Predictions of the dysregulated competing endogenous RNA signature involved in the progression of human lung adenocarcinoma

Integrated analysis of competitive endogenous RNA networks in elder patients with non-small cell lung cancer

BACKGROUND

Lung cancer is one of the most prevalent cancers and the leading cause of cancer-related deaths worldwide; non-small cell lung cancer (NSCLC) comprises approximately 80% of all lung cancer cases. This study aimed to construct a competing endogenous RNA (ceRNA) network and identify prognostic signatures in elderly patients with NSCLC.

METHODS

We extracted data from elderly patients with NSCLC from The Cancer Genome Atlas and identified differentially expressed (DE) messenger RNAs (mRNAs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to investigate the functions of DEmRNAs. The interactions between RNAs were predicted using starBase, TargetScan, miRTarBase, and miRanda. Cytoscape version 3.0 was used to construct and visualize the lncRNA-miRNA-mRNA ceRNA network. The association between the expression levels of DERNAs in the constructed ceRNA network and overall survival was determined using the survival package in R software. Furthermore, another Gene Expression Omnibus cohort was studied to externally validate the ceRNA network.

RESULTS

In total, 2865 DEmRNAs, 62 DEmiRNAs, and 131 DElncRNAs were identified. Dysregulated mRNAs are enriched in cancer-related processes and pathways. A ceRNA network was constructed using 38 miRNAs, 61 lncRNAs, and 164 mRNAs. Of these, 3 lncRNAs, 3 miRNAs, and 16 mRNAs were closely related to overall survival. The MIR99AHG-hsa-miR-31-5p-PRKCE axis has been identified as a potential ceRNA network involved in the development of NSCLC in elderly individuals. External validation of the MIR99AHG-hsa-miR-31-5p-PRKCE axis in the GSE19804 cohort showed that PRKCE was downregulated and that MIR99AHG was upregulated in the tumor tissues of elderly patients with NSCLC compared with normal lung tissues.

CONCLUSIONS

This study provides novel insights into the lncRNA-miRNA-mRNA ceRNA network and reveals potential biomarkers for the diagnosis and prognosis of elderly patients with NSCLC.

Aberrant Expression and Prognostic Potential of IL-37 in Human Lung Adenocarcinoma

Interleukin-37 (IL-37) is a relatively new IL-1 family cytokine that, due to its immunoregulatory properties, has lately gained increasing attention in basic and translational biomedical research. Emerging evidence supports the implication of this protein in any human disorder in which immune homeostasis is compromised, including cancer. The aim of this study was to explore the prognostic and/or diagnostic potential of IL-37 and its receptor SIGIRR (single immunoglobulin IL-1-related receptor) in human tumors. We utilized a series of bioinformatics tools and -omics datasets to unravel possible associations of IL-37 and SIGIRR expression levels and genetic aberrations with tumor development, histopathological parameters, distribution of tumor-infiltrating immune cells, and survival rates of patients. Our data revealed that amongst the 17 human malignancies investigated, IL-37 exhibits higher expression levels in tumors of lung adenocarcinoma (LUAD). Moreover, the expression profiles of IL-37 and SIGIRR are associated with LUAD development and tumor stage, whereas their high mRNA levels are favorable prognostic factors for the overall survival of patients. What is more, IL-37 correlates positively with a LUAD-associated transcriptomic signature, and its nucleotide changes and expression levels are linked with distinct infiltration patterns of certain cell subsets known to control LUAD anti-tumor immune responses. Our data indicate the potential value of IL-37 and its receptor SIGIRR to serve as biomarkers and/or immune-checkpoint therapeutic targets for LUAD patients. Further, the data highlight the urgent need for further exploration of this cytokine and the underlying pathogenetic mechanisms to fully elucidate its implication in LUAD development and progression.

