1
|
Takanashi Y, Kahyo T, Sekihara K, Kawase A, Setou M, Funai K. Prognostic potential of lipid profiling in cancer patients: a systematic review of mass spectrometry-based studies. Lipids Health Dis 2024; 23:154. [PMID: 38796445 PMCID: PMC11128116 DOI: 10.1186/s12944-024-02121-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 04/24/2024] [Indexed: 05/28/2024] Open
Abstract
Cancer prognosis remains a critical clinical challenge. Lipidomic analysis via mass spectrometry (MS) offers the potential for objective prognostic prediction, leveraging the distinct lipid profiles of cancer patient-derived specimens. This review aims to systematically summarize the application of MS-based lipidomic analysis in prognostic prediction for cancer patients. Our systematic review summarized 38 studies from the past decade that attempted prognostic prediction of cancer patients through lipidomics. Commonly analyzed cancers included colorectal, prostate, and breast cancers. Liquid (serum and urine) and tissue samples were equally used, with liquid chromatography-tandem MS being the most common analytical platform. The most frequently evaluated prognostic outcomes were overall survival, stage, and recurrence. Thirty-eight lipid markers (including phosphatidylcholine, ceramide, triglyceride, lysophosphatidylcholine, sphingomyelin, phosphatidylethanolamine, diacylglycerol, phosphatidic acid, phosphatidylserine, lysophosphatidylethanolamine, lysophosphatidic acid, dihydroceramide, prostaglandin, sphingosine-1-phosphate, phosphatidylinosito, fatty acid, glucosylceramide and lactosylceramide) were identified as prognostic factors, demonstrating potential for clinical application. In conclusion, the potential for developing lipidomics in cancer prognostic prediction was demonstrated. However, the field is still nascent, necessitating future studies for validating and establishing lipid markers as reliable prognostic tools in clinical practice.
Collapse
Affiliation(s)
- Yusuke Takanashi
- First Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo- ku, Hamamatsu, Shizuoka, 431-3192, Japan.
| | - Tomoaki Kahyo
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi Ward, Hamamatsu, Shizuoka, 431-3192, Japan
- International Mass Imaging Center, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo-ku, Hamamatsu, Shizuoka, 431-3192, Japan
| | - Keigo Sekihara
- First Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo- ku, Hamamatsu, Shizuoka, 431-3192, Japan
| | - Akikazu Kawase
- First Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo- ku, Hamamatsu, Shizuoka, 431-3192, Japan
| | - Mitsutoshi Setou
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi Ward, Hamamatsu, Shizuoka, 431-3192, Japan
- International Mass Imaging Center, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo-ku, Hamamatsu, Shizuoka, 431-3192, Japan
- Department of Systems Molecular Anatomy, Institute for Medical Photonics Research, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo-ku, Hamamatsu, Shizuoka, 431-3192, Japan
| | - Kazuhito Funai
- First Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo- ku, Hamamatsu, Shizuoka, 431-3192, Japan
| |
Collapse
|
2
|
Ma X, Fernández FM. Advances in mass spectrometry imaging for spatial cancer metabolomics. MASS SPECTROMETRY REVIEWS 2024; 43:235-268. [PMID: 36065601 PMCID: PMC9986357 DOI: 10.1002/mas.21804] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 05/09/2023]
Abstract
Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.
