Cancer repeatedly exploits attributes fundamental for morphogenesis to advance malignancy and metastasis. This is illustrated by lineage-specific transcription factors that regulate neural crest migration, representing frequent drivers of malignancy. One such example is the forkhead transcription factor FOXC1, where gain of function is a feature of diverse cancers that is associated with an unfavorable prognosis. Using RNA-, ChIP-sequencing and CRISPR interference, we show that Foxc1 binds a locus in a region of closed chromatin to induce expression of Arhgap36, a tissue-specific inhibitor of protein kinase A. Because PKA is a core Hedgehog (Hh) pathway inhibitor, Foxc1's induction of Arhgap36 expression increases Hh activity. The function of Sufu, a PKA substrate, and a second essential Hh pathway inhibitor, is likewise impaired. The resulting increased Hh pathway output is resistant to pharmacological inhibition of Smoothened, a phenotype of more aggressive cancers. The Foxc1-Arhgap36 relationship identified in murine cells was further evaluated in neuroblastoma, a neural crest-derived pediatric malignancy. This demonstrated in a cohort of 1348 patients that high levels of ARHGAP36 are predictive of improved 5-year survival. Accordingly, this study has identified as a novel transcription factor which enhances ARHGAP36 expression, one that induces Hh activity in multiple tissues during development. It also establishes a model by which increased levels of FOXC1 via ARHGAP36 and PKA inhibition dysregulate multiple facets of Hh signaling and provides evidence demonstrating relevance to a common neural-crest-derived malignancy.

The association between radiotherapy (RT) for localized prostate cancer and second primary cancer (SPC) remains controversial. We evaluated the incidence and risk of SPC after RT in a Korean single-center cohort.

We retrospectively analyzed 1,464 patients with localized prostate cancer treated between 2007 and 2018. SPC incidence was compared between RT and non-RT groups using Cox proportional hazards and Fine-Gray competing risk regression models. Standardized incidence ratios (SIRs) were calculated using age- and calendar year-specific incidence rates from the Korea Central Cancer Registry. Propensity score matching (PSM) was performed as a sensitivity analysis.

During follow-up, 155 patients (10.6%) developed SPC. RT was associated with a higher risk of SPC compared with non-RT in multivariable Cox analysis (hazard ratio [HR] 3.43, 95% confidence interval [CI] 2.43-4.84) and Fine-Gray competing risk regression (subdistribution HR 3.32, 95% CI 2.36-4.67). SIR analysis demonstrated a markedly increased incidence of secondary bladder cancer in the RT group (SIR 11.51, 95% CI 6.83-18.20), whereas no increase was observed in the non-RT group. In contrast, colorectal cancer showed only a modest elevation after RT. In the PSM cohort, RT remained significantly associated with SPC in univariate analysis. Exploratory analyses according to RT modality and radiation field showed no significant differences.

RT for localized prostate cancer was associated with an increased occurrence of SPC, particularly bladder cancer. These findings should be interpreted cautiously given the retrospective design and potential residual confounding.

The increasing detection of small renal masses (SRMs, ≤4 cm) has raised concern for overdiagnosis and overtreatment, as a substantial proportion of these lesions prove to be benign. Identifying preoperative predictors of benign histology may help refine treatment selection and reduce unnecessary surgery.

We performed a retrospective multicenter cohort study including patients who underwent partial or radical nephrectomy for clinical T1a renal masses at eight tertiary referral centers in South Korea between 1990 and 2023. Clinicodemographic, laboratory, and radiologic variables were compared between benign and malignant tumors. Independent predictors of benign pathology were identified using multivariable logistic regression.

Among 5,713 surgically treated renal masses, 289 (5.1%) were benign. Compared with malignant tumors, benign lesions were more common in females (58.8% vs. 28.2%, p<0.001) and were associated with lower body mass index (BMI) (23.60 kg/m² vs. 24.80 kg/m², p<0.001), higher estimated glomerular filtration rate (eGFR) (93.67 mL/min/1.73 m² vs. 84.65 mL/min/1.73 m², p<0.001), and smaller tumor size (20.60 mm vs. 24.86 mm, p<0.001). On multivariable analysis, female sex (odds ratio [OR] 3.126, p<0.001), tumor size ≤2 cm (OR 2.335, p<0.001), lower BMI (OR 0.910 per kg/m², p<0.001), and higher eGFR (OR 1.023 per mL/min/1.73 m², p<0.001) were independently associated with benign histology.

