Identification and characterization of miR-96, a potential biomarker of NSCLC, through bioinformatic analysis

  • Authors:
    • Tonghui Cai
    • Jie Long
    • Hongyan Wang
    • Wanxia Liu
    • Yajie Zhang
  • View Affiliations

  • Published online on: June 27, 2017     https://doi.org/10.3892/or.2017.5754
  • Pages: 1213-1223
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Abstract

Lung cancer is the leading cause of cancer-related death worldwide. The poor prognosis is partly due to lack of efficient methods for early diagnosis. MicroRNAs play roles in almost all aspects of cancer biology, and can be secreted into the circulation and serve as molecular biomarkers for the early diagnosis of cancer. In the present study, we determined the expression of miR-96 and the function of its target genes in lung cancer through bioinformatic analysis. Four microRNA expression profiles of lung cancer were downloaded from Gene Expression Omnibus and the data were analyzed using SPSS 16.0 software. Compared to the control group, expression of miR-96 was significantly increased in non-small cell lung cancer (NSCLC) (GSE51855), lung adenocarcinoma (GSE48414), stage I adenocarcinoma tissues (GSE63805) and the plasma of lung cancer patients (GSE68951). miR-96 was also elevated in six different NSCLC cell lines. However, the expression level of miR-96 was not related to the age, gender, clinical stage and histological subtype of the NSCLC patients. GO analysis of 78 predicted target genes of miR-96 showed that 42 of the obtained GO terms are highly associated with specific cellular processes including response to stimulus, signaling pathway, cell division, cell communication, cell migration and calcium signaling. KEGG results indicated that the miR-96 targets are mainly involved in the GnRH signaling pathway, long-term potentiation and insulin signaling pathway. In conclusion, miR-96, functioning as an oncogene, may play an important role in the development and progression of lung cancer. miR-96 may have the potential to serve as a molecular biomarker for the early diagnosis of NSCLC.

Introduction

Based on GIOBALCAN, an estimated 1.8 million new lung cancer cases and 1.6 million deaths occurred in 2012. This makes lung cancer the leading cause of cancer-related death among males worldwide and females in developed countries (1). The poor prognosis is attributed to the lack of efficient methods for early diagnosis and lack of successful treatment for metastasis. Since non-small cell lung cancer (NSCLC), which accounts for approximately 85% of all lung cancer cases, is not very sensitive to chemotherapy and/or radiation, surgery remains the treatment of choice. However, most newly diagnosed NSCLC patients cannot undergo surgery due to local invasion or distant metastasis. Therefore, it is particularly important to study the molecular mechanisms underlying NSCLC, which may provide novel molecular targets for the early diagnosis of lung cancer.

MicroRNAs (miRNAs) are a group of non-coding RNAs (~22 nucleotides) that can degrade target mRNA transcripts directly or suppress their translation through complete or partial complementarity recognizing the 3′UTR of target mRNAs (2). miRNAs have been proven to play an important role in the post-transcriptional regulation of gene expression and are involved in almost all aspects of cancer biology such as tumor transformation, growth, angiogenesis and epithelial-mesenchymal transition by inhibiting specific oncogenes or tumor-suppressor genes. Accumulating data indicate that miRNAs are present in body fluids including blood plasma and serum, urine, saliva and semen (3,4) and circulating miRNA levels are more accurate than the protein-coding gene profiles in tumor typing (5). Therefore, miRNAs are more likely to be novel molecular biomarkers in the screening and monitoring of cancer patients (6).

In our previous study, we found that DAL-1 (differentially expressed in adenocarcinoma of the lung-1; also known as EPB41L3, 4.1B) has an important role in the invasion and metastasis of NSCLC (7). By using microRNA.org, TargetScan and PicTar, we predicted four miRNAs, miR-26a, miR-26b, miR-96 and miR-223, that regulate DAL-1. Data from several studies previously showed that miR-223 does not only promote the invasion of lung cancer cells but also the metastasis of gastric cancer via targeting tumor suppressor DAL-1 (8,9). Our previous study demonstrated that both miR-26a and DAL-1 gene expression are decreased in NSCLC, and DAL-1 is not a real target gene of miR-26a (10). Both miR-26b and miR-26a belong to the miR-26 family, and miR-26b has low expression levels in many types of cancer, such as epithelial ovarian (EOC) (11), hepatocellular carcinoma (HCC) (12), as well as colorectal cancer (13). In this study, we chose miR-96 as our research target.

