Phenomics (Cham, Switzerland)最新文献

Previous observational and genomic-wide association studies (GWAS) suggested the association between several phenotypic factors and keratinocyte carcinoma, including lifestyle and dietary, photodamage-related conditions and socioeconomic status. The causal effect of these phenotypic factors in keratinocytes carcinoma etiology remains unclear. In this study, we utilized two-sample mendelian randomization analysis from multiple large-scale GWAS datasets of keratinocytes carcinoma including more than 300,000 patients. Genetic instrumental variables (IVs) were constructed by identifying single nucleotide polymorphisms (SNPs) that associate with corresponding factors. The inverse variance weighted (IVW) method and four robust MR approaches, including weighted median estimator, MR-Egger regression, simple mode and weighted mode were implemented for causal inferences and assess the sensitivity across findings. In this analysis, ease of skin tanning was identified as casual protective factor of keratinocyte carcinoma (Basal cell carcinoma: IVW OR = 0.718, 95% CI 0.654-0.788, p < 0.001; Cutaneous squamous cell carcinoma: IVW OR = 0.601, 95% CI 0.516-0.701, p < 0.001). Other phenotypic factors, such as coffee intake, alcohol consumption, smoking and socioeconomic status, indicated insignificant effects on keratinocyte carcinoma risk in the analysis, and therefore, our study does not support their roles in modifying keratinocytes carcinoma risks. Our extensive analysis provides strong evidence of the causative protective effect of ease of skin tanning in keratinocyte carcinoma. The findings suggest that individuals who are less prone to tanning may need to pay greater attention to sun protection in their daily activities to reduce the potential risk of keratinocyte cancers.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00174-w.

Type-2 diabetes mellitus (T2DM) is a global epidemic with significant societal costs. The gut microbiota, including its metabolites, plays a pivotal role in maintaining health, while gut dysbiosis is implicated in several metabolic disorders, including T2DM. Although data exists on the relationship between the gut bacteriome and metabolic disorders, further attention is needed for the mycobiome and virome. Recent advancements have begun to shed light on these connections, offering potential avenues for preventive measures. However, more comprehensive investigations are required to untangle the interrelations between different microbial kingdoms and their role in T2DM development or mitigation. This review presents a simplified overview of the alterations in the gut bacteriome in T2DM and delves into the current understanding of the mycobiome and virome's role in T2DM, along with their interactions with the cohabiting bacteriome. Subsequently, it explores into the age-related dynamics of the gut microbiome and the changes observed in the microbiome composition with the onset of T2DM. Further, we explore the basic workflow utilized in gut microbiome studies. Lastly, we discuss potential therapeutic interventions in gut microbiome research, which could contribute to the amelioration of the condition, serve as preventive measures, or pave the way towards personalized medicine.

Molecular characterization of a biological sample, e.g., with omics approaches, is fundamental for the development and implementation of personalized and precision medicine approaches. In this context, quality assessment is one of the most critical aspects. Accurate performance and interpretation of omics techniques is based on consensus, harmonization, and standardization of protocols, procedures, data analysis and reference values and materials. EATRIS, the European Infrastructure for Translational Medicine (www.EATRIS.eu), brings together resources and services to support researchers in developing their biomedical discoveries into novel translational tools and interventions for better health outcomes. Here we describe the efforts within the Horizon 2020 EATRIS-Plus project and activities of member facilities of EATRIS towards quality assessment of pre-clinical sample processing, clinical omics data generation, multi-omics data integration, and dissemination of the resources in a Multi-Omics Toolbox, which is the principal deliverable of the EATRIS-Plus project for the consolidation of EATRIS towards translational medicine.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00170-0.

Tongue analysis holds promise for disease detection and health monitoring, especially in traditional Chinese medicine. However, its subjectivity hinders clinical applications. Deep learning offers a path for automated tongue diagnosis, yet existing methods struggle to capture subtle details, and the lack of large datasets hampers the development of robust and generalizable models. To address these challenges, we introduce TonguExpert (https://www.biosino.org/TonguExpert), a free platform for archiving, analyzing, and extracting phenotypes from tongue images. Our deep learning framework integrates cutting-edge techniques for tongue segmentation and phenotype extraction. TonguExpert analyzes a massive dataset of 5992 tongue images from a Chinese population and extracts 773 phenotypes including five predicted labels and their probabilities, 355 global features (entire tongue, tongue body, and tongue coating) and 408 local features (fissures and tooth marks) in a unified process. Besides, 580 additional features for five tongue subregions are also available for future study. Notably, TonguExpert outperforms manual classification methods, achieving high accuracy (ROC-AUC 0.89-0.99 for color, 0.97 for fissures, 0.88 for tooth marks). Additionally, the model generalizes well to predict new phenotypes (e.g., greasy coating) using external datasets. This allows the model to learn from a broader spectrum of data, potentially improving its overall performance. We also release the largest publicly available dataset of tongue images and phenotypes, which is invaluable for advancing automated analysis and clinical applications of tongue diagnosis. In summary, this research advances automated tongue diagnosis, paving the way for wider clinical adoption and potentially expanding the applications in the future.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00210-9.

