Biomedical subjects

Ao Shen

Publications and source records attributed to Ao Shen.

3 recordsLinked to original sources

PubMed2026

KEAP1 loss-of-function suppresses immunogenic ferroptosis and limits PD-1 blockade efficacy through an NRF2-FSP1 pathway.

Loss-of-function mutations in Kelch-like ECH-associated protein 1 (KEAP1) frequently occur in lung adenocarcinoma and are associated with poor prognosis and limited benefit from immunotherapy. However, the mechanisms linking KEAP1 deficiency to immune evasion remain elusive. We combined clinical data analysis, in vivo tumor models, and in vitro co-culture systems to investigate how KEAP1 deficiency shapes dendritic cell (DC) biology and response to PD-1 blockade. Ferroptosis induction assays, damage-associated molecular patterns (DAMPs) quantification, cytokine profiling, and mechanistic interrogation of the FSP1-CoQ10 axis were performed to delineate pathways.KEAP1 mutations correlated with poor response to PD-1 blockade and reduced DC infiltration. In mice, KEAP1-deficient tumors exhibited accelerated growth and reduced DC and CD8+ T-cell infiltration, consistent with an immune-cold phenotype. Mechanistically, KEAP1 loss impaired DC function in vitro, as evidenced by reduced maturation, phagocytosis, and naïve CD8+ T-cell priming capacity. This defect was linked to two mechanisms. First, KEAP1-deficient tumor cells resisted ferroptosis and failed to release immunogenic DAMPs, including extracellular ATP, HMGB1, and calreticulin. Second, KEAP1 deficiency reprogrammed the cytokine secretion profile, with downregulation of CCL2, IL-6, CXCL1, and CXCL2, thereby diminishing DC recruitment and inflammatory signaling. Notably, inhibition of the FSP1-CoQ10 antioxidant axis restored ferroptosis-associated immunogenic cell death. Our study identifies KEAP1 deficiency as a driver of immune-cold tumor microenvironments and resistance to PD-1 blockade, acting through impaired ferroptosis-induced immunogenic cell death and disrupted DC function. Genetic FSP1 deletion restored ferroptosis-associated immunogenicity and DC activation in KEAP1-deficient cells, supporting FSP1 as a potential therapeutic target for further in vivo evaluation.

DAMPs↗

PubMed2025

A method for authenticating the fidelity of Cryptococcus neoformans knockout collections.

Gene knockout (KO) strain collections are important tools for discovery in microbiology. Cryptococcus neoformans, a human fungal pathogen, has an available genome-wide gene deletion collection that is widely used by the research community. We uncovered mix-ups in the assembly of the commercially available C. neoformans deletion collection of ~4,700 unique strains acquired by our laboratory. Evidence supporting a mix-up includes RNAseq analysis that identified transcripts for the gene listed as the KO. The mystery was soon solved as this same KO strain lacked RNA transcripts for a different KO strain gene found in the same plate position in an earlier partial KO collection, suggesting a plate swap between two KO collections. Therefore, we developed a quick PCR assay to distinguish the two KO collections based on the size differences between their nourseothricin (NAT)-resistance cassettes, confirmed by genome sequencing. Here, we report that nine of the first 15 plates of the 42-plate our KN99ɑ KO collection had been replaced with the corresponding plates from an earlier partial KO collection. We provide additional evidence that the remaining plates are correct, and the simple authentication method presented here serves as a quick check to identify similar mix-ups in the KO collections.IMPORTANCEGene KO strain collections are important tools for discovery in microbiology. The human fungal pathogen Cryptococcus neoformans has an available genome-wide deletion collection that is widely used by the research community. Here, we report that our KN99ɑ collection is comprised of mixed plates from two independent KO libraries and present a simple authentication method that other investigators can use to distinguish the identities of these KO collections. Above all, this article serves as a reminder to users of the 2015 KO library collection to screen the plates before undertaking large phenotyping experiments.

Cryptococcus neoformans↗

PubMed2025

Construction of a prognostic model for gastric cancer based on immune infiltration and microenvironment, and exploration of MEF2C gene function.

BACKGROUND: Advanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC. METHODS: Transcriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. Principal component analysis (PCA) modeling was performed based on gene M and N for the calculation of immune checkpoint inhibitor (ICI) Score. Then, a Nomogram was constructed and evaluated for predicting the prognosis of GC patients, based on univariate and multivariate Cox regression. Functional enrichment analysis was performed to initially investigate the potential biological mechanisms. Through Genomics of Drug Sensitivity in Cancer (GDSC) dataset, the estimated IC50 values of several chemotherapeutic drugs were calculated. Tumor-related transcription factors (TFs) were retrieved from the Cistrome Cancer database and utilized our model to screen these TFs, and weighted correlation network analysis (WGCNA) was performed to identify transcription factors strongly associated with immunotherapy in GC. Finally, 10 patients with advanced GC were enrolled from Sun Yat-sen University Cancer Center, including paired tumor tissues, paracancerous tissues and peritoneal metastases, for preparing sequencing library, in order to perform external validation. RESULTS: Lower ICI Score was correlated with improved prognosis in both the training and validation cohorts. First, lower mutant-allele tumor heterogeneity (MATH) was associated with lower ICI Score, and those GC patients with lower MATH and lower ICI Score had the best prognosis. Second, regardless of the T or N staging, the low ICI Score group had significantly higher overall survival (OS) compared to the high ICI Score group. For its mechanisms, consistently, for Camptothecin, Doxorubicin, Mitomycin, Docetaxel, Cisplatin, Vinblastine, Sorafenib and Paclitaxel, all of the IC50 values were significantly lower in the low ICI Score group compared to the high ICI Score group. As a result, based on univariate and multivariate Cox regression, ICI Score was considered to be an independent prognostic factor for GC. And our Nomogram showed good agreement between predicted and actual probabilities. Based on CIBERSORT deconvolution analysis, there was difference of immune cell composition found between high and low ICI Score groups, probably affecting the efficacy of immunotherapy. Then, MEF2C, a tumor-related transcription factor, was screened out by WGCNA analysis. Higher MEF2C expression is significantly correlated with a worse OS. Moreover, its higher expression is also negatively correlated with tumor mutation burden (TMB) and microsatellite instability (MSI), but positively correlated with several immunosuppressive molecules, indicating MEF2C may exert its influence on tumor development by upregulating immunosuppressive molecules. Finally, based on transcriptome sequencing data on 10 paired tumor tissues from Sun Yat-sen University Cancer Center, MEF2C expression was significantly lower in paracancerous tissues compared to tumor tissues and peritoneal metastases, and it was also lower in tumor tissues compared to peritoneal metastases, indicating a potential positive association between MEF2C expression and tumor invasiveness. CONCLUSIONS: Our prognostic model can effectively predict outcomes and facilitate stratification GC patients, offering valuable insights for clinical decision-making. The identified transcription factor MEF2C can serve as a biomarker for assessing the efficacy of immunotherapy for GC.

Humans↗