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Zhao M, Zhang X, Huan Q, Dong M. Metabolism-associated molecular classification of cervical cancer. BMC Womens Health 2023; 23:555. [PMID: 37884919 PMCID: PMC10605340 DOI: 10.1186/s12905-023-02712-6] [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] [Received: 05/22/2023] [Accepted: 10/16/2023] [Indexed: 10/28/2023] Open
Abstract
OBJECTIVE This study aimed to explore metabolic abnormalities in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) for metabolism-related genes. METHODS We downloaded expression data for metabolism-related genes, performed differential expression analysis, and applied weighted gene co-expression network analysis (WGCNA) to identify metabolism-related functional modules. We obtained normalised miRNA expression data and identified master methylation regulators for metabolism-related genes. Cox regression of data on metabolism-related genes was performed to screen for genes that affect the prognosis of patients with CESC. Furthermore, we selected key genes for validation. RESULTS Our results identified 3620 metabolism-related genes in CESC, 2493 of which contained related mutations. The co-occurrence of CUBN, KALRN, and HERC1 was related to the prognosis of CESC. The fraction of genome altered (FGA) closely correlated with overall survival. In expression analysis, 374 genes were related to the occurrence and prognosis of CESC. We then identified four metabolic pathway modules in WGCNA. Further analysis revealed that glycolysis/gluconeogenesis was related to endothelial cells and that arachidonic acid metabolism was related to cell proliferation. These four modules were also related to the prognosis of CESC. Among CESC-related metabolic genes, two genes were found to be regulated by microRNAs (miRNAs) and methylation, whereas another two genes were coregulated by miRNAs and mutations. CONCLUSIONS Among metabolism-related genes, 15 genes were related to the prognosis of CESC. The co-occurrence of CUBN/KALRN/HERC1 was associated with CESC prognosis. Glycolysis/gluconeogenesis was related to endothelial cells, and arachidonic acid metabolism was related to cell proliferation.
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Affiliation(s)
- Min Zhao
- School of Medicine, Mianyang Central Hospital, University of Electronic Science and Technology of China, Mianyang, 621000, Sichuan, China.
| | - Xue Zhang
- School of Life Sciences, China Medical University, Shenyang, China
| | - Qing Huan
- Shandong Key Laboratory of Reproductive Medicine, Department of Obstetrics and Gynecology, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Meng Dong
- School of Life Sciences, China Medical University, Shenyang, China
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Mar D, Babenko IM, Zhang R, Noble WS, Denisenko O, Vaisar T, Bomsztyk K. MultiomicsTracks96: A high throughput PIXUL-Matrix-based toolbox to profile frozen and FFPE tissues multiomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.16.533031. [PMID: 36993219 PMCID: PMC10055122 DOI: 10.1101/2023.03.16.533031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Background The multiome is an integrated assembly of distinct classes of molecules and molecular properties, or "omes," measured in the same biospecimen. Freezing and formalin-fixed paraffin-embedding (FFPE) are two common ways to store tissues, and these practices have generated vast biospecimen repositories. However, these biospecimens have been underutilized for multi-omic analysis due to the low throughput of current analytical technologies that impede large-scale studies. Methods Tissue sampling, preparation, and downstream analysis were integrated into a 96-well format multi-omics workflow, MultiomicsTracks96. Frozen mouse organs were sampled using the CryoGrid system, and matched FFPE samples were processed using a microtome. The 96-well format sonicator, PIXUL, was adapted to extract DNA, RNA, chromatin, and protein from tissues. The 96-well format analytical platform, Matrix, was used for chromatin immunoprecipitation (ChIP), methylated DNA immunoprecipitation (MeDIP), methylated RNA immunoprecipitation (MeRIP), and RNA reverse transcription (RT) assays followed by qPCR and sequencing. LC-MS/MS was used for protein analysis. The Segway genome segmentation algorithm was used to identify functional genomic regions, and linear regressors based on the multi-omics data were trained to predict protein expression. Results MultiomicsTracks96 was used to generate 8-dimensional datasets including RNA-seq measurements of mRNA expression; MeRIP-seq measurements of m6A and m5C; ChIP-seq measurements of H3K27Ac, H3K4m3, and Pol II; MeDIP-seq measurements of 5mC; and LC-MS/MS measurements of proteins. We observed high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles (ChIP-seq: H3K27Ac, H3K4m3, Pol II; MeDIP-seq: 5mC) was able to recapitulate and predict organ-specific super-enhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic expression profiles can be more accurately predicted by the full suite of multi-omics data, compared to using epigenomic, transcriptomic, or epitranscriptomic measurements individually. Conclusions The MultiomicsTracks96 workflow is well suited for high dimensional multi-omics studies - for instance, multiorgan animal models of disease, drug toxicities, environmental exposure, and aging as well as large-scale clinical investigations involving the use of biospecimens from existing tissue repositories.
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Song X, Zhang T, Ding H, Feng Y, Yang W, Yin X, Chen B, Liang Y, Mao Q, Xia W, Yu G, Xu L, Dong G, Jiang F. Non-genetic stratification reveals epigenetic heterogeneity and identifies vulnerabilities of glycolysis addiction in lung adenocarcinoma subtype. Oncogenesis 2022; 11:61. [PMID: 36216804 PMCID: PMC9550819 DOI: 10.1038/s41389-022-00436-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 09/12/2022] [Accepted: 09/26/2022] [Indexed: 11/12/2022] Open
Abstract
Lung adenocarcinoma (LUAD) exhibits high heterogeneity and is well known for its high genetic variation. Recently, the understanding of non-genetic variation provides a new perspective to study the heterogeneity of LUAD. Little is known about whether super-enhancers (SEs) may be primarily responsible for the inter-tumor heterogeneity of LUAD. We used super-enhancer RNA (seRNA) levels of a large-scale clinical well-annotated LUAD cohort to stratify patients into three clusters with different prognosis and other malignant characteristics. Mechanistically, estrogen-related receptor alpha (ERRα) in cluster 3-like cell lines acts as a cofactor of BRD4 to assist SE-promoter loops to activate glycolysis-related target gene expression, thereby promoting glycolysis and malignant progression, which confers a therapeutic vulnerability to glycolytic inhibitors. Our study identified three groups of patients according to seRNA levels, among which patients in cluster 3 have the worst prognosis and vulnerability of glycolysis dependency. We also proposed a 3-TF index model to stratify patients with glycolysis-addicted tumors according to tumor SE stratification.
