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Medulloblastoma is a highly heterogeneous pediatric brain tumor with five molecular subtypes, Sonic Hedgehog TP53-mutant, Sonic Hedgehog TP53-wildtype, WNT, Group 3, and Group 4, defined by the World Health Organization. The current mechanism for classification into these molecular subtypes is through the use of immunostaining, methylation, and/or genetics. We surveyed the literature and identified a number of RNA-Seq and microarray datasets in order to develop, train, test, and validate a robust classifier to identify medulloblastoma molecular subtypes through the use of transcriptomic profiling data. We have developed a GPL-3 licensed R package and a Shiny Application to enable users to quickly and robustly classify medulloblastoma samples using transcriptomic data. The classifier utilizes a large composite microarray dataset (15 individual datasets), an individual microarray study, and an RNA-Seq dataset, using gene ratios instead of gene expression measures as features for the model. Discriminating features were identified using the limma R package and samples were classified using an unweighted mean of normalized scores. We utilized two training datasets and applied the classifier in 15 separate datasets. We observed a minimum accuracy of 85.71% in the smallest dataset and a maximum of 100% accuracy in four datasets with an overall median accuracy of 97.8% across the 15 datasets, with the majority of misclassification occurring between the heterogeneous Group 3 and Group 4 subtypes. We anticipate this medulloblastoma transcriptomic subtype classifier will be broadly applicable to the cancer research and clinical communities. [ABSTRACT FROM AUTHOR]

• Cox regression was the strongest method for predicting patient survival. • Other methods like C-index, D-index, and k-means also performed well. • Methods based on dichotomization had the worst overall performance. Identifying genetic biomarkers of patient survival remains a major goal of large-scale cancer profiling studies. Using gene expression data to predict the outcome of a patient's tumor makes biomarker discovery a compelling tool for improving patient care. As genomic technologies expand, multiple data types may serve as informative biomarkers, and bioinformatic strategies have evolved around these different applications. For categorical variables such as a gene's mutation status, biomarker identification to predict survival time is straightforward. However, for continuous variables like gene expression, the available methods generate highly-variable results, and studies on best practices are lacking. We investigated the performance of eight methods that deal specifically with continuous data. K-means, Cox regression, concordance index, D-index, 25th–75th percentile split, median-split, distribution-based splitting, and KaplanScan were applied to four RNA-sequencing (RNA-seq) datasets from the Cancer Genome Atlas. The reliability of the eight methods was assessed by splitting each dataset into two groups and comparing the overlap of the results. Gene sets that had been identified from the literature for a specific tumor type served as positive controls to assess the accuracy of each biomarker using receiver operating characteristic (ROC) curves. Artificial RNA-Seq data were generated to test the robustness of these methods under fixed levels of gene expression noise. Our results show that methods based on dichotomizing tend to have consistently poor performance while C-index, D-index, and k-means perform well in most settings. Overall, the Cox regression method had the strongest performance based on tests of accuracy, reliability, and robustness. [ABSTRACT FROM AUTHOR]

Background: Inverted papilloma (IP) is a sinonasal tumor with a well‐known potential for malignant transformation. The purpose of this study was to identify the genes and pathways associated with IP, with progression to carcinoma‐in‐situ and invasive carcinoma. Methods: To determine genes and molecular pathways that may indicate progression and correlate with histologic changes, we analyzed six IP without dysplasia, five IP with carcinoma‐in‐situ, and 13 squamous cell carcinoma ex‐IP by targeted sequencing. The HTG EdgeSeq Oncology Biomarker Panel coupled with next‐generation sequencing was used to evaluate 2560 transcripts associated with solid tumors. Results: Progressive upregulation of 11 genes were observed (CALD1, COL1A1, COL3A1, COL4A2, COL5A2, FN1, ITGA5, LGALS1, MMP11, SERPINH1, SPARC) in the order of invasive carcinoma > carcinoma‐in‐situ > IP without dysplasia. When compared with IP without dysplasia, more genes are differentially expressed in invasive carcinoma than carcinoma‐in‐situ samples (341 downregulated/333 upregulated vs. 195 downregulated/156 upregulated). Gene set enrichment analysis determined three gene sets in common between the cohorts (epithelial mesenchymal transition, extracellular matrix organization, and coagulation). Conclusions: Progressive upregulation of genes specific to IP malignant degeneration has significant clinical implications. This panel of 11 genes will improve concordance of histologic classification, which can directly impact treatment and patient outcomes. Additionally, future studies on larger tumor sets may observe upregulation in the gene panel that preceded histologic changes, which may be useful for further risk stratification. [ABSTRACT FROM AUTHOR]

Long noncoding RNAs (lncRNAs) are thought to play important roles in regulating gene transcription, but few have well-defined expression patterns or known biological functions during mammalian development. Using a conservative pipeline to identify lncRNAs that have important biological functions, we identified 363 lncRNAs in the lung and foregut endoderm. Importantly, we show that these lncRNAs are spatially correlated with transcription factors across the genome. In-depth expression analyses of lncRNAs with genomic loci adjacent to the critical transcription factors Nkx2.1, Gata6, Foxa2 (forkhead box a2), and Foxf1 mimic the expression patterns of their protein-coding neighbor. Loss-of-function analysis demonstrates that two lncRNAs, LL18/NANCI (Nkx2.1-associated noncoding intergenic RNA) and LL34, play distinct roles in endoderm development by controlling expression of critical developmental transcription factors and pathways, including retinoic acid signaling. In particular, we show that LL18/NANCI acts upstream of Nkx2.1 and downstream from Wnt signaling to regulate lung endoderm gene expression. These studies reveal that lncRNAs play an important role in foregut and lung endoderm development by regulating multiple aspects of gene transcription, often through regulation of transcription factor expression. [ABSTRACT FROM AUTHOR]