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Tytuł pozycji:

Establishment and validation of a novel invasion-related gene signature for predicting the prognosis of ovarian cancer

Tytuł:
Establishment and validation of a novel invasion-related gene signature for predicting the prognosis of ovarian cancer
Autorzy:
Leilei Liang
Jian Li
Jing Yu
Jing Liu
Lin Xiu
Jia Zeng
Tiantian Wang
Ning Li
Lingying Wu
Temat:
Ovarian cancer
Invasion
6-gene signature
Risk
Prognosis
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
RC254-282
Cytology
QH573-671
Źródło:
Cancer Cell International, Vol 22, Iss 1, Pp 1-13 (2022)
Wydawca:
BMC, 2022.
Rok publikacji:
2022
Kolekcja:
LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
LCC:Cytology
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
English
ISSN:
1475-2867
Relacje:
https://doaj.org/toc/1475-2867
DOI:
10.1186/s12935-022-02502-4
Dostęp URL:
https://doaj.org/article/b602de52eb554219b83643f20d2fcf6c  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.b602de52eb554219b83643f20d2fcf6c
Czasopismo naukowe
Abstract Background Ovarian cancer (OC) is an invasive gynaecologic cancer with a high cancer-related death rate. The purpose of this study was to establish an invasion-related multigene signature to predict the prognostic risk of OC. Methods We extracted 97 invasion-related genes from The Cancer Genome Atlas (TCGA) database. Then, the ConsensusClusterPlus and limma packages were used to calculate differentially expressed genes (DEGs). To calculate the immune scores of the molecular subtypes, we used ESTIMATE to evaluate the stromal score, immune score and ESTIMATE score. MCP-counter and the GSVA package ssgsea were used to evaluate the types of infiltrating immune cells. Survival and nomogram analyses were performed to explore the prognostic value of the signature. Finally, qPCR, immunohistochemistry staining and functional assays were used to evaluate the expression and biological abilities of the signature genes in OC. Results Based on the consistent clustering of invasion-related genes, cases in the OC datasets were divided into two subtypes. A significant difference was observed in prognosis between the two subtypes. Most genes were highly expressed in the C1 group. Based on the C1 group genes, we constructed an invasion-related 6-gene prognostic risk model. Furthermore, to verify the signature, we used the TCGA-test and GSE32062 and GSE17260 chip datasets for testing and finally obtained a good risk prediction effect in those datasets. Moreover, the results of the qPCR and immunohistochemistry staining assays revealed that KIF26B, VSIG4 and COL6A6 were upregulated and that FOXJ1, MXRA5 and CXCL9 were downregulated in OC tissues. The functional study showed that the expression of KIF26B, VSIG4, COL6A6, FOXJ1, MXRA5 and CXCL9 can regulate the migration and invasion abilities of OC cells. Conclusion We developed a 6-gene prognostic stratification system (FOXJ1, MXRA5, KIF26B, VSIG4, CXCL9 and COL6A6) that is independent of clinical features. These results suggest that the signature could potentially be used to evaluate the prognostic risk of OC patients.
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