Identification of miRNAs and differentially expressed genes in early phase non-small cell lung cancer

  • Authors:
    • Wen Tian
    • Jie Liu
    • Baojing Pei
    • Xiaobo Wang
    • Yu Guo
    • Lin Yuan
  • View Affiliations

  • Published online on: January 14, 2016     https://doi.org/10.3892/or.2016.4561
  • Pages: 2171-2176
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Abstract

To explore the potential therapeutic targets of early‑stage non-small cell lung cancer (NSCLC), gene microarray analysis was conducted. The microarray data of NSCLC in stage IA, IB, IIA, and IIB (GSE50081), were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in IB vs. IA, IIA vs. IB, IIB vs. IIA were screened out via R. ToppGene Suite was used to get the enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DEGs. The GeneCoDis3 database and Cytoscape software were used to construct the transcriptional regulatory network. In total, 25, 17 and 14 DEGs were identified in IB vs. IA, IIA vs. IB, IIB vs. IIA of NSCLC, respectively. Some GO terms and pathways (e.g., extracellular space, alveolar lamellar body, bioactivation via cytochrome P450 pathway) were found significantly enriched in DEGs. Genes S100P, ALOX15B, CCL11, NLRP2, SERPINA3, FoxO4 and hsa-miR-491 may play important roles in the development of early-stage NSCLC. Thus, by bioinformatics analysis the key genes and biological processes involving in the development of early-stage NSCLC could be established, providing more potential references for the therapeutic targets.

Introduction

Lung cancer is the second most common cancer with high mortality in both men and women (1). More than 80% of lung cancer are non-small cell lung cancer (NSCLC) (2,3). In China, the incidence of lung cancer in the past few decades has doubled due to the aging population, smoking and decline in air quality (4). Although some treatments have achieved certain therapeutic effect, the overall 5-year survival rates of NSCLC patients is still below 15% (5). Most NSCLC patients are in mid-term and advanced stage when diagnosed, which could contribute its poor prognosis. The progression of NSCLC is related to many genetic factors, such as abnormal gene expression (6). Gu et al found that NSCLC, comparing to other cancers, had a significantly higher ratio of VEGF-A protein/mRNA and a significantly lower level of miR-497, suggesting the presence of the post-transcriptional control of VEGF-A in NSCLC which was obviously different from other cancers (7). It demonstrated that miR-497 played an important role in suppression of VEGF-A-mediated NSCLC by inhibiting the proliferation and invasion of cancer cells (7). Therefore, it is very important to identify potential biomarkers of NSCLC for its prevention and therapy.

With the rapid development of molecular biology and bioinformatics, we can explore the mechanisms of the development and progression of cancers at the molecular level. It also provides some significant references for the diagnosis, prevention and treatment of cancer (8). However, the mechanism and development of new target-based therapies related to lung carcinogens are still unknown. Gene microarray, as an efficient technology, has been used to detect the expression levels of some genes in cells and tissues at different stages of cancer. It is also widely used for the genome-scale detections, which may help discover the key genes associated with tumorigenesis (9).

In this study, we aimed to explore the key genes in the early and mid-stages of NSCLC via gene microarray data analysis. Differentially expressed genes (DEGs), Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways which were associated with NSCLC were identified. A transcription factor (TF) network was also constructed. Our study aimed to provide a new idea and method for a better understanding of NSCLC, so as to improve the diagnosis and treatment of early stage NSCLC.

Materials and methods

Affymetrix microarray data

Microarray data set GSE50081, submitted by Der et al (10), was obtained from the Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) database. A total of 181 gene chips were available, including 48 samples in IA stage, 79 samples in IB stage, 9 samples in IIA stage and 45 samples in IIB stage of NSCLC, respectively. Microarray data were analyzed using GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array), which included 54,675 probes to detect the expression of gene transcription levels.

Data preprocessing and screening of DEGs

The raw data were preprocessed. CEL files were normalized and converted to expression profiles using Affy package of R (11). If multiple probes corresponded to one given gene, the mean expression value was defined as the expression value. Limma package of R was used to analyze the DEGs of stage IA samples, IB samples, IIA samples and IIB samples (12). The samples were divided into three groups: IA vs. IB, IIA vs. IB and IIA vs. IIB. The gene expression values were normalized followed by the t-test of the non-paired samples (13). The multiple testing corrections were performed through Beniamini-Hochberg. FDR <0.05 and |logFC (fold change)| >2 were used as the threshold for identifying the DEGs.

