PRR11-SKA2-miR301a/454双向转录单元过表达协同促进PI3K-AKT通路激活与肺癌进展

数据集获取

源站：https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE269696

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

Gene pairing is a highly conserved and special mode of eukaryotic gene organization, and critically implicated in development and diseases including cancer. We previously found that PRR11 and SKA2 constitute a classic head-to-head gene pair. Here, we further demonstrate that PRR11, SKA2, and its intronic miR301a and miR454 constitute a more exquisite bidirectional transcription unit that are overexpressed in various types of cancers. Functional studies using lung cancer as a model system reveal that co-overexpression of PRR11, SKA2, miR301a and miR454 together remarkably accelerates cell growth, cell cycle progression and cell motility in lung cancer cells, and promotes tumor growth in mouse models in vivo, whereas CRISPRi-mediated repression of the entire transcription unit inhibits these malignant phenotypes. Mechanistically, the four component genes do not display any additive or synergistic effect, but rather compensate for each other for robustly sustained activation of PI3K-AKT pathway, with PRR11 interacting with GRB2, and SKA2 with EGFR. Notably, miR301a and miR454 exert their oncogenic functions at least partially via repressing PTEN translation. Moreover, the transcription unit presents as a prominent prognostic meta-marker for lung cancer. Collectively, these findings demonstrate the essential and coordinated roles of PRR11-SKA2-miR301a/454 bidirectional transcription unit in lung cancer progression, highlighting its potential diagnostic and therapeutic values in cancers.

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。