RESUMO
This comprehensive review delves into the intricate landscape of oral squamous cell carcinoma (OSCC) by examining the role of cathepsin B expression in its pathogenesis. OSCC, a prevalent and clinically significant oral malignancy, poses a considerable global health burden, necessitating a thorough exploration of its underlying molecular mechanisms. Cathepsin B, a lysosomal cysteine protease, emerges as a critical player in OSCC, influencing tumour initiation, invasion, and metastasis. The review begins with a brief overview of OSCC, emphasizing its epidemiological and clinical features, followed by exploring the significance of studying cathepsin B expression in this context. In the manuscript, the structure and function of cathepsin B are elucidated, providing a foundation for understanding its aberrant expression in OSCC. Clinical studies revealing correlations with tumour grade and stage, along with prognostic significance, are scrutinized, offering insights into the potential diagnostic and prognostic utility of cathepsin B. The biological functions of cathepsin B in OSCC, including its impact on tumour invasion and modulation of apoptosis, are comprehensively discussed. The Therapeutic Implications section explores targeting cathepsin B as a potential strategy, emphasizing the need for future research to overcome associated challenges. In the Conclusion section, the review synthesizes key findings, delineates implications for future research, and highlights the potential impact of cathepsin B on OSCC diagnosis and treatment, contributing to the ongoing efforts to advance our understanding of this complex malignancy, which is associated with a high mortality rate and improve clinical outcomes.
RESUMO
Cancer is one of the most devastating, fatal, dangerous, and unpredictable ailments. To reduce the risk of fatality in this disease, we need some ways to predict the disease, diagnose it faster and precisely, and predict the prognosis accurately. The incorporation of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms into the healthcare system has already proven to work wonders for patients. Artificial intelligence is a simulation of intelligence that uses data, rules, and information programmed in it to make predictions. The science of machine learning (ML) uses data to enhance performance in a variety of activities and tasks. A bigger family of machine learning techniques built on artificial neural networks and representation learning is deep learning (DL). To clarify, we require AI, ML, and DL to predict cancer risk, survival chances, cancer recurrence, cancer diagnosis, and cancer prognosis. All of these are required to improve patient's quality of life, increase their survival rates, decrease anxiety and fear to some extent, and make a proper personalized treatment plan for the suffering patient. The survival rates of people with diffuse large B-cell lymphoma (DLBCL) can be forecasted. Both solid and non-solid tumors can be diagnosed precisely with the help of AI and ML algorithms. The prognosis of the disease can also be forecasted with AI and its approaches like deep learning. This improvement in cancer care is a turning point in advanced healthcare and will deeply impact patient's life for good.