Collapse
Affiliation(s)
- Xin Ma
- School of Chemistry and Biochemistry and Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Facundo M Fernández
- School of Chemistry and Biochemistry and Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA
| |
Collapse
|
3
|
Chen X, Li YX, Cao X, Qiang MY, Liang CX, Ke LR, Cai ZC, Huang YY, Zhan ZJ, Zhou JY, Deng Y, Zhang LL, Huang HY, Li X, Mei J, Xie GT, Guo X, Lv X. Widely targeted quantitative lipidomics and prognostic model reveal plasma lipid predictors for nasopharyngeal carcinoma. Lipids Health Dis 2023; 22:81. [PMID: 37365637 DOI: 10.1186/s12944-023-01830-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/07/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Dysregulation of lipid metabolism is closely associated with cancer progression. The study aimed to establish a prognostic model to predict distant metastasis-free survival (DMFS) in patients with nasopharyngeal carcinoma (NPC), based on lipidomics. METHODS The plasma lipid profiles of 179 patients with locoregionally advanced NPC (LANPC) were measured and quantified using widely targeted quantitative lipidomics. Then, patients were randomly split into the training (125 patients, 69.8%) and validation (54 patients, 30.2%) sets. To identify distant metastasis-associated lipids, univariate Cox regression was applied to the training set (P < 0.05). A deep survival method called DeepSurv was employed to develop a proposed model based on significant lipid species (P < 0.01) and clinical biomarkers to predict DMFS. Concordance index and receiver operating curve analyses were performed to assess model effectiveness. The study also explored the potential role of lipid alterations in the prognosis of NPC. RESULTS Forty lipids were recognized as distant metastasis-associated (P < 0.05) by univariate Cox regression. The concordance indices of the proposed model were 0.764 (95% confidence interval (CI), 0.682-0.846) and 0.760 (95% CI, 0.649-0.871) in the training and validation sets, respectively. High-risk patients had poorer 5-year DMFS compared with low-risk patients (Hazard ratio, 26.18; 95% CI, 3.52-194.80; P < 0.0001). Moreover, the six lipids were significantly correlated with immunity- and inflammation-associated biomarkers and were mainly enriched in metabolic pathways. CONCLUSIONS Widely targeted quantitative lipidomics reveals plasma lipid predictors for LANPC, the prognostic model based on that demonstrated superior performance in predicting metastasis in LANPC patients.
Collapse
Affiliation(s)
- Xi Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | | | - Xun Cao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
- Department of Intensive Care Unit, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Meng-Yun Qiang
- Department of Head and Neck Radiotherapy, the Cancer Hospitalof the, University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer, Chinese Academy of Sciences , Hangzhou, 310022, China
| | - Chi-Xiong Liang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Liang-Ru Ke
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
- Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Zhuo-Chen Cai
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Ying-Ying Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Ze-Jiang Zhan
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Jia-Yu Zhou
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Ying Deng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Lu-Lu Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Hao-Yang Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Xiang Li
- Ping An Technology, Shenzhen, 518000, China
| | - Jing Mei
- Ping An Technology, Shenzhen, 518000, China
| | | | - Xiang Guo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
| | - Xing Lv
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
| |
Collapse
|
4
|
Tan X, Zhang R, Lan M, Wen C, Wang H, Guo J, Zhao X, Xu H, Deng P, Pi H, Yu Z, Yue R, Hu H. Integration of transcriptomics, metabolomics, and lipidomics reveals the mechanisms of doxorubicin-induced inflammatory responses and myocardial dysfunction in mice. Biomed Pharmacother 2023; 162:114733. [PMID: 37087977 DOI: 10.1016/j.biopha.2023.114733] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 04/25/2023] Open
Abstract
Doxorubicin (DOX) is an anthracycline antineoplastic agent that has limited clinical utility due to its dose-dependent cardiotoxicity. Although the exact mechanism remains unknown, inflammatory responses have been implicated in DOX-induced cardiotoxicity (DIC). In this study, we analyzed the transcriptomic, metabolomic as well as lipidomic changes in the DOX-treated mice to explore the underlying mechanisms of DIC. We found that continuous intraperitoneal DOX injections (3 mg/kg/d) for a period of five days significantly induced cardiac dysfunction and cardiac injury in male C57BL/6 J mice (8 weeks old). This corresponded to a significant increase in the myocardial levels of IL-4, IL-6, IL-10, IL-17 and IL-12p70. Furthermore, inflammation-related genes such as Ptgs2, Il1b, Cxcl5, Cxcl1, Cxcl2, Mmp3, Ccl2, Ccl12, Nfkbia, Fos, Mapk11 and Tnf were differentially expressed in the DOX-treated group, and enriched in the IL-17 and TNF signaling pathways. Besides, amino acids, peptides, imidazoles, toluenes, hybrid peptides, fatty acids and lipids such as Hex1Cer, Cer, SM, PG and ACCa were significantly associated with the expression pattern of inflammation-related genes. In conclusion, the integration of transcriptomic, metabolomic and lipidomic data identified potential new targets and biomarkers of DIC.
