269 research outputs found
Genome-wide interaction analysis identified low-frequency variants with sex disparity in lung cancer risk
No full textAbstract Differences by sex in lung cancer incidence and mortality have been reported which cannot be fully explained by sex differences in smoking behavior, implying existence of genetic and molecular basis for sex disparity in lung cancer development. However, the information about sex dimorphism in lung cancer risk is quite limited despite the great success in lung cancer association studies. By adopting a stringent two-stage analysis strategy, we performed a genome-wide gene–sex interaction analysis using genotypes from a lung cancer cohort including ~ 47 000 individuals with European ancestry. Three low-frequency variants (minor allele frequency < 0.05), rs17662871 [odds ratio (OR) = 0.71, P = 4.29×10−8); rs79942605 (OR = 2.17, P = 2.81×10−8) and rs208908 (OR = 0.70, P = 4.54×10−8) were identified with different risk effect of lung cancer between men and women. Further expression quantitative trait loci and functional annotation analysis suggested rs208908 affects lung cancer risk through differential regulation of Coxsackie virus and adenovirus receptor gene expression in lung tissues between men and women. Our study is one of the first studies to provide novel insights about the genetic and molecular basis for sex disparity in lung cancer development
Lung Cancer in Ever- and Never-Smokers: Findings from Multi-Population GWAS Studies
No full textAbstract
Background:
Clinical, molecular, and genetic epidemiology studies displayed remarkable differences between ever- and never-smoking lung cancer.
Methods:
We conducted a stratified multi-population (European, East Asian, and African descent) association study on 44,823 ever-smokers and 20,074 never-smokers to identify novel variants that were missed in the non-stratified analysis. Functional analysis including expression quantitative trait loci (eQTL) colocalization and DNA damage assays, and annotation studies were conducted to evaluate the functional roles of the variants. We further evaluated the impact of smoking quantity on lung cancer risk for the variants associated with ever-smoking lung cancer.
Results:
Five novel independent loci, GABRA4, intergenic region 12q24.33, LRRC4C, LINC01088, and LCNL1 were identified with the association at two or three populations (P < 5 × 10−8). Further functional analysis provided multiple lines of evidence suggesting the variants affect lung cancer risk through excessive DNA damage (GABRA4) or cis-regulation of gene expression (LCNL1). The risk of variants from 12 independent regions, including the well-known CHRNA5, associated with ever-smoking lung cancer was evaluated for never-smokers, light-smokers (packyear ≤ 20), and moderate-to-heavy-smokers (packyear > 20). Different risk patterns were observed for the variants among the different groups by smoking behavior.
Conclusions:
We identified novel variants associated with lung cancer in only ever- or never-smoking groups that were missed by prior main-effect association studies.
Impact:
Our study highlights the genetic heterogeneity between ever- and never-smoking lung cancer and provides etiologic insights into the complicated genetic architecture of this deadly cancer
Mosaic chromosomal alterations is associated with increased lung cancer risk: insight from the INTEGRAL-ILCCO cohort analysis
No full textVarious taxonomic designations of <i>V</i>. <i>ramuliflora</i> by different author.
No full textVarious taxonomic designations of V. ramuliflora by different author.</p
Data-driven modelling of nitrous oxide production in wastewater treatment processes using neural ordinary differential equations
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonNitrous oxide (N₂O) emissions from wastewater treatment facilities pose a significant environmental challenge. This study proposes a novel data-driven modelling approach using emerging neural ordinary differential equations (NODE) to capture the complex dynamics of N₂O production in typical activated sludge processes. The author established an experimental simulation platform, based on the BSM1 (benchmark simulation model no.1) plant, with the ASMG1 (activated sludge model for greenhouse gases no.1) mathematical model. This platform generates simulated monitoring data and validates the model. The author then proposes NODE-based models, analogous to traditional biokinetic models, capable of capturing the complex dynamics of N₂O generation through learning from process monitoring data. However, two primary challenges need to be overcome. First, to address inherent stiffness in the underlying dynamics, the author proposes a for training stability. Additionally, an was introduced, starting from a to establish a robust foundation, followed by refinement using the for enhanced accuracy and efficiency. Second, as monitoring data in wastewater plants typically contain confounding factors from continuous influent variations and operational adjustments, representing to the dynamics to be captured, therefore the training procedures was extended to account for these external influences. The approaches were validated on the established platform. The results demonstrate the effectiveness of the NODE-based model in capturing the intricate dynamics of N₂O production in wastewater treatment. This research presents a promising new avenue for data-driven modelling of N₂O in wastewater treatment, with the potential to improve process optimisation and emission control strategies
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