Background Lung adenocarcinoma (LUAD) is a major subtype of lung cancer and closely associated with poor prognosis. N6-methyladenosine (m6A), one of the most predominant modifications in mRNAs, is found to participate in tumorigenesis. However, the potential function of m6A RNA methylation in the tumor immune microenvironment is still murky. Methods The gene expression profile cohort and its corresponding clinical data of LUAD patients were downloaded from TCGA database and GEO database. Based on the expression of 21 m6A regulators, we identified two distinct subgroups by consensus clustering. The single-sample gene-set enrichment analysis (ssGSEA) algorithm was conducted to quantify the relative abundance of the fraction of 28 immune cell types. The prognostic model was constructed by Lasso Cox regression. Survival analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic model. Result Consensus classification separated the patients into two clusters (clusters 1 and 2). Those patients in cluster 1 showed a better prognosis and were related to higher immune scores and more immune cell infiltration. Subsequently, 457 differentially expressed genes (DEGs) between the two clusters were identified, and then a seven-gene prognostic model was constricted. The survival analysis showed poor prognosis in patients with high-risk score. The ROC curve confirmed the predictive accuracy of this prognostic risk signature. Besides, further analysis indicated that there were significant differences between the high-risk and low-risk groups in stages, status, clustering subtypes, and immunoscore. Low-risk group was related to higher immune score, more immune cell infiltration, and lower clinical stages. Moreover, multivariate analysis revealed that this prognostic model might be a powerful prognostic predictor for LUAD. Ultimately, the efficacy of this prognostic model was successfully validated in several external cohorts (GSE30219, GSE50081 and GSE72094). Conclusion Our study provides a robust signature for predicting patients’ prognosis, which might be helpful for therapeutic strategies discovery of LUAD.

Interleukin 34 (IL-34), an additional ligand of the colony-stimulating factor-1 receptor (CSF-1R), promotes the secretion of pro-inflammatory cytokines and stimulates NF-κB and JNK-related signaling pathways. However, the potential mechanism and prognostic value of IL-34 in lung adenocarcinoma (LUAD) remain obscure. In this study, IL-34 was found to be downregulated in LUAD tissues compared with para-carcinoma tissues, and loss of IL-34 expression was correlated with shorter overall survival (OS), which was validated by bioinformatics\ analysis in TCGA (The Cancer Genome Atlas) cohort and immunohistochemical analysis in the NTU (Nantong University) cohort, respectively. Subsequently, loss of IL-34 promotes negative regulation of the immune system and inhibits the infiltration of immune cells. Moreover, IL-34 deficiency was shown to be an independent adverse prognostic factor for patients with LUAD, and subgroup analysis indicated that IL-34 might contribute to the stratified management of patients with LUAD. IL-34-based nomogram model significantly improved the accuracy of prognostic predictions for OS of patients with LUAD, both in the TCGA cohort and the NTU cohort. Taken together, our data suggested that loss of IL-34 expression is associated with poor prognosis and negative regulation of the immune system of patients with LUAD, contributing to the stratified management of patients with LUAD.

Lung adenocarcinoma (LUAD) is characterized by high infiltration and rapid growth. The function of the stem cell population is to control and maintain cell regeneration. Therefore, it is necessary to study the prognostic value of stem cell-related genes in LUAD. Signature genes were screened out from 166 stem cell-related genes according to the least absolute shrinkage operator (LASSO) and subsequently multivariate Cox regression analysis, and then established risk model. Immune infiltration and nomogram model were used to evaluate the clinical efficacy of signature. A signature consisting of 10 genes was used to dichotomize the LUAD patients into two groups (cutoff, 1.314), and then validated in GSE20319 and GSE42127. There was a significant correlation between signature and clinical characteristics. Patients with high-risk had a shorter overall survival. Furthermore, significant differences were found in multiple immune cells between the high-risk group and low-risk group. A high correlation was also reflected between signature and immune infiltration. What’s more, the signature could effectively predict the efficacy of chemotherapy in patients with LUAD, and a nomogram based on signature might accurately predict the prognosis of patients with LUAD. The signature-based of stem cell-related genes might be contributed to predicting prognosis of patients with LUAD.

