1
|
Sun R, Xu H, Liu F, Zhou B, Li M, Sun X. Unveiling the intricate causal nexus between pancreatic cancer and peripheral metabolites through a comprehensive bidirectional two-sample Mendelian randomization analysis. Front Mol Biosci 2023; 10:1279157. [PMID: 37954977 PMCID: PMC10634252 DOI: 10.3389/fmolb.2023.1279157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 10/16/2023] [Indexed: 11/14/2023] Open
Abstract
Aim: Pancreatic cancer (PC) is a devastating malignancy characterized by its aggressive nature and poor prognosis. However, the relationship of PC with peripheral metabolites remains not fully investigated. The study aimed to explore the causal linkage between PC and peripheral metabolite profiles. Methods: Employing publicly accessible genome-wide association studies (GWAS) data, we conducted a bidirectional two-sample Mendelian randomization (MR) analysis. The primary analysis employed the inverse-variance weighted (IVW) method. To address potential concerns about horizontal pleiotropy, we also employed supplementary methods such as maximum likelihood, weighted median, MR-Egger regression, and MR pleiotropy residual sum and outlier (MR-PRESSO). Results: We ascertained 20 genetically determined peripheral metabolites with causal linkages to PC while high-density lipoprotein (HDL) and very low-density lipoprotein (VLDL) particles accounted for the vast majority. Specifically, HDL particles exhibited an elevated PC risk while VLDL particles displayed an opposing pattern. The converse MR analysis underscored a notable alteration in 17 peripheral metabolites due to PC, including branch chain amino acids and derivatives of glycerophospholipid. Cross-referencing the bidirectional MR results revealed a reciprocal causation of PC and X-02269 which might form a self-perpetuating loop in PC development. Additionally, 1-arachidonoylglycerophosphocholine indicated a reduced PC risk and an increase under PC influence, possibly serving as a negative feedback regulator. Conclusion: Our findings suggest a complex interplay between pancreatic cancer and peripheral metabolites, with potential implications for understanding the etiology of pancreatic cancer and identifying novel early diagnosis and therapeutic targets. Moreover, X-02269 may hold a pivotal role in PC onset and progression.
Collapse
Affiliation(s)
| | | | | | | | - Minli Li
- Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Xiangdong Sun
- Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| |
Collapse
|
2
|
Elebo N, Abdel-Shafy EA, Cacciatore S, Nweke EE. Exploiting the molecular subtypes and genetic landscape in pancreatic cancer: the quest to find effective drugs. Front Genet 2023; 14:1170571. [PMID: 37790705 PMCID: PMC10544984 DOI: 10.3389/fgene.2023.1170571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 08/29/2023] [Indexed: 10/05/2023] Open
Abstract
Pancreatic Ductal Adenocarcinoma (PDAC) is a very lethal disease that typically presents at an advanced stage and is non-compliant with most treatments. Recent technologies have helped delineate associated molecular subtypes and genetic variations yielding important insights into the pathophysiology of this disease and having implications for the identification of new therapeutic targets. Drug repurposing has been evaluated as a new paradigm in oncology to accelerate the application of approved or failed target-specific molecules for the treatment of cancer patients. This review focuses on the impact of molecular subtypes on key genomic alterations in PDAC, and the progress made thus far. Importantly, these alterations are discussed in light of the potential role of drug repurposing in PDAC.
Collapse
Affiliation(s)
- Nnenna Elebo
- Department of Surgery, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, Gauteng, South Africa
- Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
| | - Ebtesam A. Abdel-Shafy
- Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
- National Research Centre, Cairo, Egypt
| | - Stefano Cacciatore
- Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
| | - Ekene Emmanuel Nweke
- Department of Surgery, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, Gauteng, South Africa
| |
Collapse
|
3
|
Abdel-Shafy EA, Melak T, MacIntyre DA, Zadra G, Zerbini LF, Piazza S, Cacciatore S. MetChem: a new pipeline to explore structural similarity across metabolite modules. BIOINFORMATICS ADVANCES 2023; 3:vbad053. [PMID: 37424942 PMCID: PMC10322652 DOI: 10.1093/bioadv/vbad053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/28/2023] [Accepted: 04/19/2023] [Indexed: 07/11/2023]
Abstract
Summary Computational analysis and interpretation of metabolomic profiling data remains a major challenge in translational research. Exploring metabolic biomarkers and dysregulated metabolic pathways associated with a patient phenotype could offer new opportunities for targeted therapeutic intervention. Metabolite clustering based on structural similarity has the potential to uncover common underpinnings of biological processes. To address this need, we have developed the MetChem package. MetChem is a quick and simple tool that allows to classify metabolites in structurally related modules, thus revealing their functional information. Availabilityand implementation MetChem is freely available from the R archive CRAN (http://cran.r-project.org). The software is distributed under the GNU General Public License (version 3 or later).
Collapse
Affiliation(s)
| | | | - David A MacIntyre
- March of Dimes Prematurity Research Centre, Imperial College London, London SW7 2AZ, UK
- Imperial College Parturition Research Group, Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
| | - Giorgia Zadra
- Institute of Molecular Genetics, National Research Council, Pavia 27100, Italy
| | - Luiz F Zerbini
- Cancer Genomics, International Centre for Genetic Engineering and Biotechnology, Cape Town 7925, South Africa
| | - Silvano Piazza
- Computation Biology, International Centre for Genetic Engineering and Biotechnology, Trieste 34149, Italy
| | | |
Collapse
|
4
|
Ahmed D, Cacciatore S, Zerbini LF. Metabolite Analyses Using Nuclear Magnetic Resonance (NMR) Spectroscopy in Plasma of Patients with Prostate Cancer. Methods Mol Biol 2023; 2675:195-204. [PMID: 37258765 DOI: 10.1007/978-1-0716-3247-5_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy enables the detection and the quantification of a large range of molecules, including low-molecular-weight metabolites and lipids. NMR spectroscopy is a powerful approach when applied to the high-throughput analysis of plasma or serum samples allowing, in addition, the detection of total proteins, lipoproteins, and signals arising from the glycosylation of circulating acute-phase proteins. Here, we describe the usage of NMR spectroscopy for profiling the plasma or serum of patients with prostate cancer.
Collapse
Affiliation(s)
- Dalia Ahmed
- Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
- Faculty of Medical Laboratory Science, Department of Histopathology and Cytology, IBN SINA University, Khartoum, Sudan
- Faculty of Medical Laboratory Science, Department of Histopathology and Cytology, Omdurman Ahlia University, Omdurman, Sudan
| | - Stefano Cacciatore
- Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
| | - Luiz Fernando Zerbini
- Cancer Genomics Group, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa.
| |
Collapse
|
5
|
Ketavarapu V, Ravikanth V, Sasikala M, Rao GV, Devi CV, Sripadi P, Bethu MS, Amanchy R, Murthy HVV, Pandol SJ, Reddy DN. Integration of metabolites from meta-analysis with transcriptome reveals enhanced SPHK1 in PDAC with a background of pancreatitis. BMC Cancer 2022; 22:792. [PMID: 35854233 PMCID: PMC9295503 DOI: 10.1186/s12885-022-09816-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 06/22/2022] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Pathophysiology of transformation of inflammatory lesions in chronic pancreatitis (CP) to pancreatic ductal adenocarcinoma (PDAC) is not clear. METHODS We conducted a systematic review, meta-analysis of circulating metabolites, integrated this data with transcriptome analysis of human pancreatic tissues and validated using immunohistochemistry. Our aim was to establish biomarker signatures for early malignant transformation in patients with underlying CP and identify therapeutic targets. RESULTS Analysis of 19 studies revealed AUC of 0.86 (95% CI 0.81-0.91, P < 0.0001) for all the altered metabolites (n = 88). Among them, lipids showed higher differentiating efficacy between PDAC and CP; P-value (< 0.0001). Pathway enrichment analysis identified sphingomyelin metabolism (impact value-0.29, FDR of 0.45) and TCA cycle (impact value-0.18, FDR of 0.06) to be prominent pathways in differentiating PDAC from CP. Mapping circulating metabolites to corresponding genes revealed 517 altered genes. Integration of these genes with transcriptome data of CP and PDAC with a background of CP (PDAC-CP) identified three upregulated genes; PIGC, PPIB, PKM and three downregulated genes; AZGP1, EGLN1, GNMT. Comparison of CP to PDAC-CP and PDAC-CP to PDAC identified upregulation of SPHK1, a known oncogene. CONCLUSIONS Our analysis suggests plausible role for SPHK1 in development of pancreatic adenocarcinoma in long standing CP patients. SPHK1 could be further explored as diagnostic and potential therapeutic target.
