FAM224A/hsa-mir-139/RAD54B is a Competing Endogenous RNA Network that may Serve as a New Prognostic Factor and Treatment Target for Liver Cancer

Jiang, Wei; Lu, Wei; Yang, Wenjie

doi:10.1590/1678-4324-2022210652

Wei Jiang

Tianjin First Central Hospital, Department of Infectious Diseases, Tianjin, China

http://orcid.org/0000-0003-4239-1831

Wei Lu

Tianjin Medical University Cancer Institute & Hospital, Liver cancer center, Tianjin, China

http://orcid.org/0000-0003-4920-0276

Wenjie Yang ^**Correspondence: nankaidoc@nankai.edu.cn; Tel.: +86-022-23626776 (W.L.)

Tianjin First Central Hospital, Department of Infectious Diseases, Tianjin, China

http://orcid.org/0000-0002-9608-2770

Editor-in-Chief: Paulo Vitor Farago

Associate Editor: Marcelo Ricardo Vicari

*Correspondence: nankaidoc@nankai.edu.cn; Tel.: +86-022-23626776 (W.L.)

Conflicts of Interest: The authors declare no competing interests.

Figures (7)
Tables (3)

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Figure 1
RNA expression heatmap and volcano map of liver cancer samples and paracancerous samples from TCGA. (A) mRNA, (B) lncRNA, and (C) miRNA.

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Figure 2
WGCNA and co-expression network analysis. (A) Scale independence and average connectivity corresponded to different beta values. (B) β = 4; the distribution of the degree-k value of the nodes in the network approximated the power law distribution and fitted well with the ideal natural distribution function p(k) of the k value. (C) A systematic tree diagram of weighted gene co-expression network analysis was shown. Initial module partitioning is first calculated by using the dynamic rolling shear algorithm, and modules were merged on the basis of the similarity of the feature vector values of each module. Finally, 12 gene modules were obtained. (D) A heat map of the correlation of gene modules with various clinical features was presented. The purple module had the strongest negative correlation with survival time and the strongest positive correlation with clinical stage and pathological grade. (E) The eigenvalues of genes in the purple module were strongly correlated with survival time. (F) Co-expression relationship with a weight value of ≥0.7 in the purple module was illustrated. Green dots represented mRNAs, red dots indicated lncRNAs, and blue dots denoted miRNAs.

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Figure 3
Direct binding of Hsa-mir-139 to FAM224A and RAD54B. (A) Dual-luciferase reporter test revealed a binding site for FAM224A and hsa-mir-139. For mutant dual-luciferase plasmids, the overexpression of hsa-mir-139 alone leaded to a significant decrease in the signal, whereas the overexpression of both hsa-mir-139 and FAM224A caused this effect to disappear to a certain extent. This effect did not occur in the mutant assay. (B) Similar to FAM224A, RAD54B and hsa-mir-139 had binding sites. Mean ± SD; *P < 0.05; **P < 0.01, ***P < 0.001.

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Figure 4
KM survival analysis of FAM224A, hsa-mir-139, and RAD54B in patients with liver cancer. (A) FAM224A, (B) hsa-mir-139, and (C) RAD54B. TCGA data revealed that patients with high expression levels of FAM224A and RAD54B had short survival times, whereas patients with a high expression level of hsa-mir-139 had long survival times. Mean ± SD; *P < 0.05; **P < 0.01, ***P < 0.001.

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Figure 5
Effect of FAM224A on hsa-mir-139 and RAD54B expression levels. (A) FAM224A expression in HepG2 cells transfected with overexpressed plasmid and siRNA was detected through a qRT-PCR assay. FAM224A expression was successfully regulated. (B) FAM224A was upregulated after hsa-mir-139 was downregulated, and vice versa. (C) FAM224A could upregulate RAD54B expression, and vice versa. (D) hsa-mir-139 was further knocked down. The figure showed the relative expression of hsa-mir-139 before and after knockdown. (E) After hsa-mir-139 was knocked down, the control of FAM224A was still stable. (F) After hsa-mir-139 was knocked down, changes in FAM224A could no longer affect the RAD54B expression. Mean ± SD; *P < 0.05; **P < 0.01, ***P < 0.001.

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Figure 6
Effect of the changes in the FAM224A expression on multiple functions of HepG2 cells. (A) CCK-8 assay revealed that the multiplication ability of HepG2 cells improved as the FAM224A expression level increased. (B and C) Wound healing test, (D) cloning formation assays, and (E) Transwell assays on HepG2 cells were performed. (F) Statistical results of the cell clone numbers in Figure D were obtained. (G) Statistical results of the cell invasion numbers in Figure E were determined. Mean ± SD; *P < 0.05; **P < 0.01, ***P < 0.001.

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Figure 7
Knockdown of Inhibition of tumor xenograft growth via FAM224A knockdown and overexpression of hsa-mir-139 overexpression inhibited tumor xenograft growth. (A) Tumor samples from each group were provided. (B) The growth curves of tumor xenograft in subcutaneous implantation test were showed. Mean ± SD; *P < 0.05; **P < 0.01.

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Table 1
Differences in the expression levels of lncRNA and miRNA in the network between liver cancer and paracancerous samples from TCGA. logFC = log₂(cancer) - log₂(control); FDR is the P value corrected via the Benjamini-Hochberg multiple test.

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Table 2
Prediction of mRNA and lncRNA that might bind to hsa-mir-139 via the DIANA tool. miRNA target gene (miTG) score is a comprehensive combination forecast index in DIANA tool obtained with the following: miTG score∈[0,1]. The higher the miTG score, the greater the possibility of binding.

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Table 3
qRT-PCR primers

Gene	logFC	PValue	FDR	Type
AC092171.2	2.651829972	3.58E-44	8.30E-42	up
AC132192.2	2.43309798	1.59E-38	2.28E-36	up
AC004477.1	2.808776898	2.65E-37	3.31E-35	up
AC239868.2	2.181734923	1.78E-33	1.58E-31	up
AC138356.1	-2.259640873	2.20E-31	1.57E-29	down
AL606489.1	3.123917152	1.91E-31	1.38E-29	up
FAM224A	2.938576075	2.83E-07	1.01E-06	up
AC005332.8	1.652014456	3.90E-31	2.68E-29	up
SNHG4	2.439786999	4.78E-20	7.40E-20	up
hsa-mir-1197	1.368137756	0.01674537	0.047465735	up
hsa-mir-139	-1.632754924	4.62E-29	8.90E-27	down

target mRNA	miTG score	target lncRNA	miTG score
RP11-297N6.4	0.999997387	LINC00662	1
UBR4	0.994537097	chr22-38_28785274-29006793.1	1
SYT2	0.992896947	RP11-678G14.4	1
ELK1	0.979865952	XLOC_001668	1
UBE2G1	0.978283897	ANKRD62P1-PARP4P3	1
ARPC1A	0.976478901	XLOC_004143	1
FAM161A	0.975118807	NR2F1-AS1	0.999
RPS15A	0.970840751	RP11-434D2.2	0.999
ANXA2R	0.967670285	RP11-219A15.2	0.999
DNMT3A	0.966236908	XLOC_003233	0.999
GNB5	0.963469494	FAM224A	0.998
SRSF3	0.962922755	XLOC_001935	0.998
AGPAT3	0.961735116	ANKRD62P1-PARP4P3	0.998
RAD54B	0.958380461	NR2F1-AS1	0.997
TTLL6	0.956517555	AC006548.28	0.995
RAB1A	0.956047497	RP11-679B19.1	0.995
PLP1	0.953598797	CH17-264L24.1	0.992
GLYR1	0.94863118	CTC-265F19.1	0.991
RBMXL2	0.947979307
C19orf35	0.947265339
PDE4A	0.942681521
ZBTB10	0.940179895
NUTM2E	0.935623589
RGS17	0.93526549
SERF1A	0.934998234
PCBP4	0.932440217
RP13-512J5.1	0.928699663
UVRAG	0.92499542
COX16	0.924338203
DPP4	0.921635255
ERVV-1	0.912963846

RAD54B	Forward	5′-GCCAAACACTGATGATTTGTGG-3′
	Reverse	5′-CCTGAGAAGAATGCGAGATAGC-3′
FAM224A	Forward	5'-TCTCCACCTCCTTGCTTCC-3'
	Reverse	5'-TTTAAACAAGCCAGCCAAGC-3'
hsa-miR-139	Forward	5′-TCTACAGTGCACGTGTCTCCAG-3′
	Reverse	5′-ACGCAAATTCGTGAAGCGTT-3′
Beta-actin	Forward	5′-AGCACAGAGCCTCGCCTTTG-3′
	Reverse	5′-CTTCTGACCCATGCCCACCA-3′
U6	Forward	5′-CTCGCTTCGGCAGCACA-3′
	Reverse	5′-AACGCTTCACGAATTTGCGT-3′

[1] Editor-in-Chief: Paulo Vitor Farago

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FAM224A/hsa-mir-139/RAD54B is a Competing Endogenous RNA Network that may Serve as a New Prognostic Factor and Treatment Target for Liver Cancer

HIGHLIGHTS

Abstract