BioMed Research International
Volume 2017 (2017), Article ID 2563085, 9 pages
https://doi.org/10.1155/2017/2563085

Research Article

Diagnostic MicroRNA Biomarker Discovery for Non-Small-Cell Lung Cancer Adenocarcinoma by Integrative Bioinformatics Analysis

Yang Shao, Bin Liang, Fei Long, and Shu-Juan Jiang

Department of Respiratory Medicine, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250021, China

Correspondence should be addressed to Shu-Juan Jiang; shujuan-jiang@163.com

Received 3 March 2017; Accepted 10 April 2017; Published 15 June 2017

Academic Editor: Xingming Zhao

Copyright © 2017 Yang Shao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Lung cancer is the leading cause of cancer death and its incidence is ranked high in men and women worldwide. Non-small-cell lung cancer (NSCLC) adenocarcinoma is one of the most frequent histological subtypes of lung cancer. The aberration profile and the molecular mechanism driving its progression are the key for precision therapy of lung cancer, while the screening of biomarkers is essential to the precision early diagnosis and treatment of the cancer. In this work, we applied a bioinformatics method to analyze the dysregulated interaction network of microRNA-mRNA in NSCLC, based on both the gene expression data and the microRNA-gene regulation network. Considering the properties of the substructure and their biological functions, we identified the putative diagnostic biomarker microRNAs, some of which have been reported on the PubMed citations while the rest, that is, miR-204-5p, miR-567, miR-454-3p, miR-338-3p, and miR-139-5p, were predicted as the putative novel microRNA biomarker for the diagnosis of NSCLC adenocarcinoma. They were further validated by functional enrichment analysis of their target genes. These findings deserve further experimental validations for future clinical application.

1. Introduction

Lung cancer is the most death causing cancer for both men and women in the United States, and it is also the most death causing cancer in men and second in women worldwide. The incidence rate is high and ranked second for both men and women in the United States [1, 2]. Non-small-cell lung cancer (NSCLC) adenocarcinoma is one of the most common histological subtypes of lung cancer [3] and it is reported that nearly 40% of the lung cancer are adenocarcinoma, and the other two subtypes of NSCLS are squamous-cell carcinoma and large-cell carcinoma [4]. The NSCLS adenocarcinoma is reported associated with the aberrations like the epidermal growth factor receptor (EGFR) mutations and anaplastic lymphoma kinase (ALK) fusion or rearrangement [3, 5], and several drugs such as gefitinib, erlotinib, and afatinib were developed for the targeting the aberrant gene products, but only few patients are ideal for the targeted treatments [6]. In addition, the patients treated with these target drugs may acquire resistance and make the treatment invalid [7]. There are also other aberrations reported associated with the NSCLC adenocarcinoma, such as mutations or fusions happen in HER2, BRAF, NF1, MEK1, RET, ROS1, and other genes. The risk factors for NSCLS adenocarcinoma may also include air pollution, gender, age, smoking, occupation, and eating habits [8–10]. For personalized diagnosis and treatment of cancer, the expression profile characterization and the key player screening [11] are the necessary steps. With the coming of aging era and the air pollution in the developing countries, the incidence of lung cancer will keep high, and the early diagnosis of lung cancer becomes very necessary. However, we still lack sensitive and precision biomarkers for the early diagnosis or the personalized therapy of the lung cancer [7, 12, 13].

MicroRNAs are endogenous small noncoding RNAs which regulate many important biological roles and theirs aberrations may have significant effects on the cancer genesis and progression, such as cell proliferation, cell cycle, apoptosis, and tumorigenesis, and therefore are good candidates for cancer diagnosis and therapy biomarkers [14–16]. Biomarker microRNA discovery could be implemented both experimentally and computationally. The former is a routine but time-consuming and costing method, since the biological systems are complex and the mechanisms are diverse. The computational methods based on integrative analysis of different omics data have more advantages, such that it could integrate diverse omics data sets and model the biological process by network construction and then understand the aberrations at the systems level [17, 18]. Furthermore, the computational methods are also cheap, less time-consuming and could be easily validated by literature mining, association analysis, and bioinformatics functional enrichment confirmation. With more and more biomedical data available and accumulated, the computational methods will be more and more powerful for the future precision medicine strategies.

