Identification of Prognosis Biomarkers for High-Grade Serous Ovarian Cancer Based on Stemness.

In this paper, high-grade serous ovarian cancer (HGSOC) is studied, which is the most common histological subtype of ovarian cancer. We use a new analytical procedure to combine the bulk RNA-Seq sample for ovarian cancer, mRNA expression-based stemness index (mRNAsi), and single-cell data for ovarian cancer. Through integrating bulk RNA-Seq sample of cancer samples from TCGA, UCSC Xena and single-cell RNA-Seq (scRNA-Seq) data of HGSOC from GEO, and performing a series of computational analyses on them, we identify stemness markers and survival-related markers, explore stem cell populations in ovarian cancer, and provide potential treatment recommendation. As a result, 171 key genes for capturing stem cell characteristics are screened and one vital cancer stem cell subpopulation is identified. Through further analysis of these key genes and cancer stem cell subpopulation, more critical genes can be obtained as LCP2, FCGR3A, COL1A1, COL1A2, MT-CYB, CCT5, and PAPPA, are closely associated with ovarian cancer. So these genes have the potential to be used as prognostic biomarkers for ovarian cancer.

Molecular characterization and clinical relevance of m6A regulators across 33 cancer types.

The methylation of N6 adenosine (m6A) plays a critical role in diverse biological processes. However, knowledge regarding the reconstitution of m6A across cancer types is still lacking. Here, we systematically analyzed the molecular alterations and clinical relevance of m6A regulators across > 10,000 subjects representing 33 cancer types. We found that there are widespread genetic alterations to m6A regulators, and that their expression levels are significantly correlated with the activity of cancer hallmark-related pathways. Moreover, m6A regulators were found to be potentially useful for prognostic stratification, and we identified IGF2BP3 as a potential oncogene across multiple cancer types. Our results provide a valuable resource that will guide both mechanistic and therapeutic analyses of the role of m6A regulators in cancer.

D-lnc: a comprehensive database and analytical platform to dissect the modification of drugs on lncRNA expression.

Long non-coding RNAs (lncRNAs) have been proven to be implicated in the pathogenesis of various diseases. Multiple studies have demonstrated that small molecule drugs can modify lncRNA expression, which suggests a promising therapy for human diseases. Here, we constructed a comprehensive query and analytical platform D-lnc to dissect the influence of drugs on lncRNA expression. Firstly, we manually curated the experimentally validated regulations of drugs on lncRNA expression and recorded 7,825 entries between 59 drugs and 7,538 lncRNAs across five species from nearly 1,000 published papers. Secondly, we comprehensively screened the Connectivity Map (cMap) and the Gene Expression Omnibus (GEO) databases to obtain the drug-perturbed gene expression profiles. Through probe re-annotation of microarray data, we identified 19,946 putative associations between 1,279 drugs and 129 lncRNAs in cMap and 36,210 entries between 115 drugs and 2,360 lncRNAs in GEO. Finally, we developed an online analytical platform to predict the potential acting drugs or modified lncRNAs based on user input lncRNA sequence or drug structure through computing the similarities of lncRNA sequences or drug structures. In a word, D-lnc provides a comprehensive platform to detect the modification of drugs on lncRNA expression, which would facilitate the development of lncRNA-targeted therapeutics. D-lnc is freely available at http://www.jianglab.cn/D-lnc/ .

Landscape of Enhancer-Enhancer Cooperative Regulation during Human Cardiac Commitment.

Although accumulating evidence has demonstrated the key roles of enhancers in gene expression regulation, the contribution of genome-wide enhancer-enhancer interactions to developmental decisions remains unclear. Here we explored the cooperative regulation patterns among enhancers to understand their regulatory mechanism. We first filtered robust enhancers in embryonic stem cells (ESCs) through integrating bidirectional transcription, genomic location, and epigenetic modification. Genome-wide enhancer-enhancer interactions were then identified based on enhancer-promoter relationships that were derived from Hi-C data. We further explored the interacting principles of the identified enhancer-enhancer interactions. The results revealed that the observed cooperativity occurred mainly between enhancers distributed within a 1-kb to 10-Mb distance across the genome. In addition, enhancer-enhancer pairs had higher expression correlations than non-interacting pairs. Finally, we identified robust enhancers during human cardiac commitment, and we found that enhancers exhibited strong stage-specific expression patterns. We further inferred the enhancer-enhancer interactions based on RNA sequencing (RNA-seq) data in heart development, according to the regulatory principles characterized from Hi-C data. The identified enhancer-enhancer interaction networks (EEINs) presented highly dynamic linkages. Moreover, enhancers cooperatively targeted many marker genes in each developmental stage to regulate stage-specific functions, which contribute to the organization of cell identity in heart development. Our work will increase the understanding of enhancer regulation in human heart development.