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da Silva Santos R, Pascoalino Pinheiro D, Gustavo Hirth C, Barbosa Bezerra MJ, Joyce de Lima Silva-Fernandes I, Andréa da Silva Oliveira F, Viana de Holanda Barros M, Silveira Ramos E, A. Moura A, Filho ODMM, Pessoa C, Miranda Furtado CL. Hypomethylation at H19DMR in penile squamous cell carcinoma is not related to HPV infection. Epigenetics 2024; 19:2305081. [PMID: 38245880 PMCID: PMC10802203 DOI: 10.1080/15592294.2024.2305081] [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: 08/09/2023] [Accepted: 01/09/2024] [Indexed: 01/23/2024] Open
Abstract
Penile squamous cell carcinoma (SCC) is a rare and aggressive tumour mainly related to lifestyle behaviour and human papillomavirus (HPV) infection. Environmentally induced loss of imprinting (LOI) at the H19 differentially methylated region (H19DMR) is associated with many cancers in the early events of tumorigenesis and may be involved in the pathogenesis of penile SCC. We sought to evaluate the DNA methylation pattern at H19DMR and its association with HPV infection in men with penile SCC by bisulfite sequencing (bis-seq). We observed an average methylation of 32.2% ± 11.6% at the H19DMR of penile SCC and did not observe an association between the p16INK4a+ (p = 0.59) and high-risk HPV+ (p = 0.338) markers with methylation level. The average methylation did not change according to HPV positive for p16INK4a+ or hrHPV+ (35.4% ± 10%) and negative for both markers (32.4% ± 10.1%) groups. As the region analysed has a binding site for the CTCF protein, the hypomethylation at the surrounding CpG sites might alter its insulator function. In addition, there was a positive correlation between intense polymorphonuclear cell infiltration and hypomethylation at H19DMR (p = 0.035). Here, we report that hypomethylation at H19DMR in penile SCC might contribute to tumour progression and aggressiveness regardless of HPV infection.
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Affiliation(s)
- Renan da Silva Santos
- Department of Physiology and Pharmacology, Drug Research and Development Center, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | | | | | | | | | | | - Maisa Viana de Holanda Barros
- Postgraduate Program in Translational Medicine, Drug Research and Development Center, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | - Ester Silveira Ramos
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Arlindo A. Moura
- Department of Physiology and Pharmacology, Drug Research and Development Center, Federal University of Ceará, Fortaleza, Ceará, Brazil
- Department of Animal Science, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | - Odorico de Moraes Manoel Filho
- Department of Physiology and Pharmacology, Drug Research and Development Center, Federal University of Ceará, Fortaleza, Ceará, Brazil
- Postgraduate Program in Translational Medicine, Drug Research and Development Center, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | - Claudia Pessoa
- Department of Physiology and Pharmacology, Drug Research and Development Center, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | - Cristiana Libardi Miranda Furtado
- Postgraduate Program in Translational Medicine, Drug Research and Development Center, Federal University of Ceará, Fortaleza, Ceará, Brazil
- Experimental Biology Center, University of Fortaleza, Fortaleza, Ceará, Brazil
- Graduate Program in Medical Sciences, Universidade de Fortaleza, Fortaleza, Ceará, Brazil
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Bose S, Saha S, Goswami H, Shanmugam G, Sarkar K. Involvement of CCCTC-binding factor in epigenetic regulation of cancer. Mol Biol Rep 2023; 50:10383-10398. [PMID: 37840067 DOI: 10.1007/s11033-023-08879-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 10/03/2023] [Indexed: 10/17/2023]
Abstract
A major global health burden continues to be borne by the complex and multifaceted disease of cancer. Epigenetic changes, which are essential for the emergence and spread of cancer, have drawn a huge amount of attention recently. The CCCTC-binding factor (CTCF), which takes part in a wide range of cellular processes including genomic imprinting, X chromosome inactivation, 3D chromatin architecture, local modifications of histone, and RNA polymerase II-mediated gene transcription, stands out among the diverse array of epigenetic regulators. CTCF not only functions as an architectural protein but also modulates DNA methylation and histone modifications. Epigenetic regulation of cancer has already been the focus of plenty of studies. Understanding the role of CTCF in the cancer epigenetic landscape may lead to the development of novel targeted therapeutic strategies for cancer. CTCF has already earned its status as a tumor suppressor gene by acting like a homeostatic regulator of genome integrity and function. Moreover, CTCF has a direct effect on many important transcriptional regulators that control the cell cycle, apoptosis, senescence, and differentiation. As we learn more about CTCF-mediated epigenetic modifications and transcriptional regulations, the possibility of utilizing CTCF as a diagnostic marker and therapeutic target for cancer will also increase. Thus, the current review intends to promote personalized and precision-based therapeutics for cancer patients by shedding light on the complex interplay between CTCF and epigenetic processes.
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Affiliation(s)
- Sayani Bose
- Department of Biotechnology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India
| | - Srawsta Saha
- Department of Biotechnology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India
| | - Harsita Goswami
- Department of Biotechnology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India
| | - Geetha Shanmugam
- Department of Biotechnology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India
| | - Koustav Sarkar
- Department of Biotechnology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India.
