Department of Plastic and Reconstructive Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, No. 301 Middle Yanchang Road, Shanghai, China.
School of Life Sciences, Northwest University, Xi'an, 710069, China.
Clin Transl Oncol. 2024 May;26(5):1170-1186. doi: 10.1007/s12094-023-03336-w. Epub 2023 Nov 21.
Anoikis is a cell death programmed to eliminate dysfunctional or damaged cells induced by detachment from the extracellular matrix. Utilizing an anoikis-based risk stratification is anticipated to understand melanoma's prognostic and immune landscapes comprehensively.
Differential expression genes (DEGs) were analyzed between melanoma and normal skin tissues in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression data sets. Next, least absolute shrinkage and selection operator, support vector machine-recursive feature elimination algorithm, and univariate and multivariate Cox analyses on the 308 DEGs were performed to build the prognostic signature in the TCGA-melanoma data set. Finally, the signature was validated in GSE65904 and GSE22155 data sets. NOTCH3, PIK3R2, and SOD2 were validated in our clinical samples by immunohistochemistry.
The prognostic model for melanoma patients was developed utilizing ten hub anoikis-related genes. The overall survival (OS) of patients in the high-risk subgroup, which was classified by the optimal cutoff value, was remarkably shorter in the TCGA-melanoma, GSE65904, and GSE22155 data sets. Low-risk patients exhibited low immune cell infiltration and high expression of immunophenoscores and immune checkpoints. They also demonstrated increased sensitivity to various drugs, including dasatinib and dabrafenib. NOTCH3, PIK3R2, and SOD2 were notably associated with OS by univariate Cox analysis in the GSE65904 data set. The clinical melanoma samples showed remarkably higher protein expressions of NOTCH3 (P = 0.003) and PIK3R2 (P = 0.009) than the para-melanoma samples, while the SOD2 protein expression remained unchanged.
In this study, we successfully established a prognostic anoikis-connected signature using machine learning. This model may aid in evaluating patient prognosis, clinical characteristics, and immune treatment modalities for melanoma.
细胞凋亡是一种程序性细胞死亡，旨在消除因与细胞外基质分离而导致的功能失调或受损细胞。利用基于细胞凋亡的风险分层，有望全面了解黑色素瘤的预后和免疫景观。
在癌症基因组图谱（TCGA）和基因型-组织表达数据集分析黑色素瘤与正常皮肤组织之间的差异表达基因（DEGs）。接下来，对 308 个 DEGs 进行最小绝对收缩和选择算子、支持向量机递归特征消除算法、单变量和多变量 Cox 分析，以构建 TCGA 黑色素瘤数据集中的预后特征。最后，在 GSE65904 和 GSE22155 数据集中验证该特征。通过免疫组织化学验证 NOTCH3、PIK3R2 和 SOD2 在我们的临床样本中的表达。
利用十个与细胞凋亡相关的核心基因构建了黑色素瘤患者的预后模型。根据最佳截断值分类的高危亚组患者的总生存期（OS）在 TCGA 黑色素瘤、GSE65904 和 GSE22155 数据集中明显缩短。低危患者的免疫细胞浸润水平较低，免疫表型评分和免疫检查点表达水平较高。他们还表现出对各种药物的敏感性增加，包括达沙替尼和达拉非尼。在 GSE65904 数据集中，NOTCH3、PIK3R2 和 SOD2 的单变量 Cox 分析显著与 OS 相关。临床黑色素瘤样本的 NOTCH3（P=0.003）和 PIK3R2（P=0.009）蛋白表达明显高于癌旁样本，而 SOD2 蛋白表达不变。
本研究成功利用机器学习建立了预后相关的细胞凋亡连接特征模型。该模型有助于评估黑色素瘤患者的预后、临床特征和免疫治疗方式。
