长读长测序与蛋白质组学揭示多原发肺癌血液转录组与蛋白表达谱

数据集获取

源站：https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE311391

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

Multiple primary cancers (MPC) refer to the occurrence of two or more independent primary malignant tumors in the same patient, either simultaneously or metachronously. Their clinical diagnosis and differential diagnosis are challenging, treatment strategies are complex, and the incidence has been increasing in recent years. However, there is still a lack of potential biomarkers that can be used for early identification and prognosis assessment, which has become a key bottleneck in current research. This study employed Oxford Nanopore Technologies (ONT) long-read transcriptome sequencing and data-independent acquisition (DIA) proteomics to perform integrated multi-omics analysis of Whole blood samples from patients with only primary lung cancer (OPLC), lung cancer with MPC, and healthy controls (HC), systematically characterizing the molecular features of MPC at both transcript and protein levels. The results showed that MPC patients exhibited significantly increased transcript complexity, with the numbers of differentially expressed genes (DEGs), differentially expressed transcripts (DETs), and differential transcript usage (DTU) events being substantially higher than those in OPLC and HC groups. Multiple genes displayed rich isoform diversity. Functional enrichment analysis indicated that MPC-specific molecules were significantly enriched in immune- and inflammation-related pathways such as NF-κB, NOD-like receptor, Toll-like receptor, and TNF signaling. By integrating gene, transcript, and protein expression profiles, several core molecules (e.g., ATP6AP2, APMAP, CIAO2A, and ITGB2) with consistent expression across multiple regulatory levels were identified, all showing significant and consistent alterations in MPC. This study reveals the molecular characteristics of transcript isoform complexity in the peripheral blood of MPC patients through long-read sequencing combined with proteomics, providing important theoretical insights for understanding the mechanisms

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。