MicroRNAs as promising biomarkers and potential therapeutic agents in breast cancer management: a comprehensive review.

Breast cancer (BC), a complex and varied ailment, poses a significant global health burden. MicroRNAs (miRNAs) have emerged as vital regulators in BC progression, with potential implications for diagnosis and treatment. This review aims to synthesize current insights into miRNA dysregulation in BC. MiRNAs, small RNA molecules, govern gene expression post-transcriptionally and are implicated in BC initiation, metastasis, and therapy resistance. Differential expression of specific miRNAs in BC tissues versus normal breast tissue sheds light on underlying molecular mechanisms. MiRNAs also offer promise as diagnostic biomarkers due to their stable nature, accessibility in bodily fluids, and altered expression patterns in early-stage disease, augmenting conventional diagnostic methods. Beyond diagnosis, miRNAs also hold promise as therapeutic targets in BC. By modulating the expression of specific dysregulated miRNAs, it may be possible to restore normal cellular functions and overcome treatment resistance. However, several challenges need to be addressed before miRNA-based therapies can be translated into clinical practice, including the development of efficient delivery systems and rigorous evaluation through preclinical and clinical trials. MiRNAs represent a promising avenue in BC research, offering potential applications in diagnosis, prognosis, and therapeutic interventions. As our understanding of miRNA biology deepens and technology advances, further research and collaborative efforts are needed to fully exploit the diagnostic and therapeutic potential of miRNAs in BC management. Ultimately, the integration of miRNA-based approaches into clinical practice may lead to more personalized and effective strategies for combating this devastating disease.

Impact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer.

Endometrial cancer is one of the most prevalent tumours in females and holds an 83% survival rate within 5 years of diagnosis. Hypoestrogenism is a major risk factor for the development of endometrial carcinoma (EC) therefore two major types are derived, type 1 being oestrogen-dependent and type 2 being oestrogen independent. Surgery, chemotherapeutic drugs, and radiation therapy are only a few of the treatment options for EC. Treatment of gynaecologic malignancies greatly depends on diagnosis or prognostic prediction. Diagnostic imaging data and clinical course prediction are the two core pillars of artificial intelligence (AI) applications. One of the most popular imaging techniques for spotting preoperative endometrial cancer is MRI, although this technique can only produce qualitative data. When used to classify patients, AI improves the effectiveness of visual feature extraction. In general, AI has the potential to enhance the precision and effectiveness of endometrial cancer diagnosis and therapy. This review aims to highlight the current status of applications of AI in endometrial cancer and provide a comprehensive understanding of how recent advancements in AI have assisted clinicians in making better diagnosis and improving prognosis of endometrial cancer. Still, additional study is required to comprehend its strengths and limits fully.