GSE296665：小细胞肺癌中异常活跃的可变RNA剪接促进免疫逃逸 [RNA-Seq]

数据集获取

源站：https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE296665

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

The mechanisms driving cancer immune escape remain incompletely understood. In this study, we reveal that alternative RNA splicing is hyperactivated in small cell lung cancer (SCLC), particularly in the classic neuroendocrine subtypes, and is associated with poor prognosis. ASCL1, a defining transcription factor of SCLC, directly binds to and upregulates the expression of most splicing factors, including PRMT5. Elevated levels of these splicing factors correlate with an increase in skipped exon (SE) and mutually exclusive exon (MXE) events. Notably, the skipped exons in SCLC exhibit strong immunogenicity and are associated with immune suppression. Genetic or pharmacological inhibition of PRMT5 significantly suppresses SCLC progression both in vitro and in vivo. Transcriptome analyses demonstrate that PRMT5 deficiency reduces the prevalence of SEs and MXEs and thus maintains exons with high immunogenicity. Consistent with these findings, GSK3368715, a clinically safe PRMT5 inhibitor, synergizes with anti-PD1 antibody to treat SCLC by repressing alternative RNA splicing and enhancing immune responses. Taken together, we propose that hyperactive alternative RNA splicing, which excludes exons with strong immunogenicity, serves as a key mechanism for immune escape in SCLC, which highlights a potential therapeutic vulnerability for this aggressive disease.

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。