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Okruszko MA, Szabłowski M, Zarzecki M, Michnowska-Kobylińska M, Lisowski Ł, Łapińska M, Stachurska Z, Szpakowicz A, Kamiński KA, Konopińska J. Inflammation and Neurodegeneration in Glaucoma: Isolated Eye Disease or a Part of a Systemic Disorder? - Serum Proteomic Analysis. J Inflamm Res 2024; 17:1021-1037. [PMID: 38370463 PMCID: PMC10874189 DOI: 10.2147/jir.s434989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 01/23/2024] [Indexed: 02/20/2024] Open
Abstract
Introduction Glaucoma is the most common optic neuropathy and the leading cause of irreversible blindness worldwide, which affects 3.54% of the population aged 40-80 years. Despite numerous published studies, some aspects of glaucoma pathogenesis, serum biomarkers, and their potential link with other diseases remain unclear. Recent articles have proposed that autoimmune, oxidative stress and inflammation may be involved in the pathogenesis of glaucoma. Methods We investigated the serum expression of 92 inflammatory and neurotrophic factors in glaucoma patients. The study group consisted of 26 glaucoma patients and 192 healthy subjects based on digital fundography. Results Patients with glaucoma had significantly lower serum expression of IL-2Rβ, TWEAK, CX3CL1, CD6, CD5, LAP TGF-beta1, LIF-R, TRAIL, NT-3, and CCL23 and significantly higher expression of IL-22Rα1. Conclusion Our results indicate that patients with glaucoma tend to have lower levels of neuroprotective proteins and higher levels of neuroinflammatory proteins, similar to those observed in psychiatric, neurodegenerative and autoimmune diseases, indicating a potential link between these conditions and glaucoma pathogenesis.
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Affiliation(s)
| | - Maciej Szabłowski
- Department of Ophthalmology, Medical University of Bialystok, Białystok, 15-089, Poland
| | - Mateusz Zarzecki
- Department of Ophthalmology, Medical University of Bialystok, Białystok, 15-089, Poland
| | | | - Łukasz Lisowski
- Department of Ophthalmology, Medical University of Bialystok, Białystok, 15-089, Poland
| | - Magda Łapińska
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Białystok, Białystok, Poland
| | - Zofia Stachurska
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Białystok, Białystok, Poland
| | - Anna Szpakowicz
- Department of Cardiology, Medical University of Bialystok, Białystok, Poland
| | - Karol Adam Kamiński
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Białystok, Białystok, Poland
| | - Joanna Konopińska
- Department of Ophthalmology, Medical University of Bialystok, Białystok, 15-089, Poland
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Li Y, Gao J, Zheng X, Nie G, Qin J, Wang H, He T, Wheelock Å, Li CX, Cheng L, Li X. Diagnostic Prediction of portal vein thrombosis in chronic cirrhosis patients using data-driven precision medicine model. Brief Bioinform 2023; 25:bbad478. [PMID: 38221905 PMCID: PMC10788706 DOI: 10.1093/bib/bbad478] [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] [Received: 09/30/2023] [Revised: 11/16/2023] [Accepted: 12/01/2023] [Indexed: 01/16/2024] Open
Abstract
BACKGROUND Portal vein thrombosis (PVT) is a significant issue in cirrhotic patients, necessitating early detection. This study aims to develop a data-driven predictive model for PVT diagnosis in chronic hepatitis liver cirrhosis patients. METHODS We employed data from a total of 816 chronic cirrhosis patients with PVT, divided into the Lanzhou cohort (n = 468) for training and the Jilin cohort (n = 348) for validation. This dataset encompassed a wide range of variables, including general characteristics, blood parameters, ultrasonography findings and cirrhosis grading. To build our predictive model, we employed a sophisticated stacking approach, which included Support Vector Machine (SVM), Naïve Bayes and Quadratic Discriminant Analysis (QDA). RESULTS In the Lanzhou cohort, SVM and Naïve Bayes classifiers effectively classified PVT cases from non-PVT cases, among the top features of which seven were shared: Portal Velocity (PV), Prothrombin Time (PT), Portal Vein Diameter (PVD), Prothrombin Time Activity (PTA), Activated Partial Thromboplastin Time (APTT), age and Child-Pugh score (CPS). The QDA model, trained based on the seven shared features on the Lanzhou cohort and validated on the Jilin cohort, demonstrated significant differentiation between PVT and non-PVT cases (AUROC = 0.73 and AUROC = 0.86, respectively). Subsequently, comparative analysis showed that our QDA model outperformed several other machine learning methods. CONCLUSION Our study presents a comprehensive data-driven model for PVT diagnosis in cirrhotic patients, enhancing clinical decision-making. The SVM-Naïve Bayes-QDA model offers a precise approach to managing PVT in this population.
