Candidate lncRNA-microRNA-mRNA networks in predicting non-small cell lung cancer and related prognosis analysis

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.

Active Smoking Induces Aberrations in Digestive Tract Microbiota of Rats

Cigarette smoking could have certain effects on gut microbiota. Some pioneering studies have investigated effects of active smoking on the microbiome in local segments of the digestive tract, while active smoking-induced microbiome alterations in the whole digestive tract have not been fully investigated. Here, we developed a rat model of active smoking and characterized the effects of active smoking on the microbiota within multiple regions along the digestive tract. Blood glucose and some metabolic factors levels, the microbial diversity and composition, relative abundances of taxa, bacterial network correlations and predictive functional profiles were compared between the control group and active smoking group. We found that active smoking induced hyperglycemia and significant reductions in serum insulin and leptin levels. Active smoking induced region-specific shifts in microbiota structure, composition, network correlation and metabolism function along the digestive tract. Our results demonstrated that active smoking resulted in a reduced abundance of some potentially beneficial genera (i.e. Clostridium, Turicibacter) and increased abundance of potentially harmful genera (i.e. Desulfovibrio, Bilophila). Functional prediction suggested that amino acid, lipid, propanoate metabolism function could be impaired and antioxidant activity may be triggered. Active smoking may be an overlooked risk to health through its potential effects on the digestive tract microbiota, which is involved in the cause and severity of an array of chronic diseases.

Identification of lncRNA-miRNA-mRNA Networks Linked to Non-small Lung Cancer Resistance to Inhibitors of Epidermal Growth Factor Receptor

Background: Tyrosine kinase inhibitors that act against epidermal growth factor receptor (EGFR) show strong efficacy against non-small cell lung cancer (NSCLC) involving mutated EGFRs. However, most such patients eventually develop resistance to EGFR-TKIs. Numerous researches have reported that messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs) may be involved in EGFR-TKI resistance, but the comprehensive expression profile and competitive endogenous RNA (ceRNA) regulatory network between mRNAs and ncRNAs in EGFR-TKI resistance of NSCLC are incompletely known. We aimed to define a ceRNA regulatory network linking mRNAs and non-coding RNAs that may mediate this resistance.

Methods: Using datasets GSE83666, GSE75309 and GSE103352 from the Gene Expression Omnibus, we identified long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs differentially expressed between NSCLC cells that were sensitive or resistant to EGFR-TKIs. The potential biological functions of the corresponding differentially expressed genes were analyzed based KEGG pathways. We combined interactions among lncRNAs, miRNAs and mRNAs in the RNAInter database with KEGG pathways to generate transcriptional regulatory ceRNA networks associated with NSCLC resistance to EGFR-TKIs. Kaplan-Meier analysis was used to assess the ability of core ceRNA regulatory sub-networks to predict the progression-free interval and overall survival of NSCLC. The expression of two core ceRNA regulatory sub-networks in NSCLC was validated by quantitative real-time PCR.

Results: We identified 8,989 lncRNAs, 1,083 miRNAs and 3,191 mRNAs that were differentially expressed between patients who were sensitive or resistant to the inhibitors. These DEGs were linked to 968 biological processes and 31 KEGG pathways. Pearson analysis of correlations among the DEGs of lncRNAs, miRNAs and mRNAs identified 12 core ceRNA regulatory sub-networks associated with resistance to EGFR-TKIs. The two lncRNAs ABTB1 and NPTN with the hsa-miR-150–5p and mRNA SERPINE1 were significantly associated with resistance to EGFR-TKIs and survival in NSCLC. These lncRNAs and the miRNA were found to be down-regulated, and the mRNA up-regulated, in a resistant NSCLC cell line relative to the corresponding sensitive cells.

Conclusion: In this study, we provide new insights into the pathogenesis of NSCLC and the emergence of resistance to EGFR-TKIs, based on a lncRNA-miRNA-mRNA network.

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.

Long non-coding RNA anti-differentiation non-coding RNA affects proliferation, invasion, and migration of breast cancer cells by targeting miR-331

We aimed to evaluate the effects of long-chain non-coding RNA (lncRNA) anti-differentiation non-coding RNA (ANCR) on the proliferation, invasion, and migration of breast cancer cells by targeting miR-331. Forty-eight breast cancer and paracancerous tissue samples were collected. LncRNA ANCR expressions in breast cancer and adjacent tissues, human breast cancer cells and mammary epithelial cells, and miR-331 expressions in interfering cell line MDA-MB-231 (MCF-7)-shANCR, negative control MDA-MB-231 (MCF-7)-shNC and blank control MDA-MB-231 (MCF-7) were detected by real-time quantitative PCR. The correlations between lncRNA ANCR expression and clinicopathological characteristics were analyzed. Cell proliferation was detected by MTT and colony formation assays. Invasion and migration were tested by Transwell and scratch assays, respectively. The targeting relationship between ANCR and miR-331 was analyzed using the TargetScan database, and their interaction was studied using a dual-luciferase reporter assay. The expression of lncRNA ANCR in breast cancer tissue was significantly lower than that in adjacent normal tissue (p < 0.05). LncRNA ANCR was lowly expressed in various human breast cancer cell lines, being lowest in high-metastatic cell line (MDA-MB-231HM) (p < 0.05). Silencing lncRNA ANCR significantly enhanced the proliferation and invasion capacities of breast cancer cells, and promoted their tumor formation abilities in nude mice (p < 0.05). ANCR bound miR-331 targetedly, and the former negatively regulated the expression of the latter. LncRNA ANCR is lowly expressed upon breast cancer, and inhibits cell proliferation, invasion, and migration in vitro and in vivo. LncRNA ANCR exerts antitumor effects by targetedly binding miR-331 and then inhibiting its expression.

