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Abstract Bladder cancer poses significant clinical challenges due to its high metastatic potential and poor prognosis, especially when it progresses to muscle-invasive stages. Here, we show that the m6A reader YTHDC1 is downregulated in muscle-invasive bladder cancer and is negatively correlated with the expression of epithelial‒mesenchymal transition genes. The functional inhibition or depletion of YTHDC1 increased the migration and invasion of urothelial cells. Integrative analysis of multimodal sequencing datasets provided detailed insights into the molecular mechanisms mediating YTHDC1-dependent phenotypes and identified SMAD6 as a key transcript involved in the invasiveness of urothelial carcinoma of the bladder. Notably, SMAD6 mRNA colocalized less with YTHDC1 in tumoral tissues than in paratumoral tissues, indicating disrupted binding during cancer progression. Our findings establish YTHDC1-dependent m6A reading as a critical epitranscriptomic mechanism regulating bladder cancer invasiveness and provide a paradigm for the epitranscriptomic deregulation of cancer-associated networks.

Background and objective: Data on interaction of patients with artificial intelligence (AI) are limited, primarily derived from small-scale studies, cross-sectional surveys, and qualitative reviews. Most patients have not yet encountered AI in their clinical experience. This study explored patients’ confidence in AI, specifically large language models, after a direct interaction with a chatbot in a clinical setting. Through hands-on experience, the study sought to reduce potential biases due to an anticipated lack of AI experience in a real-world urological patient sample. Methods: A total of 300 patients scheduled for counseling were enrolled from February to July 2024. Participants voluntarily conversed about their medical questions with a GPT-4 powered chatbot, followed by a survey assessing their confidence in clinical capabilities of AI compared with their counseling urologists. Clinical capabilities included history taking, diagnostics, treatment recommendation, anxiety reduction, and time allocation. Key findings and limitations: Of the 292 patients who completed the study, AI was significantly preferred to physicians for consultation time allocation (p

Abstract Large language models (LLMs) are undergoing intensive research for various healthcare domains. This systematic review and meta-analysis assesses current applications, methodologies, and the performance of LLMs in clinical oncology. A mixed-methods approach was used to extract, summarize, and compare methodological approaches and outcomes. This review includes 34 studies. LLMs are primarily evaluated on their ability to answer oncologic questions across various domains. The meta-analysis highlights a significant performance variance, influenced by diverse methodologies and evaluation criteria. Furthermore, differences in inherent model capabilities, prompting strategies, and oncological subdomains contribute to heterogeneity. The lack of use of standardized and LLM-specific reporting protocols leads to methodological disparities, which must be addressed to ensure comparability in LLM research and ultimately leverage the reliable integration of LLM technologies into clinical practice.

IntroductionMuscle invasive bladder cancer (MIBC) remains a prevalent cancer with limited therapeutic options, obviating the need for innovative therapies. The epidermal growth factor receptor (EGFR) is a linchpin in tumor progression and presents a potential therapeutic target in MIBC. Additionally, the EGFR ligands AREG and EREG have shown associations with response to anti-EGFR therapy and improved progression-free survival in colorectal carcinoma.Materials and methodsWe investigated the prognostic significance of EGFR, AREG, and EREG in MIBC. Gene expression and copy number analyses were performed via qRT-PCR on tissue samples from 100 patients with MIBC who underwent radical cystectomy at the University Hospital Mannheim (MA; median age 72, interquartile range [IQR] 64–78 years, 25% female). Results were validated in 361 patients from the 2017 TCGA MIBC cohort (median age 69, IQR 60–77 years, 27% female), in the Chungbuk and MDACC cohort. Gene expressions were correlated with clinicopathologic parameters using the Mann-Whitney test, Kruskal-Wallis- test and Spearman correlation. For overall survival (OS), cancer-specific survival (CSS) and disease-free survival (DFS) gene expression was analyzed with Kaplan-Meier and Cox-proportional hazard models.ResultsSignificant gene expression differences in EGFR, AREG, and EREG could be detected in all cohorts. In the TCGA cohort, EGFR expression was significantly elevated in patients with EGFR amplification and KRAS wildtype. High AREG expression independently predicted longer OS (HR = 0.35, CI 0.19 - 0.63, p = 0.0004) and CSS (HR = 0.42, CI 0.18 – 0.95, p = 0.0378) in the MA cohort. In the TCGA cohort, high EGFR, AREG, and EREG expression correlated with shorter OS (AREG: HR = 1.57, CI 1.12 – 2.20, p = 0.0090) and DFS (EGFR: HR = 1.91, CI 1.31 – 2.8, p = 0.0008). EGFR amplification was also associated with reduced DFS.DiscussionHigh EGFR and EREG indicate worse survival in patients with MIBC. The prognostic role of AREG should further be investigated in large, prospective series. Divergent survival outcomes between the four cohorts should be interpreted cautiously, considering differences in analysis methods and demographics. Further in vitro investigations are necessary to elucidate the functional mechanisms underlying the associations observed in this study.