At present, more and more computational and bioinformatics models are developing for biomarker discovery; some of them are machine learning based [19, 20], while others are mechanism-based [14–16, 21]. The machine learning based methods need more data to train the model and the mechanism-based models are more knowledge based, and the two types of methods complement each other and promote biomarker discovery. We here applied the previous reported mechanism-based method, which is successful in microRNA biomarker discovery, to screen novel diagnostic biomarker for NSCLC adenocarcinoma.

2. Materials and Methods

The data used in our integrative analysis include the gene expression data of NSCLC adenocarcinoma from both microRNA and messenger RNA(mRNA) and the human reference microRNA-mRNA network. We need to first collect the data and reconstruct the NSCLC adenocarcinoma specific network and then to identify putative microRNA biomarkers based on the network structure and their biological functions. The screened microRNAs need to be validated by literature mining, confirmation, and bioinformatics exploration of their associations with NSCLC adenocarcinoma. The pipeline of the whole process of this work could be seen in Figure 1.

Figure 1: The procedure of data collection, identification of microRNA biomarkers with integrative analysis, and the validation of the miRNA biomarkers.

2.1. The NSCLC Adenocarcinoma Gene Expression Data Collection and the Human Reference MicroRNA-mRNA Interactions

The gene expression and microRNA expression data for the NSCLC adenocarcinoma were extracted from the public GEO database [22]. The details of data sets are listed in Table 1. The data sets we used for the construction of NSCLC adenocarcinoma specific microRNA-mRNA network are GSE63459 and GSE36681, where the GSE63459 data set is the mRNA expression data which includes 33 NSCLC adenocarcinoma samples and 32 samples as control, and the other data set is the microRNA expression data with 47 NSCLC adenocarcinoma samples and 47 control samples. The data were first normalized and the differentially expressed mRNAs were then selected with the linear models in limma R package [23, 24]. The empirical Bayes (eBayes) method was applied to calculate the value and other parameters. The Benjamini-Hochberg correction was used to adjust the values. The adjusted values less than 0.05 were regarded as significant. The human reference microRNA-mRNA interaction network was constructed based mainly on experimentally validated and the consensus predicted microRNA-mRNA interaction pairs as reported in previous studies [16, 25, 26].

Table 1: NSCLC adenocarcinoma gene expression data collected from GEO data sets.

Accession/ID
PMID
Platform
Treatment
Control
Materials
Year
mRNA/miRNA

GSE36681
22573352
GPL8179

tissue
2012
miRNA

GSE63459
26134223
GPL6883

tissue
2015
mRNA

2.2. Model Construction, Validation, and Identification of NSCLC Adenocarcinoma MicroRNA Biomarkers

The basic idea and the methods we used here are based on the models which were developed previously [26, 40–42], where two parameters are used to measure the importance of microRNAs as the potential biomarkers for a specific disease. The first is the number of genes (NOG) uniquely targeted by a certain microRNA [40, 41], and this index is reasonable to quantify the tendency to be a biomarker since the alteration of the unique interaction cannot be substituted or compensated by other microRNA-mRNA interaction pairs. The other index was proposed to quantify the transcription factor percentage (TFP) and was defined as the percentage of transcription factor (TF) genes of all the microRNA targets [26]. With these two indexes, the NSCLC adenocarcinoma specific microRNA-mRNA interaction network was constructed by mapping the detected differentially expressed microRNAs in NSCLC adenocarcinoma onto the reference human microRNA-mRNA interaction network. With the reconstructed conditional network, the abovementioned measurements, that is, the NOG and TFP, were calculated for each microRNA in the NSCLC adenocarcinoma network. MicroRNAs with significant large NOG and TFP values (Wilcoxon signed-rank test, value < 0.05) were detected as our putative biomarkers for diagnosis of NSCLC adenocarcinoma.

To validate the bioinformatics model first, we also collected reported NSCLC adenocarcinoma associated microRNAs from PubMed citations with the searching criteria “(lung adenocarcinoma OR NSCLC adenocarcinoma) AND (miRNA OR microRNA) AND (biomarker OR )”. The related PMIDs, NOGs, and TFPs of these microRNAs were also calculated.

