Identify miRNA-mRNA regulation pairs to explore potential pathogenesis of lung adenocarcinoma

Materials and Methods

miRNA and mRNA expression profiles

We downloaded the miRNA and mRNA expression profile from the TCGA database and the Gene Expression Omnibus (GEO) database. We use the GEO database web analytics tool GEO2R and “limma” and the “edgeR” R bag to filter DE-mRNAs and DE-miRNAs. The overview of the workflow steps is shown in Figure 1.

Figure 1. Flow chart for identifying the miRNA-mRNA regulatory networks and the comprehensive analysis of regulatory pairs role in lung adenocarcinoma (LUAD).

Analysis of miRNA-mRNA regulation pairs

TarBase and miRTarBase databases were used to screen miRNA-mRNA regulatory pairs. TarBase and miRTarBase are experimentally supported miRNA target index reference databases [11, 12]. Then, we further analyzed the correlation between miRNA and mRNA in TCGA-LUAD. We used DAVID, Diana-miRPath and Hiplot for functional and pathway analysis [13].

Sample collection

Formalin-fixed paraffin-embedded (FFPE) of LUAD and corresponding normal tissues were obtained from the First Affiliated Hospital of Nanjing Medical University. This study was conducted in accordance with the Helsinki Declaration and approved by the Institutional Review Committee of the First Affiliated Hospital of Nanjing Medical University (ID: 2016-SRFA-148). All specimens were collected with informed written consent of patients. The clinical characteristics of the 30 patients are listed in Table 1.

Table 1. Clinicopathological and molecular features of LUAD patients.

Variables	Number of cases	Rate (%)
	(n=30)
Age (years)
≤60	14	46.7
>60	16	53.3
Gender
Female	21	70
Male	9	30
Tumor size (cm)
≤3	21	70
> 3	9	30
TNM stage
I	21	70
II-IV	9	30
Lymph node metastasis
No	21	70
Yes	9	30
Bronchial invasion
No	24	80
Yes	6	20

Quantitative reverse transcription PCR (qRT-PCR) assay

External validation of qRT-PCR validation was performed using PrimeScript RT reagent Kit (Takara) and SYBR Premix Ex Taq II (Takara). PCR primer sequences are shown in Supplementary Table 1. We used the 2^-ΔΔCt to calculate miRNA and mRNA expression levels (ΔCt = Ct_miRNA/mRNA− Ct_normalizer; Ct: the threshold cycle) [14].

Cell culture and cell transfection

Lung adenocarcinoma cell lines A549 was obtained from the American Type Culture Collection (ATCC). The cells were seeded into 24-well plates. The miR-126-5p mimics, miR-1-3p mimics, Negative control mimics were purchased from RiboBio. When cell fusion reached 60%, cells were transfected with 20mM Opti-MEM transfection medium (Invitrogen) and Lipofectamine 2000(Invitrogen).

Cell proliferation and scratch wound healing assays

Cell Counting Kit-8 (CCK-8, Dojindo, Kumamoto, Japan) assay was used to assess cell proliferation. At indicated time points (24h, 48h, 72h, 96h), the cells were incubated in 10% CCK8 solution in culture medium at 37° C. The absorbance at 450nm was measured with a microplate reader. To examine the migratory ability of cells in vitro, the scratch wound healing assay was performed. When the cells were cultured to 80%-90% in 6-well plates, after the medium was discarded, the cells were scratched with 100 μL tip. The cells were placed in serum-free DMEM medium and observed at 0 and 24h.

Analysis of tumor-related phenotypes

We downloaded the data of single sample gene set enrichment analysis (ssGSEA) from UCSC Xena [15, 16]. The infiltrating immune cell types data were downloaded from the TCGA website [17]. ESTIMATE software was used to evaluate the stromal and immune levels of TCGA-LUAD specimens [18]. The data of TMB and methylation in TCGA-LUAD samples were obtained from the UCSC Xena platform (https://xena.ucsc.edu/) [19].

Statistical analysis

We used the IBM SPSS Statistics v.26 software, GraphPad Prism software and R language v3.6.3 (https://cran.r-project.org/) to analyze the data.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Results

Screening of differentially expressed miRNA and mRNA

A total of 7 miRNA and 13 mRNA expression datasets were downloaded from GEO database, and the information of 20 GEO datasets is shown in Table 2. As shown in Figure 2A, the GEO2R tool was used to analyze each dataset, and the DE-miRNAs and DE-mRNAs in each dataset were screened out. Then, the intersection was taken in the GEO database. A rank-sum test was performed to screen out the DE-miRNAs and DE-mRNAs in the TCGA database. A total of 11 miRNAs and 128 mRNAs were selected with differences in both databases as the final DE-miRNAs and DE-mRNAs (Table 3). We utilized DIANA-miRPath to predict the possible functions of the 11 DE-miRNAs (Figure 2B). KEGG pathway enrichment analysis revealed that the DE-mRNAs enriched in the drug metabolism, etc. (Figure 2C). The GO terms were enriched in the cell adhesion, cellular protein modification process, cytoplasm, organelle, etc.

