Biomedical subjects

Xiaowei Wang

Publications and source records attributed to Xiaowei Wang.

3 recordsLinked to original sources

PubMed2026

Beyond the "cold" barrier: Redefining the clinical paradigm of immune checkpoint inhibitor therapy in ovarian cancer.

Ovarian cancer remains an immunologically "cold" tumor, with early all-comer immune checkpoint inhibitor (ICI) trials largely negative despite underlying immunogenicity. This review takes a clinician-centric, stage-specific view linking regimen choice, treatment line, and tumor-immune context to observed outcomes. In the neoadjuvant and first-line settings, unselected ICI combinations with chemotherapy and anti-angiogenic agents failed to improve progression-free survival, whereas adding a poly (ADP-ribose) polymerase (PARP) inhibitor to ICI maintenance yielded modest gains in biomarker-enriched cohorts. In recurrent disease, single-agent ICIs produced objective response rates of 8-15%, and most randomized combinations were negative. The phase III KEYNOTE-B96 trial in platinum-resistant disease demonstrated a progression-free survival benefit in the intention-to-treat population and an overall survival benefit in tumors with programmed death ligand 1 (PD-L1) combined positive score ≥ 1 when pembrolizumab was paired with weekly paclitaxel with or without bevacizumab, underscoring the value of an immunomodulatory chemotherapy backbone in earlier lines. Ovarian clear cell carcinoma emerges as an immunotherapy-sensitive, chemo-resistant subtype that warrants dedicated stratification. We explain why single-analyte biomarkers-PD-L1, tumor mutational burden, homologous recombination deficiency/BRCA1/2-have not reliably enriched benefit and outline a multidimensional approach integrating genomic scars (e.g., mutational signature 3), immune functional state (Immunoscore, CD8⁺ tumor-infiltrating lymphocyte density and CD8⁺: regulatory T-cell ratio), and spatial architecture (inflamed, excluded, desert phenotypes). This framework aims to move beyond the all-comer era toward context-informed precision immunotherapy in ovarian cancer.

Humans↗

PubMed2025

Development and Validation an Integrated Deep Learning Model to Assist Eosinophilic Chronic Rhinosinusitis Diagnosis: A Multicenter Study.

BACKGROUND: The assessment of eosinophilic chronic rhinosinusitis (eCRS) lacks accurate non-invasive preoperative prediction methods, relying primarily on invasive histopathological sections. This study aims to use computed tomography (CT) images and clinical parameters to develop an integrated deep learning model for the preoperative identification of eCRS and further explore the biological basis of its predictions. METHODS: A total of 1098 patients with sinus CT images were included from two hospitals and were divided into training, internal, and external test sets. The region of interest of sinus lesions was manually outlined by an experienced radiologist. We utilized three deep learning models (3D-ResNet, 3D-Xception, and HR-Net) to extract features from CT images and calculate deep learning scores. The clinical signature and deep learning score were inputted into a support vector machine for classification. The receiver operating characteristic curve, sensitivity, specificity, and accuracy were used to evaluate the integrated deep learning model. Additionally, proteomic analysis was performed on 34 patients to explore the biological basis of the model's predictions. RESULTS: The area under the curve of the integrated deep learning model to predict eCRS was 0.851 (95% confidence interval [CI]: 0.77-0.93) and 0.821 (95% CI: 0.78-0.86) in the internal and external test sets. Proteomic analysis revealed that in patients predicted to be eCRS, 594 genes were dysregulated, and some of them were associated with pathways and biological processes such as chemokine signaling pathway. CONCLUSIONS: The proposed integrated deep learning model could effectively predict eCRS patients. This study provided a non-invasive way of identifying eCRS to facilitate personalized therapy, which will pave the way toward precision medicine for CRS.

Humans↗

PubMed2025

GhDMT7-mediated DNA methylation dynamics enhance starch and sucrose metabolism pathways to confer salt tolerance in cotton.

This study provides a comprehensive analysis of the impact of DNA methylation in cotton under salt stress conditions, elucidating its effects on gene expression and biological processes. Here, we determined the structures of the DNA methylation landscape across the cotton genome subjected to salt stress using whole-genome bisulfite sequencing (WGBS) and RNA-seq methodologies. We identified 4938 differentially methylated regions (DMRs) correlated with alterations in gene expression. Salt stress induced significant shifts in DNA methylation patterns, particularly in CHH contexts, suggesting context-dependent epigenetic regulation. DMRs were found to be implicated in diverse biological processes and pathways, encompassing protein metabolism, cellular homeostasis, starch and sucrose metabolism, and plant hormone signaling, all pivotal for cotton's adaptation to salt stress. Furthermore, RNA-seq analysis confirmed the impact of DNA methylation on gene expression, uncovering 9642 salt stress-responsive differentially expressed genes (DEGs). These DEGs exhibited enrichment in pathways such as carbohydrate metabolism, cell wall synthesis, and defense response, underscoring the intricate interplay between methylation and gene regulation in stress response. Moreover, the study investigated the role of the key DNA methyltransferase gene GhDMT7 in modulating cotton's response to salt stress, revealing that its downregulation enhanced cotton's salt tolerance, potentially attributed to decreased DNA methylation levels, reduced membrane damage, and enhanced antioxidant capacity. These findings elucidate the role of DNA methylation in abiotic stress resilience and provide insights for crop improvement.

Gossypium↗