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Erschienen in: Respiratory Research 1/2020

Open Access 01.12.2020 | Research

Downregulation of hsa-microRNA-204-5p and identification of its potential regulatory network in non-small cell lung cancer: RT-qPCR, bioinformatic- and meta-analyses

verfasst von: Chang-Yu Liang, Zu-Yun Li, Ting-Qing Gan, Ye-Ying Fang, Bin-Liang Gan, Wen-Jie Chen, Yi-Wu Dang, Ke Shi, Zhen-Bo Feng, Gang Chen

Erschienen in: Respiratory Research | Ausgabe 1/2020

Abstract

Background

Pulmonary malignant neoplasms have a high worldwide morbidity and mortality, so the study of these malignancies using microRNAs (miRNAs) has attracted great interest and enthusiasm. The aim of this study was to determine the clinical effect of hsa-microRNA-204-5p (miR-204-5p) and its underlying molecular mechanisms in non-small cell lung cancer (NSCLC).

Methods

Expression of miR-204-5p was investigated by real-time quantitative PCR (RT-qPCR). After data mining from public online repositories, several integrative assessment methods, including receiver operating characteristic (ROC) curves, hazard ratios (HR) with 95% confidence intervals (95% CI), and comprehensive meta-analyses, were conducted to explore the expression and clinical utility of miR-204-5p. The potential objects regulated and controlled by miR-204-5p in the course of NSCLC were identified by estimated target prediction and analysis. The regulatory network of miR-204-5p, with its target genes and transcription factors (TFs), was structured from database evidence and literature references.

Results

The expression of miR-204-5p was downregulated in NSCLC, and the downtrend was related to gender, histological type, vascular invasion, tumor size, clinicopathologic grade and lymph node metastasis (P<0.05). MiR-204-5p was useful in prognosis, but was deemed unsuitable at present as an auxiliary diagnostic or prognostic risk factor for NSCLC due to the lack of statistical significance in meta-analyses and absence of large-scale investigations. Gene enrichment and annotation analyses identified miR-204-5p candidate targets that took part in various genetic activities and biological functions. The predicted TFs, like MAX, MYC, and RUNX1, interfered in regulatory networks involving miR-204-5p and its predicted hub genes, though a modulatory loop or axis of the miRNA-TF-gene that was out of range with shortage in database prediction, experimental proof and literature confirmation.

Conclusions

The frequently observed decrease in miR-204-5p was helpful for NSCLC diagnosis. The estimated target genes and TFs contributed to the anti-oncogene effects of miR-204-5p.
Hinweise
Equal Contributors; Chang-Yu Liang and Zu-Yun Li are equally contributing co-first authors.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Abkürzungen
95% CI
95% confidence interval
DEGs
Differentially expressed genes
GEO
Gene expression omnibus
GO
Gene ontology
HR
Hazard ratio
KEGG
Kyoto encyclopedia of genes and genomes
K-M curve
Kaplan-Meier curve
LUAD
Lung adenocarcinoma
LUSC
Lung squamous cell carcinoma
MiRNA
microRNA
NSCLC
Non-small cell lung cancer
ROC
Receiver operating characteristic
RT-qPCR
Real-time quantitative PCR
SMD
Standard mean deviation
TCGA
The cancer genome atlas
TF
Transcription factor

Background

The worldwide morbidity and mortality of pulmonary cancer has remained high for decades in both genders, reflecting an increase in contributory factors like tobacco use and air pollution [15]. The two primary categories of pulmonary neoplasms are small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC accounting for approximately 80% of all pulmonary cancers. NSCLC includes adenocarcinoma, squamous cell lung carcinoma, undifferentiated large cell carcinoma, adenosquamous carcinoma and bronchioalveolar carcinoma; the first three are the best known types [6]. The survival of patients with NSCLC is still bleak due to delayed diagnosis, undisciplined treatment, incident chemoresistance, and frequent tumor recurrence [79]. Thus, thorough investigation of the molecular mechanisms underlying lung carcinogenesis remains an urgent task, for establishing new and effective guidelines for cancer screening and for identifying novel genetic targets for treatments.
One potential class of molecular targets are the microRNAs (miRNAs). These are small non-coding RNA molecules, with approximately 20 nucleotides in length, that negatively modulate expression of target genes by completely or incompletely binding to the 3′ untranslated region (UTR) of messenger RNAs (mRNAs) [1013]. The miRNAs have been proposed as novel diagnostic biomarkers and prognostic indicators for tumorigenic processes, as they play indispensable roles in cancer cell differentiation, proliferation, and apoptosis, and in metastasis and recurrence of numerous malignant tumors [10, 14]. One miRNA, hsa-microRNA-204-5p (also known as miR-204-5p, or miR-204), has attracted attention in NSCLC research, because its low expression in NSCLC tumors is associated with advanced progression, poor prognosis and severe metastatic potential [1517].
Previous studies on the mechanisms of miR-204-5p on NSCLC has mainly focused on the repression of specific mRNAs, so knowledge about its multilateral functions or its clinical prospects remains limited. Aberrant expression of miR-204-5p is now a well-established feature of pulmonary carcinogenesis; however, what is still unclear is the clinical contribution of miR-204-5p and particularly its potential role in the early detection of NSCLC. The mechanism by which miR-204-5p mediates its target mRNA-protein signaling networks to regulate tumor progression is also not yet established.
The current work describes distinctive features of miR-204-5p expression in NSCLC by integrative analysis of results from real-time quantitative polymerase chain reaction (RT-qPCR) and from sequence and genechip data from the cancer genome atlas (TCGA), Gene Expression Omnibus (GEO), and the current literature, in addition to relevant prediction materials from online tools. Our goals were to explore the possibility that miR-204-5p might be a promising indicator for NSCLC process and to identify our perspective on other underlying regulatory mechanisms at the molecular level (Fig. 1).

Methods

Patients and samples

Formalin-fixed, paraffin-embedded (FFPE) samples and corresponding non-cancerous lung tissues were obtained with prior informed consent from125 patients with NSCLC treated at Department of Pathology, the First Affiliated Hospital of Guangxi Medical University (Nanning, Guangxi, China) from January 2012 to February 2014. The research proposal was approved by the Committee on Ethics of the First Affiliated Hospital of Guangxi Medical University. All cases were pathologically distinguished and verified by two recognized experts (Zhen-bo Feng and Gang Chen). Each participant was classified based on pathological pattern, tumor size, and clinicopathologic grade according to the IASLC 2009 criteria [18].

RNA isolation and RT-qPCR

Total RNA was extracted from FFPE samples from the NSCLC and matching tissues by miRNeasy Kit (QIAGEN, KJVenlo, The Netherlands) according to the manual instructions. The RNA concentration was quantified using a NanoDrop 2000 instrument (Wilmington, DE, USA). Then, reverse transcription synthesis of complimentary DNA (cDNA) was conducted on First Strand cDNA Synthesis Kit (Thermo Scientific, USA), followed by PCR reaction on an Applied Biosystems PCR7900 instrument (Thermo Fisher Scientific, Waltham, USA). The thermal cycling steps started at 95 °C for 10 min, continued with totally 40 PCR cycles of 15 s at 95 °C and 60s at 60 °C, finally annealed at 72 °C for 5 s. RNU6B was utilized as the housekeeping miRNA for miR-204-5p. The primer sequences used in the TaqMan® MicroRNA Assays were as follows: RNU6B (Applied Biosystems,4,427,975–001093)-CGCAAGGAUGACACGCAAAUUCGUGAAGCGUUCCAUAUUUUU and miR-204-5p (Applied Biosystems, 4,427,975–000508)- UUCCCUUUGUCAUCCUAUGCCU. The RT-qPCR process was performed on an Applied Biosystems PCR7900 instrument using the protocol supplied by the manufacturer. The expression levels of the two miRNAs were compared using the 2 − ΔΔCt method [19]. All specimens were analyzed in triplicate.

Data mining from TCGA

The Illumina HiSeq miRNA-sequencing data for miR-204-5p were downloaded and extracted from TCGA up to October 31, 2018. The Xena Public Data Hubs online analysis program (https://​xena.​ucsc.​edu/​public-hubs/​) was used to calculate expression level of miR-204-5p and to assess the difference between 999 NSCLC and 91 normal tissues. The genes involved in NSCLC were also obtained from TCGA data and further analyzed with the EdgeR package. Genes with a false discovery rate (FDR)<0.05 were deemed differentially expressed genes (DEGs) and selected as standby members.

Collection and management of miR-204-5p data

Genechips data related to miR-204-5p in NSCLC were sought in the GEO database (http://​www.​ncbi.​nlm.​nih.​gov/​geo/​) up to October 31,2018.To evaluate the clinical application of miR-204-5p for NSCLC, data on documented expression of miR-204-5p between NSCLC and non-tumorous controls were collected from the following databases: PubMed, Web of Science, Wiley online library, Springerlink, Embase, Chinese National Knowledge Infrastructure, Chinese Biomedical Database, Chinese VIP and Wan Fang data resources. The data retrieval entry was as follows: (MicroRNA OR miRNA OR “Micro RNA” OR “Small Temporal RNA” OR “non-coding RNA” OR ncRNA OR “small RNA”) AND (lung OR pulmonary OR respiratory OR bronchi OR bronchioles OR alveoli OR pneumocytes OR “air way”) AND (cancer OR carcinoma OR tumor OR neoplas* OR malignan* OR adenocarcinoma).
The microarray chip data and publications had to fulfill the following conditions for inclusion in the current study:1) the research object focused on human beings, 2) the publication was in English or Chinese, 3) participants were confirmed cases with NSCLC, 4) cases included normal controls and contained at least 2 samples, and 5) pertinent expression of miR-204-5p in NSCLC and corresponding non-tumorous specimens was explored. Exclusion criteria included:1) duplicate selections of studies, conference abstracts, expert opinions, case reports, comments, letters, editorial or reviews, 2) articles with in vitro or in vivo experiments or human xenografts, 3) data with no information about miR-204-5p expression, and 4) publications not written in English or Chinese.
Items from the eligible datasets and reports included for further investigation were: series accession, the lead author, publication year, nationality, experimental platform, sample size, types of sample, research techniques, amount of miR-204-5p and threshold value. The above screening procedures were repeated by two veteran researchers.

Prediction and analyses of miR-204-5p target genes

MiRwalk 2.0, an online miRNA-target search tool that integrates 12 prediction programs (miRWalk, miRanda, miRDB, MicroT4, miRMap, miRNAMap, miRBridge, PITA, PICTAR2, RNAhybrid, RNA22 and TargetScan), was applied to predict the target genes for subsequent analyses. Only genes that co-occurred in at least six databases were deemed eligible. Due to the decrease of miR-204-5p in NSCLC, the target genes were expected to be expressed at a higher level to a large extent, so up-regulated DEGs from TCGA were adopted for further work. The final estimated objects for miR-204-5p were derived from the intersection of online databases and TCGA.
The selected candidate DEGs were then processed in the Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8 (https://​david-d.​ncifcrf.​gov/​) to obtain the gene ontology (GO) annotation as well as the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. P < 0.05 was regarded as the cut-off. Further information about the interaction between the proteins encoded by DEGs was obtained using the Search Tool for the Retrieval of Interacting Genes (STRING) (http://​www.​string-db.​org/​) and Cytoscape 3.6.1 to establish a protein-protein interaction (PPI) network for DEGs that participated in the top three GO items and KEGG pathways. In this study, the selection criterion for hub genes was based on the degree of connection among pitch points in the PPI network. The mRNA expression levels of the hub genes were also accessed from GEPIA (http://​gepia.​cancer-pku.​cn), and their protein variations were validated in the Human Protein Atlas (THPA) (https://​www.​proteinatlas.​org/​).

Transcription factor prediction

Transcription factors (TFs) that were likely to related to miRNA-204-5p and/or hub genes were predicted from public databases, followed by collection of experimentally confirmed targets from literature. Relevant TFs that were able to influence miR-204-5p were mainly predicted using three different online databases that provided estimated relationships between TFs and marker genes: Gene Transcription Regulation Database (GTRD, http://​gtrd.​biouml.​org/​), HTFtarget database (http://​bioinfo.​life.​hust.​edu.​cn/​hTFtarget#!/​) and TransmiR v2.0 database (http://​www.​cuilab.​cn/​transmir). The TFs that modulated hub genes were acquired from GTRD and HTFtarget simultaneously. Precise information was obtained from the intersection of the predictions for combinatorial utilization. The relationships between these can be described as TF-miRNA (GTRD ∩ HTFtarget ∩ TransmiR) ∩ TF-hub genes (GTRD ∩HTFtarget). The predicted transcription factor binding sites (TFBSs) were retrieved from the JASPAR database (http://​jaspar.​genereg.​net/​), and the sequences were derived from the positive-sense strand with the highest score. Literature mining was performed with combined keywords (MicroRNA OR miRNA OR “Micro RNA” OR “Small Temporal RNA” OR “non-coding RNA” OR ncRNA OR “small RNA”) AND (transcription factor OR transcriptional factor) AND (cancer OR carcinoma OR tumor OR neoplasm* OR malignant* OR adenocarcinoma) to confirm the relationships between motifs and NSCLC or other types of cancers. Synergistic co-regulatory motifs of miR-204-5p network were constructed based on the expected regulation and literature confirmation.

Statistical analysis

Results of miR-204-5p expression were reported as mean ± standard deviation (SD). Student’s t-test was used to compare differences in miR-204-5p expression measured by RT-qPCR or raw expression data. One-way analysis of variance (ANOVA) was conducted to evaluate the characteristics of miR-204-5p distribution among groups including three or more variates. Statistical analyses were performed using SPSS v22.0 (SPSS Inc., Chicago, IL, USA).
Data from GEO were first individually processed for acquisition of standard mean deviation (SMD) by meta-analysis, followed by their integration with TCGA and the literature to evaluate distinct expression and potential application prospects of miR-204-5p. The analytical methods in meta-analyses were identical to those used in previous studies [20, 21], and analysis was conducted using by Stata 12.0 (Stata Corp LP, College Station, USA). The role of miR-204-5p in NSCLC diagnosis was studied using receiver operating characteristic (ROC) and summarized receiver operating characteristics (SROC) curves were respectively constructed in accordance with the previous studies [22].
The RT-qPCR data were divided into low-level and high-level groups in according to the median expression level of miR-204-5p(Median = 3.75). The association between survival data in the two groups and miR-204-5p expression were analyzed by Kaplan-Meier (K-M) curves and univariate Cox regression analysis using SPSS v22.0. The hazard ratio (HR), 95% confidence interval (95% CI), and other data available from public resources were either extracted directly or obtained indirectly by recommendations of Tierney et al. [23]. Comprehensive meta-analysis was then preformed for the HRs to appraise the efficiency of miR-204-5p in NSCLC prognosis.
In this work, P < 0.05 was considered as statistically significant. A random effects model was considered valid when a large heterogeneity was defined with reference to I2 greater than 50% or P less than 0.1; otherwise a fixed- coefficient model was in usage [2427].

Results

Differential expression and clinical characteristics of miR-204-5p in NSCLC

Relative quantitative expression and the fundamental characteristics of miR-204-5p in the research subjects are listed in Table 1. The RT-qPCR results (Table 1 and Fig. 2a) showed a statistically significant difference in the quantitative variation of miR-204-5p between NSCLC and normal adjacent tissues (P = 0.001). Statistical differences were also found for gender, tumor size, histological type, vascular invasion, tumor node metastasis (TNM) grade, and lymph node metastasis (P<0.05). Differences of miR-204-5p in expression between pathological types were assessed by analyzing RT-qPCR data grouped into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) (Table 2, Table 3 and Fig. 2b). Apart from age and smoking behavior, lower of miR-204-5p expression was noted in sex, tumor size, vascular invasion, TNM grade, lymph node metastasis, and pathological grading in the LUAD group than in the LUSC group (P<0.05). Therefore, miR-204-5p expression was reduced in NSCLC and was related to clinical parameters other than age and smoking, especially in the LUAD group.
Table 1
Clinicopathological parameters and the expression of miR-204-5p in NSCLC. Annotation: n number, SD standard deviation, NSCLC non-small cell lung cancer. a, paired sample’s t test performed to compare miR-204-5p expression between NSCLC and the controls; Independent sample’s t test processed to assess relationships between miR-30d-5p expression and the clinicopathological parameters of NSCLC. TNM, tumor, node, metastasis; b, One-way ANOVA preformed to evaluate distributive feature of miR-204-5p in three or more groups of clinicopathological parameters
Clinicopathological parameters
n
Relevant expression of miR-204-5p (2−ΔCq)
Mean ± SD
t/F-value
p-value
Tissue
NSCLC
125
3.6760 ± 1.87670
-3.507a
0.001
Non-cancer
125
4.6487 ± 2.46888
Gender
Male
75
4.0067 ± 1.91843
2.461
0.015
Female
50
3.1800 ± 1.71357
Age (years)
< 60
57
3.9526 ± 1.81847
1.517
0.132
> = 60
68
3.4441 ± 1.90650
Smoke
No
38
4.3368 ± 1.70205
−0.108
0.914
Yes
30
4.3833 ± 1.83041
Histological type
Adenocarcinoma
101
3.4663 ± 1.82397
−2.902
0.004
Squamous carcinoma
23
4.6870 ± 1.80638
Tumor size
<=3 cm
60
3.2417 ± 1.78547
−2.540
0.012
> 3 cm
65
4.0769 ± 1.88280
Vascular invasion
No
90
4.2233 ± 1.68876
5.898
< 0.001
Yes
35
2.2686 ± 1.59609
TNM
I-II
54
4.0870 ± 1.96383
2.167
0.032
III-IV
71
3.3634 ± 1.75770
Lymph node metastasis
No
56
4.2089 ± 1.95897
2.948
0.004
Yes
69
3.2435 ± 1.70142
Pathological grading
I
17
4.2176 ± 1.94140
2.797b
0.065
II
78
3.8090 ± 1.85404
III
30
3.6760 ± 1.87670
Table 2
Clinicopathological parameters and the expression of miR-204-5p in LUAD. Annotation: LUAD, lung adenocarcinoma. a, paired sample’s t test performed to compare miR-204-5p expression between NSCLC and the controls; Independent sample’s t test processed to assess relationships between miR-30d-5p expression and the clinicopathological parameters of NSCLC. TNM, tumor, node, metastasis; b, One-way ANOVA preformed to evaluate distributive feature of miR-204-5p in three or more groups of clinicopathological parameters
Clinicopathological parameters
n
Relevant expression of miR-204-5p (2−ΔCq)
Mean ± SD
t/F-value
p-value
Tissue
LUAD
101
3.4663 ± 1.82397
-2.731a
0.007
Non-cancer
101
4.2786 ± 2.36824
Gender
Male
56
3.7768 ± 1.91937
1.934
0.056
Female
45
3.0800 ± 1.63729
Age (years)
< 60
41
3.7390 ± 1.85039
1.245
0.216
> = 60
60
3.2800 ± 1.79734
Smoke
No
26
4.1000 ± 1.67141
−0.695
0.491
Yes
18
4.4611 ± 1.72768
Tumor size
<=3 cm
53
3.0906 ± 1.72362
−2.218
0.029
> 3 cm
48
3.8813 ± 1.85915
Vascular invasion
No
70
4.1114 ± 1.63215
6.286
< 0.001
Yes
31
2.0097 ± 1.34123
TNM
I-II
44
3.8864 ± 1.87190
2.066
0.041
III-IV
57
3.1421 ± 1.73339
Lymph node metastasis
No
45
4.0556 ± 1.86822
3.027
0.003
Yes
56
2.9929 ± 1.65660
Pathological grading
I
17
4.2176 ± 1.94140
5.477b
0.006
II
61
3.6279 ± 1.81752
III
23
2.4826 ± 1.36070
Table 3
Clinicopathological parameters and the expression of miR − 204-5p in LUSC. Annotation: LUSC, lung squamous cell carcinoma. The rest were the same as Table 1. a, paired sample’s t test performed to compare miR-204-5p expression between NSCLC and the controls; Independent sample’s t test processed to assess relationships between miR-30d-5p expression and the clinicopathological parameters of NSCLC. TNM, tumor, node, metastasis; b, One-way ANOVA preformed to evaluate distributive feature of miR-204-5p in three or more groups of clinicopathological parameters
Clinicopathological parameters
n
Relevant expression of miR-204-5p (2−ΔCq)
Mean ± SD
t/F-value
p-value
Tissue
LUSC
23
4.6870 ± 1.80638
-2.264a
0.029
Non-cancer
23
6.0217 ± 2.17547
Gender
Male
18
4.8556 ± 1.68041
0.844
0.408
Female
5
4.0800 ± 2.31452
Age (years)
< 60
15
4.6933 ± 1.52572
0.020
0.985
> = 60
8
4.6750 ± 2.36628
Smoke
No
12
4.8500 ± 1.72495
0.444
0.662
Yes
11
4.5091 ± 1.95931
Tumor size
<=3 cm
7
4.3857 ± 1.96759
−0.520
0.608
> 3 cm
16
4.8188 ± 1.78222
Vascular invasion
No
20
4.6150 ± 1.86471
−0.485
0.633
Yes
3
5.1667 ± 1.56950
TNM
I-II
10
4.9700 ± 2.21512
0.650
0.523
III-IV
13
4.4692 ± 1.47783
Lymph node metastasis
No
11
4.8364 ± 2.28266
0.364
0.721
Yes
12
4.5500 ± 1.32150
Pathological grading
I
0
 
0.038b
0.848
II
16
4.6375 ± 1.80032
III
7
4.8000 ± 1.95959

Verification of miR-204-5p expression in TCGA

In this validation set, the levels of miR-204-5p were markedly decreased in NSCLC when compared to the normal control tissues (P = 0.000) (Fig. 2c). The TCGA Records were divided into TCGA-LUAD (containing 521 tumor cases and 46 controls) and TCGA-LUSC (478 tumor cases and 45 controls) due to the possibility of expression differences. The detected levels of miR-204-5p was lower in TCGA-LUAD, which was consistent with our results (P = 0.006) (Fig. 2d).

Results of data mining

Another 33 findings were selected for further analyses: 28 GEO datasets, 1 TCGA and 4 qualified publications. The first 11 investigations were involved monitoring of plasma samples, whereas the 22 analyzed solid tissues. The study by Guo W [15] was the only one derived from PubMed in this portion; papers 1 through 3 [2830] were Chinese articles. The included datasets contained 3168 NSCLC cases and 1542 control samples. After acquisition of miRNA-204-5p from GEO, the means and SDs were calculated to assess its status in NSCLC. Detailed outcomes are listed in Table 4 and scatter point plots are presented in Fig. 3, 4.
Table 4
Detailed information of all datasets used in SMD metaanalysis: eligible GEO datasets, TCGA, qualified publications and our RT-qPCR (represented as Current study). P<0.05 was considered as significant. Annotation: SMDstandard mean deviation, NOnumber, RTqPCRrealtime quantitative polymerase chain reaction. Since no citations were reflected for GSE24709, GSE46729, GSE93300, GSE19945 and GSE74190, websites were the alternatives
ID
Lead author
Year
Country
Source
Platform
Experimental type
Citation
Cancer No.
Control No.
T value
Pvalue
GSE16512
Lodes MJ
2009
USA
plasma
GPL8686
array
[31]
3
14
0.066
0.057
GSE17681
Keller A
2009
Germany
plasma
GPL9040
array
[32]
17
19
−1.104
0.009
GSE24709
Keller A
2011
Germany
plasma
GPL9040
array
[33]
28
19
2.289
0.000
GSE27486
Patnaik SK
2010
USA
plasma
GPL11432
array
[34]
22
23
1.699
0.518
GSE31568
Keller A
2011
Germany
plasma
GPL9040
array
[35]
32
70
1.527
0.363
GSE40738
Patnaik SK
2012
USA
plasma
GPL16016
array
[36]
86
59
−2.561
0.125
GSE46729
Godrey A
2014
USA
plasma
GPL8786
array
[37]
24
24
0.955
0.945
GSE61741
Keller A
2014
Germany
plasma
GPL9040
array
[38]
73
94
4.427
0.000
GSE68951
Leidinger P
2015
Germany
plasma
GPL16770
array
[39]
26
12
2.553
0.773
PMID:26497897
Guo W
2015
China
plasma
NR
RT-qPCR
[15]
126
50
NR
< 0.001
GSE93300
Liu X
2017
China
plasma
GPL21576
array
[40]
9
4
3.557
0.748
GSE2564
Lu J
2005
USA
tissue
GPL1987
array
[41]
14
4
−0.731
0.396
GSE14936
Seike M
2009
USA
tissue
GPL8879
array
[42]
26
26
−1.344
0.654
GSE15008
Tan X
2009
China
tissue
GPL8176
array
[43]
187
174
2.883
0.000
GSE16025
Raponi M
2009
USA
tissue
GPL5106
array
[44]
61
10
0.916
0.111
GSE18692
Puissegur M
2009
France
tissue
GPL4718
array
[45]
13
13
−5.072
0.617
GSE19945
Ohba T
2010
Japan
tissue
GPL9948
array
[46]
20
8
−1.305
0.289
GSE25508
Guled M
2011
Finland
tissue
GPL7731
array
[47]
26
26
1.868
0.080
GSE29248
Ma L
2010
China
tissue
GPL8179
array
[48]
6
6
−0.431
0.474
GSE36681
Jang JS
2012
USA
tissue
GPL8179
array
[49]
103
103
−3.282
0.001
GSE47525
van Jaarsveld MT
2013
Netherlands
tissue
GPL17222
array
[50]
18
14
−1.499
0.103
GSE48414
Bjaanaes MM
2014
Norway
tissue
GPL16770
array
[51]
154
20
−5.891
0.000
GSE51853
Arima C
2014
Japan
tissue
GPL7341
array
[52]
126
5
−1.63
0.103
GSE53882
Pu HY
2014
China
tissue
GPL18130
array
[53]
397
151
0.148
0.933
GSE56036
Fujita Y
2014
Japan
tissue
GPL15446
array
[54]
14
27
−0.756
0.204
GSE63805
Robles AI
2014
USA
tissue
GPL18410
array
[55]
32
30
0.449
0.074
GSE72526
Gasparini P
2015
Switzerland
tissue
GPL20275
array
[56]
67
18
− 3.904
0.000
GSE74190
Jin Y
2015
China
tissue
GPL19622
array
[57]
72
44
−1.306
0.141
GSE102286
Mitchell KA
2017
USA
tissue
GPL23871
array
[58]
91
88
−1.087
0.003
TCGA
NR
NR
NR
tissue
NR
array
NR
999
91
−3.055
0.000
Literature 1
Li LX
2017
China
tissue
NR
RT-qPCR
[28]
39
39
NR
<0.01
Literature 2
Xu YZ
2018
China
tissue
NR
RT-qPCR
[30]
60
60
9.361
0.000
Literature 3
Wang QC
2018
China
tissue
NR
RT-qPCR
[29]
72
72
11.028
<0.01
Current study
NR
NR
China
tissue
NR
RT-qPCR
NR
125
125
−3.507
0.007
Four GEO datasets assayed in plasma samples showed significant differences, but only two (GSE17681 and PMID:26497897) demonstrated the decreased level of miRNA-204-5p expression in NSCLC. Another 8 investigations performed in tissues were reflected in statistical significance except for the TCGA results; seven of these indicated a downregulation of miRNA-204-5p expression in NSCLC tissue specimens.

Integrated meta-analyses of miR-204-5p datasets in NSCLC

Each meta-analysis was first individually processed to evaluate level of miR-204-5p in the GEO data which covered 1747 NSCLC patients and 1105 control samples. Dysregulation of miR-204-5p was evident in NSCLC (SMD = − 0.098, 95% CI: − 0.310 to 0.114), but with poor statistical significance (P = 0.366) and high heterogeneity (I2 = 81.8%, P = 0.000) (Fig. 5a). Unexpected outcomes were obtained from subgroup meta-analysis, which indicated that the decrease was significantly different in both plasma (SMD = 0.374, 95% CI: 0.005 to 0.743, P = 0.047) and (SMD = − 0.098, 95% CI: − 0.310 to 0.114, P = 0.007) tissues, but also suggested a more sensitive response in cancerous tissues and evident heterogeneity (I2 > 90%, P = 0.000) as well (Fig. 5b). Random models were used to reduce the impact of heterogeneity.
An integrative meta-analysis of the entire data collection obtained from GEO, TCGA, publications and our RT-qPCR analyses was conducted to obtain a more precise assessment of miR-204-5p expression. Down-regulation of miR-204-5p in NSCLC (overall pooled SMD = − 0.447, 95% CI: − 0.750 ~ − 0.144, P = 0.004) was confirmed by the forest graph displayed in Fig. 6a, and the reduction was more significant in tissues (SMD = − 0.760, 95% CI: − 1.132 to − 0.378, P = 0.000) than in plasma (SMD = 0.224, 95% CI: − 0.301 to 0.749, P = 0.403) (Fig. 6b). Since substantial heterogeneity (I2 > 90%, P = 0.000) between data sources was noted between the data sources, a random model was adopted. The decline in miR-204-5p expression was more distinct in LUAD (SMD = − 0.258, 95% CI: − 0.685 to 0.169) than LUSC (SMD = − 0.012, 95% CI: − 0.406 to 0.382), though this subgroup analysis displayed weak statistical significance (P = 0.313) and considerable heterogeneity (I2 = 87.8%) (Fig. 6c). Furthermore, the reduction in miR-204-5p expression seemed more evident in LUAD tissues (SMD = − 0.554, 95% CI:-0.909 to − 0.199) than in plasma (SMD =1.176, 95% CI: − 0.397 to 2.748), but again the differences were not statistically significant (P = 0.236) and the data showed marked heterogeneity (I2 = 86.2%)(Fig. 6d).

