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Aging and Decoding Aging

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Dear Colleagues,

Aging is a multifactorial, dynamic process that spans from molecular and cellular scales to systemic physiology, manifesting as cumulative damage, compensatory remodeling, and altered resilience. Recent advances in artificial intelligence (AI), machine learning, and systems modeling now enable us to quantify, simulate, and predict aging trajectories in ways that were previously unattainable. This Special Issue invites contributions that leverage computational and AI-driven approaches to deepen mechanistic insight into aging, improve predictive accuracy, and suggest intervention strategies.

We welcome original research and review papers in areas including (but not limited to) the following:

AI-based modeling of molecular aging processes (e.g., transcriptomics, epigenetics, proteostasis);
Multiscale and hybrid models bridging cellular, tissue, and organismal aging;
Longitudinal trajectory prediction of healthspan, biological age, and frailty;
Generative modeling and synthetic aging simulation (e.g., imaging, tissue dynamics);
Explainable/interpretable AI approaches to aging biomarkers;
Intervention modeling and in silico experiments (e.g., for senolytics, caloric restriction);
Integration of multimodal data (omics, imaging, wearables) in aging modeling;
Ethical, reliability, and reproducibility considerations in AI models of aging;

By bringing together computational, biological, and biomedical perspectives, this Special Issue aims to promote cross-disciplinary dialogue and accelerate the development of robust AI tools that can predict, explain, and ultimately modulate the aging process.

Dr. Bingrui Li
Dr. Xuanye Cao
Dr. Wen Jiang
Guest Editors

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22 pages, 4098 KB

Open AccessArticle

Single Cell and Bulk RNA-Seq Profiling of Non-Metastatic Versus Bone-Metastatic Prostate Cancer Identifies the CXCL10-CXCR3 Axis as a Key Determinant of Tumor Microenvironment and Treatment Resistance

by Zijian Song, Likai Ren, Hong Wang, Yanqing Wang, Xinxing Du, Wei Zhou, Qi Zhang, Jiyuan Yu, Zaixu Zhao, Linxiong Ye, Kaidi Jin, Ying Liu and Wei Xue

Biomedicines 2026, 14(4), 943; https://doi.org/10.3390/biomedicines14040943 - 21 Apr 2026

Viewed by 585

Abstract

Background: Bone metastasis is a major determinant of morbidity and therapeutic failure in advanced prostate cancer (PCa); however, the transcriptional programs and tumor microenvironmental alterations driving metastatic progression remain incompletely understood. This study aimed to systematically characterize transcriptomic differences between non-metastatic and bone-metastatic PCa and to identify key microenvironmental signaling pathways involved in tumor survival and chemoresistance. Methods: Bulk RNA sequencing was performed on 49 non-metastatic and 28 bone-metastatic PCa specimens. Differential expression analysis was integrated with weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis, and immune/stromal deconvolution. Key findings were validated using in vitro functional assays, including Transwell co-culture models, small interfering RNA (siRNA)-mediated gene silencing, cell viability, apoptosis, and docetaxel resistance analyses. Results: Transcriptomic profiling identified 574 differentially expressed genes. Bone-metastatic tumors were enriched in ribosome-related and translational pathways, whereas non-metastatic tumors displayed immune-associated signatures, including natural killer (NK) cell-mediated cytotoxicity and cytokine signaling. WGCNA revealed immune-related gene modules preferentially enriched in non-metastatic disease. Immune deconvolution demonstrated significantly higher infiltration of NK cells and endothelial cells in non-metastatic tumors. Chemokine-receptor analysis highlighted upregulation of the CXCL10-CXCR3 axis in non-metastatic PCa. In vitro, PCa cells expressed CXCR3, while endothelial cells markedly increased CXCL10 expression upon co-culture. Functional assays showed that endothelial-derived CXCL10 promoted PCa cell survival, suppressed apoptosis, and conferred resistance to docetaxel via CXCR3-dependent signaling; these effects were reversed by CXCL10 or CXCR3 knockdown. Conclusions: These findings uncover a context-dependent endothelial-immune chemokine network distinguishing non-metastatic from bone-metastatic PCa and identify the CXCL10-CXCR3 axis as a critical mediator of tumor survival and chemoresistance, suggesting a potential therapeutic vulnerability in advanced prostate cancer. Full article

(This article belongs to the Special Issue Aging and Decoding Aging)

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