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Bossi LE, Palumbo C, Trojani A, Melluso A, Di Camillo B, Beghini A, Sarnataro LM, Cairoli R. A Nine-Gene Expression Signature Distinguished a Patient with Chronic Lymphocytic Leukemia Who Underwent Prolonged Periodic Fasting. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1405. [PMID: 37629695 PMCID: PMC10456711 DOI: 10.3390/medicina59081405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/27/2023]
Abstract
Background and Objectives: This study aimed to investigate the causes of continuous deep fluctuations in the absolute lymphocyte count (ALC) in an untreated patient with Chronic Lymphocytic Leukemia (CLL), who has had a favorable prognosis since the time of diagnosis. Up until now, the patient has voluntarily chosen to adopt a predominantly vegetarian and fruitarian diet, along with prolonged periods of total fasting (ranging from 4 to 39 days) each year. Materials and Methods: For this purpose, we decided to analyze the whole transcriptome profiling of peripheral blood (PB) CD19+ cells from the patient (#1) at different time-points vs. the same cells of five other untreated CLL patients who followed a varied diet. Consequently, the CLL patients were categorized as follows: the 1st group comprised patient #1 at 20 different time-points (16 time-points during nutrition and 4 time-points during fasting), whereas the 2nd group included only one time point for each of the patients (#2, #3, #4, #5, and #6) as they followed a varied diet. We performed microarray experiments using a powerful tool, the Affymetrix Human Clariom™ D Pico Assay, to generate high-fidelity biomarker signatures. Statistical analysis was employed to identify differentially expressed genes and to perform sample clustering. Results: The lymphocytosis trend in patient #1 showed recurring fluctuations since the time of diagnosis. Interestingly, we observed that approximately 4-6 weeks after the conclusion of fasting periods, the absolute lymphocyte count was reduced by about half. The gene expression profiling analysis revealed that nine genes were statistically differently expressed between the 1st group and the 2nd group. Specifically, IGLC3, RPS26, CHPT1, and PCDH9 were under expressed in the 1st group compared to the 2nd group of CLL patients. Conversely, IGHV3-43, IGKV3D-20, PLEKHA1, CYBB, and GABRB2 were over-expressed in the 1st group when compared to the 2nd group of CLL patients. Furthermore, clustering analysis validated that all the samples from patient #1 clustered together, showing clear separation from the samples of the other CLL patients. Conclusions: This study unveiled a small gene expression signature consisting of nine genes that distinguished an untreated CLL patient who followed prolonged periods of total fasting, maintaining a gradual growth trend of lymphocytosis, compared to five untreated CLL patients with a varied diet. Future investigations focusing on patient #1 could potentially shed light on the role of prolonged periodic fasting and the implication of this specific gene signature in sustaining the lymphocytosis trend and the favorable course of the disease.
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Affiliation(s)
- Luca Emanuele Bossi
- Department of Hematology and Oncology ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (A.T.); (A.M.); (L.M.S.); (R.C.)
| | - Cassandra Palumbo
- Department of Hematology and Oncology ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (A.T.); (A.M.); (L.M.S.); (R.C.)
| | - Alessandra Trojani
- Department of Hematology and Oncology ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (A.T.); (A.M.); (L.M.S.); (R.C.)
| | - Agostina Melluso
- Department of Hematology and Oncology ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (A.T.); (A.M.); (L.M.S.); (R.C.)
| | - Barbara Di Camillo
- Department of Information Engineering, University of Padova, 35020 Padua, Italy;
- Department of Comparative Biomedicine and Food Science, University of Padova, 35020 Padua, Italy
| | | | - Luca Maria Sarnataro
- Department of Hematology and Oncology ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (A.T.); (A.M.); (L.M.S.); (R.C.)
| | - Roberto Cairoli
- Department of Hematology and Oncology ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (A.T.); (A.M.); (L.M.S.); (R.C.)
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Wang Z, Tao P, Fan P, Wang J, Rong T, Hou Y, Zhou Y, Lu W, Hong L, Ma L, Zhang Y, Tong H. Insight of a lipid metabolism prognostic model to identify immune landscape and potential target for retroperitoneal liposarcoma. Front Immunol 2023; 14:1209396. [PMID: 37483592 PMCID: PMC10359070 DOI: 10.3389/fimmu.2023.1209396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 06/20/2023] [Indexed: 07/25/2023] Open
Abstract
Introduction The exploration of lipid metabolism dysregulation may provide novel perspectives for retroperitoneal liposarcoma (RPLS). In our study, we aimed to investigate potential targets and facilitate further understanding of immune landscape in RPLS, through lipid metabolism-associated genes (LMAGs) based prognostic model. Methods Gene expression profiles and corresponding clinical information of 234 cases were enrolled from two public databases and the largest retroperitoneal tumor research center of East China, including cohort-TCGA (n=58), cohort-GSE30929 (n=92), cohort-FD (n=50), cohort-scRNA-seq (n=4) and cohort-validation (n=30). Consensus clustering analysis was performed to identify lipid metabolism-associated molecular subtypes (LMSs). A prognostic risk model containing 13 LMAGs was established using LASSO algorithm and multivariate Cox analysis in cohort-TCGA. ESTIMATE, CIBERSORT, XCELL and MCP analyses were performed to visualize the immune landscape. WGCNA was used to identify three hub genes among the 13 model LMAGs, and preliminarily validated in both cohort-GSE30929 and cohort-FD. Moreover, TIMER was used to visualize the correlation between antigen-presenting cells and potential targets. Finally, single-cell RNA-sequencing (scRNA-seq) analysis of four RPLS and multiplexed immunohistochemistry (mIHC) were performed in cohort-validation to validate the discoveries of bioinformatics analysis. Results LMS1 and LMS2 were characterized as immune-infiltrated and -excluded tumors, with significant differences in molecular features and clinical prognosis, respectively. Elongation of very long chain fatty acids protein 2 (ELOVL2), the enzyme that catalyzed the elongation of long chain fatty acids, involved in the maintenance of lipid metabolism and cellular homeostasis in normal cells, was identified and negatively correlated with antigen-presenting cells and identified as a potential target in RPLS. Furthermore, ELOVL2 was enriched in LMS2 with significantly lower immunoscore and unfavorable prognosis. Finally, a high-resolution dissection through scRNA-seq was performed in four RPLS, revealing the entire tumor ecosystem and validated previous findings. Discussion The LMS subgroups and risk model based on LMAGs proposed in our study were both promising prognostic classifications for RPLS. ELOVL2 is a potential target linking lipid metabolism to immune regulations against RPLS, specifically for patients with LMS2 tumors.
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Affiliation(s)
- Zhenyu Wang
- First Affiliated Hospital, Anhui University of Science and Technology, Huainan, China
| | - Ping Tao
- Department of Laboratory Medicine, Shanghai Traditional Chinese Medicine-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Peidang Fan
- First Affiliated Hospital, Anhui University of Science and Technology, Huainan, China
| | - Jiongyuan Wang
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Tao Rong
- First Affiliated Hospital, Anhui University of Science and Technology, Huainan, China
| | - Yingyong Hou
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuhong Zhou
- Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Weiqi Lu
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Liang Hong
- Department of General Surgery, Shanghai Fifth People’s Hospital, Fudan University, Shanghai, China
| | - Lijie Ma
- Department of General Surgery, Shanghai Fifth People’s Hospital, Fudan University, Shanghai, China
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yong Zhang
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Hanxing Tong
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
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Lin X, Yang Q, Zheng D, Tian H, Chen L, Wu J, Ji Z, Chen Y, Li Z. Scientometric analysis of lipid metabolism in breast neoplasm: 2012-2021. Front Physiol 2023; 14:1042603. [PMID: 37179822 PMCID: PMC10168182 DOI: 10.3389/fphys.2023.1042603] [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: 10/04/2022] [Accepted: 04/10/2023] [Indexed: 05/15/2023] Open
Abstract
Introduction: In recent years, more and more studies have proved that lipid metabolism plays an essential role in breast cancer's proliferation and metastasisand also has a specific significance in predicting survival. Methods: This paper collected data from 725 publications related to lipid metabolism in breast neoplasm from 2012 to 2021 through the Web of Science Core Collection database. Bibliometrix, VOSviewer, and CiteSpace were used for the scientometrics analysis of countries, institutions, journals, authors, keywords, etc. Results: The number of documents published showed an increasing trend, with an average annual growth rate of 14.49%. The United States was the most productive country (n = 223, 30.76%). The journals with the largest number of publications are mostly from developed countries. Except for the retrieved topics, "lipid metabolism" (n = 272) and "breast cancer" (n = 175), the keywords that appeared most frequently were "expression" (n = 151), "fatty-acid synthase" (n = 78), "growth" (n = 72), "metabolism" (n = 67) and "cells" (n = 66). Discussion: These findings and summaries help reveal the current research status and clarify the hot spots in this field.
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Affiliation(s)
| | | | | | | | | | | | | | - Yexi Chen
- Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Zhiyang Li
- Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
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Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers. Hum Genomics 2023; 17:18. [PMID: 36879264 PMCID: PMC9990231 DOI: 10.1186/s40246-023-00465-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 02/22/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers. METHODS In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates. RESULTS The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers. CONCLUSIONS We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.
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Lu J, Liu P, Zhang R. A Metabolic Gene Signature to Predict Breast Cancer Prognosis. Front Mol Biosci 2022; 9:900433. [PMID: 35847988 PMCID: PMC9277072 DOI: 10.3389/fmolb.2022.900433] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 05/25/2022] [Indexed: 01/07/2023] Open
Abstract
Background: The existing metabolic gene signatures for predicting breast cancer outcomes only focus on gene expression data without considering clinical characteristics. Therefore, this study aimed to establish a predictive risk model combining metabolic enzyme genes and clinicopathological characteristics to predict the overall survival in patients with breast cancer. Methods: Transcriptomics and corresponding clinical data for patients with breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed metabolic genes between tumors and normal tissues were identified in the TCGA dataset (training dataset). A prognostic model was then built using univariate and multifactorial Cox proportional hazards regression analyses in the training dataset. The capability of the predictive model was then assessed using the receiver operating characteristic in both datasets. Pathway enrichment analysis and immune cell infiltration were performed using Kyoto Encyclopedia of Genes and Genomes (KEGG)/Gene Ontology (GO) enrichment and CIBERSORT algorithm, respectively. Results: In breast cancer and normal tissues, 212 metabolic enzyme genes were differentially expressed. The predictive model included four factors: age, stage, and expression of SLC35A2 and PLA2G10. Patients with breast cancer were classified into high- and low-risk groups based on the model; the high-risk group had a significantly poorer overall survival rate than the low-risk group. Furthermore, the two risk groups showed different activation of pathways and alterations in the properties of tumor microenvironment-infiltrating immune cells. Conclusion: We developed a powerful model to predict prognosis in patients with breast cancer by combining the gene expression of metabolic enzymes with clinicopathological characteristics.
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Affiliation(s)
- Jun Lu
- Hunan Normal University School of Medicine, Changsha, China
| | - Pinbo Liu
- Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Ran Zhang
- Hunan Normal University School of Medicine, Changsha, China
- *Correspondence: Ran Zhang,
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