Functional enrichment analysis

GO and KEGG pathway analysis of DEGs were conducted via ToppGene Suite (http://toppgene.cchmc.org) (14). ToppGene Suite is a one-stop portal for gene list functional enrichment, candidate gene prioritization using either functional annotations or network analysis and identification and prioritization of novel disease candidate genes in the interactome (14). Adjusted P≤0.05 was used as cut-off criterion.

Construction of transcriptional regulatory network

With the cut-offs of adjusted P<0.05, the TFs and miRNAs which interacted with target DEGs were identified by the online database GeneCoDis (http://genecodis.cnb.csic.es/) (15). These pairs were used to construct transcriptional regulatory network. Visualization of transcriptional regulatory network was performed by Cytoscape software (16).

Results

Identification of DEGs

A total of 19,851 genes were obtained after the processes of background calibration, standardization and the transformation from probe symbols to the gene symbol, which were used for the further analysis. The total 56 DEGs, consisted of 25 in IB vs. with IA, 17 in IIA vs. IB and 14 in IIB vs. IIA, as detailed in Table I. The variation in trends of the DEGs expression values in the three groups are shown in Fig. 1.

Table I

The three groups of differentially expressed genes.

Table I

The three groups of differentially expressed genes.

IB/IAlogFCP-valueIIA/IBlogFCP-valueIIB/IIAlogFCP-value
CPB2−1.463.83E-05CYP1A11.060.000686NOG−1.030.007331
IL81.130.000285SERPINA3−2.030.000757SERPINA31.590.010848
LRRK2−1.030.000594TSPAN1−1.650.00116SPRY2−1.090.012028
AGR3−1.380.000674ALOX15B1.310.001853RPL39L1.070.012364
PEBP4−1.260.000772ID1−1.230.002166NLRP21.630.012685
CYP4B1−1.350.000864MB−1.180.005791TMEM163−1.050.013372
LY6D1.130.000998S100P−2.490.006031ALOX15B−1.240.016598
ZNF385B−1.060.001022AGR2−1.330.013493TRIP131.040.023701
CYP24A11.080.00145MFAP51.320.017172MALL−1.180.026681
HSD17B6−1.030.001823PLEKHS1−1.020.017271BAMBI−1.190.027515
SFTPC−1.390.003012ANXA11.080.023946EYA21.170.030051
C16orf89−1.260.003714NLRP2−1.360.032368SLAIN1−1.040.033329
S100P1.300.004488DNER−1.110.034532PRSS211.230.035066
MMP101.170.005196CA9−1.020.036787CCL11−1.070.043612
SFTPD−1.210.006222SLPI−1.180.041679
C4BPA−1.270.006294FXYD3−1.070.044774
SFTA3−1.300.0096CCL111.160.045338
SCGB2A1−1.020.009966
SFTPB−1.010.010427
KRT6A1.740.01062
SCGB3A2−1.320.012366
MMP11.140.013572
BPIFA11.250.014045
SCGB3A1−1.190.019787
NAPSA−1.040.022362
Functional enrichment analysis of DEGs

A total of 110 enriched GO terms and 8 KEGG pathways for DEGs were identified. The top 10 enriched GO terms are listed in Table II, and 8 enriched KEGG pathways are shown in Table III. The top three pathways were bioactivation via cytochrome P450 (P=1.01E-04), Arachidonic acid metabolism (P=3.19E-04), Cytochrome P450, arranged by substrate type (P=3.57E-04).

Table II

The top 10 enriched GO terms for DEGs.

Table II

The top 10 enriched GO terms for DEGs.

CategoryGO IDGO nameGene numberP-value
CCGO:0005615Extracellular space165.44E-08
CCGO:0097208Alveolar lamellar body31.29E-06
CCGO:0042599Lamellar body35.52E-06
BPGO:0050828Regulation of liquid surface tension26.90E-06
BPGO:0016477Cell migration121.72E-05
MFGO:0070576Vitamin D 24-hydroxylase activity21.93E-05
BPGO:0048870Cell motility124.05E-05
BPGO:0051674Localization of cell124.05E-05
MFGO:0005506Iron ion binding57.45E-05
CCGO:0005771Multivesicular body38.75E-05

[i] GO, Gene Ontology; DEGs, differentially expressed genes; CC, cellular component; BP, biological process; MF, molecular function.