Female sex, lower BMI, and smaller tumor size are significant preoperative predictors of benign histology in SRMs. These findings support consideration of active surveillance or percutaneous biopsy in patients with multiple favorable predictors to minimize overtreatment.

To evaluate whether a natural language-driven analytical workflow specified through a large language model (LLM) can accurately encode, execute, and reproducibly replicate a predefined statistical process traditionally performed in SPSS, and to assess its efficiency and auditability in urological research.

A dataset of 531 patients from a prostate cancer diagnostic study was analyzed in parallel using SPSS and ChatGPT's Data Analyst environment. Equivalent analytical procedures and variables were applied to calculate sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy. Concordance between methods was assessed using absolute deviation and reproducibility rates. Processing time and user workload were also compared.

After two prompt-refinement steps to align analytical definitions, the LLM-based analysis achieved full concordance with SPSS across all diagnostic metrics, with a mean absolute deviation of <0.5%. Execution time for the stabilized artificial intelligence workflow was 8 minutes compared with 55 minutes for the conventional SPSS workflow, reflecting steady-state performance after workflow stabilization and excluding the initial prompt-refinement phase. Both methods produced identical diagnostic indices for clinically significant prostate cancer.

A natural language-driven workflow can accurately specify and reproducibly execute a predefined statistical analysis with full numerical concordance under controlled conditions, while substantially reducing execution time after workflow stabilization. These findings support LLM-based interfaces as complementary tools for reproducible and auditable analytical workflows, rather than replacements for conventional statistical engines.

Prostate cancer (PCa) is traditionally diagnosed using prostate-specific antigen (PSA)-based testing together with demographic and clinical factors. Building on this framework, we aimed to develop an AI (artificial intelligence) model for prebiopsy PCa diagnosis by integrating Korean population-relevant risk-associated single nucleotide polymorphisms (SNPs) to improve diagnostic accuracy.

Three models were developed in this study: Korean PCa-specific genomic score (GenPCa-Kor score), electronic medical record (EMR) meta-model, and Geno-EMR meta-model. From genome-wide association study summary statistics, 1,347 PCa-associated SNPs were selected for a deep neural network to derive the GenPCa-Kor score. Thirteen clinico-laboratory EMR parameters were used to build a stacking ensemble (EMR meta-model) with Light Gradient Boosting Machine, and Histogram-based Gradient Boosting Machine, and logistic regression as base learners and logistic regression as the meta-learner, using 10-fold cross-validation and Bayesian hyperparameter optimization. The Geno-EMR meta-model added the GenPCa-Kor score as a 14th feature to the same architecture.

Of 1,590 systematic biopsy-confirmed participants, 1,006 were analyzed; 757 comprised the training cohort and 249 consecutive patients comprised the independent test cohort. In the training cohort, the EMR meta-model and Geno-EMR meta-model achieved area under curves (AUCs) of 0.868 and 0.924, respectively. In the test cohort, their AUCs were 0.859 and 0.892, respectively. For clinically significant PCa (Grade Group ≥2), the Geno-EMR meta-model further improved the AUC from 0.887 to 0.911.

The Geno-EMR meta-model that integrate routine clinico-laboratory parameters with the SNP-based GenPCa-Kor score showed improved discrimination for PCa compared with the EMR meta-model alone.

Head and neck cancers (HNCs) are undergoing a major transition, driven by the changing epidemiology and advances in multidisciplinary, precision-based care aimed at improving survival, function and prevention.

Cancer care is increasingly delivered in outpatient and home settings, transferring substantial clinical responsibilities to patients and family caregivers. Although self-management interventions are widely studied, less is known about how home-based cancer self-management is experienced and co-produced by family caregivers and healthcare providers within real-world contexts.

