Unveiling Collagen’s Role in Breast Cancer: Insights into Expression Patterns, Functions and Clinical Implications

International Journal of General Medicine, Journal Year: 2024, Volume and Issue: Volume 17, P. 1773 - 1787

Published: May 1, 2024

Abstract: Collagen, the predominant protein constituent of mammalian extracellular matrix (ECM), comprises a diverse family 28 members (I–XXVIII). Beyond its structural significance, collagen is implicated in various diseases or cancers, notably breast cancer, where it influences crucial cellular processes including proliferation, metastasis, apoptosis, and drug resistance, intricately shaping cancer progression prognosis. In distinct collagens exhibit differential expression profiles, with some showing heightened diminished levels cancerous tissues cells compared to normal counterparts, suggesting specific pivotal biological functions. this review, we meticulously analyze individual utilizing Transcripts Per Million (TPM) data sourced from GEPIA2 database. Through analysis, identify that deviate patterns providing comprehensive overview their dynamics, functional roles, underlying mechanisms. Our findings shed light on recent advancements understanding intricate interplay between these aberrantly expressed cancer. This exploration aims offer valuable insights for identification potential biomarkers therapeutic targets, thereby advancing prospects more effective interventions treatment. Keywords: collagen, matrix, prognostic marker

Language: Английский

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An overview of CCN4 (WISP1) role in human diseases

Journal of Translational Medicine, Journal Year: 2024, Volume and Issue: 22(1)

Published: June 27, 2024

Abstract CCN4 (cellular communication network factor 4), a highly conserved, secreted cysteine-rich matricellular protein is emerging as key player in the development and progression of numerous disease pathologies, including cancer, fibrosis, metabolic inflammatory disorders. Over past two decades, extensive research on its family members uncovered their diverse cellular mechanisms biological functions, but not limited to cell proliferation, migration, invasion, angiogenesis, wound healing, repair, apoptosis. Recent studies have demonstrated that aberrant expression and/or associated downstream signaling vast array pathophysiological etiology, suggesting could be utilized only non-invasive diagnostic or prognostic marker, also promising therapeutic target. The cognate receptor remains elusive till date, which limits understanding mechanistic insights driven pathologies. However, agents directed against begin make way into clinic, may start change. Also, significance underexplored, hence further needed shed more light tissue specific functions better understand clinical translational benefit. This review highlights compelling evidence overlapping functional regulated by CCN4, addition addressing challenges, study limitations knowledge gaps biology potential.

Language: Английский

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Study on breast cancer detection and classification using artificial intelligence techniques and sensors

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 65 - 78

Published: Jan. 1, 2025

Language: Английский

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Identification of a Gene Signature and Prediction of Overall Survival of Patients with Stage IV Colorectal Cancer Using a Novel Machine Learning Approach

European Journal of Surgical Oncology, Journal Year: 2025, Volume and Issue: 51(5), P. 109718 - 109718

Published: Feb. 20, 2025

Language: Английский

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Artificial Intelligence and Breast Cancer Management: From Data to the Clinic

Cancer Innovation, Journal Year: 2025, Volume and Issue: 4(2)

Published: Feb. 20, 2025

Breast cancer (BC) remains a significant threat to women's health worldwide. The oncology field had an exponential growth in the abundance of medical images, clinical information, and genomic data. With its continuous advancement refinement, artificial intelligence (AI) has demonstrated exceptional capabilities processing intricate multidimensional BC-related AI proven advantageous various facets BC management, encompassing efficient screening diagnosis, precise prognosis assessment, personalized treatment planning. However, implementation into precision medicine practice presents ongoing challenges that necessitate enhanced regulation, transparency, fairness, integration multiple pathways. In this review, we provide comprehensive overview current research related BC, highlighting extensive applications throughout whole cycle management potential for innovative impact. Furthermore, article emphasizes significance constructing patient-oriented algorithms. Additionally, explore opportunities directions within burgeoning field.

Language: Английский

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In Silico Analysis of CSF2RB from Cancer Genomic Databases Reveals Heterogeneous Role in Different Breast Cancer Subtypes

Published: Jan. 1, 2025

Language: Английский

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Identification of novel biomarker RPS21 using microarray-based whole-gene expression profiling of breast cancer in Saudi women

Journal of King Saud University - Science, Journal Year: 2025, Volume and Issue: 0, P. 1 - 7

Published: Feb. 28, 2025

Breast cancer (BC) is the most common malignancy worldwide, including in Saudi Arabia. Because of its heterogeneous nature, existing diagnostic and prognostic biomarkers are not relevant for all cases. There a need to discover novel early diagnosis prognosis reduce mortality. Herein, we utilized an integrative bioinformatics approach identify potential BC. Gene expression profiling 45 BC five normal samples from KAUH, Jeddah was done with GeneChip Human Genome 1.0 ST Array. Data analyzed by LIMMA package R differentially expressed genes (DEGs) detected Arabian patients were compared American Asian datasets. Ingenuity pathway analysis tool gene ontology enrichment conducted find aberrant pathways associated Survival Kaplan -Meier plotter establish importance identified followed validation using qPCR. The association between RPS21 systematic therapeutic response checked statistical methods. Our results revealed 870, 658 567 DEGs (GSE36295) (GSE166044) (GSE15852) patients, respectively. , CXCL2 TNMD TOP2A HMMR RRM2 groups. Pathway cell cycle checkpoints regulation stathmin1 as inhibited activated pathways, protein-protein interaction (PPI) network showed role ribosome-related predicted be biomarker. findings highlight good biomarker candidate patients. It could used globally after on bigger cohorts. Functional alteration cycle, regulation, provided critical insights into molecular mechanisms driving breast tumorigenesis.

Language: Английский

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Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling

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

Published: March 17, 2025

This study proposes an advanced machine learning (ML) framework for breast cancer diagnostics by integrating transcriptomic profiling with optimized feature selection and classification techniques. A dataset of 1759 samples (987 patients, 772 healthy controls) was analyzed using Recursive Feature Elimination, Boruta, ElasticNet selection. Dimensionality reduction techniques, including Non-Negative Matrix Factorization (NMF), Autoencoders, transformer-based embeddings (BioBERT, DNABERT), were applied to enhance model interpretability. Classifiers such as XGBoost, LightGBM, ensemble voting, Multi-Layer Perceptron, Stacking trained grid search cross-validation. Model evaluation conducted accuracy, AUC, MCC, Kappa Score, ROC, PR curves, external validation performed on independent 175 samples. XGBoost LightGBM achieved the highest test accuracies (0.91 0.90) AUC values (up 0.92), particularly NMF BioBERT. The Voting method exhibited best accuracy (0.92), confirming its robustness. Transformer-based techniques significantly improved performance compared conventional approaches like PCA Decision Trees. proposed ML enhances diagnostic interpretability, demonstrating strong generalizability dataset. These findings highlight potential precision oncology personalized diagnostics.

Language: Английский

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Artificial Intelligence in Ovarian Cancer: A Systematic Review and Meta-Analysis of Predictive AI Models in Genomics, Radiomics, and Immunotherapy

AI, Journal Year: 2025, Volume and Issue: 6(4), P. 84 - 84

Published: April 18, 2025

Background/Objectives: Artificial intelligence (AI) is increasingly influencing oncological research by enabling precision medicine in ovarian cancer through enhanced prediction of therapy response and patient stratification. This systematic review meta-analysis was conducted to assess the performance AI-driven models across three key domains: genomics molecular profiling, radiomics-based imaging analysis, immunotherapy response. Methods: Relevant studies were identified a search multiple databases (2020–2025), adhering PRISMA guidelines. Results: Thirteen met inclusion criteria, involving over 10,000 patients encompassing diverse AI such as machine learning classifiers deep architectures. Pooled AUCs indicated strong predictive for genomics-based (0.78), (0.88), immunotherapy-based (0.77) models. Notably, radiogenomics-based integrating data yielded highest accuracy (AUC = 0.975), highlighting potential multi-modal approaches. Heterogeneity risk bias assessed, evidence certainty graded. Conclusions: Overall, demonstrated promise predicting therapeutic outcomes cancer, with radiomics integrated radiogenomics emerging leading strategies. Future efforts should prioritize explainability, prospective multi-center validation, integration immune spatial transcriptomic support clinical implementation individualized treatment Unlike earlier reviews, this study synthesizes broader range applications provides pooled metrics It examines methodological soundness selected highlights current gaps opportunities translation, offering comprehensive forward-looking perspective field.

Language: Английский

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Evaluating the Diagnostic Potential of Biomarker Panels in Breast Cancer and Prostate Adenocarcinoma

Health Science Reports, Journal Year: 2025, Volume and Issue: 8(5)

Published: April 29, 2025

Noninvasive diagnostic methods are essential for early cancer detection and improved patient outcomes. Circulating biomarkers, measurable indicators of pathological processes, offer a promising avenue, yet optimal panels reliable diagnosis remain undefined. This study evaluates the performance selected plasma biomarkers in distinguishing breast prostate adenocarcinoma patients from healthy individuals, using statistical analysis machine learning. We analyzed blood samples 162 participants (73 patients: 51 with 22 adenocarcinoma; 89 controls). Levels 12 cancer-associated biomarkers-including Ki67, DNMT1, BRCA1, MPO-were quantified enzyme-linked immunosorbent assays (ELISA). Statistical analyses, including Mann-Whitney U test learning models (random forest), were employed to assess predictive accuracy these between cancerous states. Biomarkers such as MPO significantly elevated groups. Random forest combinations (e.g., BRCA1-CTA-TP53) achieved perfect classification (AUC = 1.00). However, high inter-marker correlations suggested potential redundancy, underscoring need biomarker panel optimization. Our findings support accurate, noninvasive diagnostics. Further validation larger, more diverse cohorts is warranted establish clinical utility generalizability.

Language: Английский

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