Statistical assessment of functional categories of genes deregulated in pathological conditions by using microarray data.

MOTIVATION: A major challenge in current biomedical research is the identification of cellular processes deregulated in a given pathology through the analysis of gene expression profiles. To this end, predefined lists of genes, coding specific functions, are compared with a list of genes ordered according to their values of differential expression measured by suitable univariate statistics. RESULTS: We propose a statistically well-founded method for measuring the relevance of predefined lists of genes and for assessing their statistical significance starting from their raw expression levels as recorded on the microarray. We use prediction accuracy as a measure of relevance of the list. The rationale is that a functional category, coded through a list of genes, is perturbed in a given pathology if it is possible to correctly predict the occurrence of the disease in new subjects on the basis of the expression levels of the genes belonging to the list only. The accuracy is estimated with multiple random validation strategy and its statistical significance is assessed against a couple of null hypothesis, by using two independent permutation tests. The utility of the proposed methodology is illustrated by analyzing the relevance of Gene Ontology terms belonging to biological process category in colon and prostate cancer, by using three different microarray data sets and by comparing it with current approaches. AVAILABILITY: Source code for the algorithms is available from author upon request. SUPPLEMENTARY INFORMATION: Colon cancer data set and a complete description of experimental results are available at: ftp://bioftp:76bioftpxxx@marx.ba.issia.cnr.it/supp-info.htm.

Regularized least squares classifiers may predict Crohn's disease from profiles of single nucleotide polymorphisms.

In this paper we focus on the prediction of Crohn's disease (CD) susceptibility by analyzing SNP profiles for a number of defined or suggested gene polymorphisms. We assess the correlation between genetic markers and the phenotype by using well-founded methods and procedures developed in the field of statistical learning theory. To this end, we use a sample generated by a case-control study composed of 178 CD patients and 127 healthy controls. The genetic profile of each subject is composed of 16 genetic variants distributed over 11 genes. We find that regularized least squares (RLS) classifiers predict Crohn's disease with a statistically significant accuracy of 62%(p= 0.018), significantly increasing the diagnostic accuracy by at least 10% compared to that obtained with the more largely confirmed gene involved in CD predisposition, namely CARD15. This also demonstrates that our sample size is adequate for accurate and significant prediction estimates. The strength of this methodology, in contrast to classical statistical methods, is that it accounts simultaneously for the effect of several genetics markers and their possible interactions. The findings of this study show that RLS methodology is able to increase the diagnostic accuracy of CD prediction by contemporary evaluation of a large number of gene polymorphisms. This approach may be particularly useful in large-scale population screening programs, and when evaluating large datasets of gene polymorphisms (i.e. chips, microarrays). Moreover, it could shed more light on possible candidate genes with a weak genetic contribution, and for evaluating gene-gene and gene-phenotype interactions by analyzing populations with a reasonably small sample size.

On the statistical assessment of classifiers using DNA microarray data.

BACKGROUND: In this paper we present a method for the statistical assessment of cancer predictors which make use of gene expression profiles. The methodology is applied to a new data set of microarray gene expression data collected in Casa Sollievo della Sofferenza Hospital, Foggia--Italy. The data set is made up of normal (22) and tumor (25) specimens extracted from 25 patients affected by colon cancer. We propose to give answers to some questions which are relevant for the automatic diagnosis of cancer such as: Is the size of the available data set sufficient to build accurate classifiers? What is the statistical significance of the associated error rates? In what ways can accuracy be considered dependant on the adopted classification scheme? How many genes are correlated with the pathology and how many are sufficient for an accurate colon cancer classification? The method we propose answers these questions whilst avoiding the potential pitfalls hidden in the analysis and interpretation of microarray data. RESULTS: We estimate the generalization error, evaluated through the Leave-K-Out Cross Validation error, for three different classification schemes by varying the number of training examples and the number of the genes used. The statistical significance of the error rate is measured by using a permutation test. We provide a statistical analysis in terms of the frequencies of the genes involved in the classification. Using the whole set of genes, we found that the Weighted Voting Algorithm (WVA) classifier learns the distinction between normal and tumor specimens with 25 training examples, providing e = 21% (p = 0.045) as an error rate. This remains constant even when the number of examples increases. Moreover, Regularized Least Squares (RLS) and Support Vector Machines (SVM) classifiers can learn with only 15 training examples, with an error rate of e = 19% (p = 0.035) and e = 18% (p = 0.037) respectively. Moreover, the error rate decreases as the training set size increases, reaching its best performances with 35 training examples. In this case, RLS and SVM have error rates of e = 14% (p = 0.027) and e = 11% (p = 0.019). Concerning the number of genes, we found about 6000 genes (p < 0.05) correlated with the pathology, resulting from the signal-to-noise statistic. Moreover the performances of RLS and SVM classifiers do not change when 74% of genes is used. They progressively reduce up to e = 16% (p < 0.05) when only 2 genes are employed. The biological relevance of a set of genes determined by our statistical analysis and the major roles they play in colorectal tumorigenesis is discussed. CONCLUSIONS: The method proposed provides statistically significant answers to precise questions relevant for the diagnosis and prognosis of cancer. We found that, with as few as 15 examples, it is possible to train statistically significant classifiers for colon cancer diagnosis. As for the definition of the number of genes sufficient for a reliable classification of colon cancer, our results suggest that it depends on the accuracy required.

Idiopathic granulomatous mastitis. Report of a case clinically and mammographically simulating breast carcinoma.

A case of bilateral idiopathic granulomatous mastitis in a 67-year-old woman is described. The clinical presentation and mammographic findings raised strong suspicions of malignancy, excluded by the histological examination, which made it possible also to rule out possible known causes of granulomatous inflammation of the breast.

Giant cell fibroblastoma of the scrotum. A case report.

A case of giant cell fibroblastoma occurring in the scrotum of a 21-year-old male is described. It may have been a recurrence of a previous lesion in the same region, which was only clinically diagnosed as fibrolipoma when excised 10 years before. Giant cell fibroblastoma is a benign, locally recurrent tumor of youth and childhood which arises from superficial soft tissue. To our knowledge, only 2 other cases of scrotal involvement by this neoplasm have been reported.

[Primary malignant melanoma of the vagina]. / Il melanoma maligno primitivo della vagina.

We report two cases of primary malignant melanoma of the vagina explaining the essential clinical pathological and therapeutic features. They themselves, added those represented in literature, might constitute a further contribution to best definition of the unusual genital pathology, especially as regards prognostic and therapeutic aspects.

[Vulvar papillary hidrocystadenoma. Description of a case]. / Idrocistoadenoma papillifero della vulva. Descrizione di un caso.

Papillary hidradenoma of the vulva is a benign neoplasm arising from apocrine sweat glands of the skin. The treatment of choice is local excision. The prognosis for the patient is excellent. We present one case observed recently.