Biomedical subjects

Junyu Chen

Publications and source records attributed to Junyu Chen.

3 recordsLinked to original sources

PubMed2026

Role of CD25hi CD45RA+ CD4 not Treg %T cell in mediating the effect of pyruvate fermentation to acetone on intrahepatic cholangiocarcinoma.

This study aimed to elucidate the potential correlation between gut microbiota and intrahepatic cholangiocarcinoma (ICC) by investigating their causal relationship, while also exploring the possible role of immune cells as mediators in this association. We first identified gut microbiota based on phylum, class, order, family, and genus level information. Using summary-level data from a Genome-Wide Association Study (GWAS), we performed a 2-sample Mendelian randomization (MR) analysis of ICC and gut microbiota. Furthermore, we used 2-step MR to quantify the proportion of the effect of immune cell-mediated gut microbiota on ICC. MR analysis identified pyruvate fermentation to acetone (PFA) as predicting ICC risk reduction. There was no strong evidence that genetically predicted ICC had an effect on PFA risk. Furthermore, the proportion of genetically predicted PFA mediated by CD25hi CD45RA+ CD4 not Treg %T cell (CCCTT) was 3% (95% CI: 0.93-5.03%). In conclusion, our study established a causal relationship between PFA and ICC. We observed that a minor fraction of this effect was mediated by CCCTT, while the majority of the impact exerted by PFA on ICC remains elusive. However, further investigations are warranted to elucidate the mechanisms underlying the influence of gut microbiota on ICC development.

Cholangiocarcinoma↗

PubMed2025

A comparative study highlights superiority of LSTM in crop genomic prediction.

We systematically evaluated three key determinants affecting prediction accuracy and the algorithm performance differences based on fifteen state-of-the-art GP methods, and found LSTM suitable for capturing additive and epistatic effects. Genomic prediction (GP) has been developed as an important method supporting crop breeding. By utilizing the phenotype values result from GP, breeders could make decisions in the seedling stage that consequently benefit for cost saving. In recent years, machine learning emerged as an efficient technology to solve modeling problems in many fields, including crop breeding. However, numerous modeling approaches have hindered the application of GP since breeders struggle to choose. Therefore, a comprehensively methodological research with guiding significance is extremely necessary. In the present study, we systematically evaluated three key determinants affecting prediction accuracy and the algorithm performance differences based on fifteen state-of-the-art GP methods. As for genomic feature processing, we found feature selection (SNP filtering approach) performed better than feature extraction (PCA method). Specifically, the feature relationship dependent methods (GBLUP, RNN, and LSTM) as well as DNN architecture showed superior performance with feature selection. Marker density analysis showed positive correlation with prediction accuracy in a limited threshold. Comparison on effect of population size demonstrated a positive correlation between trait genetic complexity and the optimal population size required. By testing fifteen modeling methods, we found LSTM network displayed superior performance, achieving the highest average STScore (0.967) across six datasets. Further research using all cell states or the latest cell states of LSTM inputs demonstrated its architecture particularly adept with capturing additive and epistatic QTL effects among SNPs. In conclusion, our findings provide basic principles for implementing GP in breeding project to maximize prediction accuracy while maintaining cost-effectiveness.

Plant Breeding↗

PubMed2025

Novel approaches and applications in identifying DNA methylation markers of cardio-kidney-metabolic disease.

Cardio-kidney-metabolic (CKM) diseases represent a major public health challenge, accounting for a large proportion of global burden of morbidity and mortality. These conditions share risk factors, including genetic predisposition, environmental exposures, and lifestyle influences, which collectively drive disease development and progression. Epigenetic modifications, particularly DNA methylation (DNAm), serve as key mediators and biomarkers between these risk factors and disease phenotypes by regulating gene expression without altering the DNA sequence. Epigenome-wide association studies have identified DNAm markers associated with CKM diseases and related phenotypes, highlighting both shared pathways and disease-specific epigenetic signatures in inflammation, metabolic dysfunction, and aging-related processes. Longitudinal studies further demonstrate the dynamic nature of DNAm changes over time, offering insights into disease trajectories. Additionally, methylation risk scores integrating multiple epigenetic markers show promise in improving disease prediction and risk stratification beyond traditional clinical factors. To synthesize the current evidence, we conducted a targeted literature search in PubMed for English-language, peer-reviewed articles published between 2014 and the present. Future research leveraging large, well-phenotyped cohorts, advanced statistical methods, and innovative study designs will be critical for uncovering novel biomarkers, refining risk prediction models, and developing targeted epigenetic therapies to mitigate the global burden.

Humans↗