1,721,002 research outputs found
Comments on the use of a single or multiple probe-set approach for microarray-based analyses of routine molecular markers in breast cancer
No full textCell polarity, epithelial-mesenchymal transition, and cell-fate decision gene expression in ductal carcinoma in situ
Full text linkLoss of epithelial cell identity and acquisition of mesenchymal features are early events in the neoplastic transformation of mammary cells. We investigated the pattern of expression of a selected panel of genes associated with cell polarity and apical junction complex or involved in TGF-β-mediated epithelial-mesenchymal transition and cell-fate decision in a series of DCIS and corresponding patient-matched normal tissue. Additionally, we compared DCIS gene profile with that of atypical ductal hyperplasia (ADH) from the same patient. Statistical analysis identified a "core" of genes differentially expressed in both precursors with respect to the corresponding normal tissue mainly associated with a terminally differentiated luminal estrogen-dependent phenotype, in agreement with the model according to which ER-positive invasive breast cancer derives from ER-positive progenitor cells, and with an autocrine production of estrogens through androgens conversion. Although preliminary, present findings provide transcriptomic confirmation that, at least for the panel of genes considered in present study, ADH and DCIS are part of a tumorigenic multistep process and strongly arise the necessity for the regulation, maybe using aromatase inhibitors, of the intratumoral and/or circulating concentration of biologically active androgens in DCIS patients to timely hamper abnormal estrogens production and block estrogen-induced cell proliferatio
TP53 mutation, epithelial-mesenchymal transition, and stemlike features in breast cancer subtypes
Full text linkAltered p53 protein is prevalently associated with the pathologic class of triple-negative breast cancers and loss of p53 function has recently been linked to the induction of an epithelial-mesenchymal transition (EMT) and acquisition of stemness properties. We explored the association between TP53 mutational status and expression of some genes involved in the canonical TGF-β signaling pathway (the most potent EMT inducer) and in two early EMT associated events: loss of cell polarity and acquisition of stemness-associated features. We used a publicly accessible microarray dataset consisting of 251 p53-sequenced primary breast cancers. Statistical analysis indicated that mutant p53 tumors (especially those harboring a severe mutation) were consistent with the aggressive class of triple-negative cancers and that, differently from cell cultures, surgical tumors underexpressed some TGF-β related transcription factors known as involved in EMT (ID1, ID4, SMAD3, SMAD4, SMAD5, ZEB1). These unexpected findings suggest an interesting relationship between p53 mutation, mammary cell dedifferentiation, and the concomitant acquisition of stemlike properties (as indicated by the overexpression of PROM1 and NOTCH1 genes), which improve tumor cells aggressiveness as indicated by the overexpression of genes associated with cell proliferation (CDK4, CDK6, MKI67) and migration (CXCR4, MMP1)
Complementary use of cluster analysis and biplots to discover and validate patterns of gene expression in microarray data
No full textMicroarray studies are used in molecular biology to explore patterns of expression of thousands of genes. This methodology has relevantly developed in the last decades, and
so has the need for appropriate methods for analyzing highthroughput data generated from such experiments.
Identifying sets of genes and samples characterized by similar values of expression and validating these results are two of the issues related to these investigations. From a statistical
perspective there is no general agreement on these problems.
Specifically, the use of Cluster Analysis is often acritical relying on the main use of hierarchical techniques without considering
possible use of other methods. Moreover, validation of results using external datasets is still subject of discussion.
