Prognostic and immunological implications of protein kinases in gastric cancer: a focus on hub gene ABL2 and its impact on the polarization of M2 macrophages.
{"title":"Prognostic and immunological implications of protein kinases in gastric cancer: a focus on hub gene ABL2 and its impact on the polarization of M2 macrophages.","authors":"Di Chen, Ju Huang, Aiming Yang, Zhifan Xiong","doi":"10.1186/s13062-025-00636-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Protein kinases are essential cellular signal modulators involved in tumorigenesis, metastasis, immune response, and drug resistance. However, the comprehensive features and clinical significance of protein kinases in gastric cancer (GC) remain inconclusive.</p><p><strong>Methods: </strong>We analyzed the transcriptional profiles of protein kinases in GC patients from the GEO and TCGA databases. Based on differentially expressed kinase genes (DE-KGs), a novel cluster was identified to assess its association with patient survival and the tumor microenvironment (TME) in GC. Subsequently, an optimal DE-KGs-based model (DE-KGsM) was determined using 101 machine-learning algorithm combinations. This model was evaluated using multi-omics data to investigate its associations with patient prognosis, clinical features, tumor microenvironment, tumor-infiltrating immune cells (TIICs), and immunotherapy response. Furthermore, scRNA-seq analysis and TIMER algorithm were applied to determine the correlation between the hub gene (ABL2) in the DE-KGsM and Macrophages. Finally, in vitro experiments were performed to explore the immune-related mechanisms of ABL2 in GC.</p><p><strong>Results: </strong>We identified two molecular subtypes of GC patients based on 64 DE-KGs expression. Significant differences were observed in overall survival and TIIC characteristics between Cluster 1 and Cluster 2. Among these 64 DE-KGs, we identified an optimal DE-KGsM that could be a prognostic indicator in GC. TIICs and TIDE analyses exhibited that GC patients in the high-DE-KGsM score group had a higher proportion of M2 macrophages and lower response rates to ICI treatment. scRNA-seq analysis indicated that ABL2 might play an indispensable role in tumor immunity. Furthermore, in vitro experiments demonstrated that ABL2 accelerated the proliferation, migration, and invasion of GC cells, as well as the polarization of M2 macrophages.</p><p><strong>Conclusions: </strong>The DE-KGsM could be a powerful predictor of GC patients' survival and might facilitate the development of personalized therapy. Furthermore, as a hub gene in the DE-KGsM, ABL2 could be an immunological biomarker that modulates the polarization of M2 macrophages, thereby promoting GC progression.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9164,"journal":{"name":"Biology Direct","volume":"20 1","pages":"35"},"PeriodicalIF":5.7000,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934801/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biology Direct","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13062-025-00636-9","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Protein kinases are essential cellular signal modulators involved in tumorigenesis, metastasis, immune response, and drug resistance. However, the comprehensive features and clinical significance of protein kinases in gastric cancer (GC) remain inconclusive.
Methods: We analyzed the transcriptional profiles of protein kinases in GC patients from the GEO and TCGA databases. Based on differentially expressed kinase genes (DE-KGs), a novel cluster was identified to assess its association with patient survival and the tumor microenvironment (TME) in GC. Subsequently, an optimal DE-KGs-based model (DE-KGsM) was determined using 101 machine-learning algorithm combinations. This model was evaluated using multi-omics data to investigate its associations with patient prognosis, clinical features, tumor microenvironment, tumor-infiltrating immune cells (TIICs), and immunotherapy response. Furthermore, scRNA-seq analysis and TIMER algorithm were applied to determine the correlation between the hub gene (ABL2) in the DE-KGsM and Macrophages. Finally, in vitro experiments were performed to explore the immune-related mechanisms of ABL2 in GC.
Results: We identified two molecular subtypes of GC patients based on 64 DE-KGs expression. Significant differences were observed in overall survival and TIIC characteristics between Cluster 1 and Cluster 2. Among these 64 DE-KGs, we identified an optimal DE-KGsM that could be a prognostic indicator in GC. TIICs and TIDE analyses exhibited that GC patients in the high-DE-KGsM score group had a higher proportion of M2 macrophages and lower response rates to ICI treatment. scRNA-seq analysis indicated that ABL2 might play an indispensable role in tumor immunity. Furthermore, in vitro experiments demonstrated that ABL2 accelerated the proliferation, migration, and invasion of GC cells, as well as the polarization of M2 macrophages.
Conclusions: The DE-KGsM could be a powerful predictor of GC patients' survival and might facilitate the development of personalized therapy. Furthermore, as a hub gene in the DE-KGsM, ABL2 could be an immunological biomarker that modulates the polarization of M2 macrophages, thereby promoting GC progression.
期刊介绍:
Biology Direct serves the life science research community as an open access, peer-reviewed online journal, providing authors and readers with an alternative to the traditional model of peer review. Biology Direct considers original research articles, hypotheses, comments, discovery notes and reviews in subject areas currently identified as those most conducive to the open review approach, primarily those with a significant non-experimental component.