Dataset Information

Screening of T Cell-Related Long Noncoding RNA-MicroRNA-mRNA Regulatory Networks in Non-Small-Cell Lung Cancer.

ABSTRACT: Background: Lung cancer (LC) has the highest mortality rate among all the other types of cancer in the world. T cells are known to be the key factor in inducing the immune response during LC.

Objective: In this study, we aimed to screen and analyze RNAs associated with CD8(+) T cells and activated memory CD4(+) T cells in lung adenocarcinomas, a subtype of non-small-cell lung cancer (NSCLC-LUAD).

Methods: Gene expression RNA-seq data and clinical data of NSCLC-LUAD were downloaded from the XENA database. The data were divided into low scores and high scores based on the Stromal and Immune scores. Then, all the genes were screened for identifying those specifically associated with CD8(+) T cells and activated memory CD4(+) T cells. The screened genes were used for the construction of the protein-protein interaction (PPI) network and for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis along with prognosis analysis. Based on the results of the prognostic analysis, the prognostic-related genes were used to analyze long noncoding (lnc)RNA-micro(mi)RNA-mRNA networks and to predict small chemical molecules.

Results: According to the Immune and Stromal scores, a total of 885 upregulated and 29 downregulated RNAs were identified. A total of 90 differentially expressed genes (DEGs) were found to be related to CD8(+) T immune cells, and 48 DEGs were related to activated memory CD4(+) T cells. GPR174 and CD226 suggested a favorable prognosis. For CD8(+) and activated memory CD4(+) T cells, 112 and 113 PPI edges were obtained, respectively. GPR174 was found to be regulated by hsa-miR-19b-5p and hsa-miR-19b-2-5p, and both of these two miRNAs were regulated by lncRNA PCED1B-AS1. CD226 was regulated by hsa-miR-379-5p, which was in turn regulated by lncRNA RP11-81H14.2.

Conclusion: Our findings provide a deeper understanding of the T cell-related ceRNA regulatory mechanism in NSCLC-LUAD pathogenesis.

SUBMITTER: Duan J

PROVIDER: S-EPMC7684158 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Screening of T Cell-Related Long Noncoding RNA-MicroRNA-mRNA Regulatory Networks in Non-Small-Cell Lung Cancer.

Duan Jinlong J Pan Yuefen Y Yang Xi X Zhong Liping L Jin Yin Y Xu Jiamin J Zhuang Jing J Han Shuwen S

BioMed research international 20201114

<h4>Background</h4>Lung cancer (LC) has the highest mortality rate among all the other types of cancer in the world. T cells are known to be the key factor in inducing the immune response during LC.<h4>Objective</h4>In this study, we aimed to screen and analyze RNAs associated with CD8(+) T cells and activated memory CD4(+) T cells in lung adenocarcinomas, a subtype of non-small-cell lung cancer (NSCLC-LUAD).<h4>Methods</h4>Gene expression RNA-seq data and clinical data of NSCLC-LUAD were downlo ...[more]

PMID: 33274216

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Project description:BackgroundLung cancer is the most common malignant tumor, and it has a high mortality rate. However, the study of miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer (NSCLC) is insufficient. Therefore, this study explored the differential expression of mRNA and miRNA in the plasma of NSCLC patients.MethodsThe Gene Expression Omnibus (GEO) database was used to download microarray datasets, and the differentially expressed miRNAs (DEMs) were analyzed. We predicted transcription factors and target genes of the DEMs by using FunRich software and the TargetScanHuman database, respectively. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for GO annotation and KEGG enrichment analysis of downstream target genes. We constructed protein-protein interaction (PPI) and DEM-hub gene networks using the STRING database and Cytoscape software. The GSE20189 dataset was used to screen out the key hub gene. Using The Cancer Genome Atlas (TCGA) and UALCAN databases to analyze the expression and prognosis of the key hub gene and DEMs. Then, GSE17681 and GSE137140 datasets were used to validate DEMs expression. Finally, the receiver operating characteristic (ROC) curve was used to verify the ability of the DEMs to distinguish lung cancer patients from healthy patients.ResultsFour upregulated candidate DEMs (hsa-miR199a-5p, hsa-miR-186-5p, hsa-miR-328-3p, and hsa-let-7d-3p) were screened from 3 databases, and 6 upstream transcription factors and 2253 downstream target genes were predicted. These genes were mainly enriched in cancer pathways and PI3k-Akt pathways. Among the top 30 hub genes, the expression of KLHL3 was consistent with the GSE20189 dataset. Except for let-7d-3p, the expression of other DEMs and KLHL3 in tissues were consistent with those in plasma. LUSC patients with high let-7d-3p expression had poor overall survival rates (OS). External validation demonstrated that the expression of hsa-miR-199a-5p and hsa-miR-186-5p in peripheral blood of NSCLC patients was higher than the healthy controls. The ROC curve confirmed that the DEMs could better distinguish lung cancer patients from healthy people.ConclusionThe results showed that miR-199a-5p and miR-186-5p may be noninvasive diagnostic biomarkers for NSCLC patients. MiR-199a-5p-KLHL3 may be involved in the occurrence and development of NSCLC.

Dataset Information

Screening of T Cell-Related Long Noncoding RNA-MicroRNA-mRNA Regulatory Networks in Non-Small-Cell Lung Cancer.

Publications

Screening of T Cell-Related Long Noncoding RNA-MicroRNA-mRNA Regulatory Networks in Non-Small-Cell Lung Cancer.

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