Lin Wan-Yu, Huang Chien-Jung, Wang Yu-Chao
Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Discov Oncol. 2025 Jul 16;16(1):1341. doi: 10.1007/s12672-025-03184-w.
Tumor mutation burden (TMB), the total number of nonsynonymous mutations in the tumor genome, is a well-established biomarker for predicting responses to immune checkpoint blockade (ICB) therapy across various cancers. Patients with high TMB tend to exhibit better responses to ICB. Recently, targeted gene panels have been developed to estimate TMB before treatment. These panels are enriched for calcium ion-binding genes. However, a direct link between TMB and calcium ion-binding genes has not been reported in the literature to date.
The association between TMB and calcium ion-binding genes was analyzed using mutation data from The Cancer Genome Atlas (TCGA) database. In addition, a pan-cancer model was constructed to estimate TMB based solely on calcium ion-binding genes. The model's predictive power for ICB response was validated using independent datasets. Finally, enrichment analysis was performed to investigate the biological connections between calcium ion-binding genes and TMB.
Calcium ion-binding genes were enriched among the TMB-predictive model genes in 27 out of 33 cancer types. Among these, 19 cancer types exhibited strong predictive performance, with R² values greater than 0.5 in our pan-cancer model based on calcium ion-binding genes. The model effectively estimated TMB and identified ICB responders in independent datasets, including lung adenocarcinoma and melanoma. Enrichment analysis further suggested that calcium ion-binding genes may influence TMB through signal transduction pathways.
These findings establish a novel association between calcium ion-binding genes and TMB, demonstrating the feasibility of a pan-cancer TMB estimation model based on calcium ion-binding genes. This approach may enhance TMB estimation and improve ICB response prediction across multiple cancers.
肿瘤突变负荷（TMB）是肿瘤基因组中非同义突变的总数，是一种公认的生物标志物，可用于预测多种癌症对免疫检查点阻断（ICB）治疗的反应。TMB高的患者往往对ICB表现出更好的反应。最近，已开发出靶向基因panel来在治疗前估计TMB。这些panel富含钙离子结合基因。然而，迄今为止，文献中尚未报道TMB与钙离子结合基因之间的直接联系。
使用来自癌症基因组图谱（TCGA）数据库的突变数据，分析TMB与钙离子结合基因之间的关联。此外，构建了一个泛癌模型，仅基于钙离子结合基因来估计TMB。使用独立数据集验证该模型对ICB反应的预测能力。最后，进行富集分析以研究钙离子结合基因与TMB之间的生物学联系。
在33种癌症类型中的27种中，钙离子结合基因在TMB预测模型基因中富集。其中，19种癌症类型表现出强大的预测性能，在我们基于钙离子结合基因的泛癌模型中，R²值大于0.5。该模型有效地估计了TMB，并在包括肺腺癌和黑色素瘤在内的独立数据集中识别出ICB反应者。富集分析进一步表明，钙离子结合基因可能通过信号转导途径影响TMB。
这些发现建立了钙离子结合基因与TMB之间的新关联，证明了基于钙离子结合基因的泛癌TMB估计模型的可行性。这种方法可能会增强TMB估计，并改善多种癌症的ICB反应预测。
