ABSTRACTBackgroundTwo antiphagocytic (“don't eat me”) signals that allow tumor immune evasion have been discovered, including CD24 and CD47. This study explored the association between CD24/CD47 expression and macrophage infiltration and clinical outcomes in cervical cancer.MethodsRNA expression and survival data of the Cancer Genome Atlas (TCGA) cohort were extracted from OncoLnc. The macrophage infiltration level was calculated using xCell, TIMER, and ImmuCellAI. The expression of CD24 and CD47 was detected by immunohistochemistry in tissue microarrays composed of 130 clinical cervical cancer specimens from Fudan University Shanghai Cancer Center (FUSCC). Patients' medical records were also retrospectively assessed to correlate demographic and survival data.ResultsExpression levels of both CD24 and CD47 in the cancer population were higher than those in the normal population. Patients with high CD24 expression had poorer survival than those with low CD24 expression in the TCGA and FUSCC cervical cancer cohorts. Although CD47 alone was not statistically significant in predicting outcomes, patients with high CD47 and low CD11c expression, a specific marker of M1‐polarized macrophages, exhibited worse survival in the TCGA cohort.ConclusionsOur study implies that high CD24 expression is an important predictor of a worse prognosis, and CD24 blockade might have therapeutic potential for the treatment of cervical cancer. High expression levels of CD47 and low M1‐polarized macrophage infiltration predict a worse prognosis.

Frontline therapy for transplant-ineligible newly diagnosed multiple myeloma (TI-NDMM) has advanced with anti-CD38 monoclonal antibody (mAb)-based regimens. Although quadruplet combinations incorporating daratumumab and isatuximab have demonstrated improved response rates and progression-free survival (PFS), comparative overall survival (OS) data remain limited. We performed a systematic review, network meta-analysis (NMA), and reconstructed individual patient data meta-analysis comparing survival outcomes of quadruplet versus triplet regimens in TI-NDMM. This study adhered to Cochrane and PRISMA guidelines and was registered prospectively (PROSPERO CRD420251033401). A comprehensive literature search through April 2025 identified randomized clinical trials (RCT) evaluating quadruplet and triplet regimens involving daratumumab, isatuximab, bortezomib, lenalidomide, and dexamethasone in any combination, compared to their backbone regimens, reporting OS and PFS. Four RCT (n = 2,038) were included. At 60 months, estimated PFS rates were: D-VRd (66.4%), I-VRd (63.2%), D-Rd (51.9%), and VRd (42.6%). Both D-VRd and I-VRd significantly improved PFS compared with D-Rd (HR 0.65; 95% CI 0.48–0.87; and HR 0.68; 95% CI 0.52–0.89) and VRd (HR 0.51; 95% CI 0.39–0.67; and HR 0.53; 95% CI 0.41–0.67). D-Rd also showed superior PFS over VRd (HR 0.77, 95% CI 0.64–0.93; P = 0.007). At 60 months, OS rates were: D-VRd (72.8%), I-VRd (72.2%), D-Rd (67.1%), and VRd (67.0%). Pooled analyses demonstrated that quadruplets significantly improved both PFS (64.7% vs. 46.3%; HR 0.57, 95% CI 0.47–0.69; P < 0.0001) and OS (72.5% vs. 67.1%; HR 0.78, 95% CI 0.63–0.96; P = 0.02) compared to triplet regimens. The OS benefit of quadruplets was consistent in comparisons against both D-Rd (HR 0.77; 95% CI: 0.60–0.98; P = 0.04) and VRd (HR 0.77; 95% CI: 0.62–0.97; P = 0.02). In the NMA, quadruplet regimens ranked highest for complete response, PFS, and OS. This meta-analysis supports anti-CD38 mAb-based quadruplet regimens as superior frontline therapy in TI-NDMM, significantly improving overall survival.

