Analysis of MicroRNA expression profile identifies novel biomarkers for non-small cell lung cancer



Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer mortality. MicroRNAs (miRNAs), small noncoding RNAs, regulate the expression of genes that play roles in human cancer via posttranscriptional inhibition.


To identify the potential miRNA biomarkers in NSCLC, we downloaded the miRNA expression profile (ID: GSE29248) of NSCLC from the Gene Expression Omnibus (GEO) database and analyzed the differentially expressed miRNAs in NSCLC tissue compared with normal control tissue. Then the targets of these differentially expressed miRNAs were screened and used in network construction and functional enrichment analysis.


We identified a total of 17 miRNAs that showed a significantly differential expression in NSCLC tissue. We found that miR-34b and miR-520h might play important roles in the regulation of NSCLC, miR-22 might be a novel biomarker as an oncogene, and miR-448 might promote, while miR-654-3p prevents, NSCLC progression.


Our study may provide the groundwork for further clinical molecular target therapy experiments in NSCLC.

Tumori 2015; 101(1): 104 - 110




Chi Xu, Yisheng Zheng, Duohuang Lian, Shixin Ye, Jinrong Yang, Zhiyong Zeng

Article History


Financial support: This study was supported as a science and technology project of Fujian province (2012D021).
Conflict of interest: The authors have declared that no competing interests exist.

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Lung cancer is the most common cause of cancer-related mortality in patients worldwide (1), of which non-small cell lung cancer (NSCLC) accounts for 85% of all pulmonary carcinomas. Despite the recent advances in therapies for NSCLC, the high mortality rate of NSCLC patients has not decreased markedly over the years (2). It is thus worth exploring more effective and safe treatment strategies.

MicroRNAs (miRNAs), a class of endogenous short noncoding RNAs, negatively regulate gene expression by binding to their targets in both coding and untranslated regions via base-pairing with complementary sequences with the mRNA molecules (3-4-5). Over the years, an increasing number of miRNAs have emerged as involved in lung cancer pathogenesis, including in the development and metastasis process. MiRNAs have been used as biomarkers for early diagnosis, targeted therapy and clinical prognosis, which provides a new direction for diagnosis and treatment (6). In recent years, exploration of the targeted therapy of miRNAs has attracted much attention and has made great progress (2, 7-8-9).

Targeting miRNA expression in cancer is mainly classified into 2 strategies, including the direct strategy that either blocks the expression of oncogenic miRNAs by the use of oligonucleotides or substitutes for the loss of expression of a tumor suppressor miRNA via virus-based constructs, and the indirect strategy that modulates miRNA expression by targeting its transcription and processing, through drug therapies (9, 10). The let-7 microRNA directly represses cancer growth in the lung of the mouse (7). Fontana et al demonstrated that antagomir-17-5p treatment can efficiently inhibit tumor growth in vivo (8). Anti-miR cotransfections of miR-34c, miR-145 and miR-142-5p significantly repressed lung cancer cell growth (11).

MiRNA expression profiling is becoming an increasingly important tool to reveal gene activity in cancer, and many studies indicate that miRNAs can act as oncogenes and/or tumor suppressor genes (2, 9, 12). The primary aim of our study was to identify potential miRNA biomarkers in NSCLC via miRNA expression profiles.


Microarray data

We extracted miRNA expression profile (ID: GSE29248) from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, which had been deposited by Ma et al (13). This microarray study was carried out in the tissue bank of Zhoushan Hospital (China) and included 12 chips from 6 NSCLC tissues (3 adenocarcinomas and 3 squamous cell carcinomas of the lung) and 6 matched normal controls from adjacent tissues. These tissues were all obtained from male patients. Among them, 4 patients were older than 60 years and 2 patients younger than 60 years. The tumor stages were clearly defined as stage I in 1 patient, while the other 5 were in stage II, III and IV. Additionally, regarding T classifications based on the 7th edition of the Union for International Cancer Control (UICC), 2 patients had T1-T2 and 4 patients had T3-T4 cancer. The platform used was the Illumina Human v2 MicroRNA expression beadchip. We downloaded the raw data and the probe annotation file.