Collapse
Affiliation(s)
- Xin Tan
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; Academician Workstation, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Rongyi Zhang
- Department of Cardiology, Nanchong Central Hospital, The Second Clinical Institute of North Sichuan Medical College, Nanchong China; Jinan University, No. 601 Huangpu Avenue West, Guangzhou 510632, China
| | - Meide Lan
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; Academician Workstation, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Cong Wen
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; Academician Workstation, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Hao Wang
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; Academician Workstation, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Junsong Guo
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; Academician Workstation, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Xuemei Zhao
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; Academician Workstation, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Hui Xu
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; Academician Workstation, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Ping Deng
- Department of Occupational Health, Third Military Medical University, Chongqing 400038, China
| | - Huifeng Pi
- Department of Occupational Health, Third Military Medical University, Chongqing 400038, China
| | - Zhengping Yu
- Department of Occupational Health, Third Military Medical University, Chongqing 400038, China
| | - Rongchuan Yue
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; Academician Workstation, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China.
| | - Houxiang Hu
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; Academician Workstation, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; Jinan University, No. 601 Huangpu Avenue West, Guangzhou 510632, China.
| |
Collapse
|
5
|
Yang C, Zhou S, Zhu J, Sheng H, Mao W, Fu Z, Chen Z. Plasma Lipid-based Machine Learning Models Provides a Potential Diagnostic Tool for Colorectal Cancer Patients. Clin Chim Acta 2022; 536:191-199. [PMID: 36191612 DOI: 10.1016/j.cca.2022.09.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 08/23/2022] [Accepted: 09/01/2022] [Indexed: 11/29/2022]
Abstract
Colorectal cancer is the second leading cause of cancer-related death across the world. So far, screening methods for colorectal cancer are limited to blood test, imaging test, and digital rectal examination, that are either invasive or ineffective. So, this study aims to explore novel, more convenient and effective diagnostic methods for colorectal cancer. First, the experiment cohort was randomly split to train set and test set, and LC-MS-based plasma lipidomics was applied to identify lipid features in colorectal cancer. Second, univariate and multivariate analyses were performed to screen for significantly differentially expressed lipids. Third, single-lipid-based ROC analysis and multiple-lipid-based machine learning modelling were conducted to assess differential lipids' diagnostic performance. Lastly, survival analyses were used to evaluate lipids' prognostic values. In total, 41 differential lipids were screened out, 10 were upregulated and 31 were downregulated in CRC. Only CerP(d15:0_22:0+O) showed fine predictive accuracy in single-lipid-base ROC analysis. Among the four machine learning models, SVM showed best predictive performance with accuracy (in predicting test set) of 1.0000 (95%CI: 0.8806, 1.0000), that can be reached by modelling with only 14 lipids. Four lipids had significant prognostic values, that were TG(11:0_18:0_18:0) (HR: 0.34), TG(18:0_18:0_18:1) (HR: 0.34), PC(22:1_12:3) (HR: 2.22), LPC(17:0) (HR: 3.16). In conclusion, this study discovered novel lipid features that has potential diagnostic and prognostic values, and showed combination of plasma lipidomics and machine learning modelling could have outstanding diagnostic performance and may serve as a convenient and more accessible way to aid clinical diagnosis of colorectal cancer.
