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Tao Ye

Publications and source records attributed to Tao Ye.

2 recordsLinked to original sources
PubMed2026

FANCI promotes esophageal squamous cell carcinoma progression and cell cycle regulation and interacts with FANCD2.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is an aggressive malignancy with poor clinical outcomes, and reliable molecular biomarkers and therapeutic targets remain limited. Fanconi anemia group I protein (FANCI) is a core component of the Fanconi anemia (FA) pathway, but its expression pattern, clinical significance, and functional role in ESCC have not been comprehensively defined. This study aimed to investigate FANCI expression and prognostic value in ESCC, assess its effects on malignant cellular phenotypes and tumor growth, and explore its potential mechanistic relationship with Fanconi anemia group D2 protein (FANCD2) and cell-cycle regulation. METHODS: Multi-cohort analyses were performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, together with ESCC single-cell RNA sequencing (RNA-seq) data. FANCI functions were assessed by bidirectional gain- and loss-of-function experiments in vitro (proliferation, colony formation, migration, invasion, apoptosis, and cell-cycle assays) and by xenograft models in vivo. Mechanistic studies included protein-protein interaction (PPI) analyses, co-immunoprecipitation (Co-IP), and immunofluorescence (IF) colocalization. RESULTS: FANCI was consistently upregulated in ESCC across bulk transcriptomic datasets and was further supported by quantitative polymerase chain reaction (qPCR), Western blotting, and immunohistochemistry (IHC). FANCI discriminated ESCC from normal tissues in TCGA-ESCC and was independently validated in GSE53624 [area under the curve (AUC) =0.940 and 0.975, respectively]. FANCI was associated with poorer overall survival (OS) and shorter disease-free interval (DFI), and these findings were validated in an independent GEO cohort. Functionally, FANCI promoted ESCC cell proliferation, migration, and invasion, while inhibiting apoptosis; FANCI knockdown suppressed tumor growth in vivo and induced G2/M cell-cycle arrest. Mechanistically, FANCI physically interacted with FANCD2, colocalized with FANCD2 in the nucleus, and was associated with altered FANCD2 protein abundance, consistent with cell-cycle and DNA repair-related programs. Single-cell analysis indicated that FANCI was enriched in epithelial cells and associated with higher activity of malignant functional programs. In TCGA-ESCC, FANCI-high tumors showed distinct mutation profiles, a trend toward increased tumor mutation burden (TMB), and altered immune-associated signatures. CONCLUSIONS: FANCI is upregulated in ESCC and is associated with diagnostic and prognostic value. It promotes malignant phenotypes and tumor growth, potentially through a FANCI-FANCD2-linked cell-cycle/DNA repair program, supporting FANCI as a candidate biomarker and therapeutic target in ESCC.

Esophageal squamous cell carcinoma (ESCC)↗
PubMed2026

Machine learning-based clinical prediction model and multi-omics integration for assessing pancreatic cancer risk in new-onset diabetes.

BACKGROUND: Given that pancreatic cancer (PC) is typically diagnosed at an advanced stage but is often preceded by new-onset diabetes mellitus (NODM), providing a window for early detection, we sought to develop and validate an interpretable machine-learning model integrated with multi-omics profiling to identify early biomarkers of NODM-associated PC. METHODS: In a population-based cohort, individuals with NODM-associated PC and NODM without PC were identified and randomly divided (70:30) into training and validation sets after feature selection. Eight machine learning (ML) classifiers were compared using fivefold cross-validation, and model performance was evaluated in terms of discrimination, calibration, and decision curve–based clinical utility. We evaluated interpretability using the Shapley additive explanations (SHAP) analyses. Mechanistically, Olink proteomic profiling and metabolomics were analyzed through clinical classifications and model-defined risk strata. RESULTS: Categorical boosting achieved the best performance in the independent validation set (AUROC = 0.844). The NODM cohort was stratified into high- (n = 2,362) and low-risk (n = 5,030) groups, and internal validation together with SHAP analyses demonstrated consistent model performance and identified clinically interpretable predictors. Proteomic and metabolomic analyses under clinical and risk-based grouping identified 39 overlapping differentially expressed proteins and 145 overlapping metabolites with enriched across 11 shared KEGG pathways. Cross-platform validation highlighted PLTP, CRTAC1, and ITGAV as serum biomarkers with a strong potential for early NODM-PC detection. CONCLUSIONS: We developed an interpretable ML framework centered on NODM enables practical risk stratification for early PC detection by multi-omics and provides a pathway of ML-based triage followed by biomarker confirmation for earlier detection and diagnosis.

Humans↗