Biomedical subjects

Xu Shi

Publications and source records attributed to Xu Shi.

7 recordsLinked to original sources

PubMed2026

A doubly robust framework for addressing outcome-dependent selection bias in multi-cohort EHR studies.

Selection bias can hinder accurate estimation of association parameters in binary disease risk models using non-probability samples like electronic health records (EHRs). The issue is compounded when participants are recruited from multiple clinics/centers with varying selection mechanisms that may depend on the disease/outcome of interest. Traditional inverse-probability-weighted (IPW) methods, based on constructed parametric selection models, often struggle with misspecifications when selection mechanisms vary across cohorts. This paper introduces a new Joint Augmented Inverse Probability Weighted (JAIPW) method, which integrates individual-level data from multiple cohorts collected under potentially outcome-dependent selection mechanisms, with data from an external probability sample. JAIPW offers double robustness by incorporating a flexible auxiliary score model to address potential misspecifications in the selection models. We outline the asymptotic properties of the JAIPW estimator, and our simulations reveal that JAIPW achieves up to 6 times lower relative bias and 5 times lower root mean square error (RMSE) compared to the best performing joint IPW methods under scenarios with misspecified selection models. Applying JAIPW to the Michigan Genomics Initiative (MGI), a multi-clinic EHR-linked biobank, combined with external national probability samples, resulted in cancer-sex association estimates closely aligned with national benchmark estimates. We also analyzed the association between cancer and polygenic risk scores (PRS) in MGI to illustrate a situation where the exposure variable is not measured in the external probability sample.

Selection Bias↗

PubMed2025

Genetic mapping and predictive modeling of paralog synthetic lethality.

Paralogs are abundant in the human genome and thought to be a primary source of synthetic lethality, yet the vast paralogome remains largely uncharacterized. A digenic screen of 36,648 paralogous pairs in the human genome revealed that synthetic lethalities were infrequent and varied in penetrance in different tumor backgrounds. We hypothesized that the variable penetrance of synthetic lethalities resulted from complex polygenic interactions with different cellular contexts. A machine learning classifier of a subset of paralog pairs tested across 49 cancer models revealed that endogenous perturbations in related pathways predicted paralog synthetic lethality. Further, predictive modeling of paralog synthetic lethality showed that the strength of synthetic lethal interactions was largely due to the overlap and essentiality of the protein-protein interaction networks shared by the paralog pairs. Collectively, this study tested 36,648 digenic paralog interactions and delineated the key feature classes that underlie the heterogeneity of paralog synthetic lethalities.

Humans↗

PubMed2025

Privacy-Enhancing Sequential Learning under Heterogeneous Selection Bias in Multi-Site EHR Data.

OBJECTIVE: To develop privacy-enhancing statistical methods for estimation of binary disease risk model association parameters across multiple electronic health record (EHR) sites with heterogeneous selection mechanisms, without sharing raw individual-level data. We illustrate their utility through a cross-biobank analysis of smoking and 97 cancer subtypes using data from the NIH All of Us (AOU) and the Michigan Genomics Initiative (MGI). MATERIALS AND METHODS: Large-scale biobanks often follow heterogeneous recruitment strategies and store data in separate cloud-based platforms, making centralized algorithms infeasible. To address this, we propose two decentralized sequential estimators namely, Sequential Pseudo-likelihood (SPL) and Sequential Augmented Inverse Probability Weighting (SAIPW) that leverage external population-level information to adjust for selection bias, with valid variance estimation. SAIPW additionally protects against misspecification of the selection model using flexible machine learning based auxiliary outcome models. We compare SPL and SAIPW with the existing Sequential Unweighted (SUW) estimator and with centralized and meta learning extensions of IPW and AIPW in simulations under both correctly specified and misspecified selection mechanisms. We apply the methods to harmonized data from MGI ( n = 50,935) and AOU ( n = 241,563) to estimate smoking-cancer associations. RESULTS: In simulations, SUW exhibited substantial bias and poor coverage. SPL and SAIPW yielded unbiased estimates with valid coverage probabilities under correct model specification, with SAIPW remaining robust under selection model misspecification. Both approaches showed no notable efficiency loss relative to centralized methods. Meta-learning methods were efficient for large sites but failed in settings with small cohort sizes and rare outcome prevalence. In real-data analysis, strong associations were consistently identified between smoking and cancers of the lung, bladder, and larynx, aligning with established epidemiological evidence. CONCLUSION: Our framework enables valid, privacy-enhancing inference across EHR cohorts with heterogeneous selection, supporting scalable, decentralized research using real-world data.

Journal Article↗

PubMed2025

The Effect of Alcohol Intake on Brain White Matter Microstructural Integrity: A New Causal Inference Framework for Incomplete Phenomic Data.

