Open Access

Oncogenic role of miR‑183‑5p in lung adenocarcinoma: A comprehensive study of qPCR, in vitro experiments and bioinformatic analysis

  • Authors:
    • Rong‑Quan He
    • Li Gao
    • Jie Ma
    • Zu‑Yun Li
    • Xiao‑Hua Hu
    • Gang Chen
  • View Affiliations

  • Published online on: May 9, 2018     https://doi.org/10.3892/or.2018.6429
  • Pages: 83-100
  • Copyright: © He et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Despite the fact that previous studies have reported the aberrant expression of miR‑183‑5p in lung adenocarcinoma (LUAD), the oncogenic role of miR‑183‑5p in LUAD and its underlying mechanisms have remained elusive. Hence, we attempted to elucidate the clinicopathological significance of miR‑183‑5p expression in LUAD and identify the biological function of miR‑183‑5p in LUAD in this study. Meta‑analysis of Gene Expression Omnibus (GEO) data, data mining of The Cancer Genome Atlas (TCGA) and real‑time quantitative polymerase chain reaction (qPCR) were performed to evaluate the clinicopathological significance of miR‑183‑5p in LUAD. Then, the effect of miR‑183‑5p on cell growth in LUAD was assessed by in vitro experiments. Additionally, the target genes of miR‑183‑5p were identified via miRWalk v.2.0 and TCGA. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Disease Ontology (DO) analysis were further carried out for the target genes. The targetability between target genes in key KEGG pathways and miR‑183‑5p was validated by independent samples t‑test, Pearson's correlation test and immunohistochemistry results from the Human Protein Atlas (HPA). According to the results, miR‑183‑5p was overexpressed in LUAD and exhibited significant diagnostic value. Moreover, miR‑183 expression was associated with tumor progression in the TCGA data. In vitro experiments revealed the positive influence of miR‑183‑5p on cell viability and proliferation as well as the negative effect of miR‑183‑5p on caspase‑3/7 activity in LUAD, which supports the finding that target genes of miR‑183‑5p are mainly enriched in gene pathways containing cell adhesion molecules (CAMs) and gene pathways important in cancer. Therefore, we conclude that miR‑183‑5p acts as an oncogene in LUAD and participates in the pathogenesis of LUAD via the interaction networks of its target genes.

Introduction

Lung cancer (LC) ranks highly on the list of lethal cancers, with 222,500 estimated new cases and 155,870 estimated deaths in the USA in 2017. Despite improvements in diagnosis and standard therapy, the prognosis of LC patients remains poor, with a 5-year survival rate of only 18% (1). Non-small cell lung cancer (NSCLC) accounts for a large proportion of all LC cases, and lung adenocarcinoma (LUAD) is the predominant histological type of NSCLC (24). An early diagnosis and effective therapy of LUAD is beneficial for the improved survival of LUAD patients. Thus, an understanding of the molecular mechanisms of LUAD and a valid biomarker for LUAD are urgently needed.

MicroRNAs (miRNAs) are small non-coding RNAs that suppress the translation or initiate the degradation of target mRNAs by perfectly or imperfectly binding to their 3′-untranslated regions (5). Currently, there is mounting evidence that miRNAs have vital functions in the occurrence and progression of a wide variety of cancers, with essential roles in diverse biological processes such as the proliferation, apoptosis, differentiation, drug resistance and metastasis of cancer cells (69). miR-183-5p, which belongs to the miR-183 family, is located at chromosome 7q32 with a high level of homogeneity (10). Previous studies have shown that aberrantly expressed miR-183-5p is involved in the progression of a wide variety of human cancers, including epithelial ovarian cancer, breast cancer and cervical cancer (1113). Therefore, miR-183-5p has the potential to be a promising target for the effective diagnosis and therapy of cancers.

Several studies have also focused on miR-183-5p in LUAD. miR-183-5p was reported to be overexpressed in LUAD and to correlate with the tumor progression as well as poor prognosis of LUAD (14). In a study of Zhu et al, miR-183-5p exhibited a significant ability to distinguish between non-invasive and invasive LUAD (15). An in vitro study by Zhu et al declared that miR-183-5p targets PTPN4 to promote the migratory and invasive capacity of LUAD cells (16). Despite these previous findings, there is an overall lack of evaluation of the clinicopathological significance of miR-183-5p in LUAD, and the molecular mechanisms underlying its role remain unclear. Therefore, the present study aimed to explore miR-183-5p expression in LUAD and the diagnostic as well as prognostic significance of miR-183-5p in LUAD using the combined methods of Gene Expression Omnibus (GEO) meta-analysis, data retrieval from The Cancer Genome Atlas (TCGA) and real-time quantitative polymerase chain reaction (qPCR). We also endeavored to clarify the molecular function of miR-183-5p in LUAD using in vitro experiments and bioinformatic analysis of the target genes.

Materials and methods

Investigation of miR-183-5p expression in LUAD based on GEO data
Searching strategies and inclusion or exclusion criteria

miR-183-5p expression in LUAD and non-cancer tissue data from GEO microarray chips were acquired from GEO (http://www.ncbi.nlm.nih.gov/geo/) using the following search strategies: (miRNA OR miR) AND (lung OR pulmonary) AND (cancer OR neoplasm).

The preliminary retrieval results were first selected by scanning the title and abstract. The reserved studies after the initial selection were further screened according to the established inclusion and exclusion criteria. Studies that met the following characteristics were eligible for the meta-analysis: i) the study included LUAD and non-cancer tissue samples; ⅱ) the study provided expression values for miR-183-5p in LUAD and non-cancer tissues; and ⅲ) the tissue samples in the study originated from humans. Studies were excluded if i) the study included tissue samples of only either LUAD or non-cancer tissues; ⅱ) the study contained insufficient data on miR-183 expression in the tissue samples; and ⅲ) the tissue samples in the study were not from humans.

