Hasil untuk "Neoplasms. Tumors. Oncology. Including cancer and carcinogens"

Lingling Wang, Yuqing Miao, Yuexin Cheng

Venetoclax is effective in treating relapsed acute promyelocytic leukemia (APL), newly diagnosed pediatric APL, variant APL, and APL with central nervous system involvement. In newly diagnosed adult APL, venetoclax is rarely used. Herein, we present a case of newly diagnosed adult APL in a 35-year-old female with schizophrenia who received venetoclax as a cytoreduction therapy option. The patient was admitted with myocardial ischemia, the cardiac ultrasound indicating left ventricular ejection fractions (EF) of 44%, a 17-year history of schizophrenia, treated with ziprasidone, lorazepam, and clozapine. She developed differentiation syndrome (DS) shortly after receiving All-trans-retinoic acid (ATRA) and arsenic trioxide (ATO) and experienced heart arrest. In the occurrence of DS, this young female encountered numerous therapeutic conundrums, including cytoreduction of hydroxyurea being ineffective, the potential psychological worsening caused by dexamethasone use, and the cardiotoxicity of anthracyclines. We administered venetoclax 20 mg once daily as a cytoreduction therapy. The white blood cells (WBC) dropped from 72.16×109/L to 5.19×109/L in 4 days, and the proportion of promyelocytes in the peripheral blood smear decreased from 78% to 10%. Tumor lysis syndrome (TLS) did not develop since the patient received good supportive treatment. For newly diagnosed adult patients with APL who are unresponsive to traditional cytoreduction therapy, venetoclax can be an effective option.

Zhao Zhang, Hongbo Wu, Yan Huang et al.

Abstract Background More and more long non-coding RNA small nucleotide host RNA (SNHG) gene family has been confirmed to be unregulated in cervical cancer (CC) tissues, and it is significantly related to the prognosis of CC. The purpose of this study was to conduct a meta-analysis to explore the correlation between the expression level of SNHGs and the prognosis of CC. Methods Six relevant electronic databases were searched, relevant original documents were screened, and the research quality of each document was assessed based on the Newcastle–Ottawa Scale (NOS) scale. Relevant data were extracted including SNHG expression levels, survival outcomes and follow-up time. Hazard ratio (HR) and Odds ratio (OR) with 95% confidence interval (CI) were combined to assess the association between SNHG expression and overall survival (OS) TNM stage, tumor size, depth of invasion. The sensitivity analyzes and Begg’s test was conducted to explore potential publication bias. Results The results of pooling HR with 95%CI indicating the marked positive association between increasing SNHG expression and poor OS (HR: 2.046, 95%CI: 1.402–2.691). In addition, high SNHG expressions contribute to advanced TNM stage (OR: 1.476, 95%CI: 1.178–1.849), easier to lymph node metastasis (OR: 1.614, 95%CI: 1.021–2.553), bigger tumor size (OR: 1.299, 95%CI: 1.031–1.638). Meanwhile, an insignificant relationship was also found between high SNHGs expression and histological grade (OR: 1.053, 95%CI: 0.814–1.361), DM (OR: 1.659, 95%CI: 0.969–2.838), depth of invasion (OR: 1.126, 95%CI: 0.466–2.726) and age (OR: 1.115, 95%CI: 0.899–1.382). Sensitivity analysis suggests the reliability and robustness of OS, the results of Begg’s test indicated that there is no significant publication bias in the original literature. Conclusion Most SNHGs are highly expressed in CC tissues, elevated SNHG expression predicts poor prognosis of CC, SNHG may serve as a potential target for tumor therapy and a promising prognostic marker.

Yuntao Wang, Mariluz De Ornelas, Matthew T. Studenski et al.

