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

2020 ◽  
Vol 29 (3) ◽  
pp. 399-416
Author(s):  
Dan Yang ◽  
Yang He ◽  
Bo Wu ◽  
Ruxi Liu ◽  
Nan Wang ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Score System ◽  
Cerna Network ◽  
Risk Score System

BACKGROUND: Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer worldwide. Until now, the molecular mechanisms underlying LUAD progression have not been fully explained. This study aimed to construct a competing endogenous RNA (ceRNA) network to predict the progression in LUAD. METHODS: Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and mRNAs (DEGs) were identified from The Cancer Genome Atlas (TCGA) database with a |log2FC|> 1.0 and a false discovery rate (FDR) < 0.05. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network, and survival analyses were performed to analyse these DEGs involved in the ceRNA network. Subsequently, the drug-gene interaction database (DGIdb) was utilized to select candidate LUAD drugs interacting with significant DEGs. Then, lasso-penalized Cox regression and multivariate Cox regression models were used to construct the risk score system. Finally, based on the correlations between DELs and DEGs involved in the risk score system, the final ceRNA network was identified. Meanwhile, the GEPIA2 database and immunohistochemical (IHC) results were utilized to validate the expression levels of selected DEGs. RESULTS: A total of 340 DELs, 29 DEMs, and 218 DEGs were selected to construct the initial ceRNA network. Functional enrichment analyses indicated that 218 DEGs were associated with the KEGG pathway terms “microRNAs in cancer”, “pathways in cancer”, “cell cycle”, “HTLV-1 infection”, and the “PI3K-Akt signalling pathway”. K-M survival analysis of all differentially expressed genes involved in the ceRNA network identified 24 DELs, 4 DEMs, and 29 DEGs, all of which were significantly correlated with LUAD progression (P< 0.05). Furthermore, 15 LUAD drugs interacting with 29 significant DEGs were selected. After lasso-penalized Cox regression and multivariate Cox regression modelling, PRKCE, DLC1, LATS2, and DPY19L1 were incorporated into the risk score system, and the results suggested that LUAD patients who had the high-risk score always suffered from a poorer overall survival. Additionally, the correlation coefficients between these 4 DEGs and their corresponding DELs involved in the ceRNA network suggested that there were 2 significant DEL-DEG pairs, NAV2-AS2 – PRKCE (r= 0.430, P< 0.001) and NAV2-AS2 – LATS2 (r= 0.338, P< 0.001). And NAV2-AS2 – mir-31 – PRKCE and NAV2-SA2 – mir-31 – LATS2 were finally identified as ceRNA network involved in the progression of LUAD. CONCLUSIONS: The lncRNA-miRNA-mRNA ceRNA network plays an essential role in predicting the progression of LUAD. These results may improve our understanding and provide novel mechanistic insights to explore prognosis and therapeutic drugs for LUAD patients.

Comprehensive analysis of dysregulated long non-coding RNA – microRNA – mRNA involved in the progression of human lung adenocarcinoma

2020 ◽  
Author(s):  
Dan Yang ◽  
Yang He ◽  
Bo Wu ◽  
Yan Deng ◽  
Ruxi Liu ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Roc Curves ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Score System ◽  
Cerna Network ◽  
Risk Score System

Abstract Background: Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer worldwide. Until now, the molecular mechanisms underlying LUAD progression have not been fully explained. This study aimed to identify a competing endogenous RNA (ceRNA) network in LUAD.Methods: Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and mRNAs (DEGs) were identified from The Cancer Genome Atlas (TCGA) database with a |log2FC| > 1.0 and a false discovery rate (FDR) < 0.05. Then, these DELs, DEMs, and DEGs were used to construct the initial ceRNA network. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network, and survival analyses were performed to analyse these DEGs involved in the ceRNA network. Subsequently, the drug-gene interaction database (DGIdb) was utilized to select candidate LUAD drugs interacting with significant DEGs. Then, lasso-penalized Cox regression and multivariate Cox regression models were used to construct the risk score system. Kaplan-Meier (K-M) survival curves and receiver operating characteristic (ROC) curves were utilized to validate the reliability of the risk score system. Finally, based on the correlations between DELs and DEGs involved in the risk score system, the final ceRNA network was identified. Meanwhile, the GEPIA2 database and immunohistochemical (IHC) results were utilized to validate the expression levels of selected DEGs. GEPIA2 was further used to verify the correlations between DEGs and DELs.Results: A total of 340 DELs, 29 DEMs, and 218 DEGs were selected to construct the initial ceRNA network. Functional enrichment analyses indicated that 218 DEGs were significantly enriched in the GO terms “nucleoplasm”, “transcription factor complex”, “protein binding”, and “metal ion binding”, whereas these DEGs were associated with the KEGG pathway terms “microRNAs in cancer”, “pathways in cancer”, “cell cycle”, “HTLV-1 infection”, and the “PI3K-Akt signalling pathway”. K-M survival analysis of all differentially expressed genes involved in the ceRNA network identified 24 DELs, 4 DEMs, and 29 DEGs, all of which were significantly correlated with LUAD progression (P < 0.05). Furthermore, 15 LUAD drugs interacting with 29 DEGs were selected. After lasso-penalized Cox regression and multivariate Cox regression modelling, 4 DEGs, PRKCE, DLC1, LATS2, and DPY19L1, were incorporated into the risk score system. The area under the curve (AUC) values of the time-dependent ROC curves at 3 years and 5 years were both higher than 0.5. Finally, the correlation coefficients between these 4 DEGs and their corresponding DELs involved in the ceRNA network suggested that there were 2 DEL-DEG pairs, NAV2-AS2 – PRKCE (r = 0.430, P < 0.001) and NAV2-AS2 – LATS2 (r = 0.338, P < 0.001). Considering the previously constructed ceRNA network, NAV2-AS2 – mir-31 – PRKCE and NAV2-SA2 – mir-31 – LATS2 were identified.Conclusions: The lncRNA-miRNA-mRNA ceRNA network plays an essential role in LUAD. These results may improve our understanding and provide novel mechanistic insights to explore diagnostics, tumourigenesis, prognosis, and therapeutic drugs for LUAD patients.

scholarly journals Comprehensive analysis of the putative competing endogenous RNA network in human lung adenocarcinoma

2020 ◽  
Author(s):  
Dan Yang ◽  
Yang He ◽  
Bo Wu ◽  
Yan Deng ◽  
Ruxi Liu ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Roc Curves ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Score System ◽  
Cerna Network ◽  
Risk Score System

Abstract Background Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer worldwide. Until now, the molecular mechanisms underlying LUAD progression have not been fully explained. This study aimed to identify a competing endogenous RNA (ceRNA) network in LUAD. Methods Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and mRNAs (DEGs) were identified from The Cancer Genome Atlas (TCGA) database with a |log2FC| > 1.0 and a false discovery rate (FDR) < 0.05. Then, these DELs, DEMs, and DEGs were used to construct the initial ceRNA network. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network, and survival analyses were performed to analyse these DEGs involved in the ceRNA network. Subsequently, the drug-gene interaction database (DGIdb) was utilized to select candidate LUAD drugs interacting with significant DEGs. Then, lasso-penalized Cox regression and multivariate Cox regression models were used to construct the risk score system. Kaplan-Meier (K-M) survival curves and receiver operating characteristic (ROC) curves were utilized to validate the reliability of the risk score system. Finally, based on the correlations between DELs and DEGs involved in the risk score system, the final ceRNA network was identified. Results A total of 340 DELs, 29 DEMs, and 218 DEGs were selected to construct the initial ceRNA network. Functional enrichment analyses indicated that 218 DEGs were significantly enriched in the GO terms “nucleoplasm”, “transcription factor complex”, “protein binding”, and “metal ion binding”, whereas these DEGs were associated with the KEGG pathway terms “microRNAs in cancer”, “pathways in cancer”, “cell cycle”, “HTLV-1 infection”, and the “PI3K-Akt signalling pathway”. K-M survival analysis of all differentially expressed genes involved in the ceRNA network identified 24 DELs, 4 DEMs, and 29 DEGs, all of which were significantly correlated with LUAD progression (P < 0.05). Furthermore, 15 LUAD drugs interacting with 29 DEGs were selected. After lasso-penalized Cox regression and multivariate Cox regression modelling, 4 DEGs, PRKCE, DLC1, LATS2, and DPY19L1, were incorporated into the risk score system. The area under the curve (AUC) values of the time-dependent ROC curves at 3 years and 5 years were both higher than 0.5. Finally, the correlation coefficients between these 4 DEGs and their corresponding DELs involved in the ceRNA network suggested that there were 2 DEL-DEG pairs, NAV2-AS2 – PRKCE (r = 0.430, P < 0.001) and NAV2-AS2 – LATS2 (r = 0.338, P < 0.001). Considering the previously constructed ceRNA network, NAV2-AS2 – mir-31 – PRKCE and NAV2-SA2 – mir-31 – LATS2 were identified. Conclusions The lncRNA-miRNA-mRNA ceRNA network plays an essential role in LUAD. These results may improve our understanding and provide novel mechanistic insights to explore diagnostics, tumourigenesis, prognosis, and therapeutic drugs for LUAD patients.

scholarly journals Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Lixian Chen ◽  
Zhonglu Ren ◽  
Yongming Cai
Keyword(s):  
Prognostic Factors ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Competing Endogenous Rna ◽  
Cox Regression Analysis ◽  
Cerna Network

Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.

scholarly journals Identification of the Prognostic LncRNA Biomarkers and Comprehensive Analysis of LncRNA-Mediated ceRNA Network for Uterine Corpus Endometrial Carcinoma

2021 ◽  
Author(s):  
Yiran Cai ◽  
Jin Cui ◽  
Huiqun Wu
Keyword(s):  
Regression Analysis ◽  
Endometrial Carcinoma ◽  
Noncoding Rna ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Uterine Corpus ◽  
Cerna Network

Abstract Background Given that long non-coding RNAs (lncRNAs) involved in the tumor initiation or progression of the endometrium and that competing endogenous RNA (ceRNA) plays an important role in increasingly more biological processes, lncRNA-mediated ceRNA is likely to function in the pathogenesis of uterine corpus endometrial carcinoma (UCEC). Our present study aimed to explore the potential molecular mechanisms for the prognosis of UCEC through an lncRNA-mediated ceRNA network. Methods The transcriptome profiles and corresponding clinical profiles of UCEC dataset were retrieved from CPTAC and TCGA databases respectively. Differentially expressed genes (DEGs) in UCEC samples were identified via “Edge R” package. Then, an integrated bioinformatics analysis including functional enrichment analysis, tumor infiltrating immune cell(TIIC) analysis, Kaplan-Meier curve, Cox regression analysis were conducted to analyze the prognostic biomarkers. Results In the CPTAC dataset of UCEC, a ceRNA network comprised of 36 miRNAs, 123 lncRNAs and 124 targeted mRNAs was established, and 8 of 123 prognostic-related DElncRNAs(Differentially Expressed long noncoding RNA) were identified. While in the TCGA dataset, a ceRNA network comprised of 38 miRNAs, 83 lncRNAs and 110 targeted mRNAs was established, and 2 of 83 prognostic-related DElncRNAs were identified. After filtered by risk grouping and Cox regression analysis, 10 prognostic-related lncRNAs including LINC00443, LINC00483, C2orf48, TRBV11-2, MEG-8 were identified. In addition, 33 survival-related DEmRNAs(Differentially Expressed messager RNA) in two ceRNA networks were further validated in the HPA database. Finally, six lncRNA/miRNA/mRNA axes were established to elucidate prognostic regulatory roles in UCEC. Conclusion Several prognostic lncRNAs are identified and prognostic model of lncRNA-mediated ceRNA network is constructed, which promotes the understanding of UCEC development mechanisms and potential therapeutic targets.

scholarly journals Comprehensive analysis of competitive endogenous RNA associated with immune infiltration in lung adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wenjie Chen ◽  
Wen Li ◽  
Zhenkun Liu ◽  
Guangzhi Ma ◽  
Yunfu Deng ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Mast Cells ◽  
Correlation Analysis ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cerna Network ◽  
Monocytes And Macrophages ◽  
Competitive Endogenous Rna

AbstractTo identify the prognostic biomarker of the competitive endogenous RNA (ceRNA) and explore the tumor infiltrating immune cells (TIICs) which might be the potential prognostic factors in lung adenocarcinoma. In addition, we also try to explain the crosstalk between the ceRNA and TIICs to explore the molecular mechanisms involved in lung adenocarcinoma. The transcriptome data of lung adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) database, and the hypergeometric correlation of the differently expressed miRNA-lncRNA and miRNA-mRNA were analyzed based on the starBase. In addition, the Kaplan–Meier survival and Cox regression model analysis were used to identify the prognostic ceRNA network and TIICs. Correlation analysis was performed to analysis the correlation between the ceRNA network and TIICs. In the differently expressed RNAs between tumor and normal tissue, a total of 190 miRNAs, 224 lncRNAs and 3024 mRNAs were detected, and the constructed ceRNA network contained 5 lncRNAs, 92 mRNAs and 10 miRNAs. Then, six prognostic RNAs (FKBP3, GPI, LOXL2, IL22RA1, GPR37, and has-miR-148a-3p) were viewed as the key members for constructing the prognostic prediction model in the ceRNA network, and three kinds of TIICs (Monocytes, Macrophages M1, activated mast cells) were identified to be significantly related with the prognosis in lung adenocarcinoma. Correlation analysis suggested that the FKBP3 was associated with Monocytes and Macrophages M1, and the GPI was obviously related with Monocytes and Macrophages M1. Besides, the LOXL2 was associated with Monocytes and Activated mast cells, and the IL22RA1 was significantly associated with Monocytes and Macrophages M1, while the GPR37 and Macrophages M1 was closely related. The constructed ceRNA network and identified Monocytes, Macrophages M1 and activated Mast cells are all prognostic factors for lung adenocarcinoma. Moreover, the crosstalk between the ceRNA network and TIICs might be a potential molecular mechanism involved.

A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Yongmei Wang ◽  
Guimin Zhang ◽  
Ruixian Wang
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

scholarly journals Identification of Hypoxia-Related Differentially Expressed Genes and Construction of the Clinical Prognostic Predictor in Hepatocellular Carcinoma by Bioinformatic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hao Guo ◽  
Jing Zhou ◽  
Yanjun Zhang ◽  
Zhi Wang ◽  
Likun Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Model ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Clinical Prognosis ◽  
Prognostic Predictor ◽  
Key Genes

Background. Hypoxia closely relates to malignant progression and appears to be prognostic for outcome in hepatocellular carcinoma (HCC). Our research is aimed at mining the hypoxic-related genes (HRGs) and constructing a prognostic predictor (PP) model on clinical prognosis in HCC patients. Methods. RNA-sequencing data about HRGs and clinical data of patients with HCC were obtained from The Cancer Genome Atlas (TCGA) database portal. Differentially expressed HRGs between HCC and para-carcinoma tissue samples were obtained by applying the Wilcox analysis in R statistical software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used for gene functional enrichment analyses. Then, the patients who were asked to follow up for at least one month were enrolled in the following study. Cox proportional risk regression model was applied to obtain key HRGs which related to overall survival (OS) in HCC. PP was constructed and defined, and the accuracy of PP was validated by constructing the signature in a training set and validation set. Connectivity map (CMap) was used to find potential drugs, and gene set cancer analysis (GSCA) was also performed to explore the underlying molecular mechanisms. Results. Thirty-seven differentially expressed HRGs were obtained. It contained 28 upregulated and 9 downregulated genes. After the univariate Cox regression model analysis, we obtained 27 prognosis-related HRGs. Of these, 25 genes were risk factors for cancer, and 2 genes were protective factors. The PP was composed by 12 key genes (HDLBP, SAP30, PFKP, DPYSL4, SLC2A1, HMOX1, PGK1, ERO1A, LDHA, ENO2, SLC6A6, and TPI1). GSCA results showed the overall activity of these 12 key genes in 10 cancer-related pathways. Besides, CMap identified deferoxamine, crotamiton, talampicillin, and lycorine might have effects with HCC. Conclusions. This study firstly reported 12 prognostic HRGs and constructed the model of the PP. This comprehensive research of multiple databases helps us gain insight into the biological properties of HCC and provides deferoxamine, crotamiton, talampicillin, and lycorine as potential drugs to fight against HCC.

Dementia-Predicting Cognitive Risk Score and Its Correlation with Cortical Thickness in Parkinson Disease

2017 ◽  
Vol 44 (3-4) ◽  
pp. 203-212 ◽  
Author(s):  
Byoung Seok Ye ◽  
Seun Jeon ◽  
Jee Hyun Ham ◽  
Jae Jung Lee ◽  
Jong Min Lee ◽  
...  
Keyword(s):  
Parkinson Disease ◽  
Risk Score ◽  
Neuropsychological Tests ◽  
Cox Regression ◽  
Cingulate Cortex ◽  
Data Set ◽  
Score System ◽  
Cognitive Risk ◽  
Dementia Risk ◽  
Risk Score System

Background: We developed a risk score system to predict risks of developing dementia in individual Parkinson disease (PD) patients using baseline neuropsychological tests. Methods: A total of 216 nondemented PD patients underwent a baseline neuropsychological evaluation and were followed up for a mean of 2.7 (±1.1) years. Univariate Cox regression models controlled for age, gender, and education selected neuropsychological tests individually predicting dementia risk. Then, a multivariate Cox regression model combined them into a cognitive risk score system. Cortical areas correlating with cognitive risk score were investigated using a separate MRI data set from 207 nondemented PD patients. Results: Fifty-two patients (23.9%) developed dementia. The univariate Cox regression analyses identified the confrontational naming and semantic fluency tests, frontal/executive function tests, immediate verbal memory test, and visuospatial function test as predicting dementia risk. The calculated cognitive risk score (range 53-188) predicted future dementia with moderate accuracy (integrated area under the curve = 0.79; 95% CI: 0.73-0.85). A higher cognitive risk score correlated with cortical thinning in the right anteromedial temporal cortex, bilateral posterior cingulate cortex, right anterior cingulate cortex, left parahippocampal gyrus, and right superior frontal cortex in a separate MRI data set. Conclusion: The cognitive risk score system is a useful approach to predict the dementia risk among PD patients.

scholarly journals Comprehensive Bioinformatics Analysis of Lipopolysaccharide-Induced Altered Autophagy in Acute Lung Injury and Construction of Underlying Competing Endogenous RNA Regulatory Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Jian-Yu Liu ◽  
Ying-Xiao Jiang ◽  
Meng-Yu Zhang ◽  
Chen Huo ◽  
Yi-Can Yang ◽  
...  
Keyword(s):  
Acute Lung Injury ◽  
Epithelial Cell ◽  
Lung Injury ◽  
Regulatory Mechanism ◽  
Bronchial Epithelial Cell ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Bronchial Epithelial ◽  
Cerna Network

Background. Acute lung injury (ALI) is a fatal syndrome frequently induced by lipopolysaccharide (LPS) released from the bacterial cell wall. LPS could also trigger autophagy of lung bronchial epithelial cell to relieve the inflammation, while the overwhelming LPS would impair the balance of autophagy consequently inducing serious lung injury. Methods. We observed the autophagy variation of 16HBE, human bronchial epithelial cell, under exposure to different concentrations of LPS through western blot, immunofluorescence staining, and electron microscopy. Eight strands of 16HBE were divided into two groups upon 1000 ng/ml LPS stimulation or not, which were sent to be sequenced at whole transcriptome. Subsequently, we analyzed the sequencing data in functional enrichment, pathway analysis, and candidate gene selection and constructed a hsa-miR-663b-related competing endogenous RNA (ceRNA) network. Results. We set a series of concentrations of LPS to stimulate 16HBE and observed the variation of autophagy in related protein expression and autophagosome count. We found that the effective concentration of LPS was 1000 ng/ml at 12 hours of exposure and sequenced the 1000 ng/ml LPS-stimulated 16HBE. As a result, a total of 750 differentially expressed genes (DEGs), 449 differentially expressed lncRNAs (DElncRNAs), 76 differentially expressed circRNAs (DEcircRNAs), and 127 differentially expressed miRNAs (DEmiRNAs) were identified. We constructed the protein-protein interaction (PPI) network to visualize the interaction between DEGs and located 36 genes to comprehend the core discrepancy between LPS-stimulated 16HBE and the negative control group. In combined analysis of differentially expressed RNAs (DERNAs), we analyzed all the targeted relationships of ceRNA in DERNAs and figured hsa-miR-663b as a central mediator in the ceRNA network to play when LPS induced the variation of autophagy in 16HBE. Conclusion. Our research indicated that the hsa-miR-663b-related ceRNA network may contribute to the key regulatory mechanism in LPS-induced changes of autophagy and ALI.

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