MicroRNA-96 (hsa-miR-96, miR-96), located on chromosome 7 (7q31~34), belongs to the miR-183 gene family, which is the first gene cluster to be reported in the development and function of ciliated ectodermal cells and organs and is essential for the development and function of animal sensory organs (14,15). With the growing interest in the miR-183 gene family, miR-96 has been detected to be highly expressed in various human tumors and involved in cancer development by regulating key genes in tumor cell division and apoptosis (1618). Although studies have shown that miR-96 is overexpressed in lung cancer (19,20), it still remains unclear whether miR-96 could be used for early diagnosis and how miR-96 affects the progression of lung cancer. Herein, we determined the expression of miR-96 and the function of its target genes in lung cancer through bioinformatic analysis, aiming to ascertain whether it is a potential molecular biomarker for the early diagnosis of NSCLC and to obtain clues for the pathogenesis of lung cancer.

Materials and methods

Affymetrix microarray

The microRNA expression profiles of lung cancer (GSE51855, GSE48414, GSE63805, GSE68951) were downloaded from Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/), which are based on the platform of Affymetrix Human Genome U133 Plus 2.0 Array.

Probe re-annotation

Four TEX texts (GPL7341, GPL16770, GPL18410, GPL16770) were downloaded from GEO public data platform, to find the probe number of the hsa-miR-96 gene in GSE51855, GSE48414, GSE63805, GSE68951, respectively.

Cell culture

The following cell lines were cultured individually in RPMI-1640 medium (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA): human lung adenocarcinoma (A549, NCI-H1299 and pAa), human lung large cell carcinoma (NCI-H460), human lung squamous cell carcinoma (NCI-H520), human lung small cell carcinoma (NCI-H446), human lung giant-cell carcinoma (95D) and human bronchial epithelial (16HBE) cell lines. The medium was supplemented with 10% fetal bovine serum (FBS; Gibco; Thermo Fisher Scientific, Inc.), 100 U/ml penicillin and 100 mg/ml streptomycin (Hyclone; GE Healthcare Life Sciences, Logan, UT, USA). Cells were maintained in 5% CO2 at 37°C.

Real-time quantitative PCR

Specific RT primers and TaqMan probe (American ABI Company) were used for quantitative detection of hsa-miR-96 (cat no. A25576) and reference gene U6 (cat no. 4426961). Total RNAs in cells were isolated using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). The RNA yield and the ratio of absorbance at 260 to 280 nm (A260/A280 ratio) were determined with the NanoDrop 2000 spectrophotometer (NanoDrop Technologies, Montchanin, DE, USA). The cDNA synthesis and qRT-PCR were carried out using the TaqMan MicroRNA Reverse Transcription kit and TaqMan MicroRNA assays and TaqMan® Universal Master Mix, No AmpErase® UNG (all from ABI, USA), respectively, according to the manufacturers protocol. qRT-PCR was carried out using Applied Biosystems® 7500 real-time PCR systems (Applied Biosystems, Foster City, CA, USA). The experiment was repeated 3 times. The relative quantitative analysis was carried out using the ΔΔCt method and the control was used for normalization of miRNA expression.

Bioinformatic analysis of miR-96 target genes

The target genes of miR-96 were predicted using miRecords. The intersection prediction results from at least 6 miRNA target gene prediction databases were analyzed to reduce the false-positive rate. To explore the functional annotation and pathway enrichment of those predicted genes, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database analyses were conducted using a Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 online analysis tool with P<0.05 as the significant threshold to obtain significant gene sets.

Statistical analysis

All data are presented as mean ± SD and statistical analyses were processed using SPSS 16.0 statistical software. Wilcoxon's rank-sum test was used to compare the expression of miR-96 between lung cancer and normal lung tissues/plasma in GSE51855, GSE48414 and GSE68951. Wilcoxon matched-pairs signed ranks sum test was used to analyzed the miR-96 expression in GSE63805. Wilcoxon's rank-sum test and Kruskal-Wallis test were conducted to analyze the correlation of miR-96 expression with the clinicopathological features in GSE48414 and GSE51855. Independent-sample t-test was conducted to evaluate the miR-96 expression in lung cancer cell lines and 16HBE cells. A P-value of <0.05 was considered statistically significant.

Results

Expression of miR-96 in lung cancer tissues, plasma and cell lines

We analyzed four microRNA expression profiling datasets to explore the expression pattern of miR-96 in the tissues and plasma of lung cancer patients. The result indicate that, compared with the normal lung tissues, miR-96 was significantly increased in NSCLC (GSE51855, Fig. 1A and Table I, P<0.001), lung adenocarcinoma (GSE48414, Fig. 1B and Table II, P<0.001) and stage I adenocarcinoma tissues (GSE63805, Fig. 1C and Table III, P<0.001). In addition, the expression level of miR-96 in the plasma (GSE68951) of the lung cancer patients was significantly higher compared to that of the non-cancer lung disease patients (Fig. 1D and Table IV, P<0.05).

