肺腺癌中T细胞介导的细胞杀伤相关基因的预后特征

数据集获取（Figshare）

• 源站标题：Prognostic characteristics of T-cell mediated cell killing-related genes in lung adenocarcinoma
• DOI：10.6084/m9.figshare.24036616.v1
• 发布日期：2023-08-25
• 文件数：4 个
• 文件总体积：54.93 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Prognostic_characteristics_of_T-cell_mediated_cell_killing-related_genes_in_lung_adenocarcinoma/24036616

命令行下载

curl -L -o "Table S1.xlsx" "https://ndownloader.figshare.com/files/42147244"
curl -L -o "Table S2.xlsx" "https://ndownloader.figshare.com/files/42147247"
curl -L -o "Table S3.xlsx" "https://ndownloader.figshare.com/files/42147250"
curl -L -o "Table S4.xlsx" "https://ndownloader.figshare.com/files/42147253"

文件清单

• Table S1.xlsx（17.71 KB）
• Table S2.xlsx（13.87 KB）
• Table S3.xlsx（12.18 KB）
• Table S4.xlsx（11.17 KB）

文件总体积 54.93 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

Constituted by various heterogeneous cells, the tumor microenvironment (TME) is capable of promoting tumor proliferation, invasion, and metastasis through extensive crosstalk. The pivotal factor influencing the survival time of patients and their response to immunotherapy lies in the intratumoral immune environment. We obtained 112 differential genes related to T cell-mediated tumor killing in LUAD by employing bioinformatics analysis on the basis of the TCGA and TISIDB databases. Then the 6-gene prognostic risk score model (CA9, OIP5, TIMP1, SEC11C, FURIN, and TLR10) was constructed by conducting univariate LASSO as well as multivariate Cox regression analyses. The median risk score was taken as the threshold to classify the samples into two groups. Survival analysis revealed that the low-risk group exhibited a more favorable prognosis. Subsequently, the Cox regression analysis combined with clinical information (age, gender, and pathological stage) and the risk score of LUAD patients demonstrated the potential of this model as an independent prognostic factor. The nomogram established based on clinical information and a risk score in combination with the calibration curve indicated that this model had good predictive ability. Notable enrichment of the differential genes from the high- and low-risk groups was discovered in immune-associated processes or pathways, as shown by the GO and KEGG enrichment analyses. The combined use of single-sample gene enrichment analysis (ssGSEA) and immunophenoscore (IPS) demonstrated heightened immune infiltration and IPS scores in the low-risk group, indicating that immunotherapy was likely to show good efficacy in patients from this group. To sum up, the prognostic model of LUAD constructed based on T-cell-mediated cell killing-related genes was not only capable of screening the prognosis of LUAD patients but was also used for screening those LUAD patients with high sensitivity to immunotherapy. Our study offered novel insights into the clinical treatment and prognostic prediction of LUAD patients.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.24036616.v1（source: https://tandf.figshare.com/articles/dataset/Prognostic_characteristics_of_T-cell_mediated_cell_killing-related_genes_in_lung_adenocarcinoma/24036616）。