New progress in imaging diagnosis and immunotherapy of breast cancer

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: March 17, 2025

Breast cancer (BC) is a predominant malignancy among women globally, with its etiology remaining largely elusive. Diagnosis primarily relies on invasive histopathological methods, which are often limited by sample representation and processing time. Consequently, non-invasive imaging techniques such as mammography, ultrasound, Magnetic Resonance Imaging (MRI) indispensable for BC screening, diagnosis, staging, treatment monitoring. Recent advancements in technologies artificial intelligence-driven radiomics have enhanced precision medicine enabling early detection, accurate molecular subtyping, personalized therapeutic strategies. Despite reductions mortality through traditional treatments, challenges like tumor heterogeneity resistance persist. Immunotherapies, particularly PD-1/PD-L1 inhibitors, emerged promising alternatives. This review explores recent developments diagnostics immunotherapeutic approaches, aiming to inform clinical practices optimize outcomes.

Language: Английский

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Downregulated STAT3 and STAT5B are prognostic biomarkers for colorectal cancer and are associated with immune infiltration

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 18, 2025

Colorectal cancer has high incidence and mortality rates. The signal transducer activator of transcription (STAT) family plays vital roles in the tumorigenesis development colorectal cancer. expression, prognostic value, immune function STAT are becoming much more clearly. Our study collected data from several public portals such as TCGA (644 samples) GTEx database (308 clinical samples (30 samples, China). Then we systematically assessed expression level value samples. Moreover, infiltration levels prognosis-related members were explored via single cell RNA-seq spatial transcriptomics technology data. Several useful tools have been utilized CancerSEA TISIDB single-cell analysis, CBio Cancer Genomics multidimensional alterations, MethSurv DNA methylation, related R packages. found that STAT3 STAT5B significantly lower multi-omics (P < 0.001). Higher correlated with better future outcome. Nomograms developed to predict distal survival time (C-index = 0.724). functions associated inflammation, JAK/STAT pathway response. major types CD4Tconv, CD8T, CD8Tex, Tprolif, Treg widely expressed these cells. both CD244 KDR for checkpoints. downregulated great potential biomarkers novel immunotherapy targets.

Language: Английский

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The role of tumor-associated macrophages in HPV induced cervical cancer

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: April 8, 2025

Human papillomavirus (HPV), a double-stranded DNA virus linked to various malignancies, poses significant global public health challenge. In cervical cancer, persistent infection with high-risk HPV genotypes, particularly HPV-16 and HPV-18, initiates immune evasion mechanisms within the tumor microenvironment. The polarization of tumor-associated macrophages (TAMs) from M1 M2 phenotypes promotes carcinogenesis, metastasis, therapeutic resistance via establishing an immunosuppressive This review provides comprehensive overview HPV-induced pathways, including MHC downregulation, T-cell impairment, regulatory T cell induction, cGAS-STING pathway inhibition. Furthermore, describe pivotal role TAMs in cancer progression, focusing on their phenotypic plasticity, pro-tumoral functions, potential as targets. By elucidating these cellular molecular dynamics, this aims support advanced research. Targeting TAM through immunotherapies nanomedicine-based strategies represents promising strategy for enhancing patient outcomes.

Language: Английский

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Development and validation of a nomogram model of lung metastasis in breast cancer based on machine learning algorithm and cytokines

BMC Cancer, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 14, 2025

The relationship between cytokines and lung metastasis (LM) in breast cancer (BC) remains unclear current clinical methods for identifying (BCLM) lack precision, thus underscoring the need an accurate risk prediction model. This study aimed to apply machine learning algorithms key factors BCLM before developing a reliable model centered on cytokines. population-based retrospective included 326 BC patients admitted Second Affiliated Hospital of Xuzhou Medical University September 2018 2023. After randomly assigning training cohort (70%; n = 228) or validation (30%; 98) were identified using Least Absolute Shrinkage Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost) Random Forest (RF) models. Significant visualized with Venn diagram incorporated into nomogram model, performance which was then evaluated according three criteria, namely discrimination, calibration utility plots, receiver operating characteristic (ROC) curves decision curve analysis (DCA). Among cohort, 70 developed LM. A predict 5-year 10-year by incorporating five variables, endocrine therapy, hsCRP, IL6, IFN-ɑ TNF-ɑ. For cohorts had AUC values 0.786 (95% CI: 0.691-0.881) 0.627 0.441-0.813), respectively, while corresponding 0.687 0.528-0.847) 0.797 0.605-0.988), respectively. ROC further confirmed model's strong discriminative ability, plots indicated that predicted observed outcomes good agreement both cohorts. Finally, DCA demonstrated effectiveness practice. Using algorithms, this aa could effectively identify who at higher LM, providing valuable tool decision-making settings.

Language: Английский

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Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions

Biomedicines, Journal Year: 2025, Volume and Issue: 13(4), P. 951 - 951

Published: April 13, 2025

Cancer remains one of the leading causes mortality worldwide, driving need for innovative approaches in research and treatment. Artificial intelligence (AI) has emerged as a powerful tool oncology, with potential to revolutionize cancer diagnosis, treatment, management. This paper reviews recent advancements AI applications within research, focusing on early detection through computer-aided personalized treatment strategies, drug discovery. We survey AI-enhanced diagnostic explore techniques such deep learning, well integration nanomedicine immunotherapy care. Comparative analyses AI-based models versus traditional methods are presented, highlighting AI’s superior potential. Additionally, we discuss importance integrating social determinants health optimize Despite these advancements, challenges data quality, algorithmic biases, clinical validation remain, limiting widespread adoption. The review concludes discussion future directions emphasizing its reshape care by enhancing personalizing treatments targeted therapies, ultimately improving patient outcomes.

Language: Английский

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