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PubMed2004

LS Bound based gene selection for DNA microarray data.

MOTIVATION: One problem with discriminant analysis of DNA microarray data is that each sample is represented by quite a large number of genes, and many of them are irrelevant, insignificant or redundant to the discriminant problem at hand. Methods for selecting important genes are, therefore, of much significance in microarray data analysis. In the present study, a new criterion, called LS Bound measure, is proposed to address the gene selection problem. The LS Bound measure is derived from leave-one-out procedure of LS-SVMs (least squares support vector machines), and as the upper bound for leave-one-out classification results it reflects to some extent the generalization performance of gene subsets. RESULTS: We applied this LS Bound measure for gene selection on two benchmark microarray datasets: colon cancer and leukemia. We also compared the LS Bound measure with other evaluation criteria, including the well-known Fisher's ratio and Mahalanobis class separability measure, and other published gene selection algorithms, including Weighting factor and SVM Recursive Feature Elimination. The strength of the LS Bound measure is that it provides gene subsets leading to more accurate classification results than the filter method while its computational complexity is at the level of the filter method. AVAILABILITY: A companion website can be accessed at http://www.ntu.edu.sg/home5/pg02776030/lsbound/. The website contains: (1) the source code of the gene selection algorithm; (2) the complete set of tables and figures regarding the experimental study; (3) proof of the inequality (9). CONTACT: ekzmao@ntu.edu.sg.

Biomarkers, Tumor↗
PubMed2005

Molecular decomposition of complex clinical phenotypes using biologically structured analysis of microarray data.

MOTIVATION: Today, the characterization of clinical phenotypes by gene-expression patterns is widely used in clinical research. If the investigated phenotype is complex from the molecular point of view, new challenges arise and these have not been addressed systematically. For instance, the same clinical phenotype can be caused by various molecular disorders, such that one observes different characteristic expression patterns in different patients. RESULTS: In this paper we describe a novel algorithm called Structured Analysis of Microarrays (StAM), which accounts for molecular heterogeneity of complex clinical phenotypes. Our algorithm goes beyond established methodology in several aspects: in addition to the expression data, it exploits functional annotations from the Gene Ontology database to build biologically focussed classifiers. These are used to uncover potential molecular disease subentities and associate them to biological processes without compromising overall prediction accuracy. AVAILABILITY: Bioconductor compliant R package SUPPLEMENTARY INFORMATION: Complete analyses are available at http://compdiag.molgen.mpg.de/supplements/lottaz05.

Biomarkers, Tumor↗
PubMed2005

Pathway recognition and augmentation by computational analysis of microarray expression data.

MOTIVATION: We present a system, QPACA (Quantitative Pathway Analysis in Cancer) for analysis of biological data in the context of pathways. QPACA supports data visualization and both fine- and coarse-grained specifications, but, more importantly, addresses the problems of pathway recognition and pathway augmentation. RESULTS: Given a set of genes hypothesized to be part of a pathway or a coordinated process, QPACA is able to reliably distinguish true pathways from non-pathways using microarray expression data. Relying on the observation that only some of the experiments within a dataset are relevant to a specific biochemical pathway, QPACA automates selection of this subset using an optimization procedure. We present data on all human and yeast pathways found in the KEGG pathway database. In 117 out of 191 cases (61%), QPACA was able to correctly identify these positive cases as bona fide pathways with p-values measured using rigorous permutation analysis. Success in recognizing pathways was dependent on pathway size, with the largest quartile of pathways yielding 83% success. In cross-validation tests of pathway membership prediction, QPACA was able to yield enrichments for predicted pathway genes over random genes at rates of 2-fold or better the majority of the time, with rates of 10-fold or better 10-20% of the time. AVAILABILITY: The software is available for academic research use free of charge by email request. SUPPLEMENTARY INFORMATION: Data used in the paper may be downloaded from http://www.jainlab.org/downloads.html

Algorithms↗
PubMed2006

Inferring causal relationships among intermediate phenotypes and biomarkers: a case study of rheumatoid arthritis.

MOTIVATION: Genetic association analysis is based on statistical correlations which do not assign any cause-to-effect arrows between the two correlated variables. Normally, such assignment of cause and effect label is not necessary in genetic analysis since genes are always the cause and phenotypes are always the effect. However, among intermediate phenotypes and biomarkers, assigning cause and effect becomes meaningful, and causal inference can be useful. RESULTS: We show that causal inference is possible by an example in a study of rheumatoid arthritis. With the help of genotypic information, the shared epitope, the causal relationship between two biomarkers related to the disease, anti-cyclic citrullinated peptide (anti-CCP) and rheumatoid factor (RF) has been established. We emphasize the fact that third variable must be a genotype to be able to resolve potential ambiguities in causal inference. Two non-trivial conclusions have been reached by the causal inference: (1) anti-CCP is a cause of RF and (2) it is unlikely that a third confounding factor contributes to both anti-CCP and RF.

Arthritis, Rheumatoid↗
PubMed2006

Novel unsupervised feature filtering of biological data.

MOTIVATION: Many methods have been developed for selecting small informative feature subsets in large noisy data. However, unsupervised methods are scarce. Examples are using the variance of data collected for each feature, or the projection of the feature on the first principal component. We propose a novel unsupervised criterion, based on SVD-entropy, selecting a feature according to its contribution to the entropy (CE) calculated on a leave-one-out basis. This can be implemented in four ways: simple ranking according to CE values (SR); forward selection by accumulating features according to which set produces highest entropy (FS1); forward selection by accumulating features through the choice of the best CE out of the remaining ones (FS2); backward elimination (BE) of features with the lowest CE. RESULTS: We apply our methods to different benchmarks. In each case we evaluate the success of clustering the data in the selected feature spaces, by measuring Jaccard scores with respect to known classifications. We demonstrate that feature filtering according to CE outperforms the variance method and gene-shaving. There are cases where the analysis, based on a small set of selected features, outperforms the best score reported when all information was used. Our method calls for an optimal size of the relevant feature set. This turns out to be just a few percents of the number of genes in the two Leukemia datasets that we have analyzed. Moreover, the most favored selected genes turn out to have significant GO enrichment in relevant cellular processes.

Artificial Intelligence↗
PubMed2006

Simultaneous identification of differential gene expression and connectivity in inflammation, adipogenesis and cancer.

MOTIVATION: Biological differences between classes are reflected in transcriptional changes which in turn affect the levels by which essential genes are individually expressed and collectively connected. The purpose of this communication is to introduce an analytical procedure to simultaneously identify genes that are differentially expressed (DE) as well as differentially connected (DC) in two or more classes of interest. RESULTS: Our procedure is based on a two-step approach: First, mixed-model equations are applied to obtain the normalized expression levels of each gene in each class treatment. These normalized expressions form the basis to compute a measure of (possible) DE as well as the correlation structure existing among genes. Second, a two-component mixture of bi-variate distributions is fitted to identify the component that encapsulates those genes that are DE and/or DC. We demonstrate our approach using three distinct datasets including a human systemic inflammation oligonucleotide data; a spotted cDNA data dealing with bovine in vitro adipogenesis and SAGE database on cancerous and normal tissue samples.

Adipogenesis↗
PubMed2006

Clustering threshold gradient descent regularization: with applications to microarray studies.

MOTIVATION: An important goal of microarray studies is to discover genes that are associated with clinical outcomes, such as disease status and patient survival. While a typical experiment surveys gene expressions on a global scale, there may be only a small number of genes that have significant influence on a clinical outcome. Moreover, expression data have cluster structures and the genes within a cluster have correlated expressions and coordinated functions, but the effects of individual genes in the same cluster may be different. Accordingly, we seek to build statistical models with the following properties. First, the model is sparse in the sense that only a subset of the parameter vector is non-zero. Second, the cluster structures of gene expressions are properly accounted for. RESULTS: For gene expression data without pathway information, we divide genes into clusters using commonly used methods, such as K-means or hierarchical approaches. The optimal number of clusters is determined using the Gap statistic. We propose a clustering threshold gradient descent regularization (CTGDR) method, for simultaneous cluster selection and within cluster gene selection. We apply this method to binary classification and censored survival analysis. Compared to the standard TGDR and other regularization methods, the CTGDR takes into account the cluster structure and carries out feature selection at both the cluster level and within-cluster gene level. We demonstrate the CTGDR on two studies of cancer classification and two studies correlating survival of lymphoma patients with microarray expressions. AVAILABILITY: R code is available upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Algorithms↗
PubMed2005

Gene expression in Huntington's disease skeletal muscle: a potential biomarker.

Huntington's disease (HD) is an incurable and fatal neurodegenerative disorder. Improvements in the objective measurement of HD will lead to more efficient clinical trials and earlier therapeutic intervention. We hypothesized that abnormalities seen in the R6/2 mouse, a greatly accelerated HD model, might highlight subtle phenotypes in other mouse models and human HD. In this paper, we identify common gene expression changes in skeletal muscle from R6/2 mice, Hdh(CAG(150)) homozygous knock-in mice and HD patients. This HD-triggered gene expression phenotype is consistent with the beginnings of a transition from fast-twitch to slow-twitch muscle fiber types. Metabolic adaptations similar to those induced by diabetes or fasting are also present but neither metabolic disorder can explain the full phenotype of HD muscle. The HD-induced gene expression changes reflect disease progression. This raises the possibility that muscle gene expression may be used as an objective biomarker to complement clinical HD-rating systems. Furthermore, an understanding of the molecular basis of muscle dysfunction in HD should provide insight into mechanisms involved in neuronal abnormalities and neurodegeneration.

Animals↗
PubMed2026

Comprehensive Analysis of Differentially Expressed Genes and Immune Infiltration in Burn Injury: Key Biomarkers and Pathways.

BACKGROUND: Burn injuries trigger complex immune responses and gene expression changes, impacting wound healing and systemic inflammation. Understanding these changes is crucial for identifying biomarkers and therapeutic targets. METHODS: We analyzed two gene expression omnibus datasets (wound tissue [GSE8056] and blood [GSE37069]) to identify differentially expressed genes (DEGs) in burn injury samples versus controls. Immune cell proportions were assessed using CIBERSORT. Functional enrichment analyses (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) and protein-protein interaction networks were constructed to identify key genes and pathways. RESULTS: We identified 1170 upregulated and 1227 downregulated DEGs. Gene Ontology analysis revealed enrichment in neutrophil activation, inflammatory response, and extracellular matrix organization. Kyoto Encyclopedia of Genes and Genomes analysis highlighted cytokine-cytokine receptor interaction, TNF, and IL-17 signaling pathways. Immune infiltration analysis showed significant changes in neutrophils, macrophages (M1/M2), and T-cell subsets. Protein-protein interaction network analysis identified five hub genes: JUN, STAT1, Bcl2, MMP9, and TLR2. CONCLUSIONS: This study provides a comprehensive bioinformatic analysis of gene expression and immune responses in burn injuries. The identified DEGs, hub genes, and pathways offer insights into the immune response mechanisms and suggest potential targets for diagnostic and therapeutic interventions in burn injury management.

Burns↗
PubMed2023

Immunogenomics of cholangiocarcinoma.

The development of cholangiocarcinoma spans years, if not decades, during which the immune system becomes corrupted and permissive to primary tumor development and metastasis. This involves subversion of local immunity at tumor sites, as well as systemic immunity and the wider host response. While immune dysfunction is a hallmark of all cholangiocarcinoma, the specific steps of the cancer-immunity cycle that are perturbed differ between patients. Heterogeneous immune functionality impacts the evolutionary development, pathobiological behavior, and therapeutic response of these tumors. Integrative genomic analyses of thousands of primary tumors have supported a biological rationale for immune-based stratification of patients, encompassing immune cell composition and functionality. However, discerning immune alterations responsible for promoting tumor initiation, maintenance, and progression from those present as bystander events remains challenging. Functionally uncoupling the tumor-promoting or tumor-suppressing roles of immune profiles will be critical for identifying new immunomodulatory treatment strategies and associated biomarkers for patient stratification. This review will discuss the immunogenomics of cholangiocarcinoma, including the impact of genomic alterations on immune functionality, subversion of the cancer-immunity cycle, as well as clinical implications for existing and novel treatment strategies.

Humans↗
PubMed2006

Expression of stromal cell markers in distinct compartments of human skin cancers.

BACKGROUND: The importance of changes in the supporting tumor stroma for cancer initiation and progression is well established. The characteristics of an activated tumor stroma, however, are not completely understood. In an effort to better characterize the desmoplastic response to human skin tumors, we evaluated the expression pattern of three stromal cell markers, fibroblast-activation protein (FAP), endoglyx-1, and endosialin, in a series of melanocytic and epithelial skin tumors. METHODS: Immunohistochemistry using antibodies against FAP, endoglyx-1, and endosialin was carried out in skin samples obtained from 43 patients. Furthermore, microarray data from an independent set of human skin cancers were analyzed. RESULTS: FAP-positive fibroblasts were detected in all tumor tissues tested, including cases of melanocytic nevi, melanoma metastases, basal cell carcinomas, and squamous cell carcinomas. In cutaneous melanoma metastases, we identified different compartments within the stromal response on the basis of the regions of FAP expression. Endoglyx-1 expression was confined to normal and tumor blood vessel endothelium including 'hot spots' of neoangiogenesis within the cutaneous melanoma metastases. Endosialin was selectively induced in subsets of small- and medium-sized tumor blood vessels in melanoma metastases and squamous cell carcinomas. CONCLUSIONS: These data describe novel aspects of stromal marker expression in distinct compartments of human skin tumors and may point to potential targets for novel therapeutic strategies aimed at the tumor stroma.

Antigens, CD↗
PubMed2005

Ultraviolet-induced transformation of keratinocytes: possible involvement of long interspersed element-1 reverse transcriptase.

The normal human keratinocyte cell line, HaCaT, was transformed using multiple doses of ultraviolet (UV)A+B (UVA, 150-200 mJ/cm(2) and UVB, 15-20 mJ/cm(2) x 6). Malignant transformation was confirmed by upregulation of Cyclin D1 (mRNA) and formation of colonies on soft agar. To identify the genes involved in this transformation process, we have done rapid amplification of polymorphic DNA using RNA from unexposed and multiple-exposed cells. Six percent PAGE showed several differentially regulated genes in exposed cells compared with unexposed cells. Total 19 genes were identified, cloned and sequenced. Three of these 19 cloned genes showed 99% homology at both DNA and protein levels to a stretch of 540 bp (180 aa) of long interspersed element (LINE)-1 reverse transcriptase (RT) open reading frame (ORF-2). Colonies from soft agar showed upregulation of this gene compared with non-colonized (lawn on soft agar) cells as detected by RT-PCR. This data implicates LINE-1 RT (ORF-2) in UV-induced malignancy and can possibly be used as a marker for the diagnosis of UV-induced skin cancer.

Apoptosis↗
PubMed2005

Differential gene expression in colon cancer of the caecum versus the sigmoid and rectosigmoid.

BACKGROUND AND AIMS: There are epidemiological, morphological, and molecular differences between normal mucosa as well as between adenocarcinomas of the right and left side of the large bowel. The aim of this study was to investigate differences in gene expression. METHODS: Oligonucleotide microarrays (GeneChip) were used to compare gene expression in 45 single samples from normal mucosa and sporadic colorectal carcinomas (Dukes' B and C) of the caecum compared with the sigmoid and rectosigmoid. Findings were validated by real time polymerase chain reaction. RESULTS: Fifty eight genes were found to be differentially expressed between the normal mucosa of the caecum and the sigmoid and rectosigmoid (p<0.01), including pS2, S100P, and a sialyltransferase, all being expressed at higher levels in the caecum. A total of 118 and 186 genes were differentially expressed between normal and right or left sided tumours of the colon, showing more pronounced differences in Dukes' C than B tumours. Thirty genes differentially expressed in tumour tissue were common to adenocarcinomas of both sides, including known tumour markers such as the matrix metalloproteinases. Keratins 8, 19, and 20 as well as carbonic anhydrases (II, IV, VII) showed side specific expression and were downregulated in left sided tumours whereas teratocarcinoma growth factor and cyclooxygenase 2 (COX-2) were upregulated in left sided adenocarcinomas. Immunohistochemical analysis confirmed differences in side specific expression for cytokeratin 20 and COX-2. CONCLUSIONS: Differences in gene expression between normal mucosa as well as between adenocarcinomas of the caecum and sigmoid or rectosigmoid exist and should be taken into account when examining new targeted therapeutic regimens.

