Deng Zaidong, Liu Jie, Yu Yanxun V, Jin Youngnam N
Department of Neurology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China.
Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan, China.
Front Immunol. 2024 Sep 17;15:1451103. doi: 10.3389/fimmu.2024.1451103. eCollection 2024.
Immunotherapy has revolutionized skin cutaneous melanoma treatment, but response variability due to tumor heterogeneity necessitates robust biomarkers for predicting immunotherapy response.
We used weighted gene co-expression network analysis (WGCNA), consensus clustering, and 10 machine learning algorithms to develop the immunotherapy-related gene model (ITRGM) signature. Multi-omics analyses included bulk and single-cell RNA sequencing of melanoma patients, mouse bulk RNA sequencing, and pathology sections of melanoma patients.
We identified 66 consensus immunotherapy prognostic genes (CITPGs) using WGCNA and differentially expressed genes (DEGs) from two melanoma cohorts. The CITPG-high group showed better prognosis and enriched immune activities. DEGs between CITPG-high and CITPG-low groups in the TCGA-SKCM cohort were analyzed in three additional melanoma cohorts using univariate Cox regression, resulting in 44 consensus genes. Using 101 machine learning algorithm combinations, we constructed the ITRGM signature based on seven model genes. The ITRGM outperformed 37 published signatures in predicting immunotherapy prognosis across the training cohort, three testing cohorts, and a meta-cohort. It effectively stratified patients into high-risk or low-risk groups for immunotherapy response. The low-risk group, with high levels of model genes, correlated with increased immune characteristics such as tumor mutation burden and immune cell infiltration, indicating immune-hot tumors with a better prognosis. The ITRGM's relationship with the tumor immune microenvironment was further validated in our experiments using pathology sections with GBP5, an important model gene, and CD8 IHC analysis. The ITRGM also predicted better immunotherapy response in eight cohorts, including urothelial carcinoma and stomach adenocarcinoma, indicating broad applicability.
The ITRGM signature is a stable and robust predictor for stratifying melanoma patients into 'immune-hot' and 'immune-cold' tumors, enhancing prognosis and response to immunotherapy.
免疫疗法彻底改变了皮肤黑色素瘤的治疗方式，但由于肿瘤异质性导致的反应变异性需要强大的生物标志物来预测免疫疗法反应。
我们使用加权基因共表达网络分析（WGCNA）、共识聚类和10种机器学习算法来开发免疫疗法相关基因模型（ITRGM）特征。多组学分析包括黑色素瘤患者的批量和单细胞RNA测序、小鼠批量RNA测序以及黑色素瘤患者的病理切片。
我们使用WGCNA和来自两个黑色素瘤队列的差异表达基因（DEG）鉴定了66个共识免疫疗法预后基因（CITPG）。CITPG高分组显示出更好的预后和丰富的免疫活性。使用单变量Cox回归在另外三个黑色素瘤队列中分析了TCGA - SKCM队列中CITPG高分组和CITPG低分组之间的DEG，得到了44个共识基因。使用101种机器学习算法组合，我们基于7个模型基因构建了ITRGM特征。在预测训练队列、三个测试队列和一个meta队列的免疫疗法预后方面，ITRGM优于37个已发表的特征。它有效地将患者分为免疫疗法反应的高风险或低风险组。低风险组模型基因水平高，与肿瘤突变负担和免疫细胞浸润等免疫特征增加相关，表明是具有更好预后的免疫热肿瘤。在我们使用重要模型基因GBP5的病理切片和CD8免疫组化分析的实验中，进一步验证了ITRGM与肿瘤免疫微环境的关系。ITRGM在包括尿路上皮癌和胃腺癌在内的八个队列中也预测了更好的免疫疗法反应，表明其具有广泛的适用性。
ITRGM特征是一种稳定且强大的预测指标，可将黑色素瘤患者分为“免疫热”和“免疫冷”肿瘤，改善预后并增强对免疫疗法的反应。