Purpose To evaluate the reasoning capabilities of large language models (LLMs) in performing root cause analysis (RCA) of radiation oncology incidents using narrative reports from the Radiation Oncology Incident Learning System (RO-ILS), and to assess their potential utility in supporting patient safety efforts. Methods and Materials Four LLMs, Gemini 2.5 Pro, GPT-4o, o3, and Grok 3, were prompted with the 'Background and Incident Overview' sections of 19 public RO-ILS cases. Using a standardized prompt based on AAPM RCA guidelines, each model was instructed to identify root causes, lessons learned, and suggested actions. Outputs were assessed using semantic similarity metrics (cosine similarity via Sentence Transformers), semi-subjective evaluations (precision, recall, F1-score, accuracy, hallucination rate, and four performance criteria: relevance, comprehensiveness, justification, and solution quality), and subjective expert ratings (reasoning quality and overall performance) from five board-certified medical physicists. Results LLMs showed promising performance. GPT-4o had the highest cosine similarity (0.831), while Gemini 2.5 Pro had the highest recall (0.799) and accuracy (0.918). Hallucination rates ranged from 11% to 61%. Gemini 2.5 Pro outperformed others across performance criteria and received the highest expert rating (4.8/5). Statistically significant differences in accuracy, hallucination, and subjective scores were observed (p < 0.05). Conclusion LLMs show emerging promise as tools for RCA in radiation oncology. They can generate relevant, accurate analyses aligned with expert judgment and may support incident analysis and quality improvement efforts to enhance patient safety in clinical practice.

Peilong Wang, Zhengliang Liu, Yiwei Li et al.

Background: The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language models have displayed remarkable capabilities in processing complex text information. But their direct applications in specific fields like radiation oncology remain underexplored. Purpose: This study aims to investigate whether fine-tuning LLMs with domain knowledge can improve the performance on Task (1) treatment regimen generation, Task (2) treatment modality selection (photon, proton, electron, or brachytherapy), and Task (3) ICD-10 code prediction in radiation oncology. Methods: Data for 15,724 patient cases were extracted. Cases where patients had a single diagnostic record, and a clearly identifiable primary treatment plan were selected for preprocessing and manual annotation to have 7,903 cases of the patient diagnosis, treatment plan, treatment modality, and ICD-10 code. Each case was used to construct a pair consisting of patient diagnostics details and an answer (treatment regimen, treatment modality, or ICD-10 code respectively) for the supervised fine-tuning of these three tasks. Open source LLaMA2-7B and Mistral-7B models were utilized for the fine-tuning with the Low-Rank Approximations method. Accuracy and ROUGE-1 score were reported for the fine-tuned models and original models. Clinical evaluation was performed on Task (1) by radiation oncologists, while precision, recall, and F-1 score were evaluated for Task (2) and (3). One-sided Wilcoxon signed-rank tests were used to statistically analyze the results. Results: Fine-tuned LLMs outperformed original LLMs across all tasks with p-value <= 0.001. Clinical evaluation demonstrated that over 60% of the fine-tuned LLMs-generated treatment regimens were clinically acceptable. Precision, recall, and F1-score showed improved performance of fine-tuned LLMs.

Samia Saeed, Khuram Naveed

Breast cancer, the second leading cause of cancer-related deaths globally, accounts for a quarter of all cancer cases [1]. To lower this death rate, it is crucial to detect tumors early, as early-stage detection significantly improves treatment outcomes. Advances in non-invasive imaging techniques have made early detection possible through computer-aided detection (CAD) systems which rely on traditional image analysis to identify malignancies. However, there is a growing shift towards deep learning methods due to their superior effectiveness. Despite their potential, deep learning methods often struggle with accuracy due to the limited availability of large-labeled datasets for training. To address this issue, our study introduces a Contrastive Learning (CL) framework, which excels with smaller labeled datasets. In this regard, we train Resnet-50 in semi supervised CL approach using similarity index on a large amount of unlabeled mammogram data. In this regard, we use various augmentation and transformations which help improve the performance of our approach. Finally, we tune our model on a small set of labelled data that outperforms the existing state of the art. Specifically, we observed a 96.7% accuracy in detecting breast cancer on benchmark datasets INbreast and MIAS.

Yinghan Li, Yilin Yao, Junghua Lin et al.

