Hu Beiyuan, Shen Xiaotian, Qin Wei, Zhang Lan, Zou Tiantian, Dong Qiongzhu, Qin Lun-Xiu
Department of General Surgery, Huashan Hospital & Cancer Metastasis Institute, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
J Clin Transl Hepatol. 2022 Oct 28;10(5):891-900. doi: 10.14218/JCTH.2021.00296. Epub 2022 Jan 18.
Wound healing and tumor progression share some common biological features; however, how variations in wound healing patterns affect hepatocellular carcinoma (HCC) prognosis remains unclear.
We analyzed the wound healing patterns of 594 HCC samples from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) and correlated them with immune infiltration and the expression levels of immune checkpoint genes. A risk score, which we named the "heal.immune" score, was established via stepwise Cox estimation. We constructed a nomogram based on age, sex, TNM stage, and heal.immune score and explored its predictive value for HCC prognosis. Seventy-four clinical patients were enrolled in this study, and all were from Huashan Hospital of Fudan University between 2015 and 2017 to serve as an independent validation group.
We identified two distinct wound healing patterns in HCC. The biological processes of healing cluster 1 (C1) are related to metabolism, while those of healing cluster 2 (C2) are related to the inflammatory response and immune cell accumulation. A total of 565 wound healing-related genes (based on Gene Ontology) and 25 immune checkpoint genes were considered. By analyzing differentially expressed genes and implementing a stepwise Cox estimation analysis, six genes with values less than 0.02 in a multivariate Cox estimation were chosen as the "heal.immune" gene set ( and ). The "heal.immune" gene set, as an OS risk factor, was further validated in Fudan cohort. We constructed a nomogram to predict the 1-, 3- and 5-year overall survival (OS) in the TCGA cohort. The area under curve vales of the receiver characteristic operator curves were 0.82, 0.76 and 0.73 in the training group and 0.84, 0.76 and 0.72 in the test group.
We established a prognostic nomogram based on the heal.immune gene signature, which includes six wound healing- and immunity-related genes. This nomogram accurately predicts the OS of HCC patients.
伤口愈合和肿瘤进展具有一些共同的生物学特征；然而，伤口愈合模式的变化如何影响肝细胞癌（HCC）的预后仍不清楚。
我们分析了来自癌症基因组图谱（TCGA）和国际癌症基因组联盟（ICGC）的594例HCC样本的伤口愈合模式，并将其与免疫浸润和免疫检查点基因的表达水平相关联。通过逐步Cox估计建立了一个风险评分，我们将其命名为“heal.immune”评分。我们基于年龄、性别、TNM分期和heal.immune评分构建了一个列线图，并探讨了其对HCC预后的预测价值。本研究纳入了74例临床患者，均来自2015年至2017年期间复旦大学附属华山医院，作为独立验证组。
我们在HCC中识别出两种不同的伤口愈合模式。愈合簇1（C1）的生物学过程与代谢相关，而愈合簇2（C2）的生物学过程与炎症反应和免疫细胞积累相关。共考虑了565个与伤口愈合相关的基因（基于基因本体论）和25个免疫检查点基因。通过分析差异表达基因并进行逐步Cox估计分析，在多变量Cox估计中P值小于0.02的6个基因被选为“heal.immune”基因集（P<0.02）。“heal.immune”基因集作为总生存期（OS）的危险因素，在复旦队列中得到了进一步验证。我们构建了一个列线图来预测TCGA队列中的1年、3年和5年总生存期（OS）。受试者特征曲线下面积在训练组分别为0.82、0.76和0.73，在测试组分别为0.84、0.76和0.72。
我们基于heal.immune基因特征建立了一个预后列线图，其中包括6个与伤口愈合和免疫相关的基因。该列线图能准确预测HCC患者的OS。
