Cargando…

Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Wei, Zhang, Qian, Che, Li, Xie, Zhefan, Cai, Xingdong, Gong, Ling, Li, Zhu, Liu, Daishun, Liu, Shengming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939143/
https://www.ncbi.nlm.nih.gov/pubmed/35313857
http://dx.doi.org/10.1186/s12885-022-09281-1
_version_ 1784672683488706560
author Zhang, Wei
Zhang, Qian
Che, Li
Xie, Zhefan
Cai, Xingdong
Gong, Ling
Li, Zhu
Liu, Daishun
Liu, Shengming
author_facet Zhang, Wei
Zhang, Qian
Che, Li
Xie, Zhefan
Cai, Xingdong
Gong, Ling
Li, Zhu
Liu, Daishun
Liu, Shengming
author_sort Zhang, Wei
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09281-1.
format Online
Article
Text
id pubmed-8939143
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-89391432022-03-23 Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer Zhang, Wei Zhang, Qian Che, Li Xie, Zhefan Cai, Xingdong Gong, Ling Li, Zhu Liu, Daishun Liu, Shengming BMC Cancer Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09281-1. BioMed Central 2022-03-21 /pmc/articles/PMC8939143/ /pubmed/35313857 http://dx.doi.org/10.1186/s12885-022-09281-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhang, Wei
Zhang, Qian
Che, Li
Xie, Zhefan
Cai, Xingdong
Gong, Ling
Li, Zhu
Liu, Daishun
Liu, Shengming
Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer
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 Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939143/
https://www.ncbi.nlm.nih.gov/pubmed/35313857
http://dx.doi.org/10.1186/s12885-022-09281-1
work_keys_str_mv AT zhangwei usingbiologicalinformationtoanalyzepotentialmirnamrnaregulatorynetworksintheplasmaofpatientswithnonsmallcelllungcancer
AT zhangqian usingbiologicalinformationtoanalyzepotentialmirnamrnaregulatorynetworksintheplasmaofpatientswithnonsmallcelllungcancer
AT cheli usingbiologicalinformationtoanalyzepotentialmirnamrnaregulatorynetworksintheplasmaofpatientswithnonsmallcelllungcancer
AT xiezhefan usingbiologicalinformationtoanalyzepotentialmirnamrnaregulatorynetworksintheplasmaofpatientswithnonsmallcelllungcancer
AT caixingdong usingbiologicalinformationtoanalyzepotentialmirnamrnaregulatorynetworksintheplasmaofpatientswithnonsmallcelllungcancer
AT gongling usingbiologicalinformationtoanalyzepotentialmirnamrnaregulatorynetworksintheplasmaofpatientswithnonsmallcelllungcancer
AT lizhu usingbiologicalinformationtoanalyzepotentialmirnamrnaregulatorynetworksintheplasmaofpatientswithnonsmallcelllungcancer
AT liudaishun usingbiologicalinformationtoanalyzepotentialmirnamrnaregulatorynetworksintheplasmaofpatientswithnonsmallcelllungcancer
AT liushengming usingbiologicalinformationtoanalyzepotentialmirnamrnaregulatorynetworksintheplasmaofpatientswithnonsmallcelllungcancer