Summary: METTL3 is the major writer of N6-Methyladenosine (m6A) and has been associated with controversial roles in cancer. This is best illustrated in urothelial carcinoma of the bladder (UCB), where METTL3 was described to have both oncogenic and tumor-suppressive functions. Here, we reinvestigated the role of METTL3 in UCB. METTL3 knockout reduced the oncogenic phenotype and m6A levels of UCB cell lines. However, complete depletion of METTL3/m6A was not achieved due to selection of cells expressing alternative METTL3 isoforms. Systematic vulnerability and inhibitor response analyses suggested that uroepithelial cells depend on METTL3 for viability. Furthermore, expression and survival analyses of clinical data revealed a complex role for METTL3 in UCB, with decreased m6A mRNA levels in UCB tumors. Our results suggest that METTL3 expression may be a suitable diagnostic UCB biomarker, as the enzyme promotes UCB formation. However, the suitability of the enzyme as a therapeutic target should be evaluated carefully.

To date, only a single transcriptome-wide m6A sequencing study of clear cell renal cell carcinoma (ccRCC) has been reported, with no validation so far. Herein, by TCGA analysis of the KIRC cohort (n = 530 ccRCC; n = 72 normal), an external expression validation of 35 preidentified m6A targets was performed. Further in-depth expression stratification enabled assessment of m6A-driven key targets. Overall survival (OS) analysis and gene set enrichment analyses (GSEA) were conducted to assess their clinical and functional impact on ccRCC. In the hyper-up cluster significant upregulation was confirmed for NDUFA4L2, NXPH4, SAA1, and PLOD2 (40%) and in the hypo-up cluster for FCHSD1 (10%). Significant downregulation was observed for UMOD, ANK3, and CNTFR (27.3%) in the hypo-down cluster and for CHDH (25%) in the hyper-down cluster. In-depth expression stratification showed consistent dysregulation in ccRCC only for 11.67%: NDUFA4L2, NXPH4, and UMOD (NNU-panel). Patients with strong NNU panel dysregulation had significantly poorer OS (p = 0.0075). GSEA identified 13 associated and significantly upregulated gene sets (all p-values < 0.5; FDR < 0.25). External validation of the only available m6A sequencing in ccRCC consistently reduced dysregulated m6A-driven targets on the NNU panel with highly significant effects on OS. Epitranscriptomics are a promising target for developing novel therapies and for identifying prognostic markers for daily clinical practice.

