Background and objectives: The survival rate of patients with advanced oesophageal cancer is very low and can vary significantly, even among patients with the same TNM stage. It is important to look for indicators that are economical and readily available to predict overall survival. The aim of this study was to determine whether lymphocyte-to-monocyte ratio (LMR) and neutrophil-to-lymphocyte ratio (NLR) could be potential predictors of survival in patients with advanced oesophageal squamous cell carcinoma (ESCC) undergoing concurrent chemoradiotherapy. Methods: Differences in survival among 204 patients with advanced oesophageal cancer who underwent concurrent chemoradiotherapy were collected and analysed. Univariate and multivariate COX regression analyses were used to investigate the association between blood inflammatory markers and patient survival before treatment. Results: Univariate COX regression analyses showed that a history of alcohol use, neutrophil count, LMR, NLR, tumour length, and N stage were significantly associated with the survival of tumour patients receiving concurrent chemoradiotherapy. Multivariate COX regression analysis showed that NLR and LMR were predictors of outcome in tumour patients receiving chemoradiotherapy. According to receiver operating characteristic (ROC) curve analysis, the AUC of LMR and NLR was 0.734 and 0.749, and the best cutoff point for LMR and NLR was 3.03 and 2.64, respectively. Conclusions: LMR and NLR can be used to predict the survival of patients with advanced oesophageal cancer receiving concurrent chemoradiotherapy, thereby providing clinicians with suggestions for further treatment options.

The aim of this study was to determine the prognostic factors and their significance in gastric cancer (GC) patients, using the artificial neural network (ANN) and Cox regression hazard (CPH) models. A retrospective analysis was undertaken, including 289 patients with GC who had undergone gastrectomy between 2006 and 2007. According to the CPH analysis, disease stage, peritoneal dissemination, radical surgery and body mass index (BMI) were selected as the significant variables. According to the ANN model, disease stage, radical surgery, serum CA19‑9 levels, peritoneal dissemination and BMI were selected as the significant variables. The true prediction of the ANN was 85.3% and of the CPH model 81.9%. In conclusion, the present study demonstrated that the ANN model is a more powerful tool in determining the significant prognostic variables for GC patients, compared to the CPH model. Therefore, this model is recommended for determining the risk factors of such patients.

This study aimed to construct a model for using in differentiating benign and malignant nodules with the artificial neural network and to increase the objective diagnostic accuracy of US. 618 consecutive patients (528 women, 161 men) with 689 thyroid nodules (425 malignant and 264 benign nodules) were enrolled in the present study. The presence and absence of each sonographic feature was assessed for each nodule - shape, margin, echogenicity, internal composition, presence of calcifications, peripheral halo and vascularity on color Doppler. The variables meet the following criteria: important sonographic features and statistically significant difference were selected as the input layer to build the ANN for predicting the malignancy of nodules. Six sonographic features including shape (Taller than wide, p<0.001), margin (Not Well-circumscribed, p<0.001), echogenicity (Hypoechogenicity, p<0.001), internal composition (Solid, p<0.001), presence of calcifications (Microcalcification, p<0.001) and peripheral halo (Absent, p<0.001) were significantly associated with malignant nodules. A three-layer 6-8-1 feed-forward ANN model was built. In the training cohort, the accuracy of the ANN in predicting malignancy of thyroid nodules was 82.3% (AUROC = 0.818), the sensitivity and specificity was 84.5% and 79.1%, respectively. In the validation cohort, the accuracy, sensitivity and specificity was 83.1%, 83.8% and 81.8%, respectively. The AUROC was 0.828. ANN constructed by sonographic features can discriminate benign and malignant thyroid nodules with high diagnostic accuracy.

MicroRNAs play critical roles in tumor progression. Our recent study has indicated that microRNA-7 (miR-7) impairs autophagy-derived pools of glucose to suppress the glycolysis in pancreatic cancer progression. However, the roles of miR-7 in clinical significance and chemoresistance of pancreatic cancer remain unexplored. The aim of this study was to assess the expression of miR-7 in patients with pancreatic cancer and to evaluate the possibility of its usage as a prognostic molecular biomarker. MicroRNA array-based quantification analysis of 372 miRNAs was compared in serum between pancreatic cancer and healthy individuals, gemcitabine-sensitive and gemcitabine-resistance patients. We identified miR-7 showed the potential predictive power for gemcitabine-sensitive patients with pancreatic cancer. Then, the results were validated in pancreatic tissue microarray and The Cancer Genome Atlas (TCGA) dataset, demonstrating that lower miR-7 expression was correlated with more advanced tumor stages and worse prognosis in pancreatic cancer. The Cox proportional-hazards model analysis identified miR-7 to be an independent variable for prediction of the survival. Furthermore, the mechanistic exploration suggested the clinical significance of miR-7 involved its interference effect on autophagy and glycolysis in pancreatic cancer using pancreatic cancer tissue microarrays and TCGA data. Therefore, the results of the present study provide evidences that low microRNA-7 expression may contribute to tumor progression and poor prognosis in pancreatic cancer.

The aim of this study was to develop nomograms for predicting the risk of locoregional recurrence or distant metastasis in esophageal cancer patients who were treated with esophagectomy and regional lymphadenectomy. The clinicopathologic data of 408 esophageal cancer patients after esophagectomy and regional lymphadenectomy were analyzed in this study. Univariate and multivariate COX regression analyses were used to test the association between the clinicopathologic data and the risk of locoregional recurrence or distant metastasis. The nomograms were built from the COX regression model. Univariate analyses revealed that tumor length, tumor width, T-staging and perineural invasion(PNI) were significantly associated with locoregional recurrence, and that tumor length, tumor width, differentiation, T-staging, N-staging, lymph vascular space invasion(LVSI), PNI and adjuvant chemotherapy were significantly associated with distant metastasis. Multivariate analyses revealed that tumor length, tumor width and T-staging were predictors of risk of locoregional recurrence, and that differentiation, N-staging, LVSI and PNI were predictors of risk of distant metastasis. Two nomograms were constructed for a visual explanation of these two COX regression models. The bias-corrected curve showed no significant departure from the ideal curve in these two nomograms. Two nomograms were developed and validated to predict the risk of locoregional recurrence and distant metastasis in esophageal cancer patients after radical esophagectomy. The calculation outcome will help oncologists to choose adjuvant treatment regimens.

Rectal cancer is a life‑threatening disease worldwide. Chemotherapy resistance is common in rectal adenocarcinoma patients and has unfavorable survival outcomes; however, its related molecular mechanisms remain unknown. To identify genes related to the initiation and progression of rectal adenocarcinoma, three datasets were obtained from the Gene Expression Omnibus database. In total, differentially expressed genes were analyzed from 294 tumor and 277 para-carcinoma samples from patients with rectal cancer. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functions were investigated. Cytoscape software and MicroRNA Enrichment Turned Network were applied to construct a protein-protein interaction network of the dependent hub genes and related microRNAs. The Oncomine database was used to identify hub genes. Additionally, Gene Expression Profiling Interactive Analysis was applied to determine the RNA expression level. Tumor immune infiltration was assessed using the Tumor Immune Estimation Resource database. The expression profiles of hub genes between stages, and their prognostic value, were also evaluated. During this study, data from The Cancer Genome Atlas were utilized. In rectal adenocarcinoma, four hub genes including CXCL1, CXCL2, CXCL3, and GNG4 were highly expressed at the gene and RNA levels. The expression of CXCL1, CXCL2, and CXCL3 was regulated by has-miR-1-3p and had a strong positive correlation with macrophage and neutrophil. CXCL2 and CXCL3 were differentially expressed at different tumor stages. High expression levels of CXCL1 and CXCL3 predicted poor survival. In conclusion, the CXCL1 and CXCL3 genes may have potential for prognosis and molecular targeted therapy of rectal adenocarcinoma. Graphical abstract