To investigate the clinical features, treatment status and identify the prognostic factors of elderly patients with ovarian cancer. In this retrospective study, the clinical and survival data of 123 patients with ovarian cancer aged 65years or older who were treated in Shaanxi Cancer Hospital from June 2019 to June 2023 were collected, and the clinical and prognostic factors were extracted and analyzed. Among the 123 enrolled patients, 51.2% of the elderly patients with ovarian cancer received standard treatment, which includes surgery and chemotherapy with or without maintenance therapy, with no serious postoperative complications. Gene testing during treatment was just conducted in 30.9% of patients. The study found that surgery, standardized treatment, maintenance were linked to improved both PFS and OS, while age was only related to OS and > 71years old was ignificantly associated with worse OS. Multivariate analysis revealed that both surgery (HR = 0.155, 95%CI 0.05-0.484, P = 0.001) and maintenance treatment (HR = 0.163, 95%CI 0.059-0.447, P < 0.001) are identified as independent prognostic factors for PFS, whereas surgery alone (HR = 0.289, 95%CI 0.107-0.78, P = 0.014) emerged as an independent prognostic factor for OS. Our real-world study demonstrates that just a small number of elderly patients with ovarian cancer received standard treatment and gene testing during treatment;those patients who accepted surgery, standardized treatment and maintenance had a better PFS and OS; the prognosis is even worse for patients who are older than 71;surgery and maintenance treatment are identified as independent prognostic factors for PFS, whereas surgery alone emerged as an independent prognostic factor for OS.While these findings provide valuable insights, it is important to note that this was a single-center retrospective analysis, and further validation in larger, prospective cohorts is warranted.

IntroductionSurvival time models are commonly employed in medicine and health sciences when analysing data. In these time-to-event analyses, it is often necessary to dichotomise variables that are metrically measured. One example could be to assign patients to different risk groups based on an occurring event. Besides univariable methods, multivariable approaches also exist for establishing cutpoints. Up to now, these multivariable approaches have hardly been investigated.MethodsUsing a Monte Carlo simulation study, we analysed eight multivariable methods from the literature to establish a cutpoint of a biomarker in the context of a semiparametric Cox regression model. The methods are the following: maximising the chi-square statistic, maximising the chi-square statistic with a split-sample approach, maximising the c-index using either the AddFor- or Genetic algorithm, maximising the concordance probability estimator (CPE) with the AddFor- or Genetic algorithm, and minimising the Akaike information criterion (AIC). We compared these methods with each other and in addition with the univariable log-rank minimum p-value approach. The simulation parameters analysed included the cutpoint’s distance from the biomarker’s median, sample size, total censoring, censoring before the end of the follow-up time (drop-outs), and the survival time distribution. Bias and empirical standard error were used as the primary performance measures. Furthermore, each method is illustrated using two practical data examples.ResultsAll analysed methods are biased towards the biomarker’s median. Multivariable methods that estimate the cutpoint by using the lowest AIC or the maximum of the chi-square statistic have the lowest bias and empirical standard error in most simulation scenarios. The difference in bias between the methods based on maximising the c-index or maximising the CPE is minimal. Regardless of the distribution used (Weibull, Gompertz, or exponential), the respective bias shows similar dependencies on the simulation parameters.ConclusionsMultivariable methods to estimate a biomarker’s cutpoint in survival time analyses using the Cox regression model may represent a good alternative to univariable methods. Our simulation has shown that methods maximising the chi-square statistic or minimising the AIC, respectively, perform better than the univariable method using the minimum p-value approach and outperform multivariable methods based on the c-index or CPE.

Impact of immunotherapy on outcomes of cutaneous melanoma and concurrent brain metastasis: a surveillance, epidemiology, and end results analysis of 2010-2020.