This study explores the application potential of a deep learning model based on the CNN-LSTM framework in forecasting the sales volume of cancer drugs, with a focus on modeling complex time series data. As advancements in medical technology and cancer treatment continue, the demand for oncology medications is steadily increasing. Accurate forecasting of cancer drug sales plays a critical role in optimizing production planning, supply chain management, and healthcare policy formulation. The dataset used in this research comprises quarterly sales records of a specific cancer drug in Egypt from 2015 to 2024, including multidimensional information such as date, drug type, pharmaceutical company, price, sales volume, effectiveness, and drug classification. To improve prediction accuracy, a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is employed. The CNN component is responsible for extracting local temporal features from the sales data, while the LSTM component captures long-term dependencies and trends. Model performance is evaluated using two widely adopted metrics: Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The results demonstrate that the CNN-LSTM model performs well on the test set, achieving an MSE of 1.150 and an RMSE of 1.072, indicating its effectiveness in handling nonlinear and volatile sales data. This research provides theoretical and technical support for data-driven decision-making in pharmaceutical marketing and healthcare resource planning.

Pedro R. A. S. Bassi, Mehmet Can Yavuz, Kang Wang et al.

Cancers identified in CT scans are usually accompanied by detailed radiology reports, but publicly available CT datasets often lack these essential reports. This absence limits their usefulness for developing accurate report generation AI. To address this gap, we present AbdomenAtlas 3.0, the first public, high-quality abdominal CT dataset with detailed, expert-reviewed radiology reports. All reports are paired with per-voxel masks and they describe liver, kidney and pancreatic tumors. AbdomenAtlas 3.0 has 9,262 triplets of CT, mask and report--3,955 with tumors. These CT scans come from 17 public datasets. Besides creating the reports for these datasets, we expanded their number of tumor masks by 4.2x, identifying 3,011 new tumor cases. Notably, the reports in AbdomenAtlas 3.0 are more standardized, and generated faster than traditional human-made reports. They provide details like tumor size, location, attenuation and surgical resectability. These reports were created by 12 board-certified radiologists using our proposed RadGPT, a novel framework that converted radiologist-revised tumor segmentation masks into structured and narrative reports. Besides being a dataset creation tool, RadGPT can also become a fully-automatic, segmentation-assisted report generation method. We benchmarked this method and 5 state-of-the-art report generation vision-language models. Our results show that segmentation strongly improves tumor detection in AI-made reports.

Diksha Kashyap, Devanshi Gupta, Naman Kumar Mehta et al.

Addressing the growing need for organized data on tumor homing peptides (THPs), we present TumorHoPe2, a manually curated database offering extensive details on experimentally validated THPs. This represents a significant update to TumorHoPe, originally developed by our group in 2012. TumorHoPe2 now contains 1847 entries, representing 1297 unique tumor homing peptides, a substantial expansion from the 744 entries in its predecessor. For each peptide, the database provides critical information, including sequence, terminal or chemical modifications, corresponding cancer cell lines, and specific tumor types targeted. The database compiles data from two primary sources: phage display libraries, which are commonly used to identify peptide ligands targeting tumor-specific markers, and synthetic peptides, which are chemically modified to enhance properties such as stability, binding affinity, and specificity. Our dataset includes 594 chemically modified peptides, with 255 having N-terminal and 195 C-terminal modifications. These THPs have been validated against 172 cancer cell lines and demonstrate specificity for 37 distinct tumor types. To maximize utility for the research community, TumorHoPe2 is equipped with intuitive tools for data searching, filtering, and analysis, alongside a RESTful API for efficient programmatic access and integration into bioinformatics pipelines. It is freely available at https://webs.iiitd.edu.in/raghava/tumorhope2/

Daria Laslo, Efthymios Georgiou, Marius George Linguraru et al.

Predicting the spatio-temporal progression of brain tumors is essential for guiding clinical decisions in neuro-oncology. We propose a hybrid mechanistic learning framework that combines a mathematical tumor growth model with a guided denoising diffusion implicit model (DDIM) to synthesize anatomically feasible future MRIs from preceding scans. The mechanistic model, formulated as a system of ordinary differential equations, captures temporal tumor dynamics including radiotherapy effects and estimates future tumor burden. These estimates condition a gradient-guided DDIM, enabling image synthesis that aligns with both predicted growth and patient anatomy. We train our model on the BraTS adult and pediatric glioma datasets and evaluate on 60 axial slices of in-house longitudinal pediatric diffuse midline glioma (DMG) cases. Our framework generates realistic follow-up scans based on spatial similarity metrics. It also introduces tumor growth probability maps, which capture both clinically relevant extent and directionality of tumor growth as shown by 95th percentile Hausdorff Distance. The method enables biologically informed image generation in data-limited scenarios, offering generative-space-time predictions that account for mechanistic priors.

