体内 CAR 基因治疗的前景与潜在陷阱
Promises and potential pitfalls of in vivo CAR gene therapy.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Discovering biomarkers associated with infiltration of CD8(+) T cells and tumor-associated fibrosis in colon adenocarcinoma using single-cell RNA sequencing and gene co-expression network.
Discovering biomarkers associated with infiltration of CD8(+) T cells and tumor-associated fibrosis in colon adenocarcinoma using single-cell RNA sequencing and gene co-expression network.
分数与星级只用于站内排序 —— 不代表疗效、安全性或个人适用性。
本研究确定了三个与 CD8+T 细胞及 COAD 预后相关的关键基因,为 COAD 的诊断和治疗提供了新的预后生物标志物。
结直肠腺癌(COAD)是一种常见的恶性肿瘤,死亡率高。在肿瘤微环境中,CD8+ T细胞在人体抗肿瘤免疫反应中发挥关键作用。纤维化直接和间接影响肿瘤免疫治疗的反应。然而,与肿瘤相关纤维化和CD8+ T细胞浸润相关的调控基因的意义仍不确定。因此,迫切需要识别具有预后价值的生物标志物,并阐明CD8+ T细胞和肿瘤相关纤维化的确切作用。
我们对来自 GEO 数据库的 COAD 样本进行了单细胞转录组分析。为了评估 COAD 样本中的免疫浸润,我们使用了 CIBERSORT 和 ESTIMATE。此外,我们分析了 CD8 + T 细胞与免疫浸润之间的相关性。为了分析 COAD 表达的定量免疫细胞组成数据,我们进行了加权基因共表达网络分析并使用了去卷积算法。这些分析的数据来自 GEO 数据库。我们使用单因素 Cox 回归和 LASSO 分析来构建预后模型。通过 Kaplan-Meier 分析评估预测模型,并创建了生存预测列线图。此外,我们分析了预后模型与化疗药物敏感性之间的相关性。为了评估 hub 基因的表达,我们采用了免疫组织化学、实时 PCR 和 western blot 技术。
单细胞转录组分析表明,COAD肿瘤样本中CD8 + T细胞的比例更高。利用GEO数据库进行的WGCNA和去卷积分析进一步证实了COAD与CD8 + T细胞之间的关联。蛋白质-蛋白质相互作用网络分析揭示了三个枢纽基因:LARS2、SEZ6L2和SOX7。随后利用LASSO和单因素COX回归建立了一个包含这三个基因的预测模型。其中两个枢纽基因(LARS2和SEZ6L2)在COAD细胞系和组织中被发现上调,而SOX7则被观察到下调。该预后模型显示出与CD8 + T细胞的显著关联,表明这些基因可作为治疗COAD的潜在生物标志物和基因治疗靶点。
Colorectal adenocarcinoma (COAD) is a prevalent malignant tumor associated with a high mortality rate. Within the tumor microenvironment, CD8 + T cells play a pivotal role in the anti-tumor immune response within the human body. Fibrosis directly and indirectly affects the therapeutic response of tumor immunotherapy. However, the significance of regulatory genes associated with tumor-associated fibrosis and CD8 + T cell infiltration remains uncertain. Therefore, it is imperative to identify biomarkers with prognostic value and elucidate the precise role of CD8 + T cells and tumor-associated fibrosis.
We performed a single-cell transcriptome analysis of COAD samples from the GEO database. To evaluate immune infiltration in COAD samples, we utilized CIBERSORT and ESTIMATE. Furthermore, we analyzed the correlation between CD8 + T cells and immune infiltration. To analyze COAD expression's quantitative immune cell composition data, we conducted a Weighted Gene Correlation Network Analysis and utilized a deconvolution algorithm. The data for these analyses were obtained from the GEO database. We utilized univariate Cox regression and LASSO analysis to create a prognostic model. The predictive model was assessed through Kaplan-Meier analysis, and a survival prediction nomogram was created. Additionally, we analyzed the correlation between the prognostic model and chemotherapy drug sensitivity. To estimate the expression of hub genes, we employed immunohistochemistry, real-time PCR, and western blot techniques.
Single-cell transcriptome analysis has indicated a higher prevalence of CD8 + T cells in COAD tumor samples. The connection between COAD and CD8 + T cells was further confirmed by WGCNA and deconvolution analysis using the GEO database. The Protein-Protein Interaction network analysis revealed three hub genes: LARS2 , SEZ6L2 , and SOX7 . A predictive model was subsequently created using LASSO and univariate COX regression, which included these three genes. Two of these hub genes ( LARS2 and SEZ6L2 ) were found to be upregulated in COAD cell lines and tissues, while SOX7 was observed to be downregulated. The prognostic model demonstrated a significant association with CD8 + T cells, suggesting that these genes could serve as potential biomarkers and targets for gene therapy in treating COAD.
This study has identified three key genes associated with CD8 + T cells and the prognosis of COAD, providing new prognostic biomarkers for diagnosing and treating COAD.
在 PubMed 查看 → 出版商原文(DOI) 全文 PDF(PMC)· 可下载 治疗专题与资料阅读指南 资料来源与翻译说明 报告译文或资料问题 →
MEMBER ACCOUNT
登录成功会直接打开下一页。