Data extraction

The following information was extracted from the included GEO datasets to calculate an overall standardized mean difference (SMD): GSE ID, first author, publication year, country, experimental type, sample type, platform, number (N) of cases in cancer group, mean (M) ± standard deviation (SD) of miR-183-5p expression in cancer group, N of cases in the non-cancer group, and M±SD of miR-183-5p expression in the non-cancer group. To obtain the sensitivity, specificity and Youden index for the data elements, a summary receiver operating characteristic (SROC) curve was created, and a receiver operating characteristic (ROC) curve was created for each GSE dataset by SPSS v.22.0. True positivity (TP), false positivity (FP), false negativity (FN) and true negativity (TN) of each GSE dataset were calculated according to the maximum Youden index and the corresponding cut-off value.

GEO meta-analysis

The effect sizes of the selected studies were aggregated as the SMD with 95% confidence intervals (95% CI). Chi-square tests of Q and the I2 statistic were employed for the evaluation of heterogeneity between included studies. A P<0.05 or an I2>50% was considered as significant heterogeneity, which implied that a random-effect model should be applied to pool the effect sizes. Otherwise, a fixed-effect model was utilized to pool the effect sizes when P>0.05 or I2<50% (17). Then, subgroup analysis was used to detect the source of heterogeneity based on the characteristics of the studies. The impact of a single study on the overall pooling results was evaluated by sensitivity analysis through omission of each study one at a time. Additionally, Begg's and Egger's tests were carried out to confirm whether publication bias existed in the studies.

To assess the overall diagnostic value of all the included GSE datasets as well as the diagnostic value of plasma miRNA, Meta-DiSc v.1.4 was employed to plot SROC curves based on the TP, FP, FN and TN value of all studies. Value for area under curve (AUC) value ranging from 0.5 to 1 was indicative of a diagnostic capacity from poor to superior, respectively (18).

TCGA data excavation

The clinicopathological significance of miR-183-5p expression in LUAD was further analyzed with expression data of miR-183-5p precursor miR-183 in LUAD downloaded from TCGA (https://cancergenome.nih.gov/). All statistical analysis of TCGA data was performed in SPSS v.22.0 and the expression value of miR-183 was presented in the form of M±SD. The difference of miR-183 expression in two different groups of clinicopathological parameters was evaluated by independent samples t-test. When there were three or more groups of clinicopathological parameters, the distribution difference of miR-183 expression was assessed by Kruskal-Wallis (K-W) test. The diagnostic significance of miR-183 in LUAD was estimated by ROC curves and the implications of AUC for diagnostic ability of miR-183 were the same as stated above. Additionally, all the LUAD patients were divided based on the average of miR-183 expression value, and Kaplan-Meier survival curves were utilized to measure the influence of miR-183 on the prognosis of LUAD patients. P<0.05 was considered significant.

Validation of the clinicopathological significance of miR-183-5p in LUAD using qPCR
Patients

A total of 101 LUAD tissues and paired non-cancer tissues (56 males and 45 females) processed with formalin fixation and paraffin embedding were collected from the First Affiliated Hospital of Guangxi Medical University (Nanning, China) during the period from January 2012 to February 2014. The study was approved by the Research Ethics Committee of the First Affiliated Hospital of Guangxi Medical University. Signed informed consents were acquired from all the LUAD patients prior to their involvement in this study.

qPCR

The extraction and normalization of RNA as well as qPCR was carried out as described in previous studies (19,20). The coding sequence of miR-183-5p as identified through TaqMan® MicroRNA Assays (cat. no. 4427975-000416; Applied Biosystems: Thermo Fisher Scientifi, Inc., Grand Island, NY, USA) was 000417, UGUAAACAUCCUCGA CUGGAAG. A TaqMan® MicroRNA Reverse Transcription kit (4366596; Applied Biosystems: Thermo Fisher Scientifi, Inc.) was applied to perform the RT reactions in a 10-µl volume. An Applied Biosystems PCR 7900 instrument was utilized to conduct the PCR. All the experiments including blank controls were carried out in triplicate. The difference in miR-183-5p expression between LUAD and peripheral non-cancer tissues was evaluated with the method of 2−ΔCq.

Statistical analysis for qPCR

The statistical analysis for qPCR data was conducted in SPSS v.22.0. miR-183-5p expression in LUAD and non-cancer tissues was compared by paired samples t-tests and the subsequent analysis of the clinicopathological significance of miR-183-5p expression in LUAD was carried out as described in the TCGA data excavation section.

Integrated meta-analysis

To achieve an overall assessment of the clinicopathological significance of miR-183-5p in LUAD, we pooled all the expression and diagnostic data of miR-183-5p from included GSE datasets, extracted TCGA data and qPCR results to conduct an integrated meta-analysis. SMD and SROC were calculated from all the pooled studies. Then, subgroup analysis, sensitivity analysis and detection of publication bias were performed to identify the source of heterogeneity, as described above.

In vitro experiment
Cell transfections and qPCR

Three human NSCLC cell lines: H460, A-549 and H1299 were acquired from American Type Culture Collection (ATCC, Manassas, VA, USA) and were cultured in Dulbecco's modified Eagle's medium (DMEM) (Gibco; Thermo Fisher Scientific, Inc., Grand Island, NY, USA) containing 10% fetal bovine serum and penicillin-streptomycin at 37°C under a humidified atmosphere of 5% CO2. Each of the in vitro experiments was performed 3 times. Before transfection, LUAD cells were plated in 96-well plates at 2.5×103 cells/well and maintained at 37°C for 24 h. Blank control, negative mimic control, miR-183-5p mimic, negative inhibitor control and miR-183-5p inhibitor (Ambion; Thermo Fisher Scientific, Inc., Carlsbad, CA, USA) were transfected in LUAD cell lines at a final concentration of 60 nmol/l with Lipofectamine 2000 following the manufacturer's instructions. The concentration for transfections was determined based on previous studies (21,22). miR-183-5p expression was detected with qPCR in Applied Biosystems PCR 7900 system as stated previously (2326).

Effect of miR-183-5p on the biological behaviors of LUAD cells

The impact of miR-183-5p on the proliferation, viability and apoptosis of LUAD cells was measured by fluorometric resorufin viability assays, MTS assays and Apo-ONE Homogeneous Caspase-3/7 assays, as described in previous studies (25,26).

Statistical analysis of in vitro experiments

Statistical analysis was carried out in SPSS v.22.0. All data are expressed in the form of M±SD. Two-way analysis of variance (ANOVA) and Bonferroni post-tests were used for the comparisons among groups. We defined P<0.05 as statistically significant.

Network analysis of the target genes
Acquisition of target genes

Target genes of miR-183-5p came from two sources: TCGA data and miRWalk v.2.0. Downregulated genes in LUAD were downloaded from TCGA and subsequently processed with an edgeR software package. Genes with a false discovery rate (FDR) value <0.05 were selected as potential target genes. Apart from TCGA, miRWalk v.2.0 was also applied to the data to predict the target genes. Genes that appeared in at least four of the 12 software programs from miRWalk v.2.0 were considered as the possible target genes. Then, overlapping genes in the recorded lists of potential target genes from TCGA and miRWalk v.2.0 were regarded as the reliable target genes of miR-183-5p in LUAD.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis

GO analysis and KEGG pathway analysis from the online tool Database for Annotation, Visualization and Integration Discovery (DAVID) were applied to analyze functional enrichment of the target genes as stated previously (27).

Disease Ontology (DO) analysis

The clustering of the target genes in human diseases was investigated by DO analysis, which was performed by R package: clusterProfiler. DO terms with both P and Q-value <0.05 were selected as the significant terms enriched by target genes.

Protein-protein interaction (PPI) networks

PPI networks for the key KEGG pathways of LUAD were created by Search Tool for the Retrieval of Interacting Genes (STRING)/Proteins v.10.0 with all the component genes. The relationships between proteins were established from the following four channels: i) protein interactions documented in the literature; ⅱ) high-throughput experiments; ⅲ) genomic analysis and prediction; and ⅳ) studies of co-expression. Hub genes of each PPI network were confirmed by comparing the connectivity degrees of the nodes from each PPI network.

Verification of genes in key KEGG pathways as directly targeted by miR-183-5p

To further verify genes from key KEGG pathways as directly targeted by miR-183-5p, an independent samples t-test was conducted by SPSS v.22.0 to examine the differential expression of these genes in LUAD and non-cancer tissues from TCGA. GraphPad Prism v.5 was employed to perform a Pearson's correlation test for evaluating the correlation between miR-183 expression and expression of the genes from TCGA. Moreover, we downloaded immunohistochemistry results for these genes in LUAD and normal tissues from the Human Protein Atlas (HPA), a database integrating immunohistochemistry analysis of proteins in multiple human tissues and cancers (28), to confirm whether these genes were downregulated in LUAD tissues or not.

Results

Included GEO datasets

A total of 340 GEO datasets appeared after initial search and 84 GEO datasets were retained after scanning titles and abstracts (2937). According to the selection criteria, a total of 13 GEO datasets with 469 LUAD tissues and 272 non-cancer tissues were eligible for the meta-analysis. The summarization of the included studies is displayed in Tables I and II. Distribution of miR-183-5p between LUAD tissues and non-cancer tissues as well as the diagnostic value of miR-183-5p for LUAD in each GSE dataset are illustrated by scatter plots (Fig. 1) and ROC curves (Fig. 2).

Table I.

Basic information of all included GSE datasets.

Table I.

Basic information of all included GSE datasets.

IDFirst authorYear of publicationCountryExperiment typeSample typePlatformCancer (N)Cancer (M)Cancer (SD)Non-cancer (N)Non-cancer (M)Non-cancer (SD)
GSE27486Patnaik (29)2011USANon-coding RNA profiling by arrayPlasmaGPL11432  662.3680.313440.1212.011
GSE40738Patnaik (30)2012USANon-coding RNA profiling by arrayPlasmaGPL16016  452.1560.726562.1500.636
GSE93300Qua2017ChinaNon-coding RNA profiling by arrayPlasmaGPL21576  9−5.6181.490  4−8.1871.022
GSE14936Seike (31)2009USANon-coding RNA profiling by arrayTissueGPL8879  242.8510.135222.7900.069
GSE19945Yuichia2013JapanNon-coding RNA profiling by arrayTissueGPL9948  42.5151.535  8−0.8231.697
GSE25508Nymark (32)2011FinlandNon-coding RNA profiling by arrayTissueGPL7731  106.8080.600106.2490.181
GSE47525van Jaarsveld (33)2013NetherlandsNon-coding RNA profiling by arrayTissueGPL17222  72.3940.123142.4190.132
GSE48414Bjaanaes (34)2014NorwayNon-coding RNA profiling by arrayTissueGPL167701540.0691.26820−1.6780.508
GSE51853Arima (35)2014JapanNon-coding RNA profiling by arrayTissueGPL7341  76−0.3981.311  5−3.7890.234
GSE63805Robles (36)2015USANon-coding RNA profiling by arrayTissueGPL18410  327.0261.095305.5570.498
GSE74190Jina2015ChinaNon-coding RNA profiling by arrayTissueGPL19622  362.2840.272440.1212.011
GSE77380 Yoshimotoa2016JapanNon-coding RNA profiling by arrayTissueGPL16770  34.4970.99012−0.3033.193
GSE29248Ma (37)2012ChinaNon-coding RNA profiling by arrayTissueGPL8179  31,647.5103,669.657  3303.461534.985

a Unpublished data. N, number; M, mean; SD, standard deviation.

Table II.

Diagnostic data of all included GSE datasets.

Table II.

Diagnostic data of all included GSE datasets.

AuthorIDTPFPFNTN
Patnaik (29)GSE27486  2020  2  3
Patnaik (30)GSE40738  20181538
QuaGSE93300  8  0  1  4
Seike (31)GSE14936  7  3  4  6
YuichiaGSE19945  4  2  0  6
Nymark (32)GSE25508  7  1  3  9
van Jaarsveld (33)GSE47525  3  5  4  9
Bjaanaes (34)GSE48414118  03620
Arima (35)GSE51853  74  0  2  5
Robles (36)GSE63805  27  2  528
JinaGSE74190  36  3  041
YoshimotoaGSE77380  3  1  011
Ma (37)GSE29248  1  0  2  3

a Unpublished data. TP, true positivity; FP, false positivity; FN, false negativity; TN, true negativity.

Meta-analysis of GEO datasets

The pooled effect sizes from forest plots (Fig. 3A) indicated that miR-183-5p expression was significantly higher in LUAD tissues than in non-cancer tissues (SMD=1.24, 95% CI=0.76–1.72, I2=82.7%, P<0.001) with considerable study heterogeneity. To trace the origin of this heterogeneity, subgroup and sensitivity analyses were performed. Subgroup analysis based on sample types failed to locate the source of heterogeneity due to the insignificant 95% CI for the plasma subgroup (95% CI=−0.25–2.53) (Fig. 3B). The results of sensitivity analysis shown in Fig. 4A revealed that no study caused obvious deviation from the overall results. Moreover, Begg's and Egger's tests detected no publication bias (P=0.807) (Fig. 4B). With regard to the diagnostic ability of miR-183-5p for LUAD, SROC curves generated from all the included GSE datasets reported an AUC value of 0.8776 (Fig. 4C). Separating GSE datasets sampling plasma miR-183-5p from all the included GSE datasets, we obtained a SROC curve with an AUC value of 0.6813 (Fig. 4D).

Clinicopathological significance of miR-183 expression in TCGA data

As shown in Table III, miR-183 expression was significantly higher in LUAD tissues (P<0.001) and patients <60 years of age (P=0.012) compared with the matched control groups. Nevertheless, no significant relationships could be established between miR-183 expression and other clinicopathological variables of LUAD including sex, T stage, number of nodes, number of metastases, pathological stage, anatomic-organ subdivision and tumor location. In addition, the results from ROC curves indicated that miR-183 expression showed high diagnostic value for LUAD (AUC=0.985, P<0.001) (Fig. 5A). According to the log-rank test from Kaplan-Meier survival analysis, the influence of miR-183 on the prognosis of LUAD patients was not distinct (P>0.05) (data not shown).

Table III.

Relationship between miR-183 expression and clinicopathological parameters of LUAD from TCGA.

Table III.

Relationship between miR-183 expression and clinicopathological parameters of LUAD from TCGA.

miR-183 relevant expression

Clinicopathological featureNM±SDtP-value
Tissue
  Normal  4610.661±0.57225.544<0.001
  Lung cancer44113.180±1.072
Age (years)
  ≤6019913.317±1.1502.5310.012
  >6023213.054±1.005
Sex
  Female23413.166±1.0820.2880.774
  Male20713.196±1.063
T stage
  T1+T238313.209±1.0841.4330.153
  T3+T4  5812.992±0.980
Node
  No28813.155±1.092−0.7110.478
  Yes15213.231±1.037
Metastasis
  No27813.198±1.0120.5910.555
  Yes15913.133±1.173
Pathological stage
  I+II34613.155±1.084−0.690.49
  III+IV  9013.242±1.023
Anatomic-organ subdivision
  L_lower  6913.042±0.9903.159a0.532
  L_upper10513.170±1.085
  R_lower  8413.129±1.057
  R_middle  1813.226±0.913
  R_upper15413.268±1.132
Location
  Peripheral10613.264±1.0310.090.928
  Central  5413.248±1.082

{ label (or @symbol) needed for fn[@id='tfn3-or-40-01-0083'] } Independent samples t-test was performed to evaluate the relationship between miR-183 expression and the clinicopathological parameters of LUAD.

a Kruskal-Wallis test was performed to assess the distribution difference of miR-183 in three or more groups of clinicopathological parameters. LUAD, lung adenocarcinoma; TCGA, The Cancer Genome Atlas; N, number; M, mean; SD, standard deviation.

Relationship between miR-183-5p expression and the clinicopathological features of LUAD from qPCR

A total of 101 LUAD and paired non-cancer tissues were taken from patients enrolled in our study. General information of the collected samples is listed in Table IV. From the results of the paired samples t-test, miR-183-5p expression was obviously higher in LUAD tissues (6.579±3.737) than in non-cancer tissues (5.489±3.230) (P=0.038) (Table IV). Analysis from independent samples t-test suggested that patients with lymph node metastasis, vascular invasion and advanced TNM stage (III–IV) exhibited higher miR-183-5p expression than patients without those features (P=0.014, 0.038, and 0.014). With respect to the diagnostic and prognostic value of miR-183-5p for LUAD, ROC curves revealed the potential diagnostic significance of miR-183-5p for LUAD (AUC=0.587, P=0.033) (Fig. 5B) while Kaplan-Meier survival analysis yielded no significant results supporting the role of miR-183-5p as an important prognostic factor for LUAD (log-rank P>0.05).

Table IV.

Relationship between miR-183-5p expression and clinicopathological parameters of LUAD from qPCR data.

Table IV.

Relationship between miR-183-5p expression and clinicopathological parameters of LUAD from qPCR data.

miR-183-5p relevant expression

Clinical variableNM±SDtP-value
Tissue type
  LUAD1016.579±3.737−2.1030.038
  Non-cancer1015.489±3.230
Age (years)
  <60  416.556±3.613−0.0500.960
  ≥60  606.594±3.849
Sex
  Female  457.063±3.865−1.1690.245
  Male  566.190±3.618
Smoke
  No  265.648±2.4731.3460.186
  Yes  184.840±1.501
Tumor size (cm)
  ≤3  536.649±3.7170.1980.843
  >3  486.501±3.796
Lymph node metastasis
  No  455.605±2.746−2.5130.014
  Yes  567.361±4.238
Vascular invasion
  No  705.949±2.867−2.1510.038
  Yes  318.002±4.962
TNM
  I–II  455.605±2.746−2.5130.014
  III–IV  567.361±4.238
Pathological grading
  I  176.988±2.6661.526a0.466
  II  616.305±3.767
  III  237.004±4.367
EGFR amplification
  No  215.191±2.1620.2840.778
  Yes  124.972±2.067
EGFR protein expression
  Low  225.336±2.2750.8660.393
  High  114.662±1.697
EGFR mutation
  Wild-type  205.275±2.1520.5510.585
  Mutation c  134.859±2.071

{ label (or @symbol) needed for fn[@id='tfn5-or-40-01-0083'] } Independent samples t-test was performed to evaluate the relationship between miR-183-5p expression and the clinicopathological parameters of LUAD.

a Kruskal-Wallis test was performed to assess the distribution difference of miR-183-5p in three or more groups of clinicopathological parameters. LUAD, lung adenocarcinoma; qPCR, real-time quantitative polymerase chain reaction; N, number; M, mean; SD, standard deviation.

Integrated meta-analysis

The results of the integrated meta-analysis which contained a large sample with 1,011 LUAD tissues and 419 non-cancer tissues echoed with those from the GEO meta-analysis. miR-183-5p showed higher expression in LUAD tissues than in non-cancer tissues (SMD=1.27, 95% CI=0.76–1.78) (Fig. 6A). SROC curves with an AUC value of 0.890 demonstrated the significant diagnostic value of miR-183-5p for LUAD (Fig. 6B). Nevertheless, the results contained significant heterogeneity (I2=91.2%, P<0.001). Further subgroup analysis and sensitivity analysis still failed to identify the origin of the heterogeneity (Figs. 7 and 8A). Apart from that, Begg's and Egger's test revealed that no publication bias existed among all the studies analyzed (P=0.882) (Fig. 8B).

Results from in vitro experiments

Transfection efficiency of miR-183 mimic and inhibitor was monitored by qPCR. miR-183-5p expression level increased 8- to 12-fold in all the three cell lines at 96 h post-transfection with the miR-183 mimic. At 96 h after transfection with the miR-183 inhibitor, 55–85% miR-183 knockdown was observed in all the three cell lines (Fig. 9A and B). As illustrated in Fig. 9C-E, cell viability evaluated by fluorometric resorufin viability assay increased slightly at 48 h in the H460, A459 and H1299 cells transfected with the miR-183-5p mimic. The influence of miR-183-5p mimic on cell viability was observed to be most significant in H460 cells, in which cell viability was clearly increased compared to that of the blank control and negative mimic control at 72 h (P<0.01) and 96 h (P<0.001) (Fig. 9C). In A549 cell lines, cell viability in the miR-183-5p mimic group was higher than in the blank control and the negative mimic control at 72 and 96 h, although without statistical significance (P>0.05) (Fig. 9D). In the H1299 cell line, there was a sharp rise in the cell viability of the miR-183-5p mimic group at 96 h (P<0.001) (Fig. 9E). In contrast, all the column diagrams in Fig. 9 reflect a downward trend in cell viability after 48 h post-transfection with miR-183-5p inhibitor. In all three tested cell lines, cell viability decreased substantially in the miR-183-5p inhibitor group at 72 h (All P<0.01) and 96 h (All P<0.001) compared with in the blank control and the negative inhibitor control (Fig. 9). For the influence of miR-183-5p on cell proliferation, MTS assays reflected almost the same effect of miR-183-5p on cell growth as the fluorometric resorufin viability assay (Fig. 10). With regard to the effect of miR-183-5p on cell apoptosis, results from Apo-ONE Homogeneous Caspase-3/7 assays showed that the increase in caspase-3/7 activity started at 48 h in the miR-183 inhibitor group and the decline of caspase-3/7 activity started at 72 h in the miR-183 mimic group. Particularly, caspase-3/7 activity was significantly enhanced (P<0.001) at 72 and 96 h in miR-183-5p inhibitor group on the basis of the blank control and negative inhibitor group (Fig. 11), which was unanimously observed in all the three tested cells. Conversely, in the miR-183-5p mimic group, caspase-3/7 activity was significantly reduced on the basis of the blank control (P<0.001) and negative mimic control (P<0.05) in all the three tested cell lines (Fig. 11). In all the three assays, miR-183-5p stimulated cell growth and suppressed cell apoptosis in a time-dependent (Figs. 911) and dose-dependent (data not shown) manner.

Network analysis of target genes

According to the TCGA data analysis, a total of 2,609 downregulated genes with an FDR value <0.05 were identified as potential target genes of miR-183-5p. From the predictions of miRWalk v.2.0, a total of 5,065 genes occurred in at least four of the 12 online software programs. Taking genes that were found from both analyses, we obtained 432 reliable target genes of miR-183-5p in LUAD.

The selected target genes were further refined by GO and KEGG pathway analysis in DAVID. As shown in Table V, these target genes were most significantly enriched in the following terms of biological processes (BP): positive regulation of gene expression, cell adhesion and actin filament organization. With respect to cellular component (CC), the top three terms gathered by these target genes were plasma membrane, integral component of plasma membrane and receptor complex. These target genes were also significantly clustered in terms of molecular function (MF), such as Ras guanyl-nucleotide exchange factor activity, glycosaminoglycan binding and calcium ion binding. Three GO maps (Figs. 1214) illustrate the functional interactions of the target genes. With regard to KEGG pathway analysis, the four most significant signaling pathways for the target genes of miR-183-5p were cell adhesion molecules (CAMs) and pathways in cancer, endocytosis and axon guidance (Table VI). The interactions between component genes of the four most significant pathways are displayed in four PPI networks (Fig. 15). A total of 10 genes including CD34, sialophorin (SPN), WNT3A, FGFR2, WNT2B, PDGFB, AVPR2, VIPR1, PTGER4 and ADCYAP1R1 were selected as the hub genes based on their degrees of connectivity in corresponding PPI networks.

Table V.

GO analysis of the target genes.

Table V.

GO analysis of the target genes.

