| 1. |
- Chen, Chao, et al.
(författare)
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Epigenome-wide gene-age interaction analysis reveals reversed effects of PRODH DNA methylation on survival between young and elderly early-stage NSCLC patients
- 2020
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Ingår i: Aging. - : Impact Journals, LLC. - 1945-4589. ; 12:11, s. 10642-10662
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Tidskriftsartikel (refereegranskat)abstract
- DNA methylation changes during aging, but it remains unclear whether the effect of DNA methylation on lung cancer survival varies with age. Such an effect could decrease prediction accuracy and treatment efficacy. We performed a methylation-age interaction analysis using 1,230 early-stage lung adenocarcinoma patients from five cohorts. A Cox proportional hazards model was used to investigate lung adenocarcinoma and squamous cell carcinoma patients for methylation-age interactions, which were further confirmed in a validation phase. We identified one adenocarcinoma-specific CpG probe, cg14326354PRODH, with effects significantly modified by age (HRinteraction = 0.989; 95% CI: 0.986-0.994; P = 9.18×10-7). The effect of low methylation was reversed for young and elderly patients categorized by the boundary of 95% CI standard (HRyoung = 2.44; 95% CI: 1.26-4.72; P = 8.34×10-3; HRelderly = 0.58; 95% CI: 0.42-0.82; P = 1.67×10-3). Moreover, there was an antagonistic interaction between low cg14326354PRODH methylation and elderly age (HRinteraction = 0.21; 95% CI: 0.11-0.40; P = 2.20×10-6). In summary, low methylation of cg14326354PRODH might benefit survival of elderly lung adenocarcinoma patients, providing new insight to age-specific prediction and potential drug targeting.
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| 2. |
- Ji, Xinyu, et al.
(författare)
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Epigenetic–smoking interaction reveals histologically heterogeneous effects of TRIM27 DNA methylation on overall survival among early-stage NSCLC patients
- 2020
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Ingår i: Molecular Oncology. - : Wiley. - 1574-7891 .- 1878-0261. ; 14:11, s. 2759-2774
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Tidskriftsartikel (refereegranskat)abstract
- Tripartite motif containing 27 (TRIM27) is highly expressed in lung cancer, including non-small-cell lung cancer (NSCLC). Here, we profiled DNA methylation of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tumours from 613 early-stage NSCLC patients and evaluated associations between CpG methylation of TRIM27 and overall survival. Significant CpG probes were confirmed in 617 samples from The Cancer Genome Atlas. The methylation of the CpG probe cg05293407TRIM27 was significantly associated with overall survival in patients with LUSC (HR = 1.65, 95% CI: 1.30–2.09, P = 4.52 × 10−5), but not in patients with LUAD (HR = 1.08, 95% CI: 0.87–1.33, P = 0.493). As incidence of LUSC is associated with higher smoking intensity compared to LUAD, we investigated whether smoking intensity impacted on the prognostic effect of cg05293407TRIM27 methylation in NSCLC. LUSC patients had a higher average pack-year of smoking (37.49LUAD vs 54.79LUSC, P = 1.03 × 10−19) and included a higher proportion of current smokers than LUAD patients (28.24%LUAD vs 34.09%LUSC, P = 0.037). cg05293407TRIM27 was significantly associated with overall survival only in NSCLC patients with medium–high pack-year of smoking (HR = 1.58, 95% CI: 1.26–1.96, P = 5.25 × 10−5). We conclude that cg05293407TRIM27 methylation is a potential predictor of LUSC prognosis, and smoking intensity may impact on its prognostic value across the various types of NSCLC.
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| 3. |
- Zhang, Ruyang, et al.
