Identifying cancer driver genes (CDGs) remains a central challenge in cancer genomics, as frequency-based mutation approaches often miss rare but functionally important regulators. We present PICDGI, a computational framework that predicts driver-like regulatory genes by integrating dynamic gene-gene interaction modeling with single-cell RNA sequencing (scRNA-seq) data. Rather than relying on DNA mutation calls, PICDGI infers functional driver activity from time-resolved expression patterns and latent regulatory influence among genes during tumor progression. Methodologically, PICDGI employs a time-varying state-space model with variational Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling to estimate evolving gene interaction effects. The posterior distributions capture both the magnitude and uncertainty of each gene's inferred regulatory influence. From these, PICDGI derives a driver coefficient that quantifies the strength and reliability of each gene's contribution to progression-associated expression dynamics, enabling the prioritization of impactful regulators over neutral passengers. Applied to lung adenocarcinoma (LUAD) scRNA-seq data, PICDGI recovered known oncogenes and tumor suppressors and nominated novel candidate drivers, including JPH1 and CHEK1, which are implicated in calcium signaling, mitochondrial regulation, and DNA repair. These genes exhibit trajectory-aligned activity consistent with tumor evolution and immune-modulatory processes. Comparative analysis using Moran's I statistics in Monocle 3 showed that PICDGI-prioritized genes display stronger progression-associated dynamics than genes selected by spatial autocorrelation alone. We further validated PICDGI on an independent pediatric acute myeloid leukemia (AML) scRNA-seq cohort, where it consistently recovered known drivers and relapse-associated regulatory programs under fixed model parameters. By integrating interaction-informed dynamic modeling with single-cell resolution data, PICDGI provides a generalizable and biologically grounded framework for identifying rare and context-specific regulatory drivers of cancer progression, with broad applicability across tumor types.

Traditional measures of treatment success for radiotherapy in dogs with intracranial neoplasia include progression-free and overall survival time. Although important, these measures do not reflect neurologic function.

Assess tumor shrinkage by follow-up imaging and outcome using 2 neurodisability scoring systems-1 validated and 1 simplified.

One hundred six dogs with imaging-diagnosed intracranial tumors treated with 10-fraction definitive-intent radiotherapy.

Data were collected from 2 randomized trials. Neurologic function was prospectively assessed using a validated score, and a retrospective simplified score was added. Imaging was recommended every 6 months or upon clinical decline.

Diagnoses included extraparenchymal tumors (45.3%), intraparenchymal tumors (35.8%), and pituitary tumors (18.9%). Median follow-up was 581 days. The neurodisability score improved significantly before radiotherapy (median 1.0, P = .04) because of medical management, and again during treatment (median 0.0, P < .01). At peak response, 76% of dogs had no or only mild neurologic deficits. Tumor volume significantly decreased at 6 and 12 months (P < .01): median shrinkage at 6 months was -39% (extraparenchymal tumors), -83% (intraparenchymal tumors), and -47% (pituitary tumors). A moderate correlation between tumor reduction and neurodisability score was seen only at 6 months (r = 0.395, P = .002). Results were consistent across protocols.

Radiotherapy led to lasting neurologic improvement and substantial tumor reduction. Neurologic function did not always correlate with tumor volume shrinkage, emphasizing the importance of incorporating and prioritizing neurologists' functional assessments in posttreatment evaluation.

Guidelines endorse specific life expectancy (LE) cutoffs for triage of definitive local treatment of prostate cancer, but the lack of validated, prostate cancer-specific LE prediction tools limits incorporation of LE in management decisions.

We sought to provide long-term LE predictions in older men using the Prostate Cancer Comorbidity Index (PCCI), a validated tool for prediction of other-cause mortality based on age and presence and severity of major comorbidities, in nationally representative cohorts of US prostate cancer patients.

We performed an observational study of 916,890 men in the SEER-Medicare database and 243,928 men in the VA diagnosed with clinically localized prostate cancer between 2000 and 2019.

PCCI scores were calculated using ICD-9 and ICD-10 codes. Kaplan Meier and multivariable Cox proportional hazards analysis were used to measure overall survival by age-adjusted PCCI score groups.

Median follow up was 11 years in both cohorts. In SEER-Medicare, men with PCCI scores of 1-2, 3-4, 5-6, 7-9, and 10+ had median estimated LE (95%CI) of 16.6 (16.5-16.7), 12.2 (12.1-12.2), 10.7 (10.6-10.7), 9.3 (9.2-9.3), and 5.4 (5.3-5.4) years, respectively. In the VA, men with the same PCCI scores had estimated median LE (95%CI) of 16.1 (16.0-16.3), 11.2 (11.1-11.4), 9.1 (8.9-9.2), 7.5 (7.4-7.7), and 5.2 (5.0-5.3) years, respectively.

The PCCI robustly predicts long-term longevity in SEER-Medicare and VA populations, with similar LE estimates in each. LE prediction tables with their accompanying variability estimates can help clinicians implement guidelines endorsed LE cutoffs for management of older men with prostate cancer.