Liver metastasis (LM) is a leading cause of mortality in colorectal cancer (CRC), and currently, no effective therapeutic agents are available. Chanling Gao (CLG) has exhibited inhibitory effects on colorectal cancer liver metastasis (CRLM); however, its exact mechanisms of action remain unclear. The integration of artificial intelligence (AI) with precision traditional Chinese medicine offers a promising approach to enhance therapeutic strategies for CRLM. This study aims to establish a "diagnosis-prognosis" model for CRC and CRLM utilizing AI, and to explore the potential mechanisms of CLG treatment for CRC and CRLM through biological information analysis and cellular experiments. A "component-target" network was constructed for elucidating the mechanisms underpinning the therapeutic potential of CLG in CRLM through network pharmacology. Prognostic models for CRC were developed by combining Non-Negative Matrix Factorization (NMF) clustering with ten machine learning (ML) methods, using core targets identified from the network, and validated across multiple TCGA and GEO cohorts. Clinical pathological factors, survival data, biological functional enrichment, and immune landscape analyses were performed to construct a nomogram and compare the results with those of previously published studies. A diagnostic model for CRLM was developed by employing ten ML techniques in cross-combination using genes from the prognostic model, followed by an analysis of immune microenvironmental differences between CRC and CRLM at the single-cell and spatial transcriptome levels. Key targets involved in CRC onset and CRLM progression were identified, and a transcription factor (TF) regulatory network was established by screening the upstream TFs of these targets. Fourteen phenotypic functions were scored to determine their associations with key targets. Molecular docking and dynamics simulations were performed to assess the binding affinity of CLG components with key targets (TP53, CDK1, and CCNB1). In vitro cell experiments verified the inhibitory effects of CLG and its components on colorectal cancer and their regulatory roles on critical targets. The "active ingredient-target" network for CLG identified 248 intersecting targets. NMF clustering revealed two prognostic subtypes, i.e., C1 and C2, with C1 demonstrating superior prognostic outcomes over C2. Survival outcomes and immune differences between the subtypes were analyzed, resulting in the identification of 47 core targets by intersecting differentially expressed genes with previously identified targets. A CLG risk score-based prognostic ML model was constructed using ten ML cross-combination approaches and validated for survival prognosis, clinical diagnosis, and therapeutic utility across TCGA and GEO cohorts. Immune landscape analysis revealed that low-risk groups were characterized by a "hot tumor" phenotype with a favorable response to immunotherapy, whereas high-risk groups exhibited a "cold tumor" phenotype, with potential immunotherapy benefits. Independent prognostic analysis confirmed the CLG-derived risk score independently predicted prognosis. Validation against ten published models demonstrated elevated accuracy and efficacy for the proposed model.A CRLM diagnostic model was constructed using 11 genes from the prognostic model. Receiver operating characteristic (ROC) curve, calibration plot, and decision curve (DCA) analyses demonstrated its accuracy and clinical utility, indicating high predictive efficiency. Immune microenvironmental differences identified CDK1 and CCNB1 as potential biomarkers associated with CRLM onset and progression. CDK1 and CCNB1 expression levels had positive correlations with M2 macrophages, known to promote liver metastasis. The TF regulatory network revealed a regulatory relationship involving TP53, CDK1, and CCNB1, while gene set variation analysis (GSVA) demonstrated the associations of CDK1/CCNB1 with cell cycle regulation and apoptosis. Molecular docking and dynamics simulations revealed strong binding affinities of CLG components with key targets. In vitro experiments confirmed that CLG and its components effectively inhibit colorectal cancer and regulate critical gene expression. This study established an "active ingredient-target" network for CLG using network pharmacology and developed a dynamic "diagnosis-prognosis "model integrating ML based on drug targets. The clinical value of the model in CRC and CRLM patients was validated, and drug-target binding was elucidated using DL. It was discovered that CLG may inhibit CRC liver metastasis by targeting the TP53/CCNB1/CDK1 signaling pathway. These findings highlight the clinical utility of CLG and dynamic models in the prevention, diagnosis, and therapeutic management of CRC and CRLM, providing a robust foundation in bioinformatics, pharmacology, and potential targets for CRLM treatment in TCM, with broad clinical implications.

Prevalence, characteristics, and clinical outcomes of Huntington's disease in Sub-Saharan Africa: A systematic review and meta-analysis.

Extended follow-up of recurrent glioblastoma patients treated with boron neutron capture therapy (BNCT): Long-term survival from a Phase II trial (JG002) using Cyclotron Neutron Source and Boronophenylalanine.

