Hao Jie, Zhou Cancan, Wang Zheng, Ma Zhenhua, Wu Zheng, Lv Yi, Wu Rongqian
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Institute of Advanced Surgical Technology and Engineering, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Front Genet. 2023 Jun 2;14:1084275. doi: 10.3389/fgene.2023.1084275. eCollection 2023.
Pancreatic cancer is an aggressive tumor with a low 5-year survival rate and primary resistance to most therapy. Amino acid (AA) metabolism is highly correlated with tumor growth, crucial to the aggressive biological behavior of pancreatic cancer; nevertheless, the comprehensive predictive significance of genes that regulate AA metabolism in pancreatic cancer remains unknown. The mRNA expression data downloaded from The Cancer Genome Atlas (TCGA) were derived as the training cohort, and the GSE57495 cohort from Gene Expression Omnibus (GEO) database was applied as the validation cohort. Random survival forest (RSF) and the least absolute shrinkage and selection operator (LASSO) regression analysis were employed to screen genes and construct an AA metabolism-related risk signature (AMRS). Kaplan-Meier analysis and receiver operating characteristic (ROC) curve were performed to assess the prognostic value of AMRS. We performed genomic alteration analysis and explored the difference in tumor microenvironment (TME) landscape associated with KRAS and TP53 mutation in both high- and low-AMRS groups. Subsequently, the relationships between AMRS and immunotherapy and chemotherapy sensitivity were evaluated. A 17-gene AA metabolism-related risk model in the TCGA cohort was constructed according to RSF and LASSO. After stratifying patients into high- and low-AMRS groups based on the optimal cut-off value, we found that high-AMRS patients had worse overall survival (OS) in the training cohort (a median OS: 13.1 months vs. 50.1 months, < 0.0001) and validation cohort (a median OS: 16.2 vs. 30.5 months, p = 1e-04). Genetic mutation analysis revealed that KRAS and TP53 were significantly more mutated in high-AMRS group, and patients with KRAS and TP53 alterations had significantly higher risk scores than those without. Based on the analysis of TME, low-AMRS group displayed significantly higher immune score and more enrichment of T Cell CD8 cells. In addition, high-AMRS-group exhibited higher TMB and significantly lower tumor immune dysfunction and exclusion (TIDE) score and T Cells dysfunction score, which suggested a higher sensitive to immunotherapy. Moreover, high-AMRS group was also more sensitive to paclitaxel, cisplatin, and docetaxel. Overall, we constructed an AA-metabolism prognostic model, which provided a powerful prognostic predictor for the clinical treatment of pancreatic cancer.
胰腺癌是一种侵袭性肿瘤，5年生存率低，对大多数治疗具有原发性耐药性。氨基酸（AA）代谢与肿瘤生长高度相关，对胰腺癌的侵袭性生物学行为至关重要；然而，调节AA代谢的基因在胰腺癌中的综合预测意义仍不清楚。从癌症基因组图谱（TCGA）下载的mRNA表达数据作为训练队列，来自基因表达综合数据库（GEO）的GSE57495队列作为验证队列。采用随机生存森林（RSF）和最小绝对收缩和选择算子（LASSO）回归分析来筛选基因并构建AA代谢相关风险特征（AMRS）。进行Kaplan-Meier分析和受试者工作特征（ROC）曲线以评估AMRS的预后价值。我们进行了基因组改变分析，并探讨了高AMRS组和低AMRS组中与KRAS和TP53突变相关的肿瘤微环境（TME）景观差异。随后，评估了AMRS与免疫治疗和化疗敏感性之间的关系。根据RSF和LASSO在TCGA队列中构建了一个17基因的AA代谢相关风险模型。根据最佳临界值将患者分为高AMRS组和低AMRS组后，我们发现高AMRS组患者在训练队列中的总生存期（OS）较差（中位OS：13.1个月对50.1个月，< 0.0001），在验证队列中也是如此（中位OS：16.2对30.5个月，p = 1e - 04）。基因突变分析显示，高AMRS组中KRAS和TP53的突变明显更多，有KRAS和TP53改变的患者的风险评分明显高于没有改变的患者。基于TME分析，低AMRS组显示出明显更高的免疫评分和更多的T细胞CD8细胞富集。此外，高AMRS组表现出更高的肿瘤突变负荷（TMB）以及明显更低的肿瘤免疫功能障碍和排除（TIDE）评分和T细胞功能障碍评分，这表明对免疫治疗更敏感。此外，高AMRS组对紫杉醇、顺铂和多西他赛也更敏感。总体而言，我们构建了一个AA代谢预后模型，为胰腺癌的临床治疗提供了一个强大的预后预测指标。
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