Elucidation of Factors Affecting the Age-Dependent Cancer Occurrence Rates.

Cancer occurrence rates exhibit diverse age-related patterns, and understanding them may shed new and important light on the drivers of cancer evolution. This study systematically analyzes the age-dependent occurrence rates of 23 carcinoma types, focusing on their age-dependent distribution patterns, the determinants of peak occurrence ages, and the significant difference between the two genders. According to the SEER reports, these cancer types have two types of age-dependent occurrence rate (ADOR) distributions, with most having a unimodal distribution and a few having a bimodal distribution.

X-LDA: An interpretable and knowledge-informed heterogeneous graph learning framework for LncRNA-disease association prediction.

The identification of disease-related long noncoding RNAs (lncRNAs) is beneficial to unravel the intricacies of gene expression regulation and epigenetic signatures. Computational methods provide a cost-effective means to explore lncRNA-disease associations (LDAs). However, these methods often lack interpretability, leaving their predictions less convincing to biological and medical researchers.

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.

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

The methylation of N adenosine (mA) plays a critical role in diverse biological processes. However, knowledge regarding the reconstitution of mA across cancer types is still lacking. Here, we systematically analyzed the molecular alterations and clinical relevance of mA regulators across > 10,000 subjects representing 33 cancer types.

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.

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.