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive kidney cancer with poor prognosis due to its frequent late-stage diagnosis and immunosuppressive tumor microenvironment (TME). While ccRCC is responsive to immunotherapies, treatment resistance remains a major challenge, underscoring the need for new therapeutic targets. We performed integrated single-cell and bulk transcriptomic analysis of ccRCC and normal kidney tissues to characterize the immune landscape and identify key ligand-receptor interactions within the TME. Gene expression and survival data were analyzed using public datasets. Functional validation was conducted using a ccRCC xenograft mouse model treated with the CSF1R inhibitor Sotuletinib. Single-cell analysis revealed that enhanced communication between M2-like macrophages and malignant epithelial cells in ccRCC, with the CSF1-CSF1R signaling axis playing a central role. Elevated expression of CSF1 and CSF1R correlated with poor patient prognosis and increased macrophage infiltration. In vivo inhibition of CSF1R reduced tumor growth, decreased Ki67+ cell proliferation, and suppressed CD163+ M2 macrophage polarization. This study suggests a potential role of CSF1-CSF1R-mediated macrophage-epithelial crosstalk in promoting immunosuppressive TME and tumor progression in ccRCC. Importantly, CellChat-based predictions represent potential, rather than definitive, ligand-receptor interactions, and thus require further mechanistic validation. Targeting CSF1R may offer a promising strategy to modulate the immune landscape and improve therapeutic outcomes in ccRCC.

Abstract In planning clinical trials in presence of competing risks survival data, computation of sample size is typically an essential step for detecting treatment efficacy with sufficiently high power. Competing risks analysis is employed to study the main event of interest in presence of other competing events due to multiple causes of failure. Sample size calculation requires estimating the cumulative incidence functions and thus, deciding the regression modeling approach to follow. The first objective of the paper is to provide the practitioner with guidelines for estimating sample size in both fixed design and group-sequential design with interim analyses, under two of the most popular competing risks approaches: the cause-specific hazard (CSH) and the sub-distribution hazard (SDH) models. The proposed guideline procedures, which are well-known for the exponential case, are extended to more flexible parametric families, and applications are shown for the Weibull and Gompertz time-to-event distributions. The second objective is to compare sample sizes under the two different competing risks approaches. Simulation studies highlight some general recommendations. For a positive treatment effect on the competing event, the CSH approach should be preferred to determine the smallest required sample size to assess treatment effect, particularly when a short study duration is desired at no extra cost of sample size.

Background Subtotal gastrectomy is frequently performed for distally located gastric tumors and carries a lower risk of postoperative complications compared to total gastrectomy. However, due to the submucosal spread pattern and worse prognosis of poorly cohesive carcinoma (PCC), some authors advocate for routine total gastrectomy. This study aimed to compare the outcomes of subtotal versus total gastrectomy in patients with mid- and distal-located gastric PCC. Methods This single-center retrospective study included patients who underwent resection for gastric PCC between 2012 and 2024. Exclusion criteria were systemic metastasis, palliative surgery, and tumors located in the proximal one-third of the stomach. Patients were analyzed based on demographic, clinical, pathological, and survival data. Results A total of 154 patients were included. The median age was 62 years (range: 36–87), and 83 patients (53.9%) were male. Subtotal gastrectomy was performed in 70 patients (45.5%). The median pathological tumor diameter was 60 mm (IQR: 40–90). Over a median follow-up of 79 months, 33 locoregional and 81 systemic recurrences were observed among 146 patients, and 53 patients (36.3%) were alive at the time of last follow-up. Pathological stage was the only independent factor associated with overall survival, while the type of surgery (subtotal vs. total gastrectomy) did not significantly affect survival outcomes. Conclusion Our study demonstrated that subtotal gastrectomy yielded oncologic outcomes similar to those achieved with total gastrectomy in mid- and distal gastric poorly cohesive carcinoma cases.