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Chiu MH, Chang CH, Tantoh DM, Hsu TW, Hsiao CH, Zhong JH, Liaw YP. Susceptibility to hypertension based on MTHFR rs1801133 single nucleotide polymorphism and MTHFR promoter methylation. Front Cardiovasc Med 2023; 10:1159764. [PMID: 37849939 PMCID: PMC10577234 DOI: 10.3389/fcvm.2023.1159764] [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: 02/07/2023] [Accepted: 09/11/2023] [Indexed: 10/19/2023] Open
Abstract
Background The aetio-pathologenesis of hypertension is multifactorial, encompassing genetic, epigenetic, and environmental factors. The combined effect of genetic and epigenetic changes on hypertension is not known. We evaluated the independent and interactive association of MTHFR rs1801133 single nucleotide polymorphism (SNP) and MTHFR promoter methylation with hypertension among Taiwanese adults. Methods We retrieved data including, MTHFR promoter methylation, MTHFR rs1801133 genotypes (CC, CT, and TT), basic demography, personal lifestyle habits, and disease history of 1,238 individuals from the Taiwan Biobank (TWB). Results The distributions of hypertension and MTHFR promoter methylation quartiles (β < 0.1338, 0.1338 ≤ β < 0.1385, 0.1385 ≤ β < 0.1423, and β ≥ 0.1423 corresponding to Conclusion Independently, rs1801133 TT was associated with a higher risk of hypertension, but methylation was not. Based on genotypes, lower methylation was dose-dependently associated with a higher risk of hypertension in individuals with the CC genotype. Our findings suggest that MTHFR rs1801133 and MTHFR promoter methylation could jointly influence hypertension susceptibility.
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Affiliation(s)
- Ming-Huang Chiu
- Department of Pulmonology and Respiratory Care, Cathay General Hospital, Taipei City, Taiwan
| | - Chia-Hsiu Chang
- Cardiovascular Center, Cathay General Hospital, Taipei City, Taiwan
| | - Disline Manli Tantoh
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung City, Taiwan
- Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung City, Taiwan
| | - Tsui-Wen Hsu
- Superintendent Office, Institute of Medicine, Cathay General Hospital, Taipei City, Taiwan
| | - Chih-Hsuan Hsiao
- Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung City, Taiwan
| | - Ji-Han Zhong
- Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung City, Taiwan
| | - Yung-Po Liaw
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung City, Taiwan
- Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung City, Taiwan
- Institute of Medicine, Chung Shan Medical University, Taichung City, Taiwan
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Tsai HH, Shen CY, Ho CC, Hsu SY, Tantoh DM, Nfor ON, Chiu SL, Chou YH, Liaw YP. Interaction between a diabetes-related methylation site (TXNIP cg19693031) and variant (GLUT1 rs841853) on fasting blood glucose levels among non-diabetics. J Transl Med 2022; 20:87. [PMID: 35164795 PMCID: PMC8842527 DOI: 10.1186/s12967-022-03269-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/19/2022] [Indexed: 02/07/2023] Open
Abstract
Background Type 2 diabetes mellitus (T2DM) is caused by a combination of environmental, genetic, and epigenetic factors including, fasting blood glucose (FBG), genetic variant rs841853, and cg19693031 methylation. We evaluated the interaction between rs841853 and cg19693031 on the FBG levels of non-diabetic Taiwanese adults. Methods We used Taiwan Biobank (TWB) data collected between 2008 and 2016. The TWB data source contains information on basic demographics, personal lifestyles, medical history, methylation, and genotype. The study participants included 1300 people with DNA methylation data. The association of cg19693031 methylation (stratified into quartiles) with rs841853 and FBG was determined using multiple linear regression analysis. The beta-coefficients (β) and p-values were estimated. Results The mean ± standard deviation (SD) of FBG in rs841853-CC individuals (92.07 ± 7.78) did not differ significantly from that in the CA + AA individuals (91.62 ± 7.14). However, the cg19693031 methylation levels were significantly different in the two groups (0.7716 ± 0.05 in CC individuals and 0.7631 ± 0.05 in CA + AA individuals (p = 0.002). The cg19693031 methylation levels according to quartiles were β < 0.738592 (< Q1), 0.738592 ≤ 0.769992 (Q1–Q2), 0.769992 ≤ 0.800918 (Q2–Q3), and β ≥ 0.800918 (≥ Q3). FBG increased with decreasing cg19693031 methylation levels in a dose–response manner (ptrend = 0.005). The β-coefficient was − 0.0236 (p = 0.965) for Q2–Q3, 1.0317 (p = 0.058) for Q1–Q2, and 1.3336 (p = 0.019 for < Q1 compared to the reference quartile (≥ Q3). The genetic variant rs841853 was not significantly associated with FBG. However, its interaction with cg19693031 methylation was significant (p-value = 0.036). Based on stratification by rs841853 genotypes, only the CC group retained the inverse and dose–response association between FBG and cg19693031 methylation. The β (p-value) was 0.8082 (0.255) for Q2–Q3, 1.6930 (0.022) for Q1–Q2, and 2.2190 (0.004) for < Q1 compared to the reference quartile (≥ Q3). The ptrend was 0.002. Conclusion Summarily, methylation at cg19693031 was inversely associated with fasting blood glucose in a dose-dependent manner. The inverse association was more prominent in rs841853-CC individuals, suggesting that rs841853 could modulate the association between cg19693031 methylation and FBG. Our results suggest that genetic variants may be involved in epigenetic mechanisms associated with FBG, a hallmark of diabetes. Therefore, integrating genetic and epigenetic data may provide more insight into the early-onset of diabetes. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03269-y.
