结合单细胞分析和分子对接技术构建结肠腺癌预后模型并揭示抑制素亚基βb作为新治疗靶点的数据集

数据集获取（Figshare）

• 源站标题：Table 2_Combining single-cell analysis and molecular docking techniques to construct a prognostic model for colon adenocarcinoma and uncovering inhibin subunit βb as a novel therapeutic target.docx
• DOI：10.3389/fimmu.2024.1524560.s002
• 发布日期：2025-01-09
• 文件数：1 个
• 文件总体积：600.42 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Combining_single-cell_analysis_and_molecular_docking_techniques_to_construct_a_prognostic_model_for_colon_adenocarcinoma_and_uncovering_inhibin_subunit_b_as_a_novel_therapeutic_target_docx/28170824

命令行下载

curl -L -o "Table 2_Combining single-cell analysis and molecular docking techniques to construct a prognostic model for colon adenocarcinoma and uncovering inhibi.docx" "https://ndownloader.figshare.com/files/51558224"

文件清单

• Table 2_Combining single-cell analysis and molecular docking techniques to construct a prognostic model for colon adenocarcinoma and uncovering inhibi.docx（600.42 KB）

文件总体积 600.42 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

BackgroundColon adenocarcinoma (COAD) is a malignancy with a high mortality rate and complex biological characteristics and heterogeneity, which poses challenges for clinical treatment. Anoikis is a type of programmed cell death that occurs when cells lose their attachment to the extracellular matrix (ECM), and it plays a crucial role in tumor metastasis. However, the specific biological link between anoikis and COAD, as well as its mechanisms in tumor progression, remains unclear, making it a potential new direction for therapeutic strategy research.
MethodsWe employed transcriptomic data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to pinpoint differentially expressed anoikis-related genes (ARGs) in COAD. Using Cox proportional hazards models and Lasso regression analysis, we developed a prognostic signature derived from these ARGs. We also investigated the roles and interactions of these genes in the tumor microenvironment by analyzing single-cell RNA sequencing data. Additionally, we employed molecular docking techniques to evaluate the potential of inhibin subunit beta B (INHBB) as therapeutic targets and to assess the binding affinity of candidate drugs. Finally, we used gene knockout techniques to silence the key gene INHBB and explored its biological functions in vitro.
ResultsIn our study, by analyzing the expression differences of ARGs, we successfully classified patients with COAD. Kaplan-Meier survival analysis demonstrated that patients with elevated risk scores experienced poorer prognosis, a finding that was confirmed in both the training and validation cohorts. Additionally, immune infiltration analysis revealed a notable increase in immune cell presence within the tumor microenvironment of high-risk patients. Molecular docking identified potential drug candidates with high binding affinity to INHBB, including risperidone. Furthermore, in vitro experiments with INHBB showed that downregulation of its expression in COAD cell lines significantly reduced cellular viability and migration capacity.
ConclusionIn summary, our research, based on the expression characteristics of ARGs, provides new insight

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2024.1524560.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Combining_single-cell_analysis_and_molecular_docking_techniques_to_construct_a_prognostic_model_for_colon_adenocarcinoma_and_uncovering_inhibin_subunit_b_as_a_novel_therapeutic_target_docx/28170824）。