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Genetic correlations, shared risk genes and immunity landscapes between COVID-19 and venous thromboembolism: evidence from GWAS and bulk transcriptome data

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Abstract

Background

Patients with coronavirus disease 2019 (COVID-19) were vulnerable to venous thromboembolism (VTE), which further increases the risk of unfavorable outcomes. However, neither genetic correlations nor shared genes underlying COVID-19 and VTE are well understood.

Objective

This study aimed to characterize genetic correlations and common pathogenic mechanisms between COVID-19 and VTE.

Methods

We used linkage disequilibrium score (LDSC) regression and Mendelian Randomization (MR) analysis to investigate the genetic associations and causal effects between COVID-19 and VTE, respectively. Then, the COVID-19 and VTE-related datasets were obtained from the Gene Expression Omnibus (GEO) database and analyzed by bioinformatics and systems biology approaches with R software, including weighted gene co-expression network analysis (WGCNA), enrichment analysis, and single-cell transcriptome sequencing analysis. The miRNA–genes and transcription factor (TF)–genes interaction networks were conducted by NetworkAnalyst. We performed the secondary analysis of the ATAC-seq and Chip-seq datasets to address the epigenetic-regulating relationship of the shared genes.

Results

This study demonstrated positive correlations between VTE and COVID-19 by LDSC and bidirectional MR analysis. A total of 26 potential shared genes were discovered from the COVID-19 dataset (GSE196822) and the VTE dataset (GSE19151), with 19 genes showing positive associations and 7 genes exhibiting negative associations with these diseases. After incorporating two additional datasets, GSE164805 (COVID-19) and GSE48000 (VTE), two hub genes TP53I3 and SLPI were identified and showed up-regulation and diagnostic capabilities in both illnesses. Furthermore, this study illustrated the landscapes of immune processes in COVID-19 and VTE, revealing the downregulation in effector memory CD8+ T cells and activated B cells. The single-cell sequencing analysis suggested that the hub genes were predominantly expressed in the monocytes of COVID-19 patients at high levels. Additionally, we identified common regulators of hub genes, including five miRNAs (miR-1-3p, miR-203a-3p, miR-210-3p, miR-603, and miR-124-3p) and one transcription factor (RELA).

Conclusions

Collectively, our results highlighted the significant correlations between COVID-19 and VTE and pinpointed TP53I3 and SLPI as hub genes that potentially link the severity of both conditions. The hub genes and their common regulators might present an opportunity for the simultaneous treatment of these two diseases.

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Availability of data and materials

The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding authors.

References

  1. Zhang JJ, Dong X, Liu GH, Gao YD. Risk and protective factors for COVID-19 morbidity, severity, and mortality. Clin Rev Allergy Immunol. 2023;64:90–107.

    Article  CAS  PubMed  Google Scholar 

  2. Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC. Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA. 2020;324:782–93.

    Article  CAS  PubMed  Google Scholar 

  3. Clerkin KJ, Fried JA, Raikhelkar J, Sayer G, Griffin JM, Masoumi A, et al. COVID-19 and cardiovascular disease. Circulation. 2020;141:1648–55.

    Article  CAS  PubMed  Google Scholar 

  4. Chung MK, Zidar DA, Bristow MR, Cameron SJ, Chan T, Harding CV 3rd, et al. COVID-19 and cardiovascular disease: from bench to bedside. Circ Res. 2021;128:1214–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Naess IA, Christiansen SC, Romundstad P, Cannegieter SC, Rosendaal FR, Hammerstrøm J. Incidence and mortality of venous thrombosis: a population-based study. J Thromb Haemost. 2007;5:692–9.

    Article  CAS  PubMed  Google Scholar 

  6. Jiménez D, García-Sanchez A, Rali P, Muriel A, Bikdeli B, Ruiz-Artacho P, et al. Incidence of VTE and bleeding among hospitalized patients with coronavirus disease 2019: a systematic review and meta-analysis. Chest. 2021;159:1182–96.

    Article  PubMed  Google Scholar 

  7. Fernández-Capitán C, Barba R, Díaz-Pedroche MDC, Sigüenza P, Demelo-Rodriguez P, Siniscalchi C, et al. Presenting characteristics, treatment patterns, and outcomes among patients with venous thromboembolism during hospitalization for COVID-19. Semin Thromb Hemost. 2021;47:351–61.

    Article  PubMed  Google Scholar 

  8. Moores LK, Tritschler T, Brosnahan S, Carrier M, Collen JF, Doerschug K, et al. Prevention, diagnosis, and treatment of VTE in patients with coronavirus disease 2019: chest guideline and expert panel report. Chest. 2020;158:1143–63.

