Dendritic cells (DC) are highly efficient antigen-presenting cells. DC may be used to create DC vaccines against cancer, but the optimal strategies remain to be elucidated. This study aimed to examine the benefits and adverse effects of using esophageal cancer cell antigens to stimulate DC to trigger the specific immune response in patients with esophageal cancer undergoing radiotherapy. This was an observational cohort study performed at Lianshui County People’s Hospital between September 2010 and June 2012. Forty patients with esophageal cancer planned to receive radiotherapy were selected, and 28 received the DC vaccine. DC were isolated, loaded with antigens, and intradermally injected after being cultured for 1 week. One week after injection, the patients underwent a delayed-type hypersensitivity test. Serum Th1 cytokines [interleukin (IL)-2, IL-12, and interferon (IFN)-γ] and antigen-specific IFN-γ⁺CD8⁺ T cells were tested before and after vaccination. Patients were followed up for 2 years. Adverse events were monitored. Patients in the vaccine group tolerated the DC vaccine. Levels of serum IL-2 (+92.4%), IL-12 (+70.9%), and IFN-γ (+214.3%) as well as the proportion of IFN-γ⁺CD8⁺ T cells (3.0–16.4-fold) were significantly increased compared with baseline and the control group (all P<0.05). The 1- (82.1% vs. 50.0%, P=0.04) and 2-year survival (67.8% vs. 33.3%, P=0.04) was improved by vaccination. Only 2 patients showed mild fever. In conclusion, the DC vaccine triggered the specific immune response and induced the secretion of Th1 cytokines. The vaccine may lead to better survival, but this have to be confirmed. Adverse events were rare and mild.

Background: Esophageal squamous cell carcinoma (ESCC) is a highly aggressive malignancy. The aims of the present study were to screen the critical miRNA and corresponding target genes that related to development of ESCC by weighted gene correlation network analysis (WGCNA) and investigate the functions by experimental validation. Methods: Datasets of mRNA and miRNA expression data were downloaded from GEO. The R software was used for data preprocessing and differential expression gene analysis. The differentially expressed protein-coding genes (DEGs) and miRNAs (DEMs) were selected (FDR 1.5). Meanwhile, 81 expression data of ESCC patients in TCGA combined with clinic information were applied by WGCNA to create networks. The correlational analyses between each module and clinical parameters were conducted, and enrichment analyses of GO and KEGG were subsequently performed. Then, a series of experiments were conducted in ESCC cells by use of miRNA mimics. Results: In total, 4,023 DEGs and 328 DEMs were screened. After checking good genes and samples, 3,841 genes (3,696 DEGs and 145 DEMs) were used for WGCNA. As a consequence, altogether 11 gene modules were found. Among them, the brown modules were found to be strongly inversely associated with pathological grade. Meanwhile, has-mir-92b, the only miRNA in brown module, had a positive correlation with grade and negatively correlated with potential target gene (KFL4 and DCS2) in the same module. Furthermore, an increased expression of miR-92b-3p and down-regulated KLF4 and DSC2 protein was detected in the ESCC clinical samples. Up-regulated miR-92b-3p shortened G0/G1 phase and promote ESCC cells invasion and migration. Furthermore, we verified that DSC2 and KFL4 was target genes of miR-92b-3p by luciferase report assay. Conclusion: WGCNA is an efficient approach to system biology. By this procedure, miR-92b-3p was identified as an ESCC-promoting gene by target KLF4 and DCS2.

Background and objectives: Each individual studies is limited to multi-factors and potentially lead to a significant difference of results among them. The present study aim to explore the critical genes related to the development of Esophageal squamous cell carcinoma (ESCC) by integrated transcriptomics and to investigate the clinical significance by experimental validation. Methods: Datasets of protein-coding genes expression which involved in ESCC were downloaded from Gene Expression Omnibus (GEO) database. The “Robustrankaggreg” package in language was used for data integration, and the different expression genes (DEGs) were identified based the cut-off criteria as follows: adjust p-value < 0.05, |fold change (FC)| ≥ 1.5; The protein expression of seed gene in 184 cases of primary ESCC tissues and 50 tumor adjacent normal tissues (at least 5 cm away from the tumor, and defind as the controls) were detected by immunohistochemistry; The relationship between the expression level of seed genes and clinical parameter were analyze. Enumeration data were represented by frequency or percentage (%) and were tested by x² test. The P value of less than 0.05 was considered statistically significant. Results: A total of 244 DEGs were identified by comparing gene expression patterns between ESCC patients and the controls based on integrating dataset of GSE77861, GSE77861, GSE100942, GSE26886, GSE17351, GSE38129, GSE33426, GSE20347 and GSE23400; The Cyclin-dependent kinase inhibitor 3 (CDKN3) were identified the top 1 seed gene of top cluster by use of protein-protein Interaction network and plug-in Molecular Complex Detection; The level of CDKN3 mRNA was significantly increased in ESCC patients compared to controls; The positive expression rate of CDKN3 protein in ESCC tissue samples was 32 and 61.4% in control, respectively. The correlations between the expression level of CDKN3 and lymph node metastasis or clinical staging of ESCC patients are statistically significant. Conclusion: Integrated transcriptomics is an efficient approach to system biology. By this procedure, our study improved the understanding of the transcriptome status of ESCC.

Colon adenocarcinoma (COAD) is the most common histologic subtype of colorectal cancer (CRC), and its prognosis is poor. Unlike traditional research in molecular biology, which is limited to analyzing the function of a single gene or protein in malignant tumors. The Weighted gene correlation network analysis (WGCNA) technique is used to describe the gene association model among different samples in order to identify highly collaborative genes. In this study, a computational strategy was used to conduct a systematic study of prognosis-related genes (PRGs) of COAD from the TCGA database. PRGs were subsequently used for WGCNA, which included 379 COAD patient expression profiles and 39 controls. As a consequence, nine gene modules were built. Among these, the brown module had not only a negative relationship with COAD, but also simultaneously in inverse relationship with the clinical stage, stage T, stage M and stage N. C4orf19, which was identified as one of the DEG and hub genes in the brown module by calculating modular connectivity, has a negative correlation with the clinical stage and TMN stage. In addition, the downward-regulated C4orf19 protein was detected in COAD clinical specimens. Finally, in vitro experiments have confirmed that regulated C4orf19 can promote COAD cell proliferation, invasion and migration, and the biological mechanism of C4orf19 perhaps by influencing the nitrogen metabolic pathway.