肺腺癌分子亚型多组学表征与机器学习数据集

数据集获取（Figshare）

• 源站标题：Table 1_Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.xlsx
• DOI：10.3389/fimmu.2024.1497300.s001
• 发布日期：2024-11-28
• 文件数：1 个
• 文件总体积：130.30 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Multi-omics_characterization_and_machine_learning_of_lung_adenocarcinoma_molecular_subtypes_to_guide_precise_chemotherapy_and_immunotherapy_xlsx/27923763

命令行下载

curl -L -o "Table 1_Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.xlsx" "https://ndownloader.figshare.com/files/50857443"

文件清单

• Table 1_Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.xlsx（130.30 KB）

文件总体积 130.30 KB（源站 API 实测）。

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。

数据说明

BackgroundLung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-based classification system for LUAD is urgently needed to advance biological understanding.
MethodsData on clinical and pathological characteristics, genetic alterations, DNA methylation patterns, and the expression of mRNA, lncRNA, and microRNA, along with somatic mutations in LUAD patients, were gathered from the TCGA and GEO datasets. A computational workflow was utilized to merge multi-omics data from LUAD patients through 10 clustering techniques, which were paired with 10 machine learning methods to pinpoint detailed molecular subgroups and refine a prognostic risk model. The disparities in somatic mutations, copy number alterations, and immune cell infiltration between high- and low-risk groups were assessed. The effectiveness of immunotherapy in patients was evaluated through the TIDE and SubMap algorithms, supplemented by data from various immunotherapy groups. Furthermore, the Cancer Therapeutics Response Portal (CTRP) and the PRISM Repurposing dataset (PRISM) were employed to investigate new drug treatment approaches for LUAD. In the end, the role of SLC2A1 in tumor dynamics was examined using RT-PCR, immunohistochemistry, CCK-8, wound healing, and transwell tests.
ResultsBy employing multi-omics clustering, we discovered two unique cancer subtypes (CSs) linked to prognosis, with CS2 demonstrating a better outcome. A strong model made up of 17 genes was created using a random survival forest (RSF) method, which turned out to be an independent predictor of overall survival and showed reliable and impressive performance. The low-risk group not only had a better prognosis but also was more likely to display the “cold tumor” phenotype. On the other hand, individuals in the high-risk group showed a worse outlook and were more likely to respond positively to immunotherapy and six particular chemotherapy medications. Laboratory cell tests demonstrated that SLC2A1 is abundantly present in LUAD tissues and cells, greatly enhancing the proliferation and movement of LUAD cells.
ConclusionsThorough examination of mult

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2024.1497300.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Multi-omics_characterization_and_machine_learning_of_lung_adenocarcinoma_molecular_subtypes_to_guide_precise_chemotherapy_and_immunotherapy_xlsx/27923763）。