Table I.

miR-96 expression in NSCLC and normal lung tissues (GSE51855).

Table I.

miR-96 expression in NSCLC and normal lung tissues (GSE51855).

VariablesNExpression of miR-96χ2/XP-value
Total131
NSCLC126−4.3623±0.12467−3.6990.000
NA  5 −7.1807±0.12815

[i] NSCLC, non-small cell lung cancer; NA, normal lung tissues.

Table II.

miR-96 expression in lung adenocarcinoma and normal lung tissues (GSE48414).

Table II.

miR-96 expression in lung adenocarcinoma and normal lung tissues (GSE48414).

VariablesNExpression of miR-96 χ2/ZP-value
Total174
ADC154−0.548±0.12819−5.8040.000
NA  20 −1.9477±0.11705

[i] NSCLC, non-small cell lung cancer; NA, normal lung tissues.

Table III.

miR-96 expression in adenocarcinoma and adjacent non-tumor lung tissues (GSE63805).

Table III.

miR-96 expression in adenocarcinoma and adjacent non-tumor lung tissues (GSE63805).

VariablesNExpression of miR-96 χ2/ZP-value
Total62
Stage I ADC32849.71±114.63−4.6380.000
NA30 184.93±13.41056

[i] ADC, adenocarcinoma; NA, adjacent non-tumor lung tissues.

Table IV.

miR-96 expression in the serum of NSCLC and non-cancerous pulmonary disease patients (GSE68951).

Table IV.

miR-96 expression in the serum of NSCLC and non-cancerous pulmonary disease patients (GSE68951).

VariablesNExpression of miR-96 χ2/ZP-value
Total38
Lung cancer262.99409±0.0028−2.1590.031
Non-cancerous lung diseases12 2.687812±0.0226

[i] NSCLC, non-small cell lung cancer.

We subsequently examined the level of miR-96 in different types of lung cancer cell lines and bronchial epithelial 16HBE cells using qRT-PCR. As shown in Fig. 2, the expression level of miR-96 was elevated in all the 6 NSCLC cell lines but downregulated in the small cell lung cancer NCI-H446 cells. The highest expression levels for miR-96 were found in squamous cell carcinoma NCI-H520, adenocarcinoma NCI-H1299 and pAa cells (P<0.001 for each).

Correlation between miR-96 expression and clinicopathological features of NSCLC

We then analyzed the correlation between miR-96 expression and the clinicopathological features of NSCLC to further explore the potential role of miR-96 in the development and progression of lung cancer. Our results showed that the expression level of miR-96 in the tumors was not related to the age (P=0.631), sex (P=0.678), clinical stage (P=0.841) and histological subtype (P=0.051) of the NSCLC patients (GSE48414 and GSE51855, Tables V and VI).

Table V.

Correlation of miR-96 expression and the clinicopathological characteristics of the lung adenocarcinoma cases (GSE48414).

Table V.

Correlation of miR-96 expression and the clinicopathological characteristics of the lung adenocarcinoma cases (GSE48414).

VariablesNExpression of miR-96 χ2/ZP-value
Total154
Age (years)
  <6568 −0.0922±0.15804−0.4810.631
  >6586 −0.0244±0.14538
Sex
  Male670.0161±0.18784−0.4150.678
  Female87 −0.1094±0.12377
TNM classification
  I+II126−0.069±0.148670.040.841
  III+IV28 0.067259±0.04541

Table VI.

Correlation of miR-96 expression and the different histological types of NSCLC (GSE51855).

Table VI.

Correlation of miR-96 expression and the different histological types of NSCLC (GSE51855).

VariablesNExpression of miR-96 χ2/XP-value
Total126
ADC76 −4.1497±0.157387.750.051
SQC29 −4.8942±0.21236
ASC4 −4.8262±0.74010
LCC17 −4.3103±0.40235

[i] NSCLC, non-small cell lung cancer; ADC, adenocarcinoma; SQC, squamous cell carcinoma; ASC, adenosquamous carcinoma, LCC, large cell carcinoma.

Bioinformation analysis of miR-96
Prediction of miR-96 targets

Next, we used miRecords database to investigate miR-96 targets. miRecords does not only provide the target gene prediction of miRNAs but also the exact target genes regulated by miRNAs, which have already been experimentally validated. As shown in Table VII, a total of 71 target genes of miR-96 were predicted by at least six prediction softwares involved in miRecords. Ten miR-96 target genes were found and experimentally validated in the miRecords database, among which ADCY6, IRS1 and MYRIP were also in the prediction list. Finally, 71 predicted and 7 validated miR-96 targets (Table VII) were involved in the GO and KEGG analysis.

