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  • In Silico Transcriptomic Analysis of Radiation-Induced Bystander Effects in Human Fibroblasts: Uncovering Key Regulatory Networks and Ribosome Biogenesis

  • Fatemeh Zakeri,1 Sepideh Ebrahimi,2 Elnaz Farzadifar,3 Forouzan Amerizadeh,4,* Alireza Pasdar,5
    1. Department of Medical Physics, Faculty of medicine, Mashhad University of Medical Sciences
    2. Department of Clinical Biochemistry, Faculty of medicine, Shiraz University of Medical Sciences
    3. Biology Department, Faculty of Sciences, Science and Arts University
    4. Bioinformatics Research Center, Basic Sciences Research Institute, Mashhad University of Medical Sciences
    5. Department of Medical Genetics and Molecular Medicine, Faculty of Medicine, Mashhad University of Medical Sciences


  • Introduction: Radiation-induced bystander effect (RIBE) represents a non-targeted response in which non-irradiated cells undergo molecular alterations following exposure to signals released from irradiated cells. As fibroblasts constitute a major stromal population within the tumor microenvironment, their response to radiation-induced signaling may contribute to alterations in the surrounding tissue environment. However, the molecular organization of the late-stage bystander response in human fibroblasts remains insufficiently characterized. This study aimed to characterize the molecular and regulatory landscape of RIBE in immortalized human fibroblasts and to identify candidate hub proteins and pharmacological interactions associated with this response.
  • Methods: Microarray expression data from the GEO GSE32091 dataset were analyzed. The analysis focused on the 26-hour time point and included four bystander F11hTERT fibroblast samples exposed to conditioned medium from α-particle-irradiated cells and three directly irradiated fibroblast samples. Differentially expressed genes were identified using GEO2R/limma with Benjamini–Hochberg-adjusted p < 0.05. Gene Ontology and Reactome analyses were performed to determine enriched biological functions and pathways. A STRING-based protein–protein interaction network was subsequently analyzed in Cytoscape using four cytoHubba algorithms to identify central hub proteins and densely connected functional modules. Transcription factor enrichment, promoter motif analysis, DrugBank-based drug–target analysis, and molecular docking were additionally performed to explore potential regulatory and pharmacological associations.
  • Results: The analysis identified 1,595 differentially expressed genes in the bystander condition. Enrichment analysis revealed a strong predominance of RNA-related processes, particularly ribosome biogenesis, rRNA processing, RNA metabolism, and translation. Network analysis further identified 15 central proteins, with HSP90AA1, HSP90AB1, and MYC emerging as prominent molecular hubs. The network architecture also revealed several densely interconnected functional modules, suggesting coordinated regulation of the observed response. Regulatory analysis highlighted MYC-, MAX-, and E2F6-associated transcriptional signatures, while promoter analysis identified distinct regulatory motifs associated with HSP90AA1 and NACAD. Pharmacological analysis revealed multiple drug associations involving HSP90-related hubs, and docking simulations showed favorable predicted interactions for selected compounds.
  • Conclusion: The findings indicate that late-phase RIBE in immortalized human fibroblasts is characterized by coordinated alterations in RNA-related processes, particularly ribosome biogenesis, rRNA processing, and translational regulation. Integration of transcriptomic, network, regulatory, and pharmacological analyses highlighted HSP90AA1, HSP90AB1, and MYC as candidate molecular nodes within the observed response. These findings provide a systems-level framework for understanding fibroblast responses to radiation-induced bystander signaling and identify molecular candidates that can be investigated experimentally in future studies.
  • Keywords: Radiation-induced bystander effect, Fibroblasts, Transcriptomics, Ribosome biogenesis

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