IUSMMT: Survival mediation analysis of gene expression with multiple DNA methylation exposures and its application to cancers of TCGA

Shao, Zhonghe; Wang, Ting; Zhang, Meng; Jiang, Zhou; Huang, Shuiping; Zeng, Ping

doi:10.1371/journal.pcbi.1009250

Cited by 8 publications

(15 citation statements)

References 144 publications

(227 reference statements)

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“…Samples for this study were collected from the Manitoba IBD Cohort Study which was initiated in 2002 [ 22 ]. The genetic profiles of the same samples were used for the previous CNV and transcriptome-wide analysis [ 20 , 21 ].…”

Section: Methodsmentioning

confidence: 99%

“…Recently, we performed one of the first genome-wide copy number variation (CNV) analysis [ 20 ] and transcriptome-wide analysis of PC in IBD [ 21 ], which suggested that the expression level of the RBPMS gene has an association with PC status in persons with IBD [ 20 , 21 ]. Gene expression has been considered as a mediator between genetic variants and disease phenotype [ 22 ]. Hence, we hypothesized that the genetic contribution to PC susceptibility among persons with IBD could be mediated by the expression level of RBPMS .…”

Section: Introductionmentioning

confidence: 99%

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Polygenic risk and causal inference of psychiatric comorbidity in inflammatory bowel disease among patients with European ancestry

Bernstein

et al. 2022

J Transl Med

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Background Approximately 40% of persons with inflammatory bowel disease (IBD) experience psychiatric comorbidities (PC). Previous studies demonstrated the polygenetic effect on both IBD and PC. In this study, we evaluated the contribution of genetic variants to PC among the IBD population. Additionally, we evaluated whether this effect is mediated by the expression level of the RBPMS gene, which was identified in our previous studies as a potential risk factor of PC in persons with IBD. Materials and methods The polygenic risk score (PRS) was estimated among persons with IBD of European ancestry (n = 240) from the Manitoba IBD Cohort Study by using external genome-wide association studies (GWAS). The association and prediction performance were examined between the estimated PRS and PC status among persons with IBD. Finally, regression-based models were applied to explore whether the imputed expression level of the RBPMS gene is a mediator between estimated PRS and PC status in IBD. Results The estimated PRS had a significantly positive association with PC status (for the highest effect: P-value threshold = 5 × 10–3, odds ratio = 2.0, P-value = 1.5 × 10–5). Around 13% of the causal effect between the PRS and PC status in IBD was mediated by the expression level of the RBPMS gene. The area under the curve of the PRS-based PC prediction model is around 0.7 at the threshold of 5 × 10–4. Conclusion PC status in IBD depends on genetic influences among persons with European ancestry. The PRS could potentially be applied to PC risk screening to identify persons with IBD at a high risk of PC. Around 13% of this genetic influence could be explained by the expression level of the RBPMS gene.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Polygenic risk and causal inference of psychiatric comorbidity in inflammatory bowel disease among patients with European ancestry

Bernstein

et al. 2022

J Transl Med

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“…Stool is the easiest object for analysis that can be obtained. Single‐, dual‐ and multiple‐target methylation‐specific qPCR approaches show high sensitivity (>95%) and specificity (>88%) 134,135 . Detection of circulating cell‐free methylated (ccfm) DNA in blood plasma with qPCR was shown to be highly sensitive when SEPT9 and SDC2 promoters were analyzed 75,136 .…”

Section: Sensitive Detection Of Dna Methylation In Crcmentioning

confidence: 99%

“…Single-, dual-and multiple-target methylation-specific qPCR approaches show high sensitivity (>95%) and specificity (>88%). 134,135 Detection of circulating cell-free methylated (ccfm)…”

Section: Sensitive Detection Of Dna Methylation In Crcmentioning

confidence: 99%

DNA methylation biomarkers in colorectal cancer: Clinical applications for precision medicine

Fatemi

Tierling

et al. 2022

Intl Journal of Cancer

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Colorectal cancer (CRC) is the second leading cause of cancer death worldwide that is attributed to gradual long‐term accumulation of both genetic and epigenetic changes. To reduce the mortality rate of CRC and to improve treatment efficacy, it will be important to develop accurate noninvasive diagnostic tests for screening, acute and personalized diagnosis. Epigenetic changes such as DNA methylation play an important role in the development and progression of CRC. Over the last decade, a panel of DNA methylation markers has been reported showing a high accuracy and reproducibility in various semi‐invasive or noninvasive biosamples. Research to obtain comprehensive panels of markers allowing a highly sensitive and differentiating diagnosis of CRC is ongoing. Moreover, the epigenetic alterations for cancer therapy, as a precision medicine strategy will increase their therapeutic potential over time. Here, we discuss the current state of DNA methylation‐based biomarkers and their impact on CRC diagnosis. We emphasize the need to further identify and stratify methylation‐biomarkers and to develop robust and effective detection methods that are applicable for a routine clinical setting of CRC diagnostics particularly at the early stage of the disease.

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“…We drew a Venn plot about 1,936 genotype, 1,936 genes and 60 predictors (Figure S12), and found many common genes. Based on an explicit assumption of temporal ordering from genotype, gene expression and survival outcome, survival mediation analysis of gene expression with multiple genotype exposures is feasible, referring to (Shao, Wang et al 2021).…”

Section: Genotype and Gene Expressionmentioning

confidence: 99%

Efficient Feature Extraction from High Sparse Binary Genotype Data for Genetic Risk Prediction by Deep Learning Method

Shen¹,

Li²,

Yu³

et al. 2022

Preprint

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Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is that how to integrate highly sparse genetic genomics data with a mass of minor effects into prediction model for improving prediction power. We find that deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower dimensional continuous data in a non-linear way. This idea may provide benefits in risk prediction based on genome-wide data associated e.g. integrating most of the information in the genotype data. Hence, we developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for risk prediction. Specifically, we first reduced the number of biomarkers via a univariable regression model to a moderate size. Then a trainable auto-encoder was used to extract compact representations from the reduced data. Next, we performed a LASSO problem process over a grid of tuning parameter values to select the optimal combination of extracted features. Finally, we applied such feature combination to two prognostic models, and evaluated predictive effect of the models. The results of simulation studies and real data applying indicated that these highly compressed transformation features could better improve predictive performance and did not easily lead to over-fitting.

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IUSMMT: Survival mediation analysis of gene expression with multiple DNA methylation exposures and its application to cancers of TCGA

Cited by 8 publications

References 144 publications

Polygenic risk and causal inference of psychiatric comorbidity in inflammatory bowel disease among patients with European ancestry

Polygenic risk and causal inference of psychiatric comorbidity in inflammatory bowel disease among patients with European ancestry

DNA methylation biomarkers in colorectal cancer: Clinical applications for precision medicine

Efficient Feature Extraction from High Sparse Binary Genotype Data for Genetic Risk Prediction by Deep Learning Method

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