CategoryIDTermCount%P-value
GOTERM_BP_DIRECTGO:0010628Positive regulation of gene expression  214.8721.66E-06
GOTERM_BP_DIRECTGO:0007155Cell adhesion  276.2651.25E-05
GOTERM_BP_DIRECTGO:0007015Actin filament organization  102.3202.90E-05
GOTERM_BP_DIRECTGO:0007165Signal transduction  4710.9059.06E-05
GOTERM_BP_DIRECTGO:0010629Negative regulation of gene expression  122.7842.39E-04
GOTERM_BP_DIRECTGO:0032870Cellular response to hormone stimulus  71.6244.46E-04
GOTERM_BP_DIRECTGO:0009611Response to wounding  81.8564.71E-04
GOTERM_BP_DIRECTGO:0001934Positive regulation of protein phosphorylation  112.5525.30E-04
GOTERM_BP_DIRECTGO:0070374Positive regulation of ERK1 and ERK2 cascade  133.0165.39E-04
GOTERM_BP_DIRECTGO:0050665Hydrogen peroxide biosynthetic process  40.9288.20E-04
GOTERM_CC_DIRECTGO:0005886Plasma membrane14633.8752.38E-10
GOTERM_CC_DIRECTGO:0005887Integral component of plasma membrane  6414.8494.73E-08
GOTERM_CC_DIRECTGO:0043235Receptor complex  153.4807.61E-07
GOTERM_CC_DIRECTGO:0009986Cell surface  327.4251.20E-06
GOTERM_CC_DIRECTGO:0005578Proteinaceous extracellular matrix  194.4082.74E-05
GOTERM_CC_DIRECTGO:0030054Cell junction  266.0322.97E-05
GOTERM_CC_DIRECTGO:0016021Integral component of membrane15135.0353.25E-05
GOTERM_CC_DIRECTGO:0009897External side of plasma membrane  163.7127.45E-05
GOTERM_CC_DIRECTGO:0001725Stress fiber  71.6240.001128084
GOTERM_CC_DIRECTGO:0031674I band  51.1600.001440976
GOTERM_MF_DIRECTGO:0005088Ras guanyl-nucleotide exchange factor activity  112.5522.01E-04
GOTERM_MF_DIRECTGO:0005539Glycosaminoglycan binding  51.1605.23E-04
GOTERM_MF_DIRECTGO:0005509Calcium ion binding  306.9619.67E-04
GOTERM_MF_DIRECTGO:0004714Transmembrane receptor protein tyrosine kinase activity  61.3920.001299161
GOTERM_MF_DIRECTGO:0008289Lipid binding  112.5520.001702358
GOTERM_MF_DIRECTGO:0050431Transforming growth factor β binding  40.9280.004589053
GOTERM_MF_DIRECTGO:0004896Cytokine receptor activity  51.1600.007406533
GOTERM_MF_DIRECTGO:0008201Heparin binding  102.3200.008250718
GOTERM_MF_DIRECTGO:0005248Voltage-gated sodium channel activity  40.9280.008762612
GOTERM_MF_DIRECTGO:0005024Transforming growth factor β-activated receptor activity  30.6960.009159889

[i] There was a total of 93, 34 and 23 significant (P<0.05) GO terms of BP, CC and MF, respectively. Top ten GO terms for BP, CC and MF are displayed in the Table. GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function.

Table VI.

KEGG pathway analysis of the target genes.

Table VI.

KEGG pathway analysis of the target genes.

CategoryIDTermsCount%P-value
KEGG_PATHWAYhsa04514CAMs112.5522041760.001466545
KEGG_PATHWAYhsa04550Signaling pathways regulating pluripotency of stem cells102.3201856150.004641057
KEGG_PATHWAYhsa05200Pathways in cancer184.1763341070.008133914
KEGG_PATHWAYhsa04080Neuroactive ligand-receptor interaction143.2482598610.010639115
KEGG_PATHWAYhsa00380Tryptophan metabolism  51.1600928070.012685346
KEGG_PATHWAYhsa04924Renin secretion  61.3921113690.015130907
KEGG_PATHWAYhsa04670Leukocyte transendothelial migration  81.8561484920.018169594
KEGG_PATHWAYhsa04360Axon guidance  81.8561484920.026015978
KEGG_PATHWAYhsa04144Endocytosis122.7842227380.033890911
KEGG_PATHWAYhsa04024cAMP signaling pathway102.3201856150.037334507
KEGG_PATHWAYhsa04060Cytokine-cytokine receptor interaction112.5522041760.037626628
KEGG_PATHWAYhsa04725Cholinergic synapse  71.624129930.041290871
KEGG_PATHWAYhsa04911Insulin secretion  61.3921113690.044616235

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes; CAMs, cell adhesion molecules.

DO analysis by clusterProfiler revealed that the target genes were significantly associated with the following diseases: migraine, Rett syndrome, chronic pulmonary heart disease and primary pulmonary hypertension (all P<0.05 and Q<0.05). (Table VII) (Fig. 16).

Table VII.

DO analysis of the target genes.

Table VII.

DO analysis of the target genes.

IDDescriptionGene ratioBg ratioP-valueP-adjustedQ-valueGene IDCount
DOID:6364Migraine12/23173/8,0079.69E-070.0006712370.000583196 477/5241/3683/6532/1636/4842/1524/3949/3952/6323/627/35812
DOID:1206Rett syndrome   5/23117/8,0078.92E-050.0308936720.026841619 3399/1959/3952/22854/627  5
DOID:12326Chronic pulmonary heart disease   3/2315/8,0070.0002270820.0393420280.034181879659/6532/1636  3
DOID:14557Primary pulmonary hypertension   3/2315/8,0070.0002270820.0393420280.034181879659/6532/1636  3

[i] DO, Disease Ontology.

Verification of genes in key KEGG pathways as directly targeted by miR-183-5p

We conducted an independent samples t-test and Pearson's correlation test to validate the capabilities of miR-183-5p to target all the component genes in the CAM pathway. The results from the independent samples t-test suggested that expression of all these target genes was significantly lower in 535 LUAD tissues than in 59 non-cancer tissues (all P<0.001) (Fig. 17). Among the 11 component genes, five genes including PECAM1, CDH5, SIGLEC1, CD34 and CLDN18 showed significant correlation with miR-183-5p in LUAD (all P<0.05) (Fig. 18). Moreover, immunostaining evidence from the HPA database supported the downregulation of CD34 (antibody HPA036722), CADM1 (antibody CAB037266), PECAM1 (antibody HPA004690), SIGLEC1 (antibody HPA053457) and SPN (antibody HPA055244) in LUAD tissues. Fig. 19 exhibits the immunostaining results of the five genes in normal and LUAD tissues. The staining intensity of the five genes was medium or strong in normal tissues, while low to non-existent in LUAD tissues.