Microsatellite stable/proficient mismatch repair (MSS/pMMR) colorectal cancer liver metastases (CRCLM) have limited treatment options after failure of second-line systemic therapies. Hepatic artery infusion chemotherapy (HAIC) combined with targeted therapy and immunotherapy may offer improved outcomes.

This retrospective single-center study compared the efficacy and safety of HAIC (oxaliplatin, 5-fluorouracil, leucovorin) combined with regorafenib and sintilimab (HAIC-R-S) versus regorafenib monotherapy in MSS/pMMR CRCLM patients refractory to ≥ 2 lines of systemic therapy. Propensity score matching was used to balance baseline characteristics, resulting in 45 matched pairs (n = 90). Survival outcomes, tumor response, and adverse events (AEs) were analyzed.

The HAIC-R-S group showed significantly improved median progression-free survival (PFS) (6.5 vs. 3.4 months; p < 0.001) and overall survival (OS) (14.1 vs. 8.1 months; p < 0.001) compared to regorafenib alone. Objective response rate (ORR) (37.8% vs. 2.2%; p < 0.001) and disease control rate (71.1% vs. 42.2%; p = 0.006) were also superior. Grade ≥ 3 AEs were more frequent in the combination group (26.7% vs. 13.3%), primarily hematologic toxicities.

In this retrospective, hypothesis-generating study, HAIC (oxaliplatin, 5-fluorouracil, leucovorin) combined with regorafenib and sintilimab showed improved PFS, OS, and ORRs compared with regorafenib monotherapy in refractory MSS/pMMR CRCLM, albeit with increased hematologic toxicity. These findings require validation in prospective, multicenter randomized trials before clinical implementation.

Given the limited evidence on prognostic factors specifically for older patients with extensive-stage small cell lung cancer (ES-SCLC), a population with distinct clinical characteristics, this study aimed to validate whether previously reported prognostic indicators retain their predictive value in this vulnerable group. A retrospective analysis was conducted on data from 270 older ES-SCLC patients who received treatment at the Fourth Hospital of Hebei Medical University between December 2016 and June 2024. By the final follow-up date of October 15, 2024, 212 deaths had been recorded. The median progression-free survival (mPFS) was 6.7 months (95% confidence interval [CI] 6.0-7.4), and the median overall survival (mOS) was 13.1 months (95% CI 11.8-14.4). For PFS, univariate and multivariate Cox analyses identified first-line chemotherapy (CT) and old-old (≥ 75 years) as independent adverse prognostic factors. For OS, old-old, a positive smoking history, bone metastasis, and high-lactate dehydrogenase (> 250 U/L) were identified as significant adverse prognostic factors. Notably, high-pro-gastrin-releasing peptide (ProGRP) (> 69.2 pg/mL) was significantly associated with an increased risk of death during the follow-up period beyond 10 months (HR = 1.85, 95% CI 1.05-3.26, p = 0.032); conversely, no significant association was observed within the initial 10 months of follow-up (HR = 0.84, 95% CI 0.44-1.60, p = 0.604). In conclusion, these findings not only corroborate the prognostic value of previously identified risk factors in older patients with ES-SCLC but also demonstrate that the prognostic impact of ProGRP is distinctly time-dependent.

This study aimed to explore the predictive value of intratumoral proton density fat fraction (PDFF) and the clinical efficacy of hepatic arterial infusion chemotherapy (HAIC) combined with anti-programmed cell death protein 1 (anti-PD-1) therapy in advanced hepatocellular carcinoma (HCC).

In this retrospective cohort study, patients with advanced HCC received FOLFOX-HAIC or HAIC combined with anti-PD-1 (camrelizumab). Progression-free survival (PFS) was evaluated as the time-to-event outcome, while therapeutic efficacy was assessed using tumor response rates. The Kaplan-Meier method and log-rank test were used to compare PFS. In the MRI-PDFF subset, receiver operating characteristic (ROC) analysis was used to determine the optimal PDFF cutoff for predicting nonresponse (SD or PD).

Between September 2020 and August 2025, 103 patients were included, of whom 47 received HAIC monotherapy and 56 received HAIC combined with anti-PD-1 therapy. The HAIC-PD1 group demonstrated significantly longer PFS compared with the HAIC group (HR 0.423; 95% CI 0.218-0.818; p = 0.0085), and a higher objective response rate (ORR: 46.4% vs. 21.3%; p = 0.012). In the MRI-PDFF subset, baseline intratumoral PDFF was associated with treatment response. ROC analysis identified an optimal PDFF cutoff of 2.64% for predicting nonresponse (AUC 0.784; 95% CI 0.664-0.903). Patients with PDFF < 2.64% achieved a higher ORR and longer PFS compared with those with PDFF ≥ 2.64%. Longitudinal analyses showed treatment-dependent changes in PDFF after HAIC-PD1 therapy; however, ΔPDFF did not differ significantly between responders and nonresponders.

HAIC combined with anti-PD-1 therapy demonstrated superior efficacy compared with HAIC monotherapy in advanced HCC. Baseline intratumoral PDFF may serve as a promising imaging biomarker associated with treatment response in patients receiving HAIC-PD1 therapy. Its potential prognostic relevance for time-to-event outcomes requires further validation in prospective cohorts.

