AI and Cancer Research ── 2026-10-07
New papers on cancer and AI, collected from journal feeds, PubMed (searched across all journals) and OpenAlex (a public index used instead of Google Scholar), judged by Jev (an AI that makes language judgements): original research, AI central, which of 13 themes, treatment or trials, and then ranked by importance up to a daily limit. Journals are not filtered by impact factor; each paper shows its journal's IF-equivalent (OpenAlex two-year mean citedness) and the date of that value. Each entry shows the full original abstract.
Read by cancer type: Cancer biology（2） · Breast（2） · Lung（0） · Pancreatic（0） · Prostate（2） · ACC（0） · Brain（1） · Ovarian（0） · Endometrial（0） · Gastric（1） · Liver（2） · Kidney（0） · Bladder（2）
Cancer molecular biology and AI
- Abstract
Indeterminate pulmonary nodules (IPNs) detected on computed tomography (CT) pose challenges because malignancy risk assessment is essential to balance early cancer detection with unnecessary invasive procedures. Artificial intelligence (AI) is increasingly used as a decision-support tool for pulmonary nodule assessment, but its impact on clinicians’ diagnostic and management decisions remains less established than the performance of AI algorithms. This systematic review evaluated the effect of AI assistance on clinicians’ diagnostic and management decisions for CT-detected IPNs. The review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, ScienceDirect, Scopus, and Cochrane Library were searched for studies published from 2015 to 2026. Eligible studies assessed AI-assisted evaluation of indeterminate or suspicious pulmonary nodules on CT, involved clinicians, and reported diagnostic or management outcomes. Five retrospective reader-study or multireader multicase studies were included. AI assistance improved diagnostic performance and malignancy risk stratification. One study reported an AUC increase from 0.82 to 0.89 (P<0.001), with higher sensitivity, specificity, and interobserver agreement (Fleiss κ, 0.35 to 0.58; P<0.001). Another found sensitivity increased from 60% to 98% and specificity from 69% to 99%. Among advanced practice providers, AUC increased from 0.79 to 0.88, while invasive procedure recommendations for malignant nodules increased from 55% to 72%. AI detection also benefited radiologists across experience levels. Standalone AI performed worse than experienced radiologists. AI may improve clinician performance and management decisions, especially as a second-reader tool. Evidence remains limited by retrospective designs, heterogeneous systems and outcomes, and lack of randomized trials; prospective studies are needed.
Journal IF-equivalent: 0.1 (OpenAlex 2-year mean citedness, value as of 2026-10-06, retrieved 2026-10-07; not the official Clarivate IF)Reference: Fauziana Ulfa, Atika Amaliah. Impact of Artificial Intelligence Assistance on Clinicians’ Diagnostic and Management Decisions for Indeterminate Pulmonary Nodules on CT. Vitamin. 2026 Oct 6;4(4):255-77. doi:10.61132/vitamin.v4i4.2660.Checked: Abstract only - Abstract
BACKGROUND: Prognostication for patients undergoing liver resection for colorectal liver metastases (CRLM) remains challenging. Artificial intelligence-based survival models may improve individualized risk estimation. METHODS: We developed and compared classical machine learning and deep learning survival models using exclusively preoperative clinical variables from 350 patients undergoing first-time liver resection for CRLM. Models included Cox proportional hazards (CoxPH), random survival forest (RSF), support vector machine for survival (SVM), XGBoost survival, DeepSurv, and DeepHit. Model performance was evaluated using the concordance index (C-index) and the Integrated Brier Score (IBS) on a training set of 245 patients, testing set of 105 patients, and an independent internal validation set of 88 patients from the OSLO-COMET trial. Model interpretability was assessed using SHapley Additive explanation (SHAP) analysis. The results of the developed models were compared to well established Basingstoke Predictive Index (BPI). RESULTS: On internal validation cohort, CoxPH demonstrated the most consistent performance on the internal validation set (C-index 0.59), and on the test set patients (C-index 0.65), comparable to more complex machine learning and deep learning models. Although RSF achieved the highest training performance, this did not translate into superior performance both on the test and the internal validation datasets. CoxPH and DeepSurv models showed comparable 1-, 3-, and 5-year individual prediction to BPI. SHAP analysis consistently identified ASA score, lobar distribution, primary tumor location and number of metastases as the most influential predictors across models. CONCLUSIONS: Using structured preoperative clinical data, we developed machine-and deep-learning models to predict overall survival in patients with CRLM. Given that the models were based exclusively on preoperative clinical variables, their performance is encouraging. Predictive accuracy may be further improved by incorporating additional data modalities, particularly radiological features as well as by training on larger datasets.