To explore home-based cancer self-management from the perspectives of family caregivers and healthcare providers in Oman.

A qualitative descriptive design was used. Purposive sampling recruited 25 family caregivers of adult cancer survivors and 26 healthcare providers from 3 tertiary oncology institutions in Muscat, Oman. Semi-structured individual interviews were conducted and analyzed using inductive qualitative content analysis.

Participants included 51 individuals representing both caregiving and professional perspectives. Five themes emerged: relocation of clinical work to the home, structured education and skill transfer, psychological protection and emotional labor, negotiated illness disclosure, and support systems amid structural barriers. Self-management was described as a relational and system-embedded process shaped by family dynamics, professional guidance, and contextual constraints.

Home-based cancer self-management extends beyond symptom control to include emotional regulation, communication negotiation, and navigation of systemic demands. Its effectiveness depends on coordinated interaction among caregivers, healthcare providers, and supportive infrastructure.

Oncology nurses play a central role in preparing and supporting family caregivers through structured, repeated education and clear discharge guidance. Routine integration of psychosocial assessment and culturally sensitive communication strategies can strengthen treatment adherence and improve sustainability of home-based cancer care.

The incidence of early-onset colorectal cancer (EO-CRC; age <50 years) has been increasing worldwide. This single-center retrospective study aimed to compare the clinical characteristics of EO-CRC and late-onset CRC (LO-CRC; age ≥ 50 years) and to identify age-specific prognostic factors for overall survival (OS).

A total of 1,148 CRC patients were retrospectively analyzed and categorized into EO-CRC (n = 247) and LO-CRC (n = 901) groups. Clinical characteristics were compared using the Mann-Whitney U test and Chi-square test. Prognostic factors associated with OS were identified using Least absolute shrinkage and selection operator (LASSO) Cox regression followed by multivariate Cox modeling. Model performance was evaluated using the C-index, calibration curves, and time-dependent Receiver Operating Characteristic (ROC) analysis. Variable importance was further validated using a random survival forest (RSF) model.

EO-CRC patients showed higher proportions of family history, concurrent polyps, and Programmed Cell Death Ligand 1 (PD-L1) expression >10%, whereas LO-CRC patients exhibited higher rates of hypertension, diabetes, and elevated carcinoembryonic antigen (CEA) levels. Although OS did not differ significantly between groups (P = 0.460), their prognostic determinants varied markedly. In EO-CRC, distant metastasis, family history, Tumor, Node, and Metastasis (TNM) stage, PMS1 homolog 2, mismatch repair system component (PMS2), MutS Homolog 6 (MSH6), tumor size, concurrent polyps, and Ki-67 were major predictors. In LO-CRC, age, BRAF gene V600E mutation (BRAF V600E) mutation, elevated Carbohydrate antigen 19-9 (CA19-9), Ki-67, low hemoglobin, vascular invasion, MutL Homolog 1 (MLH1), and pathological type were significant contributors. The C-index values for the EO-CRC and LO-CRC models were 0.829 (SE = 0.023) and 0.751 (SE = 0.018), respectively, and all time-dependent ROC curves demonstrated Area Under the Curve (AUCs) above 0.70, indicating good predictive performance. RSF analyses further confirmed that distant metastasis and family history as the strongest predictors, while age and BRAF V600E are the strongest predictors for LO-CRC.

This study suggests that EO-CRC and LO-CRC have fundamentally different prognostic determinants: the former emphasizes genetic susceptibility and tumor invasiveness, indicating that this group of patients may benefit from early genetic counseling, MMR/MSI testing, and immune checkpoint inhibitor therapy. The latter highlights age, acquired molecular changes, and chronic systemic factors, supporting the inclusion of metabolic and geriatric assessments in routine tumor care.