Data analysis

The downloaded microarray data was normalized using robust multiarray average (RMA) (14) algorithm in R, Student’s t-Test was used to identify the significantly differentially expressed miRNAs between the NSCLC group and the normal group, based on the multitest package in R. Only miRNAs with a p value <0.05 and log2 fold change (FC) (|log2FC|) >1 were considered significantly different. The top 3 up-regulated and 3 down-regulated miRNAs were selected for further analysis.

Target genes for differentially expressed miRNAs

Target genes of the 6 most significantly differentially expressed miRNAs were retrieved from the miRecords and miRTarBase databases. MiRecords, is an integrated resource for experimentally validated animal miRNA–target interactions (MTIs), component of 1,135 records of validated MTIs between 301 miRNAs and 902 target genes in 7 animal species (15). As a database updated frequently, miRTarBase has accumulated 3,576 experimentally verified MTIs between 657 miRNAs and 2,297 target genes among 17 species by manually surveying the pertinent literature related to functional studies of miRNAs (16). In our study, we integrated target genes from the 2 databases and extracted target genes validated experimentally. Those target genes shared by both databases were deemed to be more reliable.

Interaction network analysis

To annotate functional interactions between those target genes, we constructed interaction networks to discern how they were associated with other genes in the networks. The Osprey software platform provides visualization and manipulation of complex interaction networks, using the General Repository for Interaction Datasets (GRID) and the Biomolecular Interaction Network Database (BIND) as databases (17). We used the Osprey platform to annotate functional interactions between differentially expressed miRNAs and target genes and protein–protein interaction networks (18).

Gene ontology enrichment analysis

Gene ontology (GO) analysis is increasingly used for functional studies of microarray data (19). The Database for Annotation, Visualization and Integrated Discovery (DAVID), a database for annotation, visualization and integrated discovery, provides functional annotation for large-scale genomic or transcriptomic data (20). To investigate the functions of miRNAs in the development of NSCLC, we used DAVID to identify overrepresented GO categories in biological processes, based on a hypergeometric distribution algorithm. The p value was adjusted by Henjamin-Hochberg method based on the multest package to produce the false discovery rate (FDR). The FDR <0.05 was chosen as the threshold (21).


Screening of different expressed miRNAs

The microarray data that downloaded from GEO database was normalized using the RMA algorithm. As shown in Figure 1, the median values of each sample were almost at the same level, suggesting that data were eligible for further analysis. A total of 17 differentially expressed miRNAs in NSCLC samples were selected compared with the normal samples using Student’s t-test with the p value <0.05 and |log2 FC| >1 were identified (Tab. I). The top 3 up-regulated miRNAs were hsa-miR-22, hsa-miR-518b and hsa-miR-654-3p; hsa-miR-520h, hsa-miR-448 and hsa-miR-34b were the top 3 down-regulated miRNAs. These 6 miRNAs were our research targets for further study.

Microarray gene expression profile. The pink samples are non-small cell lung cancer (NSCLC) tissue, and the blue samples are adjacent normal tissue.

MiRNAs differentially expressed in NSCLC tissue compared with normal tissue

miRNA_ID ID p Value Log2FC
MiRNAs in italics, represent the top 3 down-regulated and 3 up-regulated miRNAs, respectively.
MiRNA = microRNA; NSCLC = non-small cell lung cancer.
*the significant differentially expressed miRNA.
hsa-miR-520h ILMN_3168147 0.013034 -6.28724
hsa-miR-448 ILMN_3168228 0.01617 -6.127203
hsa-miR-34b ILMN_3168652 0.000577 -5.063579
hsa-miR-374b* ILMN_3168734 0.013277 -4.737044
hsa-miR-935 ILMN_3168678 0.044949 -3.146055
hsa-miR-17* ILMN_3167870 0.035354 -2.25328
hsa-miR-328 ILMN_3168198 0.04053 -1.879261
hsa-miR-1225-5p ILMN_3168775 0.01179 -1.798414
hsa-miR-552 ILMN_3167849 0.013464 -1.701964
hsa-miR-1298 ILMN_3168109 0.039072 -1.610049
hsa-miR-34c-5p ILMN_3167743 0.0201 -1.349674
hsa-miR-582-3p ILMN_3168780 0.046332 1.392312
hsa-miR-520d-5p ILMN_3167831 0.025494 1.760171
hsa-miR-452 ILMN_3167050 0.013007 1.823744
hsa-miR-654-3p ILMN_3168801 0.04603 2.347362
hsa-miR-518b ILMN_3167241 0.0199 2.446713
hsa-miR-22 ILMN_3168621 0.007168 2.858543

Screening of target mRNAs

As miRNAs negatively regulate gene expression by targeting mRNAs at the posttranscriptional level, it was necessary to identify putative target genes for a better understanding of their function. The total target genes for the selected miRNAs in this study were predicted from the miRecords and miRTarBase database. Finally, 31 target genes corresponding to the 6 miRNAs were obtained (Tab. II). No target gene for miR-518b was obtained.

