The relationship between LncRNAs and lung adenocarcinoma as well as their ceRNA network

BACKGROUND: More and more studies have shown that long non-coding RNA (LncRNA) as a competing endogenous RNA (ceRNA) plays an important role in lung cancer. Therefore, we analyzed the RNA expression profiles of 82 lung cancer patients which were all from Gene Expression Omnibus (GEO). METHODS: Firstly, we used BLASTN (evalue = 1e-10) to annotate the gene sets, performed in-group correction and batched normalization of the four data sets with R. Secondly, we used the limma and sva packages to compare tumor tissues with normal tissues. Then through WGCNA, we obtained the 4 gene modules most related to the trait. RESULTS: We intersected the genes of above 4 modules with the differential expression genes: 28 LncRNAs (up: 5, down: 23) and 265 mRNAs (up:11, down: 254). Based on these genes, we picked up 6 LncRNAs (CCDC39, FAM182A, SRGAP3-AS2, ADAMTS9-AS2, AC020907.2, SFTA1P), then set and visualized the LncRNA-miRNA-mRNA ceRNA network with 12 miRNAs related to 12 mRNAs. Finally, we performed downstream analysis of 265 mRNAs by Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Protein-Protein Interaction (PPI) network. CONCLUSION: After analyzing, we think this study provides a new direction for basic and clinical research related to LAD, and is expected to provide new targets for early diagnosis, prognostic evaluation and clinical treatment of lung cancer.

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Single-Cell Expression Landscape of SARS-CoV-2 Receptor ACE2 and Host Proteases in Normal and Malignant Lung Tissues from Pulmonary Adenocarcinoma Patients

Cancers ◽

10.3390/cancers13061250 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1250

Author(s):

Guangchun Han ◽

Ansam Sinjab ◽

Kieko Hara ◽

Warapen Treekitkarnmongkol ◽

Patrick Brennan ◽

...

Keyword(s):

Lung Cancer ◽

Single Cell ◽

Cancer Patients ◽

Expression Profiles ◽

Lung Cells ◽

Alveolar Type ◽

Specific Expression ◽

Lung Cancer Patients ◽

Normal Tissues ◽

Cell Expression

The novel coronavirus SARS-CoV-2 is the causative agent of the COVID-19 pandemic. Severely symptomatic COVID-19 is associated with lung inflammation, pneumonia, and respiratory failure, thereby raising concerns of elevated risk of COVID-19-associated mortality among lung cancer patients. Angiotensin-converting enzyme 2 (ACE2) is the major receptor for SARS-CoV-2 entry into lung cells. The single-cell expression landscape of ACE2 and other SARS-CoV-2-related genes in pulmonary tissues of lung cancer patients remains unknown. We sought to delineate single-cell expression profiles of ACE2 and other SARS-CoV-2-related genes in pulmonary tissues of lung adenocarcinoma (LUAD) patients. We examined the expression levels and cellular distribution of ACE2 and SARS-CoV-2-priming proteases TMPRSS2 and TMPRSS4 in 5 LUADs and 14 matched normal tissues by single-cell RNA-sequencing (scRNA-seq) analysis. scRNA-seq of 186,916 cells revealed epithelial-specific expression of ACE2, TMPRSS2, and TMPRSS4. Analysis of 70,030 LUAD- and normal-derived epithelial cells showed that ACE2 levels were highest in normal alveolar type 2 (AT2) cells and that TMPRSS2 was expressed in 65% of normal AT2 cells. Conversely, the expression of TMPRSS4 was highest and most frequently detected (75%) in lung cells with malignant features. ACE2-positive cells co-expressed genes implicated in lung pathobiology, including COPD-associated HHIP, and the scavengers CD36 and DMBT1. Notably, the viral scavenger DMBT1 was significantly positively correlated with ACE2 expression in AT2 cells. We describe normal and tumor lung epithelial populations that express SARS-CoV-2 receptor and proteases, as well as major host defense genes, thus comprising potential treatment targets for COVID-19 particularly among lung cancer patients.