Collapse
Affiliation(s)
- Chenxi Yang
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Sicheng Zhou
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Jing Zhu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Huaying Sheng
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Weimin Mao
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory of Diagnosis & Treatment Technology on Thoracic Oncology (Lung and Esophagus), Hangzhou, Zhejiang, China
| | - Zhixuan Fu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Zhongjian Chen
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory of Diagnosis & Treatment Technology on Thoracic Oncology (Lung and Esophagus), Hangzhou, Zhejiang, China.
| |
Collapse
|
6
|
Vetrano IG, Dei Cas M, Nazzi V, Eoli M, Innocenti N, Saletti V, Potenza A, Carrozzini T, Pollaci G, Gorla G, Paroni R, Ghidoni R, Gatti L. The Lipid Asset Is Unbalanced in Peripheral Nerve Sheath Tumors. Int J Mol Sci 2021; 23:ijms23010061. [PMID: 35008487 PMCID: PMC8744637 DOI: 10.3390/ijms23010061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/19/2021] [Accepted: 12/20/2021] [Indexed: 12/11/2022] Open
Abstract
Peripheral nerve sheath tumors (PNSTs) include schwannomas, neurofibromas (NFs), and plexiform neurofibromas (PNFs), among others. While they are benign tumors, according to their biological behavior, some have the potential for malignant degeneration, mainly PNFs. The specific factors contributing to the more aggressive behavior of some PNSTs compared to others are not precisely known. Considering that lipid homeostasis plays a crucial role in fibrotic/inflammatory processes and in several cancers, we hypothesized that the lipid asset was also unbalanced in this group of nerve tumors. Through untargeted lipidomics, NFs presented a significant increase in ceramide, phosphatidylcholine, and Vitamin A ester. PNFs displayed a marked decrease in 34 out of 50 lipid class analyzed. An increased level of ether- and oxidized-triacylglycerols was observed; phosphatidylcholines were reduced. After sphingolipidomic analysis, we observed six sphingolipid classes. Ceramide and dihydroceramides were statistically increased in NFs. All the glycosylated species appeared reduced in NFs, but increased in PNFs. Our findings suggested that different subtypes of PNSTs presented a specific modulation in the lipidic profile. The untargeted and targeted lipidomic approaches, which were not applied until now, contribute to better clarifying bioactive lipid roles in PNS natural history to highlight disease molecular features and pathogenesis.
Collapse
Affiliation(s)
- Ignazio G. Vetrano
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (V.N.); (N.I.)
- Correspondence:
| | - Michele Dei Cas
- Department of Health Sciences, Università degli Studi di Milano, 20142 Milan, Italy; (M.D.C.); (R.P.)
| | - Vittoria Nazzi
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (V.N.); (N.I.)
| | - Marica Eoli
- Molecular Neuro-Oncology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy;
| | - Niccolò Innocenti
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (V.N.); (N.I.)
| | - Veronica Saletti
- Developmental Neurology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy;
| | - Antonella Potenza
- Neurobiology Laboratory, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (A.P.); (T.C.); (G.P.); (G.G.); (L.G.)
| | - Tatiana Carrozzini
- Neurobiology Laboratory, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (A.P.); (T.C.); (G.P.); (G.G.); (L.G.)
| | - Giuliana Pollaci
- Neurobiology Laboratory, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (A.P.); (T.C.); (G.P.); (G.G.); (L.G.)
| | - Gemma Gorla
- Neurobiology Laboratory, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (A.P.); (T.C.); (G.P.); (G.G.); (L.G.)
| | - Rita Paroni
- Department of Health Sciences, Università degli Studi di Milano, 20142 Milan, Italy; (M.D.C.); (R.P.)
| | - Riccardo Ghidoni
- Neurorehabilitation Department, IRCCS Istituti Clinici Scientifici Maugeri, 20138 Milan, Italy;
| | - Laura Gatti
- Neurobiology Laboratory, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (A.P.); (T.C.); (G.P.); (G.G.); (L.G.)
| |
Collapse
|