Extreme self-organization in networks constructed from gene expression data

We study networks constructed from gene expression data obtained from many types of cancers. The networks are constructed by connecting vertices that belong to each others' list of K-nearest-neighbors, with K being an a priori selected non-negative integer. We introduce an order parameter for characterizing the homogeneity of the networks. On minimizing the order parameter with respect to K, degree distribution of the networks shows power-law behavior in the tails with an exponent of unity. Analysis of the eigenvalue spectrum of the networks confirms the presence of the power-law and small-world behavior. We discuss the significance of these findings in the context of evolutionary biological processes.Comment: 4 pages including 3 eps figures, revtex. Revisions as in published versio

KIAA0101 (OEACT-1), an expressionally down-regulated and growth-inhibitory gene in human hepatocellular carcinoma

BACKGROUND: Our previous cDNA array results indicated KIAA0101 as one of the differentially expressed genes in human hepatocellular carcinoma (HCC) as compared with non-cancerous liver. However, it is necessary to study its expression at protein level in HCC and its biological function for HCC cell growth. METHOD: Western blot and tissue array were performed to compare KIAA0101 protein expression level in paired human HCC and non-cancerous liver tissues from the same patients. Investigation of its subcellular localization was done by using dual fluorescence image examination and enriched mitochondrial protein Western blot analysis. The in vitro cell growth curve was used for examing the effect of over-expression of KIAA0101 in HCC cells. FACS was used to analyze the cell cycle pattern in KIAA0101 expression positive (+) and negative (-) cell populations isolated by the pMACSKK(II )system after KIAA0101 cDNA transfection. RESULTS: Western blot showed KIAA0101 protein expression was down-regulated in HCC tissues as compared with their counterpart non-cancerous liver tissues in 25 out of 30 cases. Tissue array also demonstrated the same pattern in 161 paired samples. KIAA0101 was predominantly localized in mitochondria and partially in nuclei. KIAA0101 cDNA transfection could inhibit the HCC cell growth in vitro. In cell cycle analysis, it could arrest cells at the G(1 )to S phase transition. CONCLUSION: KIAA0101 protein expression was down-regulated in HCC. This gene could inhibit the HCC cell growth in vitro and presumably by its blocking effect on cell cycle

Association between the timing of childhood adversity and epigenetic patterns across childhood and adolescence:findings from the Avon Longitudinal Study of Parents and Children (ALSPAC) prospective cohort

BACKGROUND: Childhood adversity is a potent determinant of health across development and is associated with altered DNA methylation signatures, which might be more common in children exposed during sensitive periods in development. However, it remains unclear whether adversity has persistent epigenetic associations across childhood and adolescence. We aimed to examine the relationship between time-varying adversity (defined through sensitive period, accumulation of risk, and recency life course hypotheses) and genome-wide DNA methylation, measured three times from birth to adolescence, using data from a prospective, longitudinal cohort study.METHODS: We first investigated the relationship between the timing of exposure to childhood adversity between birth and 11 years and blood DNA methylation at age 15 years in the Avon Longitudinal Study of Parents and Children (ALSPAC) prospective cohort study. Our analytic sample included ALSPAC participants with DNA methylation data and complete childhood adversity data between birth and 11 years. We analysed seven types of adversity (caregiver physical or emotional abuse, sexual or physical abuse [by anyone], maternal psychopathology, one-adult households, family instability, financial hardship, and neighbourhood disadvantage) reported by mothers five to eight times between birth and 11 years. We used the structured life course modelling approach (SLCMA) to identify time-varying associations between childhood adversity and adolescent DNA methylation. Top loci were identified using an R 2 threshold of 0·035 (ie, ≥3·5% of DNA methylation variance explained by adversity). We attempted to replicate these associations using data from the Raine Study and Future of Families and Child Wellbeing Study (FFCWS). We also assessed the persistence of adversity-DNA methylation associations we previously identified from age 7 blood DNA methylation into adolescence and the influence of adversity on DNA methylation trajectories from ages 0-15 years. FINDINGS: Of 13 988 children in the ALSPAC cohort, 609-665 children (311-337 [50-51%] boys and 298-332 [49-50%] girls) had complete data available for at least one of the seven childhood adversities and DNA methylation at 15 years. Exposure to adversity was associated with differences in DNA methylation at 15 years for 41 loci (R 2 ≥0·035). Sensitive periods were the most often selected life course hypothesis by the SLCMA. 20 (49%) of 41 loci were associated with adversities occurring between age 3 and 5 years. Exposure to one-adult households was associated with differences in DNA methylation at 20 [49%] of 41 loci, exposure to financial hardship was associated with changes at nine (22%) loci, and physical or sexual abuse was associated with changes at four (10%) loci. We replicated the direction of associations for 18 (90%) of 20 loci associated with exposure to one-adult household using adolescent blood DNA methylation from the Raine Study and 18 (64%) of 28 loci using saliva DNA methylation from the FFCWS. The directions of effects for 11 one-adult household loci were replicated in both cohorts. Differences in DNA methylation at 15 years were not present at 7 years and differences identified at 7 years were no longer apparent by 15 years. We also identified six distinct DNA methylation trajectories from these patterns of stability and persistence. INTERPRETATION: These findings highlight the time-varying effect of childhood adversity on DNA methylation profiles across development, which might link exposure to adversity to potential adverse health outcomes in children and adolescents. If replicated, these epigenetic signatures could ultimately serve as biological indicators or early warning signs of initiated disease processes, helping identify people at greater risk for the adverse health consequences of childhood adversity.FUNDING: Canadian Institutes of Health Research, Cohort and Longitudinal Studies Enhancement Resources, EU's Horizon 2020, US National Institute of Mental Health.</p

The inflammatory microenvironment in colorectal neoplasia

Peer reviewedPublisher PD

A transcriptome anatomy of human colorectal cancers

BACKGROUND: Accumulating databases in human genome research have enabled integrated genome-wide study on complicated diseases such as cancers. A practical approach is to mine a global transcriptome profile of disease from public database. New concepts of these diseases might emerge by landscaping this profile. METHODS: In this study, we clustered human colorectal normal mucosa (N), inflammatory bowel disease (IBD), adenoma (A) and cancer (T) related expression sequence tags (EST) into UniGenes via an in-house GetUni software package and analyzed the transcriptome overview of these libraries by GOTree Machine (GOTM). Additionally, we downloaded UniGene based cDNA libraries of colon and analyzed them by Xprofiler to cross validate the efficiency of GetUni. Semi-quantitative RT-PCR was used to validate the expression of β-catenin and. 7 novel genes in colorectal cancers. RESULTS: The efficiency of GetUni was successfully validated by Xprofiler and RT-PCR. Genes in library N, IBD and A were all found in library T. A total of 14,879 genes were identified with 2,355 of them having at least 2 transcripts. Differences in gene enrichment among these libraries were statistically significant in 50 signal transduction pathways and Pfam protein domains by GOTM analysis P < 0.01 Hypergeometric Test). Genes in two metabolic pathways, ribosome and glycolysis, were more enriched in the expression profiles of A and IBD than in N and T. Seven transmembrane receptor superfamily genes were typically abundant in cancers. CONCLUSION: Colorectal cancers are genetically heterogeneous. Transcription variants are common in them. Aberrations of ribosome and glycolysis pathway might be early indicators of precursor lesions in colon cancers. The electronic gene expression profile could be used to highlight the integral molecular events in colorectal cancers

Gene expression profiling of liver metastases from colorectal cancer as potential basis for treatment choice

At present no reports on gene expression profiling of liver metastases from colorectal cancer are available. We identified two different signatures using Affymetrix platform: epidermal growth factor receptor pathway was upregulated in metachronous lesions, whereas the pathway mainly related to angiogenesis was in synchronous lesions. Synchronous or metachronous liver metastases could be treated differently on the basis of different molecular pathways