Table 2. Information pertaining to the selected GEO datasets for LUAD.

	Experiment type	Source name	GEO accession	Platform	Group
					Tumor	Control
microRNA expression	Array	Tissue	GSE51853	GPL7341	76	5
			GSE135918	GPL18058	5	5
			GSE63805	GPL18410	32	30
			GSE77380	GPL16770	3	12
			GSE74190	GPL19622	36	44
			GSE36681	GPL8179	103	103
			GSE48414	GPL16770	154	20
mRNA expression	Array	Tissue	GSE1037	GPL962	12	19
			GSE116959	GPL17077	57	11
			GSE19188	GPL570	45	65
			GSE1987	GPL91	7	7
			GSE2088	GPL962	9	30
			GSE21933	GPL6254	11	21
			GSE27262	GPL570	25	25
			GSE32863	GPL6884	58	58
			GSE33532	GPL570	40	20
			GSE40275	GPL15974	8	43
			GSE43458	GPL6244	40	30
			GSE62113	GPL14951	7	9
			GSE63459	GPL6883	33	32

Table 3. The list of DE-miRNAs and DE-mRNAs (up-regulated or down-regulated in LUAD).

DE-miRNA (down)	DE-mRNA(up)	DE-miRNA(up)	DE-mRNA(down)
hsa-miR-139-5p	ANLN	hsa-miR-182-5p	ABCA8
hsa-miR-30a-3p	AURKA	hsa-miR-183-5p	ABLIM3
hsa-miR-486-5p	BAIAP2L1	hsa-miR-196a-5p	ACADL
hsa-miR-1-3p	BUB1	hsa-miR-96-5p	ADAMTSL3
hsa-miR-126-5p	CCNA2	hsa-miR-135b-5p	ADARB1
	CCNB1	hsa-miR-9-5p	ADH1B
	CDCA7		AFAP1L1
	CEACAM1		AGER
	CEACAM5		AHNAK
	CENPF		AQP4
	CTHRC1		BMP2
	CXCL13		BMPR2
	EZH2		BTNL9
	FHL2		CADM1
	HIST1H2BD		CALCRL
	HMGA1		CAT
	KIF11		CAV1
	METTL7B		CBX7
	PDIA4		CD34
	PGM2L1		CD36
	PLOD2		CD93
	PTTG1		CDH5
	S100P		CLDN18
	SPP1		CLEC1A
	STK39		CLIC5
	SULF1		CPB2
	TK1		CYBRD1
	TPX2		CYYR1
	UGT8		DACH1
	XPR1		DPT
			EDNRB
			EFEMP1
			EMCN
			FABP4
			FAM107A
			FBLN1
			FBLN5
			FCN3
			FEZ1
			FGD5
			FHL1
			FHL5
			FMO2
			FZD4
			GAS6
			GATA2
			GKN2
			GNG11
			GPC3
			GPM6A
			GPM6B
			GRASP
			GRK5
			GSTM5
			ID3
			IGSF10
			KLF4
			LDLR
			LMO2
			LMO7
			MAL
			MAOB
			METTL7A
			MFAP4
			MME
			MYH10
			MYH11
			MYL9
			PDE8B
			PDZD2
			PLEKHH2
			PODXL
			PRELP
			PTPRB
			PTPRM
			RAMP2
			RHOJ
			SASH1
			SCGB1A1
			SDPR
			SFTPC
			SH2D3C
			SLC39A8
			SLIT2
			SNRK
			SOSTDC1
			SPARCL1
			STARD13
			STX11
			STXBP6
			TBX2
			TEK
			TGFBR3
			THBD
			TIMP3
			TMEM47
			VWF
			WIF1

Figure 2. Screening and pathway analysis of DE-miRNAs and DE-mRNA. (A) The circular bar chart showing the datasets from different sources for screening differentially expressed miRNAs and mRNAs; (B) The KEGG and GO enrichment analysis of DE-miRNAs; (C) The KEGG and GO enrichment analysis of DE-mRNAs.

