Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jie Fang Road 1095, Wuhan, Hubei, China.
J Cancer Res Clin Oncol. 2023 Nov;149(17):15737-15762. doi: 10.1007/s00432-023-05316-7. Epub 2023 Sep 5.
The primary objective of this study was to construct competing endogenous RNA (ceRNA) networks and evaluate the prognostic significance of tumor-infiltrating immune cells (TIICs) and key biomarkers within the ceRNA networks in colon adenocarcinoma (COAD) patients.
Comprehensive bioinformatics tools were used to screen differentially expressed genes (DEGs), miRNAs (DEMs), and lncRNAs (DELs) related to COAD, leading to the creation of ceRNA networks. The CIBERSORT technique was employed to assess the significance of TIICs in COAD, and an immune-related prognosis prediction model was subsequently developed. Co-expression analyses were conducted to determine the relationship between key genes in ceRNA networks and immunologically significant TIICs. The study also utilized 5 GEO datasets and web-based databases to externally validate the findings.
The study revealed a statistically significant relationship between key hub genes and immune cells, as determined through co-expression analysis. Two hub regulators (SOX12 and H19) demonstrated significant prognostic value in the ceRNA-related prognostic model, and their elevated expression levels were verified across multiple CRC cell lines. Additionally, the knockdown of SOX12 led to a suppression of proliferation, migration, and invasion in colon cancer cells.
Through the construction of ceRNA networks and evaluation of TIICs, the study successfully established two risk score models and nomograms. These models serve as valuable tools for understanding the molecular processes and predicting the prognosis of COAD patients. Further validation of hub regulators SOX12 and H19 substantiates their potential role as key biomarkers in COAD.
本研究的主要目的是构建竞争性内源 RNA（ceRNA）网络，并评估 ceRNA 网络内的肿瘤浸润免疫细胞（TIICs）和关键生物标志物在结肠腺癌（COAD）患者中的预后意义。
综合生物信息学工具用于筛选与 COAD 相关的差异表达基因（DEGs）、miRNAs（DEMs）和长链非编码 RNA（DELs），从而构建 ceRNA 网络。使用 CIBERSORT 技术评估 TIICs 在 COAD 中的意义，并随后开发免疫相关预后预测模型。进行共表达分析以确定 ceRNA 网络中的关键基因与免疫相关 TIICs 之间的关系。该研究还利用了 5 个 GEO 数据集和基于网络的数据库来验证研究结果。
通过共表达分析，研究揭示了关键枢纽基因与免疫细胞之间存在统计学显著关系。两个枢纽调节剂（SOX12 和 H19）在 ceRNA 相关预后模型中表现出显著的预后价值，并且在多个 CRC 细胞系中验证了它们的高表达水平。此外，SOX12 的敲低导致结肠癌细胞的增殖、迁移和侵袭受到抑制。
通过构建 ceRNA 网络和评估 TIICs，本研究成功建立了两个风险评分模型和列线图。这些模型为理解 COAD 患者的分子过程和预测预后提供了有价值的工具。对枢纽调节剂 SOX12 和 H19 的进一步验证证实了它们作为 COAD 关键生物标志物的潜在作用。
