Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer
Abstract Background Lung 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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-03-01
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| Series: | BMC Cancer |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12885-022-09281-1 |
| _version_ | 1828317301733064704 |
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| author | Wei Zhang Qian Zhang Li Che Zhefan Xie Xingdong Cai Ling Gong Zhu Li Daishun Liu Shengming Liu |
| author_facet | Wei Zhang Qian Zhang Li Che Zhefan Xie Xingdong Cai Ling Gong Zhu Li Daishun Liu Shengming Liu |
| author_sort | Wei Zhang |
| collection | DOAJ |
| description | Abstract Background Lung 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. Methods The 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. Results Four 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. Conclusion The 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. |
| first_indexed | 2024-04-13T17:26:13Z |
| format | Article |
| id | doaj.art-a7060ec829194a2c93292507f973fb5a |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-04-13T17:26:13Z |
| publishDate | 2022-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-a7060ec829194a2c93292507f973fb5a2022-12-22T02:37:46ZengBMCBMC Cancer1471-24072022-03-0122112310.1186/s12885-022-09281-1Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancerWei Zhang0Qian Zhang1Li Che2Zhefan Xie3Xingdong Cai4Ling Gong5Zhu Li6Daishun Liu7Shengming Liu8Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Jinan UniversityDepartment of Renal Medicine, The First Affiliated Hospital of Jinan UniversityDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Jinan UniversityDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Jinan UniversityDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Jinan UniversityDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Jinan UniversityDepartment of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Zunyi Medical University (The First People’s Hospital of Zunyi)Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Zunyi Medical University (The First People’s Hospital of Zunyi)Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Jinan UniversityAbstract Background Lung 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. Methods The 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. Results Four 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. Conclusion The 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.https://doi.org/10.1186/s12885-022-09281-1Non-small cell lung cancermicroRNABioinformaticsmiRNA-mRNA regulatory network |
| spellingShingle | Wei Zhang Qian Zhang Li Che Zhefan Xie Xingdong Cai Ling Gong Zhu Li Daishun Liu Shengming Liu Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer BMC Cancer Non-small cell lung cancer microRNA Bioinformatics miRNA-mRNA regulatory network |
| title | Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer |
| title_full | Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer |
| title_fullStr | Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer |
| title_full_unstemmed | Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer |
| title_short | Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer |
| title_sort | using biological information to analyze potential mirna mrna regulatory networks in the plasma of patients with non small cell lung cancer |
| topic | Non-small cell lung cancer microRNA Bioinformatics miRNA-mRNA regulatory network |
| url | https://doi.org/10.1186/s12885-022-09281-1 |
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