A novel gene expression scoring system for accurate diagnosis of basaloid squamous cell carcinoma of the esophagus

Basaloid squamous cell carcinoma of the esophagus (BSCE) is a rare variant of squamous cell carcinoma that is difficult to distinguish from other carcinomas by preoperative endoscopic biopsy because of its histological varieties. Accurate diagnosis is essential for adequate treatment, and the methods proposed so far (e.g., immunohistochemical staining) have limitations. In this study, we tried to identify the characteristic bundles of gene expression in BSCE using comprehensive gene expression analysis (CGEA). Subsequently, we constructed a gene expression scoring system for the proper diagnosis of BSCE. Fifty-seven surgical specimens, including seven BSCEs, obtained from 30 patients who underwent esophagectomy were used for constructing the scoring system. Three hundred and twelve biopsy specimens, including eight BSCEs, obtained from 80 patients and 20 commercially available formalin-fixed paraffin-embedded (FFPE) specimens diagnosed as esophageal cancer, including 13 BSCEs, were used for validation. After our original mathematical extraction algorithm, 75 genes were extracted to distinguish BSCE from non-BSCE. The cumulative converted values (gene expression score) of the respective 75 genes from each specimen were obtained and lined up in ascending order to assess the optimal gene expression cut-off score for a definitive diagnosis of BSCE. The validation of this scoring system showed high prediction of the biopsy specimens [area under the curve (AUC)=0.981; 95% confidence interval (CI): 0.952‑1.000] and the commercially available FFPE specimens (AUC=0.901; 95% CI: 0.750-1.000). In conclusion, using CGEA in a gene expression scoring system helps in differentiating BSCE from non-BSCE with high accuracy and may contribute in improving BSCE treatment.