Browsing by Subject "intron retention"

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    Modulation of Splicing Factor Function and Alternative Splicing Outcomes
    (2022-06) Chen, Steven Xiwei; Liu, Yunlong; Lu, Xiongbin; Schneider, Bryan P.; Wek, Ronald C.
    Alternative RNA splicing is an important means of genetic control and transcriptome diversity. Alternative splicing events are frequently studied independently, and coordinated splicing controlled by common factors is often overlooked: The molecular mechanisms by which splicing regulators promote or repress specific pre-mRNA processing are still not yet well understood. It is well known that splicing factors can regulate splicing in a context-dependent manner, and the search for modulation of splicing factor activity via direct or indirect mechanisms is a worthwhile pursuit towards explaining context-dependent activity. We hypothesized that the combined analysis of hundreds of consortium RNA-seq datasets could identify trans-acting “modulators” whose expression is correlated with differential effects of a splicing factor on its target splice events in mRNAs. We first tested a genome-wide approach to identify relationships between RNA-binding proteins and their inferred modulators in kidney cancer. We then applied a more targeted approach to identify novel modulators of splicing factor SRSF1 function over dozens of its intron retention splicing targets in a neurological context using hundreds of dorsolateral prefrontal cortex samples. Our hypothesized model was further strengthened with the incorporation of genetic variants to impute gene expression in a Mendelian randomization-based approach. The modulators of intron retention splicing we identified may be associated with risk variants linked to Alzheimer’s Disease, among other neurological disorders, to explain disease-causing splicing mechanisms. Our strategy can be widely used to identify modulators of RNA-binding proteins involved in tissue-specific alternative splicing.
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    Predictive Molecular Biomarkers for Human Health Risk
    (2024-07) Jiang, Guanglong; Liu, Yunlong; Yan, Jingwen; Wan, Jun; Wang, Juexin
    Molecular biomarkers play vital roles in disease risk assessment, personalized treatment selection and therapy response monitoring. This thesis explores the use of diverse molecular biomarkers for the assessment of human health risks, primarily in cancers. MiRNAs and their isoforms (isomiR) are promising biomarker candidates due to their comprehensive regulation of gene expression and involvement in physiology and pathological processes. The first study demonstrates that genetic variations in miRNA precursor regions influence the biogenesis of isomiRs in 95 SNP-isomiR pairs. Notably, we identified a SNP (rs6505162) impacting hsa-miR-423 isomiRs, potentially linked to breast cancer pathogenesis, suggesting their potential as biomarkers in disease assessment. The findings also highlight the mechanism of genetic regulation of isomiR generation and advance our understanding of miRNA mediated post-transcriptional regulation. Secondly, we explored the predictive capacity of aberrant intron-retention neoantigen burden (INB) in predicting the response to immune checkpoint inhibitors (ICI) in metastatic cancers. Both INB and tumor mutation burden (TMB) were strong predictors of ICI therapy duration (p = 0.019 and 0.038, respectively), with patients exhibiting elevated levels demonstrating exceptional treatment duration. Patients with high INB or TMB had improved overall survival (OS) (p = 1.1×10-4). Importantly, INB and TMB were uncorrelated, indicating that they capture distinct aspects of tumor neoantigen. Together, the combined assessment of INB and TMB offers improved accuracy in predicting clinical response to ICI therapies. Finally, we extend the application of molecular biomarkers to the assessment of minimal residual disease for risk stratification in triple negative breast cancer (TNBC) with residual disease after neoadjuvant chemotherapy. Detection of circulating tumor DNA (ctDNA) was a significant predictor of inferior distant disease-free survival (DDFS) (p = 0.006), disease-free survival (DFS) (p = 0.009) and OS (p = 0.002). The combination of circulating tumor cell (CTC) and ctDNA markers provided superior sensitivity and prognostic value. In conclusion, the studies provide compelling evidence for the utility of diverse molecular biomarkers – including miRNA isoforms, abnormal splicing-based neoantigen metrics and circulating tumor DNA in disease prediction and treatment efficacy assessment. By elucidating the roles of diverse biomarkers in predicting cancer pathogenesis and therapeutic response, we pave the way towards more personalized and effective approaches to managing human health risks.