Skip to content
2000
image of Screening Co-Diagnostic Genes for Lung Adenocarcinoma and Myocardial Infarction and Analysis of the Molecular Functions and Drug Value of the Genes

Abstract

Background

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer, and myocardial infarction (MI) is an acute cardiovascular disease resulting from the disruption of coronary blood supply. Recent studies have suggested that these two diseases may share common molecular mechanisms.

Aim

The aim of this study was to discover common diagnostic genes for LUAD and MI and analyze their molecular functions and potential drug values by applying bioinformatics analysis.

Objective

The objective was to provide a theoretical basis for further research on the pathological mechanisms of LUAD and MI, contributing to the development of novel diagnostic and therapeutic strategies for the two diseases.

Methods

In this study, the datasets of LUAD and MI were obtained from TCGA and GEO databases, and differential expression analysis was performed to screen significantly differentially expressed genes (DEGs). Subsequently, disease-related genes were identified using WGCNA analysis, and the biological functions of these genes were explored by functional enrichment analysis. After screening key genes using the protein-protein interaction (PPI) network and the cytoHubba algorithm, biomarkers were determined by LASSO and SVM-RFE machine-learning methods. Finally, immune infiltration analysis and drug prediction were performed, and biomarker expression was verified by single-cell sequencing analysis.

Results

A total of 158 differentially upregulated genes were identified between LUAD and MI. WGCNA analysis screened 86 genes that were significantly associated with both diseases and were enriched in an inflammatory response and immune regulation-related pathways, such as the IL-17 signaling pathway. Ten significant genes were identified by the PPI network and cytoHubba and then reduced to 4 using LASSO and SVM-RFE. Noticeably, MMP9 was significantly overexpressed in both diseases. Immune infiltration analysis showed that MMP9 was significantly related to multiple immune cell infiltration. Drug prediction and molecular docking analysis predicted Ilomastat and Osthole as the potential target drugs. Single-cell sequencing analysis revealed that MMP9 was high-expressed in the macrophages in LUAD tissues.

Conclusion

This study identified MMP9 as a common diagnostic gene and potential therapeutic target for both LUAD and MI and revealed its role in inflammation and immune regulation through comprehensive bioinformatics analysis. These findings provided a theoretical basis for further research on the pathological mechanisms of LUAD and MI, contributing to the development of novel diagnostic and therapeutic strategies.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Loading

Article metrics loading...

/content/journals/emiddt/10.2174/0118715303374928250130113050
2025-02-11
2025-07-04
The full text of this item is not currently available.

