Background: Increasing evidence suggests the involvement of cancer stem cells (CSCs) in both oral epithelial dysplasia (OED) and oral squamous cell carcinoma (OSCC). Among the various CSC markers, aldehyde dehydrogenase (ALDH) 1, B cell-specific Moloney murine leukaemia virus integration site 1 (Bmi1), and octamer-binding protein 4 (OCT4) have been noted to increase in OSCC. The aim of the study was to analyze ALDH1, Bmi1, and OCT4 expression in OED and OSCC with clinicopathologic correlation and survival analysis. Methods: A total of 40 cases each of OED and OSCC were retrieved from departmental archives. Expression of ALDH1, Bmi1, and OCT4 was analyzed using immunohistochemistry and was correlated with clinicopathological parameters. A follow-up ranging from 6 to 52 months was considered for Kaplan-Meier survival analysis. The log-rank test was performed to analyze significant difference in survival rates. Results: The expression levels of ALDH1, Bmi1, and OCT4 increased significantly from OED through OSCC ( P < .05). The expression of ALDH1 and OCT4 showed a significant correlation with lymph node metastasis. Positive cases of ALDH1 showed a significantly reduced survival rate compared to cases showing negative expression. Kaplan-Meier survival analysis showed a significant reduction of survival rate ( P = .00) in patients showing a positive expression for all the 3 markers. Conclusion: ALDH1 and OCT4 could be used as individual prognostic markers for assessing prognosis. ALDH1, Bmi1, and OCT4 could be used as a collective panel of markers to enable surgeons in predicting the prognosis of patients and thereby carry out prompt follow-up for such cases.
Among the modern-day advancements in technology, artificial intelligence (AI) and machine learning have shown to possess a large number of applications in various fields including medicine. In the current era, cancer is one of the most common non-communicable diseases attributing to a large number of global deaths. Attempts to increase the mortality and morbidity of cancer has been ongoing research. The integration of Artificial intelligence into cancer research is being actively carried out and has provided very promising results. Major factors that play a vital role in improving cancer prognosis including early detection and accurate diagnosis employing various imaging and molecular techniques. The use of artificial intelligence as a tool in these areas has shown its potential to detect and diagnose with increased precision, which is just one of the many applications of AI in cancer research. The present review aims as delving into literature and enlisting the applications of artificial intelligence in various commonly occurring cancers.
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