基于普通转录组和孟德尔随机分析探讨肾透明细胞癌中PANoptosis相关基因的诊断价值
Exploring the Diagnostic Value of PANoptosis-Related Genes in Kidney Renal Clear Cell Carcinoma Based on Common Transcriptome and Mendelian Randomization Analysis
摘要: 肾透明细胞癌(KIRC)是一种常见的泌尿生殖系统恶性肿瘤,但其早期发现和诊断相当困难。泛凋亡(PANoptosis)强调细胞焦亡、细胞凋亡和坏死之间的作用和协调。本研究的目的是通过孟德尔随机分析方法(MR),确定PANoptosis相关基因(PRG)在KIRC中的作用,并揭示其因果关系。通过对3446个差异表达基因(DEGs)和347个PRGs进行交叉分析,我们发现66个差异表达的PANoptosis相关基因(DE-PRGs)存在表达量性状位点(eQTL)。分别进行双样本单变量和多变量MR分析后发现,PRF1 (P = 0.030,OR 95% 置信区间[CI] = 1.599 [1.047~2.441])、TNFRSF10B (P = 0. 008, OR 95% CI = 1.238 [1.058~1.447])、PDGFRB (P = 0.012, OR 95% CI = 0.546 [0.340~0.876])基因与KIRC疾病有关,以上3个标记基因同时存在时,PDGFRB基因是一个直接因素。此外,基于生物信息学分析,建立了具有良好诊断价值的KIRC模型。最后,通过数据分析和实时荧光定量PCR (RT-qPCR)验证标记基因的表达。
Abstract: Kidney renal clear cell carcinoma (KIRC) is a common genitourinary malignancy, however, it is quite difficult to detect and diagnose at an early stage. PANoptosis emphasizes the role and coordination between pyroptosis, apoptosis and necroptosis. The aim of this study is to identify the role of PANoptosis-related genes (PRG) in KIRC and reveal their causal relationship by Mendelian randomization (MR) analysis. By intersecting 3446 differentially expressed genes (DEGs) and 347 PRGs obtained, we found that 66 differentially expressed-PANoptosis related genes (DE-PRGs) existed expression quantitative trait loci (eQTL). Two-sample univariate and multivariate MR analysis were performed respectively, and identified that causal relationships between PRF1 (P = 0.030, OR 95% confidence interval [CI] = 1.599 [1.047~2.441]), TNFRSF10B (P = 0.008, OR 95% CI = 1.238 [1.058~1.447]), PDGFRB (P = 0.012, OR 95% CI = 0.546 [0.340~0.876]) genes and KIRC disease, and the PDGFRB gene is a direct factor when the 3 marker genes were present simultaneously. Furthermore, model for KIRC with good diagnostic value were established based on bioinformatic analysis. Finally, the expression of marker genes was verified by data analysis and real-time fluorescence quantitative PCR (RT-qPCR).
文章引用:王智超, 仇梦真, 高兴华, 高相芹, 张龙洋. 基于普通转录组和孟德尔随机分析探讨肾透明细胞癌中PANoptosis相关基因的诊断价值[J]. 临床医学进展, 2024, 14(4): 156-173. https://doi.org/10.12677/acm.2024.1441001

参考文献

[1] Rini, B.I., Campbell, S.C. and Escudier, B. (2009) Renal Cell Carcinoma. The Lancet, 373, 1119-1132. [Google Scholar] [CrossRef
[2] Zhan, C., Wang, Z., Xu, C., Huang, X., Su, J., Chen, B., Wang, M., Qi, Z. and Bai, P. (2021) Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma. Frontiers in Molecular Biosciences, 8, Article 609865. [Google Scholar] [CrossRef] [PubMed]
[3] Li, Y., Gong, Y., Ning, X., Peng, D., Liu, L., He, S., Gong, K., Zhang, C., Li, X. and Zhou, L. (2018) Downregulation of CLDN7 Due to Promoter Hypermethylation Is Associated with Human Clear Cell Renal Cell Carcinoma Progression and Poor Prognosis. Journal of Experimental & Clinical Cancer Research, 37, Article No. 276. [Google Scholar] [CrossRef] [PubMed]
[4] Seront, E. and Machiels, J.P. (2015) Molecular Biology and Targeted Therapies for Urothelial Carcinoma. Cancer Treatment Reviews, 41, 341-353. [Google Scholar] [CrossRef] [PubMed]
[5] Bertheloot, D., Latz, E. and Franklin, B.S. (2021) Necroptosis, Pyroptosis and Apoptosis: An Intricate Game of Cell Death. Cellular & Molecular Immunology, 18, 1106-1121. [Google Scholar] [CrossRef] [PubMed]
[6] Wang, Y. and Kanneganti, T.D. (2021) From Pyroptosis, Apoptosis and Necroptosis to PANoptosis: A Mechanistic Compendium of Programmed Cell Death Pathways. Computational and Structural Biotechnology Journal, 19, 4641-4657. [Google Scholar] [CrossRef] [PubMed]