2.3. The Literature Confirmation and Functional Validation of the Putative NSCLC Adenocarcinoma Diagnostic MicroRNA Biomarkers

For validation of the bioinformatics method and the screened microRNA biomarkers, we checked the PubMed citations and extracted the reported microRNA biomarkers for both diagnosis and prognosis of NSCLC adenocarcinoma. The identified novel microRNA biomarkers were then validated with functional enrichment analysis of the targeted genes of the microRNAs. The enrichment analysis were performed with Gene Ontology Annotations, KEGG pathway analysis, which were done by the DAVID (Database for Annotation, Visualization, and Integrated Discovery) online tool [43]. The value threshold was set to 0.05 and the FDR adjustment was used for multiple test correction. Then we calculated the enrichment based on the hypergeometric test.

3. Results and Discussion

3.1. The Validation of Bioinformatics Methods for the MicroRNA Biomarker Discovery in NSCLC Adenocarcinoma

The microRNA biomarkers for diagnosis and prognosis of NSCLC adenocarcinoma reported in PubMed citations were collected and listed in Table 2. According to the NOGs and TFPs listed in Table 2, all the microRNAs except miR-650 are characterized with high NOGs and TFPs, and therefore the performance of the microRNA biomarker discovery method based on the two measurements is reasonable and can be extended and applied to the biomarker discovery in NSCLC adenocarcinoma.

Table 2: Literature reported lung adenocarcinoma miRNA biomarkers.

Reported miRNA
Official ID
PMID
Biomarker type
Samples
Expression level
NOG
TFP

miR-155
miR-155-5p
24190459 [27]
Diagnosis
Serum
Up
71
0.21

miR-196a-5p
miR-196a-5p
27247934 [28]
Diagnosis
Tissue
Up
7
0.19

miR-218-5p
miR-218-5p
27247934 [28]
Diagnosis
Tissue
Down
10
0.12

miR-143
miR-143-3p
24286416 [29]
Diagnosis
Blood
Down
15
0.03

miR-182
miR-182-5p
19493678 [30]
Diagnosis
Tissue
Up
9
0.19

miR-650
miR-650
23991130 [31]
Prognosis
Tissue
Up
0
0

miR-141
miR-141-3p
25746592 [32]
Prognosis
Tissue
Up
22
0.17

miR-29c
miR-29c-3p
28241836 [33]
Prognosis
Tissue
Down
5
0.15

miR-23b-3p
miR-23b-3p
28055956 [34]
Prognosis
Plasma
Up
23
0.15

miR-10b-5p
miR-10b-5p
28055956 [34]
Prognosis
Plasma
Up
9
0.15

miR-21-5p
miR-21-5p
28055956 [34]
Prognosis
Plasma
Up
38
0.13

miR-126-3p
miR-126-3p
27277197 [35]
Prognosis
Tissue
Down
6
0.12

miR-451a
miR-451a
27277197 [35]
Prognosis
Tissue
Down
4
0.17

miR-25
miR-25-3p
26687391 [36]
Prognosis
Blood
Up
9
0.17

miR-145
miR-145-5p
26687391 [36]
Prognosis
Blood
Down
36
0.11

miR-210
miR-210
26687391 [36]
Prognosis
Blood
Down
1
0.18

miR-142-3p
miR-142-3p
23410826 [37]
Prognosis
Serum
Up
12
0.14

miR-29b
miR-29b-3p
22249264 [38]
Prognosis
Tissue
Down
9
0.14

miR-590
miR-590-5p
28012926 [39]
Prognosis
Tissue
Up
14
0.16

Among the reported microRNA biomarkers for NSCLC adenocarcinoma, many of them played essential roles in lung carcinogenesis and their abnormal expression patterns were highly associated with the occurrence and development of NSCLC adenocarcinoma. For example, serum miR-155 was a sensitive indicator for predicting the initiation of lung adenocarcinoma, especially combining with the index of carbohydrate antigen 125. It altered the expression of downstream proteins and activated the lung carcinogenic signal [27]. Two miRNAs, that is, miR-196a-5p and miR-218-5p, were validated to be up- and downregulated from normal to adenocarcinoma tumor tissues, respectively. Their target genes were functional in lung cancer related processes by activating or inhibiting biological activities in pathways in cancer, cell cycle, transcriptional misregulation in cancer, and small-cell lung cancer [28]. The prognostic value of miRNAs for NSCLC adenocarcinoma was also comprehensively investigated. For instance, Huang et al. [31] showed that miR-650 was able to regulate the expression of Bcl-2/Bax, which would thereby contribute to the docetaxel chemoresistance of lung adenocarcinoma cells. This miRNA was a powerful indicator for predicting the chemosensitivity of lung adenocarcinoma patients to docetaxel-based chemotherapy regimen. Zhang et al. [32] analyzed the clinical potential of miR-141 and found that this miRNAs was positively correlated with the tumor size, lymph NOGe metastasis, and TNM stage of lung adenocarcinomas. Meanwhile, Liu et al. [34] screened that the upregulation of plasma exosomal miRNAs miR-23b-3p, miR-10b-5p and miR-21-5p was strongly connected with the poor overall survival of lung adenocarcinoma patients. In order to evaluate the efficacy of maintenance treatment on lung adenocarcinoma patients with negativity for epidermal growth factor receptor (EGFR) mutations or anaplastic lymphoma kinase (ALK) translocations, Shi et al. [36] designed the experiment in which patients were divided into a pemetrexed group and a control group, respectively. As a result, the expression levels of miR-25, miR-145, and miR-210 were associated with the progression-free survival time of patients in the treatment group, which highlighted the prognostic potential of these miRNAs to the pemetrexed therapy in specific lung adenocarcinoma individuals.

3.2. The Predicted MicroRNA Biomarker for Diagnosis of NSCLC Adenocarcinoma

We performed the predictions according to the pipeline shown in Figure 1. At first, 93 differentially expressed microRNAs and 331 differentially expressed genes in NSCLC adenocarcinoma were detected, respectively. Nine microRNAs were screened by Wilcoxon signed-rank test with value < 0.05. These nine microRNAs were predicted to be biomarkers for the diagnosis of NSCLC adenocarcinoma as listed in Table 3. Their network structural features in the microRNA-mRNA interaction network were shown, which are the whole set of targeted genes, NOGs and TFPs. Four of the nine predicted microRNAs (bolded and underlined in Table 3), that is, miR-145-5p, miR-182-5p, miR-141-3p, and miR-590-3p, have been reported as biomarkers previously, the remaining five, that is, miR-204-5p, miR-567, miR-454-3p, miR-338-3p, and miR-139-5p, were recommended as novel diagnosis biomarker for NSCLC adenocarcinoma.

Table 3: Predicted putative lung adenocarcinoma microRNA biomarkers.

miRNA ID
NOG
value
TFP
value
Whole target genes

miR-145-5p
1

0.67

MMP12; ZFP36; KLF4

miR-204-5p
5

0.13

DPYSL2; EMP1; SPDEF; LMO7; SLC1A1; ALPL; MMP9; FRAS1

miR-182-5p
2

0.20

CAMK2N1; ZFP36; UBE2T; LPHN2; RGS17.

miR-567
1

0.25

SPTBN1; DUSP1; BCHE; LPHN2

miR-141-3p
2

0.14

H3F3B; TCEAL2; MYH10; LHFP; LPHN2; CCL2; KLF9

miR-454-3p
2

0.11

DPYSL2; HOXA5; FKBP11; SRPX; EDN1; LDLR; CAV2; BMPR2; SLC2A1

miR-590-3p
5

0.13

SERPINE2; PLEKHC1; SPTBN1; H3F3B; TMEM47; TIMP3; COL3A1; CXCL13; ETS2; CELSR3; LPL; SMAD6; BMPR2; LPHN2; SASH1.

miR-338-3p
2

0.25

COL1A1; FOSB; ADAMTS1; MMP9

miR-139-5p
1

1.00

FOS

3.3. Functional Enrichment Validation of the Predicted MicroRNA Biomarkers

We further performed the functional enrichment analysis to investigate the roles of genes regulated by identified microRNA biomarkers through Database for Annotation, Visualization and Integrated Discovery (DAVID) online tools. This analysis was conducted in two ways: Gene Ontology (GO) analysis and KEGG pathway analysis.