Table III

The enriched KEGG pathways for DEGs.

Table III

The enriched KEGG pathways for DEGs.

CategoryPathway nameGene numberP-value
KEGG_PATHWAYBioactivation via cytochrome P45021.01E-04
KEGG_PATHWAYArachidonic acid metabolism33.19E-04
KEGG_PATHWAYCytochrome P450 - arranged by substrate type33.57E-04
KEGG_PATHWAYCytochrome P45035.68E-04
KEGG_PATHWAYPhase 1 - functionalization of compounds38.44E-04
KEGG_PATHWAYPlasminogen activating cascade28.99E-04
KEGG_PATHWAYTGF-β signaling pathway31.19E-03
KEGG_PATHWAYFatty acid metabolic21.66E-03

[i] DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Analysis of regulatory network between TFs and miRNAs

Following the screening of TFs, we constructed a regulatory network between TFs and miRNAs including 13 TFs, 120 miRNAs and 563 edges (Fig. 2). Based on this regulatory network, TFs (e.g., hsa-miR-653, hsa-miR-548a-5p, hsa-miR-491-3p, hsa-miR-518d-3p, hsa-miR-518c, hsa-miR-330-3p and hsa-miR-374a) were obtained which closely connected with others. Each of them interacted with seven genes, respectively. ID1 was regulated by the most number of the genes including 35 TFs and miRNA. The top 10 genes which closely connected with others in the NSCLC samples of these four stages are shown in Fig. 3.

Discussion

Substantial research efforts has been made in exploring the mechanisms of NSCLC, however, the current understanding of the genetic alterations associated with the progressing NSCLC has not been elucidated. In our present study, 52 DEGs of NSCLC were identified using gene microarray analysis. These DEGs may influence the occurrence and development of NSCLC, thus, they will provide important references for the diagnosis and treatment of NSCLC.

Gene microarray data in the early and mid-stage NSCLC were obtained from the GEO database. An important coinciding gene (S100P) was found differentially expressed in IA vs. IB and IIA vs. IB, respectively. S100P is a 95-amino acid member of S100 protein family. It contains 2 EF-hand calcium-binding motifs (17) and involves in a series of cellular regulation processes, such as cell cycle progressions and differentiation (18). S100P was found to be one of the target genes studied in many cancers including NSCLC (19). The expression of S100P was associated with drug resistance, metastasis, and poor clinical outcome. According to our results, S100P was upregulated in IB vs. IA and downregulated in IIA vs. IB. It could be speculated that the expression of S100P increased in the processes of cancer migration and invasion in the early stage of NSCLC, and then its expression was inhibited by certain factors, such as the immune system.

Besides, 4 overlapped genes (ALOX15B, CCL11, NLRP2 and SERPINA3) were identified in IIA vs. IB and IIA vs. IIB. The knockdown of the human arachidonate 15-lipoxygenase type B (ALOX15B) was reported to reduce the inflammation and lipid accumulation, suggesting its active pro-inflammatory and proatherogenic role (20). Pyrin domain-containing protein 2 (NALP2) was characterized by an N-terminal pyrin domain (PYD). It was involved in the activation of caspase-1 by Toll-like receptors (21). The expression of NALP2 was regulated by inflammatory mediators which were closely related to cancer (22,23). As a member of the CC chemokine family, eotaxin-1 (CCL11) was initially regarded as an eosinophil chemoattractant, and involved in the recruitment of inflammatory cells, such as eosinophils and neutrophils (24). Overexpression CCL11 was found in various inflammatory diseases, such as allergic asthma. CCL11 was also reported as diagnostic marker for some cancers, such as prostate cancer (25,26). Collectively, the dysregulation of the 4 overlapped genes in different paired comparisons (Table I) revealed that the key genes may play different roles at different stages of NSCLC.