(författare)
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Independent Validation of Early-Stage Non-Small Cell Lung Cancer Prognostic Scores Incorporating Epigenetic and Transcriptional Biomarkers With Gene-Gene Interactions and Main Effects
- 2020
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Ingår i: Chest. - : Elsevier BV. - 0012-3692. ; 158:2, s. 808-819
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Tidskriftsartikel (refereegranskat)abstract
- Background: DNA methylation and gene expression are promising biomarkers of various cancers, including non-small cell lung cancer (NSCLC). Besides the main effects of biomarkers, the progression of complex diseases is also influenced by gene-gene (G×G) interactions. Research Question: Would screening the functional capacity of biomarkers on the basis of main effects or interactions, using multiomics data, improve the accuracy of cancer prognosis? Study Design and Methods: Biomarker screening and model validation were used to construct and validate a prognostic prediction model. NSCLC prognosis-associated biomarkers were identified on the basis of either their main effects or interactions with two types of omics data. A prognostic score incorporating epigenetic and transcriptional biomarkers, as well as clinical information, was independently validated. Results: Twenty-six pairs of biomarkers with G×G interactions and two biomarkers with main effects were significantly associated with NSCLC survival. Compared with a model using clinical information only, the accuracy of the epigenetic and transcriptional biomarker-based prognostic model, measured by area under the receiver operating characteristic curve (AUC), increased by 35.38% (95% CI, 27.09%-42.17%; P = 5.10 × 10–17) and 34.85% (95% CI, 26.33%-41.87%; P = 2.52 × 10–18) for 3- and 5-year survival, respectively, which exhibited a superior predictive ability for NSCLC survival (AUC3 year, 0.88 [95% CI, 0.83-0.93]; and AUC5 year, 0.89 [95% CI, 0.83-0.93]) in an independent Cancer Genome Atlas population. G×G interactions contributed a 65.2% and 91.3% increase in prediction accuracy for 3- and 5-year survival, respectively. Interpretation: The integration of epigenetic and transcriptional biomarkers with main effects and G×G interactions significantly improves the accuracy of prognostic prediction of early-stage NSCLC survival.
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| 4. |
- Zhang, Ruyang, et al.
(författare)
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A Large-Scale Genome-Wide Gene-Gene Interaction Study of Lung Cancer Susceptibility in Europeans With a Trans-Ethnic Validation in Asians
- 2022
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Ingår i: Journal of Thoracic Oncology. - : Elsevier. - 1556-0864 .- 1556-1380. ; 17:8, s. 974-990
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Although genome-wide association studies have been conducted to investigate genetic variation of lung tumorigenesis, little is known about gene-gene (G × G) interactions that may influence the risk of non-small cell lung cancer (NSCLC).Methods: Leveraging a total of 445,221 European-descent participants from the International Lung Cancer Consortium OncoArray project, Transdisciplinary Research in Cancer of the Lung and UK Biobank, we performed a large-scale genome-wide G × G interaction study on European NSCLC risk by a series of analyses. First, we used BiForce to evaluate and rank more than 58 billion G × G interactions from 340,958 single-nucleotide polymorphisms (SNPs). Then, the top interactions were further tested by demographically adjusted logistic regression models. Finally, we used the selected interactions to build lung cancer screening models of NSCLC, separately, for never and ever smokers.Results: With the Bonferroni correction, we identified eight statistically significant pairs of SNPs, which predominantly appeared in the 6p21.32 and 5p15.33 regions (e.g., rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.17, p = 6.57 × 10−13; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.17, p = 2.43 × 10−13; rs2858859HLA-DQA1 and rs9275572HLA-DQA2, ORinteraction = 1.15, p = 2.84 × 10−13; rs2853668TERT and rs62329694CLPTM1L, ORinteraction = 0.73, p = 2.70 × 10−13). Notably, even with much genetic heterogeneity across ethnicities, three pairs of SNPs in the 6p21.32 region identified from the European-ancestry population remained significant among an Asian population from the Nanjing Medical University Global Screening Array project (rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.13, p = 0.008; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.11, p = 5.23 × 10−4; rs3135369BTNL2 and rs9271300HLA-DQA1, ORinteraction = 0.89, p = 0.006). The interaction-empowered polygenetic risk score that integrated classical polygenetic risk score and G × G information score was remarkable in lung cancer risk stratification.Conclusions: Important G × G interactions were identified and enriched in the 5p15.33 and 6p21.32 regions, which may enhance lung cancer screening models.
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| 5. |
- Chen, Jiajin, et al.
