Department of Gynecology, Beijing Obstetrics and Gynecology Hospital, Beijing Maternal and Child Health Care Hospital, Capital Medical University, Beijing, China.
Genet Test Mol Biomarkers. 2024 Feb;28(2):70-81. doi: 10.1089/gtmb.2023.0222.
To identify potential diagnostic markers for ovarian cancer (OC) and explore the contribution of immune cells infiltration to the pathogenesis of OC. As the study cohort, two gene expression datasets of human OC (GSE27651 and GSE26712, taken as the metadata) taken from the Gene Expression Omnibus (GEO) database were combined, comprising 228 OC and 16 control samples. Analysis was performed to identify the differentially expressed genes between the OC and control samples, while support vector machine analysis using the recursive feature elimination algorithm and least absolute shrinkage and selection operator regression were performed to identify candidate biomarkers that could discriminate OC. In addition, immunohistochemistry staining was performed to verify the diagnostic value and protein expression levels of the candidate biomarkers. The GSE146553 dataset (OC = 40, control = 3) was used to further validate the diagnostic values of those biomarkers. Further, the proportions of various immune cells infiltration in the OC and control samples were evaluated using the CIBERSORT algorithm. CLEC4M, PFKP, and SCRIB were identified as potential diagnostic markers for OC in both the metadata (area under the receiver operating characteristic curve [AUC] = 0.996, AUC = 1.000, AUC = 1.000) and GSE146553 dataset (AUC = 0.983, AUC = 0.975, AUC = 0.892). Regarding immune cell infiltration, there was an increase in the infiltration of follicular helper dendritic cells, and a decrease in the infiltration of M2 macrophages and neutrophils, as well as activated natural killer (NK) cells and T cells in OC. CLEC4M showed a significantly positive correlation with neutrophils ( = 0.57, < 0.001) and resting NK cells ( = 0.42, = 0.0047), but a negative correlation with activated dendritic cells ( = -0.33, = 0.032). PFKP displayed a significantly positive correlation with activated NK cells ( = 0.36, = 0.016) and follicular helper T cells ( = 0.32, = 0.035), but a negative correlation with the naive B cells ( = -0.3, = 0.049) and resting NK cells ( = -0.41, = 0.007). SCRIB demonstrated a significantly positive correlation with plasma cells ( = 0.39, = 0.01), memory B cells ( = 0.34, = 0.025), and follicular helper T cells ( = 0.31, = 0.04), but a negative correlation with neutrophils ( = -0.46, = 0.002) and naive B cells ( = -0.48, = 0.0012). CLEC4M, PFKP, and SCRIB were identified and verified as potential diagnostic biomarkers for OC. This work and identification of the three biomarkers may provide guidance for future studies into the mechanism and treatment of OC.
为了鉴定卵巢癌（OC）的潜在诊断标志物，并探讨免疫细胞浸润对 OC 发病机制的影响。本研究将两个来自基因表达综合数据库（GEO）的人类 OC 基因表达数据集（GSE27651 和 GSE26712，作为元数据）合并作为研究队列，包含 228 例 OC 患者和 16 例对照样本。通过支持向量机分析（采用递归特征消除算法和最小绝对收缩和选择算子回归），识别 OC 样本与对照样本之间差异表达的基因，筛选候选生物标志物。此外，通过免疫组织化学染色验证候选生物标志物的诊断价值和蛋白表达水平。使用 GSE146553 数据集（OC = 40，对照 = 3）进一步验证这些标志物的诊断价值。此外，采用 CIBERSORT 算法评估 OC 样本和对照样本中各种免疫细胞浸润的比例。CLEC4M、PFKP 和 SCRIB 在元数据（受试者工作特征曲线下面积 [AUC] = 0.996、AUC = 1.000、AUC = 1.000）和 GSE146553 数据集（AUC = 0.983、AUC = 0.975、AUC = 0.892）中均被鉴定为 OC 的潜在诊断标志物。在免疫细胞浸润方面，OC 中滤泡辅助树突状细胞浸润增加，M2 巨噬细胞和中性粒细胞浸润减少，以及静息自然杀伤（NK）细胞和 T 细胞浸润减少。CLEC4M 与中性粒细胞（ = 0.57， < 0.001）和静息 NK 细胞（ = 0.42， = 0.0047）呈显著正相关，与活化的树突状细胞（ = -0.33， = 0.032）呈显著负相关。PFKP 与活化的 NK 细胞（ = 0.36， = 0.016）和滤泡辅助 T 细胞（ = 0.32， = 0.035）呈显著正相关，与幼稚 B 细胞（ = -0.3， = 0.049）和静息 NK 细胞（ = -0.41， = 0.007）呈显著负相关。SCRIB 与浆细胞（ = 0.39， = 0.01）、记忆 B 细胞（ = 0.34， = 0.025）和滤泡辅助 T 细胞（ = 0.31， = 0.04）呈显著正相关，与中性粒细胞（ = -0.46， = 0.002）和幼稚 B 细胞（ = -0.48， = 0.0012）呈显著负相关。CLEC4M、PFKP 和 SCRIB 被鉴定为 OC 的潜在诊断生物标志物，并通过验证。本工作和这三个生物标志物的鉴定可能为 OC 发病机制和治疗的进一步研究提供指导。