The regulatory network revealed that FoxO4 was closely connected with other nodes. It participated in the processes of cell cycle, aging, apoptosis, stress response, tumorigenesis and metabolisms. The expression of FoxO4 was confirmed in the studies of 8 cases of NSCLC patients. The immunohistochemistry method was used to characterize the expression of FoxO4 (27). Many studies have demonstrated the relationships between FoxO4 and a variety of cancers. According to Su et al, the transcription factor FoxO4 was downregulated and inhibited tumor proliferation and metastasis in gastric cancer (28). The expression of FoxO4 was significantly decreased in colorectal cancer, indicating that FoxO4 acted as a tumor suppressor in the development and progression of colorectal cancer (29).

miRNA is a non-coding RNA of about 20–25 nt in length, which can affect the phase of post-transcriptional process to regulate the expression levels of genes (30). The development of cancer is associated with the reduction of tumor suppressor genes which are regulated by miRNAs. In this study, hsa-miR-491 was regulated by the most number of TFs serving as modulators and biomarkers for the invasion and metastasis of oral squamous cell carcinoma (31). According to other reports, hsa-miR-491 was found to be involved in metastasis of hepatocellular carcinoma by inhibition of matrix metalloproteinase and epithelial to mesenchymal transition based on the the methods of miRNA microarray analysis, RT-PCR, western blotting, Transwell invasion and immunohistochemistry (32), and it may even play a role in stemness of stem cells and cancer stem cells in lung tumors (33). Moreover, the roles of other target genes of hsa-miR-491 have also been reported in many studies, so they may be the novel biomarkers of NSCLC.

In conclusion, a set of DEGs were screened and considered to be related to the development of NSCLC. S100P, ALOX15B, CCL11, NLRP2, SERPINA3, FoxO4 and hsa-miR-491 may act as candidate diagnostic markers for NSCLC patients. However, further experiments are still needed to confirm the results.

Acknowledgments

This study was supported by Central Laboratory of Cangzhou Central Hospital.

References

1 

Farhat FS and Houhou W: Targeted therapies in non-small cell lung carcinoma: What have we achieved so far? Ther Adv Med Oncol. 5:249–270. 2013. View Article : Google Scholar : PubMed/NCBI

2 

Tian ZQ, Li ZH, Wen SW, Zhang YF, Li Y, Cheng JG and Wang GY: Identification of commonly dysregulated genes in non-small-cell lung cancer by integrated analysis of microarray data and qRT-PCR validation. Lung. 193:583–592. 2015. View Article : Google Scholar : PubMed/NCBI

3 

Liu Q, Yu Z, Xiang Y, Wu N, Wu L, Xu B, Wang L, Yang P, Li Y and Bai L: Prognostic and predictive significance of thymidylate synthase protein expression in non-small cell lung cancer: A systematic review and meta-analysis. Cancer Biomark. 15:65–78. 2015.

4 

Wang Y, Chen J, Wu S, Hu C, Li X, Wang Y, Yang Y, Rajan N, Chen Y, Chen Y, et al: Clinical effectiveness and clinical toxicity associated with platinum-based doublets in the first-line setting for advanced non-squamous non-small cell lung cancer in Chinese patients: A retrospective cohort study. BMC Cancer. 14:9402014. View Article : Google Scholar : PubMed/NCBI

5 

Tsai MF, Wang CC and Chen JJ: Tumour suppressor HLJ1: A potential diagnostic, preventive and therapeutic target in non-small cell lung cancer. World J Clin Oncol. 5:865–873. 2014. View Article : Google Scholar : PubMed/NCBI

6 

Isa T, Tomita S, Nakachi A, Miyazato H, Shimoji H, Kusano T, Muto Y and Furukawa M: Analysis of microsatellite instability, K-ras gene mutation and p53 protein overexpression in intrahepatic cholangiocarcinoma. Hepatogastroenterology. 49:604–608. 2002.PubMed/NCBI

7 

Gu A, Lu J, Wang W, Shi C, Han B and Yao M: Role of miR-497 in VEGF-A-mediated cancer cell growth and invasion in non-small cell lung cancer. Int J Biochem Cell Biol. 70:118–125. 2015. View Article : Google Scholar : PubMed/NCBI

