Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for cancer prognosis. An important source of information for virtual validation is the high number of available cancer datasets. Nevertheless, assessing the prognostic performance of a gene expression signature along datasets is a difficult task for Biologists and Physicians and also time-consuming for Statisticians and Bioinformaticians. Therefore, to facilitate performance comparisons and validations of survival biomarkers for cancer outcomes, we developed SurvExpress, a cancer-wide gene expression database with clinical outcomes and a web-based tool that provides survival analysis and risk assessment of cancer datasets. The main input of SurvExpress is only the biomarker gene list. We generated a cancer database collecting more than 20,000 samples and 130 datasets with censored clinical information covering tumors over 20 tissues. We implemented a web interface to perform biomarker validation and comparisons in this database, where a multivariate survival analysis can be accomplished in about one minute. We show the utility and simplicity of SurvExpress in two biomarker applications for breast and lung cancer. Compared to other tools, SurvExpress is the largest, most versatile, and quickest free tool available. SurvExpress web can be accessed in http://bioinformatica.mty.itesm.mx/SurvExpress (a tutorial is included). The website was implemented in JSP, JavaScript, MySQL, and R.

The fine-needle aspiration of thyroid nodules and subsequent cytological analysis is unable to determine the diagnosis in 15 to 30% of thyroid cancer cases; patients with indeterminate cytological results undergo diagnostic surgery which is potentially unnecessary. Current gene expression biomarkers based on well-determined cytology are complex and their accuracy is inconsistent across public datasets. In the present study, we identified a robust biomarker using the differences in gene expression values specifically from cytologically indeterminate thyroid tumors and a powerful multivariate search tool coupled with a nearest centroid classifier. The biomarker is based on differences in the expression of the following genes: CCND1, CLDN16, CPE, LRP1B, MAGI3, MAPK6, MATN2, MPPED2, PFKFB2, PTPRE, PYGL, SEMA3D, SERGEF, SLC4A4 and TIMP1. This 15-gene biomarker exhibited superior accuracy independently of the cytology in six datasets, including The Cancer Genome Atlas (TCGA) thyroid dataset. In addition, this biomarker exhibited differences in the correlation coefficients between benign and malignant samples that indicate its discriminatory power, and these 15 genes have been previously related to cancer in the literature. Thus, this 15-gene biomarker provides advantages in clinical practice for the effective diagnosis of thyroid cancer.

BackgroundIn cancer, large-scale technologies such as next-generation sequencing and microarrays have produced a wide number of genomic features such as DNA copy number alterations (CNA), mRNA expression (EXPR), microRNA expression (MIRNA), and DNA somatic mutations (MUT), among others. Several analyses of a specific type of these genomic data have generated many prognostic biomarkers in cancer. However, it is uncertain which of these data is more powerful and whether the best data-type is cancer-type dependent.Therefore, our purpose is to characterize the prognostic power of models obtained from different genomic data types, cancer types, and algorithms. For this, we compared the prognostic power using the concordance and prognostic index of models obtained from EXPR, MIRNA, CNA, MUT data and their integration for ovarian serous cystadenocarcinoma (OV), multiform glioblastoma (GBM), lung adenocarcinoma (LUAD), and breast cancer (BRCA) datasets from The Cancer Genome Atlas repository. We used three different algorithms for prognostic model selection based on constrained particle swarm optimization (CPSO), network feature selection (NFS), and least absolute shrinkage and selection operator (LASSO).ResultsThe integration of the four genomic data produced models having slightly higher performance than any single genomic data. From the genomic data types, we observed better prediction using EXPR closely followed by MIRNA and CNA depending on the cancer type and method. We observed higher concordance index in BRCA, followed by LUAD, OV, and GBM. We observed very similar results between LASSO and CPSO but smaller values in NFS. Importantly, we observed that model predictions highly concur between algorithms but are highly discordant between data types, which seems to be dependent on the censoring rate of the dataset.ConclusionsGene expression (mRNA) generated higher performances, which is marginally improved when other type of genomic data is considered. The level of concordance in prognosis generated from different genomic data types seems to be dependent on censoring rate.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-015-0065-1) contains supplementary material, which is available to authorized users.

Our results characterized features associated with disease progression from MCI to AD, and, in addition, the log-rank test identified features which are associated with the risk of early conversion.

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.

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This study seeks to determine whether the relative levels of attachment to galectins 1 and 3 of cells from thyroid tissues embedded in paraffin blocks can differentiate thyroid tumors from normal tissues. A total of 48 thyroid paraffin sample blocks from 4 groups of patients were analyzed: 12 samples served as controls, 12 samples were from patients with thyroid adenoma, 12 samples were from patients with thyroid follicular carcinoma, and 12 samples were from patients with thyroid papillary carcinoma. The relative attachment of cells to galectins 1 and 3 antigens was determined using the InnoCyte™ ECM Cell Adhesion kit at different cell sample concentrations. All of the samples from thyroid tissue preparations showed attachment to galectins 1 and 3. The samples from tissues with a diagnosis of adenoma, follicular and papillary carcinoma showed an increased adherence to galectins 1 and 3 relative to the controls. Significant differences were found between the means of the adherent cells from the adenomas compared with the follicular and papillary carcinoma samples. When the outcomes from the galectins 1 and 3 cell surface binding were compared, no statistical differences were found. The cells from adenoma and carcinoma samples show more adhesion to galectins 1 and 3 than cells from the control samples. The samples prepared from follicular and papillary carcinomas show more cells adherent to galectins 1 and 3 than those from the adenomas.

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