Key Points Question Can circulating microRNAs be used as biomarkers to detect esophageal squamous cell carcinoma? Findings In this case-control study of 566 patients with esophageal squamous cell carcinoma and 4965 control patients without cancer, serum samples from patients with esophageal squamous cell carcinoma and control patients were used to establish a model to detect esophageal squamous cell carcinoma using 6 microRNAs in a training set. In the validation set, the model distinguished patients with cancer from control patients with high sensitivity and high specificity (0.96 and 0.98, respectively). Meaning The 6-microRNA model is a promising noninvasive screening tool in esophageal squamous cell carcinoma.

Both PET and NBI endoscopy is effective for detecting unknown primary tumors of squamous cell carcinomas of the neck.

Abstract. Studies on molecular mechanisms of self-renewal in normal stem cells are required for understanding the cancer stem cell. Self-renewal in many kinds of normal stem cells might be accelerated in the growth of a young organism and in the repair of damaged tissue. This study examined whether the esophagus in growing neonates provides an experimental system for studies on epithelial stem cell renewal. The esophageal epithelium consists of 3 layers, from the luminal side to the bottom: the differentiated, epibasal and basal cell layers. The basal cell layer is known to contain the stem cells for the esophageal epithelium. This basic architecture is observed both in mice and humans. We investigated the basal cells in the mouse neonate by immunostaining with a basal cell marker, nerve growth factor receptor (Ngfr), and compared the basal cell content in the esophageal epithelium between mice and humans. A mouse esophageal epithelial cell primary culture system was developed for studies on the basal cell growth and keratinocyte differentiation, and microarray analysis was conducted for obtaining expression profiles of the basal cells. It was revealed that the growth of the esophageal epithelium begins from postnatal day 3, and that the timing is consistent with membrane localization of Ngfr in the basal cell. An increase in the basal cell number by Ngf treatment is observed in in vitro mouse esophageal epithelium cultures. Furthermore, mRNA overexpression of Pdgfrb encoding platelet derived growth factor receptor ß and Egfr encoding epidermal growth factor receptor is associated with the timing of the growth of the esophageal epithelium in the neonatal mice. This study provides a new experimental model for studies on the growth of the basal cells, which are considered to include the stem cells, and on the enlargement of the body size in young organisms.

Background Non-invasive detection of early-stage cancers with accurate prediction of tumor tissue-of-origin could improve patient prognosis. Because miRNA profiles differ between organs, circulating miRNomics represent a promising method for early detection of cancers, but this has not been shown conclusively. Methods A serum miRNome-based classifier was evaluated for its ability to discriminate cancer types using advanced machine learning. The training set comprised 7,931 serum samples from patients with 13 types of solid cancers and 5,013 non-cancer samples. The validation set consisted of 1,990 cancer and 1,256 non-cancer samples. The contribution of each miRNA to the cancer-type classification was evaluated and those with a high contribution were identified. Results Cancer type was predicted with an accuracy of 0.88 (95% CI, 0.87–0.90) in all stages and an accuracy of 0.90 (95% CI, 0.88–0.91) in resectable stages (Stages 0–II). The F1-score for the discrimination of the 13 cancer types was 0.93. Optimal classification performance was achieved with at least 100 miRNAs that contributed the strongest to accurate prediction of cancer type. Assessment of tissue expression patterns of these miRNAs suggested that miRNAs secreted from the tumor environment could be used to establish cancer type-specific serum miRNomes. Conclusions This study demonstrates that large-scale serum miRNomics in combination with machine learning could lead to the development of a blood-based cancer classification system. Further investigations of the regulating mechanisms of the miRNAs that contributed strongly to accurate prediction of cancer type could pave the way for the clinical use of circulating miRNA diagnostics.

No abstract