Experimental validation of machine learning identification of KDELR3 as a signature gene for osteoarthritis hypoxia

doi:10.12307/2024.078

Abstract

Abstract: BACKGROUND: Hypoxia is strongly associated with the development and progression of osteoarthritic chondrocyte injury, but the specific targets and regulatory mechanisms are unclear.
OBJECTIVE: A machine learning approach was used to identify KDEL(Lys-Asp-Glu-Leu) receptor 3 (KDELR3) as a characteristic gene for osteoarthritis hypoxia and immune infiltration analysis, to provide new ideas and methods for the treatment of osteoarthritis.
METHODS: The osteoarthritis-related datasets were downloaded from the GEO database and the GSEA website to obtain hypoxia-related genes. The osteoarthritis datasets were batch-corrected and immune infiltration analyzed using R language, and osteoarthritis hypoxia genes were extracted for differential analysis. Differentially expressed genes were analyzed for GO function and KEGG signaling pathway. Weighted correlation network analysis (WGCNA) and machine learning were also used to screen osteoarthritis hypoxia signature genes, and in vitro cellular experiments were performed to validate expression and correlate immune infiltration analysis using the datasets and qPCR.
RESULTS AND CONCLUSION: (1) 8 492 osteoarthritis genes were obtained by batch correction and principal component analysis, mainly strongly associated with immune cells such as Macrophages M2 and Mast cells resting; 200 hypoxia genes were also obtained, resulting in 41 osteoarthritis hypoxia differentially expressed genes. (2) GO analysis involved mainly biological processes such as response to nutrient levels and glucocorticoids; cellular components such as lysosomal lumen and Golgi lumen; and molecular functions such as 14-3-3 protein binding and DNA-binding transcriptional activator activity. (3) KEGG analysis of osteoarthritis hypoxia differentially expressed genes was associated with signaling pathways such as PI3K-Akt, FoxO, and microRNAs in cancer. (4) The characteristic gene KDELR3 was obtained after using WGCNA analysis and machine learning screening. (5) The gene expression of KDELR3 was found to be higher in the test group than in the control group in the synovium (P=0.014) but lower in the meniscus (P=0.024) after validation by gene microarray. (6) In vitro chondrocyte assay showed that the expression of KDELR3 was higher in cartilage than in the control group (P=0.005), while KDELR3 was closely associated with Macrophages M0 (P=0.014) and T cells follicular helper (P=0.014). Using a machine learning approach, we confirmed that KDELR3 can be used as a hypoxic signature gene for osteoarthritis and may intervene in osteoarthritis pathogenesis by improving hypoxia, expecting to provide a new direction for better treatment of osteoarthritis.

Key words: osteoarthritis, machine learning, hypoxia, signature gene, chondrocyte, biomarker, immune infiltration analysis

CLC Number:

Xu Wenfei, Ming Chunyu, Mei Qijie, Yuan Changshen, Guo Jinrong, Zeng Chao, Duan Kan. Experimental validation of machine learning identification of KDELR3 as a signature gene for osteoarthritis hypoxia[J]. Chinese Journal of Tissue Engineering Research, 2024, 28(21): 3431-3437.

Figures/Tables 8

References

[1] TANG X, WANG S, ZHAN S, et al. The prevalence of symptomatic knee osteoarthritis in china: results from the china health and retirement longitu dinal study. Arthritis Rheumatol. 2016;68(3):648-653.
[2] CHARLIER E, DEROYER C, CIREGIA F, et al. Chondrocyte dedifferentiation and osteoarthritis (OA). Bioc hem Pharmacol. 2019;165:49-65.
[3] LEE JW, KO J, JU C, et al. Hypoxia signaling in human diseases and therapeutic targets. Exp Mol Med. 2019;51(6):1-13.
[4] HENROTIN Y, KURZ B, AIGNER T. Oxygen and reactive oxygen species in cartilage degradation: friends or foes? Osteoarthritis Cartilage. 2005;13(8): 643-654.
[5] CAMACHO DM, COLLINS KM, POWERS RK, et al. Next-Generation Machine Learning for Biological Networks. Cell. 2018;173(7):1581-1592.
[6] JAMSHIDI A, PELLETIER JP, MARTEL-PELLETIER J. Machine-learning-based patient-specific prediction models for knee osteoarthritis. Nat Rev Rheumatol. 2019;15(1):49-60.
[7] LU K, WEI S, WANG Z, et al. Identification of novel biomarkers in Hunner’s interstitial cystitis using the CIBERSORT, an algorithm based on machine learning. BMC Urol. 2021;21(1):109.
[8] CHEN L, ZHANG YH, WANG S, et al. Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways. PLoS One. 2017;12(9):e0184129.
[9] KANEHISA M, FURUMICHI M, TANABE M, et al. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45(1): 353-361.
[10] LANGFELDER P, HORVATH S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.
[11] CHEN X, ISHWARAN H. Random forests for genomic data analysis. Genomics. 2012;99(6):323-329.
[12] ZOU J, HAN Y, SO SS. Overview of artificial neural networks. Methods Mol Biol. 2008;458:15-23.
[13] LI Z, SILLANPAA MJ. Overview of LASSO-related penalized regression methods for quantitative trait mapping and genomic selection. Theor Appl Genet. 2012;125(3):419-435.
[14] GAO J, KWAN PW, SHI D. Sparse kernel learning with LASSO and Bayesian inference algorithm. Neural Netw. 2010;23(2):257-264.
[15] CHEN D, SHEN J, ZHAO W, et al. Osteoarthritis: toward a comprehensive understanding of pathological mechanism. Bone Res. 2017;5:16044.
[16] OO WM. Prospects of disease-modifying osteoarthritis drugs. Clin Geriatr Med. 2022;38(2):397-432.
[17] WONG BW, MARSCH E, TREPS L, et al. Endothelial cell metabolism in health and disease: impact of hypoxia. EMBO J. 2017;36(15):2187-2203.
[18] PFANDER D, CRAMER T, SWOBODA B. Hypoxia and HIF-1alpha in osteoarthritis. Int Orthop. 2005;29(1):6-9.
[19] YUDOH K, NAKAMURA H, MASUKO-HONGO K, et al. Catabolic stress induces expression of hypoxia-inducible factor (HIF)-1 alpha in articular chondrocytes: involvement of HIF-1 alpha in the pathogenesis of osteoarthritis. Arthritis Res Ther. 2005;7(4):904-914.
[20] MARKWAY BD, CHO H, JOHNSTONE B. Hypoxia promotes redifferentiation and suppresses markers of hypertrophy and degeneration in both healthy and osteoarthritic chondrocytes. Arthritis Res Ther. 2013;15(4):92.
[21] WU CL, HARASYMOWICZ NS, KLIMAK MA, et al. The role of macrophages in osteoarthritis and cartilage repair. Osteoarthritis Cartilage. 2020;28(5): 544-554.
[22] ZHANG H, CAI D, BAI X. Macrophages regulate the progression of osteoarthritis. Osteoarthritis Cartilage. 2020;28(5):555-561.
[23] CARDAMONE C, PARENTE R, FEO GD, et al. Mast cells as effector cells of innate immunity and regulators of adaptive immunity. Immunol Lett. 2016;178:10-14.
[24] SAVVIDOU O, MILONAKI M, GOUMENOS S, et al. Glucocorticoid signaling and osteoarthritis. Mol Cell Endocrinol. 2019;480:153-166.
[25] MATSUZAKI T, ALVAREZ-GARCIA O, MOKUDA S, et al. FoxO transcription factors modulate autophagy and proteoglycan 4 in cartilage homeostasis and osteoarthritis. Sci Transl Med. 2018;10(428):eaan0746.
[26] LIN C, SHAO Y, ZENG C, et al. Blocking PI3K/AKT signaling inhibits bone sclerosis in subchondral bone and attenuates post-traumatic osteoarthritis. J Cell Physiol. 2018;233(8):6135-6147.
[27] TRYCHTA KA, BACK S, HENDERSON MJ, et al. KDEL receptors are differentially regulated to maintain the er proteome under calcium deficiency. Cell Rep. 2018;25(7):1829-1840.
[28] SCHWARZ DS, BLOWER MD. The endoplasmic reticulum: structure, function and response to cellular signaling. Cell Mol Life Sci. 2016;73(1):79-94.
[29] OAKES SA, PAPA FR. The role of endoplasmic reticulum stress in human pathology. Annu Rev Pathol. 2015;10:173-194.
[30] TAKADA K, HIROSE J, SENBA K, et al. Enhanced apoptotic and reduced protective response in chondrocytes following endoplasmic reticulum stress in osteoarthritic cartilage. Int J Exp Pathol. 2011;92(4):232-242.
[31] RELLMANN Y, EIDHOF E, DREIER R. Review: ER stress-induced cell death in osteoarthritic cartilage. Cell Signal. 2021;78:109880.
[32] KANG M, HUANG CC, LU Y, et al. Bone regeneration is mediated by macrophage extracellular vesicles. Bone. 2020;141:115627.
[33] SHAN Y, QI C, LIU Y, et al. Increased frequency of peripheral blood follicular helper T cells and elevated serum IL21 levels in patients with knee osteoarthritis. Mol Med Rep. 2017;15(3):1095-1102.
[34] RAGIPOGLU D, DUDECK A, HAFFNER-LUNTZER M, et al. The role of mast cells in bone metabolism and bone disorders. Front Immunol. 2020;11:163.