乳腺癌免疫相关基因与细胞外基质数据表

数据集获取（Figshare）

• 源站标题：DataSheet_1_Combining Immune-Related Genes For Delineating the Extracellular Matrix and Predicting Hormone Therapy and Neoadjuvant Chemotherapy Benefits In Breast Cancer.xlsx
• DOI：10.3389/fimmu.2022.888339.s001
• 发布日期：2022-07-14
• 文件数：1 个
• 文件总体积：3.58 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Combining_Immune-Related_Genes_For_Delineating_the_Extracellular_Matrix_and_Predicting_Hormone_Therapy_and_Neoadjuvant_Chemotherapy_Benefits_In_Breast_Cancer_xlsx/20310774

命令行下载

curl -L -o "DataSheet_1_Combining Immune-Related Genes For Delineating the Extracellular Matrix and Predicting Hormone Therapy and Neoadjuvant Chemotherapy Benefi.xlsx" "https://ndownloader.figshare.com/files/36277095"

文件清单

• DataSheet_1_Combining Immune-Related Genes For Delineating the Extracellular Matrix and Predicting Hormone Therapy and Neoadjuvant Chemotherapy Benefi.xlsx（3.58 MB）

文件总体积 3.58 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

Breast cancer (BC) is the most prevalent cancer in women worldwide. A systematic approach to BC treatment, comprising adjuvant and neoadjuvant chemotherapy (NAC), as well as hormone therapy, forms the foundation of the disease’s therapeutic strategy. The extracellular matrix (ECM) is a dynamic network that exerts a robust biological effect on the tumor microenvironment (TME), and it is highly regulated by several immunological components, such as chemokines and cytokines. It has been established that the ECM promotes the development of an immunosuppressive TME. Therefore, while analyzing the ECM of BC, immune-related genes must be considered. In this study, we used bioinformatic approaches to identify the most valuable ECM-related immune genes. We used weighted gene co-expression network analysis to identify the immune-related genes that potentially regulate the ECM and then combined them with the original ECM-related gene set for further analysis. Least absolute shrinkage and selection operator (LASSO) regression and SurvivalRandomForest were used to narrow our ECM-related gene list and establish an ECM index (ECMI) to better delineate the ECM signature. We stratified BC patients into ECMI high and low groups and evaluated their clinical, biological, and genomic characteristics. We found that the ECMI is highly correlated with long-term BC survival. In terms of the biological process, this index is positively associated with the cell cycle, DNA damage repair, and homologous recombination but negatively with processes involved in angiogenesis and epithelial–mesenchymal transition. Furthermore, the tumor mutational burden, copy number variation, and DNA methylation levels were found to be related to the ECMI. In the Metabric cohort, we demonstrated that hormone therapy is more effective in patients with a low ECMI. Additionally, differentially expressed genes from the ECM-related gene list were extracted from patients with a pathologic complete response (pCR) to NAC and with residual disease (RD) to construct a neural network model for predicting the chance of achieving pCR individually. Finally, we performed qRT-PCR to validate our findings and demonstrate the i

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2022.888339.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Combining_Immune-Related_Genes_For_Delineating_the_Extracellular_Matrix_and_Predicting_Hormone_Therapy_and_Neoadjuvant_Chemotherapy_Benefits_In_Breast_Cancer_xlsx/20310774）。