Department of Oral and Maxillofacial pathology, Saveetha Dental college and Hospitals, Saveetha institute of Medical and technical sciences · Professor and Head, Department of Oral and Maxillofacial pathology, Saveetha Dental college and Hospitals, Saveetha institute of Medical and technical sciences, · Assisstant Professor,Department of Periodontics, Saveetha Dental college and Hospitals, Saveetha institute of Medical and technical sciences
Purpose: Angiotensin-converting enzyme 2 (ACE2), a critical regulator of the Circulating endocrine renin-angiotensin system, regulates cardio vascular and pulmonary homeostasis. Emerging studies gives solid evidence linking ACE2 to
various cancer types. However, the expression pattern and functional impacts of ACE2 in cancer is little known. This study systematically analysed ACE2 expression across various cancer forms, with an emphasis on head and neck squamous cell carcinoma (HNSC), using advanced non-linear dimensionality reduction techniques.
Methods: The study analysed pan-cancer dataset from The Cancer Genome Atlas (TCGA) that included 5,141 tumour samples from 10 distinct cancer types. A radial basis function (RBF) kernel was employed in Kernel Principal Component Analysis (Kernel PCA) to identify non-linear patterns in ACE2 expression. K-means clustering and tdistributed Stochastic Neighbor Embedding (t-SNE) were employed to demonstrate the high-dimensional data and to identify distinct subgroups. The Mann-Whitney U test was used to assess the statistical significance in ACE2 expression
between HNSC and other cancer types.
Results: Kernel PCA revealed three distinct ACE2 expression patterns across cancer types: high (KIRC, COADREAD), moderate (HNSC), and low (BRCA, PRAD). Comparative analysis using the Mann–Whitney U test showed a statistically significant difference in ACE2 expression between HNSC and other malignancies (p = 2.169 × 10⁻⁵). Clustering with K-means in the Kernel PCA-transformed space achieved optimal separation of expression profiles, with a silhouette score of 0.68. Compared to PCA and t-SNE, Kernel PCA demonstrated superior performance with a lower reconstruction error (0.2760) and improved interpretability of tumour-associated heterogeneity.
Conclusion: This study demonstrates the effectiveness of non-linear dimensionality reduction using Kernel Principal Component Analysis (Kernel PCA) in delineating the heterogeneity of ACE2 expression across multiple cancer types.
Head and Neck Squamous Cell Carcinoma (HNSC) was identified as a distinct molecular subtype, exhibiting unique ACE2 expression profiles. These findings support the potential utility of ACE2 profiling in cancer stratification, highlighting its relevance in precision oncology.
Hi! I can explain how to publish or share, find articles in your field, format citations and answer questions about fees and licensing. How can I help?
Answers are based on The Gufo’s published policies. Editorial decisions are made by the editorial team.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