    Article  CAS  PubMed  Google Scholar 

  9. Poissy J, Goutay J, Caplan M, Parmentier E, Duburcq T, Lassalle F, et al. Pulmonary embolism in patients with COVID-19: awareness of an increased prevalence. Circulation. 2020;142:184–6.

    Article  CAS  PubMed  Google Scholar 

  10. Filippi L, Turcato G, Milan M, Barbar S, Miozzo E, Zaboli A, et al. Long term follow-up of a multicentre cohort of COVID-19 patients with pulmonary embolism: anticoagulation management and outcomes. Thromb Res. 2023;229:73–6.

    Article  CAS  PubMed  Google Scholar 

  11. Sjoland H, Lindgren M, Toska T, Hansson PO, Sandblad KG, Alex C, et al. Pulmonary embolism and deep venous thrombosis after COVID-19: long-term risk in a population-based cohort study. Res Pract Thromb Haemost. 2023;7:100284.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Umetsu M, Kanamori H, Murakami K, Shiga T, Yachi S, Takeyama M, et al. Clinical features comparing arterial thrombosis and venous thromboembolism in hospitalized patients with COVID-19: result from the CLOT-COVID study. Ann Vasc Dis. 2023;16:115–23.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Knight R, Walker V, Ip S, Cooper JA, Bolton T, Keene S, et al. Association of COVID-19 with major arterial and venous thrombotic diseases: a population-wide cohort study of 48 million adults in England and Wales. Circulation. 2022;146:892–906.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Schulman S, Lindmarker P, Holmström M, Lärfars G, Carlsson A, Nicol P, et al. Post-thrombotic syndrome, recurrence, and death 10 years after the first episode of venous thromboembolism treated with warfarin for 6 weeks or 6 months. J Thromb Haemost. 2006;4:734–42.

    Article  CAS  PubMed  Google Scholar 

  15. Arshad N, Isaksen T, Hansen JB, Brækkan SK. Time trends in incidence rates of venous thromboembolism in a large cohort recruited from the general population. Eur J Epidemiol. 2017;32:299–305.

    Article  PubMed  Google Scholar 

  16. Bikdeli B, Madhavan MV, Jimenez D, Chuich T, Dreyfus I, Driggin E, et al. COVID-19 and thrombotic or thromboembolic disease: implications for prevention, antithrombotic therapy, and follow-up: JACC state-of-the-art review. J Am Coll Cardiol. 2020;75:2950–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Nashiry A, Sarmin Sumi S, Islam S, Quinn JMW, Moni MA. Bioinformatics and system biology approach to identify the influences of COVID-19 on cardiovascular and hypertensive comorbidities. Brief Bioinform. 2021;22:1387–401.

    Article  CAS  PubMed  Google Scholar 

  18. Song JW, Zhang C, Fan X, Meng FP, Xu Z, Xia P, et al. Immunological and inflammatory profiles in mild and severe cases of COVID-19. Nat Commun. 2020;11:3410.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ghouse J, Tragante V, Ahlberg G, Rand SA, Jespersen JB, Leinøe EB, et al. Genome-wide meta-analysis identifies 93 risk loci and enables risk prediction equivalent to monogenic forms of venous thromboembolism. Nat Genet. 2023;55:399–409.

    Article  CAS  PubMed  Google Scholar 

  20. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Holland D, Frei O, Desikan R, Fan CC, Shadrin AA, Smeland OB, et al. Beyond SNP heritability: polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model. PLoS Genet. 2020;16:e1008612.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Frei O, Holland D, Smeland OB, Shadrin AA, Fan CC, Maeland S, et al. Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation. Nat Commun. 2019;10:2417.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. eLife. https://doi.org/10.17863/CAM.27599

  24. Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 2015;30:543–52.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 2013;41:D991–5.

    Article  CAS  PubMed  Google Scholar 

  26. Li S, Zhang Q, Huang Z, Tao W, Zeng C, Yan L, et al. Comprehensive analysis of immunocyte infiltration and the key genes associated with intraplaque hemorrhage in carotid atherosclerotic plaques. Int Immunopharmacol. 2022;106:108633.

    Article  CAS  PubMed  Google Scholar 

  27. Li S, Zhang Q, Weng L, Li J. Construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy. Front Genet. 2022;13:1014264.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607-d613.

    Article  CAS  PubMed  Google Scholar 

  31. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019;47:W234–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, Vlachos IS, Tastsoglou S, Kanellos I, et al. DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions. Nucleic Acids Res. 2018;46:D239-d245.

    Article  CAS  PubMed  Google Scholar 

  35. Lachmann A, Xu H, Krishnan J, Berger SI, Mazloom AR, Ma’ayan A. ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics (Oxford, England). 2010;26:2438–44.

    CAS  PubMed  Google Scholar 

  36. Zhao Z, Zhang Z, Li J, Dong Q, Xiong J, Li Y, et al. Sustained TNF-α stimulation leads to transcriptional memory that greatly enhances signal sensitivity and robustness. Elife. 2020;9:61965.

    Article  Google Scholar 

  37. Zhao M, Joy J, Zhou W, De S, Wood WH 3rd, Becker KG, et al. Transcriptional outcomes and kinetic patterning of gene expression in response to NF-κB activation. PLoS Biol. 2018;16:e2006347.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Castro-Mondragon JA, Riudavets-Puig R, Rauluseviciute I, Lemma RB, Turchi L, Blanc-Mathieu R, et al. JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 2022;50:D165-d173.

    Article  CAS  PubMed  Google Scholar 

  39. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36:411–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Obi AT, Barnes GD, Napolitano LM, Henke PK, Wakefield TW. Venous thrombosis epidemiology, pathophysiology, and anticoagulant therapies and trials in severe acute respiratory syndrome coronavirus 2 infection. J Vasc Surg Venous Lymphat Disord. 2021;9:23–35.

    Article  PubMed  Google Scholar 

  41. Thondapu V, Montes D, Rosovsky R, Dua A, McDermott S, Lu MT, et al. Venous thrombosis, thromboembolism, biomarkers of inflammation, and coagulation in coronavirus disease 2019. J Vasc Surg Venous Lymphat Disord. 2021;9:835-844.e4.

    Article  PubMed  Google Scholar 

  42. Mohd Zawawi Z, Kalyanasundram J, Mohd Zain R, Thayan R, Basri DF, Yap WB. Prospective roles of tumor necrosis factor-alpha (TNF-α) in COVID-19: prognosis, therapeutic and management. Int J Mol Sci. 2023;24:6142.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zapponi KC, Mazetto BM, Bittar LF, Barnabé A, Santiago-Bassora FD, De Paula EV, et al. Increased adhesive properties of neutrophils and inflammatory markers in venous thromboembolism patients with residual vein occlusion and high D-dimer levels. Thromb Res. 2014;133:736–42.

    Article  CAS  PubMed  Google Scholar 

  44. Al-Samkari H, Karp Leaf RS, Dzik WH, Carlson JCT, Fogerty AE, Waheed A, et al. COVID-19 and coagulation: bleeding and thrombotic manifestations of SARS-CoV-2 infection. Blood. 2020;136:489–500.

    Article  CAS  PubMed  Google Scholar 

  45. Bao J, Li C, Zhang K, Kang H, Chen W, Gu B. Comparative analysis of laboratory indexes of severe and non-severe patients infected with COVID-19. Clin Chim Acta. 2020;509:180–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wright FL, Vogler TO, Moore EE, Moore HB, Wohlauer MV, Urban S, et al. Fibrinolysis shutdown correlation with thromboembolic events in severe COVID-19 infection. J Am Coll Surg. 2020;231:193-203.e1.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Di Minno A, Ambrosino P, Calcaterra I, Di Minno MND. COVID-19 and venous thromboembolism: a meta-analysis of literature studies. Semin Thromb Hemost. 2020;46:763–71.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Nopp S, Moik F, Jilma B, Pabinger I, Ay C. Risk of venous thromboembolism in patients with COVID-19: a systematic review and meta-analysis. Res Pract Thromb Haemost. 2020;4:1178–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Porfidia A, Valeriani E, Pola R, Porreca E, Rutjes AWS, Di Nisio M. Venous thromboembolism in patients with COVID-19: systematic review and meta-analysis. Thromb Res. 2020;196:67–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Costela-Ruiz VJ, Illescas-Montes R, Puerta-Puerta JM, Ruiz C, Melguizo-Rodríguez L. SARS-CoV-2 infection: the role of cytokines in COVID-19 disease. Cytokine Growth Factor Rev. 2020;54:62–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Karki R, Sharma BR, Tuladhar S, Williams EP, Zalduondo L, Samir P, et al. Synergism of TNF-α and IFN-γ triggers inflammatory cell death, tissue damage, and mortality in SARS-CoV-2 infection and cytokine shock syndromes. Cell. 2021;184:149-168.e17.

    Article  CAS  PubMed  Google Scholar 

  52. Bertin FR, Rys RN, Mathieu C, Laurance S, Lemarié CA, Blostein MD. Natural killer cells induce neutrophil extracellular trap formation in venous thrombosis. J Thromb Haemos. 2019;17:403–14.

    Article  Google Scholar 

  53. Nosaka M, Ishida Y, Kimura A, Kuninaka Y, Inui M, Mukaida N, et al. Absence of IFN-γ accelerates thrombus resolution through enhanced MMP-9 and VEGF expression in mice. J Clin Investig. 2011;121:2911–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Taus F, Salvagno G, Canè S, Fava C, Mazzaferri F, Carrara E, et al. Platelets promote thromboinflammation in SARS-CoV-2 Pneumonia. Arterioscler Thromb Vasc Biol. 2020;40:2975–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Moss P. The T cell immune response against SARS-CoV-2. Nat Immunol. 2022;23:186–93.

    Article  CAS  PubMed  Google Scholar 

  56. Zhuang Z, Lai X, Sun J, Chen Z, Zhang Z, Dai J, et al. Mapping and role of T cell response in SARS-CoV-2-infected mice. J Exp Med. 2021. https://doi.org/10.1084/jem.2020218710052021c.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Rha MS, Shin EC. Activation or exhaustion of CD8(+) T cells in patients with COVID-19. Cell Mol Immunol. 2021;18:2325–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Huang L, Shi Y, Gong B, Jiang L, Zhang Z, Liu X, et al. Dynamic blood single-cell immune responses in patients with COVID-19. Signal Transduct Target Ther. 2021;6:110.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Mazzoni A, Salvati L, Maggi L, Capone M, Vanni A, Spinicci M, et al. Impaired immune cell cytotoxicity in severe COVID-19 is IL-6 dependent. J Clin Investig. 2020;130:4694–703.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Rébillard RM, Charabati M, Grasmuck C, Filali-Mouhim A, Tastet O, Brassard N, et al. Identification of SARS-CoV-2-specific immune alterations in acutely ill patients. J Clin Investig. 2021. https://doi.org/10.1172/JCI145853.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Xia N, Hasselwander S, Reifenberg G, Habermeier A, Closs EI, Mimmler M, et al. B lymphocyte-deficiency in mice causes vascular dysfunction by inducing neutrophilia. Biomedicines. 2021;9:1686.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Kushnir M, Cohen HW, Billett HH. Persistent neutrophilia is a marker for an increased risk of venous thrombosis. J Thromb Thrombolysis. 2016;42:545–51.

    Article  CAS  PubMed  Google Scholar 

  63. Li B, Shang ZF, Yin JJ, Xu QZ, Liu XD, Wang Y, et al. PIG3 functions in DNA damage response through regulating DNA-PKcs homeostasis. Int J Biol Sci. 2013;9:425–34.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Polyak K, Xia Y, Zweier JL, Kinzler KW, Vogelstein B. A model for p53-induced apoptosis. Nature. 1997;389:300–5.

    Article  CAS  PubMed  Google Scholar 

  65. Kotsinas A, Aggarwal V, Tan EJ, Levy B, Gorgoulis VG. PIG3: a novel link between oxidative stress and DNA damage response in cancer. Cancer Lett. 2012;327:97–102.

    Article  CAS  PubMed  Google Scholar 

  66. Li M, Li S, Liu B, Gu MM, Zou S, Xiao BB, et al. PIG3 promotes NSCLC cell mitotic progression and is associated with poor prognosis of NSCLC patients. J Exp Clin Cancer Res. 2017;36:39.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Delgado-Roche L, Mesta F. Oxidative stress as key player in severe acute respiratory syndrome Coronavirus (SARS-CoV) infection. Arch Med Res. 2020;51:384–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Ebrahimi M, Norouzi P, Aazami H, Moosavi-Movahedi AA. Review on oxidative stress relation on COVID-19: Biomolecular and bioanalytical approach. Int J Biol Macromol. 2021;189:802–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Wang Q, Zennadi R. Oxidative stress and thrombosis during aging: the roles of oxidative stress in RBCs in venous thrombosis. Int J Mol Sci. 2020;21:4259.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Ashcroft GS, Lei K, Jin W, Longenecker G, Kulkarni AB, Greenwell-Wild T, et al. Secretory leukocyte protease inhibitor mediates non-redundant functions necessary for normal wound healing. Nat Med. 2000;6:1147–53.

    Article  CAS  PubMed  Google Scholar 

  71. Majchrzak-Gorecka M, Majewski P, Grygier B, Murzyn K, Cichy J. Secretory leukocyte protease inhibitor (SLPI), a multifunctional protein in the host defense response. Cytokine Growth Factor Rev. 2016;28:79–93.

    Article  CAS  PubMed  Google Scholar 

  72. Zani ML, Baranger K, Guyot N, Dallet-Choisy S, Moreau T. Protease inhibitors derived from elafin and SLPI and engineered to have enhanced specificity towards neutrophil serine proteases. Protein Sci. 2009;18:579–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. McKiernan PJ, McElvaney NG, Greene CM. SLPI and inflammatory lung disease in females. Biochem Soc Trans. 2011;39:1421–6.

    Article  CAS  PubMed  Google Scholar 

  74. Sibila O, Perea L, Albacar N, Moisés J, Cruz T, Mendoza N, et al. Elevated plasma levels of epithelial and endothelial cell markers in COVID-19 survivors with reduced lung diffusing capacity six months after hospital discharge. Respir Res. 2022;23:37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Sardar R, Satish D, Gupta D. Identification of novel SARS-CoV-2 drug targets by host micrornas and transcription factors co-regulatory interaction network analysis. Front Genet. 2020;11:571274.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Sarma A, Phukan H, Halder N, Madanan MG. An in-silico approach to study the possible interactions of miRNA between human and SARS-CoV2. Comput Biol Chem. 2020;88:107352.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Prasad K, Alasmari AF, Ali N, Khan R, Alghamdi A, Kumar V. Insights into the SARS-CoV-2-mediated alteration in the stress granule protein regulatory networks in humans. Pathogens (Basel, Switzerland). 2021;10:1459.

    CAS  PubMed  Google Scholar 

  78. Arora S, Singh P, Dohare R, Jha R, Ali SM. Unravelling host–pathogen interactions: ceRNA network in SARS-CoV-2 infection (COVID-19). Gene. 2020;762:145057.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Salgado-Albarrán M, Navarro-Delgado EI, Del Moral-Morales A, Alcaraz N, Baumbach J, González-Barrios R, et al. Comparative transcriptome analysis reveals key epigenetic targets in SARS-CoV-2 infection. NPJ Syst Biol Appl. 2021;7:21.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Rahman MR, Islam T, Shahjaman M, Islam MR, Lombardo SD, Bramanti P, et al. Discovering common pathogenetic processes between COVID-19 and diabetes mellitus by differential gene expression pattern analysis. Brief Bioinformatics. https://doi.org/10.3390/ijms21124259

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Acknowledgements

We acknowledge GEO databases for providing their platforms and contributors for uploading their meaningful datasets. We acknowledge the support of the National Natural Science Foundation of China (82101947), China Postdoctoral Science Foundation (2021TQ0371, 2021M703636) and the Fundamental Research Funds for the Central Universities of Central South University (2020zzts269).

Funding

This work was supported by the National Natural Science Foundation of China (82101947), China Postdoctoral Science Foundation (2021TQ0371, 2021M703636) and the Fundamental Research Funds for the Central Universities of Central South University (2020zzts269).

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  1. Di Liao

    Present address: Department of Neurology, Xiangya Hospital, Central South University, 87 Xiangya Street, Changsha, 410008, Hunan, China

Authors and Affiliations

  1. Department of Neurosurgery, Xi’an Central Hospital, Xi’an Jiaotong University, No. 161, West 5th Road, Xincheng District, Xi’an, 710003, Shanxi, China

    Langchao Yan

  2. Department of Neurosurgery, Xiangya Hospital, Central South University, 87 Xiangya Street, Changsha, 410008, Hunan, China

    Shifu Li

  3. National Clinical Research Center for Geriatric Disorders, Central South University, 87 Xiangya Street, Changsha, 410008, Hunan, China

    Shifu Li & Di Liao

  4. Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China

    Qian Hu

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Contributions

Conceptualization, LY and SL; methodology, LY; software, LY; validation, LY and SL; formal analysis, SL; investigation, QH; writing-original draft preparation, QH and DL; writing-review and editing, QH; visualization, DL; supervision, DL.

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Yan, L., Li, S., Hu, Q. et al. Genetic correlations, shared risk genes and immunity landscapes between COVID-19 and venous thromboembolism: evidence from GWAS and bulk transcriptome data. Inflamm. Res. 73, 619–640 (2024). https://doi.org/10.1007/s00011-024-01857-w

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