Abstract：
OBJECTIVE: The prognosis of colorectal cancer (CRC) is related to natural killer (NK) cells, but the molecular subtype features of CRC based on NK cells are still unknown. This study aimed to identify NK cell-related molecular subtypes of CRC and analyze the survival status and immune landscape of patients with different subtypes.
METHODS: mRNA expression data, single nucleotide variant (SNV) data, and clinical information of CRC patients were obtained from The Cancer Genome Atlas. Differentially expressed genes (DEGs) were obtained through differential analysis, and the intersection was taken with NK cell-associated genes to obtain 103 NK cell-associated CRC DEGs (NCDEGs). Based on NCDEGs, CRC samples were divided into three clusters through unsupervised clustering analysis. Survival analysis, immune analysis, Gene Set Enrichment Analysis (GSEA), and tumor mutation burden (TMB) analysis were performed. Finally, NCDEG-related small-molecule drugs were screened using the CMap database.
RESULTS: Survival analysis revealed that cluster2 had a lower survival rate than cluster1 and cluster3 (p < 0.05). Immune infiltration analysis found that the immune infiltration levels and immune checkpoint expression levels of cluster1_3 were substantially higher than those of cluster2, and the tumor purity was the opposite (p < 0.05). GSEA presented that cluster1_3 was significantly enriched in the chemokine signaling pathway, ECM receptor interaction, and antigen processing and presentation pathways (p < 0.05). The TMB of cluster1_3 was significantly higher than that of cluster2 (p < 0.05). Genes with the highest mutation rate in CRC were APC, TP53, TTN, and KRAS. Drug prediction results showed that small-molecule drugs that reverse the upregulation of NCDEGs, deoxycholic acid, dipivefrine, phenformin, and other drugs may improve the prognosis of CRC.
CONCLUSIONS: NK cell-associated CRC subtypes can be used to evaluate the tumor characteristics of CRC patients and provide an important reference for CRC patients.
METHODS: mRNA expression data, single nucleotide variant (SNV) data, and clinical information of CRC patients were obtained from The Cancer Genome Atlas. Differentially expressed genes (DEGs) were obtained through differential analysis, and the intersection was taken with NK cell-associated genes to obtain 103 NK cell-associated CRC DEGs (NCDEGs). Based on NCDEGs, CRC samples were divided into three clusters through unsupervised clustering analysis. Survival analysis, immune analysis, Gene Set Enrichment Analysis (GSEA), and tumor mutation burden (TMB) analysis were performed. Finally, NCDEG-related small-molecule drugs were screened using the CMap database.
RESULTS: Survival analysis revealed that cluster2 had a lower survival rate than cluster1 and cluster3 (p < 0.05). Immune infiltration analysis found that the immune infiltration levels and immune checkpoint expression levels of cluster1_3 were substantially higher than those of cluster2, and the tumor purity was the opposite (p < 0.05). GSEA presented that cluster1_3 was significantly enriched in the chemokine signaling pathway, ECM receptor interaction, and antigen processing and presentation pathways (p < 0.05). The TMB of cluster1_3 was significantly higher than that of cluster2 (p < 0.05). Genes with the highest mutation rate in CRC were APC, TP53, TTN, and KRAS. Drug prediction results showed that small-molecule drugs that reverse the upregulation of NCDEGs, deoxycholic acid, dipivefrine, phenformin, and other drugs may improve the prognosis of CRC.
CONCLUSIONS: NK cell-associated CRC subtypes can be used to evaluate the tumor characteristics of CRC patients and provide an important reference for CRC patients.
摘要：
目的：结直肠癌（CRC）的预后与自然杀伤（NK）细胞有关，但是基于NK细胞的CRC的分子亚型特征仍然未知。本研究旨在鉴定NK细胞相关分子亚型,分析不同亚型CRC患者的生存状况和免疫状况。
方法：mRNA表达数据，单核苷酸变异（SNV）数据，CRC患者的临床信息来自癌症基因组图谱。通过差异分析获得差异表达基因(DEGs),并与NK细胞相关基因相交，获得103个NK细胞相关CRCDEGs（NCDEGs）。基于NCDEG,通过无监督聚类分析将CRC样本分为三个簇。生存分析,免疫分析,基因集富集分析（GSEA），和肿瘤突变负荷（TMB）分析。最后,使用CMap数据库筛选NCDEG相关小分子药物。
结果：生存分析显示，Cluster2的生存率低于Cluster1和Cluster3（p<0.05）。免疫浸润分析发现，Cluster1_3的免疫浸润水平和免疫检查点表达水平明显高于Cluster2，肿瘤纯度相反（p<0.05）。GSEA表明，Cluster1_3在趋化因子信号通路中显著富集，ECM受体相互作用，以及抗原加工和呈递途径(p<0.05)。簇1_3的TMB显著高于簇2(p<0.05)。CRC中突变率最高的基因是APC，TP53,TTN,还有KRAS.药物预测结果表明,逆转NCDEGs上调的小分子药物,脱氧胆酸,Dipivefrine,苯乙双胍,其他药物可能改善CRC的预后。
结论：NK细胞相关亚型可用于评估CRC患者的肿瘤特征，为CRC患者提供重要参考。
方法：mRNA表达数据，单核苷酸变异（SNV）数据，CRC患者的临床信息来自癌症基因组图谱。通过差异分析获得差异表达基因(DEGs),并与NK细胞相关基因相交，获得103个NK细胞相关CRCDEGs（NCDEGs）。基于NCDEG,通过无监督聚类分析将CRC样本分为三个簇。生存分析,免疫分析,基因集富集分析（GSEA），和肿瘤突变负荷（TMB）分析。最后,使用CMap数据库筛选NCDEG相关小分子药物。
结果：生存分析显示，Cluster2的生存率低于Cluster1和Cluster3（p<0.05）。免疫浸润分析发现，Cluster1_3的免疫浸润水平和免疫检查点表达水平明显高于Cluster2，肿瘤纯度相反（p<0.05）。GSEA表明，Cluster1_3在趋化因子信号通路中显著富集，ECM受体相互作用，以及抗原加工和呈递途径(p<0.05)。簇1_3的TMB显著高于簇2(p<0.05)。CRC中突变率最高的基因是APC，TP53,TTN,还有KRAS.药物预测结果表明,逆转NCDEGs上调的小分子药物,脱氧胆酸,Dipivefrine,苯乙双胍,其他药物可能改善CRC的预后。
结论：NK细胞相关亚型可用于评估CRC患者的肿瘤特征，为CRC患者提供重要参考。