Table VII.

The target genes of hsa-miR-96 investigated by miRecords database.

Table VII.

The target genes of hsa-miR-96 investigated by miRecords database.

No.miRNA IDRefseqSymbolDescriptionNote
1hsa-miR-96NM_015270ADCY6Adenylate cyclase 6Predicted
2hsa-miR-96NM_198715PTGER3Prostaglandin E receptor 3 (subtype EP3)Predicted
3hsa-miR-96NM_016623FAM49BFamily with sequence similarity 49, member BPredicted
4hsa-miR-96NM_016565CHCHD8 Coiled-coil-helix-coiled-coil-helix domain containing 8Predicted
5hsa-miR-96NM_015516TSKUTsukushinPredicted
6hsa-miR-96NM_015460MYRIPMyosin VIIA and Rab interacting proteinPredicted
7hsa-miR-96NM_015215CAMTA1Calmodulin binding transcription activator 1Predicted
8hsa-miR-96NM_014946SPASTSpastinPredicted
9hsa-miR-96NM_014943ZHX2Zinc fingers and homeoboxes 2Predicted
10hsa-miR-96NM_014839LPPR4Plasticity related gene 1Predicted
11hsa-miR-96NM_012300FBXW11F-box and WD repeat domain containing 11Predicted
12hsa-miR-96NM_012257HBP1HMG-box transcription factor 1Predicted
13hsa-miR-96NM_007198PROSCProline synthetase co-transcribed homolog (bacterial)Predicted
14hsa-miR-96NM_006940SOX5SRY (sex determining region Y)-box 5Predicted
15hsa-miR-96NM_006861RAB35RAB35, member RAS oncogene familyPredicted
16hsa-miR-96NM_006791MORF4L1Mortality factor 4 like 1Predicted
17hsa-miR-96NM_017974ATG16L1ATG16 autophagy related 16-like 1 (S. cerevisiae)Predicted
18hsa-miR-96NM_018018SLC38A4Solute carrier family 38, member 4Predicted
19hsa-miR-96NM_01824311-SepSeptin 11Predicted
20hsa-miR-96NM_198459DENND2CDENN/MADD domain containing 2CPredicted
21hsa-miR-96NM_194071CREB3L2cAMP responsive element binding protein 3-like 2Predicted
22hsa-miR-96NM_033505SELISelenoprotein IPredicted
23hsa-miR-96NM_033260FOXQ1Forkhead box Q1Predicted
24hsa-miR-96NM_032560SMEK1SMEK homolog 1, suppressor of mek1 (Dictyostelium)Predicted
25hsa-miR-96NM_032373PCGF5Polycomb group ring finger 5Predicted
26hsa-miR-96NM_032139ANKRD27Ankyrin repeat domain 27 (VPS9 domain)Predicted
27hsa-miR-96NM_024915GRHL2Grainyhead-like 2 (Drosophila)Predicted
28hsa-miR-96NM_024811FLJ12529Pre-mRNA cleavage factor I, 59 kDa subunitPredicted
29hsa-miR-96NM_022041GANGiant axonal neuropathy (gigaxonin)Predicted
30hsa-miR-96NM_021229NTN4Netrin 4Predicted
31hsa-miR-96NM_020871LRCH2Leucine-rich repeats and calponin homology (CH) domain containing 2Predicted
32hsa-miR-96NM_020795NLGN2Neuroligin 2Predicted
33hsa-miR-96NM_020423SCYL3SCY1-like 3 (S. cerevisiae)Predicted
34hsa-miR-96NM_020182PMEPA1Prostate transmembrane protein, androgen induced 1Predicted
35hsa-miR-96NM_006373VAT1Vesicle amine transport protein 1 homolog (T. californica)Predicted
36hsa-miR-96NM_006283TACC1Transforming, acidic coiled-coil containing protein 1Predicted
37hsa-miR-96NM_006016CD164CD164 molecule, sialomucinPredicted
38hsa-miR-96NM_002959SORT1Sortilin 1Predicted
39hsa-miR-96NM_002923RGS2Regulator of G-protein signaling 2, 24 kDaPredicted
40hsa-miR-96NM_002833PTPN9Protein tyrosine phosphatase, non-receptor type 9Predicted
41hsa-miR-96NM_002734PRKAR1AProtein kinase, cAMP-dependent, regulatory, type I, α (tissue specific extinguisher 1)Predicted
42hsa-miR-96NM_002515NOVA1Neuro-oncological ventral antigen 1Predicted
43hsa-miR-96NM_002265KPNB1Karyopherin (importin) β 1Predicted
44hsa-miR-96NM_002223ITPR2Inositol 1,4,5-triphosphate receptor, type 2Predicted
45hsa-miR-96NM_002222ITPR1Inositol 1,4,5-triphosphate receptor, type 1Predicted
46hsa-miR-96NM_002015FOXO1Forkhead box O1Predicted
47hsa-miR-96NM_001945HBEGFHeparin-binding EGF-like growth factorPredicted
47hsa-miR-96NM_001945HBEGFHeparin-binding EGF-like growth factorPredicted
48hsa-miR-96NM_001931DLATDihydrolipoamide S-acetyltransferasePredicted
49hsa-miR-96NM_001839CNN3Calponin 3, acidicPredicted
50hsa-miR-96NM_000945PPP3R1Protein phosphatase 3 (formerly 2B), regulatory subunit B, α isoformPredicted
51hsa-miR-96NM_000935PLOD2Procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2Predicted
52hsa-miR-96NM_000663ABAT4-aminobutyrate aminotransferasePredicted
53hsa-miR-96NM_002998SDC2Syndecan 2Predicted
54hsa-miR-96NM_003060SLC22A5Solute carrier family 22 (organic cation/carnitine transporter), member 5Predicted
55hsa-miR-96NM_003182TAC1Tachykinin, precursor 1Predicted
56hsa-miR-96NM_006007ZFAND5Zinc finger, AN1-type domain 5Predicted
57hsa-miR-96NM_005766FARP1FERM, RhoGEF (ARHGEF) and pleckstrin domain protein 1 (chondrocyte-derived)Predicted
58hsa-miR-96NM_005544IRS1Insulin receptor substrate 1Predicted
59hsa-miR-96NM_005502ABCA1ATP-binding cassette, sub-family A (ABC1), member 1Predicted
60hsa-miR-96NM_005400PRKCEProtein kinase C, εPredicted
61hsa-miR-96NM_005277GPM6AGlycoprotein M6APredicted
62hsa-miR-96NM_004985KRASv-Ki-ras2 Kirsten rat sarcoma viral oncogene homologPredicted
63hsa-miR-96NM_004958FRAP1FK506 binding protein 12-rapamycin associated protein 1Predicted
64hsa-miR-96NM_004926ZFP36L1Zinc finger protein 36, C3H type-like 1Predicted
65hsa-miR-96NM_004731SLC16A7Solute carrier family 16, member 7 (monocarboxylic acid transporter 2)Predicted
66hsa-miR-96NM_004514FOXK2Forkhead box K2Predicted
67hsa-miR-96NM_004481GALNT2 UDP-N-acetyl-α-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 2 (GalNAc-T2)Predicted
68hsa-miR-96NM_003654CHST1Carbohydrate (keratan sulfate Gal-6) sulfotransferase 1Predicted
69hsa-miR-96NM_003379EZREzrinPredicted
70hsa-miR-96NM_003342UBE2G1 Ubiquitin-conjugating enzyme E2G 1 (UBC7 homolog, yeast)Predicted
71hsa-miR-96NM_000332ATXN1Ataxin 1Predicted
72 NM_000863HTR1B Validated
73 NM_198159MITF Validated
74 NM_002015.3Foxo1 Validated
75 NM_0016513AQp5 Validated
76 NM_001408CELSR2 Validated
77 NM_153437ODF2 Validated
78 NM_001005861RYK Validated
Gene ontology and KEGG pathway enrichment analysis of miR-96 target genes

Gene ontology enrichment analysis was performed to analyze 78 miR-96 target genes (Table VII). In total, 42 GO terms were obtained, which included 24 biological processes, 15 cellular components and 3 molecular functions. These 42 GO terms were sorted by P-values for further analysis and are listed in Tables VIII and IX.

Table VIII.

GO gene function (biological_process) analysis of the miR-96 targets.

Table VIII.

GO gene function (biological_process) analysis of the miR-96 targets.

GO IDGO ontologyGO termCountsP-value
GO:0009725 Biological_processResponse to hormone stimulus93.95E-04
GO:0009719 Biological_processResponse to endogenous stimulus97.57E-04
GO:0010033 Biological_processResponse to organic substance110.002442
GO:0016197 Biological_processEndosome transport40.002826
GO:0032228 Biological_processRegulation of synaptic transmission, GABAergic30.003041
GO:0016055 Biological_processWnt receptor signaling pathway50.004019
GO:0032868 Biological_processResponse to insulin stimulus40.012812
GO:0044057 Biological_processRegulation of system process60.0173
GO:0007169 Biological_processTransmembrane receptor protein tyrosine kinase signaling pathway50.023729
GO:0042325 Biological_processRegulation of phosphorylation70.025784
GO:0032870 Biological_processCellular response to hormone stimulus40.027124
GO:0001666 Biological_processResponse to hypoxia40.027651
GO:0043279 Biological_processResponse to alkaloid30.028506
GO:0050804 Biological_processRegulation of synaptic transmission40.028721
GO:0051174 Biological_processRegulation of phosphorus metabolic process70.030557
GO:0019220 Biological_processRegulation of phosphate metabolic process70.030557
GO:0070482 Biological_processResponse to oxygen levels40.03149
GO:0010648 Biological_processNegative regulation of cell communication50.032803
GO:0007612 Biological_processLearning30.03461
GO:0051969 Biological_processRegulation of transmission of nerve impulse40.034993
GO:0031998 Biological_processRegulation of fatty acid beta-oxidation20.03838
GO:0031644 Biological_processRegulation of neurological system process40.038689
GO:0043434 Biological_processResponse to peptide hormone stimulus40.039324
GO:0046907 Biological_processIntracellular transport80.040338

[i] GO, Gene Ontology.

Table IX.

GO gene function (cellular_component) analysis of the miR-96 targets.

Table IX.

GO gene function (cellular_component) analysis of the miR-96 targets.

GO IDGO ontologyTermCountP-value
GO:0042995 Cellular_componentCell projection119.15E-04
GO:0005815 Cellular_componentMicrotubule organizing center  60.005289516
GO:0005624 Cellular_componentMembrane fraction100.009055844
GO:0005626 Cellular_componentInsoluble fraction100.011347551
GO:0031095 Cellular_componentPlatelet dense tubular network membrane  20.013319656
GO:0005813 Cellular_componentCentrosome  50.017582227
GO:0031094 Cellular_componentPlatelet dense tubular network  20.017720687
GO:0000267 Cellular_componentCell fraction110.020267702
GO:0044463 Cellular_componentCell projection part  50.02029103
GO:0045202 Cellular_componentSynapse  60.020676761
GO:0012505 Cellular_componentEndomembrane system  90.021888271
GO:0005955 Cellular_componentCalcineurin complex  20.022102431
GO:0044430 Cellular_componentCytoskeletal part100.024057867
GO:0045121 Cellular_componentMembrane raft  40.025855924
GO:0005856 Cellular_componentCytoskeleton120.039405823

[i] GO, Gene Ontology.

Among the 24 biological process GO terms, the top 10 terms were: GO:0009725 (response to hormone stimulus), GO:0009719 (response to endogenous stimulus), GO:0010033 (response to organic substance), GO:0016197 (endosome transport), GO:0032228 (regulation of synaptic transmission, GABAergic), GO:0016055 (Wnt receptor signaling pathway), GO:0032868 (response to insulin stimulus), GO:0044057 (regulation of system process), GO:0007169 (transmembrane receptor protein tyrosine kinase signaling pathway) and GO:0042325 (regulation of phosphorylation) (Table VIII).

The 15 cellular component GO terms were: GO:0042995 (cell projection), GO:0005815 (microtubule organizing center), GO:0005624 (membrane fraction), GO:0005626 (insoluble fraction), GO:0031095 (platelet dense tubular network membrane), GO:0005813 (centrosome), GO:0031094 (platelet dense tubular network), GO:0000267 (cell fraction), GO:0044463 (cell projection part), GO:0045202 (synapse), GO:0012505 (endomembrane system), GO:0005955 (calcineurin complex), GO: 0044430 (cytoskeletal part), GO:0045121 (membrane raft) and GO:0005856 (cytoskeleton) (Table IX).

In regards to the molecular function of the GO terms, GO:0005220 (inositol 1,4,5-trisphosphate-sensitive calcium-release channel activity), GO:0008095 (inositol-1,4,5- trisphosphate receptor activity) and GO:0005516 (calmodulin binding) were the highest presented terms (Table X).

Table X.

GO gene function (molecular_function) analysis of the miR-96 targets.

Table X.

GO gene function (molecular_function) analysis of the miR-96 targets.

GO IDGO ontologyTermCountP-value
GO:0005220 Molecular_functionInositol 1,4,5-trisphosphate-sensitive calcium-release channel activity20.01449
GO:0008095 Molecular_function Inositol-1,4,5-trisphosphate receptor activity20.01927
GO:0005516 Molecular_functionCalmodulin binding40.03047

[i] GO, Gene Ontology.