Clinical role of miR-204-5p in NSCLC

In total,31 records, which included 4368 samples derived from 28 GEO datasets, 1 TCGA, 1 publication and our study (Table 5), were used for diagnosis meta-analysis to survey the clinical role of miR-204-5p in NSCLC. Prior to the diagnosis meta-analysis, ROC curve for every case was generated and 4-fold table data were calculated. As showed in Figs. 7 and 8, the ROC curves presented varied diagnostic value with most of them revealing relatively high region in solid tissues, in agreement with TCGA and our study (Fig. 9).
Table 5
Information and ROC fourfold table for all datasets. Annotation: No, number of NSCLC cases and the matched group, respectively; AUC, area under the receiver operating characteristic curve; TPtrue positive, FNfalse negative, FPfalse positive, TNtrue negative. Since no citations were reflected for GSE16512, GSE17681, GSE24709, GSE46729, GSE93300, GSE19945 and GSE74190, websites were the alternatives
ID
Author
Year
Country
Source
Citation
Cases/Controls No.
AUC
Threshold
Sensitivity
Specificity
TP
FP
FN
TN
GSE16512
Lodes MJ
2009
USA
plasma
[31]
3/14
0.536
−0.133
0.667
0.643
2
5
1
9
GSE17681
Keller A
2009
Germany
plasma
[32]
17/19
0.562
4.346
0.941
0.316
16
13
1
6
GSE24709
Keller A
2011
Germany
plasma
[33]
28/19
0.348
6.960
0.964
0.000
27
19
1
0
GSE27486
Patnaik SK
2010
USA
plasma
[34]
22/23
0.360
−0.025
0.045
0.957
1
1
21
22
GSE31568
Keller A
2011
Germany
plasma
[35]
32/70
0.409
5.016
0.875
0.816
28
57
4
13
GSE40738
Patnaik SK
2012
USA
plasma
[36]
86/59
0.582
−0.085
0.953
0.237
82
45
4
14
GSE46729
Godrey A
2014
USA
plasma
[37]
24/24
0.434
4.193
0.417
0.667
10
8
14
16
GSE61741
Keller A
2014
Germany
plasma
[38]
73/94
0.346
6.828
1.000
0.011
73
93
0
1
GSE68951
Leidinger P
2015
Germany
plasma
[39]
26/12
0.212
3.575
0.923
0.083
24
11
2
1
PMID:26497897
Guo W
2015
China
plasma
[15]
126/50
0.809
0.023
0.760
0.820
96
9
30
41
GSE93300
Liu X
2017
China
plasma
[40]
9/4
0.056
−3.499
1.000
0.000
9
4
0
0
GSE2564
Lu J
2005
USA
tissue
[41]
14/4
0.741
5.835
0.786
0.750
11
1
3
3
GSE14936
Seike M
2009
USA
tissue
[42]
26/26
0.607
8.545
0.500
0.731
13
7
13
19
GSE15008
Tan X
2009
China
tissue
[43]
187/174
0.447
7.941
0.294
0.776
55
39
132
135
GSE16025
Raponi M
2009
USA
tissue
[44]
61/10
0.454
4.813
0.131
1.000
8
0
53
10
GSE18692
Puissegur M
2009
France
tissue
[45]
13/13
0.917
−0.076
0.846
0.923
11
1
2
12
GSE19945
Ohba T
2010
Japan
tissue
[46]
20/8
0.769
−0.342
0.700
0.875
14
1
6
7
GSE25508
Guled M
2011
Finland
tissue
[47]
26/26
0.348
9.004
1.000
0.000
26
26
0
0
GSE29248
Ma L
2010
China
tissue
[48]
6/6
0.583
10.704
0.833
0.500
5
3
1
3
GSE36681
Jang JS
2012
USA
tissue
[49]
103/103
0.619
10.847
0.845
0.417
87
60
16
43
GSE47525
van Jaarsveld MT
2013
Netherlands
tissue
[50]
18/14
0.661
2.755
0.389
0.929
7
1
11
13
GSE48414
Bjaanaes MM
2014
Norway
tissue
[51]
154/20
0.900
1.503
0.825
0.900
127
2
27
18
GSE51853
Arima C
2014
Japan
tissue
[52]
126/5
0.821
−4.558
0.659
1.000
83
0
43
5
GSE53882
Pu HY
2014
China
tissue
[53]
397/151
0.521
0.965
0.554
0.589
220
62
177
89
GSE56036
Fujita Y
2014
Japan
tissue
[54]
14/27
0.574
3.960
0.929
0.333
13
18
1
9
GSE63805
Robles AI
2014
USA
tissue
[55]
32/30
0.468
1.443
0.250
0.933
8
2
24
28
GSE72526
Gasparini P
2015
Switzerland
tissue
[56]
67/18
0.786
1.793
0.731
0.833
49
3
18
15
GSE74190
Jin Y
2015
China
tissue
[57]
72/44
0.620
0.472
0.583
0.705
42
13
30
31
GSE102286
Mitchell KA
2017
USA
tissue
[58]
91/88
0.503
−0.529
0.714
0.443
65
49
26
39
TCGA
NR
NR
NR
tissue
NR
999/91
0.671
1.657
0.520
0.901
519
9
480
82
Current study
NR
NR
China
tissue
NR
125/125
0.613
2.350
0.320
0.864
40
17
85
108
The miR-204-5p diagnostic accuracy and its significance in NSCLC was further examined in SROC plots integrating all GEO datasets, TCGA, publications and our study to arrive at a reliable conclusion. Simultaneous subgroup analysis was conducted on the experimental sources and tumor types. The whole combined area under the curve (AUC) was 0.74 (95% CI: 0.70–0.77) with a sensitivity and specificity of 0.76 and 0.58 respectively (Fig. 10). The AUCs from different sample origins were similar to the combined AUC, whereas polarization of the sensitivity and specificity was evident in the plasma portion (Fig. 11). The AUC was larger for the entire LUAD group (0.78, 95% CI: 0.74–0.81) than for the entire LUSC group (0.66, 95% CI: 0.62–0.70), and showed higher sensitivity (0.63 to 0.32) and lower specificity (0.78 to 0.90). However, significant heterogeneity was evident by the large Q and I2 values, except in the LUSC subgroup (Table 6).
Table 6
Diagnostic accuracy evaluation of miR-204-5p by ROC analysis. Annotation: AUC, area under the receiver operating characteristic curve; 95% CI, 95% confidence interval; LL, lower limit; UL, upper limit; Q, heterogeneity Q test; Phet, P value of heterogeneity. LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma
Sample type
Study number
Enrolled number
AUC
Overall estimate
Heterogeneity
Pretest probability
95% CI (LL-UL)
sensitivity
specificity
Q
I2 (%)
Phet
Overall
31
4368
0.74
0.70–0.77
0.76
0.58
864.488
99.77
0.000
0.686
Tissue
20
3534
0.75
0.71–0.78
0.65
0.74
328.601
99.39
0.000
0.722
Plasma
11
834
0.70
0.66–0.74
0.90
0.27
279.536
99.28
0.000
0.535
LUAD
7
1269
0.78
0.74–0.81
0.63
0.78
150.286
98.67
0.000
0.742
LUSC
4
1001
0.66
0.62–0.70
0.32
0.90
3.112
35.72
0.106
0.748
LUAD-tissue
5
1211
0.79
0.75–0.82
0.61
0.81
106.761
98.13
0.000
0.752

Prognostic evaluation of miR-204-5p in NSCLC

The K-M plots of our RT-qPCR data indicated a correlation between the NSCLC survival rate and miR-204-5p expression, as patients with higher levels of miR-204-5p survived longer than those with lower expression, although the difference did not meet statistical significance (Log Rank P = 0.231) (Fig. 12a).
Only two publications were deemed eligible for prognostic assessment. The general information of 2 included references, 3 GEO datasets, and our study matched the required assessment conditions for a sum of 415 participants, as shown in Table 7. The HR and 95% CI were not included in the paper by Shi L [59], so they were calculated from the K-M survival curves, and the results with high statistical significance was considered in the selection for next step.
Table 7
Detailed information for miR-204-5p survival analysis. Annotation: HR, hazard ratio; LL, lower limit of the 95% confidence interval; UL, upper limit of the 95% confidence interval; OS, overall survival
ID
Author
Year
Country
Sample type
Citation
Cutoff
Method
Survival type
Sample size
HR
LL
UL
GSE16025
Raponi M
2009
USA
tissue
[44]
median
Univariate analysis
OS
61
1.322
0.675
2.590
GSE63805
Robles AI
2014
USA
tissue
[55]
median
Univariate analysis
OS
32
2.060
0.951
4.463
PMID:25412236
Shi L
2014
China
tissue
[59]
median
Kaplan–Meier analysis
OS
48
1.770
0.790
3.950
PMID:26497897
Guo W
2015
China
plasma
[15]
median
Univariate analysis
OS
126
1.936
1.193
3.143
GSE102286
Mitchell KA
2017
USA
tissue
[58]
median
Univariate analysis
OS
91
0.776
0.495
1.215
Current study
NR
NR
China
tissue
NR
median
Univariate analysis
OS
57
0.640
0.306
1.340
The results shown in Fig. 12b and c indicate that the use of miR-204-5p as an auxiliary prognostic risk factor for NSCLC patients is not possible at present, due to the lack of statistical significance in the prognostic meta-analysis (95% CI: 0.660 to 1.188), and a lack of large-scale investigations in plasma.

