上皮miRNA上调与进展性支气管癌前病变免疫逃逸相关[PCGA_miRNA_BX]

数据集获取

源站：https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE313145

标准获取流程：

1. 访问源站链接，注册/登录账号；
2. 进入数据集详情页，按页面指引申请权限或直接下载；
3. 部分平台提供 SDK，例如：
   • OpenDataLab: pip install openxlab 后 openxlab.dataset.get(...)
   • ModelScope: pip install modelscope 后 modelscope download
   • 天池: 登录后页面提供直链下载
     以源站「数据下载/使用文档」页面为准。

数据说明

Bronchial premalignant lesions (PMLs), precursors of lung squamous cell carcinoma, have distinct molecular subtypes. The proliferative subtype, enriched with bronchial dysplasia, had decreased expression of an antigen processing/presentation gene co-expression module in progressive/persistent versus regressive PMLs, suggesting a functional impact of these genes on immune evasion. Here, we performed miRNA sequencing, miRNA in situ hybridization (ISH), and spatial proteomics of bronchial biopsies from patients at high risk for lung cancer. A miRNA-gene network analysis identified hsa-miR-149-5p as a potential regulator of the antigen presentation gene module. Staining on adjacent biopsy tissue showed that hsa-miR-149-5p was predominantly expressed in the epithelium and up-regulated in progressive/persistent proliferative lesions. Targets of this miRNA, the transcriptional coactivator of MHC-I gene expression, NLRC5, and the genes it regulates were down-regulated in these lesions. Decreased NLRC5 expression reduced both IFN-gamma induced MHC-I surface expression and CD8 T cell cytotoxicity in lung squamous cancer cells. In PMLs, basal cells with high levels of NLRC5 were in close spatial proximity to CD8 T cells, suggesting that these cells exhibit increased functional MHC-I gene expression in vivo. These findings indicate a functional role for hsa-miR-149-5p in PML progression/persistence and suggest this axis as a potential therapeutic target for PML immunomodulation.

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。