Journal IF-equivalent: 2.7 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Kumar NP, Drejian S, Akhavi MS, Qadir HA, Fretland ÅA, Kazaryan AM, et al. Explainable AI–based prognostication of patients with resectable colorectal liver metastases using preoperative clinical parameters: is it comparable to traditional clinical scoring systems?. Front Oncol. 2026 Sep 21;16:1890766. doi:10.3389/fonc.2026.1890766.Checked: Abstract only
See all 2 Cancer biology papers →
Breast cancer: treatment, trials and AI
- Abstract
Post hoc transparency archive for a systematic review of validation design, data leakage, sample adequacy and performance-metric reporting in [18F]FDG PET/CT radiomics and artificial-intelligence models predicting pathological complete response to neoadjuvant chemotherapy in breast cancer. The archive contains the review protocol (deposited post hoc; the review was not prospectively registered), the full Boolean search strategies for all three search rounds, the study-level extraction table for the 30 included studies with PROBAST+AI judgements and reason codes, the record-level screening decisions for the 127 records assessed in the relaxed search, and the Python scripts that generate both figures in the article. Searches were last run on 1 October 2026. Every descriptive statistic reported in the article can be recomputed from the extraction table; no patient-level data were used and no full texts are redistributed. Version 1.1.0 replaces the study-level extraction table and the relaxed-search screening file with their English versions; no data values were changed. The study-level table is also supplied with the article as ESM 4 and the screening file as ESM 2.
Journal IF: unknown (could not be matched)Reference: [No authors listed]. Transparency archive for: Validation design, data leakage and performance reporting in [18F]FDG PET/CT radiomics and artificial intelligence models for predicting pathological complete response in breast cancer: a systematic review. Zenodo (CERN European Organization for Nuclear Research). [Epub ahead of print]. doi:10.5281/zenodo.23162322.Checked: Abstract only - Abstract
The apparent diffusion coefficient (ADC) is used to assess breast cancer response but may be influenced by perfusion and T2-related effects. The aim of the study was to compare ADC-only, slow diffusion coefficient (SDC)-only, and combined ADC–SDC models and to evaluate whether SDC provides information complementary to ADC for post-treatment assessment of pathological complete response (pCR). This retrospective secondary analysis included 84 patients from the ACRIN-6698/I-SPY2 dataset, including 32 with pCR. ADC was calculated from b = 0 and 800 s/mm2 and SDC from b = 600 and 800 s/mm2. Lesion-mean values and treatment-related changes were evaluated using 100 repetitions of nested fivefold cross-validation. All normalization, random-forest ranking, and LASSO tuning and selection were performed within the training data. The combined rcADC–rcSDC model achieved the highest mean cross-validated AUC of 0.724. Its aggregated out-of-fold AUC was 0.728, compared with 0.633 for rcADC alone; however, the paired AUC difference was not statistically significant. Treatment-related SDC changes may complement conventional ADC changes in post-treatment pCR assessment. Although the combined rcADC–rcSDC model achieved the highest observed discrimination, a statistically significant improvement over that of rcADC alone was not demonstrated. These exploratory findings require technical and external validation.
Journal IF-equivalent: 1.0 (OpenAlex 2-year mean citedness, value as of 2026-10-06, retrieved 2026-10-07; not the official Clarivate IF)Reference: Sakoda K, Baba S. Slow Diffusion Coefficient Derived from High b-Value Diffusion-Weighted MRI for Assessing Pathological Complete Response after Neoadjuvant Chemotherapy in Breast Cancer: An Exploratory Machine Learning Analysis. Indian J Radiol Imaging. 2026 Oct 5 [Epub ahead of print]. doi:10.1055/s-0046-1829390.Checked: Abstract only
Lung cancer: treatment, trials and AI
No new qualifying paper for this issue.
Pancreatic cancer: treatment, trials and AI
No new qualifying paper for this issue.
Prostate cancer: treatment, trials and AI
- Abstract
Objectives: To develop and test deep-learning models which detect extraprostatic extension (EPE) on biparametric (bpMRI) and multiparametric MRI (mpMRI) of the prostate in comparison to radiologists.
Methods: Consecutive patients at a large healthcare enterprise who underwent prostate MRI (2015 to 2023) with subsequent radical prostatectomy within 1 year were included. The dataset was divided into training/validation/test sets. Transfer learning models, composed of two multi-branch 3D convolutional neural networks based on a 3D ResNet-50 backbone, were trained on bpMRI (AI bp ) with an input of axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) and on mpMRI (AI mp ) with an input of axial T2WI, axial DWI, and axial post-contrast imaging. No prostate gland or tumor segmentations were performed. A logistic regression model with prostate specific antigen density was also trained with the AI mp model (AI mpPSAD ). Three fellowship-trained abdominal radiologists evaluated the test set for EPE on a 1 to 5 scoring system based on capsular appearance, on bpMRI and mpMRI for each study. Areas under the receiver operating characteristic curves (AUC) were obtained and compared with Delong test. Diagnostic statistics were also obtained.
Results: 1,232 prostate MRIs were included (1003/113/116 in the training/validation/test sets). AUC for models were as follows: AI bp (0.71), AI mp (0.72), and AI mpPSAD (0.73). While holding sensitivity constant at the Youden index for AI bp (0.77), specificity and accuracy of the AI bp , AI mp , and AI mpPSAD were 0.64, 0.69, 0.71 and 0.71, 0.73, 0.74, respectively. The ranges of AUC for readers were: bpMRI (0.66–0.71) and mpMRI (0.69–0.74). The ranges of accuracy of the readers on bpMRI was 0.60–0.68 and on mpMRI was 0.65–0.72.
Conclusion: A segmentation-free transfer learning model performs comparably to fellowship-trained radiologists on the detection of EPE on both bpMRI and mpMRI. There was a trend of increasing accuracy with the addition of post contrast sequences for all models and readers.
Journal IF: unknown (could not be matched)Reference: Tong A, Daniels A, Ginocchio L, Smereka P, Dutt T, Umapathy L, et al. Detection of extraprostatic extension of prostate cancer by transfer learning AI models versus radiologists on biparametric and multiparametric MRI. Research Square. 2026 Oct 5 [Epub ahead of print]. doi:10.21203/rs.3.rs-11001056/v1.Checked: Abstract only - Abstract
Accurate segmentation of the prostate and surrounding structures is crucial in radiotherapy planning to ensure effective treatment delivery while minimising radiation exposure to nearby tissues. However, existing deep learning (DL) segmentation studies often exclude clinically important structures such as the penile bulb (PB) and seminal vesicles (SV), rely heavily on large proprietary datasets that are difficult to acquire in clinical settings, and lack direct comparisons with traditional atlas-based methods. To address these gaps, this study provides practical insights for hospitals considering the implementation of DL systems by evaluating 2D U-Net models across six structured experiments, including one experiment comparing a 3D U-Net model. Notably, this is the first study to utilises publicly available prostate CT data in DL training and integrates public and private datasets to improve model robustness for prostate and adjacent anatomical structures. The inclusion of underrepresented structures, such as PB and SV, enhances the clinical applicability of the models. Our results demonstrated that moderate-sized datasets, ranging from 60 to approximately 300 in our study, can achieve sub-millimetre Mean Distance Agreement (MDA) for key organs at risk like the bladder (0.60 mm) and rectum (0.93 mm), reducing reliance on large-scale annotated data. Models trained on routine clinical contours performed well without extensive manual refinement, demonstrating that existing clinical data can be leveraged for model training. In comparisons with four commercial atlas-based tools, the 2D U-Net models achieved superior accuracy for key anatomical structures, including the prostate, bladder, and SV, with our deep learning model achieving an MDA of 1.28 mm for the prostate compared to the 1.4-2.5 mm range of the atlas tools. Although 3D models provided improved spatial context for small structures such as SV, 2D models proved to be a practical alternative due to lower computational demands, making them suitable for resource-limited clinical settings.
Journal IF-equivalent: 2.4 (OpenAlex 2-year mean citedness, value as of 2026-10-06, retrieved 2026-10-07; not the official Clarivate IF)Reference: Hizam DA, Saad M, Ung NM, Muaadz A, Salleh FM, Tan LK. Practical deep learning solutions for prostate cancer segmentation and implementation in radiotherapy planning. Phys Eng Sci Med. 2026 Oct 5 [Epub ahead of print]. doi:10.1007/s13246-026-01815-2.Checked: Abstract only
Adenoid cystic carcinoma: treatment, trials and AI
No new qualifying paper for this issue.
Brain tumours: treatment, trials and AI
- Abstract
INTRODUCTION: Gliomas are the most common primary malignant tumors of the central nervous system, characterized by significant biological heterogeneity and poor prognosis. Multiparametric MRI-based image analysis has shown promise in prognostic assessment; however, effective integration of deep learning (DL), radiomics, and intratumoral heterogeneity (ITH) quantification remains underexplored. This study aims to develop an integrated prognostic model incorporating clinical and multiparametric MRI-derived features and systematically evaluate the predictive performance of different feature combinations. METHODS: This retrospective, multicenter study included 183 patients from the UCSF-TCIA cohort as the training set and 109 patients from Chongqing General Hospital as an independent test set. Based on preoperative multiparametric MRI-including T1-weighted imaging (T1WI), T1-weighted contrast-enhanced imaging (T1CE), T2-weighted imaging (T2WI), T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC)-radiomics, DL, and 2D/3D ITH features were extracted. Feature selection was performed using Least Absolute Shrinkage and Selection Operator-Cox (LASSO-Cox) regression, and both single and fusion models were constructed. Feature-level integration was achieved using a two-stage LASSO-Cox regression to leverage the complementary nature of information from different sources fully. Performance was quantified using the Harrell concordance index (C-index) and validated via Kaplan-Meier survival analysis, time-dependent receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). RESULTS: The fusion model integrating DL, radiomics, ITH, and clinical features (DenseNet121_DL+Rad+ITH) achieved C-indices of 0.801 (95% CI: 0.750-0.853) and 0.710 (95% CI: 0.638-0.783) on the training and test sets, respectively, outperforming all single models. The corresponding stacking ensemble yielded a comparable test C-index of 0.701. Kaplan-Meier analysis confirmed significant risk stratification (P < 0.001). Time-dependent ROC analysis showed AUCs of 0.705, 0.721, 0.746, 0.800, and 0.873 at 12, 18, 24, 30, and 36 months. Calibration curves demonstrated good agreement between predicted and observed survival probabilities. DCA indicated favorable clinical net benefit for medium- to long-term survival prediction (24-36 months). CONCLUSIONS: An integrated prognostic model combining clinical characteristics with multiparametric MRI-derived features (DL, radiomics, and ITH) demonstrates strong prognostic potential for glioma patients. Fusion strategies generally outperform single-feature models; however, incremental gains from fusion are modest, and prospective validation in larger cohorts remains necessary.
Journal IF-equivalent: 2.7 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Yang Y, Su K, Tang K, Li K. An integrated prognostic model incorporating clinical characteristics, multiparametric MRI radiomics, deep learning features, and intratumoral heterogeneity for glioma patients. Front Oncol. 2026 Sep 21;16:1873715. doi:10.3389/fonc.2026.1873715; PMCID: PMC13635987.Checked: Full text checked
Ovarian cancer: treatment, trials and AI
No new qualifying paper for this issue.
Endometrial cancer: treatment, trials and AI
No new qualifying paper for this issue.
Gastric cancer: treatment, trials and AI
- Abstract
In locally advanced gastric cancer, a substantial number of patients relapse with liver metastasis months after an apparently curative operation, and standard tumor staging offers little warning of who is at risk. Here, we develop the Radiopathomics-Clinical Stratification Assessment (RCSA), an interpretable model that integrates three complementary sources of information: radiomic features from preoperative computed tomography, pathomic features from routine hematoxylin and eosin tumor slides, and conventional clinical features. Trained on patients from one hospital and then tested on separate internal, external, public, and prospective trial groups (NCT02555358), RCSA consistently separates high- and low-risk patients, with area under the curves between 0.862 and 0.909. Tumors it labels low-risk carry a notably more active immune environment, indicating that these patients are the ones most likely to gain from added immunotherapy. RCSA therefore turns existing hospital data into individualized guidance for postoperative follow-up and treatment.