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Lelièvre SA. Can the epigenome contribute to risk stratification for cancer onset? NAR Cancer 2021; 3:zcab043. [PMID: 34734185 PMCID: PMC8559165 DOI: 10.1093/narcan/zcab043] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 10/10/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022] Open
Abstract
The increasing burden of cancer requires identifying and protecting individuals at highest risk. The epigenome provides an indispensable complement to genetic alterations for a risk stratification approach for the following reasons: gene transcription necessary for cancer onset is directed by epigenetic modifications and many risk factors studied so far have been associated with alterations related to the epigenome. The risk level depends on the plasticity of the epigenome during phases of life particularly sensitive to environmental and dietary impacts. Modifications in the activity of DNA regulatory regions and altered chromatin compaction may accumulate, hence leading to the increase of cancer risk. Moreover, tissue architecture directs the unique organization of the epigenome for each tissue and cell type, which allows the epigenome to control cancer risk in specific organs. Investigations of epigenetic signatures of risk should help identify a continuum of alterations leading to a threshold beyond which the epigenome cannot maintain homeostasis. We propose that this threshold may be similar in the population for a given tissue, but the pace to reach this threshold will depend on the combination of germline inheritance and the risk and protective factors encountered, particularly during windows of epigenetic susceptibility, by individuals.
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Affiliation(s)
- Sophie A Lelièvre
- Institut de Cancérologie de l'Ouest (ICO)-Western Cancer Institute, Scientific Direction for Translational Research, Angers, France
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Determination of Genetic and Epigenetic Modifications-Related Prognostic Biomarkers of Breast Cancer: Genome High-Throughput Data Analysis. JOURNAL OF ONCOLOGY 2021; 2021:2143362. [PMID: 34557230 PMCID: PMC8455195 DOI: 10.1155/2021/2143362] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 08/11/2021] [Accepted: 08/26/2021] [Indexed: 12/14/2022]
Abstract
The high heterogeneity of breast cancer (BRCA) makes it more challenging to interpret the genetic variation mechanisms involved in BRCA pathogenesis and prognosis. Areas with high DNA methylation (such as CpG islands) were accompanied by copy number variation (CNV), and these genomic variations affected the level of DNA methylation. In this study, we characterized intertumor heterogeneity and analyzed the effects of CNV on DNA methylation and gene expression. In addition, we performed a Genetic Set Enrichment Analysis (GSEA) to identify key pathways for changes between patients with low and high expression of genes. Our analysis found two key genes, namely, HPDL and SOX17. The protein expressed by HPDL is 4-hydroxyphenylpyruvate dioxygenase-like protein, which has dioxygenase activity. SOX17 is a transcription factor that can inhibit Wnt signaling, promote the degradation of activated CTNNB1, and participate in cell proliferation. Our analysis found that the CNV of HPDL and SOX17 is not only related to the patient's prognosis, but also related to gene methylation and expression levels affecting the patient's survival time. Among them, the high-methylation, low-expression HPDL and SOX17 showed poor prognosis. And the addition of two copies of SOX17 is associated with a lower survival rate, while a decrease in the copy number of HPDL also suggests a poor prognosis. This study provided an effective bioinformatics basis for further exploration of molecular mechanisms related to BRCA and assessment of patient prognosis, but the development of biomarkers for diagnosis and treatment still requires further clinical data validation.
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Mahmood RI, Abbass AK, Razali N, Al-Saffar AZ, Al-Obaidi JR. Protein profile of MCF-7 breast cancer cell line treated with lectin delivered by CaCO 3NPs revealed changes in molecular chaperones, cytoskeleton, and membrane-associated proteins. Int J Biol Macromol 2021; 184:636-647. [PMID: 34174302 DOI: 10.1016/j.ijbiomac.2021.06.144] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 06/16/2021] [Accepted: 06/21/2021] [Indexed: 02/09/2023]
Abstract
The second most predominant cancer in the world and the first among women is breast cancer. We aimed to study the protein abundance profiles induced by lectin purified from the Agaricus bisporus mushroom (ABL) and conjugated with CaCO3NPs in the MCF-7 breast cancer cell line. Two-dimensional electrophoresis (2-DE) and orbitrap mass spectrometry techniques were used to reveal the protein abundance pattern induced by lectin. Flow cytometric analysis showed the accumulation of ABL-CaCO3NPs treated cells in the G1 phase than the positive control. Thirteen proteins were found different in their abundance in breast cancer cells after 24 h exposure to lectin conjugated with CaCO3NPs. Most of the identified proteins were showing a low abundance in ABL-CaCO3NPs treated cells in comparison to the positive and negative controls, including V-set and immunoglobulin domain, serum albumin, actin cytoplasmic 1, triosephosphate isomerase, tropomyosin alpha-4 chain, and endoplasmic reticulum chaperone BiP. Hornerin, tropomyosin alpha-1 chain, annexin A2, and protein disulfide-isomerase were up-regulated in comparison to the positive. Bioinformatic analyses revealed the regulation changes of these proteins mainly affected the pathways of 'Bcl-2-associated athanogene 2 signalling pathway', 'Unfolded protein response', 'Caveolar-mediated endocytosis signalling', 'Clathrin-mediated endocytosis signalling', 'Calcium signalling' and 'Sucrose degradation V', which are associated with breast cancer. We concluded that lectin altered the abundance in molecular chaperones/heat shock proteins, cytoskeletal, and metabolic proteins. Additionally, lectin induced a low abundance of MCF-7 cancer cell proteins in comparison to the positive and negative controls, including; V-set and immunoglobulin domain, serum albumin, actin cytoplasmic 1, triosephosphate isomerase, tropomyosin alpha-4 chain, and endoplasmic reticulum chaperone BiP.
