1
|
Zhang G, Zhang K. Screening and Identification of Neutrophil Extracellular Trap-related Diagnostic Biomarkers for Pediatric Sepsis by Machine Learning. Inflammation 2024:10.1007/s10753-024-02059-6. [PMID: 38795170 DOI: 10.1007/s10753-024-02059-6] [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: 02/20/2024] [Revised: 05/15/2024] [Accepted: 05/20/2024] [Indexed: 05/27/2024]
Abstract
Neutrophil extracellular trap (NET) is released by neutrophils to trap invading pathogens and can lead to dysregulation of immune responses and disease pathogenesis. However, systematic evaluation of NET-related genes (NETRGs) for the diagnosis of pediatric sepsis is still lacking. Three datasets were taken from the Gene Expression Omnibus (GEO) database: GSE13904, GSE26378, and GSE26440. After NETRGs and differentially expressed genes (DEGs) were identified in the GSE26378 dataset, crucial genes were identified by using LASSO regression analysis and random forest analysis on the genes that overlapped in both DEGs and NETRGs. These crucial genes were then employed to build a diagnostic model. The diagnostic model's effectiveness in identifying pediatric sepsis across the three datasets was confirmed through receiver operating characteristic curve (ROC) analysis. In addition, clinical pediatric sepsis samples were collected to measure the expression levels of important genes and evaluate the diagnostic model's performance using qRT-PCR in identifying pediatric sepsis in actual clinical samples. Next, using the CIBERSORT database, the relationship between invading immune cells and diagnostic markers was investigated in more detail. Lastly, to evaluate NET formation, we measured myeloperoxidase (MPO)-DNA complex levels using ELISA. A group of five important genes (MME, BST1, S100A12, FCAR, and ALPL) were found among the 13 DEGs associated with NET formation and used to create a diagnostic model for pediatric sepsis. Across all three cohorts, the sepsis group had consistently elevated expression levels of these five critical genes as compared to the normal group. Area under the curve (AUC) values of 1, 0.932, and 0.966 indicate that the diagnostic model performed exceptionally well in terms of diagnosis. Notably, when applied to the clinical samples, the diagnostic model also showed good diagnostic capacity with an AUC of 0.898, outperforming the effectiveness of traditional inflammatory markers such as PCT, CRP, WBC, and NEU%. Lastly, we discovered that children with high ratings for sepsis also had higher MPO-DNA complex levels. In conclusion, the creation and verification of a five-NETRGs diagnostic model for pediatric sepsis performs better than established markers of inflammation.
Collapse
Affiliation(s)
- Genhao Zhang
- Department of Blood Transfusion, Zhengzhou University First Affiliated Hospital, Zhengzhou, China.
| | - Kai Zhang
- Department of Medical Laboratory, Zhengzhou University Third Affiliated Hospital, Zhengzhou, China
| |
Collapse
|
2
|
Chen Y, Tu Y, Yan G, Ji X, Chen S, Niu C, Liao P. Integrated Bioinformatics Analysis for Revealing CBL is a Potential Diagnosing Biomarker and Related Immune Infiltration in Parkinson's Disease. Int J Gen Med 2024; 17:2371-2386. [PMID: 38799203 PMCID: PMC11128229 DOI: 10.2147/ijgm.s456942] [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: 02/01/2024] [Accepted: 05/16/2024] [Indexed: 05/29/2024] Open
Abstract
Purpose There is growing evidence that the immune system plays an important role in the progression of Parkinson's disease, the second most common neurodegenerative disorder. This study aims to address the comprehensive understanding of the immunopathogenesis of Parkinson's disease and explore new inflammatory biomarkers. Patients and Methods In this study, Immune-related differential expressed genes (DEIRGs) were obtained from GEO database and Immport database. The hub gene was screened in DEIRGs using LASSO regression and random forest algorithm, and the mRNA expression of the identified hub gene was validated using clinical blood samples. Results We obtained a total of 157 DEIRGs that played an important role in the immune response. The results of immune cell infiltration analysis showed that the degree of memory B cells infiltration was higher in PD patients, while the degree of Monocytes, resting mast cells and M0 macrophages infiltration was lower (p<0.05). A total of 8 hub genes were screened by machine learning methods, and RT-PCR results showed that the expression level of CBL gene in PD was significantly increased (p<0.05). Conclusion Our findings suggest that CBL is a new potential diagnostic biomarker for PD and that abnormal immune cell infiltration may influence PD development.
