鉴定并验证与肺腺癌预后相关的新的miRNA-mRNA调节网络

doi:10.24920/003966

Abstract

Objective Many studies have revealed the crucial roles of miRNA in multiple human cancers, including lung adenocarcinoma (LUAD). In this study, we sought to explore new miRNA-mRNA pairs that are associated with LUAD prognosis.
Methods A novel miRNA-mRNA regulatory network associated with prognosis in LUAD was identified and validated using the bioinformatic tools including OncomiR database, StarBase, miRnet, GEPIA2, UALCAN.
Results Twenty key miRNAs were compiled after the analysis of the expression and prognostic value in OncomiR and StarBase. Targeted mRNAs of these key miRNAs were predicted in miRnet, and the resulting mRNAs were also analyzed for their prognostic values and expression patterns in GEPIA2 and UALCAN, respectively. Further expression correlation analysis was performed in StarBase. Subsequently, a new miRNA-mRNA network was built, of which each RNA pair showed negative expression correlation, opposite expression pattern, and prognostic value. Protein-protein interaction network was under construction for the mRNAs, and 19 hub genes were determined. Enrichment analysis showed that “Cell Cycle, Mitotic” was the most significantly enriched term. Then, a miRNA-hub gene sub-network was built. We selected and validated the regulatory relationship of some miRNA-hub pairs, including hsa-miR-1976/RFC2, hsa-let-7c-5p/RFC2, hsa-let-7c-5p/ESPL1, hsa-let-7c-5p/CDC25A, and hsa-miR-101-3p/KIF2C. Moreover, over-expression of hsa-miR-1976 and hsa-let-7c-5p resulted in significant cell cycle arrest.
Conclusions Our results determined new prognosis-associated miRNA-mRNA pairs and might shed further light on the mechanism via which miRNA-mRNA network influences prognosis in LUAD.

Key words: lung adenocarcinoma, bioinformatic analysis, miRNA, prognosis, cell cycle

Funding:

Wenqin Xu, Jingjing Ye, Tianbing Chen. Identifying and Validating a Novel miRNA-mRNA Regulatory Network Associated with Prognosis in Lung Adenocarcinoma[J].Chinese Medical Sciences Journal, 2022, 37(1): 31-43.

Figures/Tables 10

Figure 1.

Table 1

Table 2

Expression details and prognostic values of key mRNAs in LUAD"

No.	Gene type	Gene Name	Expression details (Starbase)				Prognostic value (GEPIA2)	No.	Gene type	Gene Name	Expression details (Starbase)				Prognostic value (GEPIA2)
No.	Gene type	Gene Name	Cancer Exp	Normal Exp	Fold Change	P Value	Log rank P	No.	Gene type	Gene Name	Cancer Exp	Normal Exp	Fold Change	P Value	Log rank P
1	Onco-mRNA	ESPL1	1.89	0.2	9.36	1.50E-44	0.0023	22	Onco-mRNA	DDIT4	67.27	28.83	2.33	3.80E-08	0.0017
2	Onco-mRNA	CCNB2	7.76	0.86	9	3.70E-56	0.00079	23	Onco-mRNA	AHCY	46.38	22.08	2.1	1.70E-21	0.0049
3	Onco-mRNA	RRM2	11.64	1.12	10.37	1.40E-53	1.5e-05	24	Onco-mRNA	RACGAP1	8.61	2.77	3.11	1.10E-22	0.00062
4	Onco-mRNA	FANCI	4.32	0.99	4.37	2.30E-43	0.043	25	Onco-mRNA	CDCA4	8.52	2.84	3	1.60E-29	0.00081
5	Onco-mRNA	YWHAZ	120.83	61.68	1.96	1.30E-26	0.00023	26	Onco-mRNA	CDCA8	8.67	0.95	9.16	2.50E-59	0.0026
6	Onco-mRNA	GARS	34.42	15.05	2.29	1.90E-34	0.00018	27	Onco-mRNA	DNAJC9	4.59	2.3	1.99	2.10E-28	0.00057
7	Onco-mRNA	EIF4A3	19.96	11.41	1.75	1.20E-18	0.013	28	Onco-mRNA	UCK2	4.75	1.76	2.7	5.50E-09	0.00012
8	Onco-mRNA	RHOV	19.46	1.56	12.48	1.20E-26	0.0023	29	Onco-mRNA	SMUG1	7.13	3.22	2.21	7.20E-38	0.004
9	Onco-mRNA	LDHA	141.93	51.1	2.78	1.20E-37	3e-05	30	Onco-mRNA	PDIA6	76.66	33.9	2.26	6.90E-26	0.005
10	Onco-mRNA	DIABLO	1.77	1.09	1.63	3.00E-19	0.00058	31	Onco-mRNA	CACYBP	17.91	9.02	1.98	2.70E-27	0.00041
11	Onco-mRNA	MRPL12	24.41	9.6	2.54	1.20E-14	5e-04	32	Onco-mRNA	INTS7	6.82	3.39	2.01	7.70E-31	1.3e-05
12	Onco-mRNA	POLR2D	9.33	5.24	1.78	7.70E-33	0.0074	33	Onco-mRNA	SLC7A11	5.03	0.55	9.14	7.40E-18	0.036
13	Onco-mRNA	CALU	66.43	30.23	2.2	4.90E-21	0.016	34	Onco-mRNA	MRPS10	26.05	14.3	1.82	1.70E-24	0.01
14	Onco-mRNA	NME4	25.98	9.02	2.88	6.20E-30	0.014	35	Onco-mRNA	SLC2A1	38.71	3.14	12.33	1.80E-45	2.4e-05
15	Onco-mRNA	PMAIP1	10.42	3.01	3.46	7.50E-13	0.0015	36	Onco-mRNA	BIRC5	11.5	0.82	14.08	3.90E-55	0.00022
16	Onco-mRNA	RFC2	15.71	8.11	1.94	1.70E-19	0.031	37	Onco-mRNA	TTLL12	13.47	6.48	2.08	1.50E-15	0.00099
17	Onco-mRNA	IGF2BP3	1.84	0.11	16.91	1.60E-14	0.0017	38	Onco-mRNA	TIMM50	9.31	5.16	1.81	1.50E-15	0.03
18	Onco-mRNA	KPNA2	36.71	8.42	4.36	3.10E-38	0.00038	39	Onco-mRNA	CKS1B	13.34	3.69	3.62	1.90E-32	4.4e-05
19	Onco-mRNA	KIF2C	7.05	0.54	13.11	1.60E-58	0.016	40	Onco-mRNA	GPN1	13.77	8.2	1.68	7.20E-19	0.005
