[A Case of Long-Term Survival Following Two-Stage Surgery for Perforated Advanced Gastric Cancer Caused by Chemotherapy].

A 74-year-old man with bloody vomit was diagnosed as having clinical Stage Ⅳ advanced gastric cancer with lymph node metastasis around the abdominal aorta. Initially, for curative surgery, he was administered neoadjuvant chemotherapy. On day 32, in the second course of chemotherapy containing S-1 after 12 courses of chemotherapy containing S-1 and cisplatin, he developed pan-peritonitis owing to the perforation of gastric cancer caused by chemotherapy, and thus, we performed emergency omental implantation and peritoneal drainage. He was discharged from the hospital after 14 days with no trouble. His gastric cancer was judged to be resectable without retaining metastatic lymph nodes based on intraoperative findings and abdominal computed tomography. Therefore, 3 months after the emergency surgery, he underwent total gastrectomy with D1+(+No. 11d)lymphadenectomy. The postoperative course was uneventful. He rejected adjuvant chemotherapy despite our recommendation. Regrettably, intraabdominal dissemination was observed 15 months after total gastrectomy, and he then received chemotherapy again. He has remained alive for 57 months after the first visit to our hospital.

miR-877-3p as a Potential Tumour Suppressor of Oesophageal Squamous Cell Carcinoma.

BACKGROUND/AIM: MicroRNAs (miRNAs) are abnormally expressed and involved in the pathogenesis of various carcinomas. The present study aimed to identify novel miRNA genes associated with the pathogenesis and prognosis of oesophageal squamous cell carcinoma (ESCC). MATERIALS AND METHODS: The miRNA profiling of 873 genes was performed using surgically resected oesophageal tissues from 35 patients with ESCC to identify candidate miRNAs. To examine the biological activities of candidate miRNAs, their proliferative, invasive, and migratory abilities were evaluated in ESCC cells subjected to miRNA mimic-mediated over-expression. The miRNA expression levels of the selected candidate miRNAs were analysed in the resected oesophageal tissues of 76 patients with ESCC from the two cohorts and correlated with the clinicopathological parameters. RESULTS: Among the four candidate miRNAs identified by miRNA profiling, miR-877-3p was selected for subsequent analyses. In vitro analyses showed that the over-expression of miR-877-3p significantly suppressed the proliferation, invasion, and migration of ESCC cell lines compared with those of control cells. In the analyses of clinical specimens, the expression of miR-877-3p was down-regulated in ESCC tissues compared with that in adjacent normal oesophageal tissues. The down-regulation of miR-877-3p expression in ESCC tissues was significantly associated with advanced local progression and lymphatic involvement. The miR-877-3p down-regulation was also significantly associated with poor disease-free and disease-specific survival. CONCLUSION: miR-877-3p acts as a tumour suppressor gene in ESCC cells, and its down-regulation in ESCC tissues is associated with a poor prognosis. Thus, miR-877-3p may serve as a novel prognostic marker and promising therapeutic target.

The use of an artificial intelligence algorithm for circulating tumor cell detection in patients with esophageal cancer.

Despite recent advances in multidisciplinary treatments of esophageal squamous cell carcinoma (ESCC), patients frequently suffer from distant metastasis after surgery. For numerous types of cancer, circulating tumor cells (CTCs) are considered predictors of distant metastasis, therapeutic response and prognosis. However, as more markers of cytopathological heterogeneity are discovered, the overall detection process for the expression of these markers in CTCs becomes increasingly complex and time consuming. In the present study, the use of a convolutional neural network (CNN)-based artificial intelligence (AI) for CTC detection was assessed using KYSE ESCC cell lines and blood samples from patients with ESCC. The AI algorithm distinguished KYSE cells from peripheral blood-derived mononuclear cells (PBMCs) from healthy volunteers, accompanied with epithelial cell adhesion molecule (EpCAM) and nuclear DAPI staining, with an accuracy of >99.8% when the AI was trained on the same KYSE cell line. In addition, AI trained on KYSE520 distinguished KYSE30 from PBMCs with an accuracy of 99.8%, despite the marked differences in EpCAM expression between the two KYSE cell lines. The average accuracy of distinguishing KYSE cells from PBMCs for the AI and four researchers was 100 and 91.8%, respectively (P=0.011). The average time to complete cell classification for 100 images by the AI and researchers was 0.74 and 630.4 sec, respectively (P=0.012). The average number of EpCAM-positive/DAPI-positive cells detected in blood samples by the AI was 44.5 over 10 patients with ESCC and 2.4 over 5 healthy volunteers (P=0.019). These results indicated that the CNN-based image processing algorithm for CTC detection provides a higher accuracy and shorter analysis time compared to humans, suggesting its applicability for clinical use in patients with ESCC. Moreover, the finding that AI accurately identified even EpCAM-negative KYSEs suggested that the AI algorithm may distinguish CTCs based on as yet unknown features, independent of known marker expression.