Identification of Lung Cancer metabolomics profile and molecular interactions using bioinformatic methods
-
Haniyeh Rafiepoor
,
Saman Asadi
,
Alireza Ghorbankhanloo
,
Maryam Edalatifard
,
Seyyed Hamidreza Abtahi
,
Saeid Amanpour
Abstract
Lung cancer represents a significant public health challenge, representing a considerable contributor to cancer-related mortality. However, the current diagnostic methods for early detection of lung cancer are constrained by various limitations, such as inadequate clinical resources and effective screening modalities. Consequently, diagnosis frequently occurs at advanced stages, impeding timely treatment interventions. However, the emerging field of omics, including metabolomics, proteomics, and genomics, has shown some improvement in facilitating the early diagnosis of lung cancer. Metabolomics methodologies offer a comprehensive insight into the metabolic processes of cells and tissues, enabling a deeper understanding of the biological state. By analyzing endogenous metabolites within biological systems, metabolomics has exhibited substantial potential for early detection and personalized treatment of diverse cancers. In this study, we extensively explored online metabolomic databases, such as Metabolomics Workbench, to pinpoint key metabolites linked to all types of lung cancer. Furthermore, connections between metabolomic genes and 43 genes implicated in lung cancer progression have been established by employing network analysis tools like Metagenes. This integrated approach offers a comprehensive overview of the metabolic and molecular landscape of lung cancer, highlighting 10 metabolic pathways, particularly amino acid metabolism, involved in the pathogenesis of lung cancer. These findings provide valuable insights for further research and potential clinical applications in the diagnosis and management of this disease.
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39. Yang, B., M. Zhang, and T. Luo, Identification of Potential Core Genes Associated With the Progression of Stomach Adenocarcinoma Using Bioinformatic Analysis. Front Genet, 2020. 11: p. 517362.
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42. Wang, Y., et al., Tissue-based metabolomics reveals metabolic signatures and major metabolic pathways of gastric cancer with help of transcriptomic data from TCGA. Biosci Rep, 2021. 41(10).
2. Damsees, R., et al., Unravelling the predictors of late cancer presentation and diagnosis in Jordan: a cross-sectional study of patients with lung and colorectal cancers. BMJ Open, 2023. 13(5): p. e069529.
3. Gao, Y., R. Zhou, and Q. Lyu, Multiomics and machine learning in lung cancer prognosis. J Thorac Dis, 2020. 12(8): p. 4531-4535.
4. Madama D, Martins R, Pires AS, Botelho MF, Alves MG, Abrantes AM, Cordeiro CR. Metabolomic Profiling in Lung Cancer: A Systematic Review. Metabolites. 2021 Sep 17;11(9):630. doi: 10.3390/metabo11090630. PMID: 34564447; PMCID: PMC8471464.
5. Liang, S., et al., Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types. Int J Med Sci, 2024. 21(2): p. 234-252.
6. Madama, D., et al., Metabolomic Profiling in Lung Cancer: A Systematic Review. Metabolites, 2021. 11(9).
7. Chen, Y., et al. (2022). Metabolomic signatures for lung cancer screening and early detection. Journal of Thoracic Oncology, 17(3), 372-385. DOI:10.1016/j.jtho.2021.11.003
8. Shestakova, K.M., et al., Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer. Sci Rep, 2023. 13(1): p. 11072.
9. Alaa A. A. Aljabali, Mohammad A. Obeid, Rasha M. Bashatwah, Esam Qnais, Omar Gammoh, Abdelrahim Alqudah, Vijay Mishra, Yachana Mishra, Mohammad Ahmed Khan, Suhel Parvez, Mohamed El-Tanani, Taher Hatahet, Chem. Biodiversity 2025, e202402479.
10. Blimkie, T., A.H. Lee, and R.E.W. Hancock, MetaBridge: An Integrative Multi-Omics Tool for Metabolite-Enzyme Mapping. Curr Protoc Bioinformatics, 2020. 70(1): p. e98.
11. Kanehisa, M. and S. Goto, KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 2000. 28(1): p. 27-30.
12. Karp, P.D., et al., The MetaCyc Database. Nucleic Acids Res, 2002. 30(1): p. 59-61.
13. Kustatscher, G., et al., Co-regulation map of the human proteome enables identification of protein functions. Nat Biotechnol, 2019. 37(11): p. 1361-1371.
14. Detterbeck, F.C., et al., Screening for lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest, 2013. 143(5 Suppl): p. e78S-e92S.
15. Tang, Y., et al., Metabolomics workflow for lung cancer: Discovery of biomarkers. Clin Chim Acta, 2019. 495: p. 436-445.
16. Irino, Y., et al., 2-Aminobutyric acid modulates glutathione homeostasis in the myocardium. Sci Rep, 2016. 6: p. 36749.
17. Shen, H., et al., Genome-wide association study identifies genetic variants in GOT1 determining serum aspartate aminotransferase levels. J Hum Genet, 2011. 56(11): p. 801-5.
18. Stelzer, G., et al., The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinformatics, 2016. 54: p. 1.30.1-1.30.33.
19. Saxena, S., et al., Structural and functional analysis of disease-associated mutations in GOT1 gene: An in silico study. Comput Biol Med, 2021. 136: p. 104695.
20. Boulland, M.L., et al., Human IL4I1 is a secreted L-phenylalanine oxidase expressed by mature dendritic cells that inhibits T-lymphocyte proliferation. Blood, 2007. 110(1): p. 220-7.