Screening of miRNA-RNA regulatory pairs

As shown in Figure 3A, six miRNA-mRNA regulation pairs (miR-1-3p/CENPF, miR-126-5p/UGT8, miR-135b-5p/BMPR2, miR-9-5p/STARD13, miR-196a-5p/TGFBR3, miR-1-3p/UGT8) were identified. The 6 pairs of miRNA-mRNA were experimentally verified, and the 4 miRNA-mRNA pairs in TCGA-LUAD showed significant negative correlation (Figure 3B).

Figure 3. The screened miRNA-mRNA regulation pairs. (A) Six miRNA-mRNA regulatory pairs were screened from miRTarBase and TarBase databases, and the first four were verified by correlation analysis; (B) Pearson’s correlation analysis of miRNA-mRNA regulation pairs in TCGA-LUAD.

Verification of miRNAs and mRNAs expression in LUAD tissues

We used qRT-PCR to verify 4 miRNA-mRNA pairs in 30 matched tissues. In Figure 4, the expression of the miR-1-3p (P=0.0037) and miR-126-5p (P=0.0032) were down-regulated in tumor tissues, while miR-135b-5p (P=0.0037), CENPF (P<0.001) and UGT8 (P<0.001) were up-regulated. There was no significant difference in the expression of miR-9-5p (P=0.0841), BMPR2 (P=0.4522), and STARD13 (P=0.1241). Spearman correlation analysis showed that miR-1-3p was significantly correlated with CENPF expression (P<0.001, r=-0.5398), and miR-126-5p was significantly correlated with UGT8 (P=0.0116, r=-0.3239). IHC images in the HPA database evidenced higher expression of CENPF and UGT8 in LUAD tissue than in normal control and the results are shown in Supplementary Figure 1.

Figure 4. Validating the expression of the four networks by qRT-PCR. (Data are presented as mean±SEM; *p < 0.05, **p < 0.01, ***p < 0.001). (A) miR-1-3p; (B) miR-126-5p; (C) miR-135b-5p; (D) miR-9-5p; (E) CENPF; (F) UGT8; (G) BMPR2; (H) STARD13; (I) Pearson’s correlation analysis of miR-1-3p and CENPF; (J) Pearson’s correlation analysis of miR-126-5p and UGT8.

Analysis of the diagnostic efficacy

MiR-1-3p, PTPRM, miR-126-5p and UGT8 were combined as a panel using the logistic regression analysis, and the equation to predict LUAD probability was: Logit(P) = 0.813 + 0.028*miR-126-5p – 0.262*UGT8 + 1.727*miR-1-3p – 0.526*CENPF. The AUC of the panel was 0.973 (95% CI: 0.955-0.991, p<0.0001) in TCGA-LUAD and 0.771 (95% CI: 0.652-0.890, p<0.0001) in the external validation (Figure 5A, 5B). The DCA results showed that regulation pairs had good diagnostic performance (Figure 5C, 5D).

Figure 5. The ROC and DCA of the panel of miR-1-3p, miR-126-5p, CENPF and UGT8 for discriminating LUAD patients from NCs. (A) The ROC of the TCGA-LUAD (AUC = 0.973, 95% CI: 0.955-0.991, p<0.0001); (B) The ROC of the external validation (AUC = 0.771, 95% CI: 0.652-0.890, p<0.0001); (C) The DCA of the external validation; (D) The DCA of the TCGA-LUAD.

Correlation analysis of LUAD clinical-pathological features and survival analysis

According to the analysis of FIGO stages, the expression of CENPF (P=0.008) is lower in early-stage (I) than late-stage (II+III+IV) (Figure 6A). The expression of miR-1-3p (P=0.001) in female patients is higher, while CENPF (P=0.001) was lower in female patients (Figure 6B). CENPF was higher in the age≤65 group while UGT8 was lower in the age≤65 group (Figure 6C). We analyzed the association of the regulation pairs and gene mutations in KRAS, ROS1, ALK, and EGFR. The level of miR-1-3p was higher in KRAS(P=0.039), ROS1(P=0.013), ALK(P=0.02) wild-type LUAD tissues than KRAS-mutated LUAD tissues (P=0.039, P=0.013, and P=0.02, respectively). The expression of CENPF was higher in ROS1-mutated, ALK-mutated LUAD tissues and EGFR wild-type LUAD tissues (P<0.001, P=0.008, and P=0.041, respectively) shown in Figure 6D. K-M survival analysis showed that CENPF (P=0.026) correlated with prognosis, and the higher the CENPF expression level, the worse the prognosis (Figure 6E).

