头颈部鳞状细胞癌化疗耐药中ANXA1的特征（数据表2）

数据集获取（Figshare）

• 源站标题：Data Sheet 2_Characterization of ANXA1 in chemotherapy resistance of head and neck squamous cell carcinoma: insights from artificial intelligence and integrative bioinformatics analysis.docx
• DOI：10.3389/fcell.2026.1769105.s001
• 发布日期：2026-03-18
• 文件数：1 个
• 文件总体积：3.04 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Characterization_of_ANXA1_in_chemotherapy_resistance_of_head_and_neck_squamous_cell_carcinoma_insights_from_artificial_intelligence_and_integrative_bioinformatics_analysis_docx/31800862

命令行下载

curl -L -o "Data Sheet 2_Characterization of ANXA1 in chemotherapy resistance of head and neck squamous cell carcinoma: insights from artificial intelligence and .docx" "https://ndownloader.figshare.com/files/62915794"

文件清单

• Data Sheet 2_Characterization of ANXA1 in chemotherapy resistance of head and neck squamous cell carcinoma: insights from artificial intelligence and .docx（3.04 MB）

文件总体积 3.04 MB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

BackgroundHead and neck squamous cell carcinoma (HNSCC) exhibits intensive chemoresistance (CR), leading to frequent recurrence and poor prognosis; however, actionable biomarkers and therapeutic options remain limited.
MethodsBy utilizing bulk profiles of HNSCC patients (TCGA-HNSCC cohort and GSE6631) from TCGA and GEO databases, we identified CR-associated DEGs via Limma and WGCNA frameworks. Importantly, LASSO–Cox regression was utilized for the construction of a predictive model and identification of the CR-associated hub gene in TCGA-HNSCC cohort. In addition, predictive model performance was validated in the HNSCC patient bulk profile (GSE65858). Furthermore, the molecular and immune characteristics of the hub gene were estimated at HNSCC patient bulk (TCGA-HNSCC cohort) and single-cell (GSE163872) levels, especially in artificial intelligence (AI)-empowered virtual cells. Specifically, AI-driven therapeutic framework (RefLector) and molecular docking were performed for the recognition of an optimal therapeutic framework for the treatment of HNSCC by targeting the hub gene. Finally, the cariogenic role of the hub gene was evaluated in an in vitro study.
ResultsCR-associated DEGs can guide the risk stratification of HNSCC patients. ANXA1 was identified as a downregulated, malignancy-distributed, prognostic, and druggable biomarker for HNSCC patients, which was also associated with HNSCC progression. BRD-K10482608 can be considered a potential therapeutic agent for the treatment of HNSCC.
ConclusionOur study highlighted the CR in risk stratification for HNSCC patients and ANXA1 in the pathogenesis of HNSCC, which can guide personalized and precision medicine for HNSCC patients.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcell.2026.1769105.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Characterization_of_ANXA1_in_chemotherapy_resistance_of_head_and_neck_squamous_cell_carcinoma_insights_from_artificial_intelligence_and_integrative_bioinformatics_analysis_docx/31800862）。