Introduction: Temporal bone squamous cell carcinoma (TBSCC) is a rare, aggressive malignancy frequently associated with poor prognosis due to its typically late diagnosis. Objective: This study aimed to analyze diagnostic delays in TBSCC patients, assessing their impact on tumor staging and survival to improve clinical management and healthcare systems. Methods: This retrospective study was conducted at Rajshahi Medical College, Rajshahi, Bangladesh, from June 2020 to July 2021on 150 patients diagnosed with TBSCC. Patients' records were reviewed for demographic information, clinical history, diagnostic timelines, tumor staging, treatment methods, and survival outcomes. They measured diagnostic delays in three intervals: from symptom onset to the first medical visit, from the first visit to the histological diagnosis, and the overall delay. Survival data were gathered over a 3-year follow-up period. The statistical analysis was conducted on SPSS v26.0, including descriptive statistics, chi-square tests, and Kaplan-Meier survival analysis. Results: Most of the study participants were male (64%) with an average age of 58.3 years. Patients mostly lived in rural areas (61.3%) and had significant smoking histories (54.7%). There were notable diagnostic delays, with 30.7% of patients experiencing total delays of more than six months. Advanced-stage disease (Stage III-IV) was found in 66.7% of the patients. The three-year survival rate was 56.0%. Survival was strongly linked to tumor stage (Stage I: 88.9% vs Stage IV: 25.0%, p=0.007) and diagnostic delay (_3 months: 77.5% vs &gt;6 months: 32.6%, p=0.004). Conclusion: Delays in diagnosis significantly affect survival in TBSCC. Late diagnosis often results in advanced-stage cancer and lower survival rates. Early detection strategies and better access to healthcare are essential for improving outcomes in this aggressive disease. J Rang Med Col. 2025 Sep;10(2): 20-25

Abstract: Porcelain veneers represent a minimally invasive yet highly aesthetic treatment option for anterior teeth. Advances in ceramic materials, adhesive protocols, and digital workflows have significantly improved longevity and predictability. This narrative review outlines the indications, contraindications, material choices, preparation designs, adhesive cementation protocols, and complication management related to veneer therapy. Particular emphasis is placed on evidence-based decision-making and clinical nuances that influence long-term success, including case selection, occlusal planning, and periodontal considerations. The review also highlights methodological limitations in the current literature—such as heterogeneous follow-up periods, variable preparation designs, and differing adhesive strategies—that may affect interpretation of survival data. Comparative insights from long-term clinical trials and systematic reviews are integrated to provide balanced, clinically relevant guidelines.

Abstract Background: Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype, categorized by a lack of expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2, associated with poor prognosis. Therefore, need to take reliable biomarkers are essential for prognostic stratification and guiding treatment decisions. This study evaluates the clinicopathological significance of Ki-67 and p53 expression in TNBC within an Indian cohort. Methods: A prospective observational study was conducted on 23 histologically confirmed TNBC cases diagnosed between January 2023 and June 2024. Consecutive sampling was used to include all eligible, treatment-naïve patients. Clinicopathological parameters were recorded, and immunohistochemistry (IHC) for Ki-67 and p53 was performed on formalin-fixed paraffin-embedded tissue. Associations between biomarker expression and tumor grade, size, and lymph node status were analyzed using Chi-square/Fisher’s exact test and binary logistic regression. Results: The mean patient age was 48.6 years (range: 32–68). Most tumors (69.6%) were &gt;2 cm, 73.9% were Grade III, and 56.5% had lymph node metastasis. High Ki-67 expression (&gt;20%) was observed in 78.3% of cases, showing a significant association with Grade III tumors ( P = 0.01) and lymph node metastasis ( P = 0.03). p53 positivity (&gt;10%) occurred in 65.2% of cases, correlating significantly with tumor grade ( P = 0.04) but not with nodal status. Binary logistic regression confirmed Ki-67 as an independent predictor of Grade III histology (odds ratio [OR] = 6.75, P = 0.04) and nodal metastasis (OR = 5.40, P = 0.047), whereas p53 did not retain independent prognostic significance. Conclusion: High Ki-67 expression is a robust and independent prognostic marker in TNBC, associated with high-grade histology and lymphatic spread, supporting its integration into routine pathological evaluation. p53 expression shows limited prognostic utility when used alone but may have value in multi-marker panels. Larger studies with long-term survival data are warranted to validate these findings and refine biomarker-guided management strategies in TNBC.