Screening and validation of miR-204-5p target genes

In total, 4399 target genes were identified in at least six online predicted applications from miRwalk and 4371 up-regulated genes with FDR<0.05 were screened from TCGA. Subsequent analysis therefore focused on 541 over-active candidate genes from the intersection of miRwalk and TCGA.
The DAVID online tool identified 106 terms from the GO analysis and the top 3 most significantly enriched items associated with biological process (BP), cellular component (CC), and molecular function (MF) are listed in Table 8 (P < 0.05). The relevant target genes were chiefly involved in neuron projection, transcription factor activity, RNA polymerase II transcription regulation, extracellular matrix metabolism, and ion channel activity. In addition, 7 enriched pathways of KEGG analysis were collected from the same platform. As shown in Table 8, the top 3 signal pathways (P < 0.05) were connected with microRNAs in cancer, cell adhesion molecules (CAMs), and signaling pathways regulating pluripotency of stem cells.
Table 8
Top three items of GO and KEGG analysis. Annotation: GO, gene ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes
Category
ID
Term
Count
%
P value
Genes
BP
GO:0001764
neuron migration
13
0.015581
3.06E-06
PHOX2B, NDE1, SATB2, CDK5R1, CDK5R2, NAV1, SOX1, NTRK2, CELSR3, NEUROD4, DCX, FBXO45, PITX2
BP
GO:0051965
positive regulation of synapse assembly
8
0.009588
4.35E-04
SLITRK1, SRPX2, NTRK2, IL1RAP, EFNA5, TPBG, EPHB1, EPHB2
BP
GO:0008284
positive regulation of cell proliferation
17
0.020375
6.96E-04
CDC7, FGF5, HMX2, E2F3, RARG, PKHD1, SOX4, GREM1, EPHA1, GDNF, IL11, HDAC1, TFAP2B, POU3F2, EIF5A2, DPP4, DLG1
CC
GO:0043005
neuron projection
12
0.014382
2.91E-04
TENM4, TENM1, KIF5A, STMN2, SLC6A2, OPRK1, BCL11B, KIF5C, STMN4, GABBR2, DCX, CALB1
CC
GO:0005887
integral component of plasma membrane
38
0.045544
7.23E-04
GPR83, SLC5A3, SLC13A5, SLC20A2, SLC6A2, OPRK1, LRRC8D, GNRHR, CNGB3, SLC52A3, LGR4, EPHB1, EPHB2, EPCAM, ADRA2A, HCN3, HCN1, SLC12A7, GABRG2, CLCA2, RET, SLC6A17, MMP15, EPHA1, GRM1, SLC7A11, TIGIT, TENM4, EPHA7, SLC16A7, TMPRSS11D, TENM1, SLC6A8, SLC17A4, NTRK2, CLDN1, KCNH8, HAS3
CC
GO:0005667
transcription factor complex
12
0.014382
0.005459
E2F3, SATB2, BARX2, RARG, HNF1A, TRPS1, SIX1, TP63, POU3F2, TBL1X, TP73, PITX2
MF
GO:0005248
voltage-gated sodium channel activity
5
0.005993
5.84E-04
HCN1, SCN8A, SCN5A, HCN3, SCN4A
MF
GO:0005249
voltage-gated potassium channel activity
7
0.00839
8.94E-04
HCN1, KCNQ5, KCNH8, KCNA7, HCN3, CNGB3, KCNE4
MF
GO:0001077
transcriptional activator activity, RNA polymerase II core promoter proximal region sequence-specific binding
15
0.017978
0.001203
PHOX2B, FOXL2, SOX1, ONECUT2, SOX4, TP63, SIX2, HLTF, TP73, HOXC11, BCL11B, SIX1, TFAP2B, TFAP2A, POU3F2
KEGG
cfa05206
MicroRNAs in cancer
10
0.011985
0.005364
E2F1, DNMT3A, E2F3, WNT3, MMP9, IGF2BP1, TP63, CDK6, MMP16, HMGA2
KEGG
cfa04514
Cell adhesion molecules (CAMs)
8
0.009588
0.039758
TIGIT, SDC1, CLDN19, CLDN1, CNTNAP2, VCAN, NRXN1, CDH2
KEGG
cfa04550
Signaling pathways regulating pluripotency of stem cells
8
0.009588
0.04251
DVL3, FZD10, WNT3, HNF1A, INHBE, JARID2, NEUROG1, JAK3
Taking the differences in genetic expression and function into account, the PPI network of 117 DEGs from the top three GO items and KEGG pathways was explored by STRING and visualized by Cytoscape to determine the interaction between the proteins encoded by candidate target genes. As Fig. 13 shows, the network consisted of 117 nodes and 130 edges. The top 6 proteins with the highest degrees of connectivity were HDAC1 (degree = 10), SCN8A (degree = 9), DLG1 (degree = 8), EPHB2 (degree = 8), GDNF (degree = 8) and CALB1 (degree = 8).
Scatter point plots from GEPIA indicated that expression of the six hub genes was elevated in NSCLC, and that EPHB2 had the most apparent variation. Of particular interest, DLG1 and GDNF showed a pronounced trend of over-expression in LUSC (Fig. 14). Besides no record about SCN8A, THPA confirmed a similar tendency for an increased expression of HDAC1, DLG1, EPHB2 and CALB1(Fig. 15), while GDNF was not apparently changed in either normal or lung cancer tissues; no data were available for SCN8A. In addition to GDNF (aliases ATF or ATF2) identified from the available literature [60], EPHB2 and DLG1 have been proposed as suitable targets of miR-204-5p. Further comprehensive investigations and systematic evaluations are needed to confirm this hypothesis because of small sample size of THPA and a lack of statistical analysis.

TFs and the miR-204-5p regulatory network

In the present work, 61, 89, and 66 TFs related to miR-204-5p were obtained from GTRD, HTFtarget and Transmir, respectively. TF prediction was mainly matched examined for GDNF, DLG1, and EPHB2 since these genes were implicated as likely target genes. In total, GTRD and HTFtarget revealed 378 and 122 TFs of EPHB2, 408 and 171 TFs of DLG1, 271and 89 TFs with GDNF, respectively. The intersection outcome revealed MAX, MYC, and RUNX1 as the main TFs associated with miR-204-5p, GDNF, EPHB2, and DLG1(Fig. 16), while MAX was associated with miR-204-5p by TFBS prediction in JASPAR, which included some similar sequences in miR-204-5p, EPHB2, and GDNF. In addition, the putative TFBSs of MYC approached a certain degree of coincidence with MAX (Table 9). Binding competition of miRNA towards hub genes was confirmed by the miRwalk database and publication, but pairwise interactions of miR-204-5p with TFs and TFs interactions with hub genes could not be definitively constructed due to lack of database prediction, experimental proof and literature confirmation, so structural motifs of miR-204-5p networks could not be established (Fig. 17).
Table 9
The predicted transcription factors and the predicted sequences for miR-204-5p and the main hub genes
Gene
TF name
Score
Relative score
Start
End
Strand
Predicted sequence
miR-204-5p
MAX
6.92367
0.811791
21
30
+
TGACTCGTGG
DLG1
MAX
8.54191
0.861233
2277
2286
+
AAACAAGTGA
RUNX1
7.92698
0.834755
2446
2456
+
TTATGAGGTAG
EPHB2
MAX
10.4915
0.928629
402
411
+
TCCACGTGGA
MYC
11.9965
0.918300
401
412
+
ATCCACGTGGAG
GDNF
MAX
6.56373
0.800793
116
125
+
AGTCTCGTGC
MYC
6.37509
0.800941
116
127
+
AGTCTCGTGCTC
RUNX1
10.8526
0.910532
1943
1953
+
AGTTGTGGTTT