[7] Han, W., Xie, J., Li, L., Liu, Z. and Hu, X. (2009) Necrostatin-1 Reverts Shikonin-Induced Necroptosis to Apoptosis. Apoptosis, 14, 674-686. [Google Scholar] [CrossRef] [PubMed]
[8] Malireddi, R.K.S., Kesavardhana, S. and Kanneganti, T.D. (2019) ZBP1 and TAK1: Master Regulators of NLRP3 Inflammasome/Pyroptosis, Apoptosis, and Necroptosis (PAN-Optosis). Frontiers in Cellular and Infection Microbiology, 9, Article 406. [Google Scholar] [CrossRef] [PubMed]
[9] Samir, P., Malireddi, R.K.S. and Kanneganti, T.D. (2020) The PANoptosome: A Deadly Protein Complex Driving Pyroptosis, Apoptosis, and Necroptosis (PANoptosis). Frontiers in Cellular and Infection Microbiology, 10, Article 238. [Google Scholar] [CrossRef] [PubMed]
[10] Hanahan, D. (2022) Hallmarks of Cancer: New Dimensions. Cancer Discovery, 12, 31-46. [Google Scholar] [CrossRef
[11] Davey Smith, G. and Hemani, G. (2014) Mendelian Randomization: Genetic Anchors for Causal Inference in Epidemiological Studies. Human Molecular Genetics, 23, R89-R98. [Google Scholar] [CrossRef] [PubMed]
[12] Smith, G.D. and Ebrahim, S. (2002) Data Dredging, Bias, or Confounding. BMJ, 325, 1437-1438. [Google Scholar] [CrossRef] [PubMed]
[13] Lawlor, D.A., Harbord, R.M., Sterne, J.A., Timpson, N. and Davey Smith, G. (2008) Mendelian Randomization: Using Genes as Instruments for Making Causal Inferences in Epidemiology. Statistics in Medicine, 27, 1133-1163. [Google Scholar] [CrossRef] [PubMed]
[14] Burgess, S., Timpson, N.J., Ebrahim, S. and Davey Smith, G. (2015) Mendelian Randomization: Where Are We Now and Where Are We Going? International Journal of Epidemiology, 44, 379-388. [Google Scholar] [CrossRef] [PubMed]
[15] Willer, C.J., Schmidt, E.M., Sengupta, S., et al. (2013) Global Lipids Genetics Consortium. Discovery and Refinement of Loci Associated with Lipid Levels. Nature Genetics, 45, 1274-1283. [Google Scholar] [CrossRef] [PubMed]
[16] Burgess, S., Small, D.S. and Thompson, S.G. (2017) A Review of Instrumental Variable Estimators for Mendelian Randomization. Statistical Methods in Medical Research, 26, 2333-2355. [Google Scholar] [CrossRef] [PubMed]
[17] Barrett, T., Wilhite, S.E., Ledoux, P., et al. (2013) NCBI GEO: Archive for Functional Genomics Data Sets—Update. Nucleic Acids Research, 41, D991-D995. [Google Scholar] [CrossRef] [PubMed]
[18] Huo, J., Xie, W., Fan, X. and Sun, P. (2022) Pyroptosis, Apoptosis, and Necroptosis Molecular Subtype Derived Prognostic Signature Universal Applicable for Gastric Cancer—A Large Sample and Multicenter Retrospective Analysis. Computers in Biology and Medicine, 149, Article 106037. [Google Scholar] [CrossRef] [PubMed]
[19] Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W. and Smyth, G.K. (2015) Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies. Nucleic Acids Research, 43, e47. [Google Scholar] [CrossRef] [PubMed]
[20] Wu, T., Hu, E., Xu, S., et al. (2021) clusterProfiler 4.0: A Universal Enrichment Tool for Interpreting Omics Data. The Innovation, 2, Article 100141. [Google Scholar] [CrossRef] [PubMed]
[21] Porcu, E., Rüeger, S., Lepik, K., eQTLGen Consortium, BIOS Consortium, Santoni, F.A., Reymond, A. and Kutalik, Z. (2019) Mendelian Randomization Integrating GWAS and EQTL Data Reveals Genetic Determinants of Complex and Clinical Traits. Nature Communications, 10, Article No. 3300. [Google Scholar] [CrossRef] [PubMed]
[22] Lee, Y.H. (2019) Causal Association between Smoking Behavior and the Decreased Risk of Osteoarthritis: A Mendelian Randomization. Zeitschrift für Rheumatologie, 78, 461-466. [Google Scholar] [CrossRef] [PubMed]
[23] Harrell Jr., F.E. (2020) rms: Regression Modeling Strategies. R Package Version 6.0-1.
