Programmed cell death protein 1 (PD-1) and its ligand PD-L1 have attracted wide attention from researchers in the field of immunotherapy. PD-1/PD-L1 have been shown to exist in many types of cells in addition to T lymphocytes, and studies have accordingly extended from their suppressive effect on T cell activation and function to examining their role in other cells. In this review, we summarize recent research on PD-1/PD-L1 in macrophages, with the aim of furthering our understanding of PD-1/PD-L1 and their detailed roles in macrophages. This information may provide additional insights for researchers, enrich the basic theory of anti-PD-1/PD-L1 immunotherapy, and thus ultimately benefit more patients.

Background: Gamma-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the adult mammalian brain, but exerts physiologic effects other than that on neurotransmitter in non-neuronal peripheral tissues and organs. GABA may affect cancer growth through activation GABA receptors. We investigated the gene expression of GABA receptors in tissue of non-small cell lung cancers (NSCLC) and non-cancerous tissues, and found that the gene expression of GABA receptor phenotypes was correlated with tumorigenesis and clinical prognosis.Methods: Sixty-one snap-frozen human samples of NSCLC tissues and paired non-cancerous tissues (5cm away from tumor) were analyzed. Gene expression of GABA receptors was detected by Real-time quantitative PCR (RT-qPCR). Survival times in relation to the expression of GABA receptor phenotypes were analyzed. Human NSCLC cell lines H1299, A549, H520, H460 and human bronchial epithelial cell line BEAS-2B were used to determine the phenotypes of GABA inhibitory effects on cancer cell growth. The effects of exogenous administration of GABA on H1299 cell growth were examined.Results: The gene expressions were significantly higher in NSCLC tissues than in the paired non-cancerous tissues for GABA_Areceptor subunit α3 (GABR_A3,P = 0.030); for GABA_Areceptor subunit epsilon (GABR_E,P = 0.036); and GABA_Breceptor subunit 2 (GABBR₂,P = 0.005). Kaplan-Meier curves showed that patients with high expression of GABBR₂gene and low expression of GABR_A3gene had a better prognosis (P < 0.05). The administration of GABA resulted in suppressed proliferation of NSCLC cell lines in a dose- and time-dependent manner. The use of the GABA receptor antagonist CGP35348 could reverse the inhibitory effect.Conclusions: The pattern of GABA receptor gene phenotype expression may be involved in the regulation of tumorigenesis. A high expression of GABBR₂with a low expression of GABR_A3may predict a better outcome. The treatment with GABA attenuates cancer cell growth in vitro. The expression of GABA receptor may be not only promising genetic therapeutic targets but may also serve as valuable prognostic markers for NSCLC.

Background: Small cell lung cancer (SCLC) is a subtype of lung cancer with poor prognosis. In this study, we aimed to build a nomogram to predict the survival of individual with SCLC by incorporating significant clinical parameters. Methods: The patients with SCLC were enrolled from the First Affiliated Hospital of Guangzhou Medical University (GMUFAH) between 2009 and 2013. We identified and incorporated the independent prognostic factors to build a nomogram to predict the survival of SCLC patients. The predictive accuracy and discriminative ability of the nomogram were evaluated by concordance index (C-index) and calibration curve. We also compared the accuracy of the built model with the 7^th AJCC TNM and VALSG staging system. The nomogram was further validated in an independent cohort of 80 patients with SCLC from Cancer Center of Guangzhou Medical University (GMUCC) between 2009 and 2013. Results: A total of 275 patients with SCLC were included in the primary cohort, and seven independent prognostic factors were identified including age, N stage, metastasis status, histology, platelets to lymphocyte ratio (PLR), neuron specific enolase (NSE) and CYFRA21-1 as independent prognostic factors after using Cox regression model. A nomogram incorporating these prognostic factors was subsequently built. The calibration curves for possibilities of 1-, 2-year overall survival (OS) revealed optimal agreement between nomogram prediction and actual observation. The C-index of this nomogram was higher than that of TNM and VALSG staging system in both primary and validation cohort (nomogram vs. TNM, primary cohort 0.68 vs. 0.65, P<0.01, validation cohort 0.66 vs. 0.62, P<0.05; nomogram vs. VALSG, primary cohort 0.68 vs. 0.66, P<0.01, validation cohort 0.66 vs. 0.64, P<0.05). Conclusions: In this study, we established and validated a novel nomogram for the prediction of OS for the patients with SCLC. This model could provide more accurate individual prediction of survival probability of SCLC than the existing staging systems.

Background: Tumour cells interfere with normal immune functions by affecting the expression of some immune-related genes, which play roles in the prognosis of cancer patients. In recent years, immunotherapy for tumours has been widely studied, but a practical prognostic model based on immune-related genes in lung adenocarcinoma comparable to existing model has not been established and reported. Methods: We first obtained publicly accessible lung adenocarcinoma RNA expression data from The Cancer Genome Atlas (TCGA) for differential gene expression analysis and then filtered immune-related genes based on the ImmPort database. By using the lasso algorithm and multivariate Cox Proportional-Hazards (CoxPH) regression analysis, we identified candidate genes for model development and validation. The robustness of the model was further examined by comparing the model with three established gene models. Results: Gene expression data from a total of 524 lung adenocarcinoma patients from TCGA were used for model development. We identified four biomarkers (MAP3K8, CCL20, VEGFC, and ANGPTL4) that could predict overall survival in lung adenocarcinoma (HR = 1.98, 95% CI 1.48 to 2.64, P = 4.19e−06) and this model could be used as a classifier for the evaluation of low-risk and high-risk groups. This model was validated with independent microarray data and was highly comparable with previously reported gene expression signatures for lung adenocarcinoma prognosis. Conclusions: In this study, we identified a practical and robust four-gene prognostic model based on an immune gene dataset with cross-platform compatibility. This model has potential value in improving TNM staging for survival predictions in patients with lung adenocarcinoma. Impact: The study provides a method of immune relevant gene prognosis model and the identification of immune gene classifier for the prediction of lung adenocarcinoma prognosis with RNA sequencing and microarray compatibility.

Recently, LOC285194 has shown a potential tumor-suppressor function in several types of human cancers, but its function in non-small cell lung cancer (NSCLC) remains unknown. This study intended to investigate the biological role of LOC285194 and its clinical significance in NSCLC. LOC285194 was detected by qRT-PCR, and its correlation with clinicopathological features of NSCLC was analyzed. The expression of LOC285194 was knocked down or ectopically expressed in lung cancer cells (A549 and H1299) and tumor cell growth, migration and invasion in vitro were investigated. In addition, the interaction of LOC285194 and target proteins was assessed by RNA pulldown and RNA immunoprecipitation in vitro. The results revealed that the expression of LOC285194 was significantly lower in tumor tissues when compared with the corresponding non-tumor tissues (P<0.001). Its expression was correlated with the tumor size (P=0.027). Kaplan-Meier analysis revealed that patients with lower LOC285194 expression had worse disease-free survival and overall survival rates (P<0.05). RNA protein interaction analysis revealed that p53 was the direct binding target of LOC285194 in NSCLC. Bioinformatics analyses suggested that depletion of LOC285149 could affect its antitumor function through the KRAS/BRAF/SMEK pathway. Our findings indicated that LOC285194 was a novel non-coding prognostic indicator and contributed to tumor suppression by targeting p53 in NSCLC, suggesting that it may be a non-coding target for NSCLC gene therapy.

Background: Small cell lung cancer (SCLC) is a highly invasive and lethal neuroendocrine tumor. Antiangiogenic drugs have been reported in the treatment of SCLC. We aimed to provide a comprehensive evaluation of the impact of angiogenic inhibitors on SCLC survival using network meta‐analysis.Methods: The impact of five angiogenesis inhibitors, that is, vandetanib (Van), bevacizumab (Bev), Rh‐endostatin (End), sunitinib (Sun), and thalidomide (Tha), on progression‐free survival (PFS) and overall survival (OS) was evaluated by conducting a network meta‐analysis. RNA sequencing data were downloaded from publicly available databases.Results: Nine phase II and III randomized controlled trials (RCTs), that involved 1599 participants, that investigated angiogenesis inhibitors in the treatment of SCLC were included in this meta‐analysis. Sun and Bev achieved better PFS than Tha (Bev VS. Tha, HR = 0.88, 95% CI: 0.79‐0.98, Sun VS. Tha, HR = 0.80, 95% CI: 0.65‐1.00). Moreover, Sun and Bev were superior to placebo in terms of PFS (Bev VS. Placebo, HR = 0.89, 95%CI: 0.81‐0.97, Sun VS. Placebo, HR = 0.81, 95% CI: 0.66‐1.00). Based on this study, we found no significant difference of OS of SCLC. The angiogenesis pathway and expression of target genes were globally deactivated in SCLC tissue.Conclusion: Results of this network meta‐analysis indicate that the PFS outcome of SCLC with Sun or Bev drugs is superior to that of Tha. The improved therapeutic impact of angiogenesis inhibitors on SCLC needs more evidence, such as long‐term observation in clinical trials, to be validated.