Frontiers in Oncology最新文献

{"title":"From dysbiosis to precision therapy: decoding the gut-bladder axis in bladder carcinogenesis.","authors":"Ze-Qiang Liu, Xiao-Ying Yang, Jia-Hong Chen, Si-Cheng Ge, Shi-Xue Dai, Sheng-Huang Zhu, Zhi-Yong Xian","doi":"10.3389/fonc.2025.1630726","DOIUrl":"10.3389/fonc.2025.1630726","url":null,"abstract":"<p><p>The gut-bladder axis (GBA), a bidirectional network connecting gastrointestinal and urinary systems, has recently emerged as a pivotal focus in bladder cancer research. Beyond conventional risk factors, gut dysbiosis, aberrant microbial metabolites, and neuro-immune pathway disruptions have been implicated in tumorigenesis and progression. Short-chain fatty acids (SCFAs), microbial-derived metabolites, are shown to indirectly modulate tumor behavior through immune microenvironment regulation and inflammatory response attenuation. Cross-organ crosstalk is further mediated by neural pathways (e.g., vagal signaling) and shared receptors, including the Farnesoid X Receptor (FXR) and Toll-like Receptor 4 (TLR4). Novel therapies leveraging microbial ecology principles demonstrate potential, including immune checkpoint inhibitors combined with microbiota modulation (e.g., <i>Parabacteroides distasonis</i>-enhanced PD-1 efficacy), probiotics to reverse chemoresistance, and microbiota reprogramming for SCFA-targeted strategies. However, molecular mechanisms underlying GBA-host interactions remain poorly characterized. Clinical translation is hindered by limited cohort sizes and interindividual heterogeneity. Current studies, while revealing partial pathways, face methodological inconsistencies, particularly in urinary microbiome profiling, and a lack of longitudinal human data. Future breakthroughs will require multi-omics integration, organoid-based models, and interdisciplinary collaboration to address these gaps.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1630726"},"PeriodicalIF":3.5,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286802/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping the immunological landscape and emerging immunotherapeutic strategies in cervical cancer: a comprehensive review.","authors":"Xinyi Zhang, Wenyang Nie, Wenwen Shao, Qian Guo","doi":"10.3389/fonc.2025.1620501","DOIUrl":"10.3389/fonc.2025.1620501","url":null,"abstract":"<p><p>Cervical cancer continues to pose a considerable global health challenge, especially in low- and middle-income nations, although progress in screening and vaccine efforts. In recent years, immunotherapy has emerged as a promising treatment option; nevertheless, its efficacy in cervical cancer is constrained by the intricate and heterogeneous tumor immune microenvironment. Reliable biomarkers to predict which patients will benefit from immunotherapy are lacking. The heterogeneity of the immune landscape across patients adds further complexity. This paper offers a thorough examination of the immunological landscape in cervical cancer, highlighting the interactions among tumor cells, immune infiltrates, and stromal elements. Moreover, we investigate how advanced technologies-such as single-cell RNA sequencing, spatial transcriptomics, and multiplex imaging-are transforming our comprehension of immunological heterogeneity and uncovering new therapeutic targets. We seek to delineate present problems and potential pathways in the development of effective, tailored immunotherapies for cervical cancer by integrating genetic analysis with immunological insights.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1620501"},"PeriodicalIF":3.5,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lamins regulate cancer cell plasticity and chemosensitivity.","authors":"Guofang Chen, Tingyi Wei, Ao Huang, Junwei Shen, Furong Ju, Shichao Huang, Haisen Li","doi":"10.3389/fonc.2025.1599175","DOIUrl":"10.3389/fonc.2025.1599175","url":null,"abstract":"<p><strong>Background: </strong>Stem cell plasticity plays key roles in mammalian organogenesis, tissue homeostasis, and carcinogenesis. Given its tolerance to anti-tumor therapy and its promotion on immunosuppressive microenvironment, cancer cell plasticity is a major contributor to cancer recurrence and metastasis. It is necessary to explore novel avenues to resolve the limitations of current treatments.</p><p><strong>Methods: </strong>We established stable cancer cell lines harboring all lamin knockdown and then explored the effects of all lamin deficiency on cancer plasticity and tumorigenesis in both cell and subcutaneous mouse models.</p><p><strong>Results: </strong>We found that all lamin knockdown disrupts cancer cell plasticity and impairs tumor progression. The deficiency of all lamin subtypes impaired the stemness and cell cycle transition of cancer cell. Lamin knockdown modulated genomic damage and repair pathways, inhibited mitochondrial function, and triggered cellular senescence. Moreover, lamin knockdown within cancer cell suppressed cancer growth <i>in vivo</i> by enhancing the infiltration and activation of functional T cells. Mechanistically, lamin knockdown reduced the expression of inhibitory immune checkpoints and inflammatory factors in cancer cell via the HIF-1 signaling pathway, which led to the increased sensitivity of cancer cells to chemotherapy.</p><p><strong>Conclusions: </strong>Overall, our findings characterize the significance of nuclear lamins in cancer cell plasticity and offer an attractive way to improve the effectiveness of anti-cancer therapy.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1599175"},"PeriodicalIF":3.5,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-assisted diagnosis of liver tumors using non-contrast magnetic resonance imaging: a multicenter study.","authors":"Shihui Zhen, Peng Zhang, Hanxiao Huang, Zhiyu Jiang, Yankai Jiang, Jihong Sun, Liqing Zhang, Mei Ruan, Qingqing Chen, Yujun Wang, Yubo Tao, Weizhi Luo, Ming Cheng, Zhetuo Qi, Wei Lu, Hai Lin, Xiujun Cai","doi":"10.3389/fonc.2025.1582322","DOIUrl":"10.3389/fonc.2025.1582322","url":null,"abstract":"<p><strong>Objectives: </strong>Non-contrast MRI(NC-MRI) is an attractive option for liver tumors screening and follow-up. This study aims to develop and validate a deep convolutional neural network for the classification of liver lesions using non-contrast MRI.</p><p><strong>Methods: </strong>A total of 50418 enhanced MRI images from 1959 liver tumor patients across three centers were included. Inception-ResNet V2 was used to generate four models through transfer-learning for three-way lesion classification, which processed T2-weighted, diffusion-weighted (DWI) and multiphasic T1-weighted images. The models were then validated using one independent internal and two external datasets with 5172, 2916, and 1338 images, respectively. The efficacy of non-contrast models (T2,T2+DWI) in differentiating between benign and malignant liver lesions at the patient level was also evaluated and compared with radiologists. The performance of models was evaluated using the area under the receiver operating characteristic curve (AUC),sensitivity and specificity.</p><p><strong>Results: </strong>Similar to multi-sequence and enhanced image-based models, the non-contrast models showed comparable accuracy in classifying liver lesions as benign, primary malignant or metastatic. In the independent internal cohort, the T2+DWI model achieved AUC of 0.91(95% CI,0.888-0.932), 0.873(0.848-0.899), and 0.876(0.840-0.911) for three tumour categories, respectively. The sensitivities for distinguishing malignant tumors in three validation sets were 98.1%, 89.7%, and 87.5%%, with specificities over 70% in all three sets.</p><p><strong>Conclusions: </strong>Our deep-learning-based model yielded good applicability in classifying liver lesions in non-contrast MRI. It provides a potential alternative for screening liver tumors with the advantage of reducing costs, scanning time and contrast-agents risks. It is more suitable for benign tumours follow-up, surveillance of HCC and liver metastasis that need periodic repetitive examinations.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1582322"},"PeriodicalIF":3.5,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.","authors":"Liu Haixian, Pang Shu, Li Zhao, Lu Chunfeng, Li Lun","doi":"10.3389/fonc.2025.1576461","DOIUrl":"10.3389/fonc.2025.1576461","url":null,"abstract":"<p><strong>Background: </strong>With the rapid advances in artificial intelligence-particularly convolutional neural networks-researchers now exploit CT, PET/CT and other imaging modalities to predict epidermal growth factor receptor (EGFR) mutation status in non-small-cell lung cancer (NSCLC) non-invasively, rapidly and repeatably. End-to-end deep-learning models simultaneously perform feature extraction and classification, capturing not only traditional radiomic signatures such as tumour density and texture but also peri-tumoural micro-environmental cues, thereby offering a higher theoretical performance ceiling than hand-crafted radiomics coupled with classical machine learning. Nevertheless, the need for large, well-annotated datasets, the domain shifts introduced by heterogeneous scanning protocols and preprocessing pipelines, and the \"black-box\" nature of neural networks all hinder clinical adoption. To address fragmented evidence and scarce external validation, we conducted a systematic review to appraise the true performance of deep-learning and radiomics models for EGFR prediction and to identify barriers to clinical translation, thereby establishing a baseline for forthcoming multicentre prospective studies.</p><p><strong>Methods: </strong>Following PRISMA 2020, we searched PubMed, Web of Science and IEEE Xplore for studies published between 2018 and 2024. Fifty-nine original articles met the inclusion criteria. QUADAS-2 was applied to the eight studies that developed models using real-world clinical data, and details of external validation strategies and performance metrics were extracted systematically.</p><p><strong>Results: </strong>The pooled internal area under the curve (AUC) was 0.78 for radiomics-machine-learning models and 0.84 for deep-learning models. Only 17 studies (29%) reported independent external validation, where the mean AUC fell to 0.77, indicating a marked domain-shift effect. QUADAS-2 showed that 31% of studies had high risk of bias in at least one domain, most frequently in Index Test and Patient Selection.</p><p><strong>Conclusion: </strong>Although deep-learning models achieved the best internal performance, their reliance on single-centre data, the paucity of external validation and limited code availability preclude their use as stand-alone clinical decision tools. Future work should involve multicentre prospective designs, federated learning, decision-curve analysis and open sharing of models and data to verify generalisability and facilitate clinical integration.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1576461"},"PeriodicalIF":3.5,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286997/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Amide proton transfer-weighted habitat radiomics: a superior approach for preoperative prediction of lymphovascular space invasion in cervical cancer.","authors":"Jie Li, Yatong Li, Lianze Du, Qinghai Yuan, Qinghe Han","doi":"10.3389/fonc.2025.1599522","DOIUrl":"10.3389/fonc.2025.1599522","url":null,"abstract":"<p><strong>Background: </strong>Non-invasive preoperative prediction of lymphovascular space invasion (LVSI) in cervical cancer (CC) is clinically important for guiding surgical planning and adjuvant therapy, while avoiding the risks associated with invasive procedures. However, current studies using amide proton transfer-weighted (APTw) MRI for LVSI prediction typically analyze only the mean values from a limited number of intratumoral regions of interest (ROIs), which fails to fully capture tumor heterogeneity. This study investigates the added value of whole-tumor APTw habitat radiomics in predicting LVSI and its advantages over conventional analysis methods.</p><p><strong>Methods: </strong>This prospective study included consecutive adult patients with suspected CC who underwent APTw MRI between December 2022 and December 2024; a portion of the cohort has been reported previously. APTw values were extracted using two methods: (1) the conventional approach, calculating the mean signal from three ROIs on a representative slice; and (2) habitat radiomics, involving whole-tumor segmentation, k-means clustering to identify functional subregions, and radiomic feature extraction. Pathological assessment of LVSI from hysterectomy specimens served as the reference standard. Multivariable logistic regression identified variables associated with LVSI and developed diagnostic models. Model robustness was evaluated by 5-fold cross-validation, with AUC and DeLong's test used for performance assessment.</p><p><strong>Results: </strong>Among 124 patients (74 LVSI-, 50 LVSI+), the APTw_h3 model achieved a higher AUC (0.796 [95% CI: 0.709-0.882]) for predicting LVSI positivity than the clinical-radiological model (AUC = 0.733, 95% CI: 0.638-0.817). The combined model integrating clinical, radiological, and APTw_h3 features achieved the highest AUC (0.903, 95% CI: 0.841-0.952), which was significantly higher than those of both the clinical-radiological and APTw_h3 models (both <i>P</i> < 0.001). Moreover, the addition of APTw_h3 to the clinical-radiological model improved sensitivity (88% vs. 82%) and specificity (83.8% vs. 64.9%) for determining LVSI positivity.</p><p><strong>Conclusion: </strong>Whole-tumor APTw habitat radiomics demonstrates superior performance over conventional mean-value APTw analysis for preoperative prediction of LVSI in CC. Notably, integrating habitat radiomic features with clinical and radiological parameters further improves predictive accuracy, demonstrating potential for enhanced individualized patient management.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1599522"},"PeriodicalIF":3.5,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144706957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient and physician treatment preferences in relapsed/refractory follicular lymphoma: a discrete choice experiment in the United States, United Kingdom, France, Germany, Brazil, and Japan.","authors":"John G Gribben, Emmanuel Bachy, Markqayne Ray, Kathryn Krupsky, Kathleen Beusterien, Lewis Kopenhafer, Sara Beygi, Timothy Best, Graeme Ball, Oliver Will, Madhu Palivela, Anik Patel, Paola Ghione","doi":"10.3389/fonc.2025.1589722","DOIUrl":"10.3389/fonc.2025.1589722","url":null,"abstract":"<p><strong>Introduction: </strong>The objectives of this study were to identify key treatment attributes that drive physician and patient preferences for second line (2L) and third line (3L) treatments in relapsed/refractory (R/R) follicular lymphoma (FL).</p><p><strong>Methods: </strong>A multi- country, internet-based survey was administered to patients(N=195) with R/R FL and treating physicians (N=300) from the United States, United Kingdom, France, Germany, Brazil, and Japan. The survey included two discrete choice experiments - one for 2L and one for 3L treatment options - that prompted respondents to select their preferred option between two hypothetical treatment profiles varying on seven attributes associated with treatment for R/RFL: progression-free survival (PFS), overall survival (OS), serious adverse events (AE), cytokine release syndrome (CRS) events, neurological events, fatigue, and administration. Mean preference weights and relative attribute importance were estimated in each sample, overall and by country, using hierarchical Bayesian models. Physician estimates were also stratified by practice setting.</p><p><strong>Results: </strong>Treatment preferences for physicians and patients were most influenced by PFS. Beyond PFS, patients placed greater emphasis on the administration of medications, whereas physicians tended to focus more on five-year OS and toxicity profiles of agents. Preference for PFS above all other 2L and 3L treatment attributes was consistent for physicians, regardless of practice setting and country. However, patient treatment preferences varied by country.</p><p><strong>Discussion: </strong>These results offer key perspectives on how physicians and patients evaluate treatment options in 2L and 3L treatment settings; this information is essential for facilitating shared decision-making in an expanding, complex treatment landscape.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1589722"},"PeriodicalIF":3.5,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive value of serum miR-21 and miR-122 expression on the efficacy of capecitabine combined with transcatheter hepatic arterial embolization chemotherapy for liver metastasis after colorectal cancer surgery in patients with colorectal cancer and construction and verification of nomograms.","authors":"Wenfang Ma, Zukuan Chang, Shixing Li, Xiuhua Wang, Guangshao Cao, Youjie Fan","doi":"10.3389/fonc.2025.1604994","DOIUrl":"10.3389/fonc.2025.1604994","url":null,"abstract":"<p><strong>Objective: </strong>To explore the predictive value of serum miR-21 and miR-122 expressions on the efficacy of capecitabine combined with TACE for the treatment of postoperative liver metastasis in colorectal cancer patients, and to construct a nomogram model for verification.</p><p><strong>Methods: </strong>A total of 252 patients who received this treatment from January 2021 to December 2023 were included in the study. The dataset was randomly split at a 7:3 ratio into a training set (n=181) and a validation set (n=71). Serum levels of miR-21 and miR-122 before treatment were detected and the relationship with clinical pathological characteristics was analyzed. Independent risk factors were screened by multivariate Logistic regression, and a nomogram model was constructed to evaluate efficacy.</p><p><strong>Results: </strong>In the training set, there were 86 cases with effective treatment and 95 cases with ineffective treatment after operation. Multivariate analysis showed that CEA, high serum miR-21 expression, low miR-122 expression, tumor size, BMI, and age were the independent risk factors for efficacy (<i>P</i><0.05). The nomogram model exhibited C-indexes of 0.809 (training set) and 0.732 (validation set). Additionally, the average absolute errors of the calibration curves were 0.178 and 0.210, respectively. The Hosmer-Lemeshow test result was good. The Receiver operating characteristic (ROC) curve showed that the area under the curve (AUC) of the model in predicting the efficacy was 0.810 (95% <i>CI</i>: 0.734-0.885) and 0.731 (95% <i>CI</i>: 0.597-0.866) in the training set and the verification set, respectively. The sensitivities and specificities were 0.820, 0.716 and 0.600 and 0.714, respectively.</p><p><strong>Conclusion: </strong>The expression levels of serum miR-21 and miR-122 have predictive value for the efficacy of liver metastasis after colorectal cancer treatment. The nomogram model has good predictive performance, which can provide a reference for clinical decision-making. Furthermore, the identified predictive value of miR-21 and miR-122 provides a basis for exploring personalized combination therapies with targeted agents in future studies, which may help overcome the limitations of conventional chemotherapy.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1604994"},"PeriodicalIF":3.5,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286820/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global research trends and hotspots in prognostic prediction models for pancreatic cancer: a bibliometric analysis.","authors":"Siyuan Ouyang, Jing Zhang, Fuyao Liu, Qi Jiang, Wei Xing, Jie Chen, Jinggang Zhang","doi":"10.3389/fonc.2025.1588735","DOIUrl":"10.3389/fonc.2025.1588735","url":null,"abstract":"<p><strong>Background: </strong>Pancreatic cancer is a highly aggressive malignancy of the digestive system, characterized by insidious onset and rapid progression. Most cases are diagnosed at advanced stages, complicating surgical resection and presenting significant challenges for clinical treatment. Recent advancements have emphasized individualized treatment strategies tailored to patients' specific conditions. Consequently, accurate preoperative assessment is crucial, highlighting the urgent need to develop more reliable predictive models to guide personalized treatment plans.</p><p><strong>Methods: </strong>A systematic literature search was conducted using Web of Science Core Collection (WoSCC) database, covering publications from January 1, 1995, to October 25, 2024. A comprehensive bibliometric analysis was performed employing analytical tools such as VOSviewer, CiteSpace and Microsoft Excel.</p><p><strong>Results: </strong>This study includes 919 publications authored by 6716 researchers from 3727 institutions in 222 countries and regions. The articles were published in 301 journals, with 1,640 distinct keywords and 25,910 references. China led in publication volume, while the United States garnered the most citations. The top three research institutions in this field were Fudan University, Shanghai Jiao Tong University, and Sun Yat-sen University. Yu Xianjun from Fudan University emerged as the most prolific author with the highest citation count. <i>Frontiers in Oncology</i> had the highest publication volume, while the <i>Annals of Surgery</i> received the most citations. Medical imaging, biochemistry, immunology, bioinformatics, genetics, and interdisciplinary integrative research are the main research disciplines in the field of prognosis prediction for pancreatic cancer. The results of keyword co-occurrence and literature co-citation analysis revealed emerging hotspots and trends in this field, including CA19-9, CT, inflammation, machine learning, tumor microenvironment, radiomics, genes, nomograms, randomized controlled trials, long-term survival, and metastasis.</p><p><strong>Conclusion: </strong>This bibliometric analysis provides an overview of research conducted over the past three decades, offering insights into the current state of knowledge and outlining directions for future studies on prognosis prediction models for pancreatic cancer. Biochemical indicators have consistently emerged as key research focal points. The tumor microenvironment represents a currently popular research direction, while bioinformatics, medical imaging, and artificial intelligence are gaining traction as future trends in this field. In the future, prognostic models for pancreatic cancer require further refinement to ensure reliable guidance for therapeutic decision-making.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1588735"},"PeriodicalIF":3.5,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Case Report: Concurrent neurofibromatosis type 1 with papillary thyroid carcinoma and gastrointestinal stromal tumor.","authors":"Ruba Dweik, Jana Faroun, Rita Yacoub, Mohammad I Smerat, Yousef Abu Asbeh","doi":"10.3389/fonc.2025.1529765","DOIUrl":"10.3389/fonc.2025.1529765","url":null,"abstract":"<p><p>Neurofibromatosis type 1 (NF1) is a genetic disorder characterized by benign tumors such as neurofibromas and café-au-lait spots, with affected individuals at increased risk for malignant tumors, including gastrointestinal stromal tumors (GIST) and, rarely, papillary thyroid carcinoma (PTC). This case report presents a 30-year-old Palestinian woman with NF1 who experienced severe abdominal pain and melena, leading to the diagnosis of a jejunal GIST, which was surgically removed. Postoperative imaging revealed cervical and thoracic lesions. A follow-up PET scan indicated hypermetabolic masses in the thyroid and chest. Subsequent surgery confirmed the diagnosis of PTC and neurofibromas, with whole exome sequencing identifying a likely pathogenic variant in the NF1 gene. This case demonstrates the value of comprehensive evaluation and genetic counseling for NF1 patients due to the risk of multiple tumors, which points to careful monitoring for early detection and management. To our knowledge, this instance is the first reported case of concurrent GIST and PTC in a patient with NF1.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1529765"},"PeriodicalIF":3.5,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144706958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}