Ambient PM2.5 air pollution exposure and hepatocellular carcinoma incidence in the United States

VoPham, Trang; Bertrand, Kimberly A.; Tamimi, Rulla M.; Laden, Francine; Hart, Jaime E.

doi:10.1007/s10552-018-1036-x

Cited by 66 publications

(58 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The incidence of HCC is associated with the development of chronic hepatitis mainly linked to viral infection, alcohol consumption and nonalcoholic fatty liver disease (NAFLD) [1]. Lifestyles [2] and environmental pollution such as particulate matter air pollution [3] also contribute to increase burden in HCC worldwide. HCC is a heterogeneous disease and various genomic alterations associated with the etiologies and the stages of the pathology have been widely documented [4,5].…”

Section: Introductionmentioning

confidence: 99%

A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma

et al. 2020

View full text Add to dashboard Cite

Background: Integrating genome-wide gene expression patient profiles with regulatory knowledge is a challenging task because of the inherent heterogeneity, noise and incompleteness of biological data. From the computational side, several solvers for logic programs are able to perform extremely well in decision problems for combinatorial search domains. The challenge then is how to process the biological knowledge in order to feed these solvers to gain insights in a biological study. It requires formalizing the biological knowledge to give a precise interpretation of this information; currently, very few pathway databases offer this possibility. Results: The presented work proposes an automatic pipeline to extract automatically regulatory knowledge from pathway databases and generate novel computational predictions related to the state of expression or activity of biological molecules. We applied it in the context of hepatocellular carcinoma (HCC) progression, and evaluate the precision and the stability of these computational predictions. Our working base is a graph of 3383 nodes and 13,771 edges extracted from the KEGG database, in which we integrate 209 differentially expressed genes between low and high aggressive HCC across 294 patients. Our computational model predicts the shifts of expression of 146 initially non-observed biological components. Our predictions were validated at 88% using a larger experimental dataset and cross-validation techniques. In particular, we focus on the protein complexes predictions and show for the first time that NFKB1/BCL-3 complexes are activated in aggressive HCC. In spite of the large dimension of the reconstructed models, our analyses over the computational predictions discover a well constrained region where KEGG regulatory knowledge constrains gene expression of several biomolecules. These regions can offer interesting windows to perturb experimentally such complex systems. Conclusion: This new pipeline allows biologists to develop their own predictive models based on a list of genes. It facilitates the identification of new regulatory biomolecules using knowledge graphs and predictive computational methods. Our workflow is implemented in an automatic python pipeline which is publicly available at https://github. com/LokmaneChebouba/key-pipe and contains as testing data all the data used in this paper.

show abstract

Section: Introductionmentioning

confidence: 99%

A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In the end, 3,026 nodes remain not observed nor predicted. Iggy takes one minute to compute these results on a standard laptop computer [3] .…”

Section: Computational Predictions Validationmentioning

confidence: 99%

“…For this, we observed and tracked across the samples the level of precision and the quality of the information contained in the predictions (See Figure 1, step 4). [3] Laptop computer containing an Intel Core i7-5600U CPU with 4 threads of 2.60GHz and running Fedora 27 64 bits.…”

Section: Impact Of Data Incompleteness On Computational Predictionsmentioning

confidence: 99%

“…The incidence of HCC is associated with the development of chronic hepatitis mainly linked to viral infection, alcohol consumption and non-alcoholic fatty liver disease (NAFLD) [1]. Lifestyles [2] and environmental pollution such as particulate matter air pollution [3] also contribute to increase burden in HCC worldwide. HCC is a heterogeneous disease and various genomic alterations associated with the etiologies and the stages of the pathology have been widely documented [4,5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma

Folschette

Legagneux

Poret

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Suggestive evidence has shown an association between ambient air pollution and the incidence of postmenopausal breast cancer in European women (10). Ambient PM 2.5 exposure may be a risk factor for hepatocellular carcinoma in the United States (11). PM 2.5 was also involved in lung cancer burden (12).…”

Section: Introductionmentioning

confidence: 99%

Exosomes derived from PM2.5‑treated lung cancer cells promote the growth of lung cancer via the Wnt3a/β‑catenin pathway

Jiao

et al. 2018

Oncol Rep

View full text Add to dashboard Cite

Fine particulate matter (PM 2.5 ) is associated with an increased lung cancer risk. However, the effect of PM 2.5 exposure on lung cancer cells is still largely unknown. The present study revealed that A549 lung cancer cells secreted exosomes containing high levels of Wnt3a after treatment with PM 2.5 . These exosomes activated β-catenin signalling in A549 cells. These exosomes exhibited no effects on migration and invasion, but promoted proliferation of A549 cells via the Wnt3a/β-catenin pathway in vitro. These exosomes promoted A549 tumour progression in a Wnt3a-dependent fashion in vivo. These results demonstrated that PM 2.5 has a direct effect on promoting lung tumour development. Inhibition of exosome production by tumour cells or blockade of the Wnt3a/β-catenin pathway represents a promising strategy to impede PM 2.5 -mediated lung tumour progression.

show abstract

Ambient PM2.5 air pollution exposure and hepatocellular carcinoma incidence in the United States

Abstract: If confirmed in studies with individual-level PM exposure and risk factor information, these results suggest that ambient PM exposure may be a risk factor for HCC in the U.S.

Cited by 66 publications

References 60 publications

A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma

A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma

A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma

Exosomes derived from PM2.5‑treated lung cancer cells promote the growth of lung cancer via the Wnt3a/β‑catenin pathway

Contact Info

Product

Resources

About