Gastric cancer remains a major cause of cancer mortality worldwide, particularly in middle-income countries, where late diagnosis is frequent and information on its economic impact is limited. We evaluated survival outcomes and direct healthcare costs of adult patients with gastric cancer treated between 2019 and 2024 at Hospital Universitario Mayor Méderi, a tertiary referral hospital in Bogotá, Colombia, using routinely collected clinical and administrative data from a tertiary referral hospital. Overall survival and healthcare resource utilization were described, and cumulative direct medical costs over 5 years were estimated in thousands (k) of 2023 international dollars (Int$). A total of 616 adult patients with gastric cancer were identified in hospital records during the study period. Of these, 388 patients (63.0%) met the eligibility criteria and were included in the final analytic sample, whereas 228 patients (37.0%) were excluded according to the predefined exclusion criteria. The median age was 65 years [interquartile range (IQR): 57-73]; 36.9% women). Using Tumor-Node-Metastasis staging (TNM: tumor extent, regional lymph node involvement, and distant metastasis) recorded in medical charts, 36.3% had stage III and 34.8% stage IV disease. Initial treatment intent was curative in 46.6% and palliative in 53.4%. The estimated cumulative mean direct cost was Int$ 89.3 k (95% CI: 75.9 k-102.8 k) at 2 years and Int$ 161.2 k (95% CI: 92.0 k-230.4 k) at 5 years. Median overall survival was 30.7 months (95% CI: 22.7-38.7), and the estimated 5-year survival probability was 40.0%; it was 45.5% for stage III disease and 12.5% for stage IV disease. Gastric cancer imposes both high mortality and substantial economic burden on the healthcare system, largely determined by the clinical stage at diagnosis. These findings highlight the potential population-level and financial benefits of strategies aimed at earlier detection and timely access to oncologic care in middle-income countries.

Neoadjuvant chemoimmunotherapy improves pathological response in resectable non-small cell lung cancer (NSCLC), but response remains heterogeneous. PD-L1 tumor proportion score (TPS) incompletely captures spatial immune contexture. We developed and cross-center validated an immunohistochemistry (IHC)-derived equal-weight immune niche score integrating tertiary lymphoid structure (TLS) maturity, CD8-TLS proximity, CD8/FOXP3 balance, CD163/CD68 ratio, and PD-L1 TPS.

This two-center retrospective cohort included 326 patients with resectable NSCLC treated with neoadjuvant chemoimmunotherapy. The model-development cohort included 188 patients, and an institution-level external-validation cohort within the same regional medical system included 138 patients. The primary endpoint was major pathological response (MPR). Model performance was evaluated using discrimination, calibration, decision curve analysis, and ablation analysis. Sensitivity analyses tested alternative CD8-TLS thresholds, ICI-agent subgroups, missing-data approaches, Granzyme B incorporation, and alternative weighting. Exploratory analyses assessed event-free survival (EFS), overall survival (OS), interobserver reproducibility, and asthma-related pulmonary safety.

Overall, 146 patients (44.8%) achieved MPR and 42 (12.9%) achieved pathological complete response. MPR tumors had higher TLS maturity, CD8+ cells within 50 μm of TLS, CD8/FOXP3 ratio, PD-L1 TPS, lower CD163/CD68 ratio, and higher immune niche score. In external validation, the score achieved an AUC of 0.732 (95% CI, 0.648-0.816), compared with 0.564 for the clinical model, 0.635 for PD-L1 alone, 0.613 for clinical + PD-L1, and 0.692 for XGBoost, with acceptable calibration. CD8-TLS proximity (OR 1.68, 95% CI 1.03-2.79), CD163/CD68 ratio (OR 0.66, 95% CI 0.48-0.91), and the composite score (OR 2.72 per 1 SD, 95% CI 2.05-3.69) were independently associated with MPR. Performance was stable across sensitivity analyses. Exploratory EFS was more favorable in the high-score group. Asthma history was not independently associated with MPR but was associated with higher composite pulmonary adverse-event rates.

This transparent IHC-derived TLS-CD8-macrophage immune niche score showed moderate cross-center validation performance for MPR prediction after neoadjuvant chemoimmunotherapy in resectable NSCLC, while remaining pathologically interpretable. It may complement PD-L1 TPS for response stratification, pending broader multicenter and prospective validation.