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Identification of Key Genes and miRNAs in Osteosarcoma Patients with Chemoresistance by Bioinformatics Analysis

BioMed Research International ◽

10.1155/2018/4761064 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 9

Author(s):

Binbin Xie ◽

Yiran Li ◽

Rongjie Zhao ◽

Yuzi Xu ◽

Yuhui Wu ◽

...

Keyword(s):

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Regulation Of Transcription ◽

Functional Annotations ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Search Tool ◽

Poor Outcomes

Chemoresistance is a significant factor associated with poor outcomes of osteosarcoma patients. The present study aims to identify Chemoresistance-regulated gene signatures and microRNAs (miRNAs) in Gene Expression Omnibus (GEO) database. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) included positive regulation of transcription, DNA-templated, tryptophan metabolism, and the like. Then differentially expressed genes (DEGs) were uploaded to Search Tool for the Retrieval of Interacting Genes (STRING) to construct protein-protein interaction (PPI) networks, and 9 hub genes were screened, such as fucosyltransferase 3 (Lewis blood group) (FUT3) whose expression in chemoresistant samples was high, but with a better prognosis in osteosarcoma patients. Furthermore, the connection between DEGs and differentially expressed miRNAs (DEMs) was explored. GEO2R was utilized to screen out DEGs and DEMs. A total of 668 DEGs and 5 DEMs were extracted from GSE7437 and GSE30934 differentiating samples of poor and good chemotherapy reaction patients. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform GO and KEGG pathway enrichment analysis to identify potential pathways and functional annotations linked with osteosarcoma chemoresistance. The present study may provide a deeper understanding about regulatory genes of osteosarcoma chemoresistance and identify potential therapeutic targets for osteosarcoma.

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Identification of Key miRNAs in the Treatment of Dabrafenib-Resistant Melanoma

BioMed Research International ◽

10.1155/2021/5524486 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Guangyu Gao ◽

Zhen Yao ◽

Jiaofeng Shen ◽

Yulong Liu

Keyword(s):

Drug Resistance ◽

Molecular Mechanism ◽

Target Genes ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

Obvious Association

Dabrafenib resistance is a significant problem in melanoma, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism of drug resistance of dabrafenib and to explore the key genes and pathways that mediate drug resistance in melanoma. GSE117666 was downloaded from the Gene Expression Omnibus (GEO) database and 492 melanoma statistics were also downloaded from The Cancer Genome Atlas (TCGA) database. Besides, differentially expressed miRNAs (DEMs) were identified by taking advantage of the R software and GEO2R. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) and FunRich was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify potential pathways and functional annotations linked with melanoma chemoresistance. 9 DEMs and 872 mRNAs were selected after filtering. Then, target genes were uploaded to Metascape to construct protein-protein interaction (PPI) network. Also, 6 hub mRNAs were screened after performing the PPI network. Furthermore, a total of 4 out of 9 miRNAs had an obvious association with the survival rate ( P < 0.05 ) and showed a good power of risk prediction model of over survival. The present research may provide a deeper understanding of regulatory genes of dabrafenib resistance in melanoma.

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Construction and analysis of the lncRNA-miRNA-mRNA network based on competitive endogenous RNA reveal functional lncRNAs in oral cancer

10.21203/rs.3.rs-18709/v2 ◽

2020 ◽

Author(s):

Junhao Yin ◽

Xiaoli Zeng ◽

Zexin Ai ◽

Miao Yu ◽

Yang’ou Wu ◽

...

Keyword(s):