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Affiliation(s)
- Xuming Song
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 210009, Nanjing, P. R. China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, P. R. China.,The Fourth Clinical College of Nanjing Medical University, Nanjing, P. R. China
| | - Te Zhang
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 210009, Nanjing, P. R. China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, P. R. China.,The Fourth Clinical College of Nanjing Medical University, Nanjing, P. R. China
| | - Hanlin Ding
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 210009, Nanjing, P. R. China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, P. R. China.,The Fourth Clinical College of Nanjing Medical University, Nanjing, P. R. China
| | - Yipeng Feng
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 210009, Nanjing, P. R. China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, P. R. China.,The Fourth Clinical College of Nanjing Medical University, Nanjing, P. R. China
| | - Wenmin Yang
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 210009, Nanjing, P. R. China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, P. R. China.,The Fourth Clinical College of Nanjing Medical University, Nanjing, P. R. China
| | - Xuewen Yin
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, 211198, Nanjing, P. R. China
| | - Bing Chen
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 210009, Nanjing, P. R. China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, P. R. China.,The Fourth Clinical College of Nanjing Medical University, Nanjing, P. R. China
| | - Yingkuan Liang
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 210009, Nanjing, P. R. China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, P. R. China
| | - Qixing Mao
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 210009, Nanjing, P. R. China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, P. R. China
| | - Wenjie Xia
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 210009, Nanjing, P. R. China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, P. R. China
| | - Guiping Yu
- Department of Cardiothoracic Surgery, The affiliated Jiangyin Hospital of Southeast University Medical College, 214400, Jiangyin, P. R. China
| | - Lin Xu
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 210009, Nanjing, P. R. China. .,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, P. R. China. .,The Fourth Clinical College of Nanjing Medical University, Nanjing, P. R. China. .,Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, P. R. China.
| | - Gaochao Dong
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 210009, Nanjing, P. R. China. .,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, P. R. China.
| | - Feng Jiang
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 210009, Nanjing, P. R. China. .,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, P. R. China. .,The Fourth Clinical College of Nanjing Medical University, Nanjing, P. R. China.
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Cejas P, Long HW. High-Resolution ATAC-Seq Analysis of Frozen Clinical Tissues. Methods Mol Biol 2022; 2458:259-267. [PMID: 35103972 DOI: 10.1007/978-1-0716-2140-0_14] [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] [Indexed: 06/14/2023]
Abstract
The ATAC-seq method enables the genome-wide analysis of accessible chromatin revealing transcriptionally active and poised regulatory elements. The ATAC-seq analysis of clinical specimens at a single-cell resolution reveals the cellular composition of the tissue contributing to the understanding of intra-tissue heterogeneity. Here we describe our method for nuclei isolation from frozen specimens with wide applicability across tissue types, producing nuclei suitable for a number of molecular profiling methods including ATAC-seq in bulk and at a single-cell resolution.
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Affiliation(s)
- Paloma Cejas
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.
- Translational Oncology Laboratory, Hospital La Paz Institute for Health Research (IdiPAZ) and CIBERONC, La Paz University Hospital, Madrid, Spain.
| | - Henry W Long
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.
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Weichenhan D, Lipka DB, Lutsik P, Goyal A, Plass C. Epigenomic technologies for precision oncology. Semin Cancer Biol 2020; 84:60-68. [PMID: 32822861 DOI: 10.1016/j.semcancer.2020.08.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 07/28/2020] [Accepted: 08/03/2020] [Indexed: 12/15/2022]
Abstract
Epigenetic patterns in a cell control the expression of genes and consequently determine the phenotype of a cell. Cancer cells possess altered epigenomes which include aberrant patterns of DNA methylation, histone tail modifications, nucleosome positioning and of the three-dimensional chromatin organization within a nucleus. These altered epigenetic patterns are potential useful biomarkers to detect cancer cells and to classify tumor types. In addition, the cancer epigenome dictates the response of a cancer cell to therapeutic intervention and, therefore its knowledge, will allow to predict response to different therapeutic approaches. Here we review the current state-of-the-art technologies that have been developed to decipher epigenetic patterns on the genomic level and discuss how these methods are potentially useful for precision oncology.
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Affiliation(s)
- Dieter Weichenhan
- German Cancer Research Center Heidelberg, Cancer Epigenomics (B370), Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany.
| | - Daniel B Lipka
- Section of Translational Cancer Epigenomics, Division of Translational Medical Oncology, National Center for Tumor Diseases Heidelberg & German Cancer Research Center, Im Neuenheimer Feld 581, D-69120, Heidelberg, Germany; Faculty of Medicine, Medical Center, Otto-von-Guericke-University, Leipziger Straße 44, D-39120, Magdeburg, Germany.
| | - Pavlo Lutsik
- German Cancer Research Center Heidelberg, Cancer Epigenomics (B370), Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany.
| | - Ashish Goyal
- German Cancer Research Center Heidelberg, Cancer Epigenomics (B370), Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany.
| | - Christoph Plass
- German Cancer Research Center Heidelberg, Cancer Epigenomics (B370), Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany.
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