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Denoising Autoencoder, A Deep Learning Algorithm, Aids the Identification of A Novel Molecular signature of Lung Adenocarcinoma

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Genomics, Proteomics & Bioinformatics 2020年第4期18卷 468-480页

作者： Jun Wang Xueying Xie Junchao Shi Wenjun He Qi Chen Liang Chen Wanjun Gu Tong Zhou Department of Thoracic Surgery Jiangsu Province People’s Hospital and the First Affiliated Hospital of Nanjing Medical UniversityNanjing 210029China State Key Laboratory of Bioelectronics School of Biological Sciences and Medical EngineeringSoutheast UniversityNanjing 210096China Department of Physiology and Cell Biology University of NevadaReno School of MedicineRenoNV 89557USA State Key Lab of Respiratory Disease Guangzhou Medical UniversityGuangzhou 510000China

Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise to leverage very large datasets for making better predictions of disease biomarkers. Denoising autoencoder(DA) is one of the unsupervised deep learning algorithms, which is a stochastic version of autoencoder techniques. The principle of DA is to force the hidden layer of autoencoder to capture more robust features by reconstructing a clean input from a corrupted one. Here, a DA model was applied to analyze integrated transcriptomic data from 13 published lung cancer studies, which consisted of 1916 human lung tissue samples. Using DA, we discovered a Molecular signature composed of multiple genes for lung adenocarcinoma(ADC). In independent validation cohorts, the proposed Molecular signature is proved to be an effective classifier for lung cancer histological subtypes. Also, this signature successfully predicts clinical outcome in lung ADC, which is independent of traditional prognostic factors. More importantly, this signature exhibits a superior prognostic power compared with the other published prognostic genes. Our study suggests that unsupervised learning is helpful for biomarker development in the era of precision medicine.

关键词： Denoising autoencoder Unsupervised learning Lung cancer Prognosis Molecular signature

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Molecular signature of soil organic matter under different land uses in the Lake Chaohu Basin

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Eco-Environment & Health 2022年第4期1卷 212-218页

作者： Han Gao Huixin Li Chen Lin Pedro J.J.Alvarez Caroline A.Masiello Dongqiang Zhu Ao Kong Xiaolei Qu State Key Laboratory of Pollution Control and Resource Reuse School of the EnvironmentNanjing UniversityNanjing 210023China School of Finance Nanjing University of Finance and EconomicsNanjing 210023China Key Laboratory of Watershed Geographic Sciences Institute of Geography and LimnologyChinese Academy of SciencesNanjing 210008China State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control Nanjing Institute of Environmental SciencesMinistry of Ecology and Environment of ChinaNanjing 210042China Department of Civil and Environmental Engineering Rice UniversityHouston TX 77005USA Department of Earth Environmental and Planetary SciencesRice UniversityHouston TX 77005USA School of Urban and Environmental Sciences Peking UniversityBeijing 100871China

The concentration and molecular composition of soil organic matter(SOM)are important factors in mitigation against climate change as well as providing other ecosystem services.Our quantitative understanding of how land use influences SOM molecular composition and associated turnover dynamics is limited,which underscores the need for high-throughput analytical approaches and molecular marker signatures to clarify this etiology.Combining a high-throughput untargeted mass spectrometry screening and molecular markers,we show that forest,farmland and urban land uses result in distinct Molecular signatures of SOM in the Lake Chaohu Basin.molecular markers indicate that forest SOM has abundant carbon contents from vegetation and condensed organic carbon,leading to high soil organic carbon(SOC)concentration.Farmland SOM has moderate carbon contents from vegetation,and limited content of condensed organic carbon,with SOC significantly lower than that of forest soils.Urban SOM has high abundance of condensed organic carbon markers due to anthropogenic activities but relatively low in markers from vegetation.Consistently,urban soils have the highest black carbon/SOC ratio among these land uses.Overall,our results suggested that the Molecular signature of SOM varies significantly with land use in the Lake Chaohu Basin,influencing carbon dynamics.Our strategy of molecular fingerprinting and marker discovery is expected to enlighten further research on SOM Molecular signatures and cycling dynamics.

关键词： High-throughput untargeted mass spectrometry screening Mass spectrum processing algorithm Soil organic matter Molecular signature Land use

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Molecular signatures of soil-derived dissolved organic matter constrained by mineral weathering

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Fundamental Research 2023年第3期3卷 377-383页

作者： Ying-Hui Wang Peng Zhang Chen He Jian-Chun Yu Quan Shi Randy A.Dahlgren Robert G.M.Spencer Zhi-Bing Yang Jun-Jian Wang State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control School of Environmental Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan UniversityWuhan 430072China State Key Laboratory of Heavy Oil Processing China University of PetroleumBeijing 102249China Shanghai Engineering Research Center of Hadal Science and Technology College of Marine SciencesShanghai Ocean UniversityShanghai 201306China Department of Land Air and Water ResourcesUniversity of California DavisDavis 95616United States Department of Earth Ocean and Atmospheric ScienceFlorida State UniversityTallahassee 32306United States

Dissolved organic matter(DOM)in soils drives biogeochemical cycling and soil functions in different directions depending on its Molecular signature.Notably,there is a distinct paucity of information concerning how the Molecular signatures of soil DOM vary with different degrees of weathering across wide geographic scales.Herein,we resolved the DOM Molecular signatures from 22 diverse Chinese reference soils and linked them with soil organic matter and weathering-related mineralogical properties.The mixed-effects models revealed that the yields of DOM were determined by soil organic carbon content,whereas the Molecular signature of DOM was primarily constrained by the weathering-related dimension.The soil weathering index showed a positive effect on the lability and a negative effect on the aromaticity of DOM.Specifically,DOM in highly weathered acidic soils featured more amino sugars,carbohydrates,and aliphatics,as well as less O-rich polyphenols and condensed aromatics,thereby conferring a higher DOM biolability and lower DOM aromaticity.This study highlights the dominance of the weathering-related dimension in constraining the Molecular signatures and potential functions of DOM in soils across a wide geographic scale.

关键词： Soil organic matter Dissolved organic matter Chemical composition Molecular signature Mineral weathering

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Chemodiversity of soil dissolved organic matter affected by contrasting microplastics from different types of polymers

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Frontiers of Environmental Science & Engineering 2023年第12期17卷 149-162页

作者： Hong Yu Beidou Xi Lingling Shi Wenbing Tan State Key Laboratory of Environmental Criteria and Risk Assessment and State Environmental Protection Key Laboratory of Simulation and Control of Groundwater PollutionChinese Research Academy of Environmental SciencesBeijing 100012China

Chemodiversity of dissolved organic matter(DOM)is a crucial factor controlling soil nutrient availability,greenhouse gas emissions,and pollutant migration.Microplastics(MPs)are widespread pollutants in terrestrial ecosystems in many regions.However,the effects of MPs on DOM chemodiversity are not sufficiently understood,particularly under different types of polymers.Using UV-Vis spectroscopy,3D fluorescence spectroscopy,and Fourier-transform ion cyclotron resonance mass spectrometry,the effects of three prevalent MPs[polyethylene,polystyrene,and polyvinyl chloride(PVC)]on the chemical properties and composition of soil DOM were investigated via a 310-d soil incubation experiment.The results showed that MPs reduced the aromatic and hydrophobic soil DOM components by more than 20%,with PVC MPs having the greatest effect.Furthermore,as MP contents increase,the humification level of soil DOM significantly decreases.MPs increased DOM molecules with no heteroatom by 8.3%-14.0%,but decreased DOM molecules with nitrogen content by 17.0%-47.8%.This may be because MPs cause positive“priming effect,”resulting in the breakdown of bioavailable components in soil DOM.This is also related to MPs changing microbial richness and diversity and enriching microbial communities involved in lignin compositions degradation.In the presence of MPs,soil DOM chemodiversity depended on soil pH,electrical conductivity,dissolved organic carbon,soil organic matter,bacterial Shannon,and fungal Chao index.Specifically,DOM in MP-contaminated soils featured more lipids and less condensed aromatics and proteins/amino sugars,thereby conferring a lower DOM aromaticity and higher lability.

关键词： Microplastics Dissolved organic matter FT-ICR MS Chemodiversity Molecular signature

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molecular Determinants of Prognosis in Hepatocellular Carcinoma

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Journal of Clinical and Translational Hepatology 2014年第1期2卷 31-36页

作者： Jean-Charles Nault Inserm UMR-1162 Génomique fonctionnelle des Tumeurs solides IUH Paris France Université Paris Descartes Labex Immuno-Oncology Sorbonne Paris Cité Faculté de Médecine Paris France Service d'Hépatologie H(o)pital Jean Verdier AP-HPBondy and Université Paris 13 Bobigny France

Hepatocellular carcinoma is one of the leading causes of death by cancer worldwide.Prognosis of hepatocellular carcinoma is determined by characteristics of the tumor and the surrounding cirrhotic liver.Several Molecular signatures reflecting tumor biology and derived from tumor analyses predict early tumor recurrence and survival.In contrast,Molecular signatures from cirrhotic non-tumor samples are enriched in immunity/inflammation related genes and could predict late tumor recurrence.Moreover,combination of clinical,pathological,and molecular features may refine prognosis prediction in these patients.Finally,Molecular signatures from both tumor and non-tumor tissues will be helpful in the future to guide treatments in different clinical settings.

关键词： Hepatocellular carcinoma Molecular signature Prognosis Microarray Cirrhosis Liver resection

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