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Background: Lung adenocarcinoma (LUAD) is one of the tumor-related diseases with high morbidity worldwide. Epigenetic modifications such as DNA methylation changes may involve in tumorigenesis. This study aimed to explore new biomarkers that have prognostic significance of LUAD. Methods: First, we downloaded the gene expression and methylation data set from Gene Expression Omnibus. R software was then used to identify abnormally methylated differentially expressed genes (MDEGs). Next, R package Cluster Profiler was used to analyze the enrichment and pathway of the MDEGs. Analysis using STRING revealed the protein-protein interaction network. The result was then visualized by Cytoscape and obtained 10 hub genes. Afterward, they were further verified by The Cancer Genome Atlas to select candidate genes. Moreover, quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry were used to verify the expression and prognostic value of candidate genes in LUAD patients. Results: The results showed that the expressions of ADCY5 and PRKCB are indeed related to LUAD. The clinical relevance to PRKCB was confirmed by its clinical correlation analysis. Gene set enrichment analysis (GSEA) and tumor immune estimation resource (TIMER) tumor immune correlations showed that PRKCB is involved in the cancer-related Kyoto Encyclopedia of Genes and Genomes pathway and is involved in immune infiltration. It was also verified by qRT-PCR and immunohistochemistry that PRKCB was lowly expressed in LUAD patients and correlated with prognosis. Conclusions: PRKCB is relevant to prognosis of LUAD through methylation and immune infiltration.

Background: The mechanisms of hypoxia or immune microenvironment in cancer have been studiedrespectively, but the role of hypoxia immune microenvironment in non-small cell lung cancer (NSCLC) still needs further exploration.Methods: By applying the K-means algorithm, 1,121 patients with NSCLC were divided into three categories. We evaluated the constructed signature in order to link it with the prognosis, which was constructed by univariate and least absolute shrinkage operator (LASSO) Cox regression analysis.Results: A total of three clusters were obtained by clustering five Gene Expression Omnibus (GEO) data sets. Gene Set Variation Analysis (GSVA) and immune infiltration analysis were performed to explore the biological behavior. Cluster one presented an activated state of oncogenic pathways, and compared with the other two clusters, the median risk score was the highest, which was the reason for its poor survival. Cluster three showed that the immune pathway was active and the median risk score was the lowest, so the survival was the best. However, cluster two presented a state in which both immune and matrix pathways were activate. This was manifested as mutual antagonism, and its risk score was in the middle. Its survival was in the middle.Conclusions: This work revealed the role of hypoxia related genes (HRGs) modification in tumor microenvironment, which was conducive to our comprehensive analysis of the prognosis of NSCLC, and provided direction and guidance for clinical immunotherapy.

In recent years, cell membrane drug delivery systems have received increasing attention. However, drug-loaded membrane delivery systems targeting therapy in myocardial ischemia–reperfusion injury (MIRI) have been relatively rarely studied. The purpose of this study was to explore the protective effect of platelet-membrane-encapsulated Carvedilol on MIRI. We extracted platelets from the blood of adult SD rats and prepared platelet membrane vesicles (PMVs). Carvedilol, a nonselective β-blocker, was encapsulated into the PMVs. In order to determine the best encapsulation rate and drug-loading rate, three different concentrations of Carvedilol in low, medium, and high amounts were fused to the PMVs in different volume ratios (drugs/PMVs at 2:1, 1:1, 1:2, and 4:1) for determining the optimum concentration and volume ratio. By comparing other delivery methods, including abdominal injection and intravenous administration, the efficacy of PMVs-encapsulated drug-targeted delivery treatment was observed. The PMVs have the ability to target ischemic-damaged myocardial tissue, and the concentration and volume ratio at the optimum encapsulation rate and the drug-loading rate are 0.5 mg and 1:1. We verified that PMVs@Carvedilol had better therapeutic effects compared to other treatment groups, and immunofluorescence observation showed a significant improvement in the apoptosis indicators and infarction area of myocardial cells. Targeted administration of PMVs@Carvedilol may be a promising treatment for myocardial reperfusion injury, as it significantly improves postinjury cardiac function and increases drug utilization compared to other delivery methods.

Background. Esophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. This study aimed at analyzing the competing endogenous RNA (ceRNA) network and tumor-infiltrating immune cells in ESCA. Methods. The expression profiles of mRNAs, lncRNAs, and miRNAs were downloaded from the Cancer Genome Atlas database. A ceRNA network was established based on the differentially expressed RNAs by Cytoscape. CIBERSORT was applied to estimate the proportion of immune cells in ESCA. Prognosis-associated genes and immune cells were applied to establish prognostic models basing on Lasso and multivariate Cox analyses. The survival curves were constructed with Kaplan-Meier method. The predictive efficacy of the prognostic models was evaluated by the receiver operating characteristic (ROC) curves. Results. The differentially expressed mRNAs, lncRNAs, and miRNAs were identified. We constructed the ceRNA network including 23 lncRNAs, 19 miRNAs and 147 mRNAs. Five key molecules (HMGB3, HOXC8, HSPA1B, KLHL15, and RUNX3) were identified from the ceRNA network and five significant immune cells (Plasma cells, T cells follicular helper, Monocytes, Dendritic cells activated, and Neutrophils) were selected via CIBERSORT. The ROC curves based on key genes and significant immune cells all showed good sensitivity (AUC of 3-year survival: 0.739, AUC of 5-year survival: 0.899, AUC of 3-year survival: 0.824, AUC of 5-year survival: 0.876). There was certain correlation between five immune cells and five key molecules. Conclusion. This study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis.