细胞间取向在体外模拟肺癌特征中的作用

数据集获取

源站：https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE241845

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

To better understand and develop treatments for lung cancer, it is important to have reliable and physiologically relevant culture models. Traditional methods of growing lung cancer cells in two-dimensional monolayers have limitations in mimicking the complex architecture and microenvironment of lung tumors in vivo, limiting their value as reliably informative disease models. In this study, we introduce a new cell culture platform called “tumoroids,” which involves growing HCC827 lung cancer cells in three-dimensional configurations for more accurately predicting how cell-cell orientation and signaling affects the cancer-driving properties of lung cancer. By comparing transcriptional profiles of HCC827 cells grown as tumoroids versus the same cells grown in two-dimensional monolayers, we investigate how cell-cell orientation and signaling impact the cancer-driving properties of lung cancer. We examine key features associated with cancer progression, such as epithelial mesenchymal transition, replicative ability, and induction of angiogenesis. Additionally, we assess the functional characteristics of the 3D tumoroid culture system by subjecting the cells to irradiation. Through comparing the gene expression profiles of the 3D tumoroid cultures with those of primary human lung adenocarcinoma, we find that the 3D tumoroid cultures closely resemble the characteristics of primary human lung cancer. This suggests that the 3D tumoroid culture platform can serve as a valuable in vitro model for studying lung cancer, offering greater clinical relevance compared to traditional 2D cultures. Overall, this study highlights the importance of using advanced culture models like 3D tumoroids to improve our understanding of lung cancer and facilitate the development of effective treatments.

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。