A Computational Approach to Identification of Candidate Biomarkers in High-Dimensional Molecular Data.

Complex high-dimensional datasets that are challenging to analyze are frequently produced through '-omics' profiling. Typically, these datasets contain more genomic features than samples, limiting the use of multivariable statistical and machine learning-based approaches to analysis. Therefore, effective alternative approaches are urgently needed to identify features-of-interest in '-omics' data.

A miR-375/YAP axis regulates neuroendocrine differentiation and tumorigenesis in lung carcinoid cells.

Lung carcinoids are variably aggressive and mechanistically understudied neuroendocrine neoplasms (NENs). Here, we identified and elucidated the function of a miR-375/yes-associated protein (YAP) axis in lung carcinoid (H727) cells. miR-375 and YAP are respectively high and low expressed in wild-type H727 cells.

Classifying Lung Neuroendocrine Neoplasms through MicroRNA Sequence Data Mining.

Lung neuroendocrine neoplasms (NENs) can be challenging to classify due to subtle histologic differences between pathological types. MicroRNAs (miRNAs) are small RNA molecules that are valuable markers in many neoplastic diseases. To evaluate miRNAs as classificatory markers for lung NENs, we generated comprehensive miRNA expression profiles from 14 typical carcinoid (TC), 15 atypical carcinoid (AC), 11 small cell lung carcinoma (SCLC), and 15 large cell neuroendocrine carcinoma (LCNEC) samples, through barcoded small RNA sequencing.

Characterizing and classifying neuroendocrine neoplasms through microRNA sequencing and data mining.

Neuroendocrine neoplasms (NENs) are clinically diverse and incompletely characterized cancers that are challenging to classify. MicroRNAs (miRNAs) are small regulatory RNAs that can be used to classify cancers. Recently, a morphology-based classification framework for evaluating NENs from different anatomical sites was proposed by experts, with the requirement of improved molecular data integration.

Evaluating gastroenteropancreatic neuroendocrine tumors through microRNA sequencing.

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) can be challenging to evaluate histologically. MicroRNAs (miRNAs) are small RNA molecules that often are excellent biomarkers due to their abundance, cell-type and disease stage specificity and stability. To evaluate miRNAs as adjunct tissue markers for classifying and grading well-differentiated GEP-NETs, we generated and compared miRNA expression profiles from four pathological types of GEP-NETs.