In GO analysis, we did this analysis at three levels: biological process (BP), cellular component (CC), and molecular function (MF). The top 10 most significantly enriched items were shown in Figure 2. Through further literature validation, we found that most of the items have a strong relationship with lung adenocarcinoma. For example, in molecular function level, relevant research has shown that FGFR2’s function could be regulated by two proteins: Grb2 and Plcγ1 under the situation of growth factor absence. These two proteins will compete for the same protein binding site [44]. FGFR2 expression could be repressed by miR-338-3p, one of the identified miRNA biomarkers. Other items, such as cytoplasm [45], membrane-bounded organelle [46], positive regulation of metabolic process, and transcription factor binding [47, 48] have already been validated through biological and clinical experiments to have an impact on the occurrence and metastasis of lung adenocarcinoma.

Figure 2: Gene ontology (GO) analysis for genes targeted by 9 identified microRNA biomarkers. The statistical significance value ( value) has been negative 10-based log transformed. Top 10 significantly enriched items are listed for each level.

In KEGG pathway analysis, we totally found 72 significantly enriched pathways. Here we still selected top 10 most significantly enriched pathways for further investigation. These 10 pathways were listed in Table 4 and shown in Figure 3.

Table 4: Top 10 significantly enriched pathways in KEGG pathway analysis.

Term
Adj. value

Adherens junction

Pathways in cancer

Hippo signaling pathway

TGF-beta signaling pathway

Proteoglycans in cancer

Cell cycle

Prostate cancer

Hepatitis B

MAPK signaling pathway

FoxO signaling pathway

Figure 3: KEGG pathway enrichment analysis for genes targeted by 9 candidate microRNA biomarkers. The statistical significance value ( value) has been negative 10-based log transformed. The top 10 significantly enriched pathways are listed, respectively, in this figure.

Here, we investigate the relationship between these 10 pathways and lung adenocarcinoma through literature validation and we found that most of these pathways have been demonstrated to be associated with lung adenocarcinoma. Besides common pathways in lung cancer like cell cycle and MAPK signaling pathways [49, 50], there are still other pathways such as Hippo signaling pathway [51] and TGF-beta signaling pathway and proteoglycans in cancer are all supported by relevant research [52, 53]. The relevant pipeline of cell cycle and MAPK signaling pathways can be referred to in Figure 4.

Figure 4: The pipeline of cell cycle and MAPK signaling pathway. The stars in these pipelines represent the action sites of the genes regulated by 9 candidate miRNA biomarkers.

4. Conclusions

In this research, an integrative bioinformatics model considering the network structure and biological functions of the microRNA targets was used to predict novel biomarker microRNAs for the diagnosis of NSCLC adenocarcinoma. The method was first tested with the reported microRNA biomarkers of NSCLC adenocarcinoma; then we extended the model and applied it to the microRNA and gene expression data. We detected five novel biomarker microRNAs for the diagnosis of NSCLC adenocarcinoma, including miR-204-5p, miR-567, miR-454-3p, miR-338-3p, and miR-139-5p. The novel bioinformatics microRNAs were validated with bioinformatics exploring their functions associated with NSCLC adenocarcinoma.

Abbreviations

NSCLC: | Non-small-cell lung cancer
DE: | Differentially expressed
TF: | Transcription factor
NOG: | Novel out degree
TFP: | Transcription factor gene percentage
KEGG: | Kyoto encyclopedia of genes and genomes
DAVID: | Database for Annotation, Visualization, and Integrated Discovery.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

The authors would like to acknowledge the financial support of the National Natural Science Foundation of China (81370138).

References

R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2017,” CA: A Cancer Journal for Clinicians, vol. 67, no. 1, pp. 7–30, 2017.

L. A. Torre, R. L. Siegel, and A. Jemal, “Lung cancer statistics,” Advances in Experimental Medicine and Biology, vol. 893, pp. 1–19, 2016.

M. Saito, K. Shiraishi, H. Kunitoh, S. Takenoshita, J. Yokota, and T. Kohno, “Gene aberrations for precision medicine against lung adenocarcinoma,” Cancer Science, vol. 107, no. 6, pp. 713–720, 2016.

Y. Zhang, H. Wang, J. Wang et al., “Global analysis of chromosome 1 genes among patients with lung adenocarcinoma, squamous carcinoma, large-cell carcinoma, small-cell carcinoma, or non-cancer,” Cancer and Metastasis Reviews, vol. 34, no. 2, pp. 249–264, 2015.

C.-W. Xu, X.-Y. Cai, Y. Shao et al., “A case of lung adenocarcinoma with a concurrent EGFR mutation and ALK rearrangement: a case report and literature review,” Molecular Medicine Reports, vol. 12, no. 3, pp. 4370–4375, 2015.

G. Roviello, “The distinctive nature of adenocarcinoma of the lung,” OncoTargets and Therapy, vol. 8, pp. 2399–2406, 2015.

P. T. Cagle, K. Raparia, and B. P. Portier, “Emerging biomarkers in personalized therapy of lung cancer,” Advances in Experimental Medicine and Biology, vol. 890, pp. 25–36, 2016.

F. L. Wang, “Analysis of risk factors for female lung adenocarcinoma in haerbin: indoor air pollution,” Zhonghua Yu Fang Yi Xue Za Zhi, vol. 23, no. 5, pp. 270–273, 1989.

L. M. Butler, J. A. Montague, W.-P. Koh, R. Wang, M. C. Yu, and J.-M. Yuan, “Fried meat intake is a risk factor for lung adenocarcinoma in a prospective cohort of Chinese men and women in Singapore,” Carcinogenesis, vol. 34, no. 8, pp. 1794–1799, 2013.

C. Paris, C. Clement-Duchene, J. M. Vignaud et al., “Relationships between lung adenocarcinoma and gender, age, smoking and occupational risk factors: a case-case study,” Lung Cancer, vol. 68, no. 2, pp. 146–153, 2010.

J. Jiang, P. Jia, Z. Zhao, and B. Shen, “Key regulators in prostate cancer identified by co-expression module analysis,” BMC Genomics, vol. 15, no. 1, article 1015, 2014.

H. C. Jeong, “Clinical aspect of MicroRNA in lung cancer,” Tuberculosis and Respiratory Diseases, vol. 77, no. 2, pp. 60–64, 2014.

M. K. Kim, S. B. Jung, J.-S. Kim et al., “Expression of microRNA miR-126 and miR-200c is associated with prognosis in patients with non-small cell lung cancer,” Virchows Archiv, vol. 465, no. 4, pp. 463–471, 2014.

S. Shen, Y. Lin, X. Yuan et al., “Biomarker microRNAs for Diagnosis, prognosis and treatment of hepatocellular carcinoma: a functional survey and comparison,” Scientific Reports, vol. 6, article 38311, 2016.

L. Shen, Y. Lin, Z. Sun, X. Yuan, L. Chen, and B. Shen, “Knowledge-guided bioinformatics model for identifying autism spectrum disorder diagnostic microrna biomarkers,” Scientific Reports, vol. 6, article 39663, 2016.

Y. Zhu, Y. Lin, W. Yan et al., “Novel biomarker micrornas for subtyping of acute coronary syndrome: a bioinformatics approach,” BioMed Research International, vol. 2016, Article ID 4618323, 11 pages, 2016.

Y. Hu, J. Li, W. Yan et al., “Identifying novel glioma associated pathways based on systems biology level meta-analysis,” BMC Systems Biology, vol. 7, supplement 2, p. S9, 2013.

B. Shen, Shen H. -B., Tian T., Lü Q., and Hu G., “Translational bioinformatics and computational systems medicine,” Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 375641, 2 pages, 2013.

H. A. Mattison, T. Stewart, and J. Zhang, “Applying bioinformatics to proteomics: is machine learning the answer to biomarker discovery for PD and MSA?” Movement Disorders, vol. 27, no. 13, pp. 1595–1597, 2012.

V. A. Huynh-Thu, Y. Saeys, L. Wehenkel, and P. Geurts, “Statistical interpretation of machine learning-based feature importance scores for biomarker discovery,” Bioinformatics, vol. 28, no. 13, Article ID bts238, pp. 1766–1774, 2012.

X. M. Zhao, K. Q. Liu, G. Zhu et al., “Identifying cancer-related microRNAs based on gene expression data,” Bioinformatics, vol. 31, no. 8, pp. 1226–1234, 2015.