Identification and Validation of a GPX4-Related Immune Prognostic Signature for Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a commonly occurring histological subtype of lung cancer. Glutathione peroxidase 4 (GPX4) is an important regulatory factor of ferroptosis and is involved in the development of many cancers, but its prognostic significance has not been systematically described in LUAD. In this study, we focused on developing a robust GPX4-related prognostic signature (GPS) for LUAD. Data for the training cohort was extracted from The Cancer Genome Atlas, and that for the validation cohort was sourced from the GSE72094 dataset including 863 LUAD patients. GPX4-related genes were screened out by weighted gene coexpression network analysis and Spearman’s correlation analysis. Then, Cox regression and least absolute shrinkage and selection operator regression analyses were employed to construct a GPS. The ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and GSEA were utilized to evaluate the relationship between GPS and the tumor microenvironment (TME). We constructed and validated a GPS premised on four GPX4-related genes (KIF14, LATS2, PRKCE, and TM6SF1), which could classify LUAD patients into low- and high-score cohorts. The high-risk cohort presented noticeably poorer overall survival (OS) as opposed to the low-risk cohort, meaning that the GPS may be utilized as an independent predictor of the OS of LUAD. The GPS was also adversely correlated with multiple tumor-infiltrating immune cells and immune-related processes and pathways in TME. Furthermore, greater sensitivity to erlotinib and lapatinib were identified in the low-risk cohort based on the GDSC database. Our findings suggest that the GPS can effectively forecast the prognosis of LUAD patients and may possibly regulate the TME of LUAD.

Identification of Prognostic Factors Related to Super Enhancer-Regulated ceRNA Network in Metastatic Lung Adenocarcinoma

Introduction

The regulatory mechanisms of super enhancers (SEs) and ceRNA networks in LUAD progression are not well understood. We aimed to discover the prognostic-related ceRNA network regulated by SEs in metastatic LUAD.

Methods

RNA-seq data were extracted from The Cancer Genome Atlas (TCGA) database. Differentially expressed (DE) RNAs were identified by edgeR. CeRNA network was predicted and visualized using starBase and Cytoscape. H3K27ac ChIP-seq data were derived from the Gene Expression Omnibus (GEO) database, and used for SE identification. Kaplan–Meier curve and multivariate Cox model were applied for prognostic analysis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein–protein interaction (PPI) network were performed for functional analysis. SEs of AC074117.1 were verified by ChIP-qPCR in A549 and H1299 cells. MTT assay was performed to analyze cell proliferation. Luciferase activity assay was carried out to validate the target targeting relationships of ceRNA network.

Results

A total of 2355 DEmRNA, 483 DElncRNA and 155 DEmiRNA were identified between metastatic LUAD and adjacent normal tissues. CeRNA network consisting of 7 DElncRNAs, 18 DEmiRNAs and 15 DEmRNAs was constructed. Among the seven DElncRNAs in ceRNA network, only AC074117.1 was regulated by SEs. SE-regulated prognostic ceRNA sub-network consisting of FKBP3, E2F2, AC074117.1 and hsa-let-7c-5p was screened and verified. The overlapping co-expressed mRNAs of FKBP3, E2F2, AC074117.1 and hsa-let-7c-5p were mainly related to cell division and Fanconi anemia pathway. Genes in the ceRNA sub-network were correlated with DNA mismatch repair markers. Functional experiments proved that AC074117.1 was highly expressed in LUAD cells. AC074117.1 silencing notably inhibited proliferation of A549 and H1299 cells. Luciferase activity assay confirmed the direct relationship in AC074117.1-hsa-let-7c-5p-FKBP3/E2F2 network.

Conclusion

A novel prognostic ceRNA sub-network regulated by SEs was identified in metastatic LUAD. This study provided potential therapeutic targets and prognostic markers for further study of metastatic LUAD.

Bioinformatics Analysis of the MicroRNA-Metabolic Gene Regulatory Network in Neuropathic Pain and Prediction of Corresponding Potential Therapeutics

Neuropathic pain (NP) involves metabolic processes that are regulated by metabolic genes and their non-coding regulator genes such as microRNAs (miRNAs). Here, we aimed at exploring the key miRNA signatures regulating metabolic genes involved in NP pathogenesis. We downloaded NP-related data from public databases and identified differentially expressed microRNAs (miRNAs) and mRNAs through differential gene expression analysis. The miRNA target prediction was performed, and integration with the differentially expressed metabolic genes (DEMGs) was used for constructing the miRNA-DEMG network. Subsequently, functional enrichment analysis and protein–protein interaction (PPI) analysis were performed to explore the role of DEMGs in the regulatory network. The drug prediction was performed based on the DEMGs in the miRNA-DEMG network. A total of 8251 differentially expressed mRNAs (4193 upregulated and 4058 downregulated), and 959 differentially expressed miRNAs (455 upregulated and 504 downregulated) were identified. Moreover, after target gene prediction, a miRNA-DEMG network composed of 22 miRNAs and 113 mRNAs was constructed. The network was constituted of 135 nodes and 236 edges. We found that DEMGs in the network were mainly enriched in metabolic pathways and metabolic processes. A total of 1200 drugs were predicted as potential therapeutics for NP based on the differentially expressed genes, while 170 drugs were predicted for the DEMGs in the miRNA-DEMG network. Conclusively, our study predicted drugs that may be effective against the metabolic changes induced by miRNA dysregulation in NP. This information will help further reveal the pathological mechanism of NP and provide more treatment options for NP patients.

