Identifying gene‐gene interactions using penalized tensor regression

Wu, Mengyun; Huang, Jian; Ma, Shuangge

doi:10.1002/sim.7523

Cited by 25 publications

(25 citation statements)

References 53 publications

(60 reference statements)

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“…TCGA research network had large numbers of cancer studies and released the databases to the public, including thousands of microarray datasets from lung cancer samples. TCGA has been successfully used to study the association of genes with drug therapy and survival (Shah et al, ), endogenous RNA analysis (Ning et al, ), and gene–gene interactions (Wu, Huang, & Ma, ) in lung cancer. Therefore, in this study, the information of clinical data and mRNA expression in LUAD patients was retrieved from the TCGA database to explore the association of gene expression with survival.…”

Section: Discussionmentioning

confidence: 99%

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases

Liu

Ouyang

Zhou

et al. 2018

Molec Gen & Gen Med

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Background Lung adenocarcinoma (LUAD) accounts for approximately 40% of all lung cancer patients. There is an urgent need to understand the mechanisms of cancer progression in LUAD and to identify useful biomarkers to predict prognosis. Methods In this study, Oncomine database was used to identify potential genes contributed to cancer progression. Bioinformatics analysis including pathway enrichment and text mining was used to explain the potential roles of identified genes in LUAD. The Cancer Genome Atlas database was used to analyze the association of gene expression with survival result. Results Our results indicated that 80 genes were significantly dysregulated in LUAD according to four microarrays covering 356 cases of LUAD and 164 cases of normal lung tissues. Twenty genes were consistently and stably dysregulated by more than twofold. Ten of 20 genes had a relationship with overall survival or disease‐free survival in a cohort of 516 LUAD patients, and 19 genes were associated with tumor stage, gender, age, lymph node, or smoking. Low expression of AGER and high expression of CCNB1 were specifically associated with poor survival. Conclusion Our findings implicate AGER and CCNB1 might be potential biomarkers for diagnosis and prognosis targets for LUAD.

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Section: Discussionmentioning

confidence: 99%

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases

Liu

Ouyang

Zhou

et al. 2018

Molec Gen & Gen Med

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“…Recently, the 'main effects, interactions' hierarchical constraint is often used in joint analysis, where an interaction is allowed into the model only if the corresponding main effects are also identified [53][54][55][56]. Although this constraint is not strictly validated in biomedical studies, it is suggested to achieve better estimation and interpretation [53].…”

Section: Joint Analysismentioning

confidence: 99%

Robust genetic interaction analysis

2018

Briefings in Bioinformatics

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For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that genetic interactions (including gene-gene and gene-environment interactions) play important roles beyond the main genetic and environmental effects. In practical genetic interaction analyses, model mis-specification and outliers/contaminations in response variables and covariates are not uncommon, and demand robust analysis methods. Compared with their nonrobust counterparts, robust genetic interaction analysis methods are significantly less popular but are gaining attention fast. In this article, we provide a comprehensive review of robust genetic interaction analysis methods, on their methodologies and applications, for both marginal and joint analysis, and for addressing model mis-specification as well as outliers/contaminations in response variables and covariates.

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“…Accumulating evidence suggests that gene–gene interactions contribute to explaining and predicting disease outcomes or phenotypes. 9,10 The introduction of gene–gene interactions into the statistical model significantly increases the number of covariates, and, hence, aggravates the high dimensionality issue. Consider a dataset with n samples and p genetic measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to standard integrative analysis approaches that have been developed to analyse cancer data, 7,8 the proposed approach considers gene–gene interactions and some environmental risk factors. Building on existing approaches, 9,10 our proposal jointly models multiple datasets, and helps to reveal common mechanisms as well as dataset-specific cancer genomic characteristics. Li et al.…”

Section: Introductionmentioning

confidence: 99%

Semiparametric integrative interaction analysis for non-small-cell lung cancer

Wang

et al. 2020

Stat Methods Med Res

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In genomic analysis, it is significant though challenging to identify markers associated with cancer outcomes or phenotypes. Based on the biological mechanisms of cancers and the characteristics of datasets, we propose a novel integrative interaction approach under a semiparametric model, in which genetic and environmental factors are included as the parametric and nonparametric components, respectively. The goal of this approach is to identify the genetic factors and gene–gene interactions associated with cancer outcomes, while estimating the nonlinear effects of environmental factors. The proposed approach is based on the threshold gradient-directed regularisation technique. Simulation studies indicate that the proposed approach outperforms alternative methods at identifying the main effects and interactions, and has favourable estimation and prediction accuracy. We analysed non-small-cell lung carcinoma datasets from the Cancer Genome Atlas, and the results demonstrate that the proposed approach can identify markers with important implications and that it performs favourably in terms of prediction accuracy, identification stability, and computation cost.

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Identifying gene‐gene interactions using penalized tensor regression

Cited by 25 publications

References 53 publications

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases

Robust genetic interaction analysis

Semiparametric integrative interaction analysis for non-small-cell lung cancer

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