Department of Hepatology, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
Department of Hepatobiliary Surgery, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
Front Immunol. 2024 Oct 28;15:1414121. doi: 10.3389/fimmu.2024.1414121. eCollection 2024.
Hepatocellular carcinoma (HCC) patients exhibiting portal vein tumor thrombosis (PVTT) face a high risk of rapid malignant progression and poor outcomes, with this issue being compounded by a lack of effective treatment options. The integration of bulk RNA-sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) datasets focused on samples from HCC patients with PVTT has the potential to yield unprecedented insight into the dynamic changes in the tumor microenvironment (TME) and associated immunological characteristics in these patients, providing an invaluable tool for the reliable prediction of disease progression and treatment responses.
scRNA-seq data from both primary tumor (PT) and PVTT cells were downloaded from the Gene Expression Omnibus (GEO) database, while the International Cancer Genome Consortium (ICGC) and Cancer Genome Atlas (TCGA) databases were used to access bulk RNA-seq datasets. scRNA-seq, clustering, GSVA enrichment, mutational profiling, and predictive immunotherapeutic treatment analyses were conducted using these data with the goal of systematically assessing the heterogeneity of PT and PVTT cells and establishing a model capable of predicting immunotherapeutic and prognostic outcomes in patients with HCC.
These analyses revealed that PVTT cells exhibited patterns of tumor proliferation, stromal activation, and low levels of immune cell infiltration, presenting with immune desert and immune rejection-like phenotypes. PT cells, in contrast, were found to exhibit a pattern of immunoinflammatory activity. Core PVTT-associated genes were clustered into three patterns consistent with the tumor immune rejection and immune desert phenotypes. An established clustering model was capable of predicting tumor inflammatory stage, subtype, TME stromal activity, and patient outcomes. PVTT signature genes were further used to establish a risk model, with the risk scores derived from this model providing a tool to evaluate patient clinicopathological features including clinical stage, tumor differentiation, histological subtype, microsatellite instability status, and tumor mutational burden. These risk scores were also able to serve as an independent predictor of patient survival outcomes, responses to adjuvant chemotherapy, and responses to immunotherapy. experiments were used to partially validate the biological prediction results.
These results offer new insight into the biological and immunological landscape of PVTT in HCC patients, By utilizing individual patient risk scores, providing an opportunity to guide more effective immunotherapeutic interventional efforts.
肝细胞癌（HCC）患者伴有门静脉癌栓（PVTT），恶性进展迅速，预后较差，且治疗选择有限。整合 bulk RNA-seq 和单细胞 RNA-seq 数据集，聚焦 HCC 伴有 PVTT 的患者样本，有可能深入了解肿瘤微环境（TME）的动态变化，以及这些患者的相关免疫特征，为可靠预测疾病进展和治疗反应提供了宝贵的工具。
从基因表达综合数据库（GEO）下载原发性肿瘤（PT）和 PVTT 细胞的 scRNA-seq 数据，从国际癌症基因组联盟（ICGC）和癌症基因组图谱（TCGA）数据库获取 bulk RNA-seq 数据集。使用这些数据进行 scRNA-seq、聚类、GSVA 富集分析、突变分析和预测性免疫治疗分析，旨在系统评估 PT 和 PVTT 细胞的异质性，并建立能够预测 HCC 患者免疫治疗和预后结局的模型。
分析表明，PVTT 细胞表现出肿瘤增殖、基质激活和免疫细胞浸润水平低的特征，呈现出免疫荒漠和免疫排斥样表型。相比之下，PT 细胞表现出免疫炎症活性模式。核心 PVTT 相关基因聚类为三种模式，与肿瘤免疫排斥和免疫荒漠表型一致。已建立的聚类模型能够预测肿瘤炎症分期、亚型、TME 基质活性和患者结局。PVTT 特征基因进一步用于建立风险模型，该模型的风险评分可用于评估患者的临床病理特征，包括临床分期、肿瘤分化、组织学亚型、微卫星不稳定性状态和肿瘤突变负荷。这些风险评分还可以作为患者生存结局、辅助化疗反应和免疫治疗反应的独立预测因素。实验部分验证了这些生物学预测结果。
这些结果为 HCC 患者伴有 PVTT 的生物学和免疫学特征提供了新的见解。通过利用个体患者的风险评分，为更有效的免疫治疗干预提供了机会。