An Immune-Related Long Non-Coding RNA Signature to Predict the Prognosis of Ewing's Sarcoma Based on a Machine Learning Iterative Lasso Regression

The aim of this study was to construct a new immune-associated long non-coding RNA (lncRNA) signature to predict the prognosis of Ewing sarcoma (ES) and explore its molecular mechanisms. We downloaded transcriptome and clinical prognosis data from the Gene Expression Omnibus (GSE17679, which included 88 ES samples and 18 matched normal skeletal muscle samples), and used it as a training set to identify immune-related lncRNAs with different expression levels in ES. Univariable Cox regression was used to screen immune-related lncRNAs related to ES prognosis, and an immune-related lncRNA signature was constructed based on machine learning iterative lasso regression. An external verification set was used to confirm the predictive ability of the signature. Clinical feature subgroup analysis was used to explore whether the signature was an independent prognostic factor. In addition, CIBERSORT was used to explore immune cell infiltration in the high- and low-risk groups, and to analyze the correlations between the lncRNA signature and immune cell levels. Gene set enrichment and variation analyses were used to explore the possible regulatory mechanisms of the immune-related lncRNAs in ES. We also analyzed the expression of 17 common immunotherapy targets in the high- and low-risk groups to identify any that may be regulated by immune-related lncRNAs. We screened 35 immune-related lncRNAs by univariate Cox regression. Based on this, an immune-related 11-lncRNA signature was generated by machine learning iterative lasso regression. Analysis of the external validation set confirmed its high predictive ability. DPP10 antisense RNA 3 was negatively correlated with resting dendritic cell, neutrophil, and γδ T cell infiltration, and long intergenic non-protein coding RNA 1398 was positively correlated with resting dendritic cells and M2 macrophages. These lncRNAs may affect ES prognosis by regulating GSE17721_CTRL_VS_PAM3CSK4_12H_BMDC_UP, GSE2770_IL4_ACT_VS_ACT_CD4_TCELL_48H_UP, GSE29615_CTRL_VS_DAY3_ LAIV_IFLU_VACCINE_PBMC_UP, complement signaling, interleukin 2-signal transducer and activator of transcription 5 signaling, and protein secretion. The immune-related 11-lncRNA signature may also have regulatory effects on the immunotherapy targets CD40 molecule, CD70 molecule, and CD276 molecule. In conclusion, we constructed a new immune-related 11-lncRNA signature that can stratify the prognoses of patients with ES.

CTNNB1 S37C mutation causing cells proliferation and migration coupled with molecular mechanisms in lung adenocarcinoma

Background

This study aimed to investigate the potential cytological effects and molecular mechanisms of β-catenin (CTNNB1) S37C mutation in lung adenocarcinoma (LUAD).

Methods

CTNNB1 with S37C mutation were transfected into LUAD cell lines. The expression of β-catenin were determined using Western blot. Cell proliferation and migration were detected using cell counting kit-8 (CCK-8) assay and wound healing assay, respectively. Transcriptome sequencing was performed on LUAD cells with CTNNB1 S37C mutation (CTNNB1 mutation group) and LUAD cells without treatment (Control group), followed by the screening of differentially expressed genes (DEGs). Functional enrichment analysis and protein-protein interaction (PPI) analysis were performed for the DEGs. Finally, the expression of key DEGs were validated by quantitative real-time PCR (qRT-PCR).

Results

CTNNB1 with S37C mutation was successful expressed in 2 cell lines. Cells proliferation and migration were significantly promoted in mutation group in comparison with that of Control group (P<0.05). A total of 180 DEGs were revealed between Control and CTNNB1 mutation groups. These DEGs were mainly enriched in extracellular matrix function and nicotine addiction pathway. PPI network contained 51 DEGs and 45 interactions. PTPRD, GNG7 and CNTN1 were hub genes in PPI network with higher degree. CGB5 interacted with PTPRU, while IGFBP3 showed interaction with MMP1. Results of qRT-PCR confirmed the expression of several key DEGs in transcriptome analysis.

Conclusions

CTNNB1 S37C mutation contributed the LUAD cells proliferation and migration. PTPRD, IGFBP-3, MMP1 and PTPRU might play roles in the effect of CTNNB1 S37C mutation in LUAD.