|Year : 2019 | Volume
| Issue : 4 | Page : 233-235
Common and different lipidomes for lung cancer and tuberculosis: A comparative lipidomics analysis
Beuy Joob1, Viroj Wiwanitkit2
1 Sanitation 1 Medical Academic Center, Bangkok, Thailand
2 Department of Community Medicine, Dr. D.Y. Patil University, Pune, Maharashtra, India
|Date of Submission||02-Aug-2019|
|Date of Acceptance||15-Oct-2019|
|Date of Web Publication||03-Dec-2019|
Dr. Beuy Joob
Sanitation 1 Medical Academic Center, Bangkok
Source of Support: None, Conflict of Interest: None
Background: Of several lung disorders, the important diseases that are considered important burdens worldwide in the present day include lung cancer and tuberculosis. Sometimes, the diagnosis of the two mentioned lung problems is difficult. To make a differential diagnosis between negative and positive lungs for either lung cancer or tuberculosis is a critical point for further proper management. In the present day, the advanced novel biomedical technology allows the practitioner to assess the imprints of the pathological lungs. Methods: Lipidomics is a new biocochemioinformatics technique that can be useful lipidome imprint identification of medical disorders. Here, the authors perform a comparative lipidomcis analysis for lung cancer and tuberculosis. Results: There are 11 specific lipidomes for lung cancer, and there are 10 specific lipidomes for tuberculosis. There is only one overlapping lipidome, phosphatidylglycerol (PG) (34:1). Conclusion: According to the present analysis, the PG (34:1) is the specific lipidome that can help differentiate between lung cancer and tuberculosis.
Keywords: Lipidomics, lung cancer, tuberculosis
|How to cite this article:|
Joob B, Wiwanitkit V. Common and different lipidomes for lung cancer and tuberculosis: A comparative lipidomics analysis. Biomed Biotechnol Res J 2019;3:233-5
|How to cite this URL:|
Joob B, Wiwanitkit V. Common and different lipidomes for lung cancer and tuberculosis: A comparative lipidomics analysis. Biomed Biotechnol Res J [serial online] 2019 [cited 2020 Feb 22];3:233-5. Available from: http://www.bmbtrj.org/text.asp?2019/3/4/233/272189
| Introduction|| |
The lung is an important organ of human beings. It plays an important role in respiration function. The disorder of the lung can disturb the normal respiratory physiology. Of several lung disorders, the important diseases that are considered important burdens worldwide in the present day include lung cancer and tuberculosis. Sometimes, the diagnosis of the two mentioned lung problems is difficult. To make a differential diagnosis between negative and positive lungs for either lung cancer or tuberculosis is a critical point for further proper management. For example, in case of the abnormal lung with pleural effusion, the differential diagnosis for tuberculosis from lung cancer is required. In both tuberculosis and lung cancers can cause the inflammation of the lung as well as pleura. The pleural effusion can be the complication. Sometimes, the pleural effusion is sometimes difficult to differentiate diagnose. In some situations, the complex case might occur such as tuberculosis in the case with underlying malignancy. Concurrent occurrence of tuberculosis and malignancy at the same lung lobe is possible, and the reaction of tuberculosis during cancer treatment is also observable.
In the present day, the advanced novel biomedical technology allows the practitioner to assess the imprints of the pathological lungs. Lipidomics is a new biocohemioinformatics technique that can be useful limpidome imprint identification of medical disorders. Here, the authors perform a comparative lipidomcis analysis for lung cancer and tuberculosis. According to the present analysis, the phosphatidylglycerol (PG) (34:1) is the specific lipidome that can help differentiate between lung cancer and tuberculosis.
| Methods|| |
This study is a biochemioinformatics study aiming at identified the specific lipidome that is useful for differentiation between lung cancer and tuberculosis. The standard comparative lipidomics analysis was done. The comparative lipidomics analysis is the comparison of available data on lipidome from the previousin vivo orin vitro lipidome, specific lipid molecule analysis, in a specific disorder. The comparison is based on the basic mathematical technique, the set intersection principle. The set of the lidomes in each disorder was written, and the intersection was further identified and represented by Venn diagram. The primary data on the specific lipidomes from the previous lipidomics study on lung cancer and tuberculosis were used for further analysis. In comparative analysis, the Venn diagram mathematical technique was used for detecting the overlapping or intersection between sets of specific lipidomes for lung cancer and tuberculosis. This study is a bioinformatics study based on the public available data from the databases, and there is no involvement on clinical specimen, human or animals subjects. There is no requirement of written informed consent or ethical approval.
| Results|| |
According to the primary data,, the specific lipidomes for lung cancer and tuberculosis are listed in [Table 1]. There are 11 specific lipidome for lung cancer, and there are 10 specific lipidomes for tuberculosis.
According to the Venn diagram analysis, there is only one overlapping lipidome, PG (34:1). The Venn diagram is shown in [Figure 1].
| Discussion|| |
The molecular pathogenesis of tuberculosis is interesting. As a mycobacterial infection, the intracellular organelle pathogenesis, of tuberculosis can be identified. The alteration of metabolism in tuberculosis exists and tracing the specific metabolome in tuberculosis is a useful approach in diagnosis. The alteration of lipid metabolism in tuberculosis is interesting. Regarding the lung disorder, lipopolysaccharide-induced lung inflammation is reported. The identification of lipidome, lipid metabolome, is a new approach that can be used in diagnosis of lung problem. Lipid metabolism is the current focus in therapeutics for management of intracellular bacterial virulence. Indeed, the liposome, the nanolipid molecule is mentioned for the usefulness in both diagnosis and treatment purposes. Classically, the identification of lipidome in clinical practice requires the complex laboratory analysis technique. Nevertheless, the modern advance biochemioinformatics technology can help ease the analysis of lipidome.
Lipidomics is a new omics approach. This omics technique is helpful for several applications such as biomarker finding and pathogenesis clarification. Focusing on pulmonary medicine, there are some recent reports on using lipidomics for biomedical researches. The good example is the report by Eggers et al. Eggers et al. use lipidomics approach to find specific lipidomes for lung cancer. In a similar report, Wood et al. studied lipidome for tuberculosis. The alteration of lipid metabolism in tuberculosis is confirmed. Indeed, in tuberculosis, there is a macrophage cell wall lipid change., Furthermore, the lipidome is also identified for the difference in drug-resistant and susceptible tuberculosis.
In the present report, the authors used the primary data from the previous studies to find the specific lipidome that will be useful for differential diagnosis between lung cancer and tuberculosis. According to the study, a specific lipidome is detectable. In fact, the previous standard metabolomics study how that the fatty acids is a possible metabolome that is useful for classification of pleural effusion. However, the simple metabolics approach cannot identify complex liposome. The present study used the specific technique that can help the identification of specific liposome that can be used in the differential diagnosis of lung disorders.
| Conclusion|| |
According to the present analysis, the PG (34:1) is the specific lipidome that can help differentiate between lung cancer and tuberculosis.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Ferreiro L, Toubes ME, Valdés L. Contribution of pleural fluid analysis to the diagnosis of pleural effusion. Med Clin (Barc) 2015;145:171-7.
Jacobs RE, Gu P, Chachoua A. Reactivation of pulmonary tuberculosis during cancer treatment. Int J Mycobacteriol 2015;4:337-40. [Full text]
Rihawi A, Huang G, Al-Hajj A, Bootwala Z. A case of tuberculosis and adenocarcinoma coexisting in the same lung lobe. Int J Mycobacteriol 2016;5:80-2. [Full text]
Wolf C, Quinn PJ. Lipidomics: Practical aspects and applications. Prog Lipid Res 2008;47:15-36.
Taguchi R. Systems for lipidomics: Metabolomics focused on lipids. Tanpakushitsu Kakusan Koso 2004;49:1911-6.
Shade A, Handelsman J. Beyond the Venn diagram: The hunt for a core microbiome. Environ Microbiol 2012;14:4-12.
Eggers LF, Müller J, Marella C, Scholz V, Watz H, Kugler C, et al.
Lipidomes of lung cancer and tumour-free lung tissues reveal distinct molecular signatures for cancer differentiation, age, inflammation, and pulmonary emphysema. Sci Rep 2017;7:11087.
Wood PL, Tippireddy S, Feriante J. Plasma lipidomics of tuberculosis patients: Altered phosphatidylcholine remodeling. Future Sci OA 2018;4:FSO255.
Dubey RK. Assuming the role of mitochondria in mycobacterial infection. Int J Mycobacteriol 2016;5:379-83. [Full text]
Surin WR, Mundra S, Blesson CS, Gudde RS. Involvement of GAS6 protein in mouse model of lipopolysaccharide-induced lung inflammation. Biomed Biotechnol Res J 2018;2:146-51. [Full text]
Suer H, Bayram H. Liposomes as potential nanocarriers for theranostic applications in chronic inflammatory lung diseases. Biomed Biotechnol Res J 2017;1:1-8. [Full text]
Rameshwaram NR, Singh P, Ghosh S, Mukhopadhyay S. Lipid metabolism and intracellular bacterial virulence: Key to next-generation therapeutics. Future Microbiol 2018;13:1301-28.
Murphy SA, Nicolaou A. Lipidomics applications in health, disease and nutrition research. Mol Nutr Food Res 2013;57:1336-46.
Howard NC, Marin ND, Ahmed M, Rosa BA, Martin J, Bambouskova M, et al. Mycobacterium tuberculosis
carrying a rifampicin drug resistance mutation reprograms macrophage metabolism through cell wall lipid changes. Nat Microbiol 2018;3:1099-108.
Ferrara G, Bleck B, Richeldi L, Reibman J, Fabbri LM, Rom WN, et al. Mycobacterium tuberculosis
induces CCL18 expression in human macrophages. Scand J Immunol 2008;68:668-74.
Pal R, Hameed S, Kumar P, Singh S, Fatima Z. Comparative lipidomics of drug sensitive and resistant Mycobacterium tuberculosis
reveals altered lipid imprints 3 Biotech 2017;7:325.
Che N, Ma Y, Ruan H, Xu L, Wang X, Yang X, et al.
Integrated semi-targeted metabolomics analysis reveals distinct metabolic dysregulation in pleural effusion caused by tuberculosis and malignancy. Clin Chim Acta 2018;477:81-8.