Hepatocellular carcinoma (HCC) has a poor long-term prognosis due to high recurrence and cirrhosis-related mortality, even after potentially curative treatments such as liver transplantation (LT), surgical resection, or ablation. This study aimed to identify factors associated with ≥10-year survival in HCC patients.

A retrospective cohort study was conducted among HCC patients diagnosed between 2004 and 2022 using the National Cancer Database. Multivariable Cox regression was used to identify predictors of overall survival, and logistic regression was used to identify predictors of ≥10-year survival.

Among 249,600 HCC patients, 177,585 (71.2%) died within 5 years, 8613 (3.5%) died at 5-10 years, 54,988 (22.0%) were alive with <10 years of follow-up, and 8219 (3.3%) survived ≥10 years. LT, resection, and ablation were performed in 6.6%, 9.3%, and 11% of patients, respectively. Compared with ablation as the reference group, LT [adjusted odds ratio (aOR) 11.96, 95% confidence interval (CI): 11.27-13.29] and resection (aOR: 2.83, 95% CI: 2.57-3.08) increased the odds of ≥10-year survival, while non-curative treatments reduced the odds compared with ablation (aOR: 0.50, 95% CI: 0.47-0.55). Cox regression results were consistent with the logistic model, confirming the association. Decision tree analysis confirmed LT as the dominant determinant of long-term survival. Black individuals were associated with lower odds of ≥10-year survival (aOR: 0.88, 95% CI: 0.820-0.96) and decreased likelihood of receiving LT (aOR: 0.73, 95% CI: 0.55-0.96).

LT offers the best chance of ≥10-year survival in HCC. Ensuring equitable access is essential, especially for Black patients who have lower transplant rates and worse outcomes.

The efficacy of PD-1 inhibitor pucotenlimab (HX008) in solid tumors exhibits heterogeneity. This study integrated data from 6 clinical trials (covering gastric/gastroesophageal junction cancer, triple-negative breast cancer, melanoma, and dMMR/MSI-H solid tumors) using Bayesian meta-analysis, machine learning (optimal XGBoost AUC = 0.86), and network meta-analysis to construct an integrated "efficacy-prediction-safety" framework. Bayesian analysis showed pucotenlimab significantly improved outcomes versus control (ORR OR = 4.82, 95% CrI: 3.65-6.38; PFS HR = 0.41, 0.32-0.52; OS HR = 0.37, 0.26-0.51). Subgroups revealed TNBC patients with gemcitabine/cisplatin achieved highest ORR (80.6%, 62.5%-92.6%), while mucosal melanoma showed lowest response (8.7%, 1.1%-28.0%). Combination therapy demonstrated superior efficacy to monotherapy (ORR OR: 5.91 vs. 2.35). Machine learning identified 4 efficacy biomarkers (KMT2D mutation, post-treatment NLR decrease, PD-L1 CPS ≥ 1, high eotaxin) and 3 irAE risk factors (baseline NLR ≥ 4, irinotecan combination, high VEGF). Network analysis recommended regimens: gemcitabine/cisplatin for TNBC (SUCRA = 95.7%), oxaliplatin/capecitabine for G/GEJ cancer (ORR = 60.0% vs. irinotecan 27.6%, HR = 0.45). The integrated model classified high-benefit (≥ 3 points; ORR 78.2%) and low-benefit (≤ 0 points; ORR 28.3%) groups, plus high-risk (≤ -2 points; grade ≥ 3 irAEs 41.2%) and low-risk (≥ 1 point; irAEs 3.5%) groups, validated by decision curve analysis. This defines precise application scenarios and provides an extensible analytical paradigm.

Sorbin and SH3 Domain Containing 2 (SORBS2), a multifunctional scaffold protein harbouring Sorbin homology (SoHo) and Src homology 3 (SH3) domains, serves as a molecular hub in human diseases by integrating cytoskeletal remodelling, signal transduction and RNA metabolic regulation. This study systematically analyses SORBS2's molecular features, expression regulatory mechanism and disease associations. In oncology, it suppresses metastasis via enhancing the stability of certain mRNAs and immunomodulation yet exhibits oncogenic properties in triple-negative breast cancer. Cardiovascular manifestations demonstrate dose-sensitive pathology: deficiency causes arrhythmogenic cytoskeletal disorganization, while overexpression induces β-tubulin hyper polymerization and ventricular maldevelopment. Epigenetic silencing by miR-484 exacerbates metabolic liver disease, whereas defective interaction with the large-conductance Ca²⁺-activated K⁺ (BK) channel drives diabetic vasculopathy. Neurologically, it modulates synaptic remodelling and neuroinflammatory pathways. Functioning as a signalling nexus, SORBS2 interconnects chronic inflammation, oxidative stress and metabolic dysfunction, supporting 'one target for multiple diseases' strategy. Future research requires integration of single-cell omics, Artificial intelligence (AI)-based drug design and epigenetic editing for clinical translation.