(författare)
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A trans-omics assessment of gene–gene interaction in early-stage NSCLC
- 2023
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Ingår i: Molecular Oncology. - : Wiley. - 1574-7891 .- 1878-0261. ; 17:1, s. 173-187
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Tidskriftsartikel (refereegranskat)abstract
- Epigenome-wide gene–gene (G × G) interactions associated with non-small-cell lung cancer (NSCLC) survival may provide insights into molecular mechanisms and therapeutic targets. Hence, we proposed a three-step analytic strategy to identify significant and robust G × G interactions that are relevant to NSCLC survival. In the first step, among 49 billion pairs of DNA methylation probes, we identified 175 775 G × G interactions with PBonferroni ≤ 0.05 in the discovery phase of epigenomic analysis; among them, 15 534 were confirmed with P ≤ 0.05 in the validation phase. In the second step, we further performed a functional validation for these G × G interactions at the gene expression level by way of a two-phase (discovery and validation) transcriptomic analysis, and confirmed 25 significant G × G interactions enriched in the 6p21.33 and 6p22.1 regions. In the third step, we identified two G × G interactions using the trans-omics analysis, which had significant (P ≤ 0.05) epigenetic cis-regulation of transcription and robust G × G interactions at both the epigenetic and transcriptional levels. These interactions were cg14391855 × cg23937960 (βinteraction = 0.018, P = 1.87 × 10−12), which mapped to RELA × HLA-G (βinteraction = 0.218, P = 8.82 × 10−11) and cg08872738 × cg27077312 (βinteraction = −0.010, P = 1.16 × 10−11), which mapped to TUBA1B × TOMM40 (βinteraction =−0.250, P = 3.83 × 10−10). A trans-omics mediation analysis revealed that 20.3% of epigenetic effects on NSCLC survival were significantly (P = 0.034) mediated through transcriptional expression. These statistically significant trans-omics G × G interactions can also discriminate patients with high risk of mortality. In summary, we identified two G × G interactions at both the epigenetic and transcriptional levels, and our findings may provide potential clues for precision treatment of NSCLC.
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| 6. |
- Liang, Yannis Yan, et al.
(författare)
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Association between sleep duration and metabolic syndrome : linear and nonlinear Mendelian randomization analyses
- 2023
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Ingår i: Journal of Translational Medicine. - : BioMed Central (BMC). - 1479-5876. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Observational studies have found that both short and long sleep duration are associated with increased risk of metabolic syndrome (MetS). This study aimed to examine the associations of genetically determined sleep durations with MetS and its five components (i.e., central obesity, high blood pressure, dyslipidemia, hypertriglyceridemia, and hyperglycemia) among a group of elderly population.Methods In 335,727 participants of White British from the UK Biobank, linear Mendelian randomization (MR) methods were first employed to examine the causal association of genetically predicted continuous sleep duration with MetS and its each component. Nonlinear MR analyses were performed to determine the nonlinearity of these associations. The causal associations of short and long sleep duration with MetS and its components were further assessed by using genetic variants that associated with short (<= 6 h) and long sleep (>= 9 h) durations.Results Linear MR analyses demonstrated that genetically predicted 1-h longer sleep duration was associated with a 13% lower risk of MetS, a 30% lower risk of central obesity, and a 26% lower risk of hyperglycemia. Non-linear MR analyses provided evidence for non-linear associations of genetically predicted sleep duration with MetS and its five components (all P values < 0.008). Genetically predicted short sleep duration was moderately associated with MetS and its four components, including central obesity, dyslipidemia, hypertriglyceridemia, and hyperglycemia (all P values < 0.002), whereas genetically long sleep duration was not associated with MetS and any of its components.Conclusions Genetically predicted short sleep duration, but not genetically predicted long sleep duration, is a potentially causal risk factor for MetS.
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| 7. |
- Xue, Ruidong, et al.
(författare)
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Sensor Time Series Association Rule Discovery Based on Modified Discretization Method
- 2016
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Ingår i: 2016 FIRST IEEE INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND THE INTERNET (ICCCI 2016). - : IEEE. - 9781467385152 ; , s. 196-202
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Konferensbidrag (refereegranskat)abstract
- Association rule discovery from sensor time series is a challenge. Because the time series has high dimensional, numerical and continuous nature. However the general association methods can only deal with data which are symbolic and discrete. And the general association methods have high processing time consumption when the data have high dimension. So a useful framework is proposed, which is pre-processing, representation, discretization and temporal association mining. In the discretization section, a modified discretization method is proposed which can combine the advantages of other methods, such as piecewise aggregate approximation (PAA), knee point selection, symbolic aggregate approximation (SAX) and monotonicity feature extraction. In the association section, a modified Apriori algorithm is proposed to discover special patterns and normal rules.
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