20	Onco-mRNA	BZW1	20.71	11.81	1.75	5.00E-17	0.012	41	Onco-mRNA	RAD51	2.43	0.49	4.98	7.10E-40	0.0026
21	Onco-mRNA	MRPL42	3.48	2.47	1.41	4.10E-07	0.013	42	Onco-mRNA	TPI1	214.36	90.81	2.36	2.2E-26	0.0031
43	Onco-mRNA	POC1A	4.59	1.41	3.27	1.6E-32	0.00094	63	Onco-mRNA	CBX3	47.98	20.97	2.29	1.2E-42	0.00063
44	Onco-mRNA	DARS2	10.68	4.16	2.57	2E-33	0.00087	64	Onco-mRNA	RPLP0	343.61	203.06	1.69	1.3E-11	0.037
45	Onco-mRNA	CDT1	5.45	0.74	7.33	1.5E-43	0.0019	65	Onco-mRNA	FANCL	7.35	4.66	1.58	4.8E-13	5E-04
46	Onco-mRNA	KIF14	1.56	0.11	14.04	6.6E-57	0.00058	66	Onco-mRNA	SKA1	2.19	0.25	8.85	1.8E-43	0.00043
47	Onco-mRNA	HMGA1	122.27	23	5.32	5.1E-37	0.0014	67	Onco-mRNA	FKBP4	24.43	10.13	2.41	2E-18	2.4E-05
48	Onco-mRNA	ALDOA	235.13	94.94	2.48	8.5E-25	0.00021	68	Onco-mRNA	AGMAT	1.55	0.24	6.39	1.4E-49	0.0096
49	Onco-mRNA	CENPI	1.4	0.2	7.16	1.1E-39	0.014	69	Onco-mRNA	CDC25A	1.44	0.26	5.52	2.6E-38	0.014
50	Onco-mRNA	NOL10	9.62	5.24	1.84	1.4E-36	0.0054	70	Onco-mRNA	SRM	35.7	15.89	2.25	1.5E-24	0.021
51	Onco-mRNA	IMP4	15.07	8.92	1.69	1.9E-26	0.0023	71	Onco-mRNA	OIP5	2.34	0.42	5.64	1.9E-35	5.7E-05
52	Onco-mRNA	TMCO1	24.58	13.89	1.77	9E-24	0.019	72	TS-mRNA	NFIX	13.42	33.09	0.41	5.9E-25	0.007
53	Onco-mRNA	TMEM106B	9.24	4.54	2.03	1.3E-15	0.0051	73	TS-mRNA	METTL7A	19.34	64.98	0.3	3.7E-41	0.0067
54	Onco-mRNA	KIF11	5.96	0.84	7.11	4.5E-52	0.00048	74	TS-mRNA	BTG2	59.21	131.34	0.45	1.4E-17	0.0014
55	Onco-mRNA	MTHFD2	18.74	5.92	3.17	6.2E-29	0.0038	75	TS-mRNA	SNX20	2.26	2.99	0.76	0.0000055	0.012
56	Onco-mRNA	TWF1	18.66	8.99	2.08	9.5E-23	0.00014	76	TS-mRNA	PIK3R1	5.03	9.26	0.54	2.8E-20	0.00053
57	Onco-mRNA	YKT6	35.3	18.72	1.89	5E-22	0.017	77	TS-mRNA	LIFR	6.34	17.36	0.37	1.9E-25	0.035
58	Onco-mRNA	GTF2E2	12.55	7.42	1.69	9.8E-20	0.0086	78	TS-mRNA	ABCB1	0.73	1.92	0.38	5E-18	0.013
59	Onco-mRNA	SFXN1	6.8	2.19	3.11	7.2E-67	0.042	79	TS-mRNA	SNX30	6.97	12.69	0.55	2E-15	0.00091
60	Onco-mRNA	GCLC	14.69	2.82	5.21	2.9E-09	0.024	80	TS-mRNA	DOCK4	2.87	7.44	0.39	8.1E-36	0.026
61	Onco-mRNA	KPNB1	37.6	21.33	1.76	8.6E-24	0.0027	81	TS-mRNA	GPD1L	14.94	23.51	0.64	5.9E-13	0.0036
62	Onco-mRNA	TMED2	108.86	57.26	1.9	5E-25	0.002	82	TS-mRNA	SCD5	4.44	11.89	0.37	4.2E-24	0.035