21. Sadik, A., et al., IL4I1 Is a Metabolic Immune Checkpoint that Activates the AHR and Promotes Tumor Progression. Cell, 2020. 182(5): p. 1252-1270.e34.
22. Zhang, X., et al., Endogenous Indole Pyruvate Pathway for Tryptophan Metabolism Mediated by IL4I1. J Agric Food Chem, 2020. 68(39): p. 10678-10684.
23. Molinier-Frenkel, V., A. Prévost-Blondel, and F. Castellano, The IL4I1 Enzyme: A New Player in the Immunosuppressive Tumor Microenvironment. Cells, 2019. 8(7).
24. Wiemann, S., A. Kolb-Kokocinski, and A. Poustka, Alternative pre-mRNA processing regulates cell-type specific expression of the IL4l1 and NUP62 genes. BMC Biol, 2005. 3: p. 16.
25. Sun, H., et al., IL4I1 and tryptophan metabolites enhance AHR signals to facilitate colorectal cancer progression and immunosuppression. Am J Transl Res, 2022. 14(11): p. 7758-7770.
26. López de la Oliva, A.R., et al., Nuclear Translocation of Glutaminase GLS2 in Human Cancer Cells Associates with Proliferation Arrest and Differentiation. Sci Rep, 2020. 10(1): p. 2259.
27. Okazaki, A., et al., Glutaminase and poly(ADP-ribose) polymerase inhibitors suppress pyrimidine synthesis and VHL-deficient renal cancers. J Clin Invest, 2017. 127(5): p. 1631-1645.
28. Suzuki, S., et al., Phosphate-activated glutaminase (GLS2), a p53-inducible regulator of glutamine metabolism and reactive oxygen species. Proc Natl Acad Sci U S A, 2010. 107(16): p. 7461-6.
29. Plaitakis, A., M. Metaxari, and P. Shashidharan, Nerve tissue-specific (GLUD2) and housekeeping (GLUD1) human glutamate dehydrogenases are regulated by distinct allosteric mechanisms: implications for biologic function. J Neurochem, 2000. 75(5): p. 1862-9.
30. Smith, T.J., et al., Structures of bovine glutamate dehydrogenase complexes elucidate the mechanism of purine regulation. J Mol Biol, 2001. 307(2): p. 707-20.
31. Stanley, C.A., et al., Hyperinsulinism and hyperammonemia in infants with regulatory mutations of the glutamate dehydrogenase gene. N Engl J Med, 1998. 338(19): p. 1352-7.
32. Choi, M.M., et al., Identification of ADP-ribosylation site in human glutamate dehydrogenase isozymes. FEBS Lett, 2005. 579(19): p. 4125-30.
33. MacMullen, C., et al., Hyperinsulinism/hyperammonemia syndrome in children with regulatory mutations in the inhibitory guanosine triphosphate-binding domain of glutamate dehydrogenase. J Clin Endocrinol Metab, 2001. 86(4): p. 1782-7.
34. Coloff, J.L., et al., Differential Glutamate Metabolism in Proliferating and Quiescent Mammary Epithelial Cells. Cell Metab, 2016. 23(5): p. 867-80.
35. Chu, X., et al., Integration of metabolomics, genomics, and immune phenotypes reveals the causal roles of metabolites in disease. Genome Biol, 2021. 22(1): p. 198.
36. Lim, S.B., et al., A merged lung cancer transcriptome dataset for clinical predictive modeling. Sci Data, 2018. 5: p. 180136.
37. Lu, M., et al., Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis. BMC Cancer, 2021. 21(1): p. 616.
38. Wang, X., J. Yang, and X. Gao, Identification of key genes associated with lung adenocarcinoma by bioinformatics analysis. Sci Prog, 2021. 104(1): p. 36850421997276.
39. Yang, B., M. Zhang, and T. Luo, Identification of Potential Core Genes Associated With the Progression of Stomach Adenocarcinoma Using Bioinformatic Analysis. Front Genet, 2020. 11: p. 517362.
40. Fang, S., et al., Multi-Omic Integration of Blood-Based Tumor-Associated Genomic and Lipidomic Profiles Using Machine Learning Models in Metastatic Prostate Cancer. JCO Clin Cancer Inform, 2023. 7: p. e2300057.
41. Ruan, X., et al., Multi-Omics Integrative Analysis of Lung Adenocarcinoma: An in silico Profiling for Precise Medicine. Front Med (Lausanne), 2022. 9: p. 894338.
42. Wang, Y., et al., Tissue-based metabolomics reveals metabolic signatures and major metabolic pathways of gastric cancer with help of transcriptomic data from TCGA. Biosci Rep, 2021. 41(10).
| Files | ||
| Issue | Vol 16 No 2 (2024) | |
| Section | Original Articles | |
| DOI | https://doi.org/10.18502/bccr.v16i2.19442 | |
| Keywords | ||
| Keywords: metabolomics Lung cancer molecular interaction bioinformatics | ||
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How to Cite
1.
Rafiepoor H, Asadi S, Ghorbankhanloo A, Edalatifard M, Abtahi SH, Amanpour S. Identification of Lung Cancer metabolomics profile and molecular interactions using bioinformatic methods. Basic Clin Cancer Res. 2025;16(2):119-130.
