A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT.

Low-dose CT (LDCT) is widely accepted as the preferred method for detecting pulmonary nodules. However, the determination of whether a nodule is benign or malignant involves either repeated scans or invasive procedures that sample the lung tissue. Noninvasive methods to assess these nodules are needed to reduce unnecessary invasive tests.

Genome-wide maps of circulating miRNA biomarkers for ulcerative colitis.

Inflammatory Bowel Disease--comprised of Crohn's Disease and Ulcerative Colitis (UC)--is a complex, multi-factorial inflammatory disorder of the gastrointestinal tract. In this study we have explored the utility of naturally occurring circulating miRNAs as potential blood-based biomarkers for non-invasive prediction of UC incidences. Whole genome maps of circulating miRNAs in micro-vesicles, Peripheral Blood Mononuclear Cells and platelets have been constructed from a cohort of 20 UC patients and 20 normal individuals.

Large-scale integration of microarray data reveals genes and pathways common to multiple cancer types.

The global gene expression analysis of cancer and healthy tissues typically results in large numbers of genes that are significantly altered in cancer. Such data, however, has been difficult to interpret due to the high level of variation of gene lists across laboratories and the small sample sizes used in individual studies. In this investigation, we compiled microarray data obtained from the same platform family from 84 laboratories, resulting in a database containing 1,043 healthy tissue samples and 4,900 cancer samples for 13 different tissue types.

Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types.

Background: Much of the public access cancer microarray data is asymmetric, belonging to datasets containing no samples from normal tissue. Asymmetric data cannot be used in standard meta-analysis approaches (such as the inverse variance method) to obtain large sample sizes for statistical power enrichment. Noting that plenty of normal tissue microarray samples exist in studies not involving cancer, we investigated the viability and accuracy of an integrated microarray analysis approach based on significance analysis of microarrays (merged SAM) using a collection of data from separate diseased and normal samples.