References

  1. Hussain S. Bokhari H. Fan X. Malik S.I. Ijaz S. Shereen M.A. Fatima A. MicroRNAs modulation in lung cancer: Exploring dual mechanisms and clinical prospects. Biocell 2024 48 3 403 413 10.32604/biocell.2024.044801
    [Google Scholar]
  2. Ding Y. Lv J. Hua Y. Comprehensive metabolomic analysis of lung cancer patients treated with fu zheng fang. Curr. Pharm. Anal. 2022 18 9 881 891 10.2174/1573412918666220822143119
    [Google Scholar]
  3. Qian X.J. Wang J.W. Liu J.B. Yu X. The mediating role of miR-451/ETV4/MMP13 signaling axis on epithelialmesenchymal transition in promoting non-small cell lung cancer progression. Curr. Mol. Pharmacol. 2023 17 e210723218988 10.2174/1874467217666230721123554 37489792
    [Google Scholar]
  4. Qiu H. Cao S. Xu R. Cancer incidence, mortality, and burden in China: A time‐trend analysis and comparison with the United States and United Kingdom based on the global epidemiological data released in 2020. Cancer Commun. (Lond.) 2021 41 10 1037 1048 10.1002/cac2.12197 34288593
    [Google Scholar]
  5. Lantuejoul S. Mescam-Mancini L. Burroni B. McLeer-Florin A. News on molecular pathology in non-small cell lung cancer. Oncologie 2012 14 9 530 537 10.1007/s10269‑012‑2206‑1
    [Google Scholar]
  6. Wang J. Cai Y. Sheng Z. Dong Z. EGFR inhibitor CL-387785 suppresses the progression of lung adenocarcinoma. Curr. Mol. Pharmacol. 2023 16 2 211 216 10.2174/1874467215666220329212300 35352671
    [Google Scholar]
  7. Denisenko T.V. Budkevich I.N. Zhivotovsky B. Cell death-based treatment of lung adenocarcinoma. Cell Death Dis. 2018 9 2 117 10.1038/s41419‑017‑0063‑y 29371589
    [Google Scholar]
  8. Zhang L. Meng Q. Zhuang L. Gong Q. Huang X. Li X. Li S. Wang G. Wang X. miR-30a-5p/PHTF2 axis regulates the tumorigenesis and metastasis of lung adenocarcinoma. Biocell 2024 48 4 581 590 10.32604/biocell.2024.047260
    [Google Scholar]
  9. Ghosh S. Cisplatin: The first metal based anticancer drug. Bioorg. Chem. 2019 88 102925 10.1016/j.bioorg.2019.102925 31003078
    [Google Scholar]
  10. Zarrabi A. Bishayee A. Mirzaei S. Gholami M.H. Zabolian A. Saleki H. Bagherian M. Torabi S.M. Sharifzadeh S.O. Hushmandi K. Fives K.R. Khan H. Ashrafizadeh M. Resveratrol augments doxorubicin and cisplatin chemotherapy: A novel therapeutic strategy. Curr. Mol. Pharmacol. 2023 16 3 280 306 10.2174/1874467215666220415131344 35430977
    [Google Scholar]
  11. Sase K. Fujisaka Y. Shoji M. Mukai M. Cardiovascular complications associated with contemporary lung cancer treatments. Curr. Treat. Options Oncol. 2021 22 8 71 10.1007/s11864‑021‑00869‑6 34110522
    [Google Scholar]
  12. Darby S.C. Ewertz M. McGale P. Bennet A.M. Blom-Goldman U. Brønnum D. Correa C. Cutter D. Gagliardi G. Gigante B. Jensen M.B. Nisbet A. Peto R. Rahimi K. Taylor C. Hall P. Risk of ischemic heart disease in women after radiotherapy for breast cancer. N. Engl. J. Med. 2013 368 11 987 998 10.1056/NEJMoa1209825 23484825
    [Google Scholar]
  13. Lee Chuy K. Nahhas O. Dominic P. Lopez C. Tonorezos E. Sidlow R. Straus D. Gupta D. Cardiovascular complications associated with mediastinal radiation. Curr. Treat. Options Cardiovasc. Med. 2019 21 7 31 10.1007/s11936‑019‑0737‑0 31161453
    [Google Scholar]
  14. Zhang S. Liu X. Bawa-Khalfe T. Lu L.S. Lyu Y.L. Liu L.F. Yeh E.T.H. Identification of the molecular basis of doxorubicin-induced cardiotoxicity. Nat. Med. 2012 18 11 1639 1642 10.1038/nm.2919 23104132
    [Google Scholar]
  15. Cardinale D. Colombo A. Lamantia G. Colombo N. Civelli M. De Giacomi G. Rubino M. Veglia F. Fiorentini C. Cipolla C.M. Anthracycline-induced cardiomyopathy. J. Am. Coll. Cardiol. 2010 55 3 213 220 10.1016/j.jacc.2009.03.095 20117401
    [Google Scholar]
  16. Psaty B.M. Vasan R.S. The association of myocardial infarction with cancer incidence. Eur. J. Epidemiol. 2023 38 8 851 852 10.1007/s10654‑023‑01019‑y 37268804
    [Google Scholar]