KEGG pathway analysis indicated that miR-96 target genes are mainly enriched in 9 pathways (Table XI). Among these pathways, hsa04912 (GnRH signaling pathway) (Fig. 3), hsa04114 (oocyte meiosis), hsa04720 (long-term potentiation) (Fig. 4), hsa04910 (insulin signaling pathway) (Fig. 5), hsa05215 (prostate cancer) and hsa04540 (gap junction) showed significantly higher enrichment, followed by hsa04916 (melanogenesis), hsa04270 (vascular smooth muscle contraction) and hsa04930 (Type II diabetes mellitus).

Table XI.

Pathways enrichment analysis of the miR-96 tagets (KEGG).

Table XI.

Pathways enrichment analysis of the miR-96 tagets (KEGG).

GO IDName of pathwayCountP-valueGenes
hsa04912GnRH signaling pathway50.001862149NM_004985, NM_002223, NM_002222, NM_015270, NM_001945
hsa04114Oocyte meiosis50.002843987NM_012300, NM_002223, NM_002222, NM_015270, NM_000945
hsa04720Long-term potentiation40.005899778NM_004985, NM_002223, NM_002222, NM_000945
hsa04910Insulin signaling pathway50.005930538NM_004958, NM_002015, NM_004985, NM_005544, NM_002734
hsa05215Prostate cancer40.01237797NM_004958, NM_002015, NM_004985, NM_194071
hsa04540Gap junction40.01237797NM_004985, NM_002223, NM_002222, NM_015270
hsa04916Melanogenesis40.016481296NM_004985, NM_015270, NM_198159, NM_194071
hsa04270Vascular smooth muscle contraction40.02283774NM_002223, NM_002222, NM_015270, NM_005400
hsa04930Type II diabetes mellitus30.027134231NM_004958, NM_005544, NM_005400

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes.

Discussion

Owing to its elevated expression, much effort has been dedicated to study the role of miR-96 in various types of cancers (2123). In the majority of the tumors, miR-96 acts as an oncogene to promote the proliferation and invasion of cancer cells by inhibiting transcription factor FOXO1 (24), FOXO3a (25), tumor suppressor protein RECK (26) and metastasis suppressor protein MTSS1 (27). However, in pancreatic cancer, miR-96 functions as a tumor suppressor by targeting HERG1 and NUAK1 (28,29).

There is no explicit conclusion whether miR-96 could affect the development and progression of lung cancer and serve as a molecular biomarker for the clinical diagnosis of lung cancer. By analyzing four microRNA expression profiles and qRT-PCR, we showed that miR-96 was markedly increased in NSCLC, lung adenocarcinoma, stage I adenocarcinoma tissues and NSCLC cell lines. Consistent with our result, Ma et al reported that miR-96 was significantly upregulated in six NSCLC tissues and its expression was then validated in an independent set of 35 pairs of tumors and their adjacent normal tissues as well as in the serum of patients with NSCLC (19). To verify the microRNA expression signatures of lung cancer, Vosa et al performed a comprehensive meta-analysis of 20 published microRNA expression studies in lung cancer and identified a statistically significant microRNA meta-signature of seven upregulated microRNAs, including miR-21, miR-210, miR-182, miR-183, miR-31, miR-200b and miR-205. Since miR-182, miR-183 and miR-96 all belong to the miR-183 family, in conjunction with our results, miR-96 may serve as a novel molecular biomarker to distinguish early NSCLC patients from healthy individuals.

miRNAs are present not only in tissues but also in body fluids, such as blood, plasma, serum and sputum. Shen et al conducted several studies to assess the function of miRNAs in the sputum and plasma of lung cancer patients (30). They showed that the expression profile of plasma miR-21, miR-126, miR-210 and miR-486-5p produce high sensitivity and specificity in identifying stage I NSCLC patients. Zhu et al examined 70 pairs of lung cancer and non-cancerous tissues as well as serum samples. They found that miR-96 expression in tumors was positively associated with its expression in serum (31). Our data revealed that miR-96 expression in the plasma of lung cancer was significantly higher compared to that of non-cancer lung disease patients, suggesting that miR-96 may serve as a potential non-invasive marker for lung cancer diagnosis.

Although studies have shown that miR-96 is associated with poor overall survival in patients with pancreatic cancer (32), liver cancer (33) and colorectal cancer (34), our results did not demonstrate any significant correlation between the expression level of miR-96 and clinical stage as well as the histological subtype of the NSCLC patients. These discrepancies may be due to the different samples and databases that were used. Further studies are needed to confirm whether miR-96 could serve as a prognostic biomarker for lung cancer.

To date, computational methods have been widely used for the prediction of miRNAs and their target genes. However, the most commonly used miRNA target prediction websites, such as TargetScan, microRNA.org and PicTar, could not yield consistent results due to their different algorithm. miRecords is an integrated microRNA target database which includes a total of 11 established prediction programs. In this study, we selected the results predicted by at least six softwares in miRecords as the putative miR-96 target gene set and a collection of 78 predicted target genes were involved in GO/KEGG functional enrichment analysis. Since the GO hierarchy contains an added complexity by allowing terms to have multiple parents or ascendants, we used Fishers exact 0.01 to reduce the redundancy in lists of enriched GO terms. Our data showed that among the 24 biological process GO terms obtained, the top 10 terms could be roughly grouped into several different categories including response to the stimulus (GO:0009725, GO:0009719, GO:0010033 and GO:0032868), signaling pathway (GO:0016055, GO:0032868, GO:0007169) and neurotransmission (GO:0032228). Tyrosine kinase signaling (GO:0007169) is currently known as the most successful molecular-targeted therapeutic approach for lung cancer (35). The canonical Wnt signaling pathway (GO:0016055), is another important regulator of proliferation (36) and metastasis (37) of non-small lung cancer cells. In addition, the 15 cellular component GO terms were significantly enriched in various specific processes with high frequency, such as cell division (GO:0005815, GO:0005813), cell communication (GO:0042995, GO:0044463) and cell migration (GO:0042995, GO:0044463, GO:0044430, GO:0005856), indicating that miR-96 may function as a regulator for the motility, migration and invasion of tumor cells. Moreover, three highly enriched molecular function GO terms (GO: 0005220, GO:0008095, and GO:0005516) suggest a potential new role of miR-96 in regulating calcium signaling important for tumor cell proliferation, apoptosis and migration.

In the KEGG annotation, GnRH signaling pathway (hsa04912), oocyte meiosis (hsa04114), long-term potentiation (hsa04720), insulin signaling pathway (hsa04910) and prostate cancer (hsa05215) showed the highest enrichment. GnRH has been reported to participate in the self-renewal of A549-derived lung cancer stem-like cells by upregulating the JNK signaling pathway (38). Insulin, bound to insulin receptor, promotes cell proliferation through the RAS-RAF-MAP kinase signaling pathway and regulates cell survival process through (PI3K)-Akt-mammalian target of rapamycin (mTOR) pathway, playing an important role in the clinical treatment of NSCLC (39). Long-term potentiation and prostate cancer pathway, related to transcription regulation, cancer cell survival and proliferation respectively, suggest the potential function for miR-96 in cancer growth.

Although DAL-1 was not in the list of the 78 targets, DAL-1 was predicted as the target gene of miR-96 by 5 predicted databases of miRecords: MirTarget2, PicTar, PITA, RNAhybird, and TargetScan/TargetScanS (data not shown). For future studies, comprehensive screening, confirmation experiments and further bioinformatic analysis using available web tools such as Ingenuity Pathway Analysis (IPA) and STRINGProtein-Protein Interaction Networks need to be carried out on the predicted targets to explore the novel regulatory mechanism of miR-96 in cancer metastasis.

In conclusion, our results showed that miR-96, functioning as an oncogene, may play an important role in the development and progression of lung cancer. Both in tissue and plasma, miR-96 may have the potential to serve as a molecular biomarker for the early diagnosis of NSCLC.

Acknowledgements

This study was funded by the National Nature Science Foundation of China (no. 81401391), Ph.D. Programs Foundation of the Ministry of Education of China (no. 20134423110001); National Nature Science Foundation of Guangdong Province (no.S2012010010181); Science and Technology Project of Guangzhou City (no. 2014Y2-00171) and Education System Innovative Academic Team of Guangzhou City (no. 13C06); Guangzhou City-Belonged Universities Scientific Research Program (no. 2012C130); National Natural Science Foundation of Guangdong Province (no. 2015A030313452).

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Cai T, Long J, Wang H, Liu W and Zhang Y: Identification and characterization of miR-96, a potential biomarker of NSCLC, through bioinformatic analysis. Oncol Rep 38: 1213-1223, 2017.
APA
Cai, T., Long, J., Wang, H., Liu, W., & Zhang, Y. (2017). Identification and characterization of miR-96, a potential biomarker of NSCLC, through bioinformatic analysis. Oncology Reports, 38, 1213-1223. https://doi.org/10.3892/or.2017.5754
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Cai, T., Long, J., Wang, H., Liu, W., Zhang, Y."Identification and characterization of miR-96, a potential biomarker of NSCLC, through bioinformatic analysis". Oncology Reports 38.2 (2017): 1213-1223.
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Cai, T., Long, J., Wang, H., Liu, W., Zhang, Y."Identification and characterization of miR-96, a potential biomarker of NSCLC, through bioinformatic analysis". Oncology Reports 38, no. 2 (2017): 1213-1223. https://doi.org/10.3892/or.2017.5754