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Hongrong Zhang

Publications and source records attributed to Hongrong Zhang.

2 recordsLinked to original sources

Decoding spatiotemporal fibrotic and cellular immunosuppression of therapeutic T cells in live pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDA) is profoundly immunosuppressive. To help define this behavior, we present integrated experimental and computational frameworks to elucidate therapeutic T cell dynamics. Through the development of TME-CARTographer (TME-CART), a computational pipeline integrating high-dimensional data, graph theory, behavior analysis, and deep learning (DL), we present quantitative insights on 4D T cell-TME interactions in live PDA tumors. Mapping physical immunosuppression demonstrates that collagen fiber architectures direct migration while concomitantly limiting off-axis movement, creating immune exclusion zones. Expanding these findings, we establish that the collagen matrix harbors and spatially organizes immunosuppressive myeloid cells to serve as cooperative co-modulators of T cell behaviors, including migration, sampling, repulsion, and sequestration. Consistent with these findings, DL defines both linear and nonlinear collagen matrix and cellular neighborhood interactions as drivers of T cell behavior. The TME-CART DL framework also accurately predicts shifts in immunosuppression following depletion of myeloid cells. Overall, we identify synergistic barriers impeding anti-tumor T cell behaviors and present TME-CART as a discovery platform for interpreting complex 4D data to enhance the understanding and design of immunotherapies.

Journal Article

Mitochondria related gene signature serves as prognosis prediction and risk stratification of cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CHOL) is a highly aggressive biliary malignancy with poor clinical outcomes and limited effective prognostic biomarkers. Mitochondrial dysfunction participates in multiple oncological processes of CHOL, yet the prognostic roles of mitochondria‑related genes (MRGs) remain poorly understood. This study aimed to characterize MRGs expression in CHOL and develop a molecular prognostic model for predicting patient survival and guiding clinical management. METHODS: RNA sequencing (RNA-seq) and clinical data of CHOL were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) (GSE89748) databases. Differentially expressed MRGs were identified, and 10 machine learning algorithms were used to construct prognostic models. The optimal model (highest average C-index) was selected to establish a mitochondria-related risk score (MRRS), which was validated internally and externally. A nomogram integrating clinical factors and MRRS was developed, and biological mechanisms were explored via functional and immune analyses. RESULTS: A 3-MRG (MAP3K1, MRPL18, PYGB) prognostic signature was constructed, stratifying patients into high- and low-risk groups with significantly different overall survival. The model showed high predictive accuracy, with an area under the curve (AUC) up to 0.845, and MRRS was an independent prognostic factor. The signature was associated with mitochondrial pathways, and the high-risk group had distinct immune infiltration and mutation profiles. CONCLUSIONS: A validated MRG prognostic model effectively stratifies CHOL patients and has potential clinical value for prognosis prediction. Further validation in larger cohorts is needed to confirm its applicability.

Cholangiocarcinoma (CHOL)