  17. van Herk-Sukel M.P.P. Shantakumar S. Penning-van Beest F.J.A. Kamphuisen P.W. Majoor C.J. Overbeek L.I.H. Herings R.M.C. Pulmonary embolism, myocardial infarction, and ischemic stroke in lung cancer patients: Results from a longitudinal study. Lung 2013 191 5 501 509 10.1007/s00408‑013‑9485‑1 23807721
    [Google Scholar]
  18. Love M. Anders S. Huber W. Differential analysis of count data–the DESeq2 package. Genome Biol. 2014 15 550 10 1186
    [Google Scholar]
  19. Ritchie M.E. Phipson B. Wu D. Hu Y. Law C.W. Shi W. Smyth G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 43 7 e47 10.1093/nar/gkv007 25605792
    [Google Scholar]
  20. Song Z. Yu J. Wang M. Shen W. Wang C. Lu T. Shan G. Dong G. Wang Y. Zhao J. CHDTEPDB: Transcriptome expression profile database and interactive analysis platform for congenital heart disease. Congenit. Heart Dis. 2023 18 6 693 701 10.32604/chd.2024.048081
    [Google Scholar]
  21. Langfelder P. Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 2008 9 1 559 10.1186/1471‑2105‑9‑559 19114008
    [Google Scholar]
  22. Dennis G. Jr Sherman B.T. Hosack D.A. Yang J. Gao W. Lane H.C. Lempicki R.A. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 2003 4 5 P3 10.1186/gb‑2003‑4‑5‑p3 12734009
    [Google Scholar]
  23. Xu X. Huang Y. Han X. Single-nucleus RNA sequencing reveals cardiac macrophage landscape in hypoplastic left heart syndrome. Congenit. Heart Dis. 2024 19 2 233 246 10.32604/chd.2024.050231
    [Google Scholar]
  24. Szklarczyk D. Gable A.L. Nastou K.C. Lyon D. Kirsch R. Pyysalo S. Doncheva N.T. Legeay M. Fang T. Bork P. Jensen L.J. von Mering C. The STRING database in 2021: Customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021 49 D1 D605 D612 10.1093/nar/gkaa1074 33237311
    [Google Scholar]
  25. Shannon P. Markiel A. Ozier O. Baliga N.S. Wang J.T. Ramage D. Amin N. Schwikowski B. Ideker T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003 13 11 2498 2504 10.1101/gr.1239303 14597658
    [Google Scholar]
  26. Simon N. Friedman J. Hastie T. Tibshirani R. Regularization paths for cox’s proportional hazards model via coordinate descent. J. Stat. Softw. 2011 39 5 1 13 10.18637/jss.v039.i05 27065756
    [Google Scholar]
  27. Chen B. Khodadoust M.S. Liu C.L. Newman A.M. Alizadeh A.A. Profiling tumor infiltrating immune cells with cibersort. Methods Mol. Biol. 2018 1711 243 259 10.1007/978‑1‑4939‑7493‑1_12 29344893
    [Google Scholar]
  28. Seeliger D. de Groot B.L. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J. Comput. Aided Mol. Des. 2010 24 5 417 422 10.1007/s10822‑010‑9352‑6 20401516
    [Google Scholar]
  29. Stuart T. Butler A. Hoffman P. Hafemeister C. Papalexi E. Mauck W.M. 3rd Hao Y. Stoeckius M. Smibert P. Satija R. Comprehensive integration of single-cell data. Cell. 2019 177 7 1888 1902 10.1016/j.cell.2019.05.031 31178118
    [Google Scholar]
  30. Zulibiya A. Wen J. Yu H. Chen X. Xu L. Ma X. Zhang B. Single-cell RNA sequencing reveals potential for endothelial-to-mesenchymal transition in tetralogy of fallot. Congenit. Heart Dis. 2023 18 6 611 625 10.32604/chd.2023.047689
    [Google Scholar]
  31. Korsunsky I. Millard N. Fan J. Slowikowski K. Zhang F. Wei K. Baglaenko Y. Brenner M. Loh P. Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 2019 16 12 1289 1296 10.1038/s41592‑019‑0619‑0 31740819
    [Google Scholar]
  32. Han X. Liu X. Wang X. Guo W. Wen Y. Meng W. Peng D. Lv P. Zhang X. Shen H. TNF‐α‐dependent lung inflammation upregulates superoxide dismutase‐2 to promote tumor cell proliferation in lung adenocarcinoma. Mol. Carcinog. 2020 59 9 1088 1099 10.1002/mc.23239 32673443
    [Google Scholar]
  33. Cao L. Wang X. Liu X. Meng W. Guo W. Duan C. Liang X. Kang L. Lv P. Lin Q. Zhang R. Zhang X. Shen H. Tumor necrosis factor α–dependent lung inflammation promotes the progression of lung adenocarcinoma originating from alveolar type II cells by upregulating MIF-CD74. Lab. Invest. 2023 103 3 100034 10.1016/j.labinv.2022.100034 36925198
    [Google Scholar]
  34. Prabhu S.D. Frangogiannis N.G. The biological basis for cardiac repair after myocardial infarction. Circ. Res. 2016 119 1 91 112 10.1161/CIRCRESAHA.116.303577 27340270
    [Google Scholar]
  35. Zheng W. Zhou T. Zhang Y. Ding J. Xie J. Wang S. Wang Z. Wang K. Shen L. Zhu Y. Gao C. Simplified α2-macroglobulin as a TNF-α inhibitor for inflammation alleviation in osteoarthritis and myocardial infarction therapy. Biomaterials 2023 301 122247 10.1016/j.biomaterials.2023.122247 37487780
    [Google Scholar]
  36. Li Q. Liu Y. Xia X. Sun H. Gao J. Ren Q. Zhou T. Ma C. Xia J. Yin C. Activation of macrophage TBK1‐HIF‐1α‐mediated IL‐17/IL‐10 signaling by hyperglycemia aggravates the complexity of coronary atherosclerosis: An in vivo and in vitro study. FASEB J. 2021 35 5 e21609 10.1096/fj.202100086RR 33908659
    [Google Scholar]
  37. Golforoush P. Yellon D.M. Davidson S.M. Mouse models of atherosclerosis and their suitability for the study of myocardial infarction. Basic Res. Cardiol. 2020 115 6 73 10.1007/s00395‑020‑00829‑5 33258000
    [Google Scholar]
  38. Tian W. Li Y. Zhang J. Li J. Gao J. Comprehensive analysis of DNA methylation and gene expression datasets identified MMP9 and TWIST1 as important pathogenic genes of lung adenocarcinoma. DNA Cell Biol. 2018 37 4 336 346 10.1089/dna.2017.4085 29443542
    [Google Scholar]
  39. Gu J.J. Hoj J. Rouse C. Pendergast A.M. Mesenchymal stem cells promote metastasis through activation of an ABL-MMP9 signaling axis in lung cancer cells. PLoS One 2020 15 10 e0241423 10.1371/journal.pone.0241423 33119681
    [Google Scholar]
  40. Aydin S. Ugur K. Aydin S. Sahin İ. Yardim M. Biomarkers in acute myocardial infarction: Current perspectives. Vasc. Heal. Risk Manag. 2019 15 1 10 10.2147/VHRM.S166157 30697054
    [Google Scholar]
  41. Guo C. Ji W. Yang W. Deng Q. Zheng T. Wang Z. Sui W. Zhai C. Yu F. Xi B. Yu X. Xu F. Zhang Q. Zhang W. Kong J. Zhang M. Zhang C. NKRF in cardiac fibroblasts protects against cardiac remodeling post‐myocardial infarction via human antigen R. Adv. Sci. (Weinh.) 2023 10 30 2303283 10.1002/advs.202303283 37667861
    [Google Scholar]
  42. Augoff K. Hryniewicz-Jankowska A. Tabola R. Stach K. MMP9: A tough target for targeted therapy for cancer. Cancers (Basel) 2022 14 7 1847 10.3390/cancers14071847 35406619
    [Google Scholar]
  43. Lee H.S. Kim W.J. The role of matrix metalloproteinase in inflammation with a focus on infectious diseases. Int. J. Mol. Sci. 2022 23 18 10546 10.3390/ijms231810546 36142454
    [Google Scholar]
  44. Hu T. Cheng B. Matsunaga A. Zhang T. Lu X. Fang H. Mori S.F. Fang X. Wang G. Xu H. Shi H. Cowell J.K. Single-cell analysis defines highly specific leukemia-induced neutrophils and links MMP8 expression to recruitment of tumor associated neutrophils during FGFR1 driven leukemogenesis. Exp. Hematol. Oncol. 2024 13 1 49 10.1186/s40164‑024‑00514‑6 38730491
    [Google Scholar]
  45. Yin S. Liu H. Wang J. Feng S. Chen Y. Shang Y. Su X. Si F. Osthole induces apoptosis and inhibits proliferation, invasion, and migration of human cervical carcinoma hela cells. Evid. Based Complement. Alternat. Med. 2021 2021 1 7 10.1155/2021/8885093 34539807
    [Google Scholar]
  46. Marshall D.C. Lyman S.K. McCauley S. Kovalenko M. Spangler R. Liu C. Lee M. O’Sullivan C. Barry-Hamilton V. Ghermazien H. Mikels-Vigdal A. Garcia C.A. Jorgensen B. Velayo A.C. Wang R. Adamkewicz J.I. Smith V. Selective allosteric inhibition of MMP9 is efficacious in preclinical models of ulcerative colitis and colorectal cancer. PLoS One 2015 10 5 e0127063 10.1371/journal.pone.0127063 25961845
    [Google Scholar]
/content/journals/emiddt/10.2174/0118715303374928250130113050
Loading
/content/journals/emiddt/10.2174/0118715303374928250130113050
Loading

Data & Media loading...


  • Article Type:
    Research Article
Keywords: Myocardial infarction ; Lung adenocarcinoma ; MMP9 ; Molecular docking ; PPI network ; drug
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test