Integrated Single cell analysis and Machine learning for Precision Diagnosis in Human Papillomavirus-Associated cancers

Abstract

Human papillomavirus is a sexually transmitted infectious virus, that affects the skin, genital area, throat, and other body parts. Human Papillomavirus infection causes global health problems. It causes various types of warts and poses high risks for cervical cancer, oropharyngeal cancer, and head and neck cancers. Cervical cancer is the fourth most common cancer in females in the whole world. About 660,000 people were affected due to HPV and 350,000 in 2022 due to this infectious virus. It does not only cause cervical cancer about 20,000 new cases of oropharyngeal cancer are reported each year. Head and neck cancer affects the pharynx and oral cavity with 46000 annually in the USA. HPV enters into the body with first sexual intercourse. It takes 15–20 years for abnormal cells to become cancer.HPV has 200 different strains and around 14 types of HPV are considered high risk for cervical cancer and other HPV-associated cancers. Two of these types HPV 16 and HPV 18 cause about 70% of all cervical cancers and another type of cancer. In most people, the immune system clears about half of HPV infections within 6 to 12 months. But sometimes this does not happen due to a weak immune system and causes cancer. This study aims to use single-cell analysis for the heterogeneity of HPV infection in the context of cervical cancer, oropharyngeal cancer, and head and neck cancer. HPV identifies differentially expressed genes associated with these malignancies. Biomarker gene identification is affected by human papillomavirus oncoproteins like E6, and E7 causing different cancers due to the upregulation of these biomarker genes. Leveraging machine learning seeks to differentiate cervical cells, oropharyngeal cancer cells, and head and neck cancer cells, by using different classification models, thereby enhancing our understanding of their distinct molecular profiles. By elucidating the molecular signatures and spatial organization of tumor cells, immune infiltrates, and stromal components. Our approach aims to unravel the underlying complexity of HPV-associated cancers and use it for personalized therapeutic interventions to the unique characteristics of individual patients. Through this interdisciplinary synergy between single-cell analysis and machine learning, precision oncology can be advanced, and patient outcomes can be improved in the era of personalized medicine. Furthermore, it explores the potential of biomarker gene identification for early cancer screening. Through this analysis, this research contributes to the precision diagnosis of HPV-related cancers and offers promising avenues for improved diagnosis.