Neuroinflammation plays a key role in Alzheimer's disease (AD) pathophysiology, but it is not clear how neuroinflammation contributes to disease progression. We aim to investigate the role of neuroinflammation on longitudinal cognition and survival in a unique cohort with PET imaging of translocator protein (TSPO) binding tracer [11C]PK11195 and long-term follow-up. We hypothesized that higher [11C]PK11195 binding would be associated with faster cognitive decline and higher mortality. 19 participants with AD dementia, 9 participants with MCI due to AD, and 21 healthy controls (HC) with historical dynamic [11C]PK11195 PET data were included. Principal component analysis was performed to identify relevant [11C]PK11195 patterns. An additional AD ROI consisting of temporal and parietal regions was investigated. [11C]PK11195 scores in the principal components (PCs) and AD ROI were compared between groups using ANOVA. Longitudinal MMSE covering a period up to 11 years was used to measure cognitive decline. We used linear mixed models with random subject-specific intercepts and slopes corrected for age, sex and syndrome diagnosis to investigate the association of neuroinflammation with cognition in MCI and AD. Survival data were available for all MCI and AD participants, up to 15.7 years after PET. To examine the influence of neuroinflammation on survival time, we used age, sex, and syndrome diagnosis adjusted cox proportional-hazards models. Two PCs were retained. PC1 explained 55.4% of the variance and was most explained by [11C]PK11195 binding in the thalamus and entorhinal cortex. PC2 explained 15.3% of the variance and constituted of mostly the entorhinal cortex. There was no difference in [11C]PK11195 PET between AD, MCI and HCs (range F(2) = 0.157-1.231, P > 0.3). [11C]PK11195 did not predict longitudinal MMSE (PC1: β = 0.02, P = 0.73; PC2: β = 0.1, P = 0.44; AD ROI: β = 1.3, P = 0.57) or survival (PC1: HR = 0.90[95%CI: 0.80, 1.03], P = 0.13; PC2: HR = 0.96[0.75, 1.23], P = 0.72; AD ROI: HR = 0.02[0.00, 1.33], P = 0.06). Contrary to our hypothesis, we did not find evidence for [11C]PK11195 PET predicting long-term cognitive decline or survival. This may indicate that the level of [11C]PK11195 PET binding earlier in the disease trajectory is not directly linked to the long-term outcome.

Friedreich's Ataxia (FRDA) is a progressive condition leading to reduced life expectancy in European/North American cohorts, but little is known about Latin American cohorts. Herein, we assessed FRDA survival data from a large Brazilian reference center (UNICAMP). We conducted a retrospective study including patients with FRDA followed at UNICAMP between 1998 and 2025. For those patients who died, we recorded age at death. For those alive or lost to follow-up, we considered the age at last visit. Potential prognostic markers (sex, age at onset, presence of cardiomyopathy and diabetes) were explored. Statistics was carried out using Kaplan-Meier curves and log-rank tests. We gathered information on 151 patients, 24 of which died (15.9%), 125 were still alive (82.7%) and 2 were lost to follow-up (1.3%). For those who died, the mean age at death was 33 ± 10.7 years. The cause of death was known for 12 out of the 24 patients: cardiac in 7, pulmonary in 3, diabetic ketoacidosis in 1 and sepsis in 1. Shorter life expectancy was found: in men relative to women (Mean age: 54.0 yo vs. 56.8 yo, p = 0.03), in patients with classical relative to late-onset (Mean age: 52.2 yo vs. 71.0 yo, p < 0.01) and in patients with cardiomyopathy relative to those without it (Mean age: 50.8 yo vs. 65.0 yo, p < 0.01). FRDA impacts life expectancy and death is primarily from cardiac and pulmonary causes. Male sex, early onset and presence of cardiomyopathy are negative survival prognostic markers.

