A machine learning-based investigation of integrin expression patterns in cancer and metastasis.

Integrins, a family of transmembrane receptor proteins, are well known to play important roles in cancer development and metastasis. However, a comprehensive understanding of these roles has not been achieved due to the complex relationships between specific integrins, cancer types, and the stages of cancer progression. Publicly accessible repositories from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) projects provide rich datasets for exploring these relationships using machine learning (ML). In this study, integrin RNA-Seq expression data of ~ 8 healthy tissues in GTEx and corresponding tumors in TCGA were selected. Integrin expression was used to train ML models to distinguish between different healthy tissues, solid tumors, as well as normal and tumor samples from the same tissue type. These ML models can classify samples by tissue origin or disease status with high accuracy, and the integrins essential to these classifiers were identified. In some cases, the expression of only one or two integrins was needed to classify tissue type, tumor type or disease status with accuracy > 0.9. For example, expression of ITGA7 alone can distinguish healthy and cancerous breast tissue. Additionally, integrin co-expression networks in healthy and cancerous breast tissues were compared and were found to change significantly from healthy to cancer, indicating changes in functional involvement of integrins due to cancer. Integrin expression in metastatic tumors were further examined using data from the AURORA project for Metastatic Breast Cancer (MBC), and several integrins such as ITGAD, ITGA4, ITGAL, and ITGA11 were found to have significantly lower expression in metastases than in primary tumors.