Oral Cancer ◽

Expression Profiles ◽

Topological Analysis ◽

Enrichment Analysis ◽

Clinical Information ◽

R Package ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Cerna Network ◽

Competitive Endogenous Rna

Abstract Background: A growing evidence suggests that long non-coding RNAs (lncRNAs) can function as a microRNA (miRNA) sponge in various diseases including oral cancer. However, the pathophysiological function of lncRNAs remains unclear. Methods: Based on the competitive endogenous RNA (ceRNA) theory, we constructed a lncRNA-miRNA-mRNA network in oral cancer with the human expression profiles GSE74530 from the Gene Expression Omnibus (GEO) database. We used topological analysis to determine the hub lncRNAs in the regulatory ceRNA network. Then, function enrichment analysis was performed using the clusterProfiler R package. Clinical information was downloaded from The Cancer Genome Atlas (TCGA) database and survival analysis was performed with Kaplan-Meier analysis. Results: A total of 238 potential co-dysregulated competing triples were obtained in the lncRNA-associated ceRNA network in oral cancer, which consisted of 10 lncRNA nodes, 41 miRNA nodes and 122 mRNA nodes. Additionally, we found lncRNA HCG22 exhibiting superior potential as a diagnostic and prognostic marker of oral cancer. Conclusions: Our findings provide novel insights to understand the ceRNA regulation in oral cancer and identify a novel lncRNA as a potential molecular biomarker.

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Integrated analysis of lncRNA-miRNA-mRNA ceRNA network and the potential prognosis indicators in sarcomas

10.21203/rs.3.rs-21667/v1 ◽

2020 ◽

Author(s):

Ling Zhang ◽

Lu Gao ◽

Yu Zhao ◽

Xuelei Ma

Keyword(s):

Survival Analysis ◽

Protein Interactions ◽

Sequence Data ◽

Gene Prediction ◽

Expression Profiles ◽

Enrichment Analysis ◽

Integrated Analysis ◽

Pathway Enrichment Analysis ◽

Tissue Samples ◽

Cerna Network

Abstract The ceRNA network has been demonstrated to play crucial roles in multiple biological processes and the development of neoplasms, which have the potential to become diagnostic and prognosis markers and therapeutic targets. In this work, we comparing the expression profiles between sarcoma identified differentially expressed genes (DEGs), lncRNAs (DELs) and miRNAs (DEMs) in sarcomas and normal tissue samples in GEO datasets. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied to investigate the major functions of the overlapping DEGs. Then, lncRNA-miRNA interactions and miRNA-mRNA interactions were predicted, and a ceRNA regulatory network was constructed. In addition, the mRNAs included in ceRNA network were used to construct the protein-protein interactions network, and the survival analysis of sarcomas was performed according to the biomarkers included in the ceRNA network. According to the RNA sequence data from GEO dataset, 1296 DEGs were identified in sarcoma samples by combining the GO and Pathway enrichment analysis, 338 DELs were discovered after re-annotating the probes, and 36 DEGs were ascertained through intersecting two different expression miRNAs sets. Further, 448 miRNA-mRNA interactions and 454 miRNA-lncRNA interactions were obtained through target gene prediction, and then, we constructed a lncRNA-miRNA-mRNA ceRNA network containing 9 miRNAs, 69 lncRNAs and 113 mRNAs. PPI network showed that the hub up-regulated nodes include IGF1, PRKCB and GNAI3, and the hub down-regulated nodes include AR, CYCS and PPP1CB. Survival analysis revealed that the expression levels of 12 RNAs involved in the ceRNA network were associated with overall survival of sarcoma patients. Our study showed that the ceRNA network in sarcomas based on that lncRNA could serve as ceRNA and discovered the potential indicators for prognosis of sarcoma patients.

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Identification of Three lncRNAs as Potential Predictive Biomarkers of Lung Adenocarcinoma

BioMed Research International ◽

10.1155/2020/7573689 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Donghui Jin ◽

Yuxuan Song ◽

Yuan Chen ◽

Peng Zhang

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Predictive Biomarkers ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Protein Kinase Activity ◽