Breast cancer (BC) is the most common malignancy among women, with its progression and prognosis significantly influenced by the tumor microenvironment (TME). Age-related differences in TME composition lead to distinct tumor behaviors: young patients (≤ 40 years) exhibit aggressive tumors, while elderly patients (> 70 years) experience immunosenescence and reduced therapy responses. We performed single-cell RNA sequencing (scRNA-seq) analysis on tumors from 10 breast cancer patients (5 ≤ 40 years, 5 ≥ 70 years), encompassing 33,664 high-quality cells. After cell annotation and batch correction, malignant epithelial cells were identified using inferCNV. We applied pseudotime trajectory analysis, pathway enrichment, and cell–cell communication profiling to investigate age-specific TME dynamics. Survival relevance was assessed using a GEO cohort (GSE20685) of young breast cancer patients, and immunohistochemical staining was performed on clinical tumor and fibroadenoma tissues to validate protein-level expression of key ISGs. In young patients, malignant epithelial cells showed gradual upregulation of interferon-stimulated genes (ISGs) such as IFI44, IFI44L, IFIT1, and IFIT3 along the pseudotime trajectory, suggesting their involvement in early tumorigenesis. High expression of these ISGs was significantly associated with poor overall survival in a young BC cohort (GSE20685). Immunohistochemical validation further confirmed elevated IFIT3 protein levels in young tumor tissues. In contrast, elderly patients had a TME enriched in macrophages and fibroblasts, with activation of immunosuppressive pathways (e.g., SPP1, COMPLEMENT). Our integrative analysis identifies ISGs as key transcriptional drivers of tumorigenesis in young breast cancer, with potential prognostic and therapeutic value. Despite limited sample size, the combination of single-cell transcriptomics, clinical survival data, and protein-level validation provides robust evidence of age-specific TME remodeling. These findings support the development of age-tailored immunotherapy strategies targeting interferon signaling in young patients and immune checkpoint pathways (e.g., LAG3, CTLA4) in elderly individuals.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-24720-2.

Cross sectional analysis of long-term overall survival among patients taking immune checkpoint inhibitor drugs.

ABSTRACT This study introduces a novel approach to modeling competing risks in survival analysis by integrating learnable Copula functions (Clayton, Frank, and Gaussian) with deep learning architectures, including Convolutional Neural Networks (CNN), Long Short‐Term Memory (LSTM) networks, and a hybrid CNN‐LSTM model. Here, we are interested in classifying competing risks outcomes. The proposed method captures complex dependencies within the data. Our approach demonstrates improved predictive performance in survival data modeling by effectively capturing intricate dependency structures and event relationships. We validate the proposed models using both simulated data and real‐world clinical data. This research highlights the potential of integrating Copula‐based dependency structures into deep learning models for survival analysis with competing risks. The results emphasize how Copula‐based neural networks can enhance prediction accuracy and handle competing risks in survival analysis.

Disease resistance is one of the main targets of animal breeding programs. In recent years, incorporating genomic information to accelerate genetic progress has become one of the priorities of the industry. Here, we combined population scale whole-genome sequencing with differential gene expression and functional annotation analyses to study resistance to Tilapia Lake Virus (TiLV) in a breeding Nile tilapia (Oreochromis niloticus) GIFT population. Fish with survival data from a natural TiLV outbreak were sampled and genotyped for 6.7M SNPs using whole-genome resequencing and imputation. Our results confirmed a QTL located in the proximal end of Oni22, identifying 74 out of the top 99 markers associated to binary survival (BS) within a 10 Mb window. The marker explaining the highest genetic variance of TiLV resistance is located at 1.7 Mb, and presents a substitution effect of 0.15. Additionally, other SNPs in several other chromosomes explained a high percentage of the genetic variance, with an important number located in two separate regions of Oni09. These results suggest an oligogenic architecture underlying resistance to TiLV, with several QTLs with moderate effect and many with small effect. Host transcriptomic analyses identified genes differentially expressed between resistant and susceptible genotypes according to the QTL in Oni22, highlighting psmb9a. and ha1f as potential causal genes. This is the first study combining whole genome sequencing at population scale with genomic approaches to assess the underlying genomic basis for TiLV resistance. Our results confirm and narrow down a QTL underlying this key trait in a major aquaculture species worldwide and found novel QTLs in other chromosomes. The identified markers and genes have the potential to improve resistance to TiLV in Nile tilapia, significantly improving animal health and welfare.