背景:肝细胞癌(HCC)的高度异质性导致临床分期相似的患者对相同治疗的反应和预后不同。
目标:因此,研究HCC肿瘤异质性与治疗反应和预后的关系势在必行。
结果:首先,我们下载了scRNA-seq,批量RNA-seq,以及来自TCGA和GEO数据库的临床数据。我们进行了质量控制,使用SCTransform进行归一化,使用PCA降维,使用Harmony去除批量效果,使用UMAP降维,和scRNA-seq数据上基于细胞注释的标记基因。我们认识到肿瘤细胞,已鉴定的肿瘤相关基因(TRGs),并进行了细胞通讯分析。接下来,我们使用单变量Cox开发了一个预后模型,拉索,和多变量Cox分析。使用生存分析评估签名,ROC曲线,C指数,和列线图。最后,我们研究了特征在HCC的预后和免疫治疗反应方面的可预测性,评估了多种临床治疗药物,并使用qRT-PCR分析来验证预后TRGs的mRNA表达水平。
结论:总而言之,本研究阐述了肿瘤细胞异质性对HCC治疗结果和预后预测的影响。这个,反过来,增强了TNM分期系统的预测能力,并为HCC的预后评估和治疗提供了新的观点。
The highly heterogeneous nature of hepatocellular carcinoma (HCC) results in different responses and prognoses to the same treatment in patients with similar clinical stages.
Thus, it is imperative to investigate the association between HCC tumor heterogeneity and treatment response and prognosis.
At first, we downloaded scRNA-seq, bulk RNA-seq, and clinical data from TCGA and GEO databases. We conducted quality control, normalization using SCTransform, dimensionality reduction using PCA, batch effect removal using Harmony, dimensionality reduction using UMAP, and cell annotation-based marker genes on the scRNA-seq data. We recognized tumor cells, identified tumor-related genes (TRGs), and performed cell communication analysis. Next, we developed a prognostic model using univariable Cox, LASSO, and multivariate Cox analyses. The signature was evaluated using survival analysis, ROC curves, C-index, and nomogram. Last, we studied the predictability of the signature in terms of prognosis and immunotherapeutic response for HCC, assessed a variety of drugs for clinical treatment, and used the qRT-PCR analysis to validate the mRNA expression levels of prognostic TRGs.
To conclude, this study expounded upon the influence of tumor cell heterogeneity on the prediction of treatment outcomes and prognosis in HCC. This, in turn, enhances the predictive ability of the TNM staging system and furnishes novel perspectives on the prognostic assessment and therapy of HCC.