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Mao X, Chen X, Xu Z, Ding L, Luo W, Lin Y, Wang R, Xia L, Wang M, Li G. The identification of a N 6-methyladenosin-modifed immune pattern to predict immunotherapy response and survival in urothelial carcinoma. Aging (Albany NY) 2024; 16:7774-7798. [PMID: 38696324 PMCID: PMC11131986 DOI: 10.18632/aging.205782] [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: 09/14/2023] [Accepted: 03/29/2024] [Indexed: 05/04/2024]
Abstract
BACKGROUND Dysregulation of the immune system and N6-methyladenosine (m6A) contribute to immune therapy resistance and cancer progression in urothelial carcinoma (UC). This study aims to identify immune-related molecules, that are m6A-modified, and that are associated with tumor progression, poor prognosis, and immunotherapy response. METHODS We identified prognostic immune genes (PIGs) using Cox analysis and random survival forest variable hunting algorithm (RSF-VH) on immune genes retrieved from the Immunology Database and Analysis Portal database (ImmPort). The RM2Target database and MeRIP-seq analysis, combined with a hypergeometric test, assessed m6A methylation in these PIGs. We analyzed the correlation between the immune pattern and prognosis, as well as their association with clinical factors in multiple datasets. Moreover, we explored the interplay between immune patterns, tumor immune cell infiltration, and m6A regulators. RESULTS 28 PIGs were identified, of which the 10 most significant were termed methylated prognostic immune genes (MPIGs). These MPIGs were used to create an immune pattern score. Kaplan-Meier and Cox analyses indicated this pattern as an independent risk factor for UC. We observed significant associations between the immune pattern, tumor progression, and immune cell infiltration. Differential expression analysis showed correlations with m6A regulators expression. This immune pattern proved effective in predicting immunotherapy response in UC in real-world settings. CONCLUSION The study identified a m6A-modified immune pattern in UC, offering prognostic and therapeutic response predictions. This emphasizes that immune genes may influence tumor immune status and progression through m6A modifications.
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Affiliation(s)
- Xudong Mao
- Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Xianjiong Chen
- Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Zhehao Xu
- Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Lifeng Ding
- Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Wenqin Luo
- Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Yudong Lin
- Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Ruyue Wang
- Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Liqun Xia
- Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Mingchao Wang
- Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Gonghui Li
- Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
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Whittle BJ, Izuogu OG, Lowes H, Deen D, Pyle A, Coxhead J, Lawson RA, Yarnall AJ, Jackson MS, Santibanez-Koref M, Hudson G. Early-stage idiopathic Parkinson's disease is associated with reduced circular RNA expression. NPJ Parkinsons Dis 2024; 10:25. [PMID: 38245550 PMCID: PMC10799891 DOI: 10.1038/s41531-024-00636-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/08/2024] [Indexed: 01/22/2024] Open
Abstract
Neurodegeneration in Parkinson's disease (PD) precedes diagnosis by years. Early neurodegeneration may be reflected in RNA levels and measurable as a biomarker. Here, we present the largest quantification of whole blood linear and circular RNAs (circRNA) in early-stage idiopathic PD, using RNA sequencing data from two cohorts (PPMI = 259 PD, 161 Controls; ICICLE-PD = 48 PD, 48 Controls). We identified a replicable increase in TMEM252 and LMNB1 gene expression in PD. We identified novel differences in the expression of circRNAs from ESYT2, BMS1P1 and CCDC9, and replicated trends of previously reported circRNAs. Overall, using circRNA as a diagnostic biomarker in PD did not show any clear improvement over linear RNA, minimising its potential clinical utility. More interestingly, we observed a general reduction in circRNA expression in both PD cohorts, accompanied by an increase in RNASEL expression. This imbalance implicates the activation of an innate antiviral immune response and suggests a previously unknown aspect of circRNA regulation in PD.
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Affiliation(s)
- Benjamin J Whittle
- Wellcome Centre for Mitochondrial Research, Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Osagie G Izuogu
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Hannah Lowes
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Dasha Deen
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Angela Pyle
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Jon Coxhead
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Rachael A Lawson
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Alison J Yarnall
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Michael S Jackson
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | | | - Gavin Hudson
- Wellcome Centre for Mitochondrial Research, Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
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Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery. MICROARRAYS 2015; 4:389-406. [PMID: 27600230 PMCID: PMC4996376 DOI: 10.3390/microarrays4030389] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 08/16/2015] [Accepted: 08/17/2015] [Indexed: 01/24/2023]
Abstract
The diagnostic and prognostic potential of the vast quantity of publicly-available microarray data has driven the development of methods for integrating the data from different microarray platforms. Cross-platform integration, when appropriately implemented, has been shown to improve reproducibility and robustness of gene signature biomarkers. Microarray platform integration can be conceptually divided into approaches that perform early stage integration (cross-platform normalization) versus late stage data integration (meta-analysis). A growing number of statistical methods and associated software for platform integration are available to the user, however an understanding of their comparative performance and potential pitfalls is critical for best implementation. In this review we provide evidence-based, practical guidance to researchers performing cross-platform integration, particularly with an objective to discover biomarkers.