8 

Ma GF, Zhang RF, Ying KJ and Wang D: Effect evaluation of cisplatin-gemcitabine combination chemotherapy for advanced non-small cell lung cancer patients using microarray data. Eur Rev Med Pharmacol Sci. 19:578–585. 2015.PubMed/NCBI

9 

Guo W, Xie L, Zhao L and Zhao Y: mRNA and microRNA expression profiles of radioresistant NCI-H520 non-small cell lung cancer cells. Mol Med Rep. 12:1857–1867. 2015.PubMed/NCBI

10 

Der SD, Sykes J, Pintilie M, Zhu CQ, Strumpf D, Liu N, Jurisica I, Shepherd FA and Tsao MS: Validation of a histology-independent prognostic gene signature for early-stage, non-small-cell lung cancer including stage IA patients. J Thorac Oncol. 9:59–64. 2014. View Article : Google Scholar

11 

Gautier L, Cope L, Bolstad BM and Irizarry RA: Affy - analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20:307–315. 2004. View Article : Google Scholar : PubMed/NCBI

12 

Diboun I, Wernisch L, Orengo CA and Koltzenburg M: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics. 7:2522006. View Article : Google Scholar : PubMed/NCBI

13 

Yan X, Jiang Z, Bi L, Yang Y and Chen W: Salvianolic acid A attenuates TNF-α- and D-GalN-induced ER stress-mediated and mitochondrial-dependent apoptosis by modulating Bax/Bcl-2 ratio and calcium release in hepatocyte LO2 cells. Naunyn Schmiedebergs Arch Pharmacol. 388:817–830. 2015. View Article : Google Scholar : PubMed/NCBI

14 

Chen J, Bardes EE, Aronow BJ and Jegga AG: ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37:W305–W311. 2009. View Article : Google Scholar : PubMed/NCBI

15 

Nogales-Cadenas R, Carmona-Saez P, Vazquez M, Vicente C, Yang X, Tirado F, Carazo JM and Pascual-Montano A: GeneCodis: Interpreting gene lists through enrichment analysis and integration of diverse biological information. Nucleic Acids Res. 37:W317–W322. 2009. View Article : Google Scholar : PubMed/NCBI

16 

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI

17 

Becker T, Gerke V, Kube E and Weber K: S100P, a novel Ca(2+)-binding protein from human placenta. cDNA cloning, recombinant protein expression and Ca2+ binding properties. Eur J Biochem. 207:541–547. 1992. View Article : Google Scholar : PubMed/NCBI

18 

Zhang H, Wang G, Ding Y, Wang Z, Barraclough R, Rudland PS, Fernig DG and Rao Z: The crystal structure at 2A resolution of the Ca2+ -binding protein S100P. J Mol Biol. 325:785–794. 2003. View Article : Google Scholar : PubMed/NCBI

19 

Bartling B, Rehbein G, Simm A, Silber RE and Hofmann HS: Porcupine expression is associated with the expression of S100P and other cancer-related molecules in non-small cell lung carcinoma. Int J Oncol. 36:1015–1021. 2010. View Article : Google Scholar : PubMed/NCBI

20 

Magnusson LU, Lundqvist A, Karlsson MN, Skålén K, Levin M, Wiklund O, Borén J and Hultén LM: Arachidonate 15-lipoxygenase type B knockdown leads to reduced lipid accumulation and inflammation in atherosclerosis. PLoS One. 7:e431422012. View Article : Google Scholar : PubMed/NCBI

21 

Tschopp J, Martinon F and Burns K: NALPs: A novel protein family involved in inflammation. Nat Rev Mol Cell Biol. 4:95–104. 2003. View Article : Google Scholar : PubMed/NCBI

22 

Chan KS, Sano S, Kiguchi K, Anders J, Komazawa N, Takeda J and DiGiovanni J: Disruption of Stat3 reveals a critical role in both the initiation and the promotion stages of epithelial carcinogenesis. J Clin Invest. 114:720–728. 2004. View Article : Google Scholar : PubMed/NCBI

23 

Moore RJ, Owens DM, Stamp G, Arnott C, Burke F, East N, Holdsworth H, Turner L, Rollins B, Pasparakis M, et al: Mice deficient in tumor necrosis factor-α are resistant to skin carcinogenesis. Nat Med. 5:828–831. 1999. View Article : Google Scholar : PubMed/NCBI

