Cancer-Risk Module Identification and Module-Based Disease Risk Evaluation: A Case Study on Lung Cancer

Xu, Jian; Miao, Zhengqiang; Wan, Li; Zhang, Liangcai; Feng, Chenchen; He, Yuehan; Bi, Xiaoman; Wang, Liqiang; Du, Ye; Hou, Min; Hao, Dapeng; Xiao, Yun; Chen, Lina; Li, Kongning

doi:10.1371/journal.pone.0092395

Cited by 8 publications

(4 citation statements)

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“…For example, the gene H19 (cg21926276 locate) is related with both lung cancer and tumor growth, methylation of which has been thought as a sensitive marker of tobacco history [40,41]. The gene CDCA3 (cg27042065 located) is also associated with lung cancer and survival of cancer patients [42][43][44], and Song and Yang (2018) have reported that gene LOC338797 (cg26387355 located) is related with progression of tumor in lung cancer patients [45]. Besides confirming the previously reported genes, the three CpGs (cg21926276, cg27042065 and cg26387355) are also identified as novel markers for the survival of lung cancer patients.…”

Section: Plos Computational Biologymentioning

confidence: 99%

High-dimensional mediation analysis in survival models

Luo

Yan

et al. 2020

PLoS Comput Biol

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Mediation analysis with high-dimensional DNA methylation markers is important in identifying epigenetic pathways between environmental exposures and health outcomes. There have been some methodology developments of mediation analysis with high-dimensional mediators. However, high-dimensional mediation analysis methods for time-to-event outcome data are still yet to be developed. To address these challenges, we propose a new high-dimensional mediation analysis procedure for survival models by incorporating sure independent screening and minimax concave penalty techniques for variable selection, with the Sobel and the joint method for significance test of indirect effect. The simulation studies show good performance in identifying correct biomarkers, false discovery rate control, and minimum estimation bias of the proposed procedure. We also apply this approach to study the causal pathway from smoking to overall survival among lung cancer patients potentially mediated by 365,307 DNA methylations in the TCGA lung cancer cohort. Mediation analysis using a Cox proportional hazards model estimates that patients who have serious smoking history increase the risk of lung cancer through methylation markers including cg21926276,

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Section: Plos Computational Biologymentioning

confidence: 99%

High-dimensional mediation analysis in survival models

Luo

Yan

et al. 2020

PLoS Comput Biol

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“…A new method was also designed by Jia et al to identify cancer risk modules and assess disease risks based on the modules in the samples [77]. A co-expression network was first constructed followed by the identification of candidate modules and their associated cancer risk.…”

Section: Lung Cancer (Lc)mentioning

confidence: 99%

Polygenic and Network-based studies in risk identification and demystification of cancer

Hadi

Ayoub

Bachir

et al. 2022

Expert Review of Molecular Diagnostics

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Introduction: Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as cancer, are the product of genes cooperating with each other and with their environment in orchestrated communication networks. Seeing that the function of individual genes is still used to analyze cancer, the shift to using functionally interacting groups of genes as a new unit of study holds promise for demystifying cancer. Areas Covered:The literature search focused on three types of cancer, namely breast, lung, and prostate, but arguments from other cancers were also included. The aim was to prove that multigene analyses can accurately predict and prognosticate cancer risk, subtype cancer for more personalized and effective treatments, and discover anti-cancer therapies. Computational intelligence is being harnessed to analyze this type of data and is proving indispensable to scientific progress.Expert Opinion: In the future, comprehensive profiling of all kinds of patient data (e.g., serum molecules, environmental exposures) can be used to build universal networks that should help us elucidate the molecular mechanisms underlying diseases and provide appropriate preventive measures, ensuring lifelong health and longevity. Article highlights:• Diseases were originally thought to be the result of a single gene mutation, but advances in DNA sequencing have proven otherwise.• Methods for classifying genetic variants are evolving, and they all show that genetic variants need to be studied in a more robust manner.• Complex diseases occur when the right environmental factors and SNPs exist. The latter are accumulating without understanding their significance, requiring integration into multiomic studies.• Gene networks are dominated by universal laws, proving they are credible to consider as the new "units of study" instead of single genes.• Network and polygenic studies allow for more accurate prediction and prognosis of cancer risk, treatment-useful cancer subtyping, and the discovery of interesting cancer therapies.• Comprehensive network studies integrating all types of data (e.g. transcriptomics, blood serum omics, and environmental agents) are the future of medical care.

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“…Gene co-expression network analysis is a popular technique, widely used in several articles to model dependency structure among gene expression, such as: used to investigate the changes of expression patterns across the disease progression stages [5], [6], [7], [8], [9], [10], [11], used to identify hub genes and relevant pathways [12], [13], used to distinguish cancer risk modules [14] and utilized for biomarker selection in cancer prognosis [15]. Gene co-expression analysis in different stages of HIV progression is noticed to be carried out in literatures [5], [7], [16].…”

Section: Introductionmentioning

confidence: 99%