Biomedical subjects

Hongyan Li

Publications and source records attributed to Hongyan Li.

5 recordsLinked to original sources

PubMed2026

A Clinically Integrated Pediatric Patient-Derived Xenograft Program Enables Evaluation of Cohort and Patient-Specific Biology and Therapeutic Strategies.

UNLABELLED: Preclinical translational research has increasingly utilized patient-derived xenograft (PDX) models for mechanistic and experimental therapeutic studies, yet most existing models have been developed from adult cancer types. We describe the establishment of a PDX program to expand the availability of pediatric-specific PDXs for preclinical research and enable studies of pediatric cancer histologies, including ultrarare diseases. Processes for PDX generation were integrated into established clinical workflows to facilitate universal model generation. Methodologies for tissue procurement, processing, and cryopreservation were optimized to enable intra- and interinstitutional PDX model generation. Over a 6-year span, 388 PDX tumor models representing more than 40 diagnoses were generated, including ultrarare tumors and longitudinal models established from pretherapy, posttherapy, and relapse tumors from the same patient. Genomic characterization of these PDXs demonstrates excellent concordance and recapitulation of molecular alterations of the source tumor. Successful PDX generation was enhanced from relapsed samples, was higher in sarcomas compared with other solid tumor types, and was a negative prognosticator for clinical outcome. With a broad portfolio of molecularly annotated models, we demonstrate utility for validating cross-histology biomarker-driven therapeutic strategies by demonstrating antitumor activity of an MAT2A inhibitor in MTAP-deficient PDXs. Universal model creation also allows for experimental validation of therapeutic hypotheses on a patient-specific basis, as we describe the characterization of a novel RAF1 fusion (EPB41L2::RAF1) in an osteosarcoma PDX. Development of a diverse collection of pediatric PDX models enables hypothesis-driven and cross-histology studies that expand our understanding of cancer biology and aid ongoing drug prioritization efforts in rare tumors. SIGNIFICANCE: A clinically integrated, genomically annotated pediatric PDX portfolio supported by systematic benchmarking of model generation facilitates exploratory biomarker-driven and patient-specific translational studies.

Humans↗

PubMed2026

Proteomic and Metabolomic Analysis of Immune-Related Adverse Events in Patients Treated with PD-1 Inhibitors.

As a class of immune checkpoint inhibitors (ICIs), programmed cell death protein-1 (PD-1) blockade has demonstrated remarkable efficacy in the treatment of various malignancies. However, their clinical application is constrained by the high incidence of immune-related adverse events (irAEs), which arise from nonspecific immune activation and can affect multiple organ systems, with severe cases posing life-threatening risks. This study integrated high-throughput proteomic and metabolomic analyses to systematically characterize the molecular features associated with irAEs in cancer patients receiving PD-1 inhibitor therapy. The results showed that, following the first treatment, patients who developed irAEs exhibited potential involvement of the NF-κB pathway, along with lower baseline levels of SNRPA and higher expression of CD63. Metabolomic analyses further revealed that the kynurenine/tryptophan ratio was significantly elevated in the irAE group both at baseline and post-treatment compared with patients who did not develop irAEs. In addition, significant differences in the abundance of specific lipids were observed between the two groups prior to the administration of immunotherapy. Our findings provide exploratory insights into immune and metabolic alterations associated with PD-1 blockade treatment and may help generate hypotheses for future studies on early irAE risk assessment in cancer patients undergoing PD-1 blockade therapy.

Humans↗

PubMed2026

Spatially resolved single-cell atlas reveals the macroevolutionary trajectory of animal hearts.

Animal hearts display diverse anatomical structures during adaptive evolution. Here, we present a multiomics atlas of adult hearts from 27 species across chordates, arthropods, and mollusks. Joint analysis indicates that Bilateria hearts share a core gene repertoire, taking a stepwise "add-on" approach as a universal evolutionary strategy. The "proto-heart" is populated by key cell types, including cardiomyocytes, fibroblasts, endothelial cells, and neural cells, which maintained core signatures while evolving with shifts in living environments and corresponding adaptations in the cardiovascular system. Additionally, we reveal an evolutionarily conserved cardiomyocyte state dynamic potentially linked to cardiac development and stress responses. Finally, we identify a common molecular program underpinning chamber evolution from a ventricular foundation. This work establishes a resource for understanding the intrinsic mechanisms of heart evolution.

Animals↗

PubMed2026

Decoding the molecular basis of blue grain color codominance in Qingke: Integrative analysis of RNA-seq, DNA methylation, and miRNA-seq.

The grains on single spike of the F1 generation from the cross between blue- and white-grained Qingke (Hordeum vulgare L. var. nudum Hook. f.) are randomly distributed in blue and white colors. This study integrated data from RNA-seq, DNA methylation, and miRNA-seq to analyze this trait. The results showed that the HvF3'5'H gene is likely central to the development of this codominant phenotype. Through cross-validation of three omics approaches, it was found that the HvMYB gene targeted by miR858-z, as well as the WRKY24 and At3g44326 genes targeted by novel-m0152-5p, novel-m0153-5p, and novel-m0154-5p, are correlated with DNA methylation. qRT-PCR analysis confirmed that the four aforementioned genes exhibited variety-specific and developmental stage-specific expression patterns. This study dissects the regulatory network underlying the codominant blue and white grain color divergence on a single Qingke spike from a multi-omics perspective.

DNA Methylation↗

PubMed2026

Predictive Models for Hypoglycemia Risk in Haemodialysis Patients With Diabetic Kidney Disease: Systematic Review and Meta-Analysis.

AIM: To provide evidence for selecting and developing reliable clinical assessment tools for hypoglycemia in diabetic kidney disease patients during haemodialysis. DESIGN: Review. METHODS: Systematic searches were performed in 9 Chinese and English databases to collect literature regarding the development of hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease. Two reviewers independently performed literature screening, data extraction, risk-of-bias assessment, and applicability evaluation. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability of the included studies. Meta-analysis was conducted using R software. DATA SOURCES: CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, EMbase, Web of Science, and CINAHL. The search period covered from the establishment date of each database to December 2025. RESULTS: Six studies, comprising six prediction models, were included. Two studies performed internal validation, and three conducted external validation. All models reported the area under the curve, ranging from 0.813 to 0.866, and calibration measures. Four studies were rated as having a high risk of bias, while all six demonstrated good overall applicability. The meta-analysis showed that the pooled AUC value of the six studies was 0.846 (95% CI: 0.823-0.867). CONCLUSION: Research on hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease remains in the developmental stage. Although the included prediction models exhibited satisfactory apparent discriminatory ability and clinical applicability, most of the original studies suffered from a high risk of bias and lacked adequate validation. The true predictive performance and clinical application value of these models remain to be further verified. Accordingly, routine and unconditional clinical application is not recommended at this stage. Future studies should include more high-quality, multicenter external validation and develop models with high generalizability, favourable clinical applicability, and robust predictive performance to facilitate early identification of hypoglycemia risk in this population. IMPACT: This study systematically evaluated the hypoglycemia risk prediction models for diabetic kidney disease patients during haemodialysis, and the research on hypoglycemia risk prediction models for maintenance haemodialysis patients during dialysis is still in the development stage. This study provides a reference for clinical medical staff to select or develop hypoglycemia risk prediction and assessment tools for diabetic kidney disease patients during haemodialysis. REPORTING METHOD: This study was conducted in accordance with the relevant guidelines of the EQUATOR Network and followed the TRIPOD-SRMA Checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL REGISTRATION: PROSPERO: CRD420251243352.

Humans↗