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Affiliation(s)
- Rana I Mahmood
- Department of Biology, College of Science, Baghdad University, Baghdad, Iraq; Department of Biomedical Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq
| | - Amal Kh Abbass
- Department of Biology, College of Science, Baghdad University, Baghdad, Iraq
| | - Nurhanani Razali
- Department of Hygienic Sciences, Kobe Pharmaceutical University, Motoyamakita-machi, Higashinada-ku, 658-8558, Kobe, Japan; Membranology Unit, Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Kunigami-gun, Okinawa, Japan, 904-0495
| | - Ali Z Al-Saffar
- Department of Molecular and Medical Biotechnology, College of Biotechnology, Al-Nahrain University, Baghdad, Iraq
| | - Jameel R Al-Obaidi
- Department of Biology, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia.
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Ruscito I, Gasparri ML, De Marco MP, Costanzi F, Besharat AR, Papadia A, Kuehn T, Gentilini OD, Bellati F, Caserta D. The Clinical and Pathological Profile of BRCA1 Gene Methylated Breast Cancer Women: A Meta-Analysis. Cancers (Basel) 2021; 13:cancers13061391. [PMID: 33808555 PMCID: PMC8003261 DOI: 10.3390/cancers13061391] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/09/2021] [Accepted: 03/16/2021] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND DNA aberrant hypermethylation is the major cause of transcriptional silencing of the breast cancer gene 1 (BRCA1) gene in sporadic breast cancer patients. The aim of the present meta-analysis was to analyze all available studies reporting clinical characteristics of BRCA1 gene hypermethylated breast cancer in women, and to pool the results to provide a unique clinical profile of this cancer population. METHODS On September 2020, a systematic literature search was performed. Data were retrieved from PubMed, MEDLINE, and Scopus by searching the terms: "BRCA*" AND "methyl*" AND "breast". All studies evaluating the association between BRCA1 methylation status and breast cancer patients' clinicopathological features were considered for inclusion. RESULTS 465 studies were retrieved. Thirty (6.4%) studies including 3985 patients met all selection criteria. The pooled analysis data revealed a significant correlation between BRCA1 gene hypermethylation and advanced breast cancer disease stage (OR = 0.75: 95% CI: 0.58-0.97; p = 0.03, fixed effects model), lymph nodes involvement (OR = 1.22: 95% CI: 1.01-1.48; p = 0.04, fixed effects model), and pre-menopausal status (OR = 1.34: 95% CI: 1.08-1.66; p = 0.008, fixed effects model). No association could be found between BRCA1 hypermethylation and tumor histology (OR = 0.78: 95% CI: 0.59-1.03; p = 0.08, fixed effects model), tumor grading (OR = 0.78: 95% CI :0.46-1.32; p = 0.36, fixed effects model), and breast cancer molecular classification (OR = 1.59: 95% CI: 0.68-3.72; p = 0.29, random effects model). CONCLUSIONS hypermethylation of the BRCA1 gene significantly correlates with advanced breast cancer disease, lymph nodes involvement, and pre-menopausal cancer onset.
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Affiliation(s)
- Ilary Ruscito
- Gynecology Division, Department of Medical and Surgical Sciences and Translational Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189 Rome, Italy; (M.P.D.M.); (F.C.); (A.R.B.); (F.B.); (D.C.)
- Correspondence: ; Tel.: +39-06-3377-5696
| | - Maria Luisa Gasparri
- Department of Gynecology and Obstetrics, Ente Ospedaliere Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland; (M.L.G.); (A.P.)
- University of the Italian Switzerland (USI), Via Giuseppe Buffi 13, 6900 Lugano, Switzerland
| | - Maria Paola De Marco
- Gynecology Division, Department of Medical and Surgical Sciences and Translational Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189 Rome, Italy; (M.P.D.M.); (F.C.); (A.R.B.); (F.B.); (D.C.)
| | - Flavia Costanzi
- Gynecology Division, Department of Medical and Surgical Sciences and Translational Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189 Rome, Italy; (M.P.D.M.); (F.C.); (A.R.B.); (F.B.); (D.C.)
| | - Aris Raad Besharat
- Gynecology Division, Department of Medical and Surgical Sciences and Translational Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189 Rome, Italy; (M.P.D.M.); (F.C.); (A.R.B.); (F.B.); (D.C.)
| | - Andrea Papadia
- Department of Gynecology and Obstetrics, Ente Ospedaliere Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland; (M.L.G.); (A.P.)
- University of the Italian Switzerland (USI), Via Giuseppe Buffi 13, 6900 Lugano, Switzerland
| | - Thorsten Kuehn
- Interdisciplinary Breast Center, Department of Gynecology and Obstetrics, Klinikum Esslingen, 73730 Neckar, Germany;
| | - Oreste Davide Gentilini
- Breast Surgery Unit, San Raffaele University Hospital, via Olgettina 60, 20132 Milan, Italy;
| | - Filippo Bellati
- Gynecology Division, Department of Medical and Surgical Sciences and Translational Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189 Rome, Italy; (M.P.D.M.); (F.C.); (A.R.B.); (F.B.); (D.C.)
| | - Donatella Caserta
- Gynecology Division, Department of Medical and Surgical Sciences and Translational Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189 Rome, Italy; (M.P.D.M.); (F.C.); (A.R.B.); (F.B.); (D.C.)
