Bioinformatics-based Identification of Cuproptosis Genes Associated with Prognosis and Immune Microenvironment in Lung Cancer
-
摘要:
目的 通过生物信息学挖掘与肺癌患者预后和免疫微环境相关的铜死亡基因。 方法 本研究使用的肺癌数据集来源于TCGA数据库。铜死亡相关基因（cuproptosis-related gene，CRGs）获取于此前报道的文献。R软件Deseq2包鉴定肺癌组织中的差异表达基因（differentially expressed genes，DEGs）。取DEGs和CRGs的交集，以获取差异表达的铜死亡相关基因（differentially expressed CRGs，DE-CRGs）。COX分析和R软件rms包鉴定与肺癌患者预后相关的DE-CRGs。ESTIMATE和CIBERSORT算法识别DE-CRGs与免疫微环境的相关性。 结果 相较于正常组织，TCGA数据库的肺癌组织中共存在 5269 个DEGs，并与CRGs存在11个交集。11个DE-CRGs主要调控能量代谢、碳代谢和氨基酸代谢。DE-CRGs中，LIPT1是肺癌的独立风险因素，并且与临床特征（年龄、TNM分期和残留肿瘤）构建的列线图对肺癌患者生存的预测结果与患者的实际结局相近。LIPT1表达与M1和M2型巨噬细胞、活化的自然杀伤细胞细胞、CD8+ T细胞的浸润呈现正相关，与M0型巨噬细胞、激活的肥大细胞、中性粒细胞和Treg细胞显现显著的负相关。结论 LIPT1可作为肺癌的预后和免疫微环境相关的铜死亡基因，是肺癌治疗的新型生物标志物。 Abstract:Objective To Identify the cuproptosis genes associated with prognosis and immune microenvironment in lung cancer with the use of bioinformatics. Methods The lung cancer dataset used in this study was obtained from the TCGA database. The cuproptosis-related genes （CRGs） were obtained from the previously reported literature. The R software Deseq2 package was used to identify the differentially expressed genes （DEGs） in lung cancer. The intersection of DEGs and CRGs was taken to obtain the differentially expressed CRGs （DE-CRGs）. COX analysis and R software rms package were used to identify DE-CRGs associated with the prognosis of lung cancer. Estimate and Cibersort algorithms were applied to identify the correlation of DE-CRGs with the immune microenvironment. Results Compared to the normal tissues, a total of 5, 269 DEGs were present in lung cancer tissues in the TCGA database, and there were 11 shared genes between DEGs and CRGs. The 11 DE-CRGs mainly regulated the energy metabolism, carbon metabolism and amino acid metabolism. In the DE-CRGs, LIPT1 was an independent risk factor for lung cancer, and the column chart constructed with the clinical features （age, TNM staging, and residual tumor） predicted the survival of lung cancer patients in a manner similar to their actual outcomes. LIPT1 showed a positive correlation with the infiltration of M1 and M2 type macrophages, activated natural killer cells cells and CD8+ T cells, and showed a significant negative correlation with M0 type macrophages, activated mast cells, neutrophils and Treg cells. Conclusion LIPT1 may serve as a prognostic and immune microenvironment-associated cuproptosis gene, which is a novel biomarker for lung cancer therapy. -
Key words:
- Lung cancer /
- Bioinformatics /
- Cuproptosis /
- Prognosis /
- Immune microenvironment
-
图 1 肺癌中DE-CRGs的鉴定
A：火山图展示了TCGA-LUAD和TCGA-LUSC队列中DEGs的表达和差异性。每个点代表一个基因；红色和蓝色的点分别代表上调和下调DEGs；灰色的点代表表达没有显著差异性的基因；B：韦恩图展示了DEGs和CRGs的交集和并集数量。
Figure 1. Identification of DE-CRGs in lung cancer
图 2 肺癌中DE-CRGs的富集分析
A～B：气泡图展示了DE-CRGs的（A）GO和（B）KEGG富集分析的结果。点的大小代表富集到对应术语中的DE-CRGs数量；点的颜色代表校准的P值的大小，颜色越蓝代表校准的P值越小。
Figure 2. Enrichment analysis of DE-CRGs in lung cancer
图 3 肺癌中DE-CRGs相关PPI网络的构建
A：基于STING数据库和Cytoscape软件构建的DE-CRGs相关PPI网络。节点越大代表连接度越大；B：表格展示了每个节点的连接度、介数中心性和接近中心性。
Figure 3. Construction of DE-CRGS-related PPI networks in lung cancer
图 4 与肺癌患者预后相关的DE-CRGs鉴定
A：单因素和多因素COX分析识别与肺癌患者生存相关的DE-CRGs和临床特征；B：Log-rank检验鉴定LIPT1表达与患者生存可能的相关性；C：基于LIPT1、年龄、TNM分期和残留肿瘤构建的列线图，以预测肺癌患者的生存可能；D：列线图的校准曲线，以观察列线图的预后预测性能。
Figure 4. Identification of DE-CRGs associated with prognosis in patients with lung cancer
-
[1] Lahiri A,Maji A,Potdar P D,et al. Lung cancer immunotherapy: Progress,pitfalls,and promises[J]. Mol Cancer,2023,22(1):1-37. doi: 10.1186/s12943-023-01740-y [2] Sung H,Ferlay J,Siegel R L,et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin,2021,71(3):209-249. doi: 10.3322/caac.21660 [3] Nicholson A G,Tsao M S,Beasley M B,et al. The 2021 WHO classification of lung tumors: Impact of advances since 2015[J]. J Thorac Oncol,2022,17(3):362-387. doi: 10.1016/j.jtho.2021.11.003 [4] Ren X,Luo X,Wang F,et al. Recent advances in copper homeostasis-involved tumor theranostics[J]. Asian J Pharm Sci,2024,19(5):1-34. [5] Lutsenko S,Roy S,Tsvetkov P. Mammalian copper homeostasis: Physiologic roles and molecular mechanisms[J]. Physiol Rev,2025,105(1):441-491. doi: 10.1152/physrev.00011.2024 [6] Tsvetkov P,Coy S,Petrova B,et al. Copper induces cell death by targeting lipoylated TCA cycle proteins[J]. Science,2022,375(6586):1254-1261. doi: 10.1126/science.abf0529 [7] Huang Q,Tian R,Yu J,et al. Identification of PSMD11 as a novel cuproptosis- and immune-related prognostic biomarker promoting lung adenocarcinoma progression[J]. Cancer Med,2024,13(11):2-15. doi: 10.1002/cam4.7379 [8] Wu J,Fu G,Luo C,et al. Cuproptosis-related ceRNA axis triggers cell proliferation and cell cycle through CBX2 in lung adenocarcinoma[J]. BMC Pulm Med,2024,24(1):6-15. doi: 10.1186/s12890-024-02887-0 [9] Ouyang G,Wu Z,Liu Z,et al. Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning[J]. Front Immunol,2023,14(eCollection):1-16. doi: 10.3389/fimmu.2023.1251750 [10] Love M I,Huber W,Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[J]. Genome Biol,2014,15(12):2-21. doi: 10.1186/s13059-014-0550-8 [11] Ginestet C. ggplot2: Elegant graphics for data analysis[J]. Journal of the Royal Statistical Society Series A: Statistics in Society,2011,174(1):245-246. doi: 10.1111/j.1467-985X.2010.00676_9.x [12] Chen H,Boutros P C. VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R[J]. BMC Bioinformatics,2011,12:1-7. doi: 10.1186/1471-2105-12-35 [13] Wu T,Hu E,Xu S,et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data[J]. Innovation (Camb),2021,2(3):1-10. [14] Therneau T M, Grambsch P M. Modeling survival data: Extending the Cox model[M]. New York: Springer, 2000. [15] Eng K H,Schiller E,Morrell K. On representing the prognostic value of continuous gene expression biomarkers with the restricted mean survival curve[J]. Oncotarget,2015,6(34):36308-36318. doi: 10.18632/oncotarget.6121 [16] Newman A M,Liu C L,Green M R,et al. Robust enumeration of cell subsets from tissue expression profiles[J]. Nat Methods,2015,12(5):453-457. doi: 10.1038/nmeth.3337 [17] Yoshihara K,Shahmoradgoli M,Martínez E,et al. Inferring tumour purity and stromal and immune cell admixture from expression data[J]. Nat Commun,2013,4(eCollection):1-11. doi: 10.1038/ncomms3612 [18] Mirhadi S,Zhang W,Pham N A,et al. Mitochondrial aconitase ACO2 links iron homeostasis with tumorigenicity in non-small cell lung cancer[J]. Mol Cancer Res,2023,21(1):36-50. doi: 10.1158/1541-7786.MCR-22-0163 [19] Chen Q,Wang Y,Yang L,et al. PM2.5 promotes NSCLC carcinogenesis through translationally and transcriptionally activating DLAT-mediated glycolysis reprograming[J]. J Exp Clin Cancer Res,2022,41(1):1-21. doi: 10.1186/s13046-022-02437-8 [20] Cronan J E. Progress in the Enzymology of the mitochondrial diseases of lipoic acid requiring enzymes[J]. Front Genet,2020,11(eCollection):1-6. [21] Soreze Y,Boutron A,Habarou F,et al. Mutations in human lipoyltransferase gene LIPT1 cause a leigh disease with secondary deficiency for pyruvate and alpha-ketoglutarate dehydrogenase[J]. Orphanet J Rare Dis,2013,8(eCollection):2-9. doi: 10.1186/1750-1172-8-192 [22] Tort F,Ferrer-Cortès X,Thió M,et al. Mutations in the lipoyltransferase LIPT1 gene cause a fatal disease associated with a specific lipoylation defect of the 2-ketoacid dehydrogenase complexes[J]. Hum Mol Genet,2014,23(7):1907-1915. doi: 10.1093/hmg/ddt585 [23] Chen Y,Xu T,Xie F,et al. Evaluating the biological functions of the prognostic genes identified by the Pathology Atlas in bladder cancer[J]. Oncol Rep,2021,45(1):191-201. [24] Lv H,Liu X,Zeng X,et al. Comprehensive analysis of cuproptosis-related genes in immune infiltration and prognosis in melanoma[J]. Front Pharmacol,2022,13(eCollection):1-15. doi: 10.3389/fphar.2022.930041 [25] Yan C,Niu Y,Ma L,et al. System analysis based on the cuproptosis-related genes identifies LIPT1 as a novel therapy target for liver hepatocellular carcinoma[J]. J Transl Med,2022,20(1):1-18. doi: 10.1186/s12967-022-03630-1 -
下载:
点击查看大图
图(5)
计量
- 文章访问数: 38
- HTML全文浏览量: 14
- PDF下载量: 0
- 被引次数: 0