T. Barrett, T. O. Suzek, D. B. Troup et al., “NCBI GEO: mining millions of expression profiles—database and tools,” Nucleic Acids Research, vol. 35, pp. D760–D765, 2007.

G. K. Smyth, “Linear models and empirical Bayes methods for assessing differential expression in microarray experiments,” Statistical Applications in Genetics and Molecular Biology, vol. 3, no. 1, article 3, 2004.

M. E. Ritchie, B. Phipson, D. Wu et al., “limma powers differential expression analyses for RNA-sequencing and microarray studies,” Nucleic Acids Research, vol. 43, no. 7, 2015.

J. Huang, Z. Sun, W. Yan et al., “Identification of MicroRNA as sepsis biomarker based on miRNAs regulatory network analysis,” BioMed Research International, vol. 2014, Article ID 594350, 12 pages, 2014.

W. Yan, L. Xu, Z. Sun et al., “MicroRNA biomarker identification for pediatric acute myeloid leukemia based on a novel bioinformatics model,” Oncotarget, vol. 6, no. 28, pp. 26424–26436, 2015.

F. Gao, J. Chang, H. Wang, and G. Zhang, “Potential diagnostic value of miR-155 in serum from lung adenocarcinoma patients,” Oncology Reports, vol. 31, no. 1, pp. 351–357, 2014.

F. Tian, R. Li, Z. Chen et al., “Differentially expressed miRNAs in tumor, adjacent, and normal tissues of lung adenocarcinoma,” BioMed Research International, vol. 2016, Article ID 1428271, 2016.

X. L. Zeng, S. Y. Zhang, J. F. Zheng, H. Yuan, and Y. Wang, “Altered miR-143 and miR-150 expressions in peripheral blood mononuclear cells for diagnosis of non-small cell lung cancer,” Chinese Medical Journal, vol. 126, no. 23, pp. 4510–6, 2013 (English).

W. C. Cho, A. S. Chow, and J. S. Au, “Restoration of tumour suppressor hsa-miR-145 inhibits cancer cell growth in lung adenocarcinoma patients with epidermal growth factor receptor mutation,” European Journal of Cancer, vol. 45, no. 12, pp. 2197–2206, 2009.

Huang J. Y., Cui S. Y., Chen Y. T. et al., “MicroRNA-650 was a prognostic factor in human lung adenocarcinoma and confers the docetaxel chemoresistance of lung adenocarcinoma cells via regulating Bcl-2/Bax expression,” PLoS ONE, vol. 8, no. 8, Article ID e72615, 2013.

X. Zhang, P. Li, M. Rong et al., “MicroRNA-141 is a biomarker for progression of squamous cell carcinoma and adenocarcinoma of the lung: clinical analysis of 125 patients,” The Tohoku Journal of Experimental Medicine, vol. 235, no. 3, pp. 161–169, 2015.

L. Liu, N. Bi, L. Wu et al., “MicroRNA-29c functions as a tumor suppressor by targeting VEGFA in lung adenocarcinoma,” Molecular Cancer, vol. 16, article 50, no. 1, 2017.

Q. Liu, Z. Yu, S. Yuan et al., “Circulating exosomal microRNAs as prognostic biomarkers for non-small-cell lung cancer,” Oncotarget, vol. 8, no. 8, pp. 13048–13058, 2017.

Q. Chen, H. Hu, D. Jiao et al., “miR-126-3p and miR-451a correlate with clinicopathological features of lung adenocarcinoma: The underlying molecular mechanisms,” Oncology Reports, vol. 36, no. 2, pp. 209–917, 2016.

S.-B. Shi, M. Wang, J. Tian, R. Li, C.-X. Chang, and J.-L. Qi, “MicroRNA 25, microRNA 145, and microRNA 210 as biomarkers for predicting the efficacy of maintenance treatment with pemetrexed in lung adenocarcinoma patients who are negative for epidermal growth factor receptor mutations or anaplastic lymphoma kinase translocations,” Translational Research, vol. 170, pp. 1–7, 2016.

S. Kaduthanam, S. Gade, M. Meister et al., “Serum miR-142-3p is associated with early relapse in operable lung adenocarcinoma patients,” Lung Cancer, vol. 80, no. 2, pp. 223–227, 2013.

S. I. Rothschild, M. P. Tschan, E. A. Federzoni et al., “MicroRNA-29b is involved in the Src-ID1 signaling pathway and is dysregulated in human lung adenocarcinoma,” Oncogene, vol. 31, no. 38, pp. 4221–4232, 2012.

Y. Liu, F. Wang, and P. Xu, “miR-590 accelerates lung adenocarcinoma migration and invasion through directly suppressing functional target OLFM4,” Biomedicine & Pharmacotherapy, vol. 86, pp. 466–474, 2017.

J. Zhu, S. Wang, W. Zhang et al., “Screening key microRNAs for castration-resistant prostate cancer based on miRNA/mRNA functional synergistic network,” Oncotarget, vol. 6, no. 41, pp. 43819–43830, 2015.

W. Zhang, J. Zang, X. Jing et al., “Identification of candidate miRNA biomarkers from miRNA regulatory network with application to prostate cancer,” Journal of Translational Medicine, vol. 12, p. 12, 2014.

J. Chen, D. Zhang, W. Zhang et al., “Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis,” Journal of Translational Medicine, vol. 11, article 169, 2013.

G. Dennis Jr., B. T. Sherman, D. A. Hosack et al., “DAVID: database for annotation, visualization, and integrated discovery,” Genome Biology, vol. 4, no. 5, p. P3, 2003.

Z. Timsah, Z. Ahmed, C.-C. Lin et al., “Competition between Grb2 and Plcγ1 for FGFR2 regulates basal phospholipase activity and invasion,” Nature Structural and Molecular Biology, vol. 21, no. 2, pp. 180–188, 2014.

L. Li, L. Wang, K. M. Prise et al., “Akt/mTOR mediated induction of bystander effect signaling in a nucleus independent manner in irradiated human lung adenocarcinoma epithelial cells,” Oncotarget, 2017.

Z. Su, K. Wang, R. Li et al., “Overexpression of RBM5 induces autophagy in human lung adenocarcinoma cells,” World Journal of Surgical Oncology, vol. 14, no. 1, article 57, 2016.

E. Y. P. Lee, P.-L. Khong, V. H. F. Lee, W. Qian, X. Yu, and M. P. Wong, “Metabolic phenotype of stage IV lung adenocarcinoma: relationship with epidermal growth factor receptor mutation,” Clinical Nuclear Medicine, vol. 40, no. 3, pp. e190–e195, 2015.

D. D. Becker-Santos, K. L. Thu, J. C. English et al., “Developmental transcription factor NFIB is a putative target of oncofetal miRNAs and is associated with tumour aggressiveness in lung adenocarcinoma,” The Journal of Pathology, vol. 240, no. 2, pp. 161–172, 2016.

X. Wang, M. Long, K. Dong et al., “Chemotherapy agents-induced immunoresistance in lung cancer cells could be reversed by trop-2 inhibition in vitro and in vivo by interaction with MAPK signaling pathway,” Cancer Biology & Therapy, vol. 14, no. 12, pp. 1123–1132, 2013.

G. M. Dancik and D. Theodorescu, “Robust prognostic gene expression signatures in bladder cancer and lung adenocarcinoma depend on cell cycle related genes,” PLoS ONE, vol. 9, no. 1, Article ID e85249, 2014.

J. M. Kim, D. W. Kang, L. Z. Long et al., “Differential expression of Yes-associated protein is correlated with expression of cell cycle markers and pathologic TNM staging in non-small-cell lung carcinoma,” Human Pathology, vol. 42, no. 3, pp. 315–323, 2011.

R. L. Toonkel, A. C. Borczuk, and C. A. Powell, “TGF-β signaling pathway in lung adenocarcinoma invasion,” Journal of Thoracic Oncology, vol. 5, no. 2, pp. 153–157, 2010.

Y. Kusano, Y. Yoshitomi, S. Munesue, M. Okayama, and K. Oguri, “Cooperation of syndecan-2 and syndecan-4 among cell surface heparan sulfate proteoglycans in the actin cytoskeletal organization of Lewis lung carcinoma cells,” Journal of Biochemistry, vol. 135, no. 1, pp. 129–137, 2004.