吉非替尼代谢相关lncRNA预测非小细胞肺癌预后、肿瘤微环境和药物敏感性数据集

数据集获取（Figshare）

• 源站标题：DataSheet_1_Identified Gefitinib Metabolism-Related lncRNAs can be Applied to Predict Prognosis, Tumor Microenvironment, and Drug Sensitivity in Non-Small Cell Lung Cancer.pdf
• DOI：10.3389/fonc.2022.939021.s001
• 发布日期：2022-07-01
• 文件数：1 个
• 文件总体积：2.46 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Identified_Gefitinib_Metabolism-Related_lncRNAs_can_be_Applied_to_Predict_Prognosis_Tumor_Microenvironment_and_Drug_Sensitivity_in_Non-Small_Cell_Lung_Cancer_pdf/20206976

命令行下载

curl -L -o "DataSheet_1_Identified Gefitinib Metabolism-Related lncRNAs can be Applied to Predict Prognosis, Tumor Microenvironment, and Drug Sensitivity in Non-S.pdf" "https://ndownloader.figshare.com/files/36118184"

文件清单

• DataSheet_1_Identified Gefitinib Metabolism-Related lncRNAs can be Applied to Predict Prognosis, Tumor Microenvironment, and Drug Sensitivity in Non-S.pdf（2.46 MB）

文件总体积 2.46 MB（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

Gefitinib has shown promising efficacy in the treatment of patients with locally advanced or metastatic EGFR-mutated non-small cell lung cancer (NSCLC). Molecular biomarkers for gefitinib metabolism-related lncRNAs have not yet been elucidated. Here, we downloaded relevant genes and matched them to relevant lncRNAs. We then used univariate, LASSO, and multivariate regression to screen for significant genes to construct prognostic models. We investigated TME and drug sensitivity by risk score data. All lncRNAs with differential expression were selected for GO/KEGG analysis. Imvigor210 cohort was used to validate the value of the prognostic model. Finally, we performed a stemness indices difference analysis. lncRNA-constructed prognostic models were significant in the high-risk and low-risk subgroups. Immune pathways were identified in both groups at low risk. The higher the risk score the greater the value of exclusion, MDSC, and CAF. PRRophetic algorithm screened a total of 58 compounds. In conclusion, the prognostic model we constructed can accurately predict OS in NSCLC patients. Two groups of low-risk immune pathways are beneficial to patients. Gefitinib metabolism was again validated to be related to cytochrome P450 and lipid metabolism. Finally, drugs that might be used to treat NSCLC patients were screened.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2022.939021.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Identified_Gefitinib_Metabolism-Related_lncRNAs_can_be_Applied_to_Predict_Prognosis_Tumor_Microenvironment_and_Drug_Sensitivity_in_Non-Small_Cell_Lung_Cancer_pdf/20206976）。