Three hub lncRNAs associated with prognosis of endometrial cancer identified by coexpression analysis Endometrial cancer (UCEC) is a complex malignant tumor which remains the third cause of cancerrelated death of women. Although UCEC has been repeatedly researched in recent years, the molecular heterogeneity of UCEC made it hard to predict clinical outcomes of this malignant disease. This study was aimed to access the prognostic value of long noncoding RNAs (lncRNAs) in UCEC patients and to identify potential lncRNA biomarker for improving the prognosis and diagnosis of patient. We performed a comprehensive genomewide analysis of gene expression profiles and clinical data downloaded from The Cancer Genome Atlas (TCGA). After filtering out the differentially expressed genes (DEGs) from expression profiles, the coexpression modules were constructed by weighted gene coexpression network analysis. In the analysis of key modules, four hub genes (TPX2, KIF2C, AURKA and CDCA5) was filtered out, the three hub long noncoding RNAs (ENSG00000258884, ENSG00000274825 and ENSG00000271936) were discovered and verified to be potential prognostic biomarkers.

Feature Selection and Classification Reveal Key lncRNAs for Multiple Cancers Long noncoding RNA (lncRNA) plays key roles in tumorigenesis. Misexpression of lncRNA can lead to changes in expression profiles of various target genes, which are involved in cancer initiation and progression. So, identifying key lncRNAs for a cancer would help develop the cancer therapy. Usually, to identify key lncRNAs for a cancer, expression profiles of lncRNAs for normal and cancer samples are required. But, this kind of data are not available for all cancers. In the present study, a computational framework is developed to identify cancer specific key lncRNAs using the lncRNA expression of cancer patients only. The framework consists of two stateoftheart feature selection techniques  Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO); and five machine learning models  Naive Bayes, KNearest Neighbor, Random Forest, Support Vector Machine, and Deep Neural Network. For experiment, expression values of lncRNAs for 8 cancers  BLCA, CESC, COAD, HNSC, KIRP, LGG, LIHC, and LUAD  from TCGA are used. The combined dataset consists of 3,656 patients with expression values of 12,309 lncRNAs. Important features or key lncRNAs are identified by using feature selection algorithms RFE and LASSO. Capability of these key lncRNAs in classifying 8 different cancers is checked by the performance of five classification models. This study identified 37 key lncRNAs that can classify 8 different cancer types with an accuracy ranging from 94% to 97%. Finally, survival analysis supports that the discovered key lncRNAs are capable of differentiating between highrisk and lowrisk patients.