The prognostic value of GLUT1 in cancers: a systematic review and meta-analysis The prognostic value of GLUT1 in cancers: a systematic review and meta-analysis

Objective: Tumor-associated macrophages (TAMs) deserve more focus because of its pivotal role in the development of solid tumors and they are related to poor outcomes of several tumors. However, the prognostic value of tumor-infiltrating TAMs in pancreatic cancer is still controversial. Experimental design: We conduct a meta-analysis of 1699 patients in 13 studies by reviewing the studies in which the authors evaluated the prognostic value of TAMs density in pancreatic cancer. These studies were searched from inception to November 2018. Hazard ratios (HR) with their corresponding 95% confidence intervals (95% CIs) were assessed to explore the prognostic significance of TAMs in pancreatic cancer. Besides, we also conducted a subgroup analysis and sensitivity analysis. Two reviewers independently abstracted data. Results: A total of 13 studies with 1699 patients were pooled in the analysis to evaluate prognostic value of TAMs in pancreatic cancer. The pooled HRs demonstrated CD68+ TAMs correlated with worse overall survival (OS) in pancreatic cancer patients (HR =1.41, 95% CI =1.05–1.90, random effects model, I^2,=82.5%, P=0.021). And high generalized M2-TAMs density was significantly associated with poor OS (HR =1.95, 95% CI =1.63–2.33, random effects model, I^2,=59.2%, P=0.000) and disease-free survival (HR =1.83, 95% CI =1.43–2.36, fixed effects model, I^2,=0.00%, P=0.000). Pooled analysis showed no significant correlation between elevated TAMs infiltration and lymph node metastasis, tumor stage, histological grade, sex, or tumor location. Conclusion: The density of TAMs has an impact on the overall survival of pancreatic cancer patients. M2-TAMs can be recognized as a prognostic indicator in pancreatic cancer, which may serve as a potential therapeutic target in the treatment of pancreatic cancer.

Background: N6-methyladenosine (m6A) modification holds an important position in tumorigenesis and metastasis because it can change gene expression and even function in multiple levels including RNA splicing, stability, translocation and translation. In present study, we aim to conducted comprehensive investigation on m6A RNA methylation regulators and m6A-related genes in pancreatic cancer and their association with survival time. Methods: Based on Univariate Cox regression analysis, protein-protein interaction analysis, LASSO Cox regression, a risk prognostic model, STRING, Spearman and consensus clustering analysis, data from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database was used to analyze 15 m6A RNA methylation regulators that were widely reported and 1,393 m6A-related genes in m6Avar. Results: We found that 283 candidate m6A RNA methylation-related genes and 4 m6A RNA methylation regulatory factors, including RNA binding motif protein 15 (RBM15), methyltransferase like 14 (METTL14), fat mass and obesity-associated protein (FTO), and α‐ketoglutarate‐dependent dioxygenase AlkB homolog 5 (ALKBH5), differed significantly among different stages of the American Joint Committee on Cancer (AJCC) staging system. Protein-protein interaction analysis indicated epidermal growth factor receptor (EGFR), plectin-1 (PLEC), BLM RecQ like helicase (BLM), and polo like kinase 1 (PLK1) were closely related to other genes and could be considered as hub genes in the network. The results of LASSO Cox regression and the risk prognostic model indicated that AJCC stage, stage T and N, KRAS mutation status and x8q23.3 CNV fragment mutation differed significantly between the high-risk and the low-risk subgroups. The AUCs of 1 to 5 years after surgery were all more than 0.7 and increased year by year. Finally, we found KRAS mutation status and AJCC stage differed significantly among these groups after TCGA samples divided into subgroups with k=7. Moreover, we identified four m6A RNA methylation related genes expressed significantly differently among these seven subgroups, including collagen type VII alpha 1 chain (COL7A1), branched chain amino acid transaminase 1 (BCAT1), zinc finger protein 596 (ZNF596), and PLK1. Conclusions: Our study systematically analyzed the m6A RNA methylation related genes, including expression, protein-protein interaction, potential function, and prognostic value and provides important clues to further research on the function of RNA m6A methylation and its related genes in pancreatic cancer.

Effective emotion recognition algorithms can help machines better understand people and promote the development of human-computer interaction applications. In recent years, many research efforts have used benchmark expression data to train deep neural network models to achieve state-of-art results. These high-accuracy models usually contain hundreds of layers, so they require complex calculations and may not be suitable for real-world scenarios. This paper proposes a lightweight emotion recognition (LER) model to handle the latency problem under natural conditions. The three main contributions of this paper are as follows. 1) The LER model incorporates a densely connected convolution layer and model compression techniques into a framework that eliminates redundancy parameters. 2) Multichannel input is introduced in our work to preprocess the image data, which improves the learning ability of the model. 3) Experiments show that the proposed LER model has better performance on the FER2013 and FERPLUS datasets compared with other lightweight models. Compared with the VGG13 used in previous work, the LER model achieves higher accuracy and reduces the number of parameters by 97 times. Finally, the FERFIN dataset is created, which had fewer noise data and more accurate labels than the FERPLUS dataset.