Identification and Analysis of Gene Biomarkers for Ovarian Cancer.

IF 1.1 4区 生物学 Q4 GENETICS & HEREDITY Genetic testing and molecular biomarkers Pub Date : 2024-02-01 DOI:10.1089/gtmb.2023.0222
Xiaodan Wang, Chengmao Xie, Chang Lu
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Abstract

Objective: To identify potential diagnostic markers for ovarian cancer (OC) and explore the contribution of immune cells infiltration to the pathogenesis of OC. Methods: 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 n = 40, control n = 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. Results: 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 (r = 0.57, p < 0.001) and resting NK cells (r = 0.42, p = 0.0047), but a negative correlation with activated dendritic cells (r = -0.33, p = 0.032). PFKP displayed a significantly positive correlation with activated NK cells (r = 0.36, p = 0.016) and follicular helper T cells (r = 0.32, p = 0.035), but a negative correlation with the naive B cells (r = -0.3, p = 0.049) and resting NK cells (r = -0.41, p = 0.007). SCRIB demonstrated a significantly positive correlation with plasma cells (r = 0.39, p = 0.01), memory B cells (r = 0.34, p = 0.025), and follicular helper T cells (r = 0.31, p = 0.04), but a negative correlation with neutrophils (r = -0.46, p = 0.002) and naive B cells (r = -0.48, p = 0.0012). Conclusion: 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.

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卵巢癌基因生物标记物的鉴定与分析
目的确定卵巢癌(OC)的潜在诊断标志物,并探讨免疫细胞浸润对卵巢癌发病机制的影响。研究方法以基因表达总库(Gene Expression Omnibus,GEO)数据库中的两个人类卵巢癌基因表达数据集(GSE27651和GSE26712,作为元数据)为研究对象,合并了228个卵巢癌样本和16个对照样本。研究人员利用递归特征消除算法和最小绝对收缩及选择算子回归法进行支持向量机分析,以确定可鉴别 OC 的候选生物标记物。此外,还进行了免疫组化染色,以验证候选生物标志物的诊断价值和蛋白表达水平。GSE146553 数据集(OC n = 40,对照 n = 3)用于进一步验证这些生物标志物的诊断价值。此外,还使用 CIBERSORT 算法评估了 OC 和对照样本中各种免疫细胞浸润的比例。结果显示在元数据(接收者操作特征曲线下面积 [AUC] = 0.996、AUC = 1.000、AUC = 1.000)和 GSE146553 数据集(AUC = 0.983、AUC = 0.975、AUC = 0.892)中,CLEC4M、PFKP 和 SCRIB 被确定为 OC 的潜在诊断标志物。在免疫细胞浸润方面,OC 中的滤泡辅助树突状细胞浸润增加,M2 巨噬细胞和中性粒细胞以及活化的自然杀伤(NK)细胞和 T 细胞浸润减少。CLEC4M 与中性粒细胞呈显著正相关(r = 0.57,p r = 0.42,p = 0.0047),但与活化树突状细胞呈负相关(r = -0.33,p = 0.032)。PFKP 与活化的 NK 细胞(r = 0.36,p = 0.016)和滤泡辅助性 T 细胞(r = 0.32,p = 0.035)呈显著正相关,但与幼稚 B 细胞(r = -0.3,p = 0.049)和静息 NK 细胞(r = -0.41,p = 0.007)呈负相关。SCRIB 与浆细胞(r = 0.39,p = 0.01)、记忆 B 细胞(r = 0.34,p = 0.025)和滤泡辅助 T 细胞(r = 0.31,p = 0.04)呈显著正相关,但与中性粒细胞(r = -0.46,p = 0.002)和幼稚 B 细胞(r = -0.48,p = 0.0012)呈负相关。结论CLEC4M、PFKP 和 SCRIB 被鉴定并验证为 OC 的潜在诊断生物标志物。这项工作和这三个生物标志物的鉴定可为今后研究 OC 的机制和治疗提供指导。
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来源期刊
CiteScore
2.50
自引率
7.10%
发文量
63
审稿时长
1 months
期刊介绍: Genetic Testing and Molecular Biomarkers is the leading peer-reviewed journal covering all aspects of human genetic testing including molecular biomarkers. The Journal provides a forum for the development of new technology; the application of testing to decision making in an increasingly varied set of clinical situations; ethical, legal, social, and economic aspects of genetic testing; and issues concerning effective genetic counseling. This is the definitive resource for researchers, clinicians, and scientists who develop, perform, and interpret genetic tests and their results. Genetic Testing and Molecular Biomarkers coverage includes: -Diagnosis across the life span- Risk assessment- Carrier detection in individuals, couples, and populations- Novel methods and new instrumentation for genetic testing- Results of molecular, biochemical, and cytogenetic testing- Genetic counseling
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