Cosmic ray tomography is an emerging imaging technique utilizing an ambient source of radiation. One common tomography method is based on the measurement of muons scattered by the examined objects, which allows the reconstruction and discrimination of materials with different properties. From the interaction of air shower particles induced through cosmic rays with the material to be scanned, secondary particles, predominantly photons, neutrons and electrons, can be produced, which carry complementary information about the objects and their materials. However, this information is currently not fully exploited or only studied in coincidence with the incoming air shower particles. Therefore, this work presents a novel approach utilizing only the information from secondary particles to reconstruct and discriminate objects made out of a variety of materials. It also includes a detailed analysis of the kinematics of secondary particles and their dependency on material characteristics. In addition, a reconstruction algorithm to produce 3D maps of the examined volume from the measurement of secondary particles is introduced. This results in a successful reconstruction and differentiation of objects in various geometrical compositions.

Camera systems support the rapid assessment of ship traffic at ports, allowing for a better perspective of the maritime situation. However, optimal ship monitoring requires a level of automation that allows personnel to keep track of relevant variables in the maritime situation in an understandable and visualisable format. It therefore becomes important to have real-time recognition of ships present at the infrastructure, with their class and geographic position presented to the maritime situational awareness operator. This work presents a novel dataset, ShipSG, for the segmentation and georeferencing of ships in maritime monitoring scenes with a static oblique view. Moreover, an exploration of four instance segmentation methods, with a focus on robust (Mask-RCNN, DetectoRS) and real-time performances (YOLACT, Centermask-Lite) and their generalisation to other existing maritime datasets, is shown. Lastly, a method for georeferencing ship masks is proposed. This includes an automatic calculation of the pixel of the segmented ship to be georeferenced and the use of a homography to transform this pixel to geographic coordinates. DetectoRS provided the highest ship segmentation mAP of 0.747. The fastest segmentation method was Centermask-Lite, with 40.96 FPS. The accuracy of our georeferencing method was (22 ± 10) m for ships detected within a 400 m range, and (53 ± 24) m for ships over 400 m away from the camera.

Abstract Background Besides clinical stage and Gleason score, risk-stratification of prostate cancer in the pretherapeutic setting mainly relies on the serum PSA level. Yet, this is associated with many uncertainties. With regard to therapy decision-making, additional markers are needed to allow an exact risk prediction. Eukaryotic translation elongation factor 1 alpha 2 (EEF1A2) was previously suggested as driver of tumor progression and potential biomarker. In the present study its functional and prognostic relevance in prostate cancer was investigated. Methods EEF1A2 expression was analyzed in two cohorts of patients (n = 40 and n = 59) with localized PCa. Additionally data from two large expression dataset (MSKCC, Cell, 2010 with n = 131 localized, n = 19 metastatic PCa and TCGA provisional data, n = 499) of PCa patients were reanalyzed. The expression of EEF1A2 was correlated with histopathology features and biochemical recurrence (BCR). To evaluate the influence of EEF1A2 on proliferation and migration of metastatic PC3 cells, siRNA interference was used. Statistical significance was tested with t-test, Mann-Whitney-test, Pearson correlation and log-rank test. Results qRT-PCR revealed EEF1A2 to be significantly overexpressed in PCa tissue, with an increase according to tumor stage in one cohort (p = 0.0443). In silico analyses in the MSKCC cohort confirmed the overexpression of EEF1A2 in localized PCa with high Gleason score (p = 0.0142) and in metastatic lesions (p = 0.0038). Patients with EEF1A2 overexpression had a significantly shorter BCR-free survival (p = 0.0028). EEF1A2 expression was not correlated with serum PSA levels. Similar results were seen in the TCGA cohort, where EEF1A2 overexpression only occurred in tumors with Gleason 7 or higher. Patients with elevated EEF1A2 expression had a significantly shorter BCR-free survival (p = 0.043). EEF1A2 knockdown significantly impaired the migration, but not the proliferation of metastatic PC3 cells. Conclusion The overexpression of EEF1A2 is a frequent event in localized PCa and is associated with histopathology features and a shorter biochemical recurrence-free survival. Due to its independence from serum PSA levels, EEF1A2 could serve as valuable biomarker in risk-stratification of localized PCa.