非小细胞肺癌放射抗性相关预后基因签名数据集

数据集获取（Figshare）

• 源站标题：Construction of a novel radioresistance-related signature for prediction of prognosis, immune microenvironment and anti-tumour drug sensitivity in non-small cell lung cancer
• DOI：10.6084/m9.figshare.28188708.v1
• 发布日期：2025-01-11
• 文件数：9 个
• 文件总体积：2.68 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Construction_of_a_novel_radioresistance-related_signature_for_prediction_of_prognosis_immune_microenvironment_and_anti-tumour_drug_sensitivity_in_non-small_cell_lung_cancer/28188708

命令行下载

curl -L -o "Supplementary Table 1.xlsx" "https://ndownloader.figshare.com/files/51611080"
curl -L -o "Supplementary Table 2.csv" "https://ndownloader.figshare.com/files/51611083"
curl -L -o "Supplementary Table 3.csv" "https://ndownloader.figshare.com/files/51611086"
curl -L -o "Supplementary Table 4.csv" "https://ndownloader.figshare.com/files/51611089"
curl -L -o "Supplementary Table 5.csv" "https://ndownloader.figshare.com/files/51611092"
curl -L -o "Supplementary Table 6.csv" "https://ndownloader.figshare.com/files/51611095"
curl -L -o "Supplementary Table 7.csv" "https://ndownloader.figshare.com/files/51611098"
curl -L -o "Supplementary Table 8.csv" "https://ndownloader.figshare.com/files/51611101"

文件清单

• Supplementary Table 1.xlsx（1.78 MB）
• Supplementary Table 2.csv（120.09 KB）
• Supplementary Table 3.csv（694 B）
• Supplementary Table 4.csv（120 B）
• Supplementary Table 5.csv（1.09 KB）
• Supplementary Table 6.csv（109 B）
• Supplementary Table 7.csv（410 B）
• Supplementary Table 8.csv（117 B）
• Supplementary_Figure.docx（781.86 KB）

文件总体积 2.68 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

Non-small cell lung cancer (NSCLC) is a fatal disease, and radioresistance is an important factor leading to treatment failure and disease progression. The objective of this research was to detect radioresistance-related genes (RRRGs) with prognostic value in NSCLC.
The weighted gene coexpression network analysis (WGCNA) and differentially expressed genes (DEGs) analysis were performed to identify RRRGs using expression profiles from TCGA and GEO databases. The least absolute shrinkage and selection operator (LASSO) regression and random survival forest (RSF) were used to screen for prognostically relevant RRRGs. Multivariate Cox regression was used to construct a risk score model. Then, Immune landscape and drug sensitivity were evaluated. The biological functions exerted by the key gene LBH were verified by in vitro experiments.
Ninety-nine RRRGs were screened by intersecting the results of DEGs and WGCNA, then 11 hub RRRGs associated with survival were identified using machine learning algorithms (LASSO and RSF). Subsequently, an eight-gene (APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2 and TSPAN2) risk score model was established and demonstrated to be an independent prognostic factor in NSCLC on the basis of Cox regression analysis. The immune landscape and sensitivity to anti-tumour drugs showed significant disparities between patients categorized into different risk score subgroups. In vitro experiments indicated that overexpression of LBH enhanced the radiosensitivity of A549 cells, and knockdown LBH reversed the cytotoxicity induced by X-rays.
Our study developed an eight-gene risk score model with potential clinical value that can be adopted for choice of drug treatment and prognostic prediction. Its clinical routine use may assist clinicians in selecting more rational practices for individuals, which is important for improving the prognosis of NSCLC patients. These findings also provide references for the development of potential therapeutic targets.

An eight-gene (APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2, and TSPAN2) risk score model that can predict the clinical prognosis of NSCLC patients was developed and validated.
NSCLC patients in t

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.28188708.v1（source: https://tandf.figshare.com/articles/dataset/Construction_of_a_novel_radioresistance-related_signature_for_prediction_of_prognosis_immune_microenvironment_and_anti-tumour_drug_sensitivity_in_non-small_cell_lung_cancer/28188708）。