Table 1_血浆外泌体lncRNA相关特征定义肝细胞癌分子亚型并预测生存与治疗反应

数据集获取（Figshare）

• 源站标题：Table 1_Plasma exosomal lncRNA-related signatures define molecular subtypes and predict survival and treatment response in hepatocellular carcinoma.docx
• DOI：10.3389/fimmu.2025.1663943.s001
• 发布日期：2025-10-15
• 文件数：1 个
• 文件总体积：15.82 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Plasma_exosomal_lncRNA-related_signatures_define_molecular_subtypes_and_predict_survival_and_treatment_response_in_hepatocellular_carcinoma_docx/30361789

命令行下载

curl -L -o "Table 1_Plasma exosomal lncRNA-related signatures define molecular subtypes and predict survival and treatment response in hepatocellular carcinoma.docx" "https://ndownloader.figshare.com/files/58749154"

文件清单

• Table 1_Plasma exosomal lncRNA-related signatures define molecular subtypes and predict survival and treatment response in hepatocellular carcinoma.docx（15.82 KB）

文件总体积 15.82 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

BackgroundHepatocellular carcinoma (HCC) faces challenges in early diagnosis, prognosis, and treatment stratification due to molecular heterogeneity. This study aimed to establish a plasma exosomal long non-coding RNA (lncRNA)-based framework for molecular classification, prognostication, and therapeutic guidance in HCC.
MethodsThe transcriptomic data from 230 plasma exosomes and 831 HCC tissues were integrated. A competitive endogenous RNA (ceRNA) network was constructed via the miRcode, miRTarBase, TargetScan, and miRDB databases to define exosome-related genes (ERGs). Unsupervised consensus clustering was used to stratify HCC patients on the basis of ERG profiles. Prognostic models were developed and optimized via 10 machine learning algorithms with 10-fold cross-validation. Treatment responses were predicted via the SubMap, TIDE, and oncoPredict algorithms. RT-qPCR experiments were conducted to validate the expression of model genes.
ResultsWe identified 22 dysregulated plasma exosomal lncRNAs in HCC. The upregulated lncRNAs formed a ceRNA network regulating 61 ERGs and were significantly enriched in cell cycle regulation, TGF-β signaling, the p53 pathway, and ferroptosis. ERG expression stratified HCC into three subtypes (C1–C3). The C3 subtype exhibited the poorest overall survival, advanced grade and stage, an immunosuppressive microenvironment (increased Treg infiltration, elevated PD-L1/CTLA4 expression, highest TIDE score), and hyperactivation of proliferation (MYC, E2F targets) and metabolic pathways (glycolysis, mTORC1). A random survival forest-derived 6-gene risk score (G6PD, KIF20A, NDRG1, ADH1C, RECQL4, MCM4) demonstrated high prognostic accuracy. High-risk patients presented increased TP53/TTN mutations and increased tumor mutational burdens. Risk model analysis predicted differential treatment responses: low-risk patients exhibited superior anti-PD-1 immunotherapy responses, whereas high-risk patients showed increased sensitivity to DNA-damaging agents (e.g., the Wee1 inhibitor MK-1775) and sorafenib. Experimental validation confirmed consistent dysregulation of the six-gene signature (G6PD, KIF20A, NDRG1, ADH1C, RECQL4, MCM4) in HCC cell lines

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2025.1663943.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Plasma_exosomal_lncRNA-related_signatures_define_molecular_subtypes_and_predict_survival_and_treatment_response_in_hepatocellular_carcinoma_docx/30361789）。