LKB1缺失通过上调NNMT使肺癌细胞对PRMT5抑制敏感

数据集获取

源站：https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE279105

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

Cancer is a major obstacle to health of modern people, in which lung cancer has a fairly high incidence and mortality rate, according to the results of the WHO IARC agency in 2021. Lung cancer is known for its hard treatment because of its genetic complexity and heterogeneity, especially the KRAS-mutant and LKB1-lost NSCLC. Here we show that LKB1 inactivation upregulates NNMT through SIK1/2-CRTC2 axis, which decreases the ratio of SAM/SAH. Low ratio of SAM/SAH predicts high sensitivity to PRMT5 inhibition in NSCLC independent of MTA abundance, which was previously broadcast negative to PRMT5 activity in MTAP-absent cancer cells. What’s more, PRMT5 inhibition induces senescence in LKB1-mutant NSCLC cells by upregulating P21. Although there has been quite researches on the mechanism of metabolic remodeling of K/L lung cancer, the treatment methods for this type of lung cancer are still relatively limited. So this project attempts to find the unique metabolic remodeling mode of K/L mutant NSCLC and give specific treatment options.

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。