Ppi Network ◽

Survival Analyses ◽

Normal Tissues ◽

Cerna Network

Background. Lung cancer is the most common cancer and the most common cause of cancer-related death worldwide. However, the molecular mechanism of its development is unclear. It is imperative to identify more novel biomarkers. Methods. Two datasets (GSE70880 and GSE113852) were downloaded from the Gene Expression Omnibus (GEO) database and used to identify the differentially expressed genes (DEGs) between lung cancer tissues and normal tissues. Then, we constructed a competing endogenous RNA (ceRNA) network and a protein-protein interaction (PPI) network and performed gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and survival analyses to identify potential biomarkers that are related to the diagnosis and prognosis of lung cancer. Results. A total of 41 lncRNAs and 805 mRNAs were differentially expressed in lung cancer. The ceRNA network contained four lncRNAs (CLDN10-AS1, SFTA1P, SRGAP3-AS2, and ADAMTS9-AS2), 21 miRNAs, and 48 mRNAs. Functional analyses revealed that the genes in the ceRNA network were mainly enriched in cell migration, transmembrane receptor, and protein kinase activity. mRNAs DLGAP5, E2F7, MCM7, RACGAP1, and RRM2 had the highest connectivity in the PPI network. Immunohistochemistry (IHC) demonstrated that mRNAs DLGAP5, MCM7, RACGAP1, and RRM2 were upregulated in lung adenocarcinoma (LUAD). Survival analyses showed that lncRNAs CLDN10-AS1, SFTA1P, and ADAMTS9-AS2 were associated with the prognosis of LUAD. Conclusion. lncRNAs CLDN10-AS1, SFTA1P, and ADAMTS9-AS2 might be the biomarkers of LUAD. For the first time, we confirmed the important role of lncRNA CLDN10-AS1 in LUAD.

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Construction of ceRNA Coexpression Network and Screening of Molecular Targets in Colorectal Cancer

Disease Markers ◽

10.1155/2020/2860582 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Zhao Hui ◽

Wang Zhanwei ◽

Yang Xi ◽

Liu Jin ◽

Zhuang Jing ◽

...

Keyword(s):

Colorectal Cancer ◽

Protein Interactions ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Differentially Expressed ◽

Normal Tissues ◽

Cerna Network ◽

Cancer Tissues

Objective. To screen some RNAs that correlated with colorectal cancer (CRC). Methods. Differentially expressed miRNAs, lncRNAs, and mRNAs between cancer tissues and normal tissues in CRC were identified using data from the Gene Expression Omnibus (GEO) database. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interactions (PPIs) were performed to do the functional enrichment analysis. And a lncRNA-miRNA-mRNA network was constructed which correlated with CRC. RNAs in this network were subjected to analyze the relationship with the patient prognosis. Results. A total of 688, 241, and 103 differentially expressed genes (diff-mRNA), diff-lncRNA, and diff-miRNA were obtained between cancer tissues and normal tissues. A total of 315 edges were obtained in the ceRNA network. lncRNA RP11-108K3.2 and mRNA ONECUT2 correlated with prognosis. Conclusion. The identified RNAs and constructed ceRNA network could provide great sources for the researches of therapy of the CRC. And the lncRNA RP11-108K3.2 and mRNA ONECUT2 may serve as a novel prognostic predictor of CRC.

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Integrated analysis of lncRNA–miRNA–mRNA ceRNA network and the potential prognosis indicators in sarcomas

BMC Medical Genomics ◽

10.1186/s12920-021-00918-x ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Lu Gao ◽

Yu Zhao ◽

Xuelei Ma ◽

Ling Zhang

Keyword(s):

Gene Expression ◽

Survival Analysis ◽

Gene Prediction ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Integrated Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

Cerna Network

Abstract Background Competitive endogenous RNA (ceRNA) networks have revealed a new mechanism of interaction between RNAs, and play crucial roles in multiple biological processes and development of neoplasms. They might serve as diagnostic and prognosis markers as well as therapeutic targets. Methods In this work, we identified differentially expressed mRNAs (DEGs), lncRNAs (DELs) and miRNAs (DEMs) in sarcomas by comparing the gene expression profiles between sarcoma and normal muscle samples in Gene Expression Omnibus (GEO) datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were applied to investigate the primary functions of the overlapped DEGs. Then, lncRNA-miRNA and miRNA-mRNA interactions were predicted, and the ceRNA regulatory network was constructed using Cytoscape software. In addition, the protein–protein interaction (PPI) network and survival analysis were performed. Results A total of 1296 DEGs were identified in sarcoma samples by combining the GO and KEGG enrichment analyses, 338 DELs were discovered after the probes were reannotated, and 36 DEMs were ascertained through intersecting two different expression miRNAs sets. Further, through target gene prediction, a lncRNA–miRNA–mRNA ceRNA network that contained 113 mRNAs, 69 lncRNAs and 29 miRNAs was constructed. The PPI network identified the six most significant hub proteins. Survival analysis revealed that seven mRNAs, four miRNAs and one lncRNA were associated with overall survival of sarcoma patients. Conclusions Overall, we constructed a ceRNA network in sarcomas, which might provide insights for further research on the molecular mechanism and potential prognosis biomarkers.