https://cran.r-project.org/package=rms
[24] Iasonos, A., Schrag, D., Raj, G.V. and Panageas, K.S. (2008) How to Build and Interpret a Nomogram for Cancer Prognosis. Journal of Clinical Oncology, 26, 1364-1370. [Google Scholar] [CrossRef
[25] Yu, S., Hu, C., Cai, L., et al. (2020) Seven-Gene Signature Based on Glycolysis Is Closely Related to the Prognosis and Tumor Immune Infiltration of Patients with Gastric Cancer. Frontiers in Oncology, 10, Article 1778. [Google Scholar] [CrossRef] [PubMed]
[26] Newman, A.M., Liu, C.L., Green, M.R., et al. (2015) Robust Enumeration of Cell Subsets from Tissue Expression Profiles. Nature Methods, 12, 453-457. [Google Scholar] [CrossRef] [PubMed]
[27] Ru, Y., Kechris, K.J., Tabakoff, B., et al. (2014) The multiMiR R Package and Database: Integration of MicroRNA-Target Interactions Along with Their Disease and Drug Associations. Nucleic Acids Research, 42, e133. [Google Scholar] [CrossRef] [PubMed]
[28] Ljungberg, B., Albiges, L., Abu-Ghanem, Y., et al. (2022) European Association of Urology Guidelines on Renal Cell Carcinoma: The 2022 Update. European Urology, 82, 399-410. [Google Scholar] [CrossRef] [PubMed]
[29] Lopes Vendrami, C., Parada Villavicencio, C., DeJulio, T.J., et al. (2017) Differentiation of Solid Renal Tumors with Multiparametric MR Imaging. Radiographics, 37, 2026-2042. [Google Scholar] [CrossRef] [PubMed]
[30] Rocco, F., Cozzi, L.A. and Cozzi, G. (2015) Study of the Renal Segmental Arterial Anatomy with Contrast-Enhanced Multi-Detector Computed Tomography. Surgical and Radiologic Anatomy, 37, 517-526. [Google Scholar] [CrossRef] [PubMed]
[31] Kim, J.H., Sun, H.Y., Hwang, J., et al. (2016) Diagnostic Accuracy of Contrast-Enhanced Computed Tomography and Contrast-Enhanced Magnetic Resonance Imaging of Small Renal Masses in Real Practice: Sensitivity and Specificity According to Subjective Radiologic Interpretation. World Journal of Surgical Oncology, 14, Article No. 260. [Google Scholar] [CrossRef] [PubMed]
[32] Tamara, J.I., Juan, G.R., Patricia, Z.J., et al. (2022) Diagnosis and Treatment of Small Renal Masses: Where Do We Stand? Current Urology Reports, 23, 99-111. [Google Scholar] [CrossRef] [PubMed]
[33] Huang, X.L., Khan, M.I., Wang, J., et al. (2021) Role of Receptor Tyrosine Kinases Mediated Signal Transduction Pathways in Tumor Growth and Angiogenesis—New Insight and Futuristic Vision. International Journal of Biological Macromolecules, 180, 739-752. [Google Scholar] [CrossRef] [PubMed]
[34] Zou, X., Tang, X.Y., Qu, Z.Y., et al. (2022) Targeting the PDGF/PDGFR Signaling Pathway for Cancer Therapy: A Review. International Journal of Biological Macromolecules, 202, 539-557. [Google Scholar] [CrossRef] [PubMed]
[35] Balamurugan, K., Koehler, L., Dürig, J.N., et al. (2021) Structural Insights into the Modulation of PDGF/PDGFR-β Complexation by Hyaluronan Derivatives. Biological Chemistry, 402, 1441-1452. [Google Scholar] [CrossRef] [PubMed]
[36] Bedke, J., Albiges, L., Capitanio, U., et al. (2021) The 2021 Updated European Association of Urology Guidelines on Renal Cell Carcinoma: Immune Checkpoint Inhibitor-Based Combination Therapies for Treatment-Naive Metastatic Clear-Cell Renal Cell Carcinoma Are Standard of Care. European Urology, 80, 393-397. [Google Scholar] [CrossRef] [PubMed]
