Laboratory of Medical Genetics, Harbin Medical University, Harbin, 150081, China.
Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, 150081, China.
BMC Med Genomics. 2023 Jun 19;16(1):140. doi: 10.1186/s12920-023-01576-x.
Ovarian cancer (OV) has a high mortality rate all over the world, and extrachromosomal circular DNA (eccDNA) plays a key role in carcinogenesis. We wish to study more about the molecular structure of eccDNA in the UACC-1598-4 cell line and how its genes are associated with ovarian cancer prognosis.
We sequenced and annotated the eccDNA by Circle_seq of the OV cell line UACC-1598-4. To acquire the amplified genes of OV on eccDNA, the annotated eccDNA genes were intersected with the overexpression genes of OV in TCGA. Univariate Cox regression was used to find the genes on eccDNA that were linked to OV prognosis. The least absolute shrinkage and selection operator (LASSO) and cox regression models were used to create the OV prognostic model, as well as the receiver operating characteristic curve (ROC) curve and nomogram of the prediction model. By applying the median value of the risk score, the samples were separated into high-risk and low-risk groups, and the differences in immune infiltration between the two groups were examined using ssGSEA.
EccDNA in UACC-1598-4 has a length of 0-2000 bp, and some of them include the whole genes or gene fragments. These eccDNA originated from various parts of chromosomes, especially enriched in repeatmasker, introns, and coding regions. They were annotated with 2188 genes by Circle_seq. Notably, the TCGA database revealed that a total of 198 of these eccDNA genes were overexpressed in OV (p < 0.05). They were mostly enriched in pathways associated with cell adhesion, ECM receptors, and actin cytoskeleton. Univariate Cox analysis showed 13 genes associated with OV prognosis. LASSO and Cox regression analysis were used to create a risk model based on remained 9 genes. In both the training (TCGA database) and validation (International Cancer Genome Consortium, ICGC) cohorts, a 9-gene signature could successfully discriminate high-risk individuals (all p < 0.01). Immune infiltration differed significantly between the high-risk and low-risk groups. The model's area under the ROC curve was 0.67, and a nomograph was created to assist clinician.
EccDNA is found in UACC-1598-4, and part of its genes linked to OV prognosis. Patients with OV may be efficiently evaluated using a prognostic model based on eccDNA genes, including SLC7A1, NTN1, ADORA1, PADI2, SULT2B1, LINC00665, CILP2, EFNA5, TOMM.
卵巢癌（OV）在全球范围内死亡率较高，染色体外环状 DNA（eccDNA）在致癌作用中起关键作用。我们希望进一步研究 UACC-1598-4 细胞系中 eccDNA 的分子结构及其基因与卵巢癌预后的关系。
通过 OV 细胞系 UACC-1598-4 的 Circle_seq 对 eccDNA 进行测序和注释。为了获得 eccDNA 上卵巢癌的扩增基因，将注释的 eccDNA 基因与 TCGA 中卵巢癌的过表达基因进行交集。采用单因素 Cox 回归寻找与卵巢癌预后相关的 eccDNA 基因。采用最小绝对收缩和选择算子（LASSO）和 Cox 回归模型构建卵巢癌预后模型，并绘制预测模型的受试者工作特征曲线（ROC）曲线和列线图。通过应用风险评分的中位数，将样本分为高风险组和低风险组，并用 ssGSEA 检测两组之间免疫浸润的差异。
UACC-1598-4 中的 eccDNA 长度为 0-2000 bp，其中一些包含完整基因或基因片段。这些 eccDNA 来源于染色体的不同部位，特别是在重复掩蔽、内含子和编码区域中富集。通过 Circle_seq 注释了 2188 个基因。值得注意的是，TCGA 数据库显示，这些 eccDNA 基因中有 198 个在卵巢癌中过表达（p<0.05）。它们主要富集在与细胞黏附、ECM 受体和肌动蛋白细胞骨架相关的途径中。单因素 Cox 分析显示，有 13 个基因与卵巢癌的预后相关。采用 LASSO 和 Cox 回归分析，基于保留的 9 个基因构建风险模型。在训练（TCGA 数据库）和验证（国际癌症基因组联盟，ICGC）队列中，9 个基因的特征均能成功区分高风险个体（均 p<0.01）。高危组和低危组之间的免疫浸润差异有统计学意义。该模型的 ROC 曲线下面积为 0.67，并创建了一个列线图以协助临床医生。
在 UACC-1598-4 中发现了 eccDNA，其中部分基因与卵巢癌的预后相关。基于 eccDNA 基因的预后模型可有效评估卵巢癌患者，包括 SLC7A1、NTN1、ADORA1、PADI2、SULT2B1、LINC00665、CILP2、EFNA5、TOMM。
