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MUC4, MUC16, and TTN genes mutation correlated with prognosis, and predicted tumor mutation burden and immunotherapy efficacy in gastric cancer and pan‐cancer

机译:<斜体> muc4 ,<斜视> muc16 ,<斜视> ttn 基因突变与预后相关,预测肿瘤突变负担和胃癌和泛癌的免疫疗法疗效

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Dear Editor, Previous studies reported that MUC16 mutation was associated with better prognosis and higher tumor mutation burden (TMB) in gastric cancer, while TTN mutation was associated with better response to immune checkpoint blockage in solid tumors, but the potential mechanisms were still unclear. ~(1) , ~(2) Through the analysis in TCGA gastric adenocarcinoma cohort (N?=?443) and FUSCC gastric cancer cohort (N?=?177), we identified two mucin genes, MUC4 and MUC16 . These two mucin genes were selected based on mutational frequencies in gastric cancer, gene length, correlation with prognosis, and previous studies (Table S1, Figure S1). We further included the longest gene, TTN , into analysis, in consideration of its high mutation frequency and close correlation with TMB. We observed high potency MUC4 , MUC16 , and TTN had in predicting TMB in both TCGA and FUSCC cohort. MUC4 ‐, MUC16 ‐, and TTN ‐mutated cancer showed higher TMB (Figure? 1 ). Mutation numbers of MUC4 , MUC16 , and TTN were closely correlated with TMB (Table S2). In TCGA cohort, correlation coefficient reached the highest of 0.782 when combined mutation numbers of three genes together; while in FUSCC cohort, the correlation coefficient reached the highest of 0.748 for MUC16 plus TTN , and was 0.735 for three genes. The receiver operating characteristic (ROC) curve further proved the efficacy using mutation numbers to predict TMB (Figure? 1 , TMB high was defined as top 20% in each cohort, TCGA:?&20 mutations/Mb, FUSCC:?&8 mutations/Mb). In TCGA cohort, area under ROC curve (AUROC) reached the highest of 0.936 when combined three genes together; while in FUSCC cohort, AUROC reached the highest of 0.925 for MUC16 plus TTN , and was 0.915 for three genes together. The Youden index is shown in Tables S3 and S4. In both TCGA and FUSCC cohort, high TMB was correlated with better overall survival (OS) (Figure? 1 ). High mutation number of MUC4 , MUC16 , and TTN was correlated with better OS in TCGA cohort, while showing a trend of better OS in FUSCC cohort (Figure? 1 , high mutation number was defined as top 20% in each cohort). FIGURE 1 Mutation status and mutation number of MUC4 , MUC16 , and TTN predict TMB and prognosis in TCGA and FUSCC cohort. A and B, MUC4 , MUC16 , and TTN mutations showed higher TMB in TCGA and FUSCC cohort. C and D, ROC curve using gene mutation numbers to estimate TMB status in TCGA and FUSCC cohort. E and F, Kaplan‐Meier survival analysis stratified by high‐TMB and high mutation numbers in TCGA cohort. G and H, Kaplan‐Meier survival analysis stratified by high‐TMB and high mutation numbers in FUSCC cohort In order to realize whether mutation of MUC4 , MUC16 , and TTN gained functional change or just contributed to TMB, we further analyzed the gene mutation sites distribution (Figure S2). ~(3) , ~(4) Somatic mutations of MUC16 and TTN were sporadic in both TCGA and FUSCC cohort. Only MUC4 gene had a slightly high mutation rate of H4205Q in TCGA cohort (2.04%) and V3305_S3320del in FUSCC cohort (7.34%). The univariate and multivariate survival analysis showed H4205Q mutation was independently correlated with worse OS in TCGA cohort (Table S5, HR (95% CI): 2.266 (1.028‐4.994), P ?