Investigation of lipid metabolism dysregulation and the effects on immune microenvironments in pan-cancer using multiple omics data
Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite ab...
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Published in | BMC bioinformatics Vol. 20; no. S7; pp. 195 - 39 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
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BioMed Central Ltd
01.05.2019
BioMed Central BMC |
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Abstract | Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression.
Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets.
Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors. |
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AbstractList | Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression. Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets. Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors. Background Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression. Results Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets. Conclusions Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors. Keywords: Lipid metabolism, Tumor immune micro-environment, Pan-cancer, Multiple omics analysis Background Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression. Results Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets. Conclusions Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors. Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression.BACKGROUNDLipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression.Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets.RESULTSThrough systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets.Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors.CONCLUSIONSOur study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors. Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression. Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets. Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors. Abstract Background Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression. Results Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets. Conclusions Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors. |
ArticleNumber | 195 |
Audience | Academic |
Author | Xu, Yong Zhang, Yuqi Xie, Lu Li, Baoguo Li, Daixi Qin, Guangrong Hao, Yang Ouyang, Jian Wang, Yongkun |
Author_xml | – sequence: 1 givenname: Yang surname: Hao fullname: Hao, Yang – sequence: 2 givenname: Daixi surname: Li fullname: Li, Daixi – sequence: 3 givenname: Yong surname: Xu fullname: Xu, Yong – sequence: 4 givenname: Jian surname: Ouyang fullname: Ouyang, Jian – sequence: 5 givenname: Yongkun surname: Wang fullname: Wang, Yongkun – sequence: 6 givenname: Yuqi surname: Zhang fullname: Zhang, Yuqi – sequence: 7 givenname: Baoguo surname: Li fullname: Li, Baoguo – sequence: 8 givenname: Lu surname: Xie fullname: Xie, Lu – sequence: 9 givenname: Guangrong surname: Qin fullname: Qin, Guangrong |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31074374$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1074/jbc.M113.468967 10.1093/nar/24.1.238 10.1016/j.bbalip.2011.06.009 10.1016/j.cmet.2013.05.017 10.1214/aos/1176345976 10.1073/pnas.1009010107 10.1093/bioinformatics/btp616 10.1038/nmeth.3337 10.1080/2162402X.2017.1344804 10.4049/jimmunol.1501648 10.1186/s13059-016-1028-7 10.1146/annurev.bi.55.070186.000441 10.1371/journal.pone.0011584 10.1016/j.biocel.2017.08.016 10.1002/jat.2838 10.1016/j.cell.2011.02.013 10.1038/s41598-017-05415-9 10.1016/j.celrep.2018.03.077 10.3389/fimmu.2017.00248 10.3390/ijms19020609 10.1083/jcb.138.3.707 10.1164/rccm.201408-1452OC 10.1007/s12013-013-9555-2 10.1158/0008-5472.CAN-17-1492 10.1038/ng.2760 |
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Keywords | Tumor immune micro-environment Lipid metabolism Pan-cancer Multiple omics analysis |
Language | English |
License | Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
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References | B Langlois (2734_CR19) 2010; 5 K Renner (2734_CR5) 2017; 8 IV Yang (2734_CR13) 2014; 190 E Wingender (2734_CR17) 1996; 24 AA Al-Khami (2734_CR24) 2017; 6 MD Robinson (2734_CR10) 2010; 26 X Peng (2734_CR4) 2018; 23 P Sirniö (2734_CR16) 2017; 7 M Zamanian-Daryoush (2734_CR15) 2013; 288 E Currie (2734_CR2) 2013; 18 2734_CR25 DW Dawson (2734_CR23) 1997; 138 2734_CR29 2734_CR26 Shu-Guang Su (2734_CR22) 2017; 91 AM Newman (2734_CR8) 2015; 12 F Su (2734_CR14) 2010; 107 P Needleman (2734_CR11) 1986; 55 B Li (2734_CR9) 2016; 17 S Masaldan (2734_CR18) 2013; 34 MJJA Aryee (2734_CR27) 2014; 30 PK Andersen (2734_CR31) 1982; 10 H Akhavanniaki (2734_CR12) 2013; 67 E Fahy (2734_CR3) 2011; 1811 F Fang (2734_CR28) 2018; 78 JM Poczobutt (2734_CR6) 2016; 196 2734_CR30 D Hanahan (2734_CR1) 2011; 144 TI Zack (2734_CR7) 2013; 45 B Chen (2734_CR20) 1711; 2018 R Saito (2734_CR21) 2018; 19 |
References_xml | – volume: 288 start-page: 21237 issue: 29 year: 2013 ident: 2734_CR15 publication-title: J Biol Chem doi: 10.1074/jbc.M113.468967 – volume: 24 start-page: 238 issue: 1 year: 1996 ident: 2734_CR17 publication-title: Nucleic Acids Res doi: 10.1093/nar/24.1.238 – volume: 1811 start-page: 637 issue: 11 year: 2011 ident: 2734_CR3 publication-title: Biochim Biophys Acta doi: 10.1016/j.bbalip.2011.06.009 – volume: 18 start-page: 153 issue: 2 year: 2013 ident: 2734_CR2 publication-title: Cell Metab doi: 10.1016/j.cmet.2013.05.017 – volume: 10 start-page: 1100 issue: 4 year: 1982 ident: 2734_CR31 publication-title: Ann Stat doi: 10.1214/aos/1176345976 – volume: 107 start-page: 19997 issue: 46 year: 2010 ident: 2734_CR14 publication-title: Proc Natl Acad Sci U S A doi: 10.1073/pnas.1009010107 – volume: 26 start-page: 139 issue: 1 year: 2010 ident: 2734_CR10 publication-title: Bioinformatics. doi: 10.1093/bioinformatics/btp616 – ident: 2734_CR25 – volume: 12 start-page: 453 issue: 5 year: 2015 ident: 2734_CR8 publication-title: Nat Methods doi: 10.1038/nmeth.3337 – volume: 6 start-page: e1344804 issue: 10 year: 2017 ident: 2734_CR24 publication-title: Oncoimmunology. doi: 10.1080/2162402X.2017.1344804 – volume: 196 start-page: 891 issue: 2 year: 2016 ident: 2734_CR6 publication-title: J Immunol doi: 10.4049/jimmunol.1501648 – ident: 2734_CR30 – volume: 17 start-page: 174 issue: 1 year: 2016 ident: 2734_CR9 publication-title: Genome Biol doi: 10.1186/s13059-016-1028-7 – volume: 55 start-page: 69 issue: 4 year: 1986 ident: 2734_CR11 publication-title: Annu Rev Biochem doi: 10.1146/annurev.bi.55.070186.000441 – volume: 5 start-page: e11584 issue: 7 year: 2010 ident: 2734_CR19 publication-title: PloS One doi: 10.1371/journal.pone.0011584 – volume: 2018 start-page: 243 year: 1711 ident: 2734_CR20 publication-title: Methods Mol Biol – ident: 2734_CR29 – volume: 91 start-page: 53 year: 2017 ident: 2734_CR22 publication-title: The International Journal of Biochemistry & Cell Biology doi: 10.1016/j.biocel.2017.08.016 – volume: 34 start-page: 95 issue: 1 year: 2013 ident: 2734_CR18 publication-title: J Appl Toxicol doi: 10.1002/jat.2838 – volume: 144 start-page: 646 issue: 5 year: 2011 ident: 2734_CR1 publication-title: Cell. doi: 10.1016/j.cell.2011.02.013 – volume: 7 start-page: 5374 issue: 1 year: 2017 ident: 2734_CR16 publication-title: Sci Rep doi: 10.1038/s41598-017-05415-9 – volume: 30 start-page: 1363 issue: 10 year: 2014 ident: 2734_CR27 publication-title: Nature – volume: 23 start-page: 255 issue: 1 year: 2018 ident: 2734_CR4 publication-title: Cell Rep doi: 10.1016/j.celrep.2018.03.077 – volume: 8 start-page: 248 year: 2017 ident: 2734_CR5 publication-title: Front Immunol doi: 10.3389/fimmu.2017.00248 – volume: 19 start-page: 609 issue: 2 year: 2018 ident: 2734_CR21 publication-title: Int J Mol Sci doi: 10.3390/ijms19020609 – volume: 138 start-page: 707 issue: 3 year: 1997 ident: 2734_CR23 publication-title: J Cell Biol doi: 10.1083/jcb.138.3.707 – volume: 190 start-page: 1263 issue: 11 year: 2014 ident: 2734_CR13 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201408-1452OC – volume: 67 start-page: 501 issue: 2 year: 2013 ident: 2734_CR12 publication-title: Cell Biochemi Biophys doi: 10.1007/s12013-013-9555-2 – ident: 2734_CR26 – volume: 78 start-page: 631 issue: 3 year: 2018 ident: 2734_CR28 publication-title: Cancer Res doi: 10.1158/0008-5472.CAN-17-1492 – volume: 45 start-page: 1134 issue: 10 year: 2013 ident: 2734_CR7 publication-title: Nat Genet doi: 10.1038/ng.2760 |
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Snippet | Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid... Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid... Background Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of... Abstract Background Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and... |
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SubjectTerms | Analysis Arachidonic acid Backup software Biological markers Biomarkers Biomarkers, Tumor - genetics Cancer Carcinogenesis CD36 antigen Cholesterol Commonality Computational Biology - methods Correlation analysis Criminal investigation Crosstalk Deoxyribonucleic acid DNA DNA binding proteins DNA Methylation Fatty acids Gene expression Gene Expression Profiling Gene Expression Regulation, Neoplastic Genes Humans Immune response Immune system Immunosuppression Immunotherapy Lipid metabolism Lipid Metabolism - genetics Lipids Metabolism Metabolites Methylation Microenvironments Multiple omics analysis Mutation Neoplasms - genetics Neoplasms - immunology Neoplasms - metabolism Neoplasms - pathology Novels Ovarian cancer Pan-cancer Peroxisome proliferator-activated receptors Therapeutic applications Transcription (Genetics) Transcription factors Transcriptome Tumor immune micro-environment Tumor Microenvironment - genetics Tumor Microenvironment - immunology Tumorigenesis Tumors Unsaturated fatty acids |
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Title | Investigation of lipid metabolism dysregulation and the effects on immune microenvironments in pan-cancer using multiple omics data |
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