feature (gene) selection such as stepwise optimization and Monte Carlo simulation have been developed for tissue sample classification. In contrast to class prediction, few statistical and computational methods for feature selection have been applied to clustering algorithms for pattern discovery. RESULTS: An integrated scheme and corresponding program SamCluster for automatic discovery of sample classes based on gene expression profile is presented in this report. The scheme incorporates the feature selection algorithms based on the calculation of CV (coefficient of variation) and t-test into hierarchical clustering and proceeds as follows. At first, the genes with their CV greater than the pre-specified threshold are selected for cluster analysis, which results in two putative sample classes. Then, significantly differentially expressed genes in the two putative sample classes with p-values < or = 0.01, 0.05, or 0.1 from t-test are selected for further cluster analysis. The above processes were iterated until the two stable sample classes were found. Finally, the consensus sample classes are constructed from the putative classes that are derived from the different CV thresholds, and the best putative sample classes that have the minimum distance between the consensus classes and the putative classes are identified. To evaluate the performance of the feature selection for cluster analysis, the proposed scheme was applied to four expression datasets COLON, LEUKEMIA72, LEUKEMIA38, and OVARIAN. The results show that there are only 5, 1, 0, and 0 samples that have been misclassified, respectively. We conclude that the proposed scheme, SamCluster, is an efficient method for discovery of sample classes using gene expression profile. AVAILABILITY: The related program SamCluster is available upon request or from the web page http://www.sph.uth.tmc.edu:8052/hgc/Downloads.asp.

Algorithms↗

PubMed2003

Haplotype block linkage disequilibrium mapping.

Linkage disequilibrium (LD) mapping is emerging as a powerful alternative approach to identifying genes for complex disease. However, the feasibility and success of LD mapping depend largely on the extent and pattern of LD. Erratic pattern of pair-wise LD seriously compromises LD mapping. Recently discovered haplotype block structure dramatically alleviates the irregular pattern of LD and holds the promise for mapping complex disease genes. To facilitate applications of the haplotype block LD mapping, in this report we conduct theoretical analysis for haplotype block LD mapping. We present an overall LD measure of the haplotype to quantify the LD level of the haplotype block, between the haplotype blocks, and between the haplotype block and the marker locus. Most theoretical and empirical studies of the extent of LD and evaluation of the power of LD mapping have focused on pair-wise LD and single marker LD mapping. There is a lack of systematic and integrative analysis for the haplotype block LD mapping. In this report, we develop population genetic models of the haplotype blocks and analytic tools for calculation of noncentrality parameter of the statistic for the haplotype block LD mapping. We evaluate the impact of the population parameters and disease models on the power of the haplotype block LD mapping in the hope to improve its study design. We compare the powers of the single marker LD and haplotype block LD mapping. Haplotype block structure is an important discovery. Our preliminary results of theoretic analysis further demonstrate that the haplotype block LD analysis is a breakthrough in LD mapping and is a promising tool for genome-wide association studies.

Chromosome Mapping↗

PubMed2003

Randomly distributed crossovers may generate block-like patterns of linkage disequilibrium: an act of genetic drift.

There is considerable interest in identifying and characterizing block-like patterns of linkage disequilibrium (LD; haplotype blocks) in the human genome as these may facilitate the identification of complex disease genes via genome-wide association studies. Although recombination hot-spots have been suggested as the primary mechanism to explain the block-like pattern of LD, other forces, such as genetic drift, may also be important. To this end, we have studied the effect of various recombination models on patterns of LD by using extensive simulations. As expected, haplotype blocks were observed under a model allowing recombination hot-spots. However, we also observed similar block-like patterns in the models where recombination crossovers are randomly and uniformly distributed, and we demonstrate that these blocks are generated by genetic drift. We caution that genetic drift may be an alternative mechanism (in addition to recombination hot-spots) that can lead to block-like patterns of LD. Our findings highlight the necessity of characterizing haplotype blocks in world-wide populations.

Computer Simulation↗

PubMed2003

Genome-wide association study for regions of systemic sclerosis susceptibility in a Choctaw Indian population with high disease prevalence.

OBJECTIVE: Systemic sclerosis (SSc) is a complex, multisystem connective tissue disease in which genetic factors contribute to disease susceptibility. The aim of this study was to localize chromosome regions associated with susceptibility to SSc in a relatively isolated and homogeneous population of Choctaw Indians with a high prevalence of SSc. METHODS: A genome-wide microsatellite screen at 10 cM resolution (400 markers) was performed in 20 Choctaw patients with SSc and 76 ethically matched controls. Based on the results of the initial screen, fine-scale microsatellite mapping at < or =1 cM resolution was performed in 10 selected chromosome regions. Allele and marker haplotype frequencies were compared between SSc patients and controls. RESULTS: From the genome-wide screen, 12 markers showed evidence of highly significant associations with SSc in this population (P < 0.01), while 5 other markers showed significant associations (0.01 < P < 0.05). Among these markers, loci D5S410, D6S422, D15S978, and D20S107 are near the SPARC, MHC, FBN1, and TOPOI genes, respectively, confirming the results of our previous studies, which used different markers. D1S2800 and D14S63 have been reported to show linkage to systemic lupus erythematosus (SLE) in family-based studies, and D1S206, D6S422, and D6S264 are loci on 1p21.2, 6p22.3, and 6q23-27, respectively, which are in regions reported as showing linkage to SLE and other autoimmune diseases. Other markers showing unique associations with SSc were D7S510 (7p12-11), D7S661 (7q35), D8S514 (8q24.12), D19S221 (19p13.2), D19S220 (19q13.2), D22S423 (22q13.1), DXS1068 (Xp11.4), and DXS8055 (Xq21-23). Further analysis with fine-scale microsatellite mapping revealed at least 14 potential haplotypes associated with SSc. CONCLUSION: Our findings indicate that a number of genetic loci may contribute to the high prevalence of SSc in the Choctaw and are consistent with the paradigm that some autoimmune rheumatic diseases are likely to share genetic determinants.

Chromosomes, Human↗

PubMed2003

Combination of TRAIL gene therapy and chemotherapy enhances antitumor and antimetastasis effects in chemosensitive and chemoresistant breast cancers.

We recently found that breast cancer cell lines that are resistant to chemotherapy or to the recombinant TRAIL protein are susceptible to TRAIL gene therapy. However, it is unclear whether a combination of TRAIL gene therapy and chemotherapy will have enhanced antitumor activity or can be used for the treatment of metastasis. In this study, we investigated the combined effect of TRAIL gene therapy and chemotherapeutic agents, including doxorubicin, paclitaxel, vinorelbine, gemcitabine, irinotecan, and floxuridine, in different breast cancer cell lines. In all the cell lines tested, including a breast cancer cell line that is resistant to chemotherapy, the combination of TRAIL gene therapy and cytotoxic agents had either a synergistic or an additive effect. An in vivo study showed that aerosolized administration of an adenovector expressing the GFP-TRAIL fusion protein from the human telomerase reverse transcriptase promoter (designated Ad/gTRAIL) also decreased the number of lung metastases from both doxorubicin-sensitive and doxorubicin-resistant breast cancer cell lines. The combination of TRAIL gene therapy and chemotherapy resulted in a further reduction of lung metastatic nodules with minimal toxicity. These results suggest that a combination of TRAIL gene therapy and chemotherapy is effective in the treatment of metastatic diseases.

Antibiotics, Antineoplastic↗

PubMed2003

Comparison of gene expression between metastatic derivatives and their poorly metastatic parental cells implicates crucial tumor-environment interaction in metastasis of head and neck squamous cell carcinoma.

Metastasis of human head and neck cancer is a multistep and highly heterogeneous process requiring activation and deactivation of multiple and specific genes. To identify these genes, we established highly metastatic head and neck squamous cell carcinoma (HNSCC) cell lines from poorly metastatic HNSCC cells through in vivo selection using a lymph node metastatic mouse model. The very close genetic relationship between these highly metastatic cell lines and the parental cell line provided an excellent model for differential gene expression analysis using cDNA microarrays. Comparison of 6 cell lines established individually from the lymph node metastases with their poorly metastatic parental cell line revealed 33 differentially expressed genes. Some of these genes are involved in cellular signal transduction and matrix modeling. Differences in expression of members of the tumor necrosis factor, interleukin, caspase, and matrix metalloproteinase families were also examined. We found that two upregulated genes participated in the NF-kappaB regulatory pathway. Furthermore, differences in gene expression between six cell lines derived from primary tumors and six cell lines derived from lymph node metastases in the mouse model were analyzed statistically. Tissue growth factor-beta and tumor necrosis factor-related genes showed significantly altered expression in cells derived from lymph node metastases as compared with cells derived from primary tumors, suggesting that the differential growth advantage of metastatic cells requires more aggressive responses to their environment, such as a lymph node tissue.

Animals↗