Background: Esophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. The present 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: The present study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis. 相似文献
Faithful genome integrity maintenance plays an essential role in cell survival. Here, we identify the RNA demethylase ALKBH5 as a key regulator that protects cells from DNA damage and apoptosis during reactive oxygen species (ROS)-induced stress. We find that ROS significantly induces global mRNA N6-methyladenosine (m6A) levels by modulating ALKBH5 post-translational modifications (PTMs), leading to the rapid and efficient induction of thousands of genes involved in a variety of biological processes including DNA damage repair. Mechanistically, ROS promotes ALKBH5 SUMOylation through activating ERK/JNK signaling, leading to inhibition of ALKBH5 m6A demethylase activity by blocking substrate accessibility. Moreover, ERK/JNK/ALKBH5-PTMs/m6A axis is activated by ROS in hematopoietic stem/progenitor cells (HSPCs) in vivo in mice, suggesting a physiological role of this molecular pathway in the maintenance of genome stability in HSPCs. Together, our study uncovers a molecular mechanism involving ALKBH5 PTMs and increased mRNA m6A levels that protect genomic integrity of cells in response to ROS. 相似文献
With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis. 相似文献
Phosphate-solubilizing bacteria (PSB) are important plant growth-promoting rhizobacteria that can increase soil fertility through the solubilization of insoluble inorganic phosphate and organophosphorus. In this study, a PSB, Burkholderia gladioli MEL01, was isolated and identified from rice–wheat rotation rhizosphere soil. MEL01 had an excellent phosphate-solubilizing capacity (reaching 107.69 mg/L) toward insoluble inorganic phosphate rock phosphate. HPLC analysis revealed that the mechanism of phosphate solubilization of MEL01 was probably due to secreted oxalic acid and gluconic acid transformation of phosphate from insoluble to soluble. MEL01 also exhibited 4030 U/L specific chitosanase activity when cultured with chitosan fermentation medium. Interestingly, the chitosan hydrolysis product chitooligosaccharide could significantly enhance the MEL01 phosphate-solubilizing capacity. Pot experiments showed that MEL01 chitosan medium fermentation liquor (MCMFL) could promote improvement of soil available phosphorus and pakchoi growth when supplemented with phosphate rock phosphate as the phosphate fertilizer. In addition, pot experiments demonstrated that MCMFL could also promote the growth of wheat, which could decrease the amount of compound fertilizer used. Microbial diversity analysis showed that the genera Pseudomonas, Burkholderia, Mycoplana, and Cellvibrio were enriched, which might participate in synergetic phosphate solubilization. Therefore, after fermentation with chitosan and fertilization with rock phosphates, MEL01 has potential as a phosphate biofertilizer in ecological agricultural production.