Mediation analysis is a useful tool in biomedical research to investigate how molecular phenotypes, such as gene expression, mediate the effect of an exposure on health outcomes. However, commonly used mean-based total mediation effect measures may suffer from cancellation of component-wise mediation effects of opposite directions in the presence of high-dimensional omics mediators. To overcome this limitation, a variance-based R-squared total mediation effect measure has been recently proposed, which, nevertheless, relies on the computationally intensive nonparametric bootstrap for confidence interval estimation. In this work, we formulate a more efficient two-stage cross-fitted estimation procedure for the R-squared measure. To avoid potential bias, we perform iterative Sure Independence Screening (iSIS) in two subsamples to exclude the non-mediators, followed by ordinary least squares (OLS) regressions for the variance estimation. We then construct confidence intervals based on the newly-derived closed-form asymptotic distribution of the R-squared measure. Extensive simulation studies demonstrate that the proposed procedure is hundreds of times more computationally efficient than the resampling-based method with comparable coverage probability. Furthermore, when applied to the Framingham Heart Study, the proposed method replicated the established finding of gene expression mediating age-related variation in systolic blood pressure and discovered the role of gene expression profiles in the relationship between sex and high-density lipoprotein cholesterol. The proposed cross-fitted interval estimation procedure is implemented in R package RsqMed.
Background: For individuals acutely exposed to high-altitude regions, environmental hypobaric hypoxia induces several physiological or pathological responses, especially immune dysfunction. Therefore, hypoxia is a potentially life-threatening factor, which has closely related to high-altitude acclimatization. However, its specific molecular mechanism is still unclear.Methods: The four expression profiles about hypoxia and high altitude were downloaded from the Gene Expression Omnibus database in this study. Meta-analysis of GEO datasets was performed by NetworkAnalyst online tool. Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene ontology (GO) enrichment analysis, and visualization were performed using R (version 4.1.3) software, respectively. The CIBERSORT analysis was conducted on GSE46480 to examine immune cell infiltration. In addition, we experimentally verified the bioinformatics analysis with qRT-PCR.Results: The meta-analysis identified 358 differentially expressed genes (DEGs), with 209 upregulated and 149 downregulated. DEGs were mostly enriched in biological processes and pathways associated with hypoxia acclimatization at high altitudes, according to both GO and KEGG enrichment analyses. ERH, VBP1, BINP3L, TOMM5, PSMA4, and POLR2K were identified by taking intersections of the DEGs between meta-analysis and GSE46480 and verified by qRT-PCR experiments, which were inextricably linked to hypoxia. Immune infiltration analysis showed significant differences in immune cells between samples at sea level and high altitudes.Conclusion: Identifying the DEGs and pathways will improve our understanding of immune function during high-altitude hypoxia at a molecular level. Targeting hypoxia-sensitive pathways in immune cells is interesting in treating high-altitude sickness. This study provides support for further research on high-altitude acclimatization.
Longitudinal monitoring of patients with advanced cancers is crucial to evaluate both disease burden and treatment response. Current liquid biopsy approaches mostly rely on the detection of DNA-based biomarkers. However, plasma RNA analysis can unleash tremendous opportunity for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of plasma extracellular vesicles (EVs) RNA, we successfully predict consensus molecular subtypes in metastatic colorectal cancer (mCRC) patients and analogous RNA-based subtypes in patients with pancreatic ductal adenocarcinoma (PDAC). We further demonstrate the ability monitor changes in transcriptomic subtype under treatment selection pressure. Our approach also identified gene fusions and neoepitopes from plasma EVs, which were validated in matched tissue samples. These results demonstrate the feasibility of transcriptomic-based liquid biopsy platforms for precision oncology approaches, spanning from the longitudinal monitoring of tumor subtype changes to identification of expressed fusions and neoantigens as potential therapeutic targets, sans the need for tissue-based sampling
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