Transcriptome analysis identifies genetic risk markers and explores the pathogenesis for inflammatory bowel disease.

Inflammatory bowel disease (IBD) is an incurable and disabling bowel disease driven by multiple risk factors that severely limit patients' quality of life. We integrated the RNA-sequencing data of 1238 IBD patients, and investigated the pathogenesis of IBD by combining transcriptional element prediction analysis and immune-related analysis. Here, we first determined that KIAA1109 is inhibited in IBD patients.

LCAT1 is an oncogenic LncRNA by stabilizing the IGF2BP2-CDC6 axis.

Long non-coding RNAs (lncRNAs) is known to play vital roles in modulating tumorigenesis. We previously reported that LCAT1, a novel lncRNA, promotes the growth and metastasis of lung cancer cells both in vitro and in vivo. However, the underlying mechanism(s) of LCAT1 as an oncogenic regulator remains elusive.

circFBXO7/miR-96-5p/MTSS1 axis is an important regulator in the Wnt signaling pathway in ovarian cancer.

Background: CircRNAs are a novel class of evolutionarily conserved noncoding RNA molecules that form covalently closed continuous loop structures without 5' caps and 3' poly(A) tails. Accumulating evidence suggests that circRNAs play important regulatory roles in cancer and are promising biomarkers for cancer diagnosis and prognosis, as well as targets for cancer therapy. In this study, we identify and explore the role of a novel circRNA, circFBXO7, in ovarian cancer.

Global identification and characterization of tRNA-derived RNA fragment landscapes across human cancers.

Transfer RNA-derived RNA fragments (tRFs) are a class of small non-coding RNAs that are abundant in many organisms, but their role in cancer has not been fully explored. Here, we report a functional genomic landscape of tRFs in 8118 specimens across 15 cancer types from The Cancer Genome Atlas. These tRFs exhibited characteristics of widespread expression, high sequence conservation, cytoplasmic localization, specific patterns of tRNA cleavage and conserved cleavage in tissues.

LCAT3, a novel m6A-regulated long non-coding RNA, plays an oncogenic role in lung cancer via binding with FUBP1 to activate c-MYC.

Background: Long non-coding RNAs (lncRNAs) are important epigenetic regulators, which play critical roles in diverse physiological and pathological processes. However, the regulatory mechanism of lncRNAs in lung carcinogenesis remains elusive. Here, we characterized a novel oncogenic lncRNA, designated as Lung Cancer Associated Transcript 3 (LCAT3).

An integrated epigenomic-transcriptomic landscape of lung cancer reveals novel methylation driver genes of diagnostic and therapeutic relevance.

Aberrant DNA methylation occurs commonly during carcinogenesis and is of clinical value in human cancers. However, knowledge of the impact of DNA methylation changes on lung carcinogenesis and progression remains limited. Genome-wide DNA methylation profiles were surveyed in 18 pairs of tumors and adjacent normal tissues from non-small cell lung cancer (NSCLC) patients using Reduced Representation Bisulfite Sequencing (RRBS).

Long noncoding RNA LCAT1 functions as a ceRNA to regulate RAC1 function by sponging miR-4715-5p in lung cancer.

Introduction: Long noncoding RNAs (lncRNAs) are emerging as key players in the development and progression of cancer. However, the biological role and clinical significance of most lncRNAs in lung carcinogenesis remain unclear. In this study, we identified and explored the role of a novel lncRNA, lung cancer associated transcript 1 (LCAT1), in lung cancer.

DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies.

Although rapid progress has been made in computational approaches for prioritizing cancer driver genes, research is far from achieving the ultimate goal of discovering a complete catalog of genes truly associated with cancer. Driver gene lists predicted from these computational tools lack consistency and are prone to false positives. Here, we developed an approach (DriverML) integrating Rao's score test and supervised machine learning to identify cancer driver genes.