Biomedical subjects

Yu Wang

Publications and source records attributed to Yu Wang.

At least 19 recordsLinked to original sources

PubMed2026

Integrated Pan-Cancer, Single-Cell, and Spatial Transcriptomic Analyses Identify ZDHHC12 as a Biomarker Associated with Macrophage Infiltration and the Immune Landscape in Glioma.

BACKGROUND: The tumor immune microenvironment (TME) critically influences cancer progression and therapeutic response. However, the pan-cancer expression landscape, prognostic relevance, and spatial distribution of ZDHHC12 remain incompletely characterized. This study investigated the prognostic value of ZDHHC12 and its associations with immune microenvironmental features and drug sensitivity. METHODS: Data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets were used to evaluate ZDHHC12 expression and prognosis across cancer types. Immune infiltration analyses, single-cell RNA sequencing, and spatial transcriptomics were integrated to characterize the associations of ZDHHC12 with the cancer immunity cycle and the spatial architecture of glioma. Drug sensitivity and immunotherapy-related metrics were assessed using pharmacogenomic databases and computational prediction models. RESULTS: ZDHHC12 was aberrantly expressed across multiple tumors and was associated with patient prognosis. Its expression was broadly correlated with immune cell recruitment- and activation-related signatures. In glioma, single-cell and spatial transcriptomic analyses showed enrichment of ZDHHC12 in monocyte/macrophage populations and spatial co-localization with BAK1, CD68, and CD163. ZDHHC12 expression was also associated with predicted drug sensitivity and immunotherapy-related metrics. CONCLUSION: ZDHHC12 may serve as a candidate pan-cancer prognostic biomarker. In glioma, its expression is associated with macrophage-enriched and immunosuppressive microenvironmental features. Functional studies are required to establish causality and determine its therapeutic relevance.

GBM↗

PubMed2026

Comparison of clinical efficacy and gut microbiota characteristics in children with ASD treated with fecal microbiota transplantation and ketogenic diet.

OBJECTIVE: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social communication and interaction, along with restricted, repetitive patterns of behavior. It is often accompanied by gastrointestinal dysfunction and gut microbiota dysbiosis. Fecal Microbiota Transplantation (FMT) and the Ketogenic Diet (KD) are interventions targeting the gut microbiota for ASD. METHODS: 30 participants were diagnosed with ASD according to DSM-5 and ADOS-2. ASD core symptoms were evaluated with CARS and ABC. Gut microbiota composition was analyzed by shotgun metagenomic sequencing. RESULTS: Both groups demonstrated significant improvements in core symptoms. In the FMT group, the mean CARS score significantly decreased from 34.87 to 33.53 (p < 0.01); in the KD group, it declined from 35.13 to 33 (p < 0.01). The mean ABC score reduced from 79.93 to 69.33 (p = 0.064) in the FMT group and from 63.07 to 42.73 (p < 0.01) in the KD group. Following the intervention, no statistically significant changes were observed in α-diversity or β-diversity within either group. LEfSe analysis revealed distinct post-intervention microbial signatures: FMT significantly enriched butyrate-producing taxa (Wujia chipingensis, Eubacterium sp. MSJ-33, and Butyrivibrio crossotus), while KD elevated Blautia massiliensis and decreased propionate metabolism -associated taxa (Veillonella sp. S12025-13 and Veillonella nakazawae). KEGG enrichment analysis revealed that KD enriched propionate metabolism (Fold enrichment = 3.747, q = 0.010) and aromatic compound degradation (Fold enrichment = 3.591, q = 0.010). CONCLUSIONS: Both interventions significantly improved clinical symptoms among children with ASD, potentially through distinct patterns of gut microbiota modulation. CLINICAL TRIALS NUMBER: NCT06348433 (03/21/2024).

Child↗

PubMed2026

Publisher Correction: PTX3 induces the expression of PD-L1 and promotes breast cancer immune escape : Liyuan Jing.

Explore the source record for details and available documents.

Published Erratum↗

PubMed2026

Development and validation of a serum peptidomic signature for early detection of asymptomatic ovarian cancer: A multi-center prospective study.

Early detection of asymptomatic ovarian cancer (asym-OC) remains a critical challenge, the failure of which underlies its high mortality. Performing serum peptidomic profiling of 843 participants in the cohort SOCFCP, we distill 1,081 initial features into a 7-marker panel for asym-OC detection via a biology-informed machine-learning (ML)-based feature selection strategy. Three markers significantly revert toward non-OC levels after surgery. Integrating the panel with age, CA125, and HE4, we develop and externally validate (n = 159) a LightGBM model, ProMS+. For early-stage OC detection, ProMS+ shows a specificity of 92.6% at 95.0% sensitivity, outperforming CA125 (44.7%), HE4 (11.2%), and Risk of Ovarian Malignancy Algorithm (ROMA) (24.0%), with an area under the curve (AUC) of 0.993. In a simulated high-risk population (n = 100,000; OC prevalence = 1%), ProMS+ yields a high AUC (0.983) and a higher positive predictive value than CA125, HE4, and Age + CA125 + HE4 combined model (0.201 vs. 0.027, 0.090, and 0.064). ProMS+ offers a promising, non-invasive, and interpretable approach for the early detection of asym-OC.

Humans↗

PubMed2026

A three-gene radioresistance signature predicts tumor progression in cervical cancer.

BACKGROUND: As a primary curative treatment for locally advanced cervical cancer, radiotherapy is frequently undermined by radioresistant tumor cells that evade cell death and subsequently drive post-treatment tumor progression. This study aimed to identify candidate genes associated with radioresistance in cervical cancer and to explore their potential in predicting unfavorable outcomes among radioresistant patients, thereby providing a reference for future research. METHODS: We screened for co-expressed genes using transcriptomic data from radiation non-complete response (NCR) cervical cancer patients in Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Cox regression analyses were conducted to identify the most significant radioresistance-associated genes for constructing a prognostic model. The predictive performance of this model was further validated through logistic regression, weighted gene co-expression network analysis (WGCNA), and pan-cancer analyses. Quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) was performed to quantify the expression levels of key genes in cervical cancer tissue samples from radiosensitive and radioresistant patients. RESULTS: The resulting prognostic model comprised three genes: MTMR11, VANGL1, and CD46. This gene panel was significantly associated with the prognosis of cervical cancer patients receiving radiotherapy and showed acceptable predictive performance across multiple cancer types. qRT-PCR analysis revealed that the expression patterns of MTMR11 and VANGL1 were generally consistent with radioresistance of cervical cancer, whereas CD46 exhibited an unexpected expression trend. CONCLUSIONS: Our findings indicate that MTMR11, VANGL1, and CD46 are associated with radioresistance and prognosis in cervical cancer. Their potential clinical utility, especially in predicting radiotherapy response at the individual patient level, requires further validation in larger, independent, and prospective cohorts.

Cervical cancer↗

PubMed2026

Strategy for enhanced production of A40926B0 in Nonomuraea gerenzanensis using an efficient CRISPR/AsCas12f1 system.

The global emergence of vancomycin-resistant Gram-positive pathogens underscores the urgent need for efficient production of novel lipoglycopeptide antibiotics. Dalbavancin, a last-resort therapeutic agent, relies on its key biosynthetic precursor A40926B0, whose industrial manufacture is severely limited by the low yield of wild-type Nonomuraea gerenzanensis and inefficient genetic tools for this rare actinomycete. Here, we developed a high-efficiency CRISPR/AsCas12f1 genome editing system and applied systematic metabolic engineering to boost A40926B0 biosynthesis. First, conjugation conditions were optimized to elevate the transfer efficiency in N. gerenzanensis D11. The hypercompact AsCas12f1 nuclease showed markedly lower cytotoxicity than SpCas9 and enabled 100% gene deletion efficiency with preferred PAMs (TTTG, CTTG, GTTG). Second, we strengthened the shikimate pathway via multiple genetic strategies: overexpressing feedback-resistant DAHP synthase (aroG fbr ) and chorismate mutase/prephenate dehydrogenase (tyrA fbr ), as well as knocking out pheA. This manipulation blocks the phenylalanine synthetic branch and redirects metabolic flux toward the l-tyrosine branch. Third, we engineered the branched-chain fatty acid (BCFA) pathway via promoter replacement of bkdA2B2C2, LipAB, fabF and deletion of acdH to enhance isododecanoyl side-chain supply. The combinatorial engineering yielded strain B-13, which produced 1740 mg/L A40926B0 in shake flasks. Finally, 50-L fed-batch fermentation with continuous maltodextrin feeding further increased the titer to 1817 mg/L, the highest reported titer to date. This work establishes a robust CRISPR editing tool for N. gerenzanensis and provides valuable engineering references for precursor-oriented strain improvement targeting lipoglycopeptide antibiotics, offering insights for the industrial scale production of A40926B0.