Table 2

Table 3

Expression correlations of miRNA-mRNA pairs"

No.	miRNA	gene	Correlation		No.	miRNA	gene	Correlation		No.	miRNA	gene	Correlation
No.	miRNA	gene	R	P value	No.	miRNA	gene	R	P value	No.	miRNA	gene	R	P value
1	hsa-let-7c-5p	ESPL1	-0.431	1.25E-24	34	hsa-mir-140-3p	CDCA8	-0.268	7.27E-10	67	hsa-mir-30d-5p	MTHFD2	-0.401	3.74E-21
2	hsa-let-7c-5p	CCNB2	-0.384	2.04E-19	35	hsa-mir-140-3p	CDC25A	-0.203	3.79E-06	68	hsa-mir-30d-5p	TWF1	-0.4	4.84E-21
3	hsa-let-7c-5p	RRM2	-0.384	1.81E-19	36	hsa-mir-140-3p	DNAJC9	-0.171	0.000106	69	hsa-mir-30d-5p	YKT6	-0.344	1.21E-15
4	hsa-let-7c-5p	CDC25A	-0.361	3.13E-17	37	hsa-mir-140-3p	UCK2	-0.103	0.0202	70	hsa-mir-30d-5p	GTF2E2	-0.329	2.20E-14
5	hsa-let-7c-5p	HMGA1	-0.348	5.20E-16	38	hsa-mir-150-5p	SMUG1	-0.264	1.23E-09	71	hsa-mir-30d-5p	SFXN1	-0.301	3.59E-12
6	hsa-let-7c-5p	FANCI	-0.332	1.17E-14	39	hsa-mir-150-5p	PDIA6	-0.251	8.44E-09	72	hsa-mir-30d-5p	GCLC	-0.245	1.91E-08
7	hsa-let-7c-5p	YWHAZ	-0.296	7.73E-12	40	hsa-mir-150-5p	CACYBP	-0.245	2.00E-08	73	hsa-mir-30d-5p	KPNB1	-0.245	1.95E-08
8	hsa-let-7c-5p	GARS	-0.288	3.19E-11	41	hsa-mir-150-5p	ALDOA	-0.239	4.19E-08	74	hsa-mir-30d-5p	TMED2	-0.214	9.84E-07
9	hsa-let-7c-5p	BZW1	-0.282	7.79E-11	42	hsa-mir-150-5p	INTS7	-0.188	1.93E-05	75	hsa-mir-30d-5p	CBX3	-0.19	1.53E-05
10	hsa-let-7c-5p	EIF4A3	-0.278	1.50E-10	43	hsa-mir-150-5p	SLC7A11	-0.154	0.000489	76	hsa-mir-30d-5p	RPLP0	-0.129	0.00346
11	hsa-let-7c-5p	RHOV	-0.278	1.44E-10	44	hsa-mir-150-5p	MRPS10	-0.153	0.000499	77	hsa-mir-30d-5p	FANCL	-0.119	0.00722
12	hsa-let-7c-5p	LDHA	-0.27	5.22E-10	45	hsa-mir-150-5p	SLC2A1	-0.146	0.000919	78	hsa-mir-328-3p	RAD51	-0.188	1.76E-05
13	hsa-let-7c-5p	DIABLO	-0.254	5.90E-09	46	hsa-mir-150-5p	BIRC5	-0.137	0.00182	79	hsa-mir-328-3p	SKA1	-0.141	0.00133
14	hsa-let-7c-5p	MRPL12	-0.247	1.53E-08	47	hsa-mir-150-5p	TTLL12	-0.133	0.00258	80	hsa-mir-328-3p	FKBP4	-0.1	0.0243
15	hsa-let-7c-5p	POLR2D	-0.225	2.52E-07	48	hsa-mir-150-5p	TIMM50	-0.109	0.0139	81	hsa-mir-195-3p	AGMAT	-0.188	1.86E-05
16	hsa-let-7c-5p	CALU	-0.214	9.69E-07	49	hsa-mir-1976	CKS1B	-0.344	1.16E-15	82	hsa-mir-195-3p	CDC25A	-0.302	2.89E-12
17	hsa-let-7c-5p	NME4	-0.181	0.0000372	50	hsa-mir-1976	GPN1	-0.338	4.14E-15	83	hsa-mir-490-3p	SRM	-0.149	0.000748
18	hsa-let-7c-5p	PMAIP1	-0.169	0.000123	51	hsa-mir-1976	RAD51	-0.285	4.67E-11	84	hsa-mir-490-3p	RAD51	-0.151	0.0006
19	hsa-let-7c-5p	INTS7	-0.163	0.000218	52	hsa-mir-1976	TPI1	-0.28	1.10E-10	85	hsa-mir-526b-5p	OIP5	0.114	0.00992
20	hsa-let-7c-5p	RFC2	-0.145	0.000963	53	hsa-mir-1976	POC1A	-0.261	2.02E-09	86	hsa-mir-1293	NFIX	-0.235	7.15E-08
21	hsa-let-7c-5p	IGF2BP3	-0.14	0.00147	54	hsa-mir-1976	DARS2	-0.235	7.65E-08	87	hsa-mir-873-5p	METTL7A	-0.19	1.56E-05
22	hsa-mir-101-3p	RRM2	-0.41	3.49E-22	55	hsa-mir-1976	CDT1	-0.215	9.35E-07	88	hsa-mir-873-5p	BTG2	-0.173	8.56E-05
23	hsa-mir-101-3p	KPNA2	-0.388	7.83E-20	56	hsa-mir-1976	KIF14	-0.199	5.61E-06	89	hsa-mir-550a-5p	SNX20	-0.153	0.000531
24	hsa-mir-101-3p	KIF2C	-0.373	2.46E-18	57	hsa-mir-1976	HMGA1	-0.198	6.42E-06	90	hsa-mir-21-5p	BTG2	-0.301	3.61E-12
25	hsa-mir-101-3p	BIRC5	-0.301	3.66E-12	58	hsa-mir-1976	ALDOA	-0.187	2.06E-05	91	hsa-mir-21-5p	PIK3R1	-0.218	6.69E-07
26	hsa-mir-101-3p	BZW1	-0.261	2.12E-09	59	hsa-mir-1976	CENPI	-0.185	2.55E-05	92	hsa-mir-21-5p	LIFR	-0.198	6.29E-06
27	hsa-mir-101-3p	MRPL42	-0.22	5.27E-07	60	hsa-mir-1976	NOL10	-0.18	4.16E-05	93	hsa-mir-21-5p	ABCB1	-0.185	2.44E-05
28	hsa-mir-101-3p	GCLC	-0.216	7.94E-07	61	hsa-mir-1976	IMP4	-0.173	8.01E-05	94	hsa-mir-21-5p	SNX30	-0.172	8.75E-05
29	hsa-mir-101-3p	DDIT4	-0.1	0.0237	62	hsa-mir-1976	RFC2	-0.168	0.00013	95	hsa-mir-21-5p	DOCK4	-0.137	0.00184
30	hsa-mir-140-3p	TPI1	-0.403	1.99E-21	63	hsa-mir-1976	TMCO1	-0.16	0.000273	96	hsa-mir-21-5p	GPD1L	-0.133	0.00265
31	hsa-mir-140-3p	AHCY	-0.341	2.01E-15	64	hsa-mir-1976	TMEM106B	-0.108	0.0141	97	hsa-mir-9-5p	SCD5	-0.1	0.023
32	hsa-mir-140-3p	RACGAP1	-0.296	8.31E-12	65	hsa-mir-30d-5p	RRM2	-0.535	2.68E-39
33	hsa-mir-140-3p	CDCA4	-0.269	5.98E-10	66	hsa-mir-30d-5p	KIF11	-0.416	7.76E-23

Table 3

Figure 2.

Figure 3.

Table 4

Table 5

Figure 4.

Figure 5.

References 28.

1.	Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019; 69(1):7-34. doi: 10.3322/caac.21551. doi: 10.3322/caac.21551
2.	Torre LA, Siegel RL, Jemal A. Lung Cancer Statistics. Adv Exp Med Biol 2016; 893:1-19. doi: 10.1007/978-3-319-24223-1_1. doi: 10.1007/978-3-319-24223-1_1
3.	He L, He X, Lim LP, et al. A microRNA component of the p53 tumour suppressor network. Nature 2007; 447(7148):1130-4. doi: 10.1038/nature05939. doi: 10.1038/nature05939
4.	Misso G, Di Martino MT, De Rosa G, et al. miR-34: a new weapon against cancer?. Mol Ther Nucleic Acids 2014; 3(9):e194. doi: 10.1038/mtna.2014.47. doi: 10.1038/mtna.2014.47
5.	Shi Y, Liu C, Liu X, et al. The microRNA miR-34a inhibits non-small cell lung cancer (NSCLC) growth and the CD44hi stem-like NSCLC cells. PLoS One 2014; 9(3):e90022. doi: 10.1371/journal.pone.0090022. doi: 10.1371/journal.pone.0090022
6.	Cortez MA, Ivan C, Valdecanas D, et al. PDL1 regulation by p53 via miR-34. J Natl Cancer Inst 2016; 108(1): djv303. doi: 10.1093/jnci/djv303. doi: 10.1093/jnci/djv303
7.	Ceppi P, Mudduluru G, Kumarswamy R, et al. Loss of miR-200c expression induces an aggressive, invasive, and chemoresistant phenotype in non-small cell lung cancer. Mol Cancer Res 2010; 8(9):1207-16. doi: 10.1158/1541-7786.MCR-10-0052. doi: 10.1158/1541-7786.MCR-10-0052
8.	Takeyama Y, Sato M, Horio M, et al. Knockdown of ZEB1, a master epithelial-to-mesenchymal transition (EMT) gene, suppresses anchorage-independent cell growth of lung cancer cells. Cancer Lett 2010; 296(2):216-24. doi: 10.1016/j.canlet.2010.04.008. doi: 10.1016/j.canlet.2010.04.008
9.	Gibbons DL, Lin W, Creighton CJ, et al. Contextual extracellular cues promote tumor cell EMT and metastasis by regulating miR-200 family expression. Genes Dev 2009; 23(18):2140-51. doi: 10.1101/gad.1820209. doi: 10.1101/gad.1820209
10.	Cortez MA, Valdecanas D, Zhang X, et al. Therapeutic delivery of miR-200c enhances radiosensitivity in lung cancer. Mol Ther 2014; 22(8):1494-503. doi: 10.1038/mt.2014.79. doi: 10.1038/mt.2014.79
11.	Wang X, Ding Y, Da B, et al. Identification of potential prognostic long noncoding RNA signatures based on a competing endogenous RNA network in lung adenocarcinoma. Oncol Rep 2018; 40(6):3199-212. doi: 10.3892/or.2018.6719. doi: 10.3892/or.2018.6719
12.	Wang XW, Guo QQ, Wei Y, et al. Construction of a competing endogenous RNA network using differentially expressed lncRNAs, miRNAs and mRNAs in nonsmall cell lung cancer. Oncol Rep 2019; 42(6):2402-15. doi: 10.3892/or.2019.7378. doi: 10.3892/or.2019.7378
13.	Wong NW, Chen Y, Chen S, et al. OncomiR: an online resource for exploring pan-cancer microRNA dysregulation. Bioinformatics 2018; 34(4):713-5. doi: 10.1093/bioinformatics/btx627. doi: 10.1093/bioinformatics/btx627
14.	Li JH, Liu S, Zhou H, et al. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 2014; 42(Database issue):D92-7. doi: 10.1093/nar/gkt1248. doi: 10.1093/nar/gkt1248
15.	Chang L, Zhou G, Soufan O, et al. miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology. Nucleic Acids Res 2020; 48(W1):W244-W251. doi: 10.1093/nar/gkaa467. doi: 10.1093/nar/gkaa467
16.	Chandrashekar DS, Bashel B, Balasubramanya SAH, et al. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 2017; 19(8):649-58. doi: 10.1016/j.neo.2017.05.002. doi: S1476-5586(17)30179-3 pmid: 28732212
17.	Chou CH, Shrestha S, Yang CD, et al. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res 2018; 46(D1):D296-D302. doi: 10.1093/nar/gkx1067. doi: 10.1093/nar/gkx1067
18.	Pathan M, Keerthikumar S, Ang CS, et al. FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics 2015; 15(15):2597-601. doi: 10.1002/pmic.201400515. doi: 10.1002/pmic.201400515
19.	Szklarczyk D, Morris J H, Cook H, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 2017; 45(D1):D362-D368. doi: 10.1093/nar/gkw937. doi: 10.1093/nar/gkw937
20.	Reddy KB. MicroRNA (miRNA) in cancer. Cancer Cell Int 2015; 15:38. doi: 10.1186/s12935-015-0185-1. doi: 10.1186/s12935-015-0185-1
21.	Fang L, Cai J, Chen B, et al. Aberrantly expressed miR-582-3p maintains lung cancer stem cell-like traits by activating Wnt/beta-catenin signalling. Nat Commun 2015; 6:8640. doi: 10.1038/ncomms9640. doi: 10.1038/ncomms9640
22.	Luo J, Zhu H, Jiang H, et al. The effects of aberrant expression of LncRNA DGCR5/miR-873-5p/TUSC3 in lung cancer cell progression. Cancer Med 2018; 7(7):3331-41. doi: 10.1002/cam4.1566. doi: 10.1002/cam4.1566
23.	Hosseini SM, Soltani BM, Tavallaei M, et al. Clinically Significant Dysregulation of hsa-miR-30d-5p and hsa-let-7b Expression in Patients with Surgically Resected Non-Small Cell Lung Cancer. Avicenna J Med Biotechnol 2018; 10(2):98-104.
24.	Wang P, Zou F, Zhang X, et al. microRNA-21 negatively regulates Cdc25A and cell cycle progression in colon cancer cells. Cancer Res 2009; 69(20):8157-65. doi: 10.1158/0008-5472.CAN-09-1996. doi: 10.1158/0008-5472.CAN-09-1996
25.	Hu T, Shen H, Li J, et al. RFC2, a direct target of miR-744, modulates the cell cycle and promotes the proliferation of CRC cells. J Cell Physiol 2020; 235(11):8319-33. doi: 10.1002/jcp.29676. doi: 10.1002/jcp.29676
26.	Gan H, Lin L, Hu N, et al. KIF2C exerts an oncogenic role in nonsmall cell lung cancer and is negatively regulated by miR-325-3p. Cell Biochem Funct 2019; 37(6):424-31. doi: 10.1002/cbf.3420. doi: 10.1002/cbf.3420
27.	Huang D, Zheng Z, Huang J, et al. Diagnostic signifcance of circulating let-7c-5p and its potential target gene network in non-small cell lung cancers. Int J Clin Exp Med 2016; 9:9828-42.
28.	Chen G, Hu J, Huang Z, et al. MicroRNA-1976 functions as a tumor suppressor and serves as a prognostic indicator in non-small cell lung cancer by directly targeting PLCE1. Biochem Biophys Res Commun 2016; 473(4):1144-1151. doi: 10.1016/j.bbrc.2016.04.030. doi: 10.1016/j.bbrc.2016.04.030

miRNA name	miRNA type	Expression details				Prognostic value
miRNA name	miRNA type	Cancer Exp	Normal Exp	Fold Change	P Value	Hazard Ration	log-Rank P
hsa-miR-328-3p	Down-regulated / good prognosis	36.93	674.10	0.05	0.0002	0.69	0.014
hsa-miR-1976	Down-regulated / good prognosis	14.73	114.84	0.13	6E-11	0.66	0.0053
hsa-let-7c-5p	Down-regulated / good prognosis	1258.23	5235.5	0.24	2E-18	0.74	0.043
hsa-miR-146b-3p	Down-regulated / good prognosis	227.52	966.56	0.24	0.0002	0.74	0.045
hsa-miR-150-5p	Down-regulated / good prognosis	1108.92	3574.06	0.31	0.0084	0.74	0.046
hsa-miR-140-3p	Down-regulated / good prognosis	908.08	2111.63	0.43	6E-15	0.73	0.035
hsa-miR-30d-5p	Down-regulated / good prognosis	13296.36	23797.41	0.56	2E-05	0.62	0.0017
hsa-miR-101-3p	Down-regulated / good prognosis	11939.12	14362.07	0.83	1E-09	0.65	0.0038
hsa-miR-490-3p	Down-regulated / good prognosis	0.57	2.69	0.21	3E-09	0.72	0.033
hsa-miR-195-3p	Down-regulated / good prognosis	3.42	13.21	0.26	3E-06	0.59	0.00046
hsa-miR-1468-5p	Down-regulated / good prognosis	4.66	9.25	0.5	0.0026	0.64	0.0034
hsa-miR-526b-5p	Down-regulated / good prognosis	7.11	11.19	0.64	2E-07	1.50	0.0093
hsa-miR-582-3p	Up-regulated / poor prognosis	440.1	126.36	3.48	5E-07	1.36	0.039
hsa-miR-21-5p	Up-regulated / poor prognosis	358144.55	38949.90	9.20	6E-101	1.45	0.014
hsa-miR-9-5p	Up-regulated / poor prognosis	3568.81	35.32	101.04	3E-19	1.49	0.0079
hsa-miR-550a-5p	Up-regulated / poor prognosis	4.77	3.24	1.47	0.016	1.50	0.0067
hsa-miR-582-5p	Up-regulated / poor prognosis	20.23	6.00	3.37	5E-05	1.52	0.0051
hsa-miR-873-5p	Up-regulated / poor prognosis	0.47	0.06	8.40	0.037	1.57	0.0031
hsa-miR-1293	Up-regulated / poor prognosis	0.30	0.01	30.34	0.0065	1.88	0.000025
hsa-miR-31-5p	Up-regulated / poor prognosis	17.01	0.42	40.94	1E-08	1.39	0.032

Biological pathway	Genes mapped (from input data set)
Cell Cycle, Mitotic	CCNB2; RRM2; CDC25A; RFC2; KIF2C; BIRC5; CDCA8; CKS1B; CDT1; CENPI; SKA1
Validated targets of C-MYC transcriptional activation	CDC25A; HMGA1; LDHA; PMAIP1; BIRC5; SLC2A1

Primer name	sequence (5’ to 3’)	Primer name	sequence (5’ to 3’)
Q-H-CDT1-F	TGTCAAGGAGCACCACAAGGC	Q-H-BIRC5-F	TCTCAAGGACCACCGCATCT
Q-H-CDT1-R	CGATGTCGGGTACTTCATCCA	Q-H-BIRC5-R	CCAAGTCTGGCTCGTTCTCA
Q-H-CENPI-F	CTCAGATGGGCTCAGTGCTAA	Q-H-KIF2C-F	AGAATTTCGGGCTACTTTGG
Q-H-CENPI-R	TGAAGGAACTGTGACCGACAA	Q-H-KIF2C-R	TGCTTATTCAGTGGGCGTTT
Q-H-RAD51-F	TGTCAAGGAGCACCACAAGGC	Q-H-KPNA2-F	CGTCGCAGAATAGAGGTCAA
Q-H-RAD51-R	CGATGTCGGGTACTTCATCCA	Q-H-KPNA2-R	GCAGCGGAGAAGTAGCATCA
Q-H-KIF14-F	AAAAGAAAGTCTGGGTGGA	Q-H-CCNB2-F	CGAGGACATTGATAACGAAG
Q-H-KIF14-R	CTGTATCGTTCAGGGTCAA	Q-H-CCNB2-R	AACTGGCTGAACCTGTAAAA
Q-H-CKS1B-F	TCATGCTGCCCAAGGACATA	Q-H-CDC25A-F	AGGGTCTGGGCAGTGATTAT
Q-H-CKS1B-R	GGACCCATCCCTGACTCTGC	Q-H-CDC25A-R	AACAGCTTCTGAGGTAGGGA
Q-H-RFC2-F	CTTCTCAGGATTTGGCTTCA	Q-H-FANCI-F	GCTCTGAGGAAGCTGGAACA
Q-H-RFC2-R	GTAGGCTTCGTCAATGTTGG	Q-H-FANCI-R	AGGGCAGTGAGAATGATAGG
Q-H-ESPL1-F	GCTTGTGATGCCATCCTGAGGG	Q-H-CDCA8-F	TGAGTCAGACAGGCAGAACC
Q-H-ESPL1-R	GCCTCTGGGCTTCCTTGTGC	Q-H-CDCA8-R	TTCCTCCAAGGGCGAAGTAG
Q-H-RRM2-F	AACTTGGTGGAGCGATTTAG	Q-H-RACGAP1-F	AGCGAAGTGCTCTGGATGTT
Q-H-RRM2-R	ATAGGTAGCCTCTTTGTCCC	Q-H-RACGAP1-R	CATTGCTGCTGGATGGTTGG
Q-H-ACTB-F	ACTCTTCCAGCCTTCCTTCC
Q-H-ACTB-R	TGTTGGCGTACAGGTCTTTG

[1]	Li Wenxing, Zhang Yanli. Novel Long Non-coding RNA Markers for Prognostic Prediction of Patients with Bladder Cancer [J]. Chinese Medical Sciences Journal, 2020, 35(3): 239-247.
[2]	Zhu Weihua,Xie Wenyong,Zhang Zhedong,Li Shu,Zhang Dafang,Liu Yijun,Zhu Jiye,Leng Xisheng. Postoperative Complications and Survival Analysis of Surgical Resection for Hilar Cholangiocarcinoma: A Retrospective Study of Fifty-Nine Consecutive Patients [J]. Chinese Medical Sciences Journal, 2020, 35(2): 157-169.
[3]	Chen Qiang, Zhang Liwei, Huang Dangsheng, Zhang Chunhong, Wang Qiushuang, Shen Dong, Xiong Minjun, Yang Feifei. Five-year Clinical Outcomes of CAD Patients Complicated with Diabetes after StentBoost-optimized Percutaneous Coronary Intervention [J]. Chinese Medical Sciences Journal, 2019, 34(3): 177-183.
[4]	Wang Guorong, Wang Zhiwei, Jin Zhengyu. Application and Progress of Texture Analysis in the Therapeutic Effect Prediction and Prognosis of Neoadjuvant Chemoradiotherapy for Colorectal Cancer [J]. Chinese Medical Sciences Journal, 2019, 34(1): 45-50.
[5]	Liu Yongsheng, Zhao Yu. Progress in Intraoperative Neurophysiological Monitoring for the Surgical Treatment of Thoracic Spinal Stenosis [J]. Chinese Medical Sciences Journal, 2017, 32(4): 260-264.
[6]	Meng-yi Wang, Zhe-yu Niu, Xiang-Gao, Li Zhou, Quan Liao, Yu-pei Zhao. Prognostic Impact of Cell Division Cycle Associated 2 Expression on Pancreatic Ductal Adenocarcinoma [J]. Chinese Medical Sciences Journal, 2016, 31(3): 149-154.
[7]	Min Xu, Zheng-song Gu, Cun-zu Wang, Xiao-feng Lu, Ding-chao Xiang, Zhi-cheng Yuan, Qiao-yu Li, Min Wu. Impact of Intraoperative Blood Pressure Control and Temporary Parent Artery Blocking on Prognosis in Cerebral Aneurysms Surgery [J]. Chinese Medical Sciences Journal, 2016, 31(2): 89-94.
[8]	Shu-bo Tian, Jian-chun Yu*, Wei-ming Kang, Zhi-qiang Ma, Xin Ye, Chao Yan, Ya-kai Huang. Effect of Neoadjuvant Chemotherapy Treatment on Prognosis of Patients with Advanced Gastric Cancer: a Retrospective Study [J]. Chinese Medical Sciences Journal, 2015, 30(2): 84-89.
[9]	Ying-qiu Pan, Wei-wu Shi, Dan-ping Xu, Hui-hui Xu, Mei-ying Zhou, Wei-hua Yan*. Associations Between Epidermal Growth Factor Receptor Gene Mutation and Serum Tumor Markers in Advanced Lung Adenocarcinomas: A Retrospective Study [J]. Chinese Medical Sciences Journal, 2014, 29(3): 156-161.
[10]	Xiao-dong Qu, Resha Shrestha and Mao-de Wang* . Risk Factors Analysis on Traumatic Brain Injury Prognosis [J]. Chinese Medical Sciences Journal, 2011, 26(2): 98-102.

Early edition

Current issue

Archive

Specialties & Topics

Author center

Reviewer center

Identifying and Validating a Novel miRNA-mRNA Regulatory Network Associated with Prognosis in Lung Adenocarcinoma

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 28.

Related Articles 10

Metrics

Recommended 10