Figure 6. Correlation analysis of LUAD clinical-pathological features and survival analysis with the expression of miRNA and mRNA expression levels. (Data are presented as mean±SEM; *p < 0.05; **p < 0.01; ***p < 0.001). (A) Stage I vs. Stage II-IV; (B) Female vs. Male; (C) Age ≤ 65 vs. Age > 65; (D) mutation vs. wild-type of KRAS, ROS1, ALK, EGFR; (E) The survival analysis of miR-1-3p, CENPF, miR-126-5p and UGT8 in TCGA-LUAD.

Validation of cell experiments

We transfected miR-1-3p mimic and miR-126-5p mimic into A549 cells to established miR-1-3p and miR-126-5p overexpressed cells to investigate the potential function in regulating LUAD cell proliferation. The expression level of miR-1-3p and miR-126-5p in A549 cells upregulated significantly after transfecting miR-1-3p and miR-126-5p mimic (Figure 7A). We found that the mRNA levels of CENPF and UGT8 were declined after transfecting miR-1-3p mimics and miR-126-5p mimics respectively (Figure 7A). Then CCK8 assay was performed to testify the effects of miR-1-3p and miR-126-5p on cell proliferation. MiR-1-3p and miR-126-5p overexpressed significantly inhibited cell proliferation of A549 cells after transfecting 48h, 72h and 96h (Figure 7B). To gain further insight into the role of miR-1-3p and miR-126-5p in LUAD cell migration was performed in A549 cells transfected with miR-1-3p and miR-126-5p mimics or Negative control. The overexpression of miR-1-3p and miR-126-5p significantly inhibited LUAD cell migration (Figure 7C).

Figure 7. The validation of biological function assays. (Data are presented as mean±SD; *p < 0.05; **p < 0.01; ***p < 0.001). (A) Comparison of miRNA and mRNA expression levels between transfected miR-1-3p mimic and miR-126-5p mimic and negative control; (B) CCK-8 assay was performed to assess cell proliferation; (C) Wound-healing assay was conducted to explore LUAD cell migration.

Analysis of tumor-related phenotypes

The miRNA-mRNA regulation pairs correlated with mRNA synthesis pathways, such as transport of the SLBP dependent mature mRNA (Figure 8A). Therefore, we further analyzed the correlation between miRNA-mRNA regulation pairs and immune cells, and explored its role in tumor immunity. There are 19 different types of immune cells between tumor tissue and normal tissue, as shown in Supplementary Table 2. MiR-1-3p and CENPF correlated with macrophages m0, mast cells resting, and dendritic cells resting, etc. while miR-126-5p and UGT8 were related to plasma cells (Figure 8B). As shown in Figure 8C, miR-1-3p and CENPF have some correlation with TMB and tumor microenvironment, but not with DNA methylation.

Figure 8. Pearson’s correlation analysis of immune-related phenotypes and regulatory pairs in TCGA-LUAD. (A) ssGSEA; (B) Immune cells; (C) Global methylation, tumor mutation burden and tumor microenvironment factors.

Abbreviations

miRNA: microRNA; LUAD: Lung Adenocarcinoma; GEO: gene expression omnibus; TCGA: the cancer genome atlas; qRT-PCR: Quantitative Reverse Transcription-Polymerase Chain Reaction; ROC: receiver operating characteristic curve; DCA: decision curve analysis; DE-miRNAs: differential miRNAs; DE-mRNAs: differential mRNAs; dbDEMC: database of Differentially Expressed MiRNAs in human Cancers; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes Genomes; ssGSEA: Single sample gene set Enrichment analysis; ESTIMATE: Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data; TMB: tumor mutation burden; AUC: Area Under Curve; CI: confidence interval; NSCLC: non-small cell lung cancer.

Author Contributions

Conception: Z.W, and Z.J; Interpretation or analysis of data: Z.J, F.X.C, and Z.X; Preparation of the manuscript: F.X.C, Z.X, and L.C; Experimental verification: F.X.C; Revision for important intellectual content: F.X.C, Z.X, and L.C; Supervision: P.S and Z.S.Y.

Conflicts of Interest

The authors report no conflicts of interest in this work.

Ethical Statement and Consent

The study was conducted in accordance with the guidelines of the Hospital Ethics Committee and approved by the Institutional Review Boards of the First Affiliated Hospital of Nanjing Medical University (ID: 2016-SRFA-148). This study was conducted in accordance with the Declaration of Helsinki. All specimens were collected with informed written consent of patients.

Funding

This work was supported by the Natural Science Foundation of China [Grant number: 81672400] and the Graduate Research and Practice innovation Plan of Graduate Education Innovation Project in Jiangsu Province [Grant number: JX10213729].

Editorial Note

This corresponding author has a verified history of publications using a personal email address for correspondence.

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