Biomedical subjects

Yining Zhao

Publications and source records attributed to Yining Zhao.

3 recordsLinked to original sources

PubMed2026

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagnosis, limited treatment options, and a lack of reliable biomarkers for early detection and prognosis. In this study, we integrated DNA methylation data from TCGA and ICGC cohorts, categorizing samples based on survival time, and identified 688 differentially methylated CpG sites, along with 224 CpG biomarkers significantly associated with patient survival through statistical and machine learning-based analyses. We developed a random forest model to predict patient survival, achieving 85.2% accuracy for short-survival patients and 70.0% for long-survival patients in the validation set. External dataset validation further confirmed the model's robustness and accuracy. De novo motif analysis of genomic regions surrounding the 224 CpG biomarkers identified TWIST1 and FOXA2 as key transcriptional regulators enriched in survival-associated CpG sites, linking their activity to patient survival outcomes. Collectively, our findings highlight valuable epigenetic biomarkers and provide a predictive model to assess PDAC risk levels post-surgery, offering the potential for improved patient stratification and personalized therapeutic strategies.

DNA methylation↗

PubMed2026

CasY7: An optimized Cas12i system for enhanced genome editing in monocot crops.

The CRISPR-Cas12 family nucleases, particularly the Cas12i subtypes, are considered promising alternatives to Cas9 for genome editing in plants. We previously developed a new Cas12i variant, CasY7, which has been successfully applied in clinical trials; its performance in plants remains to be investigated. Initial testing in stable transgenic maize and rice showed that the codon-optimized CasY7 (pCasY7e1) achieved average editing efficiencies of 58.7% and 62.3% across five target sites, respectively, outperforming the typical Cpf1 (pCpf1) control that targets the same sites. To further enhance activity, we fused T5 exonuclease to CasY7 (pCasY7e2), which shifted mutation profiles toward larger deletions, and subsequently integrated an MS2 aptamer into the crRNA scaffold (pCasY7e3). The optimized pCasY7e3 system increased editing efficiencies to 87.7% in maize and 82.9% in rice-approximately 2.7-fold higher than pCpf1. We further demonstrated multiplexed editing in maize, generating biallelic dwarf mutants, and validated functionality in hexaploid wheat with editing efficiencies up to 58.8%. Overall, our comprehensive validation across 942 transgenic plants confirmed robust editing in maize, rice, and wheat, establishing CasY7 as a high-efficiency addition to the CRISPR toolkit.

Zea mays↗

PubMed2025

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagnosis, limited treatment options, and a lack of reliable biomarkers for early detection and prognosis. In this study, we integrated DNA methylation data from TCGA and ICGC cohorts, categorizing samples based on survival time, and identified 684 differentially methylated CpG sites, along with 224 CpG biomarkers significantly associated with patient survival through statistical and machine learning-based analyses. We developed a random forest model to predict patient survival, achieving 85.2% accuracy for short-survival patients and 70.0% for long-survival patients in the validation set. External dataset validation further confirmed the model's robustness and accuracy. De novo motif analysis of genomic regions surrounding the 224 CpG biomarkers identified TWIST1 and FOXA2 as key transcriptional regulators enriched in survival-associated CpG sites, linking their activity to patient survival outcomes. Collectively, our findings highlight valuable epigenetic biomarkers and provide a predictive model to assess PDAC risk levels post-surgery, offering the potential for improved patient stratification and personalized therapeutic strategies.

Journal Article↗