Presence of activating KRAS mutations correlates significantly with expression of tumour suppressor genes DCN and TPM1 in colorectal cancer

<p>Abstract</p> <p>Background</p> <p>Despite identification of the major genes and pathways involved in the development of colorectal cancer (CRC), it has become obvious that several steps in these pathways might be bypassed by other as yet unknown genetic events that lead towards CRC. Therefore we wanted to improve our understanding of the genetic mechanisms of CRC development.</p> <p>Methods</p> <p>We used microarrays to identify novel genes involved in the development of CRC. Real time PCR was used for mRNA expression as well as to search for chromosomal abnormalities within candidate genes. The correlation between the expression obtained by real time PCR and the presence of the <it>KRAS </it>mutation was investigated.</p> <p>Results</p> <p>We detected significant previously undescribed underexpression in CRC for genes <it>SLC26A3</it>, <it>TPM1 </it>and <it>DCN</it>, with a suggested tumour suppressor role. We also describe the correlation between <it>TPM1 </it>and <it>DCN </it>expression and the presence of <it>KRAS </it>mutations in CRC. When searching for chromosomal abnormalities, we found deletion of the <it>TPM1 </it>gene in one case of CRC, but no deletions of <it>DCN </it>and <it>SLC26A3 </it>were found.</p> <p>Conclusion</p> <p>Our study provides further evidence of decreased mRNA expression of three important tumour suppressor genes in cases of CRC, thus implicating them in the development of this type of cancer. Moreover, we found underexpression of the <it>TPM1 </it>gene in a case of CRCs without <it>KRAS </it>mutations, showing that <it>TPM1 </it>might serve as an alternative path of development of CRC. This downregulation could in some cases be mediated by deletion of the <it>TPM1 </it>gene. On the other hand, the correlation of <it>DCN </it>underexpression with the presence of <it>KRAS </it>mutations suggests that <it>DCN </it>expression is affected by the presence of activating <it>KRAS </it>mutations, lowering the amount of the important tumour suppressor protein decorin.</p

A feature selection method for classification within functional genomics experiments based on the proportional overlapping score

Background: Microarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a feature's relevance to a classification task.Results: We apply POS, along-with four widely used gene selection methods, to several benchmark gene expression datasets. The experimental results of classification error rates computed using the Random Forest, k Nearest Neighbor and Support Vector Machine classifiers show that POS achieves a better performance.Conclusions: A novel gene selection method, POS, is proposed. POS analyzes the expressions overlap across classes taking into account the proportions of overlapping samples. It robustly defines a mask for each gene that allows it to minimize the effect of expression outliers. The constructed masks along-with a novel gene score are exploited to produce the selected subset of genes

A Population Proportion approach for ranking differentially expressed genes

<p>Abstract</p> <p>Background</p> <p>DNA microarrays are used to investigate differences in gene expression between two or more classes of samples. Most currently used approaches compare mean expression levels between classes and are not geared to find genes whose expression is significantly different in only a subset of samples in a class. However, biological variability can lead to situations where key genes are differentially expressed in only a subset of samples. To facilitate the identification of such genes, a new method is reported.</p> <p>Methods</p> <p>The key difference between the Population Proportion Ranking Method (PPRM) presented here and almost all other methods currently used is in the quantification of variability. PPRM quantifies variability in terms of inter-sample ratios and can be used to calculate the relative merit of differentially expressed genes with a specified difference in expression level between at least some samples in the two classes, which at the same time have lower than a specified variability within each class.</p> <p>Results</p> <p>PPRM is tested on simulated data and on three publicly available cancer data sets. It is compared to the t test, PPST, COPA, OS, ORT and MOST using the simulated data. Under the conditions tested, it performs as well or better than the other methods tested under low intra-class variability and better than t test, PPST, COPA and OS when a gene is differentially expressed in only a subset of samples. It performs better than ORT and MOST in recognizing non differentially expressed genes with high variability in expression levels across all samples. For biological data, the success of predictor genes identified in appropriately classifying an independent sample is reported.</p