Jakub R. Kaczmarzyk, Sarah C. Van Alsten, Alyssa J. Cozzo et al.

Transcriptomic assays such as the PAM50-based ROR-P score guide recurrence risk stratification in non-metastatic, ER-positive, HER2-negative breast cancer but are not universally accessible. Histopathology is routinely available and may offer a scalable alternative. We introduce MAKO, a benchmarking framework evaluating 12 pathology foundation models and two non-pathology baselines for predicting ROR-P scores from H&E-stained whole slide images using attention-based multiple instance learning. Models were trained and validated on the Carolina Breast Cancer Study and externally tested on TCGA BRCA. Several foundation models outperformed baselines across classification, regression, and survival tasks. CONCH achieved the highest ROC AUC, while H-optimus-0 and Virchow2 showed top correlation with continuous ROR-P scores. All pathology models stratified CBCS participants by recurrence similarly to transcriptomic ROR-P. Tumor regions were necessary and sufficient for high-risk predictions, and we identified candidate tissue biomarkers of recurrence. These results highlight the promise of interpretable, histology-based risk models in precision oncology.

Óscar A. Martín, Javier Sánchez

Neural networks have become the standard technique for medical diagnostics, especially in cancer detection and classification. This work evaluates the performance of Vision Transformers architectures, including Swin Transformer and MaxViT, in several datasets of magnetic resonance imaging (MRI) and computed tomography (CT) scans. We used three training sets of images with brain, lung, and kidney tumors. Each dataset includes different classification labels, from brain gliomas and meningiomas to benign and malignant lung conditions and kidney anomalies such as cysts and cancers. This work aims to analyze the behavior of the neural networks in each dataset and the benefits of combining different image modalities and tumor classes. We designed several experiments by fine-tuning the models on combined and individual datasets. The results revealed that the Swin Transformer provided high accuracy, achieving up to 99\% on average for individual datasets and 99.4\% accuracy for the combined dataset. This research highlights the adaptability of Transformer-based models to various image modalities and features. However, challenges persist, including limited annotated data and interpretability issues. Future work will expand this study by incorporating other image modalities and enhancing diagnostic capabilities. Integrating these models across diverse datasets could mark a significant advance in precision medicine, paving the way for more efficient and comprehensive healthcare solutions.

Sayan Das, Arghadip Biswas

Brain tumors pose a significant threat to human life, therefore it is very much necessary to detect them accurately in the early stages for better diagnosis and treatment. Brain tumors can be detected by the radiologist manually from the MRI scan images of the patients. However, the incidence of brain tumors has risen amongst children and adolescents in recent years, resulting in a substantial volume of data, as a result, it is time-consuming and difficult to detect manually. With the emergence of Artificial intelligence in the modern world and its vast application in the medical field, we can make an approach to the CAD (Computer Aided Diagnosis) system for the early detection of Brain tumors automatically. All the existing models for this task are not completely generalized and perform poorly on the validation data. So, we have proposed two novel Deep Learning Architectures - (a) SAETCN (Self-Attention Enhancement Tumor Classification Network) for the classification of different kinds of brain tumors. We have achieved an accuracy of 99.38% on the validation dataset making it one of the few Novel Deep learning-based architecture that is capable of detecting brain tumors accurately. We have trained the model on the dataset, which contains images of 3 types of tumors (glioma, meningioma, and pituitary tumors) and non-tumor cases. and (b) SAS-Net (Self-Attentive Segmentation Network) for the accurate segmentation of brain tumors. We have achieved an overall pixel accuracy of 99.23%.

Xiangde Luo, Xiyue Wang, Feyisope Eweje et al.