Adenocarcinoma↗
PubMed2005

Gene expression of myogenic factors and phenotype-specific markers in electrically stimulated muscle of paraplegics.

The transcription factors myogenin and MyoD have been suggested to be involved in maintaining slow and fast muscle-fiber phenotypes, respectively, in rodents. Whether this is also the case in human muscle is unknown. To test this, 4 wk of chronic, low-frequency electrical stimulation training of the tibialis anterior muscle of paraplegic subjects were used to evoke a fast-to-slow transformation in muscle phenotype. It was hypothesized that this would result from an upregulation of myogenin and a downregulation of MyoD. The training evoked the expected mRNA increase for slow fiber-specific markers myosin heavy chain I and 3-hydroxyacyl-CoA dehydrogenase A, whereas an mRNA decrease was seen for fast fiber-specific markers myosin heavy chain IIx and glycerol phosphate dehydrogenase. Although the slow fiber-specific markers citrate synthase and muscle fatty acid binding protein did not display a significant increase in mRNA, they did tend to increase. As hypothesized, myogenin mRNA was upregulated. However, contrary to the hypothesis, MyoD mRNA also increased, although later than myogenin. The mRNA levels of the other myogenic regulatory factor family members, myogenic factor 5 and myogenic regulatory factor 4, and the myocyte enhancer factor (MEF) family members, MEF-2A and MEF-2C, did not change. The results indicate that myogenin is indeed involved in the regulation of the slow oxidative phenotype in human skeletal muscle fibers, whereas MyoD appears to have a more complex regulatory function.

Adaptation, Physiological↗
PubMed2004

Promise and challenge: Markers of prostate cancer detection, diagnosis and prognosis.

Approximately 1 man in 6 will be diagnosed with prostate cancer during his life lifetime, and over 200,000 men in the U.S. are diagnosed with prostate cancer annually. Since the widespread adoption of PSA testing, about 60-70% of men at risk in the U.S. have had a blood test for prostate cancer. With this, prostate cancer death rates have decreased, yet only slightly. Thirty thousand men still die each year from this disease. PSA testing fails to identify a small but significant proportion of aggressive cancers, and only about 30% of men with a "positive" PSA have a positive biopsy. Additionally, of men who are treated for prostate cancer, about 25% require additional treatment, presumably due to disease recurrence. Also of concern is the growing evidence that there are some prostate cancers for which treatment may not be necessary. Very long-term studies from the U.S. and Europe, following men with prostate cancer have found that some tumors do not progress over time. In these individuals, prostate cancer treatment is unnecessary and harmful as these men do not benefit from treatment but will be at risk of treatment-related side effects and complications. They suggest a fundamental problem with prostate cancer: it is not possible, at this time, to predict the natural history of the disease. It is for these reasons that the most important challenge in prostate cancer today is the inability to predict the behavior of an individual tumor in an individual patient. Here we review issues related to performance and validation of biomarkers with a focus on "doing no harm", and bearing in mind that it is the ultimate goal of early detection to save lives. Improved diagnostic and prognostic biomarkers are needed for prostate cancer, and the use of these markers should ultimately translate into increased life span and quality of life. The ultimate goal would be to not only have accurate biomarkers suitable for early diagnosis, but also biomarkers that identify men at greatest risk of developing aggressive disease. Technology has been brought to bear on this problem, and the major approaches are genomics, expression analysis, and proteomics. Proteomics and DNA methylation assays may soon be used in sensitive and specific diagnostic testing of serum and tissues for cancer. Expression arrays may be used to establish both a more specific diagnosis and prognosis for a particular tumor. The proteome is only beginning to be understood, and alternative splicing and post-translational modifications of proteins such as glycosylation and phosphorylation are challenging areas of study. Finally, risk assessment and prognosis are being pursued through analysis of genomic polymorphisms (single nucleotide polymorphisms, SNPs). This huge task is only beginning, and requires the combined expertise of molecular epidemiologists, oncologists, surgeons, pathologists, and basic scientists.

Biomarkers, Tumor↗