Collapse
Affiliation(s)
- Vijayasarathy Ketavarapu
- grid.410866.d0000 0004 1803 177XAsian Healthcare Foundation, Asian Institute of Gastroenterology, Mindspace Rd, Gachibowli, Hyderabad, Telangana 500032 India
| | - Vishnubhotla Ravikanth
- grid.410866.d0000 0004 1803 177XAsian Healthcare Foundation, Asian Institute of Gastroenterology, Mindspace Rd, Gachibowli, Hyderabad, Telangana 500032 India
| | - Mitnala Sasikala
- grid.410866.d0000 0004 1803 177XAsian Healthcare Foundation, Asian Institute of Gastroenterology, Mindspace Rd, Gachibowli, Hyderabad, Telangana 500032 India
| | - G. V. Rao
- grid.410866.d0000 0004 1803 177XAIG Hospitals, Mindspace Rd, Gachibowli, Hyderabad, Telangana 500032 India
| | - Ch. Venkataramana Devi
- grid.412419.b0000 0001 1456 3750Department of Biochemistry, University College of Science, Osmania University, Hyderabad, 500 007 India
| | - Prabhakar Sripadi
- grid.417636.10000 0004 0636 1405Centre for Mass Spectrometry, Analytical & Structural Chemistry Department, CSIR-Indian Institute of Chemical Technology, Tarnaka, Hyderabad, 500 007 India
| | - Murali Satyanarayana Bethu
- grid.410865.eDivision of Applied Biology, CSIR-IICT (Indian Institute of Chemical Technology), Ministry of Science and Technology (GOI), Hyderabad, Telangana 500007 India ,grid.240614.50000 0001 2181 8635Department of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center, Elm &Carlton Streets, Buffalo, New York, 14221 USA
| | - Ramars Amanchy
- grid.410865.eDivision of Applied Biology, CSIR-IICT (Indian Institute of Chemical Technology), Ministry of Science and Technology (GOI), Hyderabad, Telangana 500007 India
| | - H. V. V. Murthy
- grid.410866.d0000 0004 1803 177XAsian Healthcare Foundation, Asian Institute of Gastroenterology, Mindspace Rd, Gachibowli, Hyderabad, Telangana 500032 India
| | - Stephen J. Pandol
- grid.50956.3f0000 0001 2152 9905Department of Medicine, Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - D. Nageshwar Reddy
- grid.410866.d0000 0004 1803 177XAIG Hospitals, Mindspace Rd, Gachibowli, Hyderabad, Telangana 500032 India
| |
Collapse
|
6
|
Zinga MM, Abdel-Shafy E, Melak T, Vignoli A, Piazza S, Zerbini LF, Tenori L, Cacciatore S. KODAMA exploratory analysis in metabolic phenotyping. Front Mol Biosci 2022; 9:1070394. [PMID: 36733493 PMCID: PMC9887019 DOI: 10.3389/fmolb.2022.1070394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 12/28/2022] [Indexed: 01/18/2023] Open
Abstract
KODAMA is a valuable tool in metabolomics research to perform exploratory analysis. The advanced analytical technologies commonly used for metabolic phenotyping, mass spectrometry, and nuclear magnetic resonance spectroscopy push out a bunch of high-dimensional data. These complex datasets necessitate tailored statistical analysis able to highlight potentially interesting patterns from a noisy background. Hence, the visualization of metabolomics data for exploratory analysis revolves around dimensionality reduction. KODAMA excels at revealing local structures in high-dimensional data, such as metabolomics data. KODAMA has a high capacity to detect different underlying relationships in experimental datasets and correlate extracted features with accompanying metadata. Here, we describe the main application of KODAMA exploratory analysis in metabolomics research.
Collapse
Affiliation(s)
- Maria Mgella Zinga
- Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
- Department of Medical Parasitology and Entomology, Catholic University of Health and Allied Sciences, Mwanza, Tanzania
| | - Ebtesam Abdel-Shafy
- Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
- National Research Centre, Cairo, Egypt
| | - Tadele Melak
- Computation Biology, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
- Department of clinical chemistry, University of Gondar, Gondar, Ethiopia
| | - Alessia Vignoli
- Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy
| | - Silvano Piazza
- Computation Biology, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
| | - Luiz Fernando Zerbini
- Cancer Genomics, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy
| | - Stefano Cacciatore
- Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
- Institute of Reproductive and Developmental Biology, Imperial College London, London, United Kingdom
- *Correspondence: Stefano Cacciatore,
| |
Collapse
|