Immune cell landscape analysis reveals prognostic immune cells and its potential mechanism in squamous cell lung carcinoma

Background

Squamous cell lung carcinoma (LUSC) was closely associated with smoking which was known to have a distant immunosuppression effect. In this study, we aimed to explore the relationship between immune cells and clinical outcomes of LUSC patients with smoking history.

Methods

The immune cell infiltration and RNA expression profiles of LUSC patients were collected from The Cancer Genome Atlas (TCGA). Then, the correlation between immune cell infiltration and clinical characteristics was explored. According to the level of immune cell infiltration, LUSC patients with smoking history were divided into high or low group to screen the differentially expressed lncRNAs and mRNAs. The prediction of target genes was performed by miRanda. Finally, the prognostic value of a certain signature was confirmed in an independent dataset.

Results

Higher abundance of tumor-infiltrating T follicular helper (Tfh) cells together with a lower abundance of resting memory CD4 T cells had been found in LUSC current reformed smokers for ≤15 years and current smoking patients. Moreover, Tfh cell infiltration was not only associated with better overall survival (OS) but also varied from different degrees of TNM stage. Low expression of lncRNA PWRN1 and its potential regulating genes DMRTB1, PIRT, APOBEC1, and ZPBP2 were associated with better OS. Combining PWRN1 and four regulating genes as a signature, patients with higher-level expression of the signature had shorter survival time in not only the TCGA but also in the GEO dataset.

Conclusions

It was found that Tfh cells presented higher infiltration in LUSC current reformed smokers for ≤15 years and current smokers, while resting memory CD4 T cells had lower infiltration. The signature consisting of PWRN1 as well as its predicted targeted mRNAs was dysregulated in different levels of Tfh cell infiltration and might indicate patients' OS.

Clinical significance and functions of miR-203a-3p/AVL9 axis in human non-small-cell lung cancer

Aim: We aimed to investigate the clinical significance and biological function of miR-203a-3p in non-small-cell lung cancer (NSCLC). Methods: The association between miR-203a-3p expression and clinicopathological parameters in NSCLC was assessed by χ² test. Kaplan-Meier method and Cox regression model were applied to evaluate the prognosis value of miR-203a-3p. The biological function of miR-203-3p was explored using CCK-8 and transwell assays. Results: Significantly downregulated miR-203a-3p was associated with TNM stage, lymph node metastasis and poor prognosis. AVL9 was identified as a direct target of miR-203a-3p. Functionally, we found overexpression of miR-203a-3p inhibited cell proliferation, migration and invasion in NSCLC cells by targeting AVL9. Conclusion: Collectively, targeting the miR-203a-3p/AVL9 axis might help to develop useful therapeutic target for NSCLC.

Identification of Prognostic miRNA Signature and Lymph Node Metastasis-Related Key Genes in Cervical Cancer

Background

miRNAs and genes can serve as biomarkers for the prognosis and therapy of cervical tumors whose metastasis into lymph nodes is closely associated with disease progression and poor prognosis.

Methods

R software and Bioconductor packages were employed to identify differentially expressed miRNAs (DEMs) from The Cancer Genome Atlas (TCGA) database. GEO2R detected differentially expressed genes (DEGs) in the GSE7410 dataset originating from the Gene Expression Omnibus (GEO). A Cox proportional hazard regression model was established to select prognostic miRNA biomarkers. Online tools such as TargetScan and miRDB predicted target genes, and overlapping DEGs and target genes were defined as consensus genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) function annotations were performed to discern the potential functions of consensus genes. STRING and Cytoscape screened key genes and constructed a regulatory network.

Results

A combination of four miRNAs (down-regulated miR-502 and miR-145, up-regulated miR-142 and miR-33b) was identified as an independent prognostic signature of cervical cancer. A total of 94 consensus genes were significantly enriched in 7 KEGG pathways and 19 GO function annotations including the cAMP signaling pathway, the plasma membrane, integral components of the plasma membrane, cell adhesion, etc. The module analysis suggested that CXCL12, IGF1, PTPRC CDH5, RAD51B, REV3L, and WDHD1 are key genes that significantly correlate with cervical cancer lymph node metastasis.

Conclusions

This study demonstrates that a four-miRNA signature can be a prognostic biomarker, and seven key genes are significantly associated with lymph node metastasis in cervical cancer patients. These miRNAs and key genes have the potential to be therapeutic targets for cervical cancer. Among them, two miRNAs (miR-502 and miR-33b) and two key genes (PTPRC and CDH5) were first reported to be potential novel biomarkers for cervical cancer. The current study further characterizes the progression of lymph node metastasis and mechanism of cervical tumors; therefore, it provides a novel diagnostic indicator and therapeutic targets for future clinical treatments.