Discussion

The data presented here verified the decrease in miR-204-5p expression in NSCLC by comprehensive analysis of RT-qPCR, microarrays, sequencing data, and publications and revealed an obvious decrease in cancerous tissues and the LUAD subtype. An auxiliary role for miR-204-5p was also identified by in NSCLC, particularly in tissues and LUAD, which was verified by the meta-analysis. Unfortunately, the prognostic implications for miR-204-5p were weak and showed no statistical significance in the meta-analysis, due to a shortage of large-scale investigations. Nevertheless, the information provided by GO annotation and KEGG analysis indicated that the target genes of miR-204-5p were associated with neuron projection, transcription factor activity, RNA polymerase II transcription regulation, extracellular matrix (ECM) metabolism, and ion channel activity, as well as were connected with microRNAs in cancer, cell adhesion molecules (CAMs) and signaling pathways regulating pluripotency of stem cells. Three of the six hub genes, GDNF, EPHB2 and DLG1, were selected for continued research due to their distinct characteristics in NSCLC. TF prediction revealed speculatively functional relations of MAX, MYC, and RUNX1 between miR-204-5p and these three genes, although only MAX demonstrated the TFBS sequences connected with miR-204-5p upon further query.
At present, miR-204-5p has aroused considerable interest in cancer research for its dual function as an oncogene and tumor suppressor [14]. MiR-204-5p is clearly attenuated in NSCLC, and its expression is negatively linked with tumor size, clinical stage, and metastasis [2830, 59, 61]. Downregulation of miR-204 occurs in part due to its hypermethylation in the promoter region [59]. Elevated expression of miR-204-5p depresses NSCLC migration and invasion by targeting Janus kinase 2 (JAK2) [17], restrains proliferation of NSCLC cells by regulating SIX homeobox 1 (SIX1) and attenuates LUAD angiogenesis potentially by JAK2-signal transducer and activator of transcription 3 (JAK2-STAT3) pathway [16]. In addition, miR-204-5p serves as a cancer suppressor gene by modulating oncogenic Wnt/FZD signaling pathways [62], inhibiting NUAK family kinase 1 (NUAK1) in NSCLC [59], and mediating a long-noncoding RNA (lncRNA) MALAT1 effect on the epithelial-to-mesenchymal transition (EMT) and cells invasion [63]. Our results confirmed the downregulation of miR-204-5p expression in NSCLC and revealed a constant level of decline in LUAD.
The integrative meta-analysis also indicated a promising role for miR-204-5p for NSCLC screening, as did subgroup SROC curves, even though the sample origins were different. The variation in miR-204-5p expression in tissues was helpful in diagnosis of LUAD than of LUSC. Considering the invasive work, high cost, and cumbersome procedure of tissue biopsy, analysis of blood circulating miR-204-5p was considered an attractive screening indicator. However, low sensitivity, high specificity, and small sample sizes of the currently available data mean that more research and detailed profiling at all levels are needed to provide information to confirm the effectiveness of blood screening.
The correlation between the low miR-204-5p and high risk of death in patients with NSCLC [15, 59] was evident in our study but failed to reach statistical significance. The subsequent integrative meta-analysis was conducted to gain insights into the potential usefulness of miR-204-5p in NSCLC prognosis, but the data from the literature and from the present study regarding the ability of miR-204-5p to predict survival times of patients with NSCLC are conflicting. Consequently, no conclusion can be made in terms of miR-204-5p for NSCLC, at least for now.
The GO analysis indicated that the estimated target genes were mainly enriched in neuron projection, transcription factor activity, RNA polymerase II transcription regulation, ECM metabolism and ion channel activity, suggesting a potential involvement of miR-204-5p in the molecular function and signal modulation associated with NSCLC biological processes. The KEGG pathway analysis indicated that some of candidate genes were participated in microRNAs in cancer, CAMs, and signaling pathways regulating pluripotency of stem cells. Like other miRNAs, miR-204-5p plays an indispensable role in cancer proliferation, migration, and metastasis by regulating the tumor microenvironment, such as ECM structure and CAM metabolism [64]. Cancer stem cells (CSCs) attain stemness by complicated processes and signaling pathways, such as JAK-STAT, nuclear factor kappa B, Sonic hedgehog, transforming growth factor beta, Wnt/β-catenin, and PI3K/AKT [65, 66]. Many miRNAs take part in processes that maintain a balance between differentiation and quiescence of pulmonary CSCs, adjust the tumor microenvironment and affect cell cycle progression via regulation of these signaling pathways [67]. Consequently, miR-204-5p and its target genes could serve as important determinants of NSCLC pathogenesis and development.
Continued investigation of the hub genes involved in GO enrichment and KEGG Pathway analysis identified six leading relevant genes that were screened out due to binding to 3’UTR of miRNA. However, only GDNF has been investigated for a direct relationship with miR-204-5p in NSCLC [60].
GDNF, also called ATF or ATF2, is a well-characterized oncogene that promotes tumor growth, invasion, and metastasis, in addition to tumor microenvironment alterations [68]. GDNF expression occurs high level in NSCLC, though a significant difference exists with regard to factors such as race, gender, age, smoking status, and histologic subtype [69]. GDNF is upregulated at the transcript level in LUSC [70], and is hypermethylated in tumor tissues [71]. It also facilitates demethylation of the fibromodulin promoter and promotes subsequent angiogenesis in human glioblastomas [72]. Nerve-derived GDNF increases programmed death ligand 1 (PD-L1) levels in head and neck squamous cell carcinoma cells by activating the JAK2-STAT1 signaling pathway, which in turn promotes the evasion of cancer cells from immune system surveillance in the nerve-cancer microenvironment [73]. Recent research in colorectal cancer (CRC) has indicated that miR-196a-5p exerts its function in cell proliferation and migration by regulating GDNF expression [74], while miR-451 influences drug resistance in renal cell carcinoma by targeting GDNF [75]. GDNF is also targeted and regulated by miR-204-5p which inversely affects GDNF mRNA and protein levels, to inhibit NSCLC growth, migration, and cell cycle alteration and promote apoptosis [60]. Therefore, the interaction between miR-204-5p and GDNF appears to be critical in the development and progression of NSCLC and requires thorough research.
DLG1 is a vital participant in the control of cellular processes like polarity, proliferation and migration, so its dysregulation and mutation give rise to pathologies that include oncogenic processes [76]. DLG1 is mainly identified as a tumor suppressor, since overexpression is observed early in the onset of cervical cancer (CeCa) [77] and elevated DLG1 promotes intestinal tumorigenesis [78], predicts poor prognosis in people with CRC [79] and increases the invasiveness of NSCLC cell lines [80]. Increased phosphorylation of the DLG1 SH3-Hook region promotes interaction with the PDZ ligand of PKCα and accelerates cell migration [80]. The lncRNA DLG1-AS1 acts as a competitive inhibitor that influences the activity of miR-107 on its target gene ZHX1, thereby inducing cancer cell proliferation [81]. Moreover, DLG1 deficiency results in incorrect spindle polarity and a delay in cells transiting orientation [78], which disrupts cellular structure and distribution [82]. Interestingly, DLG1 protein levels are significantly lower in NSCLC and hepatocellular carcinoma (HCC) than in the corresponding normal tissues [83, 84], but are nearly undetectable in poorly differentiated stages of colon adenocarcinoma [85], in contrast to our findings and the existing literature. One possible reason is that DLG1 dysregulation in advanced tumor progression or in more malignant forms depends on its spatial/temporal distribution. Future research should focus on this possibility.
A series of studies have reported a direct correlation between EPHB2 expression and numerous human malignancies, including NSCLC. EPHB2 activates bidirectional signaling cascades and its upregulation predicts poor survival in LUAD [86], CRC [87], breast cancer [88] and malignant mesothelioma [89]. One study indicated that EPHB2 enhances cellular growth, migration and invasion in CeCa by a competitive inhibition that counteracts the miR-204 effect on cell cycle arrest, Bax overexpression and PI3K/AKT signaling pathway deactivation via competitive inhibition [90]. Expression of miRNAs also significantly suppresses EPHB2 expression, resulting in a decrease of tubulogenesis and angiogenesis [91, 92]. EPHB2 affects cell viability in medulloblastoma in part by promotion of the G2/M phase of the cell cycle [93]. Activation of EPHB2 promotes the progression of cutaneous squamous cell carcinoma cells by accelerating the production of invasive proteinases like MMP13 and MMP1 [94]. Nevertheless, some publications highlight EPHB2 declines in CRC, which was supposedly attributable to EMT modulation [95] and epigenetic modification of promoter [96, 97]. Future research could identify where whether specific differences in the interaction of EPHB2 and miR-204-5p are associated with NSCLC.
As discussed above, the function of miR-204-5p in NSCLC is also influenced by TFs as well, but an unanswered question is whether TFs regulate miR-204-5p or whether TFs could be adjusted or controlled by this miRNA in some way. Although great achievements have been made in understanding the biological behavior of miR-204-5p and its mRNA targets, integrative analysis of miRNA-TF-gene regulatory networks is still needed, as TFs are undoubtedly involved in pulmonary cancer initiation, progression, dissemination, recurrence, and even drug resistance [98]. At least ten kinds of TF-miRNA synergistic regulatory networks apparently function in NSCLC [99]. In addition to combining with and regulating its target genes, miR-204-5p also attenuates some angiogenic inducers like hypoxia inducible factor-1α (HIF-1α) to impair angiogenesis in LUAD [16]. Another study has demonstrated a dependence of miR-204-5p level on promoter hypermethylation and support by positive feedback of three TFs, c-MYB, ETS1 and RUNX2 [100]. Osterix, a transcription factor that is essential and specific for osteogenesis, coordinately modulates miR-204-5p and its endogenous competitors, as well as ultimately establishing a feed-forward loop (FFL) ultimately [101]. Activation of STAT3 suppresses miR-204-5p activities, in turn affecting proliferation and apoptotic resistance in human pulmonary arterial hypertension and nasopharyngeal carcinoma [102, 103]. A positive FFL between Hepatitis B virus, miR-204-5p, and STAT3 appears to contribute to HCC incidence [104].
Other work has suggested that miR-204-5p reciprocally represses TrkB expression; however, TrkB expression noticeably increases JAK2 and STAT3 phosphorylation. The phospho-STAT3 then directly binds to promoter sequence of miR-204-5p, resulting in increased clonogenic proliferation, migration and invasion in endometrial carcinoma, via a feed-backward-loop (FBL) motif of TF-miRNA-target gene [105]. The activity of the IL-6R/STAT3/miR-204-5p FBL also leads to chemosensitivity [106]. The possibility exists that, at molecular level, the cellular activities involved in NSCLC progression, including tumor cell proliferation, differentiation, invasion, apoptosis, recurrence, and even drug resistance, are associated with downregulation of miR-204-5p and account for the direct upregulation of its target genes as well as unidirectional or bidirectional activation of TFs.
Among the three TFs expected to take part in miR-204-5p networks, MAX forms a dimer-complex system of transcriptional regulation with other family members, which include MYC, Mad and Mxi1, and is implicated in cell proliferation, differentiation and apoptosis [107]. The sequence similarity in MAX and MYC TFBS in the current work was predicted based on the existence of the multiprotein complex. An increased expression of miR-22 in leukemia cells reduces the MAX expression level, blocking cell cycle progression at the G1 phase [108]. MAX expression in HCC activates Linc00176, which is a competing endogenous lncRNA (ceRNA) of tumor-suppressive miRNA, resulting in cell cycle acceleration and reduction of apoptosis by reducing the levels of miR-9 and miR-185 [109]. In CRC, a MAX/MYC heterodimer induced by elevated HIF-2α mediates transcriptional repression of hypoxia-related miR-15-16, leading to tumor angiogenesis and hematogenous metastasis by further loss of post-transcriptional restriction towards fibroblast growth factor-2 [110]. MYC, also known as MYCC and c-Myc, is frequently amplified in numerous human cancers via transcriptional regulation of specific target genes, including miRNA and lncRNA [111]. MiR-296 − 3p directly targets PRKCA to impair FAK-Ras-MYC signaling, thereby accelerating its own transcription in a FBL that obstructs the EMT signal and progression through the cell cycle, following suppression of cell proliferation, metastasis and chemosensitivity in LUAD [112]. Another study has suggested that miR-342-3p is capable of indirectly adjusting MYC by directly repressing E2F1, a MYC-collaborating molecule [52]. In breast tumor, MYC expression correlates positively with miR-203b-3p and miR-203a-3p but negatively with BCL2L1 expression, resulting in formation of a TF-FFL [113]. MIR7-3HG restrains MYC dephosphorylation by downregulation of AMBRA1 to form a positive feedback loop for its own expression and further contributing significantly to autophagic control [114].
As a crucial hematopoietic transcription factor, RUNX1 is well-documented in chromosomal translocations and in several types of carcinogenesis processes [115]. RUNX1 is positioned in the center of miRNA circuits relevant for malignant hematopoiesis in transcriptional programs [116, 117]. A RUNX1-microRNA-139-HCP5 axis shows a positive FBL for mediating the tumor-suppressive effects of glioma cells [118]. A miR-18a-RUNX1-ZO-1 regulatory network also increases the permeability of the blood-tumor barrier (BTB), thereby providing novel potential targets for drug transportation across the BTB as an attractive strategy for glioma treatment [119]. By binding to the miRNA promoter, RUNX1 increases the transcriptional level of miR-27a in breast cancer and concomitantly the decreases expression of ZBTB10, a direct target gene of miR-27a, to promote endothelial differentiation and subsequent angiogenesis and tumor metastasis [120]. Conversely, reduced expression of Runx1 in breast cancer cells leads to elevated expression of both pre-miR-378 and PPARGC1B, which is a host gene of miR-378, to create a FBL on that reduces cell migration and invasion [121]. The miR-204-5p circuits and its hub genes and TFs still await identification, but TFBS prediction was capable of offering fresh perspectives, and likewise, assisting in new theoretical insights into potential regulatory mechanisms. Based on the available data, MAX would appear likely to be a vital TF involved in miR-204-5p-mRNA interactions, since it was the only assumed attachment that focused on the upstream region of genetic sequences in the current work.
This study had drawbacks and limitations. One limitation is that the findings indicate a great heterogeneity between the data sources, which were then explored via a random effects model and subgroup meta-analyses. The trial quality was also generally poor due to heterogeneity that remained above 50%. One possible cause of the statistical heterogeneity is that the data were generated using different sources, operating protocols, and detection metrics. Univariate survival analysis is also the only appraisal method for determining the prognostic significance of miR-204-5p. A carefully designed evaluation system should be developed to provide a more in-depth assessment of this issue. In particular, time limits and tight budgets have prevented a satisfactory generation of experimental proof to validate the function of miR-204-5p regulatory networks with the target genes and TFs. In addition, the biological progression and molecular regulation of NSCLC is complicated, so other mechanisms mediated by miRNA circuits should also be addressed with in-depth research.

