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 paracarcinoma 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.

Objective To investigate the value of neuron‐specific enolase (NSE), neutrophil‐to‐lymphocyte ratio (NLR) and lymph node metastasis in predicating distant metastasis in patients with limited‐stage small cell lung cancer (LD‐SCLC). Methods Clinical pathological data of LD‐SCLC patients in the First Affiliated Hospital of Wenzhou Medical University between August 2009 and October 2017 were retrospectively analyzed. The age, gender, smoking, TNM, NSE, NLR, chemotherapy cycle, radiotherapy, surgery and new metastasis of lymph nodes of 47 cases with distant metastasis and 47 cases without distant metastasis in 1 year were compared. Finally, factors influencing distant metastasis were determined as the predictors. The receiver operating characteristic (ROC) curve model was established based on logistic regression analysis of the factors obtained. Results Distant metastasis mainly involved brain (17/47), liver (17/47) and bone (17/47). Univariate analysis showed that patients with new lymph node metastasis, high NSE, pretreatment hilar lymph node metastasis and NLR were more prone to have distant metastasis. Multivariate analysis showed that new lymph node metastasis, high NSE, NLR and pretreatment hilar lymph node metastasis were independent predictors. The predictive model established using these predictors had an AUC of 0.872 (95%CI: 0.803‐0.941), a sensitivity of 76.60% and a speciality of 80.85%. Conclusion The new lymph node metastasis, NLR and NSE are predictors of distant metastasis, and thus, may have a profound impact on treatment decision making. Patients with lower NLR and NSE expression levels and less new metastasis of lymph nodes have a lower distant metastasis rate.

The miR-17-92 cluster (miR-17, miR-18a, miR-19a, miR-20a, miR-19b-1 and miR-92a) contributes to the occurrence and development of various diseases by inhibiting multiple target genes. Here, we explored the effects of miR-18a on insulin sensitivity. Quantitative real-time PCR indicated that serum miR-18a levels were lower in type 2 diabetes mellitus patients than in healthy controls, suggesting that miR-18a may influence blood glucose levels. Global overexpression of miR-18a in transgenic mice increased their glucose tolerance and insulin sensitivity, while it reduced expression of the phosphatase and tensin homolog deleted on chromosome ten (PTEN) in their skeletal muscle and adipose tissue. Western blotting indicated that overexpressing miR-18a in 3T3-L1 and C2C12 cells enhanced insulin-stimulated AKT phosphorylation and suppressed PTEN expression, while inhibiting miR-18a had the opposite effects. These results suggest that miR-18a improves insulin sensitivity by downregulating PTEN . This makes miR-18a a potentially useful target for the treatment of diabetes mellitus in the future.

BackgroundHerpes simplex virus type 1 (HSV‐1)‐mediated oncolytic therapy is a promising cancer treatment modality. However, viral tropism is considered to be one of the major stumbling blocks to the development of HSV‐1 as an anticancer agent.MethodsThe surface of oncolytic HSV‐1 G207 was covalently modified with folate‐poly (ethylene glycol) conjugate (FA‐PEG). The specificities and tumor targeting efficiencies of modified or unmodified G207 particles were analyzed by a real‐time polymerase chain reaction at the level of cell attachment and entry. Immune responses were assessed by an interleukin‐6 release assay from RAW264.7 macrophages. Biodistribution and in vivo antitumoral activity after intravenous delivery was evaluated in BALB/c nude mice bearing subcutaneous KB xenograft tumors.ResultsFA‐PEG‐HSV exhibited enhanced targeting specificity for folate receptor over‐expressing tumor cells and had lower immunogenicity than the unmodified HSV. In vivo, the FA‐PEG‐HSV group revealed an increased anti‐tumor efficiency and tumor targeting specificity compared to the naked HSV.ConclusionsThese results indicate that folate‐conjugated HSV G207 presents a folate receptor‐targeted oncolytic virus with a potential therapeutic value via retargeting to tumor cells.

As a fine-grained classification problem, food image classification faces many difficulties in the specific implementation. Different countries and regions have different eating habits. In particular, Asian food images have a complicated structure, and the related classification methods are still very scarce. There is an urgent need to develop a feature extraction and fusion scheme based on the characteristics of Asian food images. To solve the above problems, we proposed an image classification model SLGC (SURF-Local and Global Color) that combines image segmentation and feature fusion. By studying the unique structure of Asian foods, the color features of the images are merged into the representation vectors in the local and global dimensions, respectively, thereby further enhancing the effect of feature extraction. The experimental results show that the SLGC model can express the intrinsic characteristics of Asian food images more comprehensively and improve classification accuracy.