铁死亡相关长非编码RNA特征预测头颈部鳞状细胞癌预后的补充数据表1

数据集获取（Figshare）

• 源站标题：DataSheet1_Ferroptosis-Related Long Non-Coding RNA Signature Contributes to the Prediction of Prognosis Outcomes in Head and Neck Squamous Cell Carcinomas.PDF
• DOI：10.3389/fgene.2021.785839.s001
• 发布日期：2021-12-17
• 文件数：1 个
• 文件总体积：538.67 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet1_Ferroptosis-Related_Long_Non-Coding_RNA_Signature_Contributes_to_the_Prediction_of_Prognosis_Outcomes_in_Head_and_Neck_Squamous_Cell_Carcinomas_PDF/17254970

命令行下载

curl -L -o "DataSheet1_Ferroptosis-Related Long Non-Coding RNA Signature Contributes to the Prediction of Prognosis Outcomes in Head and Neck Squamous Cell Carcin.PDF" "https://ndownloader.figshare.com/files/31879073"

文件清单

• DataSheet1_Ferroptosis-Related Long Non-Coding RNA Signature Contributes to the Prediction of Prognosis Outcomes in Head and Neck Squamous Cell Carcin.PDF（538.67 KB）

文件总体积 538.67 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

Background: Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor, which makes the prognosis prediction challenging. Ferroptosis is an iron-dependent form of non-apoptotic regulated cell death, which could affect cancer development. However, the prognostic value of ferroptosis-related long non-coding RNA (lncRNA) in HNSCC is still limited.
Methods: In the current study, we employed the DESeq2 method to characterize the differentially expressed ferroptosis-related genes (FEGs) between cancer and normal samples. Next, the FEG-related lncRNAs (FElncRNAs) were identified using Spearman’s correlation analysis and multiple permutation hypotheses. Subsequently, LASSO and stepwise multivariate Cox regression analyses were undertaken to recognize the prognosis-related FElncRNA signature (PFLS) and risk scores.
Results: Herein, we first identified 60 dysregulated FEGs and their co-expressed FElncRNAs in HNSCC. Then, we recognized a set of six FElncRNAs PFLS (SLCO4A1-AS1, C1RL-AS1, PCED1B-AS1, HOXB-AS3, MIR9-3HG, and SFTA1P) for predicting patients’ prognostic risks and survival outcomes. We also assessed the efficiency of PFLS in the test set and an external validation cohort. Further parsing of the tumor immune microenvironment showed the PFLS was closely associated with immune cell infiltration abundances. Notably, the low-risk group of the PFLS showed a higher MHC score and cytolytic activity (CYT) score than the high-risk group, implying the low-risk group may have greater tumor surveillance and killing ability. In addition, we observed that the expression levels of two immune checkpoints (ICPs), i.e., programmed cell death protein 1 (PD-1) and programmed cell death 1 ligand 1 (PD-L1), showed significant associations with patients’ risk score, prompting the role of the PFLS in ICP blockade therapy. Finally, we also constructed a drug–PFLS network to reinforce the clinical utilities of the PFLS.
Conclusion: In summary, our study indicated that FElncRNAs played an important role in HNSCC survival prediction. Identification of PFLS will contribute to the development of novel anticancer therapeutic strategies.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fgene.2021.785839.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet1_Ferroptosis-Related_Long_Non-Coding_RNA_Signature_Contributes_to_the_Prediction_of_Prognosis_Outcomes_in_Head_and_Neck_Squamous_Cell_Carcinomas_PDF/17254970）。