Lynch syndrome (LS) is an inherited condition caused by germline mutations in genes involved in DNA mismatch repair (MMR), which could increase the risk of developing colorectal cancer and other types of cancers. Current understanding of MMR gene mutations cannot fully account for the genetic predisposition to LS-associated colon cancer. This study uncovered a novel germline mutation, EPCAM c.661 A > G, in members of a three-generation family using next-generation sequencing technique, which was related to a vertically transmitted risk for LS-associated colon cancer. Genetically, EPCAM c.661 A > G was proposed to modulate the transcriptional activity of MSH2 through the DNA methylation alteration, as well as influence the stability of EpCAM protein. Through spatial transcriptomic analysis, we discovered a "cold" tumor microenvironment feature and distinct cellular interactions among epithelial cell subpopulations. In conclusion, these findings highlight the importance of identifying and characterizing novel pathogenic mutations of MMR genes to better understand the genetic basis of LS and its association with colon cancer.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00202-9.

Cervical cancer (CC) is the second most common cancer of female reproductive system. However, satisfactory prognostic model for CC remains to be established. In this study, we perform whole-exome sequencing on formalin-fixed and paraffin-embedded tumor specimens extracted from 67 recurrent and 28 matched non-recurrent CC patients. As a result, four core mutated genes (i.e., DCHS2, DNAH10, RYR1, and WDFY4) that are differentially presented in recurrent and non-recurrent CC patients are screened out to construct a recurrence-free related score (RRS) model capable of predicting CC prognosis in our cohort, which is further confirmed in TCGA CESC cohort. Moreover, combining tumor mutational burden (TMB) and RRS into an integrated RRS/TMB model enables better stratification of CC patients with distinct prognosis in both cohorts. Increased infiltration of multiple immune cell types, enriched interferon signaling pathway, and elevated cytolytic activity are evident in tumors from patients with a higher RRS and/or a higher TMB. In summary, this study establishes a novel mutation-based prognostic model for CC, the predictive value of which can be attributable to immunological mechanisms. This study will provide insight into the utilization of mutational analysis in guiding therapeutic strategies for CC patients.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00158-w.

Most genome-wide association studies (GWAS) of Venous Thromboembolism (VTE) have used data from individuals of European descent, however, genetic factors for VTE have not been fully identified in Chinese populations, which causes the limited use of existing polygenic risk scores (PRS) to identify subpopulations at high risk of VTE for prevention. We, therefore, aimed to curate all the potential VTE-related single-nucleotide polymorphisms (SNPs) for the construction of a new improved PRS model based on the self-adapting method, and then evaluate its utility and effectiveness in the stratification of VTE risk in Chinese populations. We comprehensively analyzed the mutation spectrum of VTE-associated SNPs in the Chinese cohort, and ranked their individual risk effects independently using risk ratio, logistic regression coefficient, and penalty regression coefficient as evaluation criteria. By integrating various algorithms and evaluating their performance, we trained the optimal prediction model of VTE risk in the Chinese population with the least SNP features, established an adaptive PRS model with progressive SNP overlay, and tested it on an independent Chinese population cohort. Self-adaptive polygenic risk score model based on all 318 SNPs or on the 44 most strongly associated SNPs performed similarly (areas under receiver-operating characteristic curves (AUCs) of 0.739 and 0.709, respectively) on the testing dataset of the Chinese VTE cohort, and that achieve the overall best level of the AUC from a conventional PRS model based on known genetic risk factors (0.620-0.718). In addition, we observed the self-adaptive PRS model was an independent effective risk stratification indicator beyond other clinical characteristics including age and smoking status. Our data revealed that only 44 SNPs-derived PRS model can be effectively used in discriminating subpopulations at high risk of VTE. To become clinically useful, our model could benefit from a practically feasible VTE screening program for precision prevention in Chinese populations.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00192-8.