Histopathology is essential for disease diagnosis and treatment decision-making. Recent advances in artificial intelligence (AI) have enabled the development of pathology foundation models that learn rich visual representations from large-scale whole-slide images (WSIs). However, existing models are often trained on disparate datasets using varying strategies, leading to inconsistent performance and limited generalizability. Here, we introduce ELF (Ensemble Learning of Foundation models), a novel framework that integrates five state-of-the-art pathology foundation models to generate unified slide-level representations. Trained on 53,699 WSIs spanning 20 anatomical sites, ELF leverages ensemble learning to capture complementary information from diverse models while maintaining high data efficiency. Unlike traditional tile-level models, ELF's slide-level architecture is particularly advantageous in clinical contexts where data are limited, such as therapeutic response prediction. We evaluated ELF across a wide range of clinical applications, including disease classification, biomarker detection, and response prediction to major anticancer therapies, cytotoxic chemotherapy, targeted therapy, and immunotherapy, across multiple cancer types. ELF consistently outperformed all constituent foundation models and existing slide-level models, demonstrating superior accuracy and robustness. Our results highlight the power of ensemble learning for pathology foundation models and suggest ELF as a scalable and generalizable solution for advancing AI-assisted precision oncology.

Jincheng Wang, Ti Tong, Kun Zhang et al.

Abstract Objective The efficacy and safety of subxiphoid thoracoscopic thymectomy (SVATS) for early thymoma are unknown. The purposes of this meta-analysis were to evaluate the effectiveness and safety of SVATS for early thymoma, to compare it with unilateral intercostal approach video thoracoscopic surgery (IVATS) thymectomy, and to investigate the clinical efficacy of modified subxiphoid thoracoscopic thymectomy (MSVATS) for early anterior mediastinal thymoma. Methods Original articles describing subxiphoid and unilateral intercostal approaches for thoracoscopic thymectomy to treat early thymoma published up to March 2023 were searched from PubMed, Embase, and the Cochrane Library. Standardized mean differences (SMDs) and 95% confidence intervals (CIs) were calculated and analyzed for heterogeneity. Clinical data were retrospectively collected from all Masaoka stage I and II thymoma patients who underwent modified subxiphoid and unilateral intercostal approach thoracoscopic thymectomies between September 2020 and March 2023. The operative time, intraoperative bleeding, postoperative drainage, extubation time, postoperative hospital stay, postoperative visual analog pain score (VAS), and postoperative complications were compared, and the clinical advantages of the modified subxiphoid approach for early-stage anterior mediastinal thymoma were analyzed. Results A total of 1607 cases were included in the seven studies in this paper. Of these, 591 cases underwent SVATS thymectomies, and 1016 cases underwent IVATS thymectomies. SVATS thymectomy was compared with IVATS thymectomy in terms of age (SMD = − 0.09, 95% CI: −0.20 to − 0.03, I2 = 20%, p = 0.13), body mass index (BMI; SMD = − 0.10, 95% CI: −0.21 to − 0.01, I2 = 0%, p = 0.08), thymoma size (SMD = − 0.01, 95% CI: −0.01, I2 = 0%, p = 0.08), operative time (SMD = − 0.70, 95% CI: −1.43–0.03, I2 = 97%, p = 0.06), intraoperative bleeding (SMD = − 0.30. 95% CI: −0.66–0.06, I2 = 89%, p = 0.10), time to extubation (SMD = − 0.34, 95%CI: −0.73–0.05, I2 = 91%, p = 0.09), postoperative hospital stay (SMD = − 0.40, 95% CI: −0.93–0.12, I2 = 93%, p = 0.13), and postoperative complications (odds ratio [OR] = 0.94, 95% CI: 0.42–2.12, I2 = 57%, p = 0.88), which were not statistically significantly different between the SVATS and IVATS groups. However, the postoperative drainage in the SVATS group was less than that in the IVATS group (SMD = − 0.43, 95%CI: −0.84 to − 0.02, I2 = 88%, p = 0.04), and the difference was statistically significant. More importantly, the postoperative VAS was lower in the SVATS group on days 1 (SMD = − 1.73, 95%CI: −2.27 to − 1.19, I2 = 93%, p < 0.00001), 3 (SMD = − 1.88, 95%CI: −2.84 to − 0.81, I2 = 97%, p = 0.0005), and 7 (SMD = − 1.18, 95%CI: −2.28 to − 0.08, I2 = 97%, p = 0.04) than in the IVATS group, and these differences were statistically significant. A total of 117 patients undergoing thoracoscopic thymectomy for early thymoma in the Department of Thoracic Surgery of the Second Hospital of Jilin University were retrospectively collected and included in the analysis, for which a modified subxiphoid approach was used in 42 cases and a unilateral intercostal approach was used in 75 cases. The differences between the two groups (MSVATS vs. IVATS) in general clinical characteristics such as age, sex, tumor diameter, Masaoka stage, Word Health Organization (WHO) stage, and intraoperative and postoperative conditions, including operative time, postoperative drainage, extubation time, postoperative hospital stay, and postoperative complication rates, were not statistically significant (p > 0.05), while BMI, intraoperative bleeding, and VAS on postoperative days 1, 3, and 7 were all statistically significant (p < 0.05) in the MSVATS group compared with the IVATS group. Conclusion The meta-analysis showed that the conventional subxiphoid approach was superior in terms of postoperative drainage and postoperative VAS pain scores compared with the unilateral intercostal approach. Moreover, the modified subxiphoid approach had significant advantages in intraoperative bleeding and postoperative VAS pain scores compared with the unilateral intercostal approach. These results indicate that MSVATS can provide more convenient operation conditions, a better pleural cavity view, and a more complete thymectomy in the treatment of early thymoma, indicating that is a safe and feasible minimally invasive surgical method.

