Unlocking Overexpressed Membrane Proteins to Guide Breast Cancer Precision Medicine

Simple Summary Breast cancer (BC) is a global health concern, hindered by the limited effectiveness and adverse effects of current treatments. To address this, we propose a method for identifying membrane proteins in tumors, offering potential targets for BC therapy and diagnosis by analyzing gene expressions in breast tumor and healthy tissues using bioinformatics tools, like TCGA, UALCAN, TNM Plot, and LinkedOmics. Four transcripts (LRRC15, EFNA3, TSPAN13, and CA12) were identified with heightened expressions in BC tissue. These transcripts showed high accuracy in identifying tumor samples and were consistently elevated across all BC molecular subtypes. Tissue microarray (TMA) analysis confirmed an increased expression in tumor tissues compared to adjacent breast tissue. The study underscores the potential of LRRC15, EFNA3, TSPAN13, and CA12 as biomarkers for enhancing BC diagnosis and as promising therapeutic targets with reduced side effects and improved efficacy.Abstract Breast cancer (BC) is a prevalent form of cancer affecting women worldwide. However, the effectiveness of current BC drugs is limited by issues such as systemic toxicity, drug resistance, and severe side effects. Consequently, there is an urgent need for new therapeutic targets and improved tumor tracking methods. This study aims to address these challenges by proposing a strategy for identifying membrane proteins in tumors that can be targeted for specific BC therapy and diagnosis. The strategy involves the analyses of gene expressions in breast tumor and non-tumor tissues and other healthy tissues by using comprehensive bioinformatics analysis from The Cancer Genome Atlas (TCGA), UALCAN, TNM Plot, and LinkedOmics. By employing this strategy, we identified four transcripts (LRRC15, EFNA3, TSPAN13, and CA12) that encoded membrane proteins with an increased expression in BC tissue compared to healthy tissue. These four transcripts also demonstrated high accuracy, specificity, and accuracy in identifying tumor samples, as confirmed by the ROC curve. Additionally, tissue microarray (TMA) analysis revealed increased expressions of the four proteins in tumor tissues across all molecular subtypes compared to the adjacent breast tissue. Moreover, the analysis of human interactome data demonstrated the important roles of these proteins in various cancer-related pathways. Taken together, these findings suggest that LRRC15, EFNA3, TSPAN13, and CA12 can serve as potential biomarkers for improving cancer diagnosis screening and as suitable targets for therapy with reduced side effects and enhanced efficacy.