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.
乳腺癌是全球范围内影响女性健康的常见恶性肿瘤。然而，现有乳腺癌药物的疗效常受全身毒性、耐药性及严重副作用等问题限制。因此，亟需寻找新的治疗靶点并改进肿瘤追踪方法。本研究旨在通过提出一种识别肿瘤膜蛋白的策略，以解决上述挑战，该策略可为特异性乳腺癌治疗与诊断提供靶点。我们利用癌症基因组图谱（TCGA）、UALCAN、TNM Plot及LinkedOmics数据库进行综合生物信息学分析，系统比较了乳腺肿瘤组织、非肿瘤组织及其他健康组织的基因表达谱。通过该策略，我们筛选出四种在乳腺癌组织中表达显著高于健康组织的膜蛋白编码转录本（LRRC15、EFNA3、TSPAN13和CA12）。受试者工作特征曲线证实，这四种转录本在识别肿瘤样本时具有高准确性、特异性及精确度。组织芯片分析进一步显示，与癌旁组织相比，所有分子亚型的肿瘤组织中这四种蛋白表达均显著上调。此外，人类相互作用组数据分析表明这些蛋白在多种癌症相关通路中发挥重要作用。综上所述，LRRC15、EFNA3、TSPAN13和CA12可作为潜在的生物标志物以提升癌症诊断筛查效能，并有望成为副作用更小、疗效更佳的治疗靶点。
Unlocking Overexpressed Membrane Proteins to Guide Breast Cancer Precision Medicine
肿瘤(癌症)患者之家