Wu Riping, Li Qiaolian, Wu Fan, Shi Chunmei, Chen Qiang
Fujian Medical University Union Hospital, Fuzhou, Fujian Province, People's Republic of China.
Fujian Medical University, Fuzhou, Fujian Province, People's Republic of China.
Onco Targets Ther. 2020 Apr 21;13:3335-3346. doi: 10.2147/OTT.S244351. eCollection 2020.
The peritoneum is the most common metastatic site of gastric cancer and is associated with a dismal prognosis. However, there is no reliable biomarker for predicting peritoneal metastasis (PM).
Whole-exome sequencing (WES) was performed on formalin-fixed, paraffin-embedded (FFPE) samples from 63 patients with stage I-III gastric cancer and circulating tumor DNA (ctDNA) samples from 10 patients with stage IV gastric cancer. Differentially expressed genes (DEGs) were identified between the PM and non-PM groups and analyzed by multiple bioinformatics analyses. Univariate and multivariate Cox regression analyses were used to identify the risk factors for PM and a risk score model was developed.
The number of mutant genes and the tumor mutation burden (TMB) in the PM group were higher than those in the non-PM group (p < 0.05). There was a significant positive correlation between the number of mutant genes and the TMB (R = 0.9997). The risk of PM was significantly higher in the high TMB group than in the low TMB group (p = 0.045). Forty-nine DEGs were identified as associated with PM in gastric cancer. CDC27 mutations were associated with a higher risk for PM and poor survival. The CDC27 mutations were located in the Apc3 region, the TPR region, and the phosphorylation region, and new mutation sites were not included in the TCGA database. Multivariable Cox regression analysis demonstrated that pathological T stage, poor tumor differentiation, Borrmann type, and CDC27 mutations were independent predictive factors of PM. A risk score model was constructed that demonstrated good performance.
Through WES, we identified 49 DEGs relevant to PM in gastric cancer. CDC27 mutations were independently associated with PM by statistical and bioinformatics analyses. A risk score model was built and was demonstrated to effectively discriminate gastric cancer patients with and without PM.
腹膜是胃癌最常见的转移部位,且与预后不良相关。然而,目前尚无可靠的生物标志物可用于预测腹膜转移(PM)。
对63例I - III期胃癌患者的福尔马林固定石蜡包埋(FFPE)样本以及10例IV期胃癌患者的循环肿瘤DNA(ctDNA)样本进行全外显子测序(WES)。在PM组和非PM组之间鉴定差异表达基因(DEG),并通过多种生物信息学分析进行分析。采用单因素和多因素Cox回归分析确定PM的危险因素,并建立风险评分模型。
PM组的突变基因数量和肿瘤突变负荷(TMB)高于非PM组(p < 0.05)。突变基因数量与TMB之间存在显著正相关(R = 0.9997)。高TMB组的PM风险显著高于低TMB组(p = 0.045)。49个DEG被鉴定为与胃癌中的PM相关。CDC27突变与更高的PM风险和较差的生存率相关。CDC27突变位于Apc3区域、TPR区域和磷酸化区域,且新的突变位点未包含在TCGA数据库中。多变量Cox回归分析表明,病理T分期、肿瘤低分化、Borrmann分型和CDC27突变是PM的独立预测因素。构建了一个表现良好的风险评分模型。
通过WES,我们鉴定出49个与胃癌PM相关的DEG。通过统计和生物信息学分析,CDC27突变与PM独立相关。建立了一个风险评分模型,并证明其能有效区分有和没有PM的胃癌患者。