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Wang M, Ngo V, Wang W. Deciphering the genetic code of DNA methylation. Brief Bioinform 2021; 22:6082840. [PMID: 33432324 DOI: 10.1093/bib/bbaa424] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 12/03/2020] [Accepted: 12/22/2020] [Indexed: 12/17/2022] Open
Abstract
DNA methylation plays crucial roles in many biological processes and abnormal DNA methylation patterns are often observed in diseases. Recent studies have shed light on cis-acting DNA elements that regulate locus-specific DNA methylation, which involves transcription factors, histone modification and DNA secondary structures. In addition, several recent studies have surveyed DNA motifs that regulate DNA methylation and suggest potential applications in diagnosis and prognosis. Here, we discuss the current biological foundation for the cis-acting genetic code that regulates DNA methylation. We review the computational models that predict DNA methylation with genetic features and discuss the biological insights revealed from these models. We also provide an in-depth discussion on how to leverage such knowledge in clinical applications, particularly in the context of liquid biopsy for early cancer diagnosis and treatment.
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Affiliation(s)
- Mengchi Wang
- Bioinformatics and Systems Biology at University of California, USA
| | - Vu Ngo
- Bioinformatics and Systems Biology at University of California, USA
| | - Wei Wang
- Bioinformatics and Systems Biology, Department of Chemistry and Biochemistry, and Department of Cellular and Molecular Medicine at University of California, USA
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10
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Romanowska J, Haaland ØA, Jugessur A, Gjerdevik M, Xu Z, Taylor J, Wilcox AJ, Jonassen I, Lie RT, Gjessing HK. Gene-methylation interactions: discovering region-wise DNA methylation levels that modify SNP-associated disease risk. Clin Epigenetics 2020; 12:109. [PMID: 32678018 PMCID: PMC7367265 DOI: 10.1186/s13148-020-00881-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 06/10/2020] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Current technology allows rapid assessment of DNA sequences and methylation levels at a single-site resolution for hundreds of thousands of sites in the human genome, in thousands of individuals simultaneously. This has led to an increase in epigenome-wide association studies (EWAS) of complex traits, particularly those that are poorly explained by previous genome-wide association studies (GWAS). However, the genome and epigenome are intertwined, e.g., DNA methylation is known to affect gene expression through, for example, genomic imprinting. There is thus a need to go beyond single-omics data analyses and develop interaction models that allow a meaningful combination of information from EWAS and GWAS. RESULTS We present two new methods for genetic association analyses that treat offspring DNA methylation levels as environmental exposure. Our approach searches for statistical interactions between SNP alleles and DNA methylation (G ×Me) and between parent-of-origin effects and DNA methylation (PoO ×Me), using case-parent triads or dyads. We use summarized methylation levels over nearby genomic region to ease biological interpretation. The methods were tested on a dataset of parent-offspring dyads, with EWAS data on the offspring. Our results showed that methylation levels around a SNP can significantly alter the estimated relative risk. Moreover, we show how a control dataset can identify false positives. CONCLUSIONS The new methods, G ×Me and PoO ×Me, integrate DNA methylation in the assessment of genetic relative risks and thus enable a more comprehensive biological interpretation of genome-wide scans. Moreover, our strategy of condensing DNA methylation levels within regions helps overcome specific disadvantages of using sparse chip-based measurements. The methods are implemented in the freely available R package Haplin ( https://cran.r-project.org/package=Haplin ), enabling fast scans of multi-omics datasets.
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Affiliation(s)
- Julia Romanowska
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, N-5020, Norway.
- Computational Biology Unit, University of Bergen, Bergen, N-5020, Norway.
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, N-0213, Norway.
| | - Øystein A Haaland
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, N-5020, Norway
| | - Astanand Jugessur
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, N-5020, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, N-0213, Norway
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, N-0473, Norway
| | - Miriam Gjerdevik
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, N-5020, Norway
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, N-0473, Norway
| | - Zongli Xu
- National Institute of Environmental Health Sciences, Research Triangle Park, 27709, NC, USA
| | - Jack Taylor
- National Institute of Environmental Health Sciences, Research Triangle Park, 27709, NC, USA
| | - Allen J Wilcox
- National Institute of Environmental Health Sciences, Research Triangle Park, 27709, NC, USA
| | - Inge Jonassen
- Computational Biology Unit, University of Bergen, Bergen, N-5020, Norway
| | - Rolv T Lie
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, N-5020, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, N-0213, Norway
| | - Håkon K Gjessing
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, N-5020, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, N-0213, Norway
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11
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Wang X, Lin FK, Li JR, Wang HS. A Comprehensive Risk Assessment Model for Ovarian Cancer Patients with Phospho-STAT3 and IL-31 as Immune Infiltration Relevant Genes. Onco Targets Ther 2020; 13:5617-5628. [PMID: 32606776 PMCID: PMC7305843 DOI: 10.2147/ott.s254494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 05/22/2020] [Indexed: 11/23/2022] Open
Abstract
Introduction Ovarian carcinoma is a malignant tumor with a high mortality rate and a lack of effective treatment options for patients at advanced stages. For improving outcomes and helping patients with poor prognosis, choose a suitable therapy and an excellent risk assessment model and new treatment options are needed. Materials and Methods Ovarian cancer gene expression profile of GSE32062 was downloaded from the NCBI GEO database for screening differentially expressed genes (DEGs) between well and poor prognosis groups using limma package in R (version 3.4.1). Prognosis-related genes and clinical prognostic factors were obtained from univariate and multivariate Cox regression analyses, and a comprehensive risk assessment model was constructed using a Pathway Dysregulation Score (PDS) matrix, Cox-Proportional Hazards (Cox-PH) regression, as well as L1-least absolute shrinkage and selection operator (L1-LASSO) penalization. Then, significant DEGs were converted to pathways and optimal prognosis-related pathways were screened. Finally, risk prediction models based on pathways, genes involved in pathways, and comprehensive clinical risk factors with pathways were built. Their prognostic functions were assessed in verification sets. Besides, genes involved in immune-pathways were checked for immune infiltration using immunohistochemistry. Results A superior risk assessment model involving 9 optimal combinations of pathways and one clinical factor was constructed. The pathway-based model was found to be superior to the gene-based model. Phospho-STAT3 (from JAK-STAT signaling pathway) and IL-31 (from DEGs) were found to be related to immune infiltration. Conclusion We have generated a comprehensive risk assessment model consisting of a clinical risk factor and pathways that showed a possible bright foreground. The set of significant pathways might play as a better prognosis model which is more accurate and applicable than the DEG set. Besides, p-STAT3 and IL-31 showing correlation to immune infiltration of ovarian cancer tissues may be potential therapeutic targets for treating ovarian cancers.
