Calcium ion-binding genes can predict tumor mutation burden and immune checkpoint blockade response in a pan-cancer model
Abstract Background 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 IC...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
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| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-025-03184-w |
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| Summary: | Abstract Background 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. Methods 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. Results 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. Conclusions 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. |
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| ISSN: | 2730-6011 |