Rare germline deleterious variants increase susceptibility for lung cancer.

Although multiple common susceptibility loci for lung cancer (LC) have been identified by genome-wide association studies, they can explain only a small portion of heritability. The etiological contribution of rare deleterious variants (RDVs) to LC risk is not fully characterized and may account for part of the missing heritability. Here, we sequenced the whole exomes of 2777 participants from the Environment and Genetics in Lung cancer Etiology study, a homogenous population including 1461 LC cases and 1316 controls. In single-variant analyses, we identified a new RDV, rs77187983 [EHBP1, odds ratio (OR) = 3.13, 95% confidence interval (CI) = 1.34-7.30, P = 0.008] and replicated two previously reported RDVs, rs11571833 (BRCA2, OR = 2.18; 95% CI = 1.25-3.81, P = 0.006) and rs752672077 (MPZL2, OR = 3.70, 95% CI = 1.04-13.15, P = 0.044). In gene-based analyses, we confirmed BRCA2 (P = 0.007) and ATM (P = 0.014) associations with LC risk and identified TRIB3 (P = 0.009), involved in maintaining genome stability and DNA repair, as a new candidate susceptibility gene. Furthermore, cases were enriched with RDVs in homologous recombination repair [carrier frequency (CF) = 22.9% versus 19.5%, P = 0.017] and Fanconi anemia (CF = 12.5% versus 10.2%, P = 0.036) pathways. Our results were not significant after multiple testing corrections but were enriched in cases versus controls from large scale public biobank resources, including The Cancer Genome Atlas, FinnGen and UK Biobank. Our study identifies novel candidate genes and highlights the importance of RDVs in DNA repair-related genes for LC susceptibility. These findings improve our understanding of LC heritability and may contribute to the development of risk stratification and prevention strategies.

Gene Expression Nebulas (GEN): a comprehensive data portal integrating transcriptomic profiles across multiple species at both bulk and single-cell levels.

Transcriptomic profiling is critical to uncovering functional elements from transcriptional and post-transcriptional aspects. Here, we present Gene Expression Nebulas (GEN, https://ngdc.cncb.ac.cn/gen/), an open-access data portal integrating transcriptomic profiles under various biological contexts. GEN features a curated collection of high-quality bulk and single-cell RNA sequencing datasets by using standardized data processing pipelines and a structured curation model. Currently, GEN houses a large number of gene expression profiles from 323 datasets (157 bulk and 166 single-cell), covering 50 500 samples and 15 540 169 cells across 30 species, which are further categorized into six biological contexts. Moreover, GEN integrates a full range of transcriptomic profiles on expression, RNA editing and alternative splicing for 10 bulk datasets, providing opportunities for users to conduct integrative analysis at both transcriptional and post-transcriptional levels. In addition, GEN provides abundant gene annotations based on value-added curation of transcriptomic profiles and delivers online services for data analysis and visualization. Collectively, GEN presents a comprehensive collection of transcriptomic profiles across multiple species, thus serving as a fundamental resource for better understanding genetic regulatory architecture and functional mechanisms from tissues to cells.

The association between poor oral health and risk of breast cancer in the UK Biobank.

PURPOSE: Specific oral health conditions may be risk factors for breast cancer. This study aimed to investigate the associations of oral health conditions with breast cancer risk. METHODS: A total of 234,363 women from the UK Biobank prospective cohort were included in this study. We examined the association of self-reported painful/bleeding gums, loose teeth, mouth ulcers, toothache, and use of dentures with the risk of breast cancer. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) for the associations were calculated with adjustment for multiple confounders. RESULTS: No associations of self-reported painful/bleeding gums (HR = 1.04, 95% CI 0.98-1.10), loose teeth (HR = 0.92, 95% CI 0.82-1.02), mouth ulcers (HR = 0.99, 95% CI 0.93-1.06), toothache (HR = 1.03, 95% CI 0.92-1.14), or denture use (HR = 0.96, 95% CI 0.91-1.02) with breast cancer risk were found. No statistical heterogeneity was observed in analyses stratified by baseline smoking and menopausal status. CONCLUSION: We observed no association between self-reported oral health conditions with the risk of breast cancer. Additional research with clinical examinations or oral health biomarkers in diverse populations is warranted.

EWAS Data Hub: a resource of DNA methylation array data and metadata.

