基于TCGA数据库筛选与三阴型乳腺癌预后相关的miRNAs及其基因网络

doi:10.13481/j.1671-587X.20240212

Abstract

Abstract:

Objective To identify the key microRNAs （miRNAs） and their target genes associated with the prognosis of triple-negative breast cancer （TNBC） and to discuss their roles in regulatory biological functions and signaling pathways， and to provide the theoretical basis for the selection of prognostic biomarkers for the patients with TNBC. Methods The differentially expressed miRNAs between the TNBC patients and non-TNBC patients were selected based on The Cancer Genome Atlas （TCGA） Database； survival analysis was used to clarify the miRNAs related to the prognosis of the patients；miRDB and miRWalk3.0 online Databases were used to screen for the miRNA target gene； Cytoscape 3.8.2 software was used to elucidate the miRNA-messenger RNA （mRNA） regulatory network；the limma package in R software was used to screen the differentially expressed miRNAs of the TNBC patients and non-TNBC patients； the clusterProfiler package in R software was used to analyze the biological functions and pathways involved by the target genes. Results The survival analysis results showed that miR-9， miR-17， miR-31， miR-146a， miR-188， and miR-190b were associated with the prognosis of the TNBC patients， and low expression levels of these miRNAs were correlated with the poorer prognosis of the patients. A total of 224 target genes regulated by these six miRNAs were identified. The Gene Ontology （GO） functional enrichment analysis results showed that the target genes were primarily involved in the development of mammary alveoli， positive regulation of DNA transcription， and angiogenesis.The Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis results showed that the target genes were mainly enriched in cytokine-cytokine receptor interaction and insulin secretion signaling pathways. The network analysis results identified the key genes in the regulatory network， including solute carrier family 24 member 2 （SLC24A2）， vav guanine nucleotide exchange factor 3 （VAV3）， tripartite motif containing 36 （TRIM36）， synaptotagmin 1 （SYT1）， pleckstrin and Sec7 domain containing 3 （PSD3）， peroxisome proliferator-activated receptor α （PPARA）， RNA polymerase Ⅲ subunit G （POLR3G）， pleomorphic adenoma gene 1 （PLAG1）， ubiquitin associated and SH3 domain containing B （UBASH3B）， and SH3 domain and tetramerization domain containing 2 （SH3TC2）； the interactions of miRNA-mRNA of miR-9-SYT1， miR-9-KIF13B， miR-9-KITLG， miR-17-SLC24A2， miR-31-SLC24A2，miR-146a-SYT1， miR-146a-KIF13B， miR-188-SLC24A2， and miR-188-SLC24A2 were the closest. Conclusion MiR-9， miR-17， miR-31， miR-146a， miR-188， miR-190b and their target genes are involved in physiological processes such as mammary alveolar development and angiogenesis， which are closely associated with the poor prognosis of the patients with TNBC.

Key words: Triple negative breast cancer, MicroRNA, Prognosis, Regulatory network, Target gene

CLC Number:

R737.9

Yueying SONG,Chao GAO,Wenjun CHEN,Aiyu SHAO,Yichun QIAO,Zhuolin LI. Screening of miRNAs related to prognosis of triple-negative breast cancer and its gene network based on TCGA Database[J].Journal of Jilin University(Medicine Edition), 2024, 50(2): 392-399.

Figures/Tables 7

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References 33

1	KWAN M L， BERNARD P S， KROENKE C H， et al. Breastfeeding， PAM50 tumor subtype， and breast cancer prognosis and survival［J］.J Natl Cancer Inst， 2015， 107（7）： djv087.
2	YUAN Z Y， WANG S S， GAO Y， et al. Clinical characteristics and prognosis of triple-negative breast cancer： a report of 305 cases［J］.Ai Zheng，2008，27（6）： 561-565.
3	EBERLEIN T J. Race， breast cancer subtypes， and survival in the Carolina breast cancer study［J］. Yearb Surg， 2007， 2007： 304-305.
4	NAKAJIMA H， FUJIWARA I， MIZUTA N， et al. Prognosis of Japanese breast cancer based on hormone receptor and HER2 expression determined by immunohistochemical staining［J］. World J Surg， 2008， 32（11）： 2477-2482.
5	BABYSHKINA N， MALINOVSKAYA E， PATALYAK S， et al. Neoadjuvant chemotherapy for different molecular breast cancer subtypes： a retrospective study in Russian population［J］. Med Oncol， 2014， 31（9）： 165.
6	GROUP I B C S， COLLEONI M， GELBER S， et al. Tamoxifen after adjuvant chemotherapy for premenopausal women with lymph node-positive breast cancer： international Breast Cancer Study Group Trial 13-93［J］. J Clin Oncol， 2006， 24（9）： 1332-1341.
7	Straver M E， Rutgers E J T， Rodenhuis S，et al.The relevance of breast cancer subtypes in the outcome of neoadjuvant chemotherapy［J］. Annal surgical Oncol， 2010， 17（9）：2411-2418.
8	RAKHAE A， EL-SAYEDM E， GREENA R， et al. Prognostic markers in triple-negative breast cancer［J］. Cancer， 2007， 109（1）： 25-32.
9	XIONG B， LEI X F， ZHANG L， et al. miR-103 regulates triple negative breast cancer cells migration and invasion through targeting olfactomedin 4［J］. Biomed Pharmacother， 2017， 89： 1401-1408.
10	LIANG Z X， BIAN X H， SHIM H. Downregulation of microRNA-206 promotes invasion and angiogenesis of triple negative breast cancer［J］. Biochem Biophys Res Commun， 2016， 477（3）： 461-466.
11	LEE R C， FEINBAUM R L， AMBROS V. The C. elegans heterochronic gene Lin-4 encodes small RNAs with antisense complementarity to Lin-14［J］. Cell， 1993， 75（5）： 843-854.
12	SANTOVITO D， MEZZETTI A， CIPOLLONE F. microRNAs and atherosclerosis： new actors for an old movie［J］. Nutr Metab Cardiovasc Dis， 2012， 22（11）： 937-943.
13	RUPAIMOOLE R， CALIN G A， LOPEZ-BERESTEIN G， et al. miRNA deregulation in cancer cells and the tumor microenvironment［J］. Cancer Discov， 2016， 6（3）： 235-246.
14	CHUA J H， ARMUGAM A， JEYASEELAN K. microRNAs： biogenesis， function and applications［J］. Curr Opin Mol Ther， 2009， 11（2）： 189-199.
15	LING H， FABBRI M， CALIN G A. microRNAs and other non-coding RNAs as targets for anticancer drug development［J］. Nat Rev Drug Discov， 2013， 12（11）： 847-865.
16	VOLINIA S， CALIN G A， LIU C G，et al.A microRNA expression signature of human solid tumors defines cancer gene targets［J］. Proc Natl Acad Sci U S A， 2006， 103（7）： 2257-2261.
17	JANG M H， KIM H J， GWAK J M， et al. Prognostic value of microRNA-9 and microRNA-155 expression in triple-negative breast cancer［J］. Hum Pathol， 2017， 68： 69-78.
18	FAN C N， LIU N， ZHENG D， et al. microRNA-206 inhibits metastasis of triple-negative breast cancer by targeting transmembrane 4 L6 family member 1［J］. Cancer Manag Res， 2019， 11： 6755-6764.
19	BARONI S， ROMERO-CORDOBA S， PLANTAMURA I， et al. Exosome-mediated delivery of miR-9 induces cancer-associated fibroblast-like properties in human breast fibroblasts［J］. Cell Death Dis， 2016， 7（7）： e2312.
20	D'IPPOLITO E， PLANTAMURA I， BONGIOVANNI L，et al. miR-9 and miR-200 regulate PDGFRβ-mediated endothelial differentiation of tumor cells in triple-negative breast cancer［J］. Cancer Res， 2016，76（18）： 5562-5572.
21	JANG M H， KIM H J， GWAK J M， et al. Prognostic value of microRNA-9 and microRNA-155 expression in triple-negative breast cancer［J］. Hum Pathol， 2017， 68： 69-78.
22	D'IPPOLITO E， PLANTAMURA I， BONGIOVANNI L，et al. miR-9 and miR-200 regulate PDGFRβ-mediated endothelial differentiation of tumor cells in triple-negative breast cancer［J］. Cancer Res， 2016， 76（18）： 5562-5572.
23	LI J， LAI Y H， MA J Y， et al. miR-17-5p suppresses cell proliferation and invasion by targeting ETV1 in triple-negative breast cancer［J］. BMC Cancer， 2017， 17（1）： 745.
24	LUO L J， YANG F， DING J J， et al. miR-31 inhibits migration and invasion by targeting SATB2 in triple negative breast cancer［J］. Gene， 2016， 594（1）： 47-58.
25	SI C S， YU Q， YAO Y F. Effect of miR-146a-5p on proliferation and metastasis of triple-negative breast cancer via regulation of SOX5［J］. Exp Ther Med， 2018， 15（5）： 4515-4521.
26	ZAVALA V， PÉREZ-MORENO E， TAPIA T， et al. miR-146a and miR-638 in BRCA1-deficient triple negative breast cancer tumors， as potential biomarkers for improved overall survival［J］. Cancer Biomark， 2016， 16（1）： 99-107.
27	NAOREM L D， MUTHAIYAN M， VENKATESAN A. Identification of dysregulated miRNAs in triple negative breast cancer： a meta-analysis approach［J］. J Cell Physiol， 2019， 234（7）： 11768-11779.
28	HAMILTON N， AUSTIN D， MÁRQUEZ-GARBÁN D， et al. Receptors for insulin-like growth factor-2 and androgens as therapeutic targets in triple-negative breast cancer［J］. Int J Mol Sci， 2017， 18（11）： 2305.
29	HAMILTON N， AUSTIN D， MÁRQUEZ-GARBÁN D，et al. Receptors for insulin-like growth factor-2 and androgens as therapeutic targets in triple-negative breast cancer［J］. Int J Mol Sci， 2017， 18（11）： 2305.
30	WANG Z S， LI Y F， XIAO Y J， et al. Integrin α9 depletion promotes β-catenin degradation to suppress triple-negative breast cancer tumor growth and metastasis［J］. Int J Cancer， 2019， 145（10）： 2767-2780.
31	RIGIRACCIOLO D C， NOHATA N， LAPPANO R， et al. IGF-1/IGF-1R/FAK/YAP transduction signaling prompts growth effects in triple-negative breast cancer （TNBC） cells［J］. Cells， 2020， 9（4）： 1010.
32	SHAHEEN S， FAWAZ F， SSHA H，et al. Differential expression and pathway analysis in drug-resistant triple-negative breast cancer cell lines using RNASeq analysis［J］. Int J Mol Sci，2018，19（6）：1810.
33	WANG S， XIA D， WANG X，et al. C/EBPβ regulates the JAK/STAT signaling pathway in triple-negative breast cancer［J］. FEBS Open Bio， 2021， 11： 1250-1258.

Gene ID	Log₂FC	P	Gene expression status
Hsa-miR-34c	1.02	3.55E-08	Low expression
Hsa-miR-30a	1.04	8.15E-10	Low expression
Hsa-miR-887	1.06	4.66E-09	Low expression
Hsa-miR-182	1.06	4.86E-15	Low expression
Hsa-miR-196a-2	1.06	1.13E-06	Low expression
Hsa-miR-342	1.48	7.62E-27	Low expression
Hsa-miR-153-2	1.59	7.98E-13	Low expression
Hsa-miR-10a	1.79	2.97E-21	Low expression
Hsa-miR-149	1.88	1.14E-23	Low expression
Hsa-miR-375	2.13	9.27E-17	Low expression
Hsa-miR-653	2.34	9.83E-20	Low expression
Hsa-miR-184	3.43	1.19E-17	Low expression
Hsa-miR-190b	3.46	6.87E-47	Low expression
Hsa-miR-877	1.00	4.99E-10	High expression
Hsa-miR-130b	1.01	9.70E-25	High expression
Hsa-miR-511	1.03	2.52E-12	High expression
Hsa-miR-188	1.03	1.33E-13	High expression
Hsa-miR-146a	1.05	1.79E-15	High expression
Hsa-miR-942	1.07	2.55E-19	High expression
Hsa-miR-937	1.17	3.56E-08	High expression
Hsa-miR-19a	1.24	6.77E-20	High expression
Hsa-miR-17	1.26	9.58E-30	High expression
Hsa-miR-31	1.31	5.37E-11	High expression
Hsa-miR-505	1.31	3.12E-32	High expression
Hsa-miR-455	1.51	6.81E-31	High expression
Hsa-miR-9-3	1.56	4.18E-11	High expression
Hsa-miR-9-1	1.57	3.58E-11	High expression
Hsa-miR-9-2	1.57	3.58E-11	High expression
Hsa-miR-584	1.62	7.46E-29	High expression
Hsa-miR-483	1.65	1.16E-13	High expression
Hsa-miR-224	1.84	1.76E-18	High expression
Hsa-miR-452	1.86	7.46E-29	High expression
Hsa-miR-18a	1.92	7.34E-48	High expression
Hsa-miR-1269a	2.23	3.35E-06	High expression
Hsa-miR-135b	2.88	5.38E-34	High expression
Hsa-miR-577	3.37	9.58E-30	High expression
Hsa-miR-934	4.78	8.82E-55	High expression

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Screening of miRNAs related to prognosis of triple-negative breast cancer and its gene network based on TCGA Database

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