Tugce Kutuk, Ece Atak, Alessandro Villa et al.

Radiation therapy (RT) plays a crucial role in the treatment of head and neck cancers (HNCs). This paper emphasizes the importance of effective communication and collaboration between radiation oncologists and dental specialists in the HNC care pathway. It also provides an overview of the role of RT in HNC treatment and illustrates the interdisciplinary collaboration between these teams to optimize patient care, expedite treatment, and prevent post-treatment oral complications. The methods utilized include a thorough analysis of existing research articles, case reports, and clinical guidelines, with terms such as ‘dental management’, ‘oral oncology’, ‘head and neck cancer’, and ‘radiotherapy’ included for this review. The findings underscore the significance of the early involvement of dental specialists in the treatment planning phase to assess and prepare patients for RT, including strategies such as prophylactic tooth extraction to mitigate potential oral complications. Furthermore, post-treatment oral health follow-up and management by dental specialists are crucial in minimizing the incidence and severity of RT-induced oral sequelae. In conclusion, these proactive measures help minimize dental and oral complications before, during, and after treatment.

Shuyan Sheng, Bangjie Chen, Ruiyao Xu et al.

Abstract Background Numerous studies have shown that Schistosoma japonicum infection correlates with an increased risk of liver hepatocellular carcinoma (LIHC). However, data regarding the role of this infection in LIHC oncogenesis are scarce. This study aimed to investigate the potential mechanisms of hepatocarcinogenesis associated with Schistosoma japonicum infection. Methods By examining chronic liver disease as a mediator, we identified the genes contributing to Schistosoma japonicum infection and LIHC. We selected 15 key differentially expressed genes (DEGs) using weighted gene co-expression network analysis (WGCNA) and random survival forest models. Consensus clustering revealed two subgroups with distinct prognoses. Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression identified six prognostic DEGs, forming an Schistosoma japonicum infection-associated signature for strong prognosis prediction. This signature, which is an independent LIHC risk factor, was significantly correlated with clinical variables. Four DEGs, including BMI1, were selected based on their protein expression levels in cancerous and normal tissues. We confirmed BMI1's role in LIHC using Schistosoma japonicum-infected mouse models and molecular experiments. Results We identified a series of DEGs that mediate schistosomiasis, the parasitic disease caused by Schistosoma japonicum infection, and hepatocarcinogenesis, and constructed a suitable prognostic model. We analyzed the mechanisms by which these DEGs regulate disease and present the differences in prognosis between the different genotypes. Finally, we verified our findings using molecular biology experiments. Conclusion Bioinformatics and molecular biology analyses confirmed a relationship between schistosomiasis and liver hepatocellular cancer. Furthermore, we validated the role of a potential oncoprotein factor that may be associated with infection and carcinogenesis. These findings enhance our understanding of Schistosoma japonicum infection's role in LIHC carcinogenesis.

Hou M, Chen J, Yang L et al.

Maolin Hou,1,&ast; Jinghua Chen,2,&ast; Le Yang,3,&ast; Lei Qin,3 Jie Liu,4 Haibo Zhao,5 Yujin Guo,6 Qing-Qing Yu,6 Qiujie Zhang5 1Department of Internal Medicine, Siziwangqi People’s Hospital, Wulancabu, 011800, People’s Republic of China; 2Department of Oncology, the First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, Jinan, 250000, People’s Republic of China; 3Department of Gastrointestinal Surgery, Jining, 272000, People’s Republic of China; 4Department of Pediatric Intensive Care Unit, Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University, Jinan, 250000, People’s Republic of China; 5Department of Oncology, Jining, 272000, People’s Republic of China; 6Department of Clinical Pharmacology, Jining, 272000, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Qiujie Zhang, Department of Oncology, Jining No. 1 People’s Hospital, Jining, 272000, People’s Republic of China, Email zhangqiujie86@163.com Qing-Qing Yu, Department of Clinical Pharmacology, Jining No. 1 People’s Hospital, Jining, 272000, People’s Republic of China, Email yuqingqing_lucky@163.comIntroduction: Gastric cancer, the fifth most common malignant tumor in the world, poses a serious threat to human health. However, the role of fatty acid metabolism (FAM) in gastric cancer remains incompletely understood. We aim to provide guidance for clinical decisions by utilizing public database of gastric adenocarcinoma to establish an FAM-related gene subtypes via machine learning algorithm.Methods: The intersection of FMGs from KEGG, Hallmark, and Reactome bioinformatics databases and the DEGs of the TCGA-STAD cohort was used to decompose the gene matrix related to establish FAM-related gene subtypes by NMF. Comparison of immune infiltrating differences between subtypes using ESTIMATE and Cibersort algorithms. The multifactor Cox regression to identify independent risk genes for patient prognosis based on the subtypes. A prognostic model including independent risk genes was built using random survival forest and Cox regression. IHC validation in gastric cancer and adjacent tissues confirmed the above gene expression level.Results: 71 DEGs related to FMGs of STAD were identified, which was used to established the FAM-related gene subtypes, C1 and C2. The immune infiltrating analysis showed that most immune features of C2 were significantly upregulated compared to C1. The independent risk genes were CGβ 8, UPK1B, and OR51G based on the subtypes. A gastric cancer prognostic model consisting of independent risk genes was constructed and patients were classified into high-risk and low-risk groups with survival differential analysis. Finally, IHC showed that CGβ 8 and UPK1B expression were upregulated in gastric cancer, while OR51G2 did not detect differences in expression.Conclusion: The study developed a machine learning-based gastric cancer prognosis risk model using FMGs. This model effectively stratifies patients according to their risk levels and provides valuable insights for clinical decision-making, enabling accurate evaluation of patient prognosis.Keywords: Gastric cancer, fatty acid metabolism-related genes, machine learning, genomics, signature

Arju Hossain, Asif Ahsan, Imran Hasan et al.

Purpose Ultraviolet radiation causes skin cancer, but the exact mechanism by which it occurs and the most effective methods of intervention to prevent it are yet unknown. For this purpose, our study will use bioinformatics and systems biology approaches to discover potential biomarkers of skin cancer for early diagnosis and prevention of disease with applicable clinical treatments. Methods This study compared gene expression and protein levels in ultraviolet-mediated cultured keratinocytes and adjacent normal skin tissue using RNA sequencing data from the National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) database. Then, pathway analysis was employed with a selection of hub genes from the protein-protein interaction (PPI) network and the survival and expression profiles. Finally, potential clinical biomarkers were validated by receiver operating characteristic (ROC) curve analysis. Results We identified 32 shared differentially expressed genes (DEGs) by analyzing three different subsets of the GSE85443 dataset. Skin cancer development is related to the control of several DEGs through cyclin-dependent protein serine/threonine kinase activity, cell cycle regulation, and activation of the NIMA kinase pathways. The cytoHubba plugin in Cytoscape identified 12 hub genes from PPI; among these 3 DEGs, namely, AURKA, CDK4 , and PLK1 were significantly associated with survival ( P  < 0.05) and highly expressed in skin cancer tissues. For validation purposes, ROC curve analysis indicated two biomarkers: AURKA (area under the curve (AUC) value = 0.8) and PLK1 (AUC value = 0.7), which were in an acceptable range. Conclusions Further translational research, including clinical experiments, teratogenicity tests, and in-vitro or in-vivo studies, will be performed to evaluate the expression of these identified biomarkers regarding the prognosis of skin cancer patients.

Plummer R, Greystoke A, Naylor G et al.

Ruth Plummer,1 Alastair Greystoke,1 Gregory Naylor,2 Debashis Sarker,3,4 ANM Kaiser Anam,4 Hans Prenen,5 Laure-Anne Teuwen,5 Eric Van Cutsem,6 Jeroen Dekervel,6 Beate Haugk,1 Thomas Ness,1 Sujata Bhoi,7 Malene Jensen,7 Tom Morris,7 Pia Baumann,7 Niclas Sjögren,8 Karin Tunblad,7 Hans Wallberg,7 Fredrik Öberg,7 Thomas R Jeffry Evans2 1Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK; 2Beatson West of Scotland Cancer Centre, University of Glasgow, Glasgow, UK; 3School of Cancer and Pharmaceutical Sciences, King’s College London, London, UK; 4Department of Medical Oncology, Guy’s Hospital, London, UK; 5Department of Oncology, Antwerp University Hospital, Edegem, Belgium; 6Department of Oncology, University Hospitals Gasthuisberg Leuven and KU Leuven, Leuven, Belgium; 7Medivir AB, Huddinge, Sweden; 8SDS Life Science, Stockholm, SwedenCorrespondence: Pia Baumann, Medivir AB, Box 1086, SE-141 22, Huddinge, Sweden, Tel +46 739163897, Email pia.baumann@medivir.comPurpose: To evaluate safety, preliminary efficacy, pharmacokinetics, and pharmacodynamics, of fostroxacitabine bralpamide (fostrox, MIV-818), a novel oral troxacitabine nucleotide prodrug designed to direct exposure to the liver, while minimizing systemic toxicity.Patients and Methods: Fostrox monotherapy was administered in an open-label, single-arm, first-in-human, phase 1a/1b study, in patients with hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma, or solid tumor liver metastases. The first part (1a) consisted of intra/inter-patient escalating doses (3 mg to 70 mg) QD for up to 5 days, and the second part (1b), doses of 40 mg QD for 5 days, in 21-day cycles. Safety and tolerability were evaluated by the Safety Review Committee, and efficacy was assessed every 6 weeks with CT or MRI using RECIST 1.1 and mRECIST.Results: Nineteen patients were treated with fostrox. Most common adverse events (AEs) were hematological and increased AST. Grade 3 treatment related AEs (TRAE) were seen in 53% of the patients, with transient neutropenia and thrombocytopenia as the most common. No grade 5 AE was observed. Recommended Phase 2 dose of fostrox was 40 mg QD for 5 days in 21-day cycles. Preliminary efficacy showed a clinical benefit rate in the liver of 53% and stable disease (SD) as best response in 10 patients. Liver targeting with fostrox was confirmed with higher exposure of troxacitabine and its metabolites in liver compared to plasma. Systemic exposure of fostrox was generally low with troxacitabine as main analyte. Biopsies demonstrated tumor-selective, drug-induced DNA damage.Conclusion: The phase 1a/1b monotherapy study of fostrox, in patients with liver tumors, showed a tumor selective effect in the liver and that 40 mg QD for 5 days in 21-day cycles is safe and tolerable. Safety and preliminary efficacy in patients with advanced HCC supports clinical development of fostrox in combination with other modes of action in HCC.Keywords: phase 1, fostrox, hepatocellular carcinoma, nucleotide prodrug, pharmacokinetics, pharmacodynamics

Mobina Tousian, Christian Solis Calero, Julio Cesar Perez Sansalvador

The tumor microenvironment (TME) plays a critical role in cancer cell proliferation, invasion, and resistance to therapy. A principal component of the TME is the tumor immune microenvironment (TIME), which includes various immune cells such as macrophages. Depending on the signals received from environmental elements like IL-4 or IFN-$γ$, macrophages can exhibit pro-inflammatory (M1) or anti-inflammatory (M2) phenotypes. This study uses an enhanced agent-based model to simulate interactions within the TIME, focusing on the dynamic behavior of macrophages. We examine the response of cancer cell populations to alterations in macrophages, categorized into three different behaviors: M0 (initial-inactive), M1 (immune-upholding), and M2 (immune-repressing), as well as environmental differentiations. The results highlight the significant impact of macrophage modulation on tumor proliferation and suggest potential therapeutic strategies targeting these immune cells.