Biology subjects

Yang Li

Publications and source records attributed to Yang Li.

4 recordsLinked to original sources

bioRxiv2016

Recurrently deregulated lncRNAs associated with HCC tumorigenesis and metastasis revealed by genomic, epigenomic and transcriptomic profiling in paired primary tumor and PVTT samples

Hepatocellular carcinoma (HCC) are highly potent to invade the portal venous system and subsequently develop into the portal vein tumor thrombosis (PVTT). PVTT could induce intrahepatic metastasis, which is closely associated with poor prognosis. A comprehensive systematic characterization of long noncoding RNAs (lncRNAs) associated with HCC metastasis has not been reported. Here, we first assayed 60 clinical samples (matched primary tumor, adjacent normal tissue, and PVTT) from 20 HCC patients using total RNA sequencing. We identified and characterized 8,603 novel lncRNAs from 9.6 billion sequenced reads, indicating specific expression of these lncRNAs in our samples. On the other hand, the expression patterns of 3,212 known and novel recurrently deregulated lncRNAs (in >=20% of our patients) were well correlated with clinical data in a TCGA cohort and published liver cancer data. Some lncRNAs (e.g., RP11-166D19.1/MIR100HG) were shown to be useful as putative biomarkers for prognosis and metastasis. Moreover, matched array data from 60 samples showed that copy number variations (CNVs) and alterations in DNA methylation contributed to the observed recurrent deregulation of 716 lncRNAs. Subsequently, using a coding-noncoding co-expression network, we found that many recurrently deregulated lncRNAs were enriched in clusters of genes related to cell adhesion, immune response, and metabolic processes. Candidate lncRNAs related to metastasis, such as HAND2-AS1, were further validated using RNAi-based loss-of-function assays. The results of our integrative analysis provide a valuable resource regarding functional lncRNAs and novel biomarkers associated with HCC tumorigenesis and metastasis.

Genomics↗

bioRxiv2016

Allele-Specific Quantification of Structural Variations in Cancer Genomes

One of the hallmarks of cancer genome is aneuploidy, resulting in abnormal copy numbers of alleles. Structural variations (SVs) can further modify the aneuploid cancer genomes into a mixture of rearranged genomic segments with extensive range of somatic copy number alterations (CNAs). Indeed, aneuploid cancer genomes have significantly higher rate of CNAs and SVs. However, although methods have been developed to identify SVs and allele-specific copy number of genome (ASCNG) separately, no existing algorithm can simultaneously analyze SVs and ASCNG. Such integrated approach is particularly important to fully understand the complexity of cancer genomes. Here we introduce a new algorithm called Weaver to provide allele-specific quantification of SVs and CNAs in aneuploid cancer genomes. Weaver uses a probabilistic graphical model by utilizing cancer whole genome sequencing data to simultaneously estimate the digital copy number and inter-connectivity of SVs. Our simulation evaluation, comparison with single-molecule Optical Mapping analysis, and real data applications (including MCF-7, HeLa, and TCGA whole genome sequencing samples) demonstrated that Weaver is highly accurate and can greatly refine the analysis of complex cancer genome structure.

Bioinformatics↗

bioRxiv2015

Hypothesis-free identification of modulators of genetic risk factors

Genetic risk factors often localize in non-coding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying the association of genetic risk factors with disease. More mechanistic insights can be derived from knowledge of the context, such as cell type or the activity of signaling pathways, influencing the nature and strength of eQTLs. Here, we generated peripheral blood RNA-seq data from 2,116 unrelated Dutch individuals and systematically identified these context-dependent eQTLs using a hypothesis-free strategy that does not require prior knowledge on the identity of the modifiers. Out of the 23,060 significant cis-regulated genes (false discovery rate < 0.05), 2,743 genes (12%) show context-dependent eQTL effects. The majority of those were influenced by cell type composition, revealing eQTLs that are particularly strong in cell types such as CD4+ T-cells, erythrocytes, and even lowly abundant eosinophils. A set of 145 cis-eQTLs were influenced by the activity of the type I interferon signaling pathway and we identified several cis-eQTLs that are modulated by specific transcription factors that bind to the eQTL SNPs. This demonstrates that large-scale eQTL studies in unchallenged individuals can complement perturbation experiments to gain better insight in regulatory networks and their stimuli.

Genetics↗

bioRxiv2014

Cell specific eQTL analysis without sorting cells

Expression quantitative trait locus (eQTL) mapping on tissue, organ or whole organism data can detect associations that are generic across cell types. We describe a new method to focus upon specific cell types without first needing to sort cells. We applied the method to whole blood data from 5,683 samples and demonstrate that SNPs associated with Crohn's disease preferentially affect gene expression within neutrophils.

Genetics↗