Although substance use, such as alcohol intake, is known to be associated with cognitive decline during aging, its direct influence on the central nervous system remains incompletely understood. In this study, we investigate the influence of alcohol intake frequency on reduction of brain white matter microstructural integrity in the fornix, a brain region considered a promising marker of age-related microstructural degeneration, using a large UK Biobank (UKB) cohort with extensive phenomic data reflecting a comprehensive lifestyle profile. Two major challenges arise: (a) potentially nonlinear confounding effects from phenomic variables and (b) a limited proportion of participants with complete phenomic data. To address these challenges, we develop a novel ensemble learning framework tailored for robust causal inference and introduce a data integration step to incorporate information from UKB participants with incomplete phenomic data, improving estimation efficiency. Our analysis reveals that daily alcohol intake may significantly reduce fractional anisotropy, a neuroimaging-derived measure of white matter structural integrity, in the fornix and increase systolic and diastolic blood pressure levels. Moreover, extensive numerical studies demonstrate the superiority of our method over competing approaches in terms of estimation bias, while outcome regression-based estimators may be preferred when minimizing mean squared error is prioritized. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Brain aging↗

PubMed2025

Bayesian identification of differentially expressed isoforms using a novel joint model of RNA-seq data.

We develop a Bayesian approach, BayesIso, to identify differentially expressed isoforms from RNA-seq data. The approach features a novel joint model of the sample variability and the deferential state of isoforms. Specifically, the within-sample variability and the between-sample variability of each isoform are modeled by a Poisson-Lognormal model and a Gamma-Gamma model, respectively. Using a Bayesian framework, the differential state of each isoform and the model parameters are jointly estimated by a Markov Chain Monte Carlo (MCMC) method. Extensive studies using simulation and real data demonstrate that BayesIso can effectively detect isoforms of less differentially expressed and differential transcripts for genes with multiple isoforms. We applied the approach to breast cancer RNA-seq data and uncovered a unique set of isoforms that form key pathways associated with breast cancer recurrence. First, PI3K/AKT/mTOR signaling and PTEN signaling pathways are identified as being involved in breast cancer development. Further integrated with protein-protein interaction data, pathways of Jak-STAT, mTOR, MAPK and Wnt signaling are revealed in association with breast cancer recurrence. Finally, several pathways are activated in the early recurrence of breast cancer. In tumors that occur early, members of pathways of cellular metabolism and cell cycle (such as CD36 and TOP2A) are upregulated, while immune response genes such as NFATC1 are downregulated.

Humans↗

PubMed2006

Inhibition of nuclear factor-kappaB activation is essential for membrane-associated TNF-alpha-induced apoptosis in HL-60 cells.

The killing of tumour cells that are resistant to soluble TNF-alpha (sTNF-alpha) by the membrane-bound form of TNF-alpha (mTNF-alpha) suggests that different intracellular signalling pathways are involved. We found that mTNF-alpha induced apoptosis in HL-60 cells and failed to cause degradation of inhibitor of kappa B alpha (IkappaB-alpha) and translocation and activation of nuclear factor kappa B (NF-kappaB), whereas sTNF-alpha failed to induce apoptosis, but lowered cytoplasmic inhibitor of kappa B alpha, induced translocation of NF-kappaB to the nucleus and experimentally increased activity of the regulated luciferase. Furthermore, mTNF-alpha upregulated the expression of TNF receptor associated factor (TRAF) 1 and failed to induce TRAF1 and TRAF2 membrane translocation, but led to cytoplasmic colocalization. In contrast, sTNF-alpha stimulated the expression of TRAF1 and TRAF2, recruiting both molecules onto the cell membrane poststimulation. These results suggest that the increased susceptibility of HL-60 cells to mTNF-alpha may be due to the failure of TRAF2 membrane translocation caused by the upregulation of TRAF1 expression and formation of a TRAF1/TRAF2 complex in the cytoplasm, thereby inhibiting NF-kappaB activation and inducing apoptosis.

Active Transport, Cell Nucleus↗

PubMed2003

The role of thiol reduction in hydroquinone-induced apoptosis in HEK293 cells.

Hydroquinone (HQ) is a chemical used as a reducing agent, antioxidant, polymerization inhibitor, and chemical intermediate. It has a minor use as a bleaching agent in dermatologic preparations. HQ also occurs as a main metabolite of benzene. In the present study, HQ-induced apoptosis was evaluated by cell morphology changes, determination of phosphatidylserine (PS) externalization and analysis of sub-G1 cells. The effect of HQ on intracellular thiol concentration, including glutathione and protein thiol, and the effect of N-acetylcysteine (NAC) and buthionine sulfoximine (BSO) pretreatment on HQ-induced apoptosis were investigated. The results showed that HQ was able to induce typical apoptosis in HEK293 cells (human embryonic kidney cells) in a dose-dependent manner. Intracellular thiol, including glutathione and protein thiol, was decreased following treatment with HQ. NAC, a precursor of intracellular GSH synthesis, significantly inhibited HQ-induced apoptosis. However, BSO, a specific inhibitor of intracellular GSH synthesis, enhanced HQ-induced apoptosis significantly. Taken together, the present study demonstrates that HQ is able to induce apoptosis in HEK293 cells, most probably through depletion of intracellular thiol. The results also suggest that, at least in HEK293 cells, the control of intracellular redox homeostasis has a central role in the regulation of cell death induced by HQ.

Acetylcysteine↗