[37] Bedke, J., Albiges, L., Capitanio, U., et al. (2021) Updated European Association of Urology Guidelines on Renal Cell Carcinoma: Nivolumab plus Cabozantinib Joins Immune Checkpoint Inhibition Combination Therapies for Treatment-Naïve Metastatic Clear-Cell Renal Cell Carcinoma. European Urology, 79, 339-342. [Google Scholar] [CrossRef] [PubMed]
[38] Cavalcanti, E., Ignazzi, A., De Michele, F. and Caruso, M.L. (2019) PDGFRα Expression as a Novel Therapeutic Marker in Well-Differentiated Neuroendocrine Tumors. Cancer Biology, 20, 423-430. [Google Scholar] [CrossRef] [PubMed]
[39] Primac, I., Maquoi, E., Blacher, S., et al. (2019) Stromal Integrin α11 Regulates PDGFR-β Signaling and Promotes Breast Cancer Progression. Journal of Clinical Investigation, 129, 4609-4628. [Google Scholar] [CrossRef
[40] Voskoboinik, I., Whisstock, J.C. and Trapani, J.A. (2015) Perforin and Granzymes: Function, Dysfunction and Human Pathology. Nature Reviews Immunology, 15, 388-400. [Google Scholar] [CrossRef] [PubMed]
[41] Law, R.H. et al. (2010) The Structural Basis for Membrane Binding and Pore Formation by Lymphocyte Perforin. Nature, 468, 447-451. [Google Scholar] [CrossRef] [PubMed]
[42] Hadders, M.A., Beringer, D.X. and Gros, P. (2007) Structure of C8α-MACPF Reveals Mechanism of Membrane Attack in Complement Immune Defense. Science, 317, 1552-1554. [Google Scholar] [CrossRef] [PubMed]
[43] Voskoboinik, I., Dunstone, M.A., Baran, K., Whisstock, J.C. and Trapani, J.A. (2010) Perforin: Structure, Function, and Role in Human Immunopathology. Immunological Reviews, 235, 35-54. [Google Scholar] [CrossRef] [PubMed]
[44] Yang, A., Wilson, N.S. and Ashkenazi, A. (2010) Proapoptotic DR4 and DR5 Signaling in Cancer Cells: Toward Clinical Translation. Current Opinion in Cell Biology, 22, 837-844. [Google Scholar] [CrossRef] [PubMed]
[45] Kischkel, F.C., Lawrence, D.A., Chuntharapai, A., Schow, P., Kim, K.J. and Ashkenazi, A. (2000) Apo2L/TRAIL-Dependent Recruitment of Endogenous FADD and Caspase-8 to Death Receptors 4 and 5. Immunity, 12, 611-620. [Google Scholar] [CrossRef
[46] Wilson, N.S., Dixit, V. and Ashkenazi, A. (2009) Death Receptor Signal Transducers: Nodes of Coordination in Immune Signaling Networks. Nature Immunology, 10, 348-355. [Google Scholar] [CrossRef] [PubMed]
[47] Ashkenazi, A. (2008) Directing Cancer Cells to Self-Destruct with Pro-Apoptotic Receptor Agonists. Nature Reviews Drug Discovery, 7, 1001-1012. [Google Scholar] [CrossRef] [PubMed]
[48] Breunig, C., Pahl, J., Küblbeck, M., et al. (2017) MicroRNA-519a-3p Mediates Apoptosis Resistance in Breast Cancer Cells and Their Escape from Recognition by Natural Killer Cells. Cell Death & Disease, 8, e2973. [Google Scholar] [CrossRef] [PubMed]
[49] An, Y., Jeon, J., Sun, L., et al. (2021) Death Agonist Antibody against TRAILR2/DR5/TNFRSF10B Enhances Birinapant Anti-Tumor Activity in HPV-Positive Head and Neck Squamous Cell Carcinomas. Scientific Reports, 11, Article No. 6392. [Google Scholar] [CrossRef] [PubMed]