Target genes of differentially expressed miRNAs from miRecords and miRTarBase databases

miRNA Target in miRTarBase Target in miRecords
MiRNA = microRNA.
*The predicted target genes of down-regulated miRNAs.
The predicted target genes of up-regulated miRNAs.
hsa-miR-520h ABCG2, CDKN1A, ID1, ID3, VEGFA ABCG2
hsa-miR-34b CDK4, CDK6, MET, MYC, ZAP70, VEGFA, MYC, NOTCH4, NOTCH2, HMGA2, Notch1, Bcl-2
hsa-miR-448 SATB1 -
hsa-miR-654-3p CDKN1A -

Interaction network construction of differentially expressed miRNA target genes

We used the Osprey software for all interactions between the 6 differentially expressed miRNAs and their target genes, along with the target gene interaction network (Fig. 2). Notch homolog 1 (NOTCH1) and vascular endothelial growth factor A (VEGFA) were identified as hub nodes in the network of down-regulated miRNAs, suggesting that these genes and their interactive genes may play important roles in the development of NSCLC. VEGFA was directly targeted by miR-24b and miR-520h. MiR-22, which was the centralized miRNA gene in the interaction network of up-regulated miRNAs, may have a vital role in NSCLC cells by regulating (phosphatase and tensin homolog deleted on chromosome 10 (PTEN), one of the most widely investigated tumor suppressor genes.

Interaction network constructed between differentially expressed (microRNAs (miRNAs) and their interactive genes. A) Interaction network of the top 3 down-regulated miRNAs and their interactive genes; B) interaction network of the top 3 up-regulated miRNAs and their interactive genes. The gray nodes stand for differentially expressed miRNAs, and the white for their target genes. The arrows stand for miRNA-regulated target genes, and the lines indicate interactions between target genes.

GO enrichment analysis

The biological processes of the total selected target genes and miRNAs from the constructed network were enriched using the GO database. In this study, 24 and 12 enriched terms were found, respectively, corresponding to the 3 down-regulated and the 3 up-regulated miRNAs with our cutoff criterion (Tab. III). Developmental process and responding to chemical stimulus were identified as significant biological processes via gene functional enrichment analysis, implying that the significant genes may play key roles in NSCLC pathogenesis via the development processes or act as signal factors.

The enriched GO terms in differentially expressed miRNA target interaction network

GO ID Corr p value x Description
Corr = Corrected; GO = gene ontology; miRNA = microRNA; X = number of genes in the enrichment.
*Enriched GO terms in the interaction network of the down-regulated miRNA.
Enriched GO terms in the interaction network of the up-regulated miRNA.
32502 9.54E-07 15 Developmental process
48513 2.02E-06 12 Organ development
48523 2.68E-06 12 Negative regulation of cellular process
10468 2.82E-06 14 Regulation of gene expression
48731 3.51E-06 13 System development
48522 5.00E-06 12 Positive regulation of cellular process
48519 5.31E-06 12 Negative regulation of biological process
19222 7.19E-06 15 Regulation of metabolic process
30154 8.35E-06 11 Cell differentiation
48856 8.35E-06 13 Anatomical structure development
48869 9.68E-06 11 Cellular developmental process
48518 1.07E-05 12 Positive regulation of biological process
60255 1.07E-05 14 Regulation of macromolecule metabolic process
32501 2.01E-05 15 Multicellular organismal process
50896 2.03E-05 14 Response to stimulus
10556 8.38E-05 12 Regulation of macromolecule biosynthetic process
31326 1.23E-04 12 Regulation of cellular biosynthetic process
50794 1.23E-04 16 Regulation of cellular process
9889 1.29E-04 12 Regulation of biosynthetic process
31323 1.38E-04 13 Regulation of cellular metabolic process
50789 2.15E-04 16 Regulation of biological process
65007 4.16E-04 16 Biological regulation
80090 4.40E-04 12 Regulation of primary metabolic process
9987 1.35E-02 16 Cellular process
GO ID Corr p value x Description
42221 2.98E-06 11 Response to chemical stimulus
48519 2.53E-04 10 Negative regulation of biological process
32501 2.53E-04 13 Multicellular organismal process
50896 3.32E-04 12 Response to stimulus
32502 8.84E-04 11 Developmental process
65007 1.40E-03 14 Biological regulation
50794 1.62E-02 12 Regulation of cellular process
44238 1.78E-02 11 Primary metabolic process
50789 2.10E-02 12 Regulation of biological process
44237 2.70E-02 10 Cellular metabolic process
8152 2.70E-02 11 Metabolic process
9987 4.89E-02 13 Cellular process


Recently, dysregulation of miRNA expression was found to contribute to the initiation and progression of cancer. Depending on their target genes (22) miRNAs can act either as oncogenes or tumor suppressor genes (23).

In this study, we analyzed the miRNA profiling of NSCLC from the GEO database to identify new miRNA targets for therapeutic intervention. A total of 17 miRNAs displayed a significantly differential expression in NSCLC tissue compared with adjacent nonmalignant tissue. Furthermore, we performed interaction network analysis of the top 3 up-regulated (hsa-miR-22, hsa-miR-518b and hsa-miR-654-3p) and top 3 down-regulated differentially expressed miRNAs (hsa-miR-520h, hsa-miR-448 and hsa-miR-34b). Since miRNAs play important roles in posttranscriptional regression by targeting mRNAs, we performed interaction network analysis of these miRNAs and their target genes.

NOTCH1 and VEGFA were the identified hubs of miR-34b and miR-520h in the network of down-regulated miRNAs and targets (Fig. 2A). MiR-34b was found to be a tumor suppressor in colorectal cancer (24), prostate cancer (25) and gastric cancer (26). Overexpression of miR-34b could inhibit prostate cancer progression via down-regulating of DNMTs (human DNA methyltransferases) to induce promoter demethylation and AKT pathways (27). Besides, miR-34b plays an important role in estrogen expression in breast cancer (28). Meanwhile, miR-520h is a regulator of ABCG2 gene in inhibiting the tumor migration and invasion of pancreatic cancer cells (29). Resveratrol-mediated miR-520h regulation was associated with tumor suppressing ability in lung cancer (30). Down-regulation of miR-520h was shown to be important to the tumor-suppressive function of E1A (31). NOTCH signaling plays an important role in many types of cancer (32). VEGFA, one of the initiators of NOTCH signaling pathways through induction of higher expression of Delta 4 (33), is one of the ligands of NOTCH1 (34). The release of VEGFA and platelet activation by thrombin generation plays an important role in NSCLC (35). Also, VEGFA levels were significantly higher in NSCLC patients than in the controls, and this could be used as a prognostic biomarker in advanced NSCLC (36). NOTCH1, a well-known cancer-causing gene implicated in a number of malignancies, has been shown to play a far more critical role in NSCLC than previously thought, in the latest study from the Florida campus of the Scripps Research Institute (37). The NOTCH3 signaling pathway plays a crucial role in NSCLC via the cell cycle alterations induced by LiC1 (38). On the basis of this study, oncogene NOTCH1 is required for the survival of cancer cells, as it represses p53, a well-known tumor suppressor protein preventing mutations. Our study showed that both the down-regulated MiR-34b and miR-520h could directly target VEGFA; we speculated that both of the 2 miRNAs might play important roles in the regulation of NSCLC.

Meanwhile, our findings showed that down-regulated miR-448 interacted with special AT-rich sequence-binding protein 1 (SATB1) in the network (Fig. 2A). MiR-448 is related to the metastasis and malignancy of breast cancer cells (39). SATB1 (encoded by the SATB1 gene) is a cancer progression associated protein (40). Overexpression of SATB1 could promote prostate cancer cell growth and invasion (41). SATB1 is a novel tumor antigen in cancer immunotherapy (42). Also, the missing expression of SATB1 has been related to a poor prognosis for lung squamous cell carcinoma (43). Thus, SATB1 might be crucial in NSCLC progression or prognosis. MiR-448 played a role in regulating the epithelial-mesenchymal transition of breast cancer cells by targeting SATB1 (44). On the basis of our results, we speculated that miR-448 might be important in NSCLC formation via the regulation of SATB1.