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Affiliation(s)
- Xue Wang
- Department of Obstetrics & Gynecology, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, People's Republic of China.,Shanghai Jiao Tong University School of Medicine, Shanghai 200025, People's Republic of China
| | - Fei-Kai Lin
- Department of Obstetrics & Gynecology, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, People's Republic of China.,Shanghai Jiao Tong University School of Medicine, Shanghai 200025, People's Republic of China
| | - Jia-Rui Li
- Department of Obstetrics & Gynecology, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, People's Republic of China
| | - Hu-Sheng Wang
- Department of Obstetrics & Gynecology, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, People's Republic of China
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12
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Wu J, Mamidi TKK, Zhang L, Hicks C. Deconvolution of the Genomic and Epigenomic Interaction Landscape of Triple-Negative Breast Cancer. Cancers (Basel) 2019; 11:cancers11111692. [PMID: 31683572 PMCID: PMC6896043 DOI: 10.3390/cancers11111692] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 10/07/2019] [Accepted: 10/19/2019] [Indexed: 12/26/2022] Open
Abstract
Triple-negative breast cancer (TNBC) is the most aggressive form of breast cancer. Emerging evidenced suggests that both genetics and epigenetic factors play a role in the pathogenesis of TNBC. However, oncogenic interactions and cooperation between genomic and epigenomic variation have not been characterized. The objective of this study was to deconvolute the genomic and epigenomic interaction landscape in TNBC using an integrative genomics approach, which integrates information on germline, somatic, epigenomic and gene expression variation. We hypothesized that TNBC originates from a complex interplay between genomic (both germline and somatic variation) and epigenomic variation. We further hypothesized that these complex arrays of interacting genomic and epigenomic factors affect entire molecular networks and signaling pathways which, in turn, drive TNBC. We addressed these hypotheses using germline variation from genome-wide association studies and somatic, epigenomic and gene expression variation from The Cancer Genome Atlas (TCGA). The investigation revealed signatures of functionally related genes containing germline, somatic and epigenetic variations. DNA methylation had an effect on gene expression. Network and pathway analysis revealed molecule networks and signaling pathways enriched for germline, somatic and epigenomic variation, among them: Role of BRCA1 in DNA Damage Response, Hereditary Breast Cancer Signaling, Molecular Mechanisms of Cancer, Estrogen-Dependent Breast Cancer, p53, MYC Mediated Apoptosis, and PTEN Signaling pathways. The investigation revealed that integrative genomics is a powerful approach for deconvoluting the genomic-epigenomic interaction landscape in TNBC. Further studies are needed to understand the biological mechanisms underlying oncogenic interactions between genomic and epigenomic factors in TNBC.
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Affiliation(s)
- Jiande Wu
- Department of Genetics, Louisiana State University Health Sciences Center, School of Medicine, 533 Bolivar Street, New Orleans, LA 70112, USA.
| | - Tarun Karthik Kumar Mamidi
- Graduate Biomedical Sciences, The University of Alabama at Birmingham, 1825 University Blvd, Birmingham, AL 35233, USA.
| | - Lu Zhang
- Department of Public Health Sciences, Clemson University, 513 Edwards Hall, Clemson, SC 29634, USA.
| | - Chindo Hicks
- Department of Genetics, Louisiana State University Health Sciences Center, School of Medicine, 533 Bolivar Street, New Orleans, LA 70112, USA.
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13
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Mauger F, Deleuze JF. Technological advances in studying epigenetics biomarkers of prognostic potential for clinical research. PROGNOSTIC EPIGENETICS 2019:45-83. [DOI: 10.1016/b978-0-12-814259-2.00003-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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14
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Combining DNA methylation and RNA sequencing data of cancer for supervised knowledge extraction. BioData Min 2018; 11:22. [PMID: 30386434 PMCID: PMC6203208 DOI: 10.1186/s13040-018-0184-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 10/11/2018] [Indexed: 11/26/2022] Open
Abstract
Background In the Next Generation Sequencing (NGS) era a large amount of biological data is being sequenced, analyzed, and stored in many public databases, whose interoperability is often required to allow an enhanced accessibility. The combination of heterogeneous NGS genomic data is an open challenge: the analysis of data from different experiments is a fundamental practice for the study of diseases. In this work, we propose to combine DNA methylation and RNA sequencing NGS experiments at gene level for supervised knowledge extraction in cancer. Methods We retrieve DNA methylation and RNA sequencing datasets from The Cancer Genome Atlas (TCGA), focusing on the Breast Invasive Carcinoma (BRCA), the Thyroid Carcinoma (THCA), and the Kidney Renal Papillary Cell Carcinoma (KIRP). We combine the RNA sequencing gene expression values with the gene methylation quantity, as a new measure that we define for representing the methylation quantity associated to a gene. Additionally, we propose to analyze the combined data through tree- and rule-based classification algorithms (C4.5, Random Forest, RIPPER, and CAMUR). Results We extract more than 15,000 classification models (composed of gene sets), which allow to distinguish the tumoral samples from the normal ones with an average accuracy of 95%. From the integrated experiments we obtain about 5000 classification models that consider both the gene measures related to the RNA sequencing and the DNA methylation experiments. Conclusions We compare the sets of genes obtained from the classifications on RNA sequencing and DNA methylation data with the genes obtained from the integration of the two experiments. The comparison results in several genes that are in common among the single experiments and the integrated ones (733 for BRCA, 35 for KIRP, and 861 for THCA) and 509 genes that are in common among the different experiments. Finally, we investigate the possible relationships among the different analyzed tumors by extracting a core set of 13 genes that appear in all tumors. A preliminary functional analysis confirms the relation of part of those genes (5 out of 13 and 279 out of 509) with cancer, suggesting to focus further studies on the new individuated ones. Electronic supplementary material The online version of this article (10.1186/s13040-018-0184-6) contains supplementary material, which is available to authorized users.
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15
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Aberg KA, Shabalin AA, Chan RF, Zhao M, Kumar G, van Grootheest G, Clark SL, Xie LY, Milaneschi Y, Penninx BWJH, van den Oord EJCG. Convergence of evidence from a methylome-wide CpG-SNP association study and GWAS of major depressive disorder. Transl Psychiatry 2018; 8:162. [PMID: 30135428 PMCID: PMC6105579 DOI: 10.1038/s41398-018-0205-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 06/04/2018] [Accepted: 06/10/2018] [Indexed: 01/19/2023] Open
Abstract
DNA methylation is an epigenetic modification that provides stability and diversity to the cellular phenotype. It is influenced by both genetic sequence variation and environmental factors, and can therefore potentially account for variation of heritable phenotypes and disorders. Therefore, methylome-wide association studies (MWAS) are promising complements to genome-wide association studies (GWAS) of genetic variants. Of particular interest are methylation sites (CpGs) that are created or destroyed by the alleles of single-nucleotide polymorphisms (SNPs), as these so-called CpG-SNPs may show variation in methylation levels on top of what can be explained by the sequence variation. Using sequencing-based data from 1132 major depressive disorder (MDD) cases and controls, we performed a MWAS of 970,414 common CpG-SNPs. The analysis identified 27 suggestively significant (P < 1.00 × 10-5) CpG-SNPs associations. Furthermore, the MWAS results were over-represented (odds ratios ranging 1.36-5.00; P ranging 4.9 × 10-3-8.1 × 10-2) among findings from three recent GWAS for MDD-related phenotypes. Overlapping loci included, e.g., ROBO2, ASIC2, and DCC. As the CpG-SNP analysis accounts for the number of alleles that creates CpGs, the methylation differences could not be explained by differences in allele frequencies. Thus, the results show that the MWAS and GWASs provide independent lines of evidence for the involvement of these loci in MDD. In conclusion, our methylation study of MDD contributes novel information about loci of relevance that complements previous findings and generates new hypothesis about MDD etiology, such as that the functional effects of genetic association may be partly mediated and/or enhanced by the methylation status in these loci.
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Affiliation(s)
- Karolina A. Aberg
- 0000 0004 0458 8737grid.224260.0Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA USA
| | - Andrey A. Shabalin
- 0000 0004 0458 8737grid.224260.0Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA USA
| | - Robin F. Chan
- 0000 0004 0458 8737grid.224260.0Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA USA
| | - Min Zhao
- 0000 0004 0458 8737grid.224260.0Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA USA
| | - Gaurav Kumar
- 0000 0004 0458 8737grid.224260.0Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA USA
| | - Gerard van Grootheest
- 0000 0004 0435 165Xgrid.16872.3aDepartment of Psychiatry, Amsterdam Neuroscience, VU University Medical Center/GGZ inGeest, Amsterdam, The Netherlands
| | - Shaunna L. Clark
- 0000 0004 0458 8737grid.224260.0Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA USA
| | - Lin Y. Xie
- 0000 0004 0458 8737grid.224260.0Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA USA
| | - Yuri Milaneschi
- 0000 0004 0435 165Xgrid.16872.3aDepartment of Psychiatry, Amsterdam Neuroscience, VU University Medical Center/GGZ inGeest, Amsterdam, The Netherlands
| | - Brenda W. J. H. Penninx
- 0000 0004 0435 165Xgrid.16872.3aDepartment of Psychiatry, Amsterdam Neuroscience, VU University Medical Center/GGZ inGeest, Amsterdam, The Netherlands
| | - Edwin J. C. G. van den Oord
- 0000 0004 0458 8737grid.224260.0Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA USA
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16
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Iuliano A, Occhipinti A, Angelini C, De Feis I, Liò P. Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods. Front Genet 2018; 9:206. [PMID: 29963073 PMCID: PMC6011013 DOI: 10.3389/fgene.2018.00206] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 05/24/2018] [Indexed: 12/30/2022] Open
Abstract
Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of ad-hoc statistical methods. In this work, we propose novel screening-network methods that predict patient survival outcome by screening key survival-related genes and we assess the capability of the proposed approaches using METABRIC dataset. In particular, we first identify a subset of genes by using variable screening techniques on gene expression data. Then, we perform Cox regression analysis by incorporating network information associated with the selected subset of genes. The novelty of this work consists in the improved prediction of survival responses due to the different types of screenings (i.e., a biomedical-driven, data-driven and a combination of the two) before building the network-penalized model. Indeed, the combination of the two screening approaches allows us to use the available biological knowledge on breast cancer and complement it with additional information emerging from the data used for the analysis. Moreover, we also illustrate how to extend the proposed approaches to integrate an additional omic layer, such as copy number aberrations, and we show that such strategies can further improve our prediction capabilities. In conclusion, our approaches allow to discriminate patients in high-and low-risk groups using few potential biomarkers and therefore, can help clinicians to provide more precise prognoses and to facilitate the subsequent clinical management of patients at risk of disease.
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Affiliation(s)
- Antonella Iuliano
- Istituto per le Applicazioni del Calcolo "Mauro Picone", Consiglio Nazionale delle Ricerche, Naples, Italy.,Telethon Institute of Genetics and Medicine, Pozzuoli, Italy
| | | | - Claudia Angelini
- Istituto per le Applicazioni del Calcolo "Mauro Picone", Consiglio Nazionale delle Ricerche, Naples, Italy
| | - Italia De Feis
- Istituto per le Applicazioni del Calcolo "Mauro Picone", Consiglio Nazionale delle Ricerche, Naples, Italy
| | - Pietro Liò
- Computer Laboratory, University of Cambridge, Cambridge, United Kingdom
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17
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Zhou J, Hang D, Jiang Y, Chen J, Han J, Zhou W, Jin G, Ma H, Dai J. Evaluation of genetic variants in autophagy pathway genes as prognostic biomarkers for breast cancer. Gene 2017; 627:549-555. [PMID: 28669927 DOI: 10.1016/j.gene.2017.06.053] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 06/10/2017] [Accepted: 06/28/2017] [Indexed: 12/13/2022]
Abstract
Autophagy-related genes (ATGs) play a critical role in the development of various diseases including cancer. However, the role of ATGs in breast cancer survival remains unclear. This study aims to investigate whether genetic variants in core ATGs are correlated with the prognosis of breast cancer. A total of 14 potentially functional variants in core ATGs were genotyped in 790 breast cancer patients. The association of each variant with breast cancer-specific survival was evaluated by log-rank test and Cox regression model. In silico analysis was also performed to evaluate the potential function of selected variants. We found that one variant in ATG7 rs8154 (A>G) was significantly associated with breast cancer-specific survival after adjusted for age and clinical stage (HR=1.61, 95% CI: 1.12-2.31, P=0.010). Stratified analysis showed that the prognostic role of rs8154 was significant in subgroups of elder age, elder menarche age, and postmenopausal status (all P<0.001). Interaction effects were also detected between rs8154 and these grouping variables. In silico analysis revealed that rs8154 was annotated to be expression quantitative trait loci (eQTL) and methylation quantitative trait loci (meQTL) based on Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets (both P<0.05). In addition, upregulated expression of ATG7 in breast cancer tissues was observed and is significantly associated with poorer overall survival (log-rank P=0.015), poorer relapse free survival (log-rank P=0.017), and poorer distant metastasis free survival (log-rank P=0.034) in different datasets. Summarily, ATG7 variant rs8154 represents a novel prognostic marker for breast cancer patients, which may shed light on clinical risk stratification and therapeutic decision making.
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Affiliation(s)
- Jing Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Dong Hang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Yue Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Jiaping Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Jing Han
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Wen Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Juncheng Dai
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China.
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18
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Zhu Z, Li L, Ye Z, Fu T, Du Y, Shi A, Wu D, Li K, Zhu Y, Wang C, Fan Z. Prognostic value of routine laboratory variables in prediction of breast cancer recurrence. Sci Rep 2017; 7:8135. [PMID: 28811593 PMCID: PMC5557903 DOI: 10.1038/s41598-017-08240-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 07/10/2017] [Indexed: 11/23/2022] Open
Abstract
The prognostic value of routine laboratory variables in breast cancer has been largely overlooked. Based on laboratory tests commonly performed in clinical practice, we aimed to develop a new model to predict disease free survival (DFS) after surgical removal of primary breast cancer. In a cohort of 1,596 breast cancer patients, we analyzed the associations of 33 laboratory variables with patient DFS. Based on 3 significant laboratory variables (hemoglobin, alkaline phosphatase, and international normalized ratio), together with important demographic and clinical variables, we developed a prognostic model, achieving the area under the curve of 0.79. We categorized patients into 3 risk groups according to the prognostic index developed from the final model. Compared with the patients in the low-risk group, those in the medium- and high-risk group had a significantly increased risk of recurrence with a hazard ratio (HR) of 1.75 (95% confidence interval [CI] 1.30–2.38) and 4.66 (95% CI 3.54–6.14), respectively. The results from the training set were validated in the testing set. Overall, our prognostic model incorporating readily available routine laboratory tests is powerful in identifying breast cancer patients who are at high risk of recurrence. Further study is warranted to validate its clinical application.
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Affiliation(s)
- Zhu Zhu
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin, 130021, China.,Department of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Ling Li
- Department of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Zhong Ye
- Department of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Tong Fu
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin, 130021, China
| | - Ye Du
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin, 130021, China
| | - Aiping Shi
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin, 130021, China
| | - Di Wu
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin, 130021, China
| | - Ke Li
- Department of Emergency, The First Hospital of Jilin University, Changchun, Jilin, 130021, China
| | - Yifan Zhu
- Department of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Chun Wang
- Department of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, 19107, USA. .,Department of Environmental Health, School of Public Health, Nantong University, Nantong, Jiangsu, 226000, China.
| | - Zhimin Fan
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin, 130021, China.
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