Epigenome-Wide Association Study (EWAS) has become an effective strategy to explore epigenetic basis of complex traits. Over the past decade, a large amount of epigenetic data, especially those sourced from DNA methylation array, has been accumulated as the result of numerous EWAS projects. We present EWAS Data Hub (https://bigd.big.ac.cn/ewas/datahub), a resource for collecting and normalizing DNA methylation array data as well as archiving associated metadata. The current release of EWAS Data Hub integrates a comprehensive collection of DNA methylation array data from 75 344 samples and employs an effective normalization method to remove batch effects among different datasets. Accordingly, taking advantages of both massive high-quality DNA methylation data and standardized metadata, EWAS Data Hub provides reference DNA methylation profiles under different contexts, involving 81 tissues/cell types (that contain 25 brain parts and 25 blood cell types), six ancestry categories, and 67 diseases (including 39 cancers). In summary, EWAS Data Hub bears great promise to aid the retrieval and discovery of methylation-based biomarkers for phenotype characterization, clinical treatment and health care.

Tracing Lung Cancer Risk Factors Through Mutational Signatures in Never-Smokers.

Epidemiologic studies often rely on questionnaire data, exposure measurement tools, and/or biomarkers to identify risk factors and the underlying carcinogenic processes. An emerging and promising complementary approach to investigate cancer etiology is the study of somatic "mutational signatures" that endogenous and exogenous processes imprint on the cellular genome. These signatures can be identified from a complex web of somatic mutations thanks to advances in DNA sequencing technology and analytical algorithms. This approach is at the core of the Sherlock-Lung study (2018-ongoing), a retrospective case-only study of over 2,000 lung cancers in never-smokers (LCINS), using different patterns of mutations observed within LCINS tumors to trace back possible exposures or endogenous processes. Whole genome and transcriptome sequencing, genome-wide methylation, microbiome, and other analyses are integrated with data from histological and radiological imaging, lifestyle, demographic characteristics, environmental and occupational exposures, and medical records to classify LCINS into subtypes that could reveal distinct risk factors. To date, we have received samples and data from 1,370 LCINS cases from 17 study sites worldwide and whole-genome sequencing has been completed on 1,257 samples. Here, we present the Sherlock-Lung study design and analytical strategy, also illustrating some empirical challenges and the potential for this approach in future epidemiologic studies.

Plant editosome database: a curated database of RNA editosome in plants.

RNA editing plays an important role in plant development and growth, enlisting a number of editing factors in the editing process and accordingly revealing the diversity of plant editosomes for RNA editing. However, there is no resource available thus far that integrates editosome data for a variety of plants. Here, we present Plant Editosome Database (PED; http://bigd.big.ac.cn/ped), a curated database of RNA editosome in plants that is dedicated to the curation, integration and standardization of plant editosome data. Unlike extant relevant databases, PED incorporates high-quality editosome data manually curated from related publications and organelle genome annotations. In the current version, PED integrates a complete collection of 98 RNA editing factors and 20 836 RNA editing events, covering 203 organelle genes and 1621 associated species. In addition, it contains functional effects of editing factors in regulating plant phenotypes and includes detailed experimental evidence. Together, PED serves as an important resource to help researchers investigate the RNA editing process across a wide range of plants and thus would be of broad utility for the global plant research community.

EWAS Atlas: a curated knowledgebase of epigenome-wide association studies.

Epigenome-Wide Association Study (EWAS) has become increasingly significant in identifying the associations between epigenetic variations and different biological traits. In this study, we develop EWAS Atlas (http://bigd.big.ac.cn/ewas), a curated knowledgebase of EWAS that provides a comprehensive collection of EWAS knowledge. Unlike extant data-oriented epigenetic resources, EWAS Atlas features manual curation of EWAS knowledge from extensive publications. In the current implementation, EWAS Atlas focuses on DNA methylation-one of the key epigenetic marks; it integrates a large number of 329 172 high-quality EWAS associations, involving 112 tissues/cell lines and covering 305 traits, 1830 cohorts and 390 ontology entities, which are completely based on manual curation from 649 studies reported in 401 publications. In addition, it is equipped with a powerful trait enrichment analysis tool, which is capable of profiling trait-trait and trait-epigenome relationships. Future developments include regular curation of recent EWAS publications, incorporation of more epigenetic marks and possible integration of EWAS with GWAS. Collectively, EWAS Atlas is dedicated to the curation, integration and standardization of EWAS knowledge and has the great potential to help researchers dissect molecular mechanisms of epigenetic modifications associated with biological traits.