Journal IF-equivalent: 18.1 (OpenAlex 2-year mean citedness, value as of 2026-10-06, retrieved 2026-10-07; not the official Clarivate IF)Reference: Ding P, Yang J, Guo H, Chen S, Liu Y, Han X, et al. Multimodal radiopathomics model predicts postoperative metachronous liver metastasis in gastric cancer. Nat Commun. 2026 Sep 4;17(1):10518. doi:10.1038/s41467-026-76382-x.Checked: Abstract only
Liver cancer: treatment, trials and AI
- Abstract
BACKGROUND: The T cell-inflamed gene expression profile (GEP) provides a biomarker for immunotherapy response across various cancers. However, specific biomarkers for immunotherapy response in hepatocellular carcinoma (HCC) are lacking. In this study, we established a computed tomography (CT)-based radiomics model to predict the T cell-inflamed GEP and immunotherapy response in HCC. METHODS: This retrospective, multicenter study included 270 patients with HCC, who were divided into training ( n = 227) and test sets ( n = 43). All included patients had available contrast-enhanced CT and RNA sequencing data. The T cell-inflamed GEP consisted of 18 genes derived from RNA sequencing data, and patients were classified into GEP-high and GEP-low groups. A support vector machine was used to establish the radiomics model. The immunotherapy dataset comprised patients who underwent contrast-enhanced CT and received anti-programmed cell death protein 1/programmed cell death ligand 1 immunotherapy at three hospitals (209 lesions). This immunotherapy dataset was used to evaluate the association between the predicted GEP status and immunotherapy response. Additionally, overall survival was compared between patients with and without predicted GEP-high lesions in the immunotherapy dataset. RESULTS: The areas under the receiver operating characteristic curve of the radiomics model for predicting GEP in the training and test sets were 0.824 (95% confidence interval [CI] = 0.757-0.892) and 0.827 (95% CI: 0.674-0.980), respectively. In the immunotherapy dataset, the response rate of liver lesions at 6 months was significantly higher in the GEP-high group than in the GEP-low group (48.1% vs . 9.3%, p < 0.001). The areas under the receiver operating characteristic curve of the radiomics model for predicting immunotherapy response at 6 months was 0.750 (95% CI: 0.614-0.886). Median overall survival among patients with GEP-high lesions was 17.5 months, which notably exceeded the value of 8.3 months for patients without GEP-high lesions. CONCLUSIONS: This study provides the first CT-based radiomics model for predicting the T cell-inflamed GEP in HCC. This model highlights the potential of the T cell-inflamed GEP as a noninvasive biomarker to help guide immunotherapy selection.
Journal IF-equivalent: 4.2 (OpenAlex 2-year mean citedness, value as of 2026-10-06, retrieved 2026-10-07; not the official Clarivate IF)Reference: Tang M, Sun K, Wu D, Zou Y, Zhou J, Li B, et al. A Computed Tomography Radiomics Approach for Assessing the T Cell‐Inflamed Gene Expression Profile in Hepatocellular Carcinoma and Its Association With Immunotherapy Response. Health Care Sci. 2026 Oct 4 [Epub ahead of print]. doi:10.1002/hcs2.70106; PMCID: PMC13635722.Checked: Full text checked - Abstract
OBJECTIVE: To develop and externally validate an interpretable deep learning model for pre-treatment prediction of early clinically significant immune-related adverse events in patients with hepatocellular carcinoma receiving immune checkpoint inhibitor-based therapy. METHODS: We conducted a multicenter retrospective cohort study of patients with hepatocellular carcinoma receiving immune checkpoint inhibitor-based therapy, with atezolizumab plus bevacizumab serving as the representative regimen in this cohort. The study population was divided into a training cohort and an independent external validation cohort. Early immune-related adverse events were defined as clinically significant events occurring within three months after treatment initiation. Five prediction models were constructed using baseline clinical and circulating immunological variables obtained prior to treatment, including TabNet, logistic regression, random forest, extreme gradient boosting, and support vector machine models. Model performance was evaluated using discrimination, calibration, and clinical utility metrics, and model interpretability was assessed using feature attribution analyses. RESULTS: Among all models, the TabNet model showed the most favorable overall performance profile, considering discrimination, calibration, clinical utility, and external validation performance. Calibration analysis showed good agreement between predicted risks and observed event rates, while decision curve analysis and clinical impact assessment indicated meaningful clinical utility across a range of threshold probabilities. Interpretability analyses identified the CD4 to CD8 ratio, serum immunoglobulin G level, albumin, platelet count, and the proportion of CD19-positive B cells as key contributors to model-predicted early immune-related adverse event risk. Risk-stratified feature clustering further suggested that immune-related features and host functional reserve may represent complementary dimensions associated with model-predicted early immune-related toxicity risk. CONCLUSIONS: An interpretable deep learning model was developed and externally validated for pre-treatment prediction of early clinically significant immune-related adverse events in hepatocellular carcinoma patients receiving immune checkpoint inhibitors.
Journal IF-equivalent: 5.5 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Pan H, Zhao C, Sun H, Zhao H, Tang G, Du X, et al. Development and external validation of an interpretable deep learning model for early immune-related adverse events in hepatocellular carcinoma. Front Immunol. 2026 Sep 21;17:1886946. doi:10.3389/fimmu.2026.1886946; PMCID: PMC13635132.Checked: Full text checked
Kidney cancer: treatment, trials and AI
No new qualifying paper for this issue.
Bladder cancer: treatment, trials and AI
- Abstract
Upper-tract urothelial carcinoma (UTUC) is rare, constitutes < 5% of urothelial cancers, and, given its insidious clinical presentation, is typically diagnosed at advanced stages; post-radical nephroureterectomy recurrence remains challenging. Postoperative management is primarily guided by pathological factors, including T-stage and lymph-node status, which cannot accurately identify patients who would benefit from treatment intensification, particularly considering concerns regarding cisplatin-associated nephrotoxicity. Using an artificial intelligence (AI)-informed pathology model, we predicted postoperative recurrence using UTUC’s quantitative nuclear features. Among 222 patients with UTUC, support vector machine (SVM) and random forest (RF) models were trained using pT3 cases ( n = 68) comprising sufficient recurrence events. Patient-level model performance was evaluated and validated using an independent test cohort ( n = 50; pT1 = 12, pT2 = 11, pT3 = 22, and pT4 = 5). RF and SVM models achieved patient-level accuracies of 77.3% and 63.6%, respectively, in pT3 cases and overall accuracies of 80% and 68%, respectively, in the independent pT1–pT4 cohort. Model-derived risk stratification (1 point per model with predicted recurrence probability ≥ 0.5 and classifying patients into low, intermediate, and high-risk groups [0, 1, and 2 points, respectively]) significantly discriminated recurrence-free survival in the independent test cohort (pT1–pT4), distinguishing risk groups (low-risk: n = 7, intermediate-risk: n = 8, high-risk: n = 35; log-rank p = 0.01); the low-risk group had no recurrence.
Journal IF-equivalent: 5.2 (OpenAlex 2-year mean citedness, value as of 2026-10-04, retrieved 2026-10-05; not the official Clarivate IF)Reference: Muraoka R, Saito A, Tokuyama N, Matsubara S, Shen B, Tsujino T, et al. A novel machine-learning model using nuclear features to predict upper tract urothelial carcinoma recurrence. Sci Rep. 2026 Oct 5 [Epub ahead of print]. doi:10.1038/s41598-026-72946-5.Checked: Abstract only - Abstract
Ubiquitination-regulated molecular events exert crucial regulatory effects on the initiation and progression of bladder cancer (BCa), yet clinically actionable ubiquitination-related prognostic models are still in short supply. This study is designed to construct a ubiquitination-associated prognostic signature for BCa via machine learning strategies. Ten machine learning algorithms with 101 parameter combinations analyzed BCa transcriptomes. The prognostic model was validated using time-dependent receiver operating characteristic curves, Kaplan-Meier survival analysis, nomogram construction, and multivariate Cox regression. To elucidate underlying biological functions, we conducted immune microenvironment profiling (via CIBERSORT algorithm) and gene set enrichment analysis (GSEA). Molecular docking experiments were performed using PubChem compound libraries, Protein Data Bank structures, and the CB-DOCK2 platform to screen potential drug targets. Subsequent experimental validation included Polymerase Chain Reaction (PCR), cell migration assays (Transwell and wound healing assays), and cell proliferation evaluations (colony formation and CCK-8 assays). Transcriptomic analysis identified 106 ubiquitination-related differentially expressed genes (DEGs) specific to BCa, among which 9 prognosis-related genes were filtered out via univariate Cox regression analysis. Our machine learning-based ubiquitination-related prognosis-associated signature showed superior performance over conventional clinical predictors, enabling accurate stratification of BCa patients into high- and low-risk subgroups. Computational docking results revealed strong binding affinities between the E3 ubiquitin ligase NRDP1 and classic agents. Notably, targeted knockdown of NRDP1 substantially impaired the migration capabilities of BCa cells. This study introduces a BCa machine learning derived prognostic model, identifies NRDP1 as a key BCa prognostic and therapeutic target, and highlights the value of integrating machine learning and ubiquitination biology.
Journal IF-equivalent: 5.2 (OpenAlex 2-year mean citedness, value as of 2026-10-04, retrieved 2026-10-05; not the official Clarivate IF)Reference: Zhong Z, Zheng F, Li S, He J, Yuan Y, Deng X, et al. Machine learning-driven identification of a ubiquitination-related prognostic signature and potential target NRDP1 in bladder cancer. Sci Rep. 2026 Oct 5;16(1):30948. doi:10.1038/s41598-026-67956-2.Checked: Abstract only