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Affiliation(s)
- Ying Li
- The First Hospital of Lanzhou University, Lanzhou, China
| | - Jing Gao
- Respiratory Medicine Unit, Department of Medicine & Centre for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Heart and Lung Centre, Department of Pulmonary Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Xubin Zheng
- School of Computing and Information Technology, Great Bay University, Guangdong, China
| | - Guole Nie
- The First Hospital of Lanzhou University, Lanzhou, China
| | - Jican Qin
- School of Computing and Information Technology, Great Bay University, Guangdong, China
| | - Haiping Wang
- The First Hospital of Lanzhou University, Lanzhou, China
| | - Tao He
- Jilin Hepato-Biliary Diseases Hospital, Changchun, China
| | - Åsa Wheelock
- Respiratory Medicine Unit, Department of Medicine & Centre for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm, Sweden
| | - Chuan-Xing Li
- Respiratory Medicine Unit, Department of Medicine & Centre for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Lixin Cheng
- Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen, China
| | - Xun Li
- The First Hospital of Lanzhou University, Lanzhou, China
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3
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Liu X, Hong C, Jiang Y, Li W, Chen Y, Ma Y, Zhao P, Li T, Chen H, Liu X, Cheng L. Co-expression module analysis reveals high expression homogeneity for both coding and non-coding genes in sepsis. BMC Genomics 2023; 24:418. [PMID: 37488493 PMCID: PMC10364430 DOI: 10.1186/s12864-023-09460-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 06/16/2023] [Indexed: 07/26/2023] Open
Abstract
Sepsis is a life-threatening condition characterized by a harmful host response to infection with organ dysfunction. Annually about 20 million people are dead owing to sepsis and its mortality rates is as high as 20%. However, no studies have been carried out to investigate sepsis from the system biology point of view, as previous research predominantly focused on individual genes without considering their interactions and associations. Here, we conducted a comprehensive exploration of genome-wide expression alterations in both mRNAs and long non-coding RNAs (lncRNAs) in sepsis, using six microarray datasets. Co-expression networks were conducted to identify mRNA and lncRNA modules, respectively. Comparing these sepsis modules with normal modules, we observed a homogeneous expression pattern within the mRNA/lncRNA members, with the majority of them displaying consistent expression direction. Moreover, we identified consistent modules across diverse datasets, consisting of 20 common mRNA members and two lncRNAs, namely CHRM3-AS2 and PRKCQ-AS1, which are potential regulators of sepsis. Our results reveal that the up-regulated common mRNAs are mainly involved in the processes of neutrophil mediated immunity, while the down-regulated mRNAs and lncRNAs are significantly overrepresented in T-cell mediated immunity functions. This study sheds light on the co-expression patterns of mRNAs and lncRNAs in sepsis, providing a novel perspective and insight into the sepsis transcriptome, which may facilitate the exploration of candidate therapeutic targets and molecular biomarkers for sepsis.
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Affiliation(s)
- Xiaojun Liu
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Chengying Hong
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Yichun Jiang
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Wei Li
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Youlian Chen
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Yonghui Ma
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Pengfei Zhao
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Tiyuan Li
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Huaisheng Chen
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China.
| | - Xueyan Liu
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China.
| | - Lixin Cheng
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China.
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de la Varga-Martínez O, Martín-Fernández M, Heredia-Rodríguez M, Ceballos F, Cubero-Gallego H, Priede-Vimbela JM, Bardají-Carrillo M, Sánchez-de Prada L, López-Herrero R, Jorge-Monjas P, Tamayo E, Gómez-Sánchez E. Influence of Renal Dysfunction on the Differential Behaviour of Procalcitonin for the Diagnosis of Postoperative Infection in Cardiac Surgery. J Clin Med 2022; 11:jcm11247274. [PMID: 36555891 PMCID: PMC9781060 DOI: 10.3390/jcm11247274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 10/26/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022] Open
Abstract
Background: procalcitonin is a valuable marker in the diagnosis of bacterial infections; however, the impairment of renal function can influence its diagnostic precision. The objective of this study is to evaluate the differential behaviour of procalcitonin, as well as its usefulness in the diagnosis of postoperative pulmonary infection after cardiac surgery, depending on the presence or absence of impaired renal function. Materials and methods: A total of 805 adult patients undergoing cardiac surgery with extracorporeal circulation (CBP) were prospectively recruited, comparing the behaviour of biomarkers between the groups with and without postoperative pneumonia and according to the presence or absence of renal dysfunction. Results: Pulmonary infection was diagnosed in 42 patients (5.21%). In total, 228 patients (28.32%) presented postoperative renal dysfunction. Procalcitonin was significantly higher in infected patients, even in the presence of renal dysfunction. The optimal procalcitonin threshold differed markedly in patients with renal dysfunction compared to patients without renal dysfunction (1 vs. 0.78 ng/mL p < 0.05). The diagnostic accuracy of procalcitonin increased significantly when the procalcitonin threshold was adapted to renal function. Conclusions: Procalcitonin is an accurate marker of postoperative infection in cardiac surgery, even in the presence of renal dysfunction. Renal function is an important determinant of procalcitonin levels and, therefore, its diagnostic thresholds must be adapted in the presence of renal dysfunction.
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Affiliation(s)
- Olga de la Varga-Martínez
- Department of Anaesthesiology, Infanta Leonor University Hospital, Gran Via del Este 80, 28031 Madrid, Spain
- BioCritic, Group for Biomedical Research in Critical Care Medicine, Ramon y Cajal Ave. 7, 47005 Valladolid, Spain
- Correspondence: ; Tel.: +34-911918000
| | - Marta Martín-Fernández
- BioCritic, Group for Biomedical Research in Critical Care Medicine, Ramon y Cajal Ave. 7, 47005 Valladolid, Spain
- Center for Biomedical Research in Infectious Diseases Network (CIBERINFEC), Carlos III Health Institute, 28029 Madrid, Spain
- Department of Medicine, Faculty of Medicine, Universidad de Valladolid, 47005 Valladolid, Spain
| | - María Heredia-Rodríguez
- BioCritic, Group for Biomedical Research in Critical Care Medicine, Ramon y Cajal Ave. 7, 47005 Valladolid, Spain
- Center for Biomedical Research in Infectious Diseases Network (CIBERINFEC), Carlos III Health Institute, 28029 Madrid, Spain
- Department of Anaesthesiology, Clinical University Hospital of Salamanca, P.° de San Vicente, 58, 37007 Salamanca, Spain
| | - Francisco Ceballos
- Viral Infection and Immunity Unit, National Center for Microbiology, Carlos III Health Institute, 28029 Madrid, Spain
| | - Hector Cubero-Gallego
- Interventional Cardiology Unit, Cardiology Department, Hospital del Mar, 08003 Barcelona, Spain
| | - Juan Manuel Priede-Vimbela
- Department of Anaesthesiology, Clinic University Hospital of Valladolid, Ramon y Cajal Ave. 3, 47003 Valladolid, Spain
| | - Miguel Bardají-Carrillo
- Department of Anaesthesiology, Clinic University Hospital of Valladolid, Ramon y Cajal Ave. 3, 47003 Valladolid, Spain
| | - Laura Sánchez-de Prada
- BioCritic, Group for Biomedical Research in Critical Care Medicine, Ramon y Cajal Ave. 7, 47005 Valladolid, Spain
- Microbiology and Immunology Department, Hospital Clínico Universitario de Valladolid, 47003 Valladolid, Spain
| | - Rocío López-Herrero
- BioCritic, Group for Biomedical Research in Critical Care Medicine, Ramon y Cajal Ave. 7, 47005 Valladolid, Spain
- Department of Anaesthesiology, Clinic University Hospital of Valladolid, Ramon y Cajal Ave. 3, 47003 Valladolid, Spain
| | - Pablo Jorge-Monjas
- BioCritic, Group for Biomedical Research in Critical Care Medicine, Ramon y Cajal Ave. 7, 47005 Valladolid, Spain
- Center for Biomedical Research in Infectious Diseases Network (CIBERINFEC), Carlos III Health Institute, 28029 Madrid, Spain
- Department of Anaesthesiology, Clinic University Hospital of Valladolid, Ramon y Cajal Ave. 3, 47003 Valladolid, Spain
- Department of Surgery, Faculty of Medicine, Universidad de Valladolid, 47005 Valladolid, Spain
| | - Eduardo Tamayo
- BioCritic, Group for Biomedical Research in Critical Care Medicine, Ramon y Cajal Ave. 7, 47005 Valladolid, Spain
- Center for Biomedical Research in Infectious Diseases Network (CIBERINFEC), Carlos III Health Institute, 28029 Madrid, Spain
- Department of Anaesthesiology, Clinic University Hospital of Valladolid, Ramon y Cajal Ave. 3, 47003 Valladolid, Spain
- Department of Surgery, Faculty of Medicine, Universidad de Valladolid, 47005 Valladolid, Spain
| | - Esther Gómez-Sánchez
- BioCritic, Group for Biomedical Research in Critical Care Medicine, Ramon y Cajal Ave. 7, 47005 Valladolid, Spain
- Center for Biomedical Research in Infectious Diseases Network (CIBERINFEC), Carlos III Health Institute, 28029 Madrid, Spain
- Department of Anaesthesiology, Clinic University Hospital of Valladolid, Ramon y Cajal Ave. 3, 47003 Valladolid, Spain
- Department of Surgery, Faculty of Medicine, Universidad de Valladolid, 47005 Valladolid, Spain
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5
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Xu C, Li W, Li T, Yuan J, Pang X, Liu T, Liang B, Cheng L, Sun X, Dong S. Iron metabolism-related genes reveal predictive value of acute coronary syndrome. Front Pharmacol 2022; 13:1040845. [PMID: 36330096 PMCID: PMC9622999 DOI: 10.3389/fphar.2022.1040845] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/04/2022] [Indexed: 11/25/2022] Open
Abstract
Iron deficiency has detrimental effects in patients with acute coronary syndrome (ACS), which is a common nutritional disorder and inflammation-related disease affects up to one-third people worldwide. However, the specific role of iron metabolism in ACS progression is opaque. In this study, we construct an iron metabolism-related genes (IMRGs) based molecular signature of ACS and to identify novel iron metabolism gene markers for early stage of ACS. The IMRGs were mainly collected from Molecular Signatures Database (mSigDB) and two relevant studies. Two blood transcriptome datasets GSE61144 and GSE60993 were used for constructing the prediction model of ACS. After differential analysis, 22 IMRGs were differentially expressed and defined as DEIGs in the training set. Then, the 22 DEIGs were trained by the Elastic Net to build the prediction model. Five genes, PADI4, HLA-DQA1, LCN2, CD7, and VNN1, were determined using multiple Elastic Net calculations and retained to obtain the optimal performance. Finally, the generated model iron metabolism-related gene signature (imSig) was assessed by the validation set GSE60993 using a series of evaluation measurements. Compared with other machine learning methods, the performance of imSig using Elastic Net was superior in the validation set. Elastic Net consistently scores the higher than Lasso and Logistic regression in the validation set in terms of ROC, PRC, Sensitivity, and Specificity. The prediction model based on iron metabolism-related genes may assist in ACS early diagnosis.
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Affiliation(s)
- Cong Xu
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Wanyang Li
- School of Mathematics, South China University of Technology, Guangzhou, China
| | - Tangzhiming Li
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Jie Yuan
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Xinli Pang
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Tao Liu
- International Digital Economy Academy, Shenzhen, China
| | - Benhui Liang
- Department of Cardiology, Xiangya Hospital, Central South University, Changsha, China
| | - Lixin Cheng
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
- *Correspondence: Lixin Cheng, ; Xin Sun, ; Shaohong Dong,
| | - Xin Sun
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
- *Correspondence: Lixin Cheng, ; Xin Sun, ; Shaohong Dong,
| | - Shaohong Dong
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
- *Correspondence: Lixin Cheng, ; Xin Sun, ; Shaohong Dong,
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6
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Li H, Zheng X, Gao J, Leung KS, Wong MH, Yang S, Liu Y, Dong M, Bai H, Ye X, Cheng L. Whole transcriptome analysis reveals non-coding RNA's competing endogenous gene pairs as novel form of motifs in serous ovarian cancer. Comput Biol Med 2022; 148:105881. [DOI: 10.1016/j.compbiomed.2022.105881] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/30/2022] [Accepted: 07/16/2022] [Indexed: 11/03/2022]
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7
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Wu Q, Zheng X, Leung KS, Wong MH, Tsui SKW, Cheng L. meGPS: a multi-omics signature for hepatocellular carcinoma detection integrating methylome and transcriptome data. Bioinformatics 2022; 38:3513-3522. [PMID: 35674358 DOI: 10.1093/bioinformatics/btac379] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 05/08/2022] [Accepted: 06/01/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Hepatocellular carcinoma (HCC) is a primary malignancy with poor prognosis. Recently, multi-omics molecular-level measurement enables HCC diagnosis and prognosis prediction, which is crucial for early intervention of personalized therapy to diminish mortality. Here, we introduce a novel strategy utilizing DNA methylation and RNA expression data to achieve a multi-omics gene pair signature (GPS) for HCC discrimination. RESULTS The immune genes with negative correlations between expression and promoter methylation are enriched in the highly connected cancer-related pathway network, which are considered as the candidates for HCC detection. After that, we separately construct a methylation GPS (mGPS) and an expression GPS (eGPS), and then assemble them as a meGPS with five gene pairs, in which the significant methylation and expression changes occur between HCC tumor and non-tumor groups. Reliable performance has been validated by independent tissue (age, gender, and etiology) and blood datasets. This study proposes a procedure for multi-omics GPS identification and develops a novel HCC signature using both methylome and transcriptome data, suggesting potential molecular targets for the detection and therapy of HCC. AVAILABILITY AND IMPLEMENTATION Models are available at https://github.com/bioinformaticStudy/meGPS.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qiong Wu
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China.,School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.,Department of Paediatrics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Xubin Zheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Man-Hon Wong
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Stephen Kwok-Wing Tsui
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Lixin Cheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
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8
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Wang R, Zheng X, Wang J, Wan S, Song F, Wong MH, Leung KS, Cheng L. Improving bulk RNA-seq classification by transferring gene signature from single cells in acute myeloid leukemia. Brief Bioinform 2022; 23:6523149. [PMID: 35136933 DOI: 10.1093/bib/bbac002] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 12/22/2021] [Accepted: 01/04/2022] [Indexed: 12/13/2022] Open
Abstract
The advances in single-cell RNA sequencing (scRNA-seq) technologies enable the characterization of transcriptomic profiles at the cellular level and demonstrate great promise in bulk sample analysis thereby offering opportunities to transfer gene signature from scRNA-seq to bulk data. However, the gene expression signatures identified from single cells are typically inapplicable to bulk RNA-seq data due to the profiling differences of distinct sequencing technologies. Here, we propose single-cell pair-wise gene expression (scPAGE), a novel method to develop single-cell gene pair signatures (scGPSs) that were beneficial to bulk RNA-seq classification to transfer knowledge across platforms. PAGE was adopted to tackle the challenge of profiling differences. We applied the method to acute myeloid leukemia (AML) and identified the scGPS from mouse scRNA-seq that allowed discriminating between AML and control cells. The scGPS was validated in bulk RNA-seq datasets and demonstrated better performance (average area under the curve [AUC] = 0.96) than the conventional gene expression strategies (average AUC$\le$ 0.88) suggesting its potential in disclosing the molecular mechanism of AML. The scGPS also outperformed its bulk counterpart, which highlighted the benefit of gene signature transfer. Furthermore, we confirmed the utility of scPAGE in sepsis as an example of other disease scenarios. scPAGE leveraged the advantages of single-cell profiles to enhance the analysis of bulk samples revealing great potential of transferring knowledge from single-cell to bulk transcriptome studies.
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Affiliation(s)
- Ran Wang
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Xubin Zheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Jun Wang
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China
| | - Shibiao Wan
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Fangda Song
- School of Data Science, The Chinese University of Hong Kong, Shenzhen 518000, China
| | - Man Hon Wong
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Kwong Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Lixin Cheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China
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