Editome Disease Knowledgebase (EDK): a curated knowledgebase of editome-disease associations in human.

RNA editing, as an essential co-/post-transcriptional RNA modification type, plays critical roles in many biological processes and involves with a variety of human diseases. Although several databases have been developed to collect RNA editing data in both model and non-model animals, there still lacks a resource integrating associations between editome and human disease. In this study, we present Editome-Disease Knowledgebase (EDK; http://bigd.big.ac.cn/edk), an integrated knowledgebase of RNA editome-disease associations manually curated from published literatures. In the current version, EDK incorporates 61 diseases associated with 248 experimentally validated abnormal editing events located in 32 mRNAs, 16 miRNAs, 1 lncRNA and 11 viruses, and 44 aberrant activities involved with 6 editing enzymes, which together are curated from more than 200 publications. In addition, to facilitate standardization of editome-disease knowledge integration, we propose a data curation model in EDK, factoring an abundance of relevant information to fully capture the context of editome-disease associations. Taken together, EDK is a comprehensive collection of editome-disease associations and bears the great utility in aid of better understanding the RNA editing machinery and complex molecular mechanisms associated with human diseases.

ICG: a wiki-driven knowledgebase of internal control genes for RT-qPCR normalization.

Real-time quantitative PCR (RT-qPCR) has become a widely used method for accurate expression profiling of targeted mRNA and ncRNA. Selection of appropriate internal control genes for RT-qPCR normalization is an elementary prerequisite for reliable expression measurement. Here, we present ICG (http://icg.big.ac.cn), a wiki-driven knowledgebase for community curation of experimentally validated internal control genes as well as their associated experimental conditions. Unlike extant related databases that focus on qPCR primers in model organisms (mainly human and mouse), ICG features harnessing collective intelligence in community integration of internal control genes for a variety of species. Specifically, it integrates a comprehensive collection of more than 750 internal control genes for 73 animals, 115 plants, 12 fungi and 9 bacteria, and incorporates detailed information on recommended application scenarios corresponding to specific experimental conditions, which, collectively, are of great help for researchers to adopt appropriate internal control genes for their own experiments. Taken together, ICG serves as a publicly editable and open-content encyclopaedia of internal control genes and accordingly bears broad utility for reliable RT-qPCR normalization and gene expression characterization in both model and non-model organisms.

Serum-plasma matched metabolomics for comprehensive characterization of benign thyroid nodule and papillary thyroid carcinoma.

Metabolomics offers a noninvasive methodology to identify metabolic markers for pathogenesis and diagnosis of diseases. This work aimed to characterize circulating metabolic signatures of benign thyroid nodule (BTN) and papillary thyroid carcinoma (PTC) via serum-plasma matched metabolomics. A cohort of 1,540 serum-plasma matched samples and 114 tissues were obtained from healthy volunteers, BTN and PTC patients enrolled from 6 independent centers. Untargeted metabolomics was determined by liquid chromatography-quadrupole time-of-flight mass spectrometric and multivariate statistical analyses. The use of serum-plasma matched samples afforded a broad-scope detection of 1,570 metabolic features. Metabolic phenotypes revealed significant pattern differences for healthy versus BTN and healthy versus PTC. Perturbed metabolic pathways related mainly to amino acid and lipid metabolism. It is worth noting that, BTN and PTC showed no significant differences but rather overlap in circulating metabolic signatures, and this observation was replicated in all study centers. For differential diagnosis of healthy versus thyroid nodules (BTN + PTC), a panel of 6 metabolic markers, namely myo-inositol, α-N-phenylacetyl-L-glutamine, proline betaine, L-glutamic acid, LysoPC(18:0) and LysoPC(18:1) provided area under the curve of 97.68% in the discovery phase and predictive accuracies of 84.78-98.18% in the 4 validation centers. Taken together, serum-plasma matched metabolomics showed significant differences in circulating metabolites for healthy versus nodules but not for BTN versus PTC. Our results highlight the true metabolic nature of thyroid nodules, and potentially decrease overtreatment that exposes patients to unnecessary risks.