Comprehensive analysis of an immune infiltrate-related competitive endogenous RNA network reveals potential prognostic biomarkers for non-small cell lung cancer.

Globally, non-small cell lung cancer (NSCLC) is the most common malignancy and its prognosis remains poor because of the lack of reliable early diagnostic biomarkers. The competitive endogenous RNA (ceRNA) network plays an important role in the tumorigenesis and prognosis of NSCLC. Tumor immune microenvironment (TIME) is valuable for predicting the response to immunotherapy and determining the prognosis of NSCLC patients. To understand the TIME-related ceRNA network, the RNA profiling datasets from the Genotype-Tissue Expression and The Cancer Genome Atlas databases were analyzed to identify the mRNAs, microRNAs, and lncRNAs associated with the differentially expressed genes. Weighted gene co-expression network analysis revealed that the brown module of mRNAs and the turquoise module of lncRNAs were the most important. Interactions among microRNAs, lncRNAs, and mRNAs were prognosticated using miRcode, miRDB, TargetScan, miRTarBase, and starBase databases. A prognostic model consisting of 13 mRNAs was established using univariate and multivariate Cox regression analyses and validated by the receiver operating characteristic (ROC) curve. The 22 immune infiltrating cell types were analyzed using the CIBERSORT algorithm, and results showed that the high-risk score of this model was related to poor prognosis and an immunosuppressive TIME. A lncRNA-miRNA-mRNA ceRNA network that included 69 differentially expressed lncRNAs (DElncRNAs) was constructed based on the five mRNAs obtained from the prognostic model. ROC survival analysis further showed that the seven DElncRNAs had a substantial prognostic value for the overall survival (OS) in NSCLC patients; the area under the curve was 0.65. In addition, the high-risk group showed drug resistance to several chemotherapeutic and targeted drugs including cisplatin, paclitaxel, docetaxel, gemcitabine, and gefitinib. The differential expression of five mRNAs and seven lncRNAs in the ceRNA network was supported by the results of the HPA database and RT-qPCR analyses. This comprehensive analysis of a ceRNA network identified a set of biomarkers for prognosis and TIME prediction in NSCLC.

Construction of a Prognostic Immune Signature for Squamous-Cell Lung Cancer to Predict Survival.

Background: Limited treatment strategies are available for squamous-cell lung cancer (SQLC) patients. Few studies have addressed whether immune-related genes (IRGs) or the tumor immune microenvironment can predict the prognosis for SQLC patients. Our study aimed to construct a signature predict prognosis for SQLC patients based on IRGs. Methods: We constructed and validated a signature from SQLC patients in The Cancer Genome Atlas (TCGA) using bioinformatics analysis. The underlying mechanisms of the signature were also explored with immune cells and mutation profiles. Results: A total of 464 eligible SQLC patients from TCGA dataset were enrolled and were randomly divided into the training cohort (n = 232) and the testing cohort (n = 232). Eight differentially expressed IRGs were identified and applied to construct the immune signature in the training cohort. The signature showed a significant difference in overall survival (OS) between low-risk and high-risk cohorts (P < 0.001), with an area under the curve of 0.76. The predictive capability was verified with the testing and total cohorts. Multivariate analysis revealed that the 8-IRG signature served as an independent prognostic factor for OS in SQLC patients. Naive B cells, resting memory CD4 T cells, follicular helper T cells, and M2 macrophages were found to significantly associate with OS. There was no statistical difference in terms of tumor mutational burden between the high-risk and low-risk cohorts. Conclusion: Our study constructed and validated an 8-IRG signature prognostic model that predicts clinical outcomes for SQLC patients. However, this signature model needs further validation with a larger number of patients.

Genistein inhibits angiogenesis developed during rheumatoid arthritis through the IL-6/JAK2/STAT3/VEGF signalling pathway.

BACKGROUND: Angiogenesis plays an important role in the development of rheumatoid arthritis (RA), which increases the supply of nutrients, cytokines, and inflammatory cells to the synovial membrane. Genistein (GEN), a soy-derived isoflavone, has been validated that can effectively inhibit the angiogenesis of several tumours. We thus carried out a study in vitro to investigate the effect of GEN in vascular endothelial growth factor (VEGF) expression and angiogenesis induced by the inflammatory environment of RA. METHODS: MH7A cells were used to verify whether GEN can inhibit the expression of VEGF in MH7A cells under inflammatory conditions and demonstrate the mechanism. EA.hy926 â€‹cells were used to verify whether GEN can inhibit the migration and tube formation of vascular endothelial cells in inflammatory environment. RESULTS: GEN dose-dependently inhibited the expression and secretion of interleukin (IL)-6 and VEGF, as well as the nucleus translocation of Signal transducer and activator of transcription 3 (STAT3) in MH7A. Furthermore, GEN inhibited IL-6-induced vascular endothelial cell migration and tube formation in vitro. CONCLUSION: GEN inhibits IL-6-induced VEGF expression and angiogenesis partially through the Janus kinase 2 (JAK2)/STAT3 pathway in RA, which has provided a novel insight into the antiangiogenic activity of GEN in RA. THE TRANSLATIONAL POTENTIAL OF THIS ARTICLE: Our study provides scientific guidance for the clinical translational research of GEN in the RA treatment.