Peng Caijiao, Li Licheng, Luo Guangxia, Tan Shanmei, Xia Ruming, Zeng Lanjuan
Department of Gynecological Oncology, The Fourth Affiliated Hospital of Jishou University, Huaihua, China.
Department of Gynecological Oncology, the First People's Hospital of Huaihua, Huaihua, China.
Front Oncol. 2022 Aug 26;12:986885. doi: 10.3389/fonc.2022.986885. eCollection 2022.
M2 macrophages play an important role in cancer development. However, the underlying biological fator affecting M2 macrophages infiltration in ovarian cancer (OV) has not been elucidated.
R software v 4.0.0 was used for all the analysis. The expression profile and clinical information of OV patients enrolled in this study were all downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases.
The CIBERSORT algorithm was used to quantify the M2 macrophage infiltration in OV tissue, which was found a risk factor for patients survival. Based on the limma package, a total of 196 DEGs were identified between OV patients with high and low M2 macrophage infiltration, which were defined as M2 macrophages related genes. Finally, the genes PTGFR, LILRA2 and KCNA1 were identified for prognosis model construction, which showed a great prediction efficiency in both training and validation cohorts (Training cohort, 1-year AUC = 0.661, 3-year AUC = 0.682, 8-year AUC = 0.846; Validation cohort, 1-year AUC = 0.642, 3-year AUC = 0.716, 5-year AUC = 0.741). Clinical correlation showed that the riskscore was associated with the worse clinical features. Pathway enrichment analysis showed that in high risk patients, the pathway of epithelial-mesenchymal transition (EMT), TNF-α signaling NFKB, IL2/STAT5 signaling, apical junction, inflammatory response, KRAS signaling, myogenesis were activated. Moreover, we found that the PTGFR, LILRA2 and KCNA1 were all positively correlated with M2 macrophage infiltration and PTGFR was significantly associated with the pathway of autophagy regulation. Moreover, we found that the low risk patients might be more sensitive to cisplatin, while high risk patient might be more sensitive to axitinib, bexarotene, bortezomib, nilotinib, pazopanib.
In this study, we identified the genes associated with M2 macrophage infiltration and developed a model that could effectively predict the prognosis of OV patients.
M2巨噬细胞在癌症发展中起重要作用。然而，影响卵巢癌（OV）中M2巨噬细胞浸润的潜在生物学因素尚未阐明。
所有分析均使用R软件v 4.0.0。本研究纳入的OV患者的表达谱和临床信息均从癌症基因组图谱和基因表达综合数据库下载。
使用CIBERSORT算法量化OV组织中M2巨噬细胞浸润情况，发现其为患者生存的危险因素。基于limma软件包，在M2巨噬细胞浸润高和低的OV患者之间共鉴定出196个差异表达基因（DEGs），这些基因被定义为M2巨噬细胞相关基因。最后，鉴定出用于构建预后模型的基因PTGFR、LILRA2和KCNA1，其在训练和验证队列中均显示出良好的预测效率（训练队列，1年AUC = 0.661，3年AUC = 0.682，8年AUC = 0.846；验证队列，1年AUC = 0.642，3年AUC = 0.716，5年AUC = 0.741）。临床相关性分析表明，风险评分与较差的临床特征相关。通路富集分析表明，在高风险患者中，上皮-间质转化（EMT）、TNF-α信号通路-NFKB、IL2/STAT5信号通路、顶端连接、炎症反应、KRAS信号通路、肌生成等通路被激活。此外，我们发现PTGFR、LILRA2和KCNA全与M2巨噬细胞浸润呈正相关，且PTGFR与自噬调节通路显著相关。此外，我们发现低风险患者可能对顺铂更敏感，而高风险患者可能对阿西替尼、贝沙罗汀、硼替佐米、尼洛替尼、帕唑帕尼更敏感。
在本研究中，我们鉴定了与M2巨噬细胞浸润相关的基因，并开发了一种可有效预测OV患者预后的模型。