In addition, up-regulated miR-22 and miR-654-3p are believed to be crucial in the regulation of NSCLC, as their target genes are PTEN and CDKN1A, respectively. MiR-22 is a putative oncogenic miRNA in promoting cancer progression, which has the ability to suppress cell progression of lung cancer through the posttranscriptional regulation of ErbB3 (45). PTEN, a lipid phosphatase, which serves as an important tumor suppressor gene, has been a hotspot of clinical and basic research for a long time, since loss of heterozygosity in human cancers was first discovered in 1993 (46). Studies have demonstrated that miR-21, miR-22 and miR-205 are related to promotion of the growth, metastasis and chemoresistance or radioresistance, by targeting PTEN in NSCLC cells (47, 48). Many disorders occur in NSCLC tissues with methylation genes such as retinoic acid receptor β-2, tissue inhibitor of metalloproteinase 3 and death-associated protein kinase (49). In addition, the loss of expression of PTEN is caused by promoter methylation in NSCLC (50). PTEN functions as a dual-specificity phosphatase mainly targeting phosphatidylinositol (3, 4, 5) trisphosphate to be phosphatidylinositol 3-kinase (PI3K) in the PTEN/PI3K/AKT pathway, which is highly involved in tumorigenesis, regulating the signaling of multiple biological processes such as apoptosis, metabolism, cell proliferation and cell growth (51-52-53-54). Thus, miR-22 might be a putative oncogenic miRNA in promoting cancer progression. Likewise, miR-654-3p is a prostate cancer cell–related regulator, which functions in preventing cell proliferation, apoptosis, migration and invasion in breast cancer (55). However, the role of miR-654-3p in lung cancer has not been fully discovered. Cyclin-dependent kinase inhibitor 1A (CDKN1A), also called p21, WAF1, p21CIP1 or CDKN1, is a negative regulator of cell cycle progression at the G1 phase transition (56). P21WAF1/CIP1 was targeted by miR-224 in promoting the chemoresistance of lung adenocarcinoma (57). P21(WAF1/CIP1) could be induced by garcinol via the down-regulation of p38-MAPK signaling in lung cancer (58). On the basis of our findings, it was shown that CDKN1A was the target of miR-654-3p, suggesting that miR-654-3p might play a role in preventing NSCLC via the targeting of CDKN1A.

Additionally, 5 of our selected miRNAs (except for miR-224) were not consistent with the results in the study by Ma et al (13), due to the different methods used in our studies. We used Student’s t-test in the multitest package to compare all of the individual expression values of miRNAs, and we selected differentially expressed miRNAs with a p value <0.05 and |log2 FC| >1 as threshold, while Ma et al calculated the mean expression values of miRNAs and then selected differentially expressed miRNAs using the SAM method, with a FDR <0.1 as the cutoff criteria. Besides, our analysis results were verified using the Limma package in R and the GEO2R software in the GEO database. Thus the selected miRNAs were different from those in the study by Ma et al Therefore, further experimental validation is required.

In conclusion, we identified the differentially expressed miRNAs in NSCLC tissue compared with noncancerous tissue. Our analysis identified several differentially expressed miRNA target genes, which might play crucial roles in NSCLC, including Notch1, VEGFA, SATB1, CDKN1A and PTEN. Further, we speculated that miR-34b and miR-520h might play important roles in the regulation of NSCLC by targeting genes in the notch signing. MiR-22 might be useful as a novel therapeutic agent as an oncogene. MiR-448 might function in promoting NSCLC cell metastasis via targeting SATB1, while miR-654-3p might play a role in preventing NSCLC by interacting with CDKN1A. Our study provides the groundwork for further clinical molecular target therapy experiments.


Financial support: This study was supported as a science and technology project of Fujian province (2012D021).
Conflict of interest: The authors have declared that no competing interests exist.
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  • Department of Thoracic and Cardiovascular Surgery, Fuzhou General Hospital, Fujian Medical University, Fuzhou, Fujian - PR China
  • Department of Respiratory and Critical Care Medicine, Fuzong Clinical College of Fujian Medical University, Fuzhou General Hospital, Fuzhou, Fujian - PR China
  • C. Xu and Y. Zheng contributed equally to this work and should be considered co–first authors

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