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Chikina MD, Gerald CP, Li X, Ge Y, Pincas H, Nair VD, Wong AK, Krishnan A, Troyanskaya OG, Raymond D, Saunders-Pullman R, Bressman SB, Yue Z, Sealfon SC. Low-variance RNAs identify Parkinson's disease molecular signature in blood. Mov Disord 2015; 30:813-21. [PMID: 25786808 DOI: 10.1002/mds.26205] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2014] [Revised: 01/23/2015] [Accepted: 02/09/2015] [Indexed: 12/20/2022] Open
Abstract
The diagnosis of Parkinson's disease (PD) is usually not established until advanced neurodegeneration leads to clinically detectable symptoms. Previous blood PD transcriptome studies show low concordance, possibly resulting from the use of microarray technology, which has high measurement variation. The Leucine-rich repeat kinase 2 (LRRK2) G2019S mutation predisposes to PD. Using preclinical and clinical studies, we sought to develop a novel statistically motivated transcriptomic-based approach to identify a molecular signature in the blood of Ashkenazi Jewish PD patients, including LRRK2 mutation carriers. Using a digital gene expression platform to quantify 175 messenger RNA (mRNA) markers with low coefficients of variation (CV), we first compared whole-blood transcript levels in mouse models (1) overexpressing wild-type (WT) LRRK2, (2) overexpressing G2019S LRRK2, (3) lacking LRRK2 (knockout), and (4) and in WT controls. We then studied an Ashkenazi Jewish cohort of 34 symptomatic PD patients (both WT LRRK2 and G2019S LRRK2) and 32 asymptomatic controls. The expression profiles distinguished the four mouse groups with different genetic background. In patients, we detected significant differences in blood transcript levels both between individuals differing in LRRK2 genotype and between PD patients and controls. Discriminatory PD markers included genes associated with innate and adaptive immunity and inflammatory disease. Notably, gene expression patterns in levodopa-treated PD patients were significantly closer to those of healthy controls in a dose-dependent manner. We identify whole-blood mRNA signatures correlating with LRRK2 genotype and with PD disease state. This approach may provide insight into pathogenesis and a route to early disease detection.
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Affiliation(s)
- Maria D Chikina
- Departments of Neurology and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Christophe P Gerald
- Departments of Neurology and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Xianting Li
- Departments of Neurology and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Yongchao Ge
- Departments of Neurology and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Hanna Pincas
- Departments of Neurology and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Venugopalan D Nair
- Departments of Neurology and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Aaron K Wong
- Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
| | - Arjun Krishnan
- Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
| | - Olga G Troyanskaya
- Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
| | - Deborah Raymond
- Department of Neurology, Mount Sinai Beth Israel, New York, New York, USA
| | | | - Susan B Bressman
- Department of Neurology, Mount Sinai Beth Israel, New York, New York, USA
| | - Zhenyu Yue
- Departments of Neurology and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Stuart C Sealfon
- Departments of Neurology and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Parker HS, Corrada Bravo H, Leek JT. Removing batch effects for prediction problems with frozen surrogate variable analysis. PeerJ 2014; 2:e561. [PMID: 25332844 PMCID: PMC4179553 DOI: 10.7717/peerj.561] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2014] [Accepted: 08/15/2014] [Indexed: 01/06/2023] Open
Abstract
Batch effects are responsible for the failure of promising genomic prognostic signatures, major ambiguities in published genomic results, and retractions of widely-publicized findings. Batch effect corrections have been developed to remove these artifacts, but they are designed to be used in population studies. But genomic technologies are beginning to be used in clinical applications where samples are analyzed one at a time for diagnostic, prognostic, and predictive applications. There are currently no batch correction methods that have been developed specifically for prediction. In this paper, we propose an new method called frozen surrogate variable analysis (fSVA) that borrows strength from a training set for individual sample batch correction. We show that fSVA improves prediction accuracy in simulations and in public genomic studies. fSVA is available as part of the sva Bioconductor package.
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Affiliation(s)
- Hilary S. Parker
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Héctor Corrada Bravo
- Center for Bioinformatics and Computational Biology, Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Jeffrey T. Leek
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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