Dataset Information

Analysis of differential expression profile of miRNA in peripheral blood of patients with lung cancer.

ABSTRACT:

Purpose

To identify potential molecular targets for lung cancer intervention and diagnosis, we analyzed the differential miRNA expression of peripheral blood between lung cancer patients and healthy controls.

Methods

Three pairs of cases' and controls' peripheral blood samples were evaluated for miRNA expression by microarray. 12 miRNAs were selected for RT-PCR validation and target genes prediction. In addition, 4 miRNAs were selected for future validation by RT-PCR in a large sample of 145 cases and 55 frequency-matched healthy controls.

Results

A total of 338 differentially expressed miRNAs were screened and identified by microarray. According to the fold changes, the top ten upregulated miRNAs were hsa-miR-124-3p, hsa-miR-379-5p, hsa-miR-3655, hsa-miR-450b-5p, hsa-miR-29a-5p, hsa-miR-200a-3p, hsa-miR-542-3p, hsa-miR-138-5p, hsa-miR-219a-2-3p, and hsa-miR-4701-3p, and the top ten downregulated miRNAs were hsa-miR-34c-5p, hsa-miR-135a-5p, hsa-miR-132-3p, hsa-miR-3178, hsa-miR-4449, hsa-miR-4999-3p, hsa-miR-1246, hsa-miR-4424, hsa-miR-1252-5p, and hsa-miR-24-2-5p. RT-PCR verification of the 12 miRNAs revealed that 5 of 8 upregulated miRNAs, 2 of 4 downregulated miRNAs showed a significant difference between the cases and controls (P < .05). A large number of target genes and their functional set showed overlapping among the 453 predicted target genes of the 12 miRNAs (P < .01). RT-PCR in the large sample confirmed the significant differential expression level of hsa-miR-29a-5p, hsa-miR-135a-5p, hsa-miR-542-3p, and hsa-miR-4491 between cases and controls (P < .05), and three of these microRNA, except hsa-miR-29a-5p, were significant after Bonferroni correction for adjustment of multiple comparisons.

Conclusion

There was a significant difference in miRNAs expression in the peripheral blood between lung cancer patients and healthy controls, and 4 miRNAs were validated by a large-size sample.

SUBMITTER: He Q

PROVIDER: S-EPMC6868404 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analysis of differential expression profile of miRNA in peripheral blood of patients with lung cancer.

He Qingfang Q Fang Yirong Y Lu Feng F Pan Jin J Wang Lixin L Gong Weiwei W Fei Fangrong F Cui Jingjie J Zhong Jieming J Hu Ruying R Liang Mingbin M Fang Le L Wang Hao H Yu Min M Zhang Zuo-Feng ZF

Journal of clinical laboratory analysis 20190920 9

<h4>Purpose</h4>To identify potential molecular targets for lung cancer intervention and diagnosis, we analyzed the differential miRNA expression of peripheral blood between lung cancer patients and healthy controls.<h4>Methods</h4>Three pairs of cases' and controls' peripheral blood samples were evaluated for miRNA expression by microarray. 12 miRNAs were selected for RT-PCR validation and target genes prediction. In addition, 4 miRNAs were selected for future validation by RT-PCR in a large sa ...[more]

PMID: 31541491

Similar Datasets

Project description:BACKGROUND:The current diagnostic methods and treatments still fail to lower the incidence of anthracycline-induced cardiotoxicity effectively. In this study, we aimed to (1) analyze the cardiotoxicity-related genes after breast cancer chemotherapy in gene expression database and (2) carry out bioinformatic analysis to identify cardiotoxicity-related abnormal expressions, the biomarkers of such abnormal expressions, and the key regulatory pathways after breast cancer chemotherapy. METHODS:Cardiotoxicity-related gene expression data (GSE40447) after breast cancer chemotherapy was acquired from the Gene Expression Omnibus (GEO) database. The biomarker expression data of women with chemotherapy-induced cardiotoxicity (group A), chemotherapy history but no cardiotoxicity (group B), and confirmatory diagnosis of breast cancer but normal ejection fraction before chemotherapy (group C) were analyzed to obtain the mRNA with differential expressions and predict the micro RNAs (miRNAs) regulating the differential expressions. The miRanda formula and functional enrichment analysis were used to screen abnormal miRNAs. Then, the Gene Ontology (GO) analysis was adapted to further screen the miRNAs related to cardiotoxicity after breast cancer chemotherapy. RESULT:The data of differential analysis of biomarker expression of groups A, B, and C using the GSE40447-related gene expression profile database showed that there were 30 intersection genes. The differentially expressed mRNAs were predicted using the miRanda and Target Scan software, and a total of 2978 miRNAs were obtained by taking the intersections. Further, the GO analysis and targeted regulatory relationship between miRNA and target genes were used to establish miRNA-gene interaction network to screen and obtain seven cardiotoxicity-related miRNAs with relatively high centrality, including hsa-miR-4638-3p, hsa-miR-5096, hsa-miR-4763-5p, hsa-miR-1273 g-3p, hsa-miR6192, hsa-miR-4726-5p and hsa-miR-1273a. Among them, hsa-miR-4638-3p and hsa-miR-1273 g-3p had the highest centrality. The PCR verification results were consistent with those of the chip data. There are differentially expressed miRNAs in the peripheral blood of breast cancer patients with anthracycline cardiotoxicity. Among them, hsa-miR-4638-3p and hsa-miR-1273 g-3p are closely associated with the onset of anthracycline cardiotoxicity in patients with breast cancer. The signaling pathway is mainly concentrated in TGF-β signaling pathway and adhesion signaling pathway. CONCLUSIONS:Changes in expression of hsa-miR-4638-3p and hsa-miR-1273 g-3p may contribute to the detection of anthracyclines induced cardiac toxicity, and their potential function may be related to TGF-β signaling pathway and adhesion signaling pathway.

Project description:ObjectiveTo identify new biomarkers and molecular pathogenesis of Down syndrome (DS) by analyzing differentially expressed miRNAs in the placentas and their biological pathways.MethodsWhole transcriptome sequencing was used to identify the differentially expressed miRNAs in DS (n=3) and normal placental samples (n=3) diagnosed by prenatal diagnosis. The target genes were predicted using miRWalk, Targetscan and miRDB, and GO and KEGG pathway analyses were performed for gene enrichment studies.ResultsWe identified a total of 82 differentially expressed miRNAs in the placental tissues of DS, including 29 up-regulated miRNAs (fold change ≥2, P < 0.05) and 15 down-regulated miRNAs (fold change ≥2, P < 0.05), among which 10 miRNAs with relatively high expression abundance were selected for further analysis, including 4 up-regulated and 6 down-regulated miRNAs. These selected miRNAs shared the common target genes BTBD3 and AUTS2, both of which were associated with neurodevelopment. GO analysis showed that the target genes of the selected miRNAs were mainly enriched in protein binding, hydrolytic enzymes, metal ion binding protein combining, transferase activity, nucleotide, cytoplasmic constituents, nucleus composition, transcriptional regulation, RNA metabolism regulation, DNA-dependent RNA polymerase Ⅱ promoter transcriptional regulation, eye development, and sensory organ development. KEGG enrichment analysis showed that the target genes of these differentially expressed miRNAs were involved in the signaling pathways including tumor-related signaling pathway, PI3K-Akt signaling pathway, Ras signaling pathway, Rap1 signaling pathway, cytoskeletal regulatory signaling pathway, purine metabolization-related signaling pathway and P53 signaling pathway.ConclusionThe differentially expressed miRNAs may play important roles in placental damage and pregnancy pathology in DS and provide clues for the prevention and treatment of mental retardation-related diseases.

Dataset Information

Analysis of differential expression profile of miRNA in peripheral blood of patients with lung cancer.

Purpose

Methods

Results

Conclusion

Publications

Analysis of differential expression profile of miRNA in peripheral blood of patients with lung cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets