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Xu Z, Li L. Identification and validation of potential genes for the diagnosis of sepsis by bioinformatics and 2-sample Mendelian randomization study. Medicine (Baltimore) 2024; 103:e38917. [PMID: 39029061 DOI: 10.1097/md.0000000000038917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/21/2024] Open
Abstract
This integrated study combines bioinformatics, machine learning, and Mendelian randomization (MR) to discover and validate molecular biomarkers for sepsis diagnosis. Methods include differential expression analysis, weighted gene co-expression network analysis (WGCNA) for identifying sepsis-related modules and hub genes, and functional enrichment analyses to explore the roles of hub genes. Machine learning algorithms identify 3 diagnostic genes - CD177, LDHA, and MCEMP1 - consistently highly expressed in sepsis patients. The nomogram model effectively predicts sepsis risk, supported by receiver operator characteristic (ROC) curves. Correlations between diagnostic genes and immune cell infiltration are observed. MR analysis reveals a positive causal relationship between MCEMP1 and sepsis risk. In conclusion, this study presents potential sepsis diagnostic biomarkers, highlighting the genetic association of MCEMP1 with sepsis for insights into early diagnosis.
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Affiliation(s)
- Zhongbo Xu
- Emergency Department, Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
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Xie J, Zheng X, Yan J, Li Q, Jin N, Wang S, Zhao P, Li S, Ding W, Cheng L, Geng Q. Deep learning model to discriminate diverse infection types based on pairwise analysis of host gene expression. iScience 2024; 27:109908. [PMID: 38827397 PMCID: PMC11141160 DOI: 10.1016/j.isci.2024.109908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 03/01/2024] [Accepted: 05/03/2024] [Indexed: 06/04/2024] Open
Abstract
Accurate detection of pathogens, particularly distinguishing between Gram-positive and Gram-negative bacteria, could improve disease treatment. Host gene expression can capture the immune system's response to infections caused by various pathogens. Here, we present a deep neural network model, bvnGPS2, which incorporates the attention mechanism based on a large-scale integrated host transcriptome dataset to precisely identify Gram-positive and Gram-negative bacterial infections as well as viral infections. We performed analysis of 4,949 blood samples across 40 cohorts from 10 countries using our previously designed omics data integration method, iPAGE, to select discriminant gene pairs and train the bvnGPS2. The performance of the model was evaluated on six independent cohorts comprising 374 samples. Overall, our deep neural network model shows robust capability to accurately identify specific infections, paving the way for precise medicine strategies in infection treatment and potentially also for identifying subtypes of other diseases.
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Affiliation(s)
- Jize Xie
- Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen People’s Hospital (First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University), Shenzhen 518020, China
- John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai, China
| | - Xubin Zheng
- Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen People’s Hospital (First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University), Shenzhen 518020, China
- Great Bay University, Dongguan, China
| | - Jianlong Yan
- Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen People’s Hospital (First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University), Shenzhen 518020, China
| | - Qizhi Li
- John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai, China
| | - Nana Jin
- Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen People’s Hospital (First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University), Shenzhen 518020, China
- Health Data Science Center, Shenzhen People’s Hospital, Shenzhen 518020, China
| | - Shuojia Wang
- Guangdong Provincial Clinical Research Center for Geriatrics, 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
- Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen People’s Hospital (First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University), Shenzhen 518020, China
| | - Shuai Li
- John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai, China
| | - Wanfu Ding
- Guangdong Provincial Clinical Research Center for Geriatrics, 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
- Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen People’s Hospital (First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University), Shenzhen 518020, China
- Health Data Science Center, Shenzhen People’s Hospital, Shenzhen 518020, China
| | - Qingshan Geng
- Guangdong Provincial Clinical Research Center for Geriatrics, 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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Ding X, Liang W, Xia H, Liu Y, Liu S, Xia X, Zhu X, Pei Y, Zhang D. Analysis of Immune and Prognostic-Related lncRNA PRKCQ-AS1 for Predicting Prognosis and Regulating Effect in Sepsis. J Inflamm Res 2024; 17:279-299. [PMID: 38229689 PMCID: PMC10790647 DOI: 10.2147/jir.s433057] [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: 08/28/2023] [Accepted: 12/07/2023] [Indexed: 01/18/2024] Open
Abstract
Background Sepsis was a high mortality and great harm systemic inflammatory response syndrome caused by infection. lncRNAs were potential prognostic marker and therapeutic target. Therefore, we expect to screen and analyze lncRNAs with potential prognostic markers in sepsis. Methods Transcriptome sequencing and limma was used to screen dysregulated RNAs. Key RNAs were screened by correlation analysis, lncRNA-mRNA co-expression and weighted gene co-expression network analysis. Immune infiltration, gene set enrichment analysis and gene set variation analysis were used to analyze the immune correlation. Kaplan-Meier curve, receiver operator characteristic curve, Cox regression analysis and nomogram were used to analyze the correlation between key RNAs and prognosis. Sepsis model was established by lipopolysaccharide-induced HUVECs injury, and then cell viability and migration ability were detected by cell counting kit-8 and wound healing assay. The levels of apoptosis-related proteins and inflammatory cytokines were detected by RT-qPCR and Western blot. Reactive Oxygen Species and superoxide dismutase were detected by commercial kit. Results Fourteen key differentially expressed lncRNAs and 663 key differentially expressed genes were obtained. And these lncRNAs were closely related to immune cells, especially T cell activation, immune response and inflammation. Subsequently, Subsequently, lncRNA PRKCQ-AS1 was identified as the regulator for further investigation in sepsis. RT-qPCR results showed that PRKCQ-AS1 expression was up-regulated in clinical samples and sepsis model cells, which was an independent prognostic factor in sepsis patients. Immune correlation analysis showed that PRKCQ-AS1 was involved in the immune response and inflammatory process of sepsis. Cell function tests confirmed that PRKCQ-AS1 could inhibit sepsis model cells viability and promote cell apoptosis, inflammatory damage and oxidative stress. Conclusion We constructed immune-related lncRNA-mRNA regulatory networks in the progression of sepsis and confirmed that PRKCQ-AS1 is an important prognostic factor affecting the progression of sepsis and is involved in immune response.
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Affiliation(s)
- Xian Ding
- Department of Emergency, Third Affiliated Hospital of Naval Medical University, Shanghai, People’s Republic of China
| | - Wenqi Liang
- Department of Emergency, Shanghai Changhai Hospital, Naval Medical University, Shanghai, People’s Republic of China
| | - Hongjuan Xia
- Department of Emergency, Third Affiliated Hospital of Naval Medical University, Shanghai, People’s Republic of China
| | - Yuee Liu
- Department of Emergency, Shanghai Changhai Hospital, Naval Medical University, Shanghai, People’s Republic of China
| | - Shuxiong Liu
- Department of Emergency, Third Affiliated Hospital of Naval Medical University, Shanghai, People’s Republic of China
| | - Xinyu Xia
- Department of Emergency, Third Affiliated Hospital of Naval Medical University, Shanghai, People’s Republic of China
| | - Xiaoli Zhu
- Department of Emergency, Third Affiliated Hospital of Naval Medical University, Shanghai, People’s Republic of China
| | - Yongyan Pei
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Zhongshan, People’s Republic of China
| | - Dewen Zhang
- Longhua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
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Wang X, Cui X, Wang W, Sun J, Wang Y, Han W, Xie X, Zhu Z, Zhang X, Yu L, Liu D. Deciphering essential druggable genes reveals potential immune-inflammatory axis in hepatocellular carcinoma. Comput Biol Med 2023; 167:107625. [PMID: 37918266 DOI: 10.1016/j.compbiomed.2023.107625] [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: 07/06/2023] [Revised: 09/30/2023] [Accepted: 10/23/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is a malignant tumor with a high mortality rate and poor prognosis in patients. Its pathogenesis is a complex process of multi-factors and multi-steps. However, the etiology and exact molecular mechanism are not completely clear. METHODS Here, we constructed a specific-expressed network based on RNA sequencing data. Gene and miRNA expression profiles and clinical evidence were integrated to detect hepatocellular carcinoma survival modules. Finally, we attempted to identify potential key biomarkers and drug targets by integrating drug sensitivity analysis and immune infiltration analysis. RESULTS A total of 42 prognostic modules for hepatocellular carcinoma were detected. The prognostic modules were significantly enriched with known cancer-related molecules and 12.93 % molecules of prognostic modules had been found were the targets of small molecule drug. In addition, we found that 38 of 42 (90.48 %) essential genes were associated with the proportions of at least one of the 7 immune cell types. CONCLUSION We integrated clinical prognosis information, RNA sequencing data, and drug activity data to explore risk modules of hepatocellular carcinoma. Through drug sensitivity analysis and immune infiltration analysis, we assessed the value of hub genes in the modules as potential biomarkers and drug targets for hepatocellular carcinoma. The protocol provides new insight into parsing the molecular mechanism and theoretical basis of hepatocellular carcinoma.
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Affiliation(s)
- Xiaoren Wang
- Department of Infectious Disease, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xudong Cui
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China
| | - Wencan Wang
- Guangzhou National Laboratory, Guangzhou, China
| | - Jia Sun
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China
| | - Yan Wang
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China
| | - Wanru Han
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China
| | - Xiaotong Xie
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China
| | - Zhu Zhu
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China
| | - Xijun Zhang
- E.N.T. Department, The Fourth Hospital of Harbin Medical University, Harbin, China
| | - Lei Yu
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China.
| | - Dabin Liu
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China.
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