International journal of medical sciences · 2026
Development and Validation of Novel Senescence-TIME Biomarkers for Predicting the Prognosis and Immunotherapy Responsiveness of SKCM Patients.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Fan Shiying, Lu Shuya, Wu Zilong, Li Xu, Gao Yisong, Mao Gan, Cai Mao, Song Tianyu, Peng Zuojie, Li Chong, Tao Kaixiong, Li Wei
Аннотация
BACKGROUND: Increasing evidence indicates that tumor cellular senescence can impair antitumor immunity and promote skin cutaneous melanoma (SKCM) progression. However, effective methods for assessing tumor cellular senescent status and tumor immune microenvironment (TIME) status remain lacking. This study intends to establish a novel Senescence-TIME Risk Score (STIRS) based on senescence and TIME related genes to predict prognosis and immunotherapy responsiveness in SKCM patients, thereby providing new strategies for current clinical personalized treatment.
METHODS: We identified distinct senescent microenvironment patterns using t-distributed stochastic neighbor embedding (t-SNE) based on a set of senescence marker genes and predicted the TIME in SKCM using the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm. Based on this, we divided the SKCM cohort into three groups: low-senescence & high-immunity, high-senescence & low-immunity, and mixed. We analyzed differentially expressed genes (DEGs) between the first two groups. Gene ontology (GO) enrichment analysis, kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, gene set enrichment analysis (GSEA), and the construction of protein-protein interaction (PPI) network were used to investigate the functional relevance of DEGs. We screened DEGs using least absolute shrinkage and selection operation (LASSO) regression and random forest (RF) algorithm to construct the STIRS. Patients were grouped by the median of STIRS, and differences in expression of immune cells and immune checkpoints between groups were examined. The predictive capability of STIRS for immunotherapy was validated. Finally, we knocked down the core risk gene in the B16 cell line to validate its function.
RESULTS: We identified 994 DEGs predominantly enriched in TIME- and senescence-related pathways. The constructed STIRS comprises six signature genes. Patients in the high-STIRS group exhibited significantly poorer survival than those in the low-STIRS group, and STIRS negatively correlated with immune response and immunotherapy responsiveness. A nomogram integrating STIRS and clinical indicators demonstrated satisfactory predictive performance for SKCM patient prognosis. These findings validate the STIRS model as a reliable independent prognostic indicator. Additionally, knockdown of the core risk gene keratin 17 (KRT17) inhibited the invasion and proliferation of B16 cells, demonstrating the role of KRT17 in the progression of SKCM.
CONCLUSION: This study proposed a novel STIRS model and selected the core risk gene KRT17 for functional validation, which had potential as a prognostic tool and a guide for creating personalized therapies for SKCM patients.
METHODS: We identified distinct senescent microenvironment patterns using t-distributed stochastic neighbor embedding (t-SNE) based on a set of senescence marker genes and predicted the TIME in SKCM using the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm. Based on this, we divided the SKCM cohort into three groups: low-senescence & high-immunity, high-senescence & low-immunity, and mixed. We analyzed differentially expressed genes (DEGs) between the first two groups. Gene ontology (GO) enrichment analysis, kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, gene set enrichment analysis (GSEA), and the construction of protein-protein interaction (PPI) network were used to investigate the functional relevance of DEGs. We screened DEGs using least absolute shrinkage and selection operation (LASSO) regression and random forest (RF) algorithm to construct the STIRS. Patients were grouped by the median of STIRS, and differences in expression of immune cells and immune checkpoints between groups were examined. The predictive capability of STIRS for immunotherapy was validated. Finally, we knocked down the core risk gene in the B16 cell line to validate its function.
RESULTS: We identified 994 DEGs predominantly enriched in TIME- and senescence-related pathways. The constructed STIRS comprises six signature genes. Patients in the high-STIRS group exhibited significantly poorer survival than those in the low-STIRS group, and STIRS negatively correlated with immune response and immunotherapy responsiveness. A nomogram integrating STIRS and clinical indicators demonstrated satisfactory predictive performance for SKCM patient prognosis. These findings validate the STIRS model as a reliable independent prognostic indicator. Additionally, knockdown of the core risk gene keratin 17 (KRT17) inhibited the invasion and proliferation of B16 cells, demonstrating the role of KRT17 in the progression of SKCM.
CONCLUSION: This study proposed a novel STIRS model and selected the core risk gene KRT17 for functional validation, which had potential as a prognostic tool and a guide for creating personalized therapies for SKCM patients.