24 

Menzies-Gow A, Ying S, Sabroe I, Stubbs VL, Soler D, Williams TJ and Kay AB: Eotaxin (CCL11) and eotaxin-2 (CCL24) induce recruitment of eosinophils, basophils, neutrophils, and macrophages as well as features of early- and late-phase allergic reactions following cutaneous injection in human atopic and nonatopic volunteers. J Immunol. 169:2712–2718. 2002. View Article : Google Scholar : PubMed/NCBI

25 

Campbell EM, Kunkel SL, Strieter RM and Lukacs NW: Temporal role of chemokines in a murine model of cockroach allergen-induced airway hyperreactivity and eosinophilia. J Immunol. 161:7047–7053. 1998.PubMed/NCBI

26 

Yawalkar N, Uguccioni M, Schärer J, Braunwalder J, Karlen S, Dewald B, Braathen LR and Baggiolini M: Enhanced expression of eotaxin and CCR3 in atopic dermatitis. J Invest Dermatol. 113:43–48. 1999. View Article : Google Scholar : PubMed/NCBI

27 

Xu MM, Mao GX, Liu J, Li JC, Huang H, Liu YF and Liu JH: Low expression of the FoxO4 gene may contribute to the phenomenon of EMT in non-small cell lung cancer. Asian Pac J Cancer Prev. 15:4013–4018. 2014. View Article : Google Scholar : PubMed/NCBI

28 

Su L, Liu X, Chai N, Lv L, Wang R, Li X, Nie Y, Shi Y and Fan D: The transcription factor FOXO4 is down-regulated and inhibits tumor proliferation and metastasis in gastric cancer. BMC Cancer. 14:3782014. View Article : Google Scholar : PubMed/NCBI

29 

Liu XQ, Tang SH, Zhang ZY and Jin HF: Expression and clinical significance of FOX04 in colorectal cancer. Chinese J Cell Mol Immunol. 27:969–971. 2011.In Chinese.

30 

Salic K and De Windt LJ: MicroRNAs as biomarkers for myocardial infarction. Curr Atheroscler Rep. 14:193–200. 2012. View Article : Google Scholar : PubMed/NCBI

31 

Huang WC, Chan SH, Jang TH, Chang JW, Ko YC, Yen TC, Chiang SL, Chiang WF, Shieh TY, Liao CT, et al: miRNA-491-5p and GIT1 serve as modulators and biomarkers for oral squamous cell carcinoma invasion and metastasis. Cancer Res. 74:751–764. 2014. View Article : Google Scholar

32 

Zhou Y, Li Y, Ye J, Jiang R, Yan H, Yang X, Liu Q and Zhang J: MicroRNA-491 is involved in metastasis of hepatocellular carcinoma by inhibitions of matrix metalloproteinase and epithelial to mesenchymal transition. Liver Int. 33:1271–1280. 2013. View Article : Google Scholar : PubMed/NCBI

33 

Mowla SJ, Naeli P, Mirzadeh AF and Tavallaei M: Evaluating the expression of miR-491 as a potential stemness marker in lung tumor and non-tumor tissue. Cell J (Yakhteh). 15(Suppl 1): 632013.

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Spandidos Publications style
Tian W, Liu J, Pei B, Wang X, Guo Y and Yuan L: Identification of miRNAs and differentially expressed genes in early phase non-small cell lung cancer. Oncol Rep 35: 2171-2176, 2016.
APA
Tian, W., Liu, J., Pei, B., Wang, X., Guo, Y., & Yuan, L. (2016). Identification of miRNAs and differentially expressed genes in early phase non-small cell lung cancer. Oncology Reports, 35, 2171-2176. https://doi.org/10.3892/or.2016.4561
MLA
Tian, W., Liu, J., Pei, B., Wang, X., Guo, Y., Yuan, L."Identification of miRNAs and differentially expressed genes in early phase non-small cell lung cancer". Oncology Reports 35.4 (2016): 2171-2176.
Chicago
Tian, W., Liu, J., Pei, B., Wang, X., Guo, Y., Yuan, L."Identification of miRNAs and differentially expressed genes in early phase non-small cell lung cancer". Oncology Reports 35, no. 4 (2016): 2171-2176. https://doi.org/10.3892/or.2016.4561