Collapse
Affiliation(s)
- Yanchen Chen
- Department of Laboratory Medicine, North Sichuan Medical College, Nanchong, People’s Republic of China
- Department of Clinical Laboratory, Chongqing General Hospital, Chongqing, People’s Republic of China
| | - Yuqin Tu
- Department of Clinical Laboratory, Chongqing General Hospital, Chongqing, People’s Republic of China
- Chongqing Medical University, Chongqing, People’s Republic of China
| | - Guiling Yan
- Department of Clinical Laboratory, Chongqing General Hospital, Chongqing, People’s Republic of China
- Chongqing Medical University, Chongqing, People’s Republic of China
| | - Xinyao Ji
- Department of Clinical Laboratory, Chongqing General Hospital, Chongqing, People’s Republic of China
- Chongqing Medical University, Chongqing, People’s Republic of China
| | - Shu Chen
- Department of Clinical Laboratory, Chongqing General Hospital, Chongqing, People’s Republic of China
- Chongqing Medical University, Chongqing, People’s Republic of China
| | - Changchun Niu
- Department of Clinical Laboratory, Chongqing General Hospital, Chongqing, People’s Republic of China
| | - Pu Liao
- Department of Laboratory Medicine, North Sichuan Medical College, Nanchong, People’s Republic of China
- Department of Clinical Laboratory, Chongqing General Hospital, Chongqing, People’s Republic of China
| |
Collapse
|
3
|
Xiao Y, Zhang G. Predictive Value of a Diagnostic Five-Gene Biomarker for Pediatric Sepsis. J Inflamm Res 2024; 17:2063-2071. [PMID: 38595339 PMCID: PMC11002788 DOI: 10.2147/jir.s447588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 03/26/2024] [Indexed: 04/11/2024] Open
Abstract
Background Pediatric sepsis has a very high morbidity and mortality rate. The purpose of this study was to evaluate diagnostic biomarkers and immune cell infiltration in pediatric sepsis. Methods Three datasets (GSE13904, GSE26378, and GSE26440) were downloaded from the gene expression omnibus (GEO) database. After identifying overlapping genes in differentially expressed genes (DEGs) and modular sepsis genes selected via a weighted gene co-expression network (WGCNA) in the GSE26378 dataset, pivotal genes were further identified by using LASSO regression and random forest analysis to construct a diagnostic model. Receiver operating characteristic curve (ROC) analysis was used to validate the efficacy of the diagnostic model for pediatric sepsis. Furthermore, we used qRT-PCR to detect the expression levels of pivotal genes and validate the diagnostic model's ability to diagnose pediatric sepsis in 65 actual clinical samples. Results Among 294 overlapping genes of DEGs and modular sepsis genes, five pivotal genes (STOM, MS4A4A, CD177, MMP8, and MCEMP1) were screened to construct a diagnostic model of pediatric sepsis. The expression of the five pivotal genes was higher in the sepsis group than in the normal group. The diagnostic model showed good diagnostic ability with AUCs of 1, 0.986, and 0.968. More importantly, the diagnostic model showed good diagnostic ability with AUCs of 0.937 in the 65 clinical samples and showed better efficacy compared to conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%). Conclusion We developed and tested a five-gene diagnostic model that can reliably identify pediatric sepsis and also suggest prospective candidate genes for peripheral blood diagnostic testing in pediatric sepsis patients.
Collapse
Affiliation(s)
- Yulong Xiao
- Department of Medical Laboratory, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
| | - Genhao Zhang
- Department of Blood Transfusion, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
| |
Collapse
|
4
|
Casini F, Valentino MS, Lorenzo MG, Caiazzo R, Coppola C, David D, Di Tonno R, Giacomet V. Use of transcriptomics for diagnosis of infections and sepsis in children: A narrative review. Acta Paediatr 2024; 113:670-676. [PMID: 38243675 DOI: 10.1111/apa.17119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 01/08/2024] [Accepted: 01/11/2024] [Indexed: 01/21/2024]
Abstract
AIM The aim of this review was to summarise the most recent evidence about the use of omics-based techniques as an instrument for a more rapid and accurate characterisation of respiratory tract infections, neurological infections and sepsis in paediatrics. METHODS We performed a narrative review using PubMed and a set of inclusion criteria: English language articles, clinical trials, meta-analysis and reviews including only paediatric population inherited to this topic in the last 15 years. RESULTS The examined studies suggest that host gene expression signatures are an effective method to characterise the different types of infections, to distinguish infection from colonisation and, in some cases, to assess the severity of the disease in children. CONCLUSIONS 'Omics-based techniques' may help to define the aetiology of infections in paediatrics, representing a useful tool to choose the most appropriate therapies and limit antibiotic resistance.
Collapse
Affiliation(s)
- Francesca Casini
- Pediatric Department, "Vittore Buzzi" Children's Hospital, Milan, Italy
| | - Maria Sole Valentino
- Pediatric Infectious Disease Unit, Luigi Sacco Hospital, University of Milan, Milan, Italy
| | - Marc Garcia Lorenzo
- Pediatric Infectious Disease Unit, Luigