Cancer Investigation最新文献

{"title":"Selection of Criteria in the Diagnosis Approach of Paraneoplastic Fever in Adults With Solid Neoplasia Using a Delphi Method.","authors":"Matthieu Bainaud, Arnaud Saillant, Nicolas Isambert, Mathieu Puyade, Clément Beuvon","doi":"10.1080/07357907.2025.2561037","DOIUrl":"https://doi.org/10.1080/07357907.2025.2561037","url":null,"abstract":"<p><strong>Purpose: </strong>Paraneoplastic fever (PF) is an exclusion diagnosis that affects around 10% of patients in oncology, combining fever of unknown origin and the presence of cancer. There is no consensus or guidelines in the literature about the minimum criteria required for the diagnosis of (PF). The objective of this survey was to select clinical and paraclinical criteria to establish the diagnosis of PF.</p><p><strong>Methods: </strong>After a review of the literature, 23 categories and 48 items were set up in an online survey. A two-round Delphi questionnaire survey was carried out from May to August 2021 with the participation of experts in several specialties in France and abroad.</p><p><strong>Results: </strong>Thirty-seven and 33 experts responded in the first and second rounds respectively. Nine items obtained consensus. Among them, the need to rule out suspected infection by a directed bacteriological statement, an up-to-date imaging and doppler ultrasound of the lower limbs was highly consensual. No biological criteria were retained. Thirty-six propositions did not reach consensus and five were considered useless in this setting.</p><p><strong>Conclusion: </strong>The 9 selected criteria confirm the importance to eliminating differential fever aetiologies whereas no specific clinical or biological markers were retained. This survey constitute the first consensus of experts in this field.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"1-13"},"PeriodicalIF":1.9,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145190913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progress in Development of Lung Cancer Survival Prediction Models Using Machine Learning Based on SEER Database.","authors":"Ye Zhang, Jiaye Wang, Shiyu Hu, Yufen Xu, Qi Yang, Wenyu Chen","doi":"10.1080/07357907.2025.2563716","DOIUrl":"https://doi.org/10.1080/07357907.2025.2563716","url":null,"abstract":"<p><p>The SEER (Surveillance, Epidemiology, and End Results) database, a comprehensive public repository of clinical oncology data, has been increasingly used to construct clinical prediction models for predicting the prognosis of cancer. With the advances in machine learning, various algorithms including logistic regression (LR), support vector machines (SVM), decision trees (DT), random forest (RF), artificial neural networks (ANN), and extreme gradient boosting (XGBoost) have been successively employed in the development of lung cancer survival prediction models (LCSPMs). This study combs through the progress of these machine learning algorithms in constructing lung cancer survival prediction models, points out the problems of data imbalance, poor model interpretability, and lack of external validation, and clarifies the future development direction.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"1-12"},"PeriodicalIF":1.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interplay Between ERK1/2 Signaling Pathway and Estradiol Receptor Modulates ER Targeted Genes Involved in Progression of Estrogen Responsive Breast Cancers.","authors":"Rajeshwari H Patil, Kavya K, Naveen Kumar M, Paturu Kondaiah","doi":"10.1080/07357907.2025.2563715","DOIUrl":"https://doi.org/10.1080/07357907.2025.2563715","url":null,"abstract":"<p><p>Breast cancer is a leading global health concern, while the endocrine resistance in breast cancer poses a critical challenge, directly undermining the long-term effectiveness of hormone therapies and significantly impacting patient survival and treatment outcomes. Hence, the present study aims to elucidate the non-genomic mechanism of ERK1/2 signalling pathway, in conjunction with ER and GPR30 receptors involved in regulation of breast cancer progression in MCF-7 and T47D cells. We assessed cell proliferation using MTT and Trypan blue assays, expression studies by reverse transcription quantitative PCR and western blot analysis, the migratory abilities of cells by scratch-wound healing assay. Our results revealed significant down (90%) regulation of E2-induced ERK phosphorylation, inturn suppression of proliferation rate by 30% and migration by 35% using small molecular inhibitors of ERK in MCF-7 and T47D cells confirming ERK as the central direct target for breast cancer proliferation and development. Collectively, our results suggest that E2-induced 1.5-fold upregulation of phospho ERK1/2 expression promotes breast cancer cell proliferation and migration via a Src/EGFR/ERK pathway. These findings provide a novel strategy of combining endocrine therapy with targeted agents (ERK inhibitors), a cornerstone in managing endocrine-resistant condition, delaying progression and improving outcomes in the treatment of breast cancer.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"1-14"},"PeriodicalIF":1.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Supervised Learning Method for Breast Cancer Detection with Image Feature Set and Modified U-Net Segmentation Using Whole Slide Image.","authors":"Sangishetti Karunakar, Praveen Pappula","doi":"10.1080/07357907.2025.2562535","DOIUrl":"https://doi.org/10.1080/07357907.2025.2562535","url":null,"abstract":"<p><p>Breast cancer (BC) is the second most prevalent cause of death for women and the most frequently diagnosed malignancy. Early identification of this deadly illness lowers treatment costs while significantly improving survival rates. In contrast, skilled radiologists and pathologists analyze radiographic and histopathological images, respectively. In addition to being expensive, the procedure is prone to errors. The paper offers a solution to these challenges by presenting an innovative approach that combines a Modified U-Net architecture with sophisticated self-supervised learning methods to the accuracy and efficiency of breast cancer detection in WSIs. The proposed model improves the accuracy of tumor detection by integrating a multi-stage process: starting with Gaussian filtering for image preprocessing to remove noise, followed by the Modified U-Net for precise tumor segmentation including multi-scale processing and attention mechanisms. Feature extraction is achieved through the Bag of Visual Words (BoW), Improved Local Gradient and Intensity Pattern (LGIP), and Pyramidal Histogram of Oriented Gradients (PHOG) techniques to capture diverse image characteristics. The classification phase employs an Improved Self-Supervised Learning (ISSL) method, which improves feature representation via a novel loss function and an improved Multiple Instance Pooling (IMIP) mechanism. This method is designed to overcome the limitations of conventional techniques by offering clearer tumor boundaries and more accurate classifications, thereby improving the overall reliability and efficacy of breast cancer detection in clinical practice. Moreover, the ISSL strategy yielded the highest performance metrics, including an accuracy of 0.924, a sensitivity of 0.886, and a negative predictive value (NPV) of 0.943.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"1-22"},"PeriodicalIF":1.9,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AURKA Enhances Antitumor Immunity by Activating CD4+ T Cell Proliferation in Colorectal Cancer.","authors":"Yidong Xu, Wei Wang, Jiazi Yu, Jianpei Zhao, Xiaoyu Dai, Zhongchen Liu","doi":"10.1080/07357907.2025.2559403","DOIUrl":"https://doi.org/10.1080/07357907.2025.2559403","url":null,"abstract":"<p><strong>Introduction: </strong>Colorectal cancer (CRC) ranks third globally in cancer incidence. Aurora Kinase A (AURKA) critically regulates tumor proliferation and microenvironment, yet its dual CRC roles remain unclear.</p><p><strong>Methods: </strong>We integrated bulk RNA-seq, scRNA-seq, and 10x Visium spatial transcriptomics to profile AURKA. Immune infiltration was assessed via CIBERSORT/ssGSEA. Clinical validation used IHC/HE staining. Immunotherapy associations were tested in ICB cohorts and murine models.</p><p><strong>Results: </strong>Pan-cancer analysis showed CRC-specific AURKA prognostic value (<i>p</i> < 0.05). High AURKA correlated with prolonged OS (median 68 vs 42 months; log-rank <i>P </i>= 0.034), conventional adenocarcinoma (<i>p</i> < 0.001), left-sided tumors (<i>p</i> < 0.001), and absent perineural invasion (<i>p</i> = 0.041). Pathway analyses linked AURKA to cell cycle (G2/M checkpoint) and immune pathways (IL-2/STAT5). Spatial transcriptomics identified peritumoral niches (clusters 6/7/12) co-expressing AURKA, CD4, MKI67, and immune-activation markers (HLA-DRB1, CXCL10). IHC confirmed AURKA-CD4 + T-cell correlation (R = 0.66, <i>p</i> < 0.05). scRNA-seq revealed AURKA dominance in proliferating T cells. High AURKA predicted anti-PD-1 response (HR = 0.44, <i>p</i> = 0.003) and CD4+ memory T-cell expansion in murine models.</p><p><strong>Conclusion: </strong>AURKA dually regulates tumor proliferation and immune engagement. Its spatial enrichment in T-cell niches supports its use as an immunotherapy biomarker.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"1-18"},"PeriodicalIF":1.9,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Advances in Nanocarrier Systems for the Co-Delivery of siRNA and Chemotherapeutic Drug for Breast Cancer Therapy.","authors":"Neha Laxane, Khushwant S Yadav","doi":"10.1080/07357907.2025.2559088","DOIUrl":"https://doi.org/10.1080/07357907.2025.2559088","url":null,"abstract":"<p><p>Breast cancer's heterogeneity demands innovative therapies. Co-delivery of therapeutics using nanocarriers, especially siRNA combined with other chemotherapeutic drugs, presents a promising avenue. These systems safeguard siRNA, enhance its cellular uptake, and facilitate simultaneous targeting of multiple oncogenic pathways. This multifaceted approach holds potential for superior efficacy and reduced toxicity, addressing the limitations of conventional treatments and paving the way for improved breast cancer therapy.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"1-20"},"PeriodicalIF":1.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145079660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Histopathological Image Analysis and Enhanced Diagnostic Accuracy Explainability for Oral Cancer Detection.","authors":"V P Gladis Pushparathi, S R Sylaja Vallee Narayan, R S Pratheeba, V Naveen","doi":"10.1080/07357907.2025.2559103","DOIUrl":"https://doi.org/10.1080/07357907.2025.2559103","url":null,"abstract":"<p><p>Deep learning (DL) has transformed medical imaging, particularly in the realm of Oral Cancer (OC) diagnosis using histopathological images. Timely detection of OC is essential for enhancing precision medicine and saving lives. However, incorrect diagnosis may impede effective treatment. In this study, we have proposed a DL model for OC classification, enhanced diagnosis decision-making, and interpretability. We achieve this by starting with color normalization of histopathology images using the Vahadane Three-Stain Parameter Normalization and watershed segmentation method, followed by tiling and augmentation. Key features are selected using the Weighted Fisher Score (WFS) to address class imbalance. The U-Net classifier has been improved by using feature-based inputs instead of full images, reducing computational complexity and training time. The integration of Vahadane normalization for consistent preprocessing across samples, WFS, and Explainable Artificial Intelligence (XAI) addresses critical challenges in histopathological image analysis. The proposed model surpasses existing approaches with a classification accuracy of 99.54% and outperforms DenseNet201 and VGG10 in precision and reliability. The efficiency in handling imbalanced datasets and explainability features make it suitable for early precise OC detection, which can reduce diagnostic errors and enhance treatment outcomes.​.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"1-14"},"PeriodicalIF":1.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145063503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Specific Genomic Alterations and Molecular Mechanisms of Liver Metastases in Patients with Lung Adenocarcinoma.","authors":"Lingyi Yang, Lin Gao, Ruiqi Qian, Xiuqin Zhang, Xurui Shen","doi":"10.1080/07357907.2025.2558087","DOIUrl":"https://doi.org/10.1080/07357907.2025.2558087","url":null,"abstract":"<p><p>Given the limited diagnostic technologies and treatment options available for lung adenocarcinoma (LUAD) patients with liver metastases, it is crucial to identify potential genomic signatures associated with liver metastasis, which could significantly contribute to the development of improved diagnostic tools and treatment strategies for LUAD patients with liver metastases. In this study, we identified specific genetic alterations in tumor samples with liver metastases by targeted capture sequencing. The results showed that the significantly higher mutation frequencies of <i>KRAS</i>, <i>STK11</i> and <i>ERBB2</i> in LUAD patients with liver metastases and <i>ERBB2</i> and <i>STK11</i> mutations found in both tumor tissues and plasma samples from patients with liver metastases. In addition, the higher mutation frequencies of <i>KRAS</i> and <i>STK11</i> in the group with early-stage liver metastasis suggested that mutations in <i>KRAS</i> and <i>STK11</i> may play crucial roles in promoting liver metastases in LUAD patients at an early stage. Furthermore, the significantly higher TMB in the late-stage liver metastasis group indicated that patients with late-stage liver metastasis may have a better response to immunotherapy compared to those with early-stage liver metastasis. These findings provide valuable insights for developing detection tools and tailoring individualized treatments for such patients.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"1-13"},"PeriodicalIF":1.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends of Female Breast Cancer Burden in China over 25 Years: A Join Point Regression and Age-Period-Cohort Analysis Based on the GBD (1997-2021).","authors":"Yuanyan Tang, Jia Zhu, Zhengren Liu","doi":"10.1080/07357907.2025.2554631","DOIUrl":"https://doi.org/10.1080/07357907.2025.2554631","url":null,"abstract":"<p><strong>Background: </strong>Breast cancer (BC) is one of the most prevalent malignant tumors among women globally. The incidence and mortality rates of female BC exhibit significant variation across different countries and regions.</p><p><strong>Objective: </strong>This study analyzed the trends of BC among Chinese women from 1997 to 2021 to support evidence-based for the prevention, screening and treatment strategies of female BC in China.</p><p><strong>Methods: </strong>We extracted data on BC incidence, mortality, prevalence, disability-adjusted life years (DALYs), years lived with disability (YLDs) and years of life lost (YLLs) among Chinese women from 1997 to 2021 from the Global Burden of Disease (GBD)database. Join point regression analysis was used to identify the major turning points of disease burden trends, and to calculate the annual percentage change (APC) and average annual percentage change (AAPC). We applied age-period-cohort (A-P-C) models to separately evaluate the effects of age, period, and cohort on trends in female BC in China.</p><p><strong>Results: </strong>In 2021, the age standardized incidence rate (ASIR) and DALYs of female BC in China were 37.12 (95% CI: 28.23,46.95) and 281.54(95% CI: 216.87,358.11) per 100,000 women respectively. The AAPC values of the incidence and mortality of female BC were 2.42% (95% CI 2.04-2.80) and -0.49% (95% CI -0.70--0.28) respectively (p < 0.05). A-P-C model indicated that both the rates of incidence, prevalence and deaths increased with age from 1997 to 2021. The period effect analysis revealed that the prevalence and incidence risk of BC peaked between 2015 and 2020, with the highest rate ratio (RR) value 1.28 (95% CI 1.25-1.31) and 1.22 (95% CI 1.19-1.25). The cohort born in 2002 exhibited the lowest risk of mortality and the highest risk of incidence and prevalence.</p><p><strong>Conclusions: </strong>Over the past 25 years, the large population size and aging population structure in China have led to female BC becoming an important public health issue. Effective preventive strategies and individualized treatment approaches are urgently required to enhance the control of BC in China.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"1-13"},"PeriodicalIF":1.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144999772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Are Current Health Policies Ready to Deliver Life-Saving AML Treatments to Vulnerable Populations?","authors":"Jose Eric M Lacsa","doi":"10.1080/07357907.2025.2556430","DOIUrl":"https://doi.org/10.1080/07357907.2025.2556430","url":null,"abstract":"","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"1"},"PeriodicalIF":1.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144999781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}