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Abstract：
Pancreatic ductal adenocarcinoma (PDAC) has the highest mortality rate among all solid tumors. Tumorigenesis is promoted by the oncogene KRAS, and KRAS mutations are prevalent in patients with PDAC. Therefore, a comprehensive understanding of the interactions between KRAS mutations and PDAC may expediate the development of therapeutic strategies for reversing the progression of malignant tumors. Our study aims at establishing and validating a prediction model of KRAS mutations in patients with PDAC based on survival analysis and mRNA expression.
A total of 184 and 412 patients with PDAC from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC), respectively, were included in the study.
After tumor mutation profile and copy number variation (CNV) analyses, we established and validated a prediction model of KRAS mutations, based on survival analysis and mRNA expression, that contained seven genes: CSTF2, FAF2, KIF20B, AKR1A1, APOM, KRT6C, and CD70. We confirmed that the model has a good predictive ability for the prognosis of overall survival (OS) in patients with KRAS-mutated PDAC. Then, we analyzed differential biological pathways, especially the ferroptosis pathway, through principal component analysis, pathway enrichment analysis, Gene Ontology (GO) enrichment analysis, and gene set enrichment analysis (GSEA), with which patients were classified into low- or high-risk groups. Pathway enrichment results revealed enrichment in the cytokine-cytokine receptor interaction, metabolism of xenobiotics by cytochrome P450, and viral protein interaction with cytokine and cytokine receptor pathways. Most of the enriched pathways are metabolic pathways predominantly enriched by downregulated genes, suggesting numerous downregulated metabolic pathways in the high-risk group. Subsequent tumor immune infiltration analysis indicated that neutrophil infiltration, resting CD4 memory T cells, and resting natural killer (NK) cells correlated with the risk score. After verifying that the seven gene expression levels in different KRAS-mutated pancreatic cancer cell lines were similar to that in the model, we screened potential drugs related to the risk score.
This study established, analyzed, and validated a model for predicting the prognosis of PDAC based on risk stratification according to KRAS mutations, and identified differential pathways and highly effective drugs.
A total of 184 and 412 patients with PDAC from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC), respectively, were included in the study.
After tumor mutation profile and copy number variation (CNV) analyses, we established and validated a prediction model of KRAS mutations, based on survival analysis and mRNA expression, that contained seven genes: CSTF2, FAF2, KIF20B, AKR1A1, APOM, KRT6C, and CD70. We confirmed that the model has a good predictive ability for the prognosis of overall survival (OS) in patients with KRAS-mutated PDAC. Then, we analyzed differential biological pathways, especially the ferroptosis pathway, through principal component analysis, pathway enrichment analysis, Gene Ontology (GO) enrichment analysis, and gene set enrichment analysis (GSEA), with which patients were classified into low- or high-risk groups. Pathway enrichment results revealed enrichment in the cytokine-cytokine receptor interaction, metabolism of xenobiotics by cytochrome P450, and viral protein interaction with cytokine and cytokine receptor pathways. Most of the enriched pathways are metabolic pathways predominantly enriched by downregulated genes, suggesting numerous downregulated metabolic pathways in the high-risk group. Subsequent tumor immune infiltration analysis indicated that neutrophil infiltration, resting CD4 memory T cells, and resting natural killer (NK) cells correlated with the risk score. After verifying that the seven gene expression levels in different KRAS-mutated pancreatic cancer cell lines were similar to that in the model, we screened potential drugs related to the risk score.
This study established, analyzed, and validated a model for predicting the prognosis of PDAC based on risk stratification according to KRAS mutations, and identified differential pathways and highly effective drugs.
摘要：
■胰腺导管腺癌（PDAC）在所有实体瘤中死亡率最高。癌基因KRAS促进了肿瘤发生，和KRAS突变在PDAC患者中普遍存在。因此,全面了解KRAS突变与PDAC之间的相互作用,可能有助于开发逆转恶性肿瘤进展的治疗策略.我们的研究旨在建立和验证基于生存分析和mRNA表达的PDAC患者KRAS突变的预测模型。
■来自癌症基因组图谱（TCGA）数据库和国际癌症基因组联盟（ICGC）的共有184和412名PDAC患者，分别,包括在研究中。
■在肿瘤突变谱和拷贝数变异（CNV）分析后，我们建立并验证了KRAS突变的预测模型，基于生存分析和mRNA表达，包含七个基因：CSTF2，FAF2，KIF20B，AKR1A1,APOM,KRT6C,CD70我们证实，该模型对KRAS突变的PDAC患者的总体生存（OS）预后具有良好的预测能力。然后，我们分析了不同的生物学途径，尤其是铁性凋亡途径，通过主成分分析，途径富集分析，基因本体论（GO）富集分析，和基因集富集分析(GSEA)，将患者分为低危组或高危组。通路富集结果显示细胞因子-细胞因子受体相互作用富集,细胞色素P450和病毒蛋白与细胞因子和细胞因子受体途径相互作用的异源物质代谢。大多数富集途径是主要由下调基因富集的代谢途径，提示在高危人群中有许多下调的代谢途径。随后肿瘤免疫浸润分析显示中性粒细胞浸润，静息CD4记忆T细胞，静息自然杀伤（NK）细胞与风险评分相关。在验证了不同KRAS突变的胰腺癌细胞系中7种基因表达水平与模型相似后,我们筛选了与风险评分相关的潜在药物.
■这项研究建立了，分析,并验证了基于KRAS突变的危险分层预测PDAC预后的模型,并确定了不同的途径和高效的药物。
■来自癌症基因组图谱（TCGA）数据库和国际癌症基因组联盟（ICGC）的共有184和412名PDAC患者，分别,包括在研究中。
■在肿瘤突变谱和拷贝数变异（CNV）分析后，我们建立并验证了KRAS突变的预测模型，基于生存分析和mRNA表达，包含七个基因：CSTF2，FAF2，KIF20B，AKR1A1,APOM,KRT6C,CD70我们证实，该模型对KRAS突变的PDAC患者的总体生存（OS）预后具有良好的预测能力。然后，我们分析了不同的生物学途径，尤其是铁性凋亡途径，通过主成分分析，途径富集分析，基因本体论（GO）富集分析，和基因集富集分析(GSEA)，将患者分为低危组或高危组。通路富集结果显示细胞因子-细胞因子受体相互作用富集,细胞色素P450和病毒蛋白与细胞因子和细胞因子受体途径相互作用的异源物质代谢。大多数富集途径是主要由下调基因富集的代谢途径，提示在高危人群中有许多下调的代谢途径。随后肿瘤免疫浸润分析显示中性粒细胞浸润，静息CD4记忆T细胞，静息自然杀伤（NK）细胞与风险评分相关。在验证了不同KRAS突变的胰腺癌细胞系中7种基因表达水平与模型相似后,我们筛选了与风险评分相关的潜在药物.
■这项研究建立了，分析,并验证了基于KRAS突变的危险分层预测PDAC预后的模型,并确定了不同的途径和高效的药物。
