AI and Cancer Research ── Prostate ── 2026-10-30
Prostate cancer: treatment, trials and AI
- Abstract
Prostate cancer (PCa) demonstrates complex associations with type 2 diabetes mellitus (T2DM) and ageing, though their interconnected molecular mechanisms remain poorly defined. This study aimed to identify and validate diagnostic biomarkers at the intersection of these pathways to enable early detection and risk stratification in high-risk populations. Bioinformatic analysis was performed on PCa and T2DM transcriptomic data downloaded from the GEO database, followed by differential expression analysis, weighted gene co-expression network analysis (WGCNA), and functional enrichment. Eight machine learning algorithms were applied to identify hub ageing-related genes (ARGs), with SHAP analysis interpreting feature importance. A nomogram model was constructed and validated across multiple cohorts. Immune infiltration, drug sensitivity, single-cell RNA sequencing, and spatial transcriptomics were used for mechanistic exploration. Three key ARGs (SOX4, PRNP, and FOXO4) were identified and used to develop a diagnostic model with high accuracy (AUCs: 0.726-0.905). SOX4 promoted immune evasion, PRNP exhibited context-dependent duality, and FOXO4 functioned as a tumor suppressor. Spatial and single-cell analyses revealed cell-type-specific expression and fibroblast-epithelial communication via MIF, LRP1, and FZD1. Drug sensitivity profiling identified abiraterone and docetaxel as potential therapeutics for high- and low-risk patients, respectively. In conclusion, SOX4, PRNP, and FOXO4 are key biomarkers linking PCa, T2DM, and ageing, and the diagnostic model shows robust performance and clinical utility, providing insights into microenvironment-driven mechanisms and personalized treatment strategies for PCa patients with T2DM.
Journal IF-equivalent: 5.1 (OpenAlex 2-year mean citedness, value as of 2026-10-29, retrieved 2026-10-30; not the official Clarivate IF)Reference: Zhang W, Zhang X, Zhang C, Shi X, Deng Y, Gao Y, et al. Identification of ageing-related genes in prostate cancer with type 2 Diabetes using machine learning: a multi-omics study focusing on SOX4, PRNP, and FOXO4. Artif Cells Nanomed Biotechnol. 2026 Oct 28;54(1):890-907. doi:10.1080/21691401.2026.2737622. PMID: 42952172.Checked: Abstract only - Abstract
Objective. Prediction of dose-averaged linear energy transfer (LETd) may facilitate LET-aware evaluation of proton therapy plans. This study developed a cascade deep learning framework for simultaneous three-dimensional prediction of dose and LETd distributions for conventional dose-based optimization (DoseOpt) and LETd-incorporated optimization (LETOpt) in prostate intensity-modulated proton therapy. Approach. A total of 150 patients with prostate cancer were included, each with DoseOpt and LETOpt plans. Planning CT images and anatomical structure masks were used as inputs. The cascade network transferred an intermediate latent representation from the dose prediction pathway to the LETd prediction pathway. Stepwise, multitask, and anatomy-only LETd (only-LET) networks served as comparators. Model performance was evaluated using five-fold cross-validation. Main results. All dose-predicting models showed high spatial agreement, with cascade Dice similarity coefficients of approximately 0.93, 0.96, and 0.95 for the 50%, 75%, and 95% prescription isodose volumes. No significant differences in structure-specific dose errors remained after false discovery rate correction. The cascade model achieved the lowest global LETd MAE for DoseOpt and LETOpt, 0.108 ± 0.025 and 0.111 ± 0.025 keV/μm, respectively, significantly lower than those of the multitask, stepwise, and only-LET models. Structure-specific LETd performance was endpoint dependent, with favorable cascade performance for several CTV and organ-at-risk endpoints. LETOpt primarily redistributed LETd while largely preserving dose, and the cascade model reproduced the principal plan-specific changes, including reductions in rectal and rectal-wall LETd. Mean inference time was 0.505 ± 0.020 s per patient. Significance. The proposed framework enables rapid simultaneous prediction of dose and LETd for predefined planning strategies from patient anatomy without requiring a precomputed dose distribution, supporting LET-aware plan comparison and providing a basis for future LETd-guided biological optimization.
Journal IF-equivalent: 2.9 (OpenAlex 2-year mean citedness, value as of 2026-10-28, retrieved 2026-10-29; not the official Clarivate IF)Reference: Liu Q, Wei S, Peng H, Tang Y, Chen X, Dai J, et al. A cascade deep learning network for simultaneous prediction of dose and linear energy transfer in proton therapy of prostate cancer. Phys Med Biol. 2026 Oct 27 [Epub ahead of print]. doi:10.1088/1361-6560/aeba33. PMID: 42945684.Checked: Abstract only - Abstract
Prostate cancer (PCa) exhibits marked biological heterogeneity, resulting in substantial differences in disease recurrence and clinical outcomes and highlighting the need for improved molecular risk stratification. In this study, we developed a machine learning–driven prognostic framework to identify transcriptomic features associated with disease-free survival (DFS) and stratify patients according to recurrence risk. Differentially expressed genes between prostate cancer and normal prostate tissue were initially screened, and their prognostic relevance was evaluated in the TCGA-PRAD cohort. A prognostic signature was subsequently developed using LASSO-Cox and multivariate Cox regression, generating a patient-level risk score for recurrence stratification. The final prognostic model comprised 33 genes and effectively separated patients into high- and low-risk groups, with significantly different DFS outcomes. The model demonstrated good prognostic discrimination, as indicated by the concordance index and time-dependent area under the receiver operating characteristic curve (AUC), and significantly separated patients into high- and low-risk groups. Random Survival Forest analysis further supported the prognostic relevance of the selected features, while SHAP analysis identified the relative contribution of individual genes to patient-level risk prediction. Drug–gene interaction analysis further identified a subset of the 33 signature genes targeted by FDA-approved drugs, providing hypothesis-generating information regarding potential therapeutic relevance. The results support the potential of the 33-gene signature for molecular risk stratification and recurrence prediction in PCa. However, the model was developed and evaluated within the TCGA-PRAD cohort and requires independent external and prospective validation before clinical translation.
Journal IF-equivalent: 0.6 (OpenAlex 2-year mean citedness, value as of 2026-10-28, retrieved 2026-10-29; not the official Clarivate IF)Reference: de Melo NC, de Oliveira DC, de Carvalho LM. Unveiling Novel Potential Biomarkers in Prostate Cancer: A Comprehensive Bioinformatics and Machine Learning Approach. AI Med. 2026 Oct 26;1(4):27. doi:10.3390/aimed1040027.Checked: Abstract only