单细胞测序数据揭示DMKN在卵巢癌中的诊断及预测价值

Single cell sequencing data reveal the diagnostic and predictive value of DMKN in ovarian cancer

目的 通过绘制高级别浆液性卵巢癌(High-grade serous ovarian cancer, HGSOC)、交界性卵巢癌及正常卵巢的单细胞转录组图谱,寻找能诊断并预测卵巢癌预后的标志物。方法 利用实验室前期测序的单细胞数据(SRA数据库:PRJNA756768),分析HGSOC、交界性卵巢癌及正常卵巢组织间的差异表达基因,通过功能富集筛选与肿瘤进展相关的细胞亚群、细胞通讯分析各个亚群的交流情况、拟时序分析探究细胞分化轨迹,确定和肿瘤进展最相关的亚群,再结合癌症基因组图谱(The Cancer Genome Atlas, TCGA)中卵巢癌(Ovarian cancer, OC)转录组数据和患者预后,最终筛选出卵巢癌患者诊断及预测生存的生物标志物。结果 采用t-分布领域嵌入算法(t-SNE)降维后,获得9个细胞亚群:内皮细胞,髓系细胞,纤维细胞,T细胞,基质细胞,B细胞及3种上皮细胞亚群(C1,C4,C7)。进一步分析发现C4群拷贝数变异(Copy number variation, CNV)在HGSOC中分值最高,高于交界性卵巢癌及正常卵巢,与预后负相关,而DMKN是该群关键的标志性基因。TCGA数据库中卵巢癌转录组分析显示DMKN与预后不良密切相关(P=0.026),且DMKN对卵巢癌的诊断效能显著(AUC=0.906)。结论 本研究基于单细胞测序数据筛选到DMKN,能很好地诊断并预测卵巢癌预后,该研究为卵巢癌的诊断及预后的预测提供了新的思路。

Objective The aim of this study was to draw single-cell transcriptome profiles of high-grade serous ovarian cancer(HGSOC),borderline ovarian cancer(OC),and normal ovaries in order to identify biomarkers that can diagnose and predict the prognosis of OC.Methods The differentially expressed genes between HGSOC,borderline OC,and normal ovarian tissues were analyzed using single-cell data sequenced(SRA database:PRJNA756768).The cell subsets associated with tumor progression were screened by functional enrichment,cell communication between different subsets was analyzed by Cellchat,and cell differentiation trajectories were explored by pseudotime analysis to finally determine the subsets most relevant to tumor progression.Combined with OC transcriptome data of OC from the Cancer Genome Atlas(TCGA)with patient prognosis,biomarkers for diagnosing and predicting survival of OC patients were ultimately screened.Results After using t-distribution stochastic neighbor embedding(t-SNE)for dimensionality reduction,nine cell subpopulations were obtained:endothelial cells,myeloid cells,fibroblasts,T cells,stromal cells,B cells,and 3 epithelial cell subpopulations(C1,C4,and C7).Further analysis revealed that copy number variation(CNV)in the C4 group had the highest score in HGSOC,higher than those of borderline OC and normal ovaries,and was negatively correlated with prognosis.DMKN was a key marker gene in this group.Transcriptome analysis of OC in the TCGA database showed a close correlation between DMKN and poor prognosis(P=0.026),and the diagnostic efficacy of DMKN for OC was significant(AUC=0.906).Conclusion This study is based on single-cell sequencing data to screen for DMKN,which can effectively diagnose and predict the prognosis of OC.This study provides new ideas for the diagnosis and prognosis prediction of OC.