Conclusion

As the most frequent type of pulmonary cancer, NSCLC deserves more effort in achieving the goal of early detection and timely treatment. The findings presented in the current research demonstrated an attenuation of miR-204-5p expression in NSCLC, this decrease was more frequently observed in cancerous tissues and in the LUAD subtype and was, in part, helpful for diagnosis. The activities of miR-204-5p as an anti-oncogene were induced by its regulatory axes or circuits with target genes and TFs that participated in specific genetic pathways and biological processes.

Acknowledgements

The authors appreciate all the participants and the patients for making contributions to this work.
The research proposal was ratified by Committee on Ethics of the First Affiliated Hospital of Guangxi Medical University. Consent was obtained from each subject or their legally authorized representative at the time of enrollment.
Not applicable.

Competing interests

The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://​creativecommons.​org/​licenses/​by/​4.​0/​), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated.

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Metadaten
Titel
Downregulation of hsa-microRNA-204-5p and identification of its potential regulatory network in non-small cell lung cancer: RT-qPCR, bioinformatic- and meta-analyses
verfasst von
Chang-Yu Liang
Zu-Yun Li
Ting-Qing Gan
Ye-Ying Fang
Bin-Liang Gan
Wen-Jie Chen
Yi-Wu Dang
Ke Shi
Zhen-Bo Feng
Gang Chen
Publikationsdatum
01.12.2020
Verlag
BioMed Central
Erschienen in
Respiratory Research / Ausgabe 1/2020
Elektronische ISSN: 1465-993X
DOI
https://doi.org/10.1186/s12931-020-1274-9

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