A40926B0↗

PubMed2026

Genomic and genetic dissection underlying seedling drought resilience in oats.

Drought threatens global crop yields, and common oat, a vital nutritional source for food and feed, is particularly constrained in the semi‑arid regions where it is widely cultivated. Here, we report two high-quality genome assemblies for drought-resilient (Borris37) and drought-sensitive (XymC06) oat accessions with distinct seedling survival rates and genome sizes of 10.92 Gb and 10.96 Gb, and construct comprehensive landscapes of insertion‑deletions (InDels) and structural variants (SVs). Integrating population-level genomic, transcriptomic and phenotypic (seedling survival rate), we demonstrate that InDels and SVs underpin divergent drought resilience and identify 52 candidate genes associated with drought resistance whose expression is significantly modulated by these variants. Borris37 accumulates 36 favorable alleles of these genes. An InDel in the AsNF-YB3 promoter enhances binding to AsARF1, upregulating AsNF‑YB3 under drought, and overexpression of AsNF‑YB3 reduces ROS accumulation. Our findings provide resources and targets for drought‑resistance breeding in oat, thereby supporting global food security.

Drought Resistance↗

PubMed2026

Large-scale whole-genome sequencing reveals the landscape and health implications of de novo mutations.

De novo mutations (DNMs) are an important source of congenital diseases. With delayed parenthood and assisted reproductive technology (ART) use increasing, it is essential to elucidate how these reproductive factors influence DNMs and whether resulting mutations influence offspring health. Here we performed whole-genome sequencing of 24,030 individuals from 7,851 parent-offspring families, identifying 390,924 de novo single-nucleotide variants (dnSNVs). Paternal and maternal aging exhibited distinct mutational patterns, with maternal DNM accumulation accelerating at advanced ages. Increased paternal dnSNVs partially accounted for the association between advanced parental age and shorter gestational duration. Moreover, ART showed age-independent, procedure-specific effects: intracytoplasmic sperm injection (ICSI) and ovarian stimulation were associated with increased paternal and maternal dnSNVs, respectively, and ICSI-associated paternal dnSNVs also partially accounted for the association between ICSI and shorter gestational duration. In vitro embryo manipulation was associated with increased early post-zygotic mosaic mutations, particularly C > A substitutions linked to delayed neurocognitive development at 1 year. Collectively, these findings advance understanding of the determinants and consequences of de novo mutagenesis.

Journal Article↗

PubMed2026

AI proteomics: from protein identification to virtual cells.

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.

Proteomics↗

PubMed2026

Efficient homologous replacement and deletion of large genomic fragments through template-jumping prime editing in rice.

Homologous replacement of genomic sequences with large DNA fragments (> 100 bp) holds great potential for crop breeding, yet an efficient method to achieve such edits is lacking in plants. Here, in rice, we developed template-jumping prime editing (TJ-PE), a recently reported PE strategy for large targeted insertion, as an efficient tool for homologous replacement with DNA fragments ranging from dozens to hundreds of base pairs, and using TJ-PE, we replaced genomic fragments of up to 340 bp with homologous fragments of the same length. In addition, our TJ-PE tool also enabled precise deletion of 944- to 2024-bp fragments in rice, with efficiencies of up to 34.6% for c. 2000-bp precise deletions. Collectively, this study expands the editing scope of PE in rice and establishes TJ-PE as a generalist tool for precise deletion and replacement of large DNA fragments.

Oryza↗

PubMed2026

Targeting pancreatic cancer progression: The formononetin and salvianolic acid B combination suppresses JAK/STAT signaling via MBOAT2 downregulation.

OBJECTIVE: Formononetin and salvianolic acid B (FcS) are the primary bioactive components of the Astragalus mongholicus-Salvia miltiorrhiza herbal pair, a classic combination for treating pancreatic cancer associated with qi deficiency and blood stasis. This study elucidates the therapeutic potential and mechanisms of FcS in the treatment of pancreatic cancer. METHODS: A zebrafish xenograft model was used to screen bioactive combinations derived from A. mongholicus and S. miltiorrhiza, identifying FcS as a candidate with antitumor activity. Its efficacy was evaluated in vivo using the zebrafish model, orthotopic LSL-KrasG12D/+, LSL-Trp53R172H/+ and Pdx-1-Cre (KPC) mice, and subcutaneous xenograft models. Cell viability and proliferation were assessed using cell counting kit-8, 5-ethynyl-2'-deoxyuridine and colony formation assays, and migration and invasion were evaluated by wound healing and transwell assays. Membrane-bound O-acyltransferase 2 (MBOAT2) was identified as a potential target through a molecular docking study and the Cancer Genome Atlas (TCGA) analysis. MBOAT2 knockdown cells were used to explore its roles and the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway in FcS-mediated inhibition. RESULTS: In the zebrafish model, FcS strongly inhibited pancreatic tumor growth. FcS reduced tumor volume, the expression of proliferation marker Ki-67, and proliferating cell nuclear antigen in KPC mice. In vitro, FcS inhibited pancreatic cancer cell viability, proliferation, migration and invasion, which was accompanied by downregulation of MBOAT2 expression. TCGA analysis linked high MBOAT2 expression to aggressive phenotypes. MBOAT2 knockdown reduced the survival, proliferation and invasion of BxPC-3 cells. Rescue experiments revealed that MBOAT2 knockdown attenuated the antitumor effects of FcS, possibly through modulation of the JAK/STAT signaling pathway. FcS also inhibited tumor proliferation in xenograft models, and MBOAT2 expression was elevated in tumor tissues from pancreatic cancer patients. CONCLUSION: FcS suppresses pancreatic cancer progression via MBOAT2 downregulation and JAK/STAT pathway inhibition, which highlights MBOAT2 as a potential therapeutic target. Please cite this article as: Xu Y, Xu CS, Jin HB, Gu WG, Shen HZ, Lu L, Chen Y, Xu DC, Zhang XF, Yang JF, Wang Y. Targeting pancreatic cancer progression: The formononetin and salvianolic acid B combination suppresses JAK/STAT signaling via MBOAT2 downregulation. J Integr Med. 2026; 24(5):725-741.

Animals↗

PubMed2026

Oxidative Damage Fine-Tunes G-Quadruplex Structures in Human Gene Promoters.

Oxidative damage can convert guanine (G) into 8-oxoguanine (O8G), resulting in altered gene expression and genome instability. However, the underlying molecular mechanisms remain poorly understood. Herein, we show that the NEIL3 gene proximal promoter sequence forms a mixture of parallel and hybrid G-quadruplex structures (NEIL3-G4s), exhibiting intrinsic structural polymorphism. Strikingly, site-specific O8G modifications significantly reduce this polymorphism, promoting the stabilization of either the parallel or the (3+1) hybrid-1 G4 topology. A single G-to-O8G substitution is sufficient to trigger a clear structural transition from the parallel to the (3+1) hybrid-1 G4, highlighting the profound impact of O8G on G4-mediated epigenetic regulation. We have determined the NMR solution structures of both native and O8G-modified NEIL3-G4s, providing mechanistic insights into how O8G induces specific G4 structural rearrangements. Functional analysis demonstrates that both forms of NEIL3-G4s can form in extended DNA contexts and inhibit DNA polymerase activity. Under oxidative stress, the formation of NEIL3-G4s correlates with elevated NEIL3 gene expression, suggesting that they play a role as sensors of oxidative damage and function as molecular switches for gene upregulation. Collectively, these findings underscore the crucial role of O8G-induced G4 structural plasticity in the cellular response to oxidative stress and in regulating gene expression.

G-Quadruplexes↗

PubMed2025

Genomic insights into karyotype evolution and adaptive mechanisms in Polygonaceae species.

Polygonaceae, with ecological versatility and global distribution, is an ideal system for investigating plant adaptation. However, the genomic mechanisms underlying its karyotype evolution and environmental resilience remain unclear. We herein present chromosome-level genomes of 11 species from 10 Polygonaceae genera. Our analyses reveal that Gypsy retrotransposons are key drivers of genome size variations in Polygonaceae. We reconstructed a Polygonaceae ancestral karyotype comprising 28 proto-chromosomes and elucidated evolutionary trajectories via extensive chromosomal rearrangements. Furthermore, we constructed a cross-genus super pan-genome for Polygonaceae, identifying 80,055 gene families, of which 9,845 (12.30%) are core gene families. Private genes are found to contribute significantly to interspecific differences in adaptability. Notably, gene copy number variations are identified as a critical factor influencing adaptations to diverse niches involving species-specific increases in metabolic pathways. This study provides a genomic framework for Polygonaceae karyotype plasticity and adaptive innovation, offering insights into plant evolution under environmental challenges.

Karyotype↗

PubMed2025

ZIPcnv: accurate and efficient inference of copy number variations from shallow whole-genome sequencing.

MOTIVATION: Shallow whole-genome sequencing (sWGS), a rapid and cost-effective sequencing technology, has gradually been widely adopted for CNV analyses. However, with genome‑wide coverage of only 0.1-5×, sWGS data display a pronounced zero‑inflation phenomenon-a large fraction of loci has zero sequencing reads. Zero inflation causes read counts to fluctuate by several‑fold between adjacent windows. As a result, random upward blips in coverage can be misinterpreted as copy‑number gains (false positives), and true deletions often become indistinguishable from pervasive zero‑coverage noise. In addition, existing CNV detection tools developed for sWGS data often struggle to adapt across different CNV sizes. These combined effects severely constrain the accuracy of CNV inference. RESULTS: To address above challenges, we propose ZIPcnv, a novel CNV detection tool specifically designed for sWGS data. First, we apply a segment sliding window to smooth the raw read depth signal, which transforms the original zero-inflated statistical characteristics into approximately normal distribution characteristics. We then design a statistical process model that robustly detects persistent shifts under high background noise using a cumulative sum strategy, classifying genomic regions into candidate and non-candidate CNV regions. Finally, dynamic sliding windows are used for one-pass detection of CNVs of varying lengths, with window size adapting to the CNV region size. We evaluated the performance of ZIPcnv on simulated data and 190 real whole-genome sequencing samples. Experimental results show that ZIPcnv consistently outperforms currently popular CNV detection tools. AVAILABILITY AND IMPLEMENTATION: The ZIPcnv source code is freely available at https://github.com/Nevermore233/ZIPcnv.

DNA Copy Number Variations↗

PubMed2025

Genomic mapping of diabetic kidney disease biomarkers and identification of potential inhibitors through virtual screening.

BACKGROUND: Diabetic kidney disease (DKD) is a common and serious complication of diabetes mellitus, marked by a multifactorial pathogenesis and the absence of sensitive diagnostic biomarkers. Identifying novel molecular targets and therapeutic options is essential to improve early diagnosis and treatment outcomes. METHODS: To uncover potential biomarkers and therapeutic candidates, we performed an integrated genomic analysis using microarray and RNA-seq datasets from the Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) databases. Differentially expressed genes (DEGs) were identified and subjected to protein-protein interaction (PPI) network analysis. Key genes were further explored through virtual screening of an FDA-approved compound library using molecular docking techniques. Drug-likeness was assessed via Lipinski's rule of five. RESULTS: A total of 40 DEGs were identified, among which ISCU (downregulated; involved in iron-sulfur cluster biogenesis) and AP1S2 (upregulated; associated with vesicular trafficking) emerged as potential biomarkers. PPI analysis revealed their involvement in critical DKD-related pathways, such as extracellular matrix remodeling and oxidative stress. Virtual screening identified six FDA-approved compounds with high binding affinity (≤-7.96 kcal/mol) to ISCU, notably ZINC000001576020, all of which complied with Lipinski's rule. CONCLUSIONS: This in-silico study nominates ISCU and AP1S2 as candidate diagnostic biomarkers for DKD and identifies computationally prioritized inhibitors targeting ISCU. These findings require experimental validation but provide a molecular framework for precision diagnosis and therapeutic development. These findings offer new molecular insights that could inform precision diagnosis and personalized treatment strategies for diabetic kidney disease.

Diabetic Nephropathies↗

PubMed2025

Mismatch-introduced crRNA guided PCR-CRISPR/Cas12a platform improves EGFR point mutation detection in single tumor cell.

Dynamic monitoring of epidermal growth factor receptor (EGFR) mutations is essential for the early identification of resistance and treatment adaptation. Single-cell heterogeneity analysis is crucial for precision cancer medicine, yet sensitive and specific detection methods for individual tumor cells remain challenging. Here, we develop a PCR-CRISPR/Cas12a platform enhanced by the incorporation of mismatched base in crRNA at specific site for single-cell point mutation detection. This platform demonstrated high specificity and sensitivity, detecting point mutation at a frequency of 0.1% and in as low as 1.02 ng of genomic DNA, which represents an improvement over the amplification-refractory mutation system PCR (ARMS-PCR). Notably, the accuracy of the platform is highly consistent with next-generation sequencing (NGS), as evidenced by Kappa test values surpassing 0.9. By utilizing a conical-pore membrane with optimized porosity for single circulating tumor cell (CTC) enrichment, our platform enables point mutations detection in individual tumor cells, offering potential enhancements in precision and reliability for EGFR mutation analysis. This novel methodology holds potential for more accurate and personalized cancer treatment strategies.

Humans↗

PubMed2025

Redistribution of super-enhancers promotes malignancy in human hepatocellular carcinoma.

INTRODUCTION: Super-enhancers (SEs) are defined as the regulatory region where intensive transcriptional cofactors bind. Dysregulation of SEs is related to multiple diseases, however, its role in hepatocellular carcinoma (HCC) remains elusive. OBJECTIVES: This work aimed to reveal the dysregulation of SEs in HCC and the therapeutic potential for HCC treatment. METHODS: Fifteen HCC and twelve paracancerous samples underwent chromatin immunoprecipitation (ChIP) sequencing targeting H3K27ac, and subsequently the SEs were identified by the Rank Ordering of Super-Enhancers algorithm. Differential SEs featured by tumor or paracancerous tissues were identified, and cross-referenced with the differential expression genes and prognosis-related genes in 2 independent public or in-house HCC cohorts. The SE region of HSPA4 was deleted in the genome of HCCLM3 cell by CRISPR-Cas9, named HSPA4-SE-KO cells. The potential druggable transcriptional factors were identified by CRCmapper, GeneMANIA and Drug Gene Interaction Database (DGID). RESULTS: Five targets, including CDKN2C, HSPA4, GGH, PDGFA, and CAP2, were identified as HCC-gain SEs with oncogenic potential, which were further validated experimentally by SE inhibitors and ChIP targeting H3K27ac and BRD4. Cell proliferation and migration assays further confirmed that silencing of these HCC-gain SEs significantly suppressed the malignant phenotype of HCC cell lines. HSPA4 appeared strongest oncogenic functions among these targets, which was further verified by HCC mouse xenograft models and clinical sample investigation. Moreover, HSPA4-SE-KO cells obtained significantly suppressed HSPA4 expression and retarded tumorigenic capability. Finally, dysregulation of transcriptional factors engaged in the oncogenic role of SEs, and Danthron that targeting RXRA were identified from DGID for HCC treatment. CONCLUSION: The dysregulated SE landscape of HCC promoted the malignancy phenotype by the upregulation of oncogenes, and SE-regulatory network might be potential drug targets for HCC treatment. Our study deepened the insight of epigenetic dysregulation in HCC, offering the groundwork for SEs as potential therapeutic targets of HCC treatment.

Humans↗

PubMed2025

Systematic mining and quantification reveal the dominant contribution of non-HLA variations to acute graft-versus-host disease.

Human leukocyte antigen (HLA) disparity between donors and recipients is a key determinant triggering intense alloreactivity, leading to a lethal complication, namely, acute graft-versus-host disease (aGVHD), after allogeneic transplantation. Moreover, aGVHD remains a cause of mortality after HLA-matched allogeneic transplantation. Protocols for HLA-haploidentical hematopoietic cell transplantation (haploHCT) have been established successfully and widely applied, further highlighting the urgency of performing panoramic screening of non-HLA variations correlated with aGVHD. On the basis of our time-consecutive large haploHCT cohort (with a homogenous discovery set and an extended confirmatory set), we first delineated the genetic landscape of 1366 samples to quantitatively model aGVHD risk by assessing the contributions of HLA and non-HLA genes together with clinical factors. In addition to identifying multiple loss-of-function (LoF) risk variations in non-HLA coding genes, our data-driven study revealed that non-HLA genetic variations, independent of HLA disparity, contributed the most to the occurrence of aGVHD. This unexpected major effect was verified in an independent cohort that received HLA-identical sibling HCT. Subsequent functional experiments further revealed the roles of a representative non-HLA LoF gene and LoF gene pair in regulating the alloreactivity of primary human T cells. Our findings highlight the importance of non-HLA genetic risk in the new era of transplantation and propose a new direction to explore the immunogenetic mechanism of alloreactivity and to optimize donor selection strategies for allogeneic transplantation.

Humans↗