In this paper we show the use of several clustering algorithms to discover common patterns of expression, and propose a rank based passive projection of Principal Components for validation purposes. Results from a study involving 23 cell lines and 76
genes are presented
Cell identity disruption in breast cancer precursors
Full text linkMammary epithelial cell identity depends on a set of genes epigenetically-regulated by maintenance proteins, the best-characterized of which belong to the Trithorax and Polycomb groups. Perturbations in expression of these proteins may disrupt cell identity and trigger tumor initiation
Epithelial cell identity in hyperplastic precursors of breast cancer
Full text linkIntroduction: In the adult human breast, hyperplastic enlarged lobular unit (HELU) and atypical ductal hyperplasia (ADH) are two common abnormalities that frequently coexist with ductal carcinoma in situ (DCIS). For this reason, they have been proposed as the early steps in a biological continuum toward breast cancer. Methods: We investigated in silico the expression of 369 genes experimentally recognized as involved in establishing and maintaining epithelial cell identity and mammary gland remodeling, in HELUs or ADHs with respect to the corresponding patient-matched normal tissue. Results: Despite the common luminal origin, HELUs and ADHs proved to be characterized by distinct gene profiles that overlap for 5 genes only. While HELUs were associated with the overexpression of progesterone receptor (PGR), ADHs were characterized by the overexpression of estrogen receptor 1 (ESR1) coupled with the overexpression of some proliferation-associated genes. Conclusions: This unexpected finding contradicts the notion that in differentiated luminal cells the expression of estrogen receptor (ER) is dissociated from cell proliferation and suggests that the establishing of an ER-dependent signaling is able to sustain cell proliferation in an autocrine manner as an early event in tumor initiation. Although clinical evidence indicates that only a fraction of HELUs and ADHs evolve to invasive cancer, present findings warn that exposure to synthetic progestins, frequently administered as hormone-replacement therapy, and estrogens, when abnormally produced by adipose cells and persistently present in the stroma surrounding the mammary gland, may cause these hyperplastic lesions
Prognosis in node-negative primary breast cancer : a neural network analysis of risk profiles using routinely assessed factors
No full textBackground: The present study investigated complex time-dependent effects of routinely assessed factors on the risk of breast cancer recurrence over follow-up time, with a partial logistic artificial neural network (PLANN) model. Patients and methods: PLANN was applied to data from 1793 patients with node-negative breast cancer, not submitted to any adjuvant treatment and with a minimal potential follow-up of 10 years. Results: The shape of the hazard function changed according to histology, which showed a time-dependent effect, partly modulated by estrogen receptors (ERs). Age and progesterone receptors (PgR) showed protective effects; the latter was more evident for short follow-up and high ER values. Tumour size and ER content showed time-dependent unfavourable effects at early and long follow-up times, respectively. Predicted values of disease recurrence probability at 2 years of follow-up showed that low steroid-receptor content, young age and large tumour size were associated with the highest risk of relapse. Although the oldest patients with high ER content seem to be those most protected overall, high risk predictions tend to spread also to higher steroid-receptor contents, intermediate ages and small tumour size, with an increase in follow-up time. Conclusion: PLANN with suitable visualisation techniques provided thorough insights into the dynamics of breast cancer recurrence for improving individual risk staging of node-negative breast cancer patients
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Conditional independence relations among biological markers may improve clinical decision as in the case of triple negative breast cancers
Full text linkAbstract The associations existing among different biomarkers are important in clinical settings because they contribute to the characterisation of specific pathways related to the natural history of the disease, genetic and environmental determinants. Despite the availability of binary/linear (or at least monotonic) correlation indices, the full exploitation of molecular information depends on the knowledge of direct/indirect conditional independence (and eventually causal) relationships among biomarkers, and with target variables in the population of interest. In other words, that depends on inferences which are performed on the joint multivariate distribution of markers and target variables. Graphical models, such as Bayesian Networks, are well suited to this purpose. Therefore, we reconsidered a previously published case study on classical biomarkers in breast cancer, namely estrogen receptor (ER), progesterone receptor (PR), a proliferative index (Ki67/MIB-1) and to protein HER2/neu (NEU) and p53, to infer conditional independence relations existing in the joint distribution by inferring (learning) the structure of graphs entailing those relations of independence. We also examined the conditional distribution of a special molecular phenotype, called triple-negative, in which ER, PR and NEU were absent. We confirmed that ER is a key marker and we found that it was able to define subpopulations of patients characterized by different conditional independence relations among biomarkers. We also found a preliminary evidence that, given a triple-negative profile, the distribution of p53 protein is mostly supported in 'zero' and 'high' states providing useful information in selecting patients that could benefit from an adjuvant anthracyclines/alkylating agent-based chemotherapy.</p
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