Discussion

In this report, we used a combined method of GEO meta-analysis, TCGA data mining, qPCR, integrated meta-analysis, in vitro experiments and bioinformatic analysis of the target genes to comprehensively investigate the clinicopathological significance of miR-183-5p in LUAD and its underlying molecular basis. The results from this report revealed that overexpressed miR-183-5p had considerable diagnostic value in LUAD and was associated with the malignant progression of LUAD. Further in vitro experiments and bioinformatic analysis of the target genes of miR-183-5p showed the positive effect of miR-183-5p on cell growth in LUAD and revealed the specific biological processes and gene pathways common to miR-183-5p target genes, which may provide new insights into the oncogenesis of LUAD.

GEO meta-analysis, TCGA data mining, qPCR and the integrated meta-analysis in our study concordantly reported overexpression of miR-183-5p in LUAD tissues, which was in agreement with previous studies (38,39). The diagnostic value of miR-183-5p in LUAD was also studied. We found that miR-183-5p possessed strong diagnostic capacity for distinguishing LUAD tissues from non-cancer tissues via ROC curves derived from all the included GSE datasets, TCGA data mining and the integrated meta-analysis. ROC curves from qPCR also demonstrated the diagnostic potential of miR-183-5p for LUAD, although with weak statistical significance. The difference between ROC curves from all the included GSE datasets, TCGA data mining, qPCR and integrated meta-analysis might be explained by the type and number of samples. It should be noted that all the non-cancer tissues from qPCR were matched with the corresponding LUAD tissues, which is not the case in the GEO meta-analysis, the TCGA data mining or the integrated meta-analysis. Now that sensitive, less non-invasive biomarker was necessary for the early detection of LUAD, we singled out GSE datasets sampling plasma miR-183-5p to assess the diagnostic capacity of plasma miR-183-5p. Nevertheless, the results indicated a poorer diagnostic ability of plasma miR-183-5p in our study compared with miR-183-5p from both tissues and plasma. We conjectured that the less significant diagnostic ability of plasma miR-183-5p might be attributed to the lower levels of miR-183-5p in blood samples than in tissues, which is related to the phenomenon that LUAD cells in lung tissues assimilating exosomes containing miRNAs from the blood as a supplement of transcribed essential miRNAs (4042). Considering the results of all the diagnostic evaluations, we are optimistic about the prospect of miR-183-5p as a diagnostic target for LUAD.

To investigate the role of miR-183-5p in the development of LUAD, we analyzed the relationship between miR-183-5p and the clinicopathological characteristics of LUAD. Given the positive correlation between miR-183-5p overexpression and lymph node metastasis, vascular invasion and advanced TNM stage in LUAD, it can be inferred that miR-183-5p may enhance the malignant properties of LUAD. To answer the question of how miR-183-5p affects the pathogenesis of LUAD, we conducted in vitro experiments to determine the influence of miR-183-5p on cell growth in LUAD.

As suggested by fluorometric resorufin viability assay and MTS assay, the proliferative ability of LUAD cells was augmented by miR-183-5p. The promotion of tumor cell growth by miR-183-5p has also been shown in in vitro experiments with pediatric acute myeloid leukemia, tongue squamous cell carcinoma and endometrial cancer (4346). To ascertain whether the knockdown of miR-183-5p caused apoptosis or just inhibited the proliferation of the cancer cells, we performed Apo-ONE Homogeneous Caspase-3/7 assays. Caspase-3 and −7 are effector caspases that execute cell apoptosis via cleaving relevant cellular substrates (46). The negative effect of miR-183-5p on caspase-3/7 activity indicated that the knockdown of miR-183-5p caused apoptosis of LUAD cells. Therefore, we hypothesized that miR-183-5p may accelerate the malignant progression of LUAD by enhancing tumor growth and inhibiting tumor apoptosis. It was discovered in previous studies that miR-183-5p modulated the expression of tumor-suppressor genes such as PDCD4 and SOCS-6 to accelerate the proliferation of cancer cells (47,48). In an in vitro experiment by Yan et al, the reduced cell growth and increased cell apoptosis by miR-183-5p inhibitor was correlated with upregulated caspase-3 and downregulated anti-apoptotic protein BCL-xl (43). These findings might provide interpretation of how miR-183-5p contributed to cell growth and suppressed cell apoptosis in LUAD.

In order to develop effective therapeutic and diagnostic targets for LUAD, it is not sufficient to merely identify the relationship between miR-183-5p expression and the malignant properties of LUAD. Therefore, we further attempted to explore the underlying molecular basis of the cell growth-promoting effects of miR-183-5p in LUAD via in silico analysis of miR-183-5p target genes. The significant terms identified from the GO analysis including positive regulation of gene expression, cell adhesion, actin filament organization, Ras guanyl-nucleotide exchange factor activity, glycosaminoglycan binding and calcium ion binding imply that target genes regulated by miR-183-5p may participate in these biological functions to cause the occurrence and development of LUAD. We can also draw conclusions from the KEGG pathway analysis. Among all the listed KEGG pathways significantly identified by analysis of the target genes of miR-183-5p, we noted that the top two pathways, CAMs and pathways in cancer, were closely associated with the formation of human cancers. CAMs are cell surface proteins that mediate the adhesion between cells and the extracellular matrix. This feature allows CAMs to stimulate the motility, invasion and angiogenesis of tumor cells (49,50). We speculate that miR-183-5p may target downstream CAMs to contribute to the progression of LUAD. Apart from CAMs and pathways in cancer, other pathways, such as endocytosis and axon guidance, were also identified as significantly enriched by analysis of the target genes of miR-183-5p. Involvement of target genes in these additional pathways may have an impact on LUAD; this awaits further study. In addition to the above results, we noted from DO analysis that the target genes were significantly assembled in chronic pulmonary heart disease and primary pulmonary hypertension, the function of target genes in chronic pulmonary heart disease and primary pulmonary hypertension may constitute part of the pathogenesis of LUAD.

