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Expression and prognosis analyses of Dectin-1 cluster genes in patients with lung adenocarcinoma (LUAD) and the association with immune checkpoint molecules 被引量:1

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摘要 Reviews The Dectin-1 cluster comprises seven members:CLEC-12A,CLEC-12B,CLEC-1A,CLEC-7A,CLEC-2,CLEC-9A and OLR1.These members have been demonstrated to be involved in the tumorigenesis,progression,and metastasis of several cancers.However,little is known about their roles in human lung adenocarcinoma(LUAD).The expression patterns of the Dectin-1 cluster were analyzed via the ONCOMINE and GEPIA databases.We evaluated the prognostic value of the Dectin-1 cluster in patients with LUAD using the Kaplan-Meier plotter and GEPIA.Differential expression was validated with the EMBL-EBI database,and protein expression was analyzed with the HPA database.In addition,protein-protein interaction network,GO,and KEGG analyses were conducted.Finally,the correlations between CLEC-12A and immune molecules(immune inhibitors and MHC molecules)were investigated via TISIDB and GEPIA.The expression levels of Dectin-1 cluster genes were downregulated in LUAD tissues compared to those in normal lung tissues.The expression levels of CLEC-12A,CLEC-12B,CLEC-2,and CLEC-9A correlated with tumor stage,and CLEC-12A and CLEC-12B were significantly associated with survival in patients with LUAD.The seven genes mostly participated in immune regulation processes and were involved in autoimmune disorders and hematological malignancies.Finally,correlation analyses revealed CLEC-12A expression was associated with most immune inhibitors and MHCs.CLEC-12A was positively related to PD-1,PD-L1,PD-L2,CTLA4,TIM3,and LAG3.In conclusion,our findings suggest that CLEC-12A and CLEC-12B can be used as prognostic biomarkers in LUAD.CLEC-12A expression was associated with immune checkpoint molecules,and CLEC-12A may be a potential assistant target to improve the efficacy of immune checkpoint inhibitors immunotherapy.
出处 《BIOCELL》 SCIE 2021年第3期649-663,共15页 生物细胞(英文)
基金 supported by the China Postdoctoral Science Foundation[Grant No.2018M643495] Technology Department of Sichuan Province[Grant No.2020YJ0049].
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