Multi-omics profiling reveal cells with novel oncogenic cluster, TRAP1low/CAMSAP3low, emerge more aggressive behavior and poor-prognosis in early-stage endometrial cancer

生物 转录组 蛋白质组学 癌症研究 癌症 计算生物学 肿瘤科 生物信息学 基因 基因表达 遗传学 医学
作者
Xiaodan Mao,Xiaoyue Tang,Jingxuan Ye,Xu Shuxia,Li Wang,Xianhua Liu,Qibin Wu,Xite Lin,Maotong Zhang,Jiang‐Feng Liu,Juntao Yang,Pengming Sun
出处
期刊:Molecular Cancer [Springer Nature]
卷期号:23 (1)
标识
DOI:10.1186/s12943-024-02039-2
摘要

Abstract The clinical heterogeneity of early-stage endometrial cancer (EC) is worthy of further study to identify high-quality prognostic markers and their potential role in aggressive tumor behavior. Mutation of TP53 was considered as an important primary triage in modified molecular typing for EC, it still cannot precisely predict the prognosis of EC. After proteomic analysis of cancer and para-cancerous tissues from 24 early-stage endometrioid EC patients with different survival outcomes, 13 differentially expressed proteins were screen out while 2 proteins enriched in p53 signaling pathway were further identified by single-cell transcriptome (scRNA-seq). Interestingly, tumor necrosis factor type-1 receptor-associated protein (TRAP1) and calmodulin-regulated spectrin-associated protein family member 3 (CAMSAP3) were found to be significantly downregulated in the specific cell cluster. Expectedly, the signature genes of TRAP1 low /CAMSAP3 low cluster included classical oncogenes. Moreover, close cellular interactions were observed between myeloid cells and the TRAP1 low /CAMSAP3 low cluster after systematically elucidating their relationship with tumor microenvironment (TME). The expression of TRAP1 and CAMSAP3 was verified by immunohistochemistry. Thus, a novel prediction model combining TRAP1, CAMSAP3 and TP53 was construct by multi-omics. Compared with the area under the curve, it demonstrated a significantly improvemrnt in the diagnostic efficacy in EC patients from TCGA bank. In conclusion, this work improved the current knowledge regarding the prognosis of early-stage EC through proteomics and scRNA-seq. These findings may lead to improvements in precise risk stratification of early-stage EC patients.
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