Although the regulatory network of target genes was intricate, we focused on hub genes to shed light on the molecular mechanism of miR-183-5p in LUAD. Ten genes were screened out as hub genes from the four most significant KEGG pathways. However, several of the hub genes including CD34, PDGFB, FGFR2 and WNT3A were described in previous studies to relate to the tumorigenesis or poor prognosis of LUAD (5154). Although there have not been studies investigating the expression of SPN, WNT2B and PTGER4 in LUAD, all the three genes were reported to act as an oncogene in other cancers. SPN could induce cell adhesion and migration as well as suppress apoptosis when secreted by activated leukocytes (55). WNT2B controlled multiple biological events, including cell proliferation, differentiation and migration to serve as an oncogene in numerous human cancers (5658). PTGER4 could transduce a series of signaling pathways such as Akt, ERK1/2 and early growth response factor-1 to mediate cancer cell survival and tumor development with its overexpression in a wide variety of cancers (59). Thus, we are skeptical about the regulatory targeting relationship between miR-183-5p and these genes. Whether these genes were the direct targets of miR-183-5p needs to be verified in future study. Apart from these genes, we found evidence that VIPR1 was downregulated in LUAD and served as a tumor-suppressor gene in the study of Mlakar et al (60), which lend credence to the assumption that miR-183-5p directly targets VIPR1. In addition to the hub genes, component genes in CAMs pathway validated by Pearson's correlation test and immunostaining results from HPA were also worthy for attention. Despite that we found no literature evidence of the downregulation of expression in LUAD for all component genes in the CAMs pathway, the tumor suppressor roles of SIGLEC1 and CADM1 human cancers were explored in previous research. The study conducted by Strömvall et al confirmed that reduced expression of SIGLEC1 in metastasis-free regional lymph nodes was responsible for the inhibited antitumor immune response (61). CADM1, a membrane-spanning glycoprotein that participates in the process of attenuating cell proliferation and activating cell apoptosis, was downregulated in various cancers such as breast, prostate, pancreatic and hepatocellular cancer (62). We believe that miR-183-5p-regulated downexpression of genes such as VIPR1, SIGLEC1 and CADM1 may help explain the oncogenic function of miR-183-5p in LUAD. Future experiments are warranted to identify the interactions between miR-183-5p and these genes in LUAD.

Despite the research progress in our study, there were also limitations. In our GEO and integrated meta-analysis, great heterogeneity existed in the pooled studies, and even the subgroup analysis or sensitivity analysis failed to solve the problem, which unfortunately impacts the reliability of our results. This intractable heterogeneity may partly be attributed to the different proportion of LUAD and non-cancer tissues in the studies or the varying experimental platforms of studies. The number of LUAD tissues exceeded non-cancer tissues in GSE27486, GSE93300, GSE14936, GSE48414, GSE51853, GSE63805, GSE74190 and the TCGA data; conversely, the cases of non-cancer tissues outnumbered LUAD tissues in GSE19945, GSE40738, GSE47525 and GSE77380. The experimental platforms of studies also varied from each other. A feasible way to prevent this problem may be to enroll larger study cohorts with a balanced proportion of LUAD and non-cancer tissues in future studies. Another flaw of this study was that neither western blotting nor flow cytometry was conducted in in vitro experiments. Western blotting and flow cytometry should be conducted after knockdown or overexpression of miR-183-5p in the LC cell lines. Nevertheless, we failed to perform western blotting and flow cytometry due to the restriction of the experimental condition. This imperfect design of the in vitro experiment should be improved in future research.

In conclusion, our study confirmed that overexpression of miR-183-5p may play an oncogenic role in LUAD through involvement in the regulatory networks of its target genes. miR-183-5p is anticipated to be a novel diagnostic and therapeutic target for lung adenocarcinoma.

Acknowledgements

Not applicable.

Funding

The study was supported by funds from the National Natural Science Foundation of China (NSFC81560469, NSFC81360327), the Natural Science Foundation of Guangxi, China (2015GXNSFCA139009) and teh Guangxi Medical University Training Program for Distinguished Young Scholars (2017).

Availability of data and materials

The datasets generated and analysed during the current study are available in Gene Expression omnibus (GEO; https://www.ncbi.nlm.nih.gov/pubmed) and TCGA data portal (https://portal.gdc.cancer.gov/).

Authors' contributions

RQH and LG analyzed and interpreted data and drafted the manuscript. JM, ZYL, XHH and GC provided information from database. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The study was approved by the Research Ethics Committee of the First Affiliated Hospital of Guangxi Medical University. Signed informed consents were acquired from all the LUAD patients prior to their involvement in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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July-2018
Volume 40 Issue 1

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Online ISSN:1791-2431

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Spandidos Publications style
He RQ, Gao L, Ma J, Li ZY, Hu XH and Chen G: Oncogenic role of miR‑183‑5p in lung adenocarcinoma: A comprehensive study of qPCR, in vitro experiments and bioinformatic analysis. Oncol Rep 40: 83-100, 2018
APA
He, R., Gao, L., Ma, J., Li, Z., Hu, X., & Chen, G. (2018). Oncogenic role of miR‑183‑5p in lung adenocarcinoma: A comprehensive study of qPCR, in vitro experiments and bioinformatic analysis. Oncology Reports, 40, 83-100. https://doi.org/10.3892/or.2018.6429
MLA
He, R., Gao, L., Ma, J., Li, Z., Hu, X., Chen, G."Oncogenic role of miR‑183‑5p in lung adenocarcinoma: A comprehensive study of qPCR, in vitro experiments and bioinformatic analysis". Oncology Reports 40.1 (2018): 83-100.
Chicago
He, R., Gao, L., Ma, J., Li, Z., Hu, X., Chen, G."Oncogenic role of miR‑183‑5p in lung adenocarcinoma: A comprehensive study of qPCR, in vitro experiments and bioinformatic analysis". Oncology Reports 40, no. 1 (2018): 83-100. https://doi.org/10.3892/or.2018.6429