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Differentially expressed genes SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma

10.21203/rs.3.rs-24106/v1 ◽

2020 ◽

Author(s):

Sheng Chang ◽

Yang Cao

Keyword(s):

Signaling Pathway ◽

Differentially Expressed Genes ◽

Osteogenic Sarcoma ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

Potential Biomarkers

Abstract Background: Osteosarcoma (osteogenic sarcoma, OS) is a primary cause of morbidity and mortality and is associated with poor prognosis in the field of orthopedic. Globally, rates of OS are highest among 15 to 25-year-old adolescent. However, the mechanism of gene regulation and signaling pathway is unknown. Material and Methods: GSE9508, including 34 OS samples and 5 non-malignant bone samples, was gained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were picked out by GEO2R online R soft tool. Furthermore, the protein-protein interaction (PPI) network between the DEGs was molded utilizing STRING online software. Afterward, PPI network of DEGs was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out on DAVID online tool and visualized via cytoscape software. Subsequently, module analysis of PPI was performed by using MCODE app. What’s more, prognosis-related genes were screened by using online databases including GEPIA, UALCAN and cBioPortal databases. Results: Totally, 671 DEGs were picked out, including 501 up-regulated genes and 170 down-regulated genes. Moreover, 22 hub genes were identified to be significantly expressed in PPI network (16 up-regulated and 6 down-regulated). We found that spliceosome signaling pathway may provide a potential target in OS. Furthermore, on the basis of common crucial pathway, PRPF38A and SNRPC were closely associated with spliceosome. Conclusion: This study showed that SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma.

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Identification of Potential Core Genes and Potential Drugs in Vestibular Schwannoma

10.21203/rs.3.rs-715254/v1 ◽

2021 ◽

Author(s):

Junqiang Yan ◽

Anran Liu ◽

Jiarui Huang ◽

Jiannan Wu ◽

Hongxia Ma ◽

...

Keyword(s):

Vestibular Schwannoma ◽

Protein Interactions ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Pathway Enrichment Analysis ◽

Data Sets ◽

Protein Protein Interactions ◽

Potential Core ◽

Normal Tissues ◽

Therapeutic Drugs

Abstract Vestibular schwannoma is a common intracranial benign tumor, but the current drug treatment effect is not obvious. Surgical treatment can usually lead to residual problems such as nerve damage. Therefore, there is no clear molecular target to facilitate better clinical treatment. We analyzed three microarray data sets (GSE39645, GSE54934 and GSE108524) derived from the Gene Expression Omnibus database (GEO). The GEO2R was used to screen for the differentially expressed genes (DEG) between vestibular schwannomas and normal tissues. The ontology function of genes and genome pathway enrichment analysis were performed using annotation, visualizative and comprehensive discovery databases to identify the pathways and functional annotation of DEGs. The protein-protein interactions of these DEGs were analyzed by searching the interaction gene database and visualized by Cytoscape software. The potential therapeutic drugs for vestibular schwannoma were searched by online gene drug interaction analysis.A total of 226 up-regulated and 148 down-regulated DEGs were identified. Among them, ten hub genes with high connectivity (EGFR, PPARG, CD86, CSF1R, SPP1, CDH2, CCND1, CAV1, CYBB and NCAM1) were selected as the central genes that may be closely related to the pathogenesis of vestibular schwannoma, which can be potential treatment targets of vestibular schwannoma. Afatinib and osimerinib may be potential therapeutic drugs.

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