=?.043), while V3305_S3320del was independently correlated with better OS in FUSCC cohort (Table S6, HR (95% CI): 0.221 (0.053‐0.928), P ?=?.039). Of note, TMB and mutation numbers were not independent prognostic factors, which indicated them as marker for prognosis but not determining factors. We further clarified the potential mechanism why MUC4 and MUC16 mutation, high TMB and high mutation numbers were correlated with prognosis. In both TCGA and FUSCC cohort, patients with MUC4 mutation showed lower T stage, while MUC16 ‐mutated patients showed lower N stage. High TMB and high mutation numbers were correlated with lower N stage in TCGA cohort but lower T stage in FUSCC cohort (Table S7, P ?&?.05). In both cohorts, MUC4 , MUC16 , and TTN mutation showed alternations in cell signaling pathway, immune checkpoint expression, and immune cell infiltration (Figures S3–S6). High TMB cancers upregulated myc, cell cycle, metabolism, and DNA repair pathways; at the same time, they also showed upregulation of immune response pathway and high infiltration of CD8 T cells, CD4 T cells, macrophage 1, and macrophage 2 cells (Figure S7). These findings indicated that high TMB was accompanied with high genetic instability. Under this circumstance, oncogenes and metabolism genes had more opportunity generating mutations, but also more neoantigens were produced to stimulate immune response. ~(5) So the prognosis might be based on a comprehensive consideration of disease status and treatment. ~(6) High mutation numbers showed similar results on cell signaling pathways, immune cell infiltration, and immune checkpoint expression to TMB, indicating that gene mutation number might be the maker of TMB (Figure S7). We verified our hypothesis in
机译:亲爱的编辑,以前的研究报告说,MUC16突变与预后较好和胃癌高的肿瘤突变负担(TMB)相关,而TTN突变与实体瘤的免疫检查点堵塞更好的反应有关,但潜在的机制仍不清楚。 〜(1), - (2)通过在TCGA胃腺癌队列分析(N =?443)和FUSCC胃癌队列(N =?177),我们确定了两个粘蛋白基因,MUC4和MUC16。基于在胃癌,基因长度,与预后的关系,和先前的研究(表S1,图S1)突变频率选择这两种粘蛋白的基因。我们还具备最长的基因,TTN,为分析,考虑到其较高的突变频率,并与TMB密切相关。我们观察到高效力MUC4,MUC16,和TTN不得不在两个TCGA和FUSCC队列预测TMB。 MUC4 - ,MUC16 - ,和TTN -mutated癌症显示出更高的TMB(图1?)。 MUC4,MUC16,和TTN的突变数字与TMB(表S2)密切相关。在TCGA队列中,当组合突变号码的三个基因一起相关系数达到最高的0.782;而在FUSCC队列,相关系数达到了最高的0.748的MUC16加TTN,和三个基因是0.735。 ?接收器操作特性(ROC)曲线进一步证实使用突变编号,以预测TMB(图1中的功效,TMB高于各队列,TCGA定义为最高的20%产品:& 20个突变/ MB,FUSCC:&GT; 8个突变/ MB)。在TCGA队列中,当三个基因组合在一起ROC曲线(AUROC)下面积达到最高的0.936;而在FUSCC队列,AUROC达到最高的为0.925加MUC16 TTN,和三个基因为0.915在一起。约登指数示于表S3和S4。在这两种TCGA和FUSCC队列中,高TMB用更好的总体生存(OS)(图?1)相关。 MUC4,MUC16,和TTN的高突变数目与TCGA队列更好OS有相关性,同时表现出更好的OS在FUSCC队列的趋势(图?1,高突变数在各组群定义为顶部20%)。图1突变状态和MUC4,MUC16的突变数量和TTN预测TMB和预后TCGA和FUSCC队列。 A和B,MUC4,MUC16,和TTN突变表明在TCGA和FUSCC队列更高TMB。 C和d,采用基因突变数量估计在TCGA和FUSCC队列TMB状态ROC曲线。通过高TMB和TCGA队列高突变号分层E和F,Kaplan-Meier生存分析。通过高TMB和FUSCC队列高突变号分层为了G和H,Kaplan-Meier生存分析,以实现MUC4,MUC16,和TTN突变是否获得了功能性变化,或只是促成TMB,我们进一步分析了基因突变位点分布(图S2)。 MUC16的〜(3),〜(4)的体细胞突变和TTN分别在两个TCGA和FUSCC队列零星。只有MUC4基因TCGA队列(2.04%)和V3305_S3320del在FUSCC队列(7.34%)有H4205Q的稍微高突变率。单变量和多变量生存分析显示H4205Q突变在TCGA队列与较差的OS独立相关(表S5,HR(95%CI):2.266(1.028-4.994),P =?043),而V3305_S3320del独立与相关更好OS在FUSCC队列(表S6,HR(95%CI):0.221(0.053-0.928),P =?039?)。值得注意的是,TMB和变异的数字是不独立的预后因素,这表明他们作为标志物的预后,但不是决定性因素。我们进一步阐明为什么MUC4和MUC16突变,高TMB和高突变数均与预后相关的潜在机制。在这两种TCGA和FUSCC队列,患者MUC4突变显示出较低的T台上,而MUC16 -mutated患者表现低位的N阶段。高TMB和高突变数字与低位的N阶段中TCGA队列,但较低的T台上FUSCC队列有相关性(?LT表S7,P&; 0.05)。在这两个组群,MUC4,MUC16,并在细胞信号传导途径,免疫检查点表达,和免疫细胞浸润(图S3-S6)TTN突变表明交替。高TMB癌症中上调MYC,细胞周期,代谢和DNA修复途径;同时,它们还表现出免疫应答途径和CD8 T细胞,CD4 + T细胞的浸润高,巨噬细胞1和巨噬细胞2个细胞(图S7)的上调。这些结果表明,高TMB伴随高遗传不稳定性。在这种情况下,原癌基因和代谢的基因有更多的机会发生突变,但制作也比较新抗原刺激免疫反应。 〜(5)所以预后可能基于综合考虑疾病的地位和待遇。 〜(6)高突变数字显示对细胞的信号传导途径,免疫细胞浸润,和免疫检查点表达TMB类似的结果,这表明基因突变数目可能是TMB(图S7)的制造者。我们证实了我们的假设中

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