Gong Xumeng, Hou Dong, Zhou Shengning, Tan Jianan, Zhong Guangyu, Yang Bing, Xie Lang, Han Fanghai, Zhong Lin
Department of Surgical Oncology, Yuebei People's Hospital, Shaoguan, Guangdong, China.
Department of Head-Neck and Breast Surgery, Yuebei People's Hospital of Shantou University, Shaoguan, Guangdong, China.
Front Oncol. 2023 May 18;13:1144775. doi: 10.3389/fonc.2023.1144775. eCollection 2023.
To explore the relationship between flavin-containing monooxygenases (FMOs) and peritoneal metastasis (PM) in gastric cancer (GC).
TIMER 2.0 was used to perform pan-cancer analysis and assess the correlation between the expression of FMOs and cancers. A dataset from The Cancer Genome Atlas (TCGA) was used to analyze the correlation between FMOs and clinicopathological features of GC. PM is well established as the most common mode of metastasis in GC. To further analyze the correlation between FMOs and PM of GC, a dataset was obtained from the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database. The results were validated by immunohistochemistry. The relationship between FMOs and PM of GC was explored, and a novel PM risk signature was constructed by least absolute shrinkage and selection operator (LASSO) regression analysis. The regression model's validity was tested by multisampling. A nomogram was established based on the model for predicting PM in GC patients. The mechanism of FMOs in GC patients presenting with PM was assessed by conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses in TCGA and GEO datasets. Finally, the potential relationship between FMOs and immunotherapy was analyzed.
The pan-cancer analysis in TCGA and GEO datasets showed that FMO1 was upregulated, while FMO2 and FMO4 were downregulated in GC. Moreover, FMO1 and FMO2 correlated positively with the T and N stage of GC in the TCGA dataset. FMO1 and FMO2 expression was a risk factor for GC (hazard ratio: 1.112 and 1.185). The overexpression of FMO1 was significantly correlated with worse disease-free-survival (DFS) and overall survival (OS). However, no relationship was found between FMO2 expression in GC and DFS and OS. PM was highly prevalent among GC patients and typically associated with a worse prognosis. FMO1 was highly expressed in GC with PM. FMO1 and FMO2 were positively correlated with PM in GC. We identified a 12-gene panel for predicting the PM risk signature by LASSO (Area Under Curve (AUC) = 0.948, 95%CI: 0.896-1.000). A 10-gene panel for PM prediction was identified (AUC = 0.932, 95%CI: 0.874-0.990), comprising FMO1 and FMO2. To establish a model for clinical application, a 7-gene panel was established (AUC = 0.927, 95% CI: 0.877-0.977) and successfully validated by multisampling. (AUC = 0.892, 95% CI: 0.878-0.906). GO and KEGG analyses suggest that FMO1 and FMO2 regulate the extracellular matrix and cell adhesion. FMO1 and FMO2 were positively correlated with the immune score of GC, and their expression was associated with the infiltration of immune cells.
PM in GC is strongly correlated with FMOs. Overall, FMO1 and FMO2 have huge prospects for application as novel diagnostic and therapeutic targets.
探讨含黄素单加氧酶（FMOs）与胃癌（GC）腹膜转移（PM）之间的关系。
使用TIMER 2.0进行泛癌分析，并评估FMOs表达与癌症之间的相关性。来自癌症基因组图谱（TCGA）的数据集用于分析FMOs与GC临床病理特征之间的相关性。PM是GC最常见的转移方式。为了进一步分析FMOs与GC的PM之间的相关性，从美国国立生物技术信息中心基因表达综合数据库（GEO）获取了一个数据集。结果通过免疫组织化学验证。探讨了FMOs与GC的PM之间的关系，并通过最小绝对收缩和选择算子（LASSO）回归分析构建了一种新的PM风险特征。通过多次抽样检验回归模型的有效性。基于该模型建立了一个列线图，用于预测GC患者的PM。通过在TCGA和GEO数据集中进行基因本体（GO）和京都基因与基因组百科全书（KEGG）分析，评估了FMOs在出现PM的GC患者中的作用机制。最后，分析了FMOs与免疫治疗之间的潜在关系。
TCGA和GEO数据集中的泛癌分析表明，GC中FMO1上调，而FMO2和FMO4下调。此外，在TCGA数据集中，FMO1和FMO2与GC的T和N分期呈正相关。FMO1和FMO2表达是GC的危险因素（风险比：1.112和1.185）。FMO1的过表达与无病生存期（DFS）和总生存期（OS）较差显著相关。然而，未发现GC中FMO2表达与DFS和OS之间存在关联。PM在GC患者中非常普遍，通常与较差的预后相关。FMO1在伴有PM的GC中高表达。FMO1和FMO2与GC中的PM呈正相关。我们通过LASSO确定了一个用于预测PM风险特征的12基因panel（曲线下面积（AUC）=0.948，95%CI：0.896 - 1.000）。确定了一个用于PM预测的10基因panel（AUC = 0.932，95%CI：0.874 - 0.990），包括FMO1和FMO2。为建立临床应用模型，建立了一个7基因panel（AUC = 0.927，95%CI：0.877 - 0.977）并通过多次抽样成功验证（AUC = 0.892，95%CI：0.878 - 0.906）。GO和KEGG分析表明，FMO1和FMO2调节细胞外基质和细胞黏附。FMO1和FMO2与GC的免疫评分呈正相关，且它们的表达与免疫细胞浸润有关。
GC中的PM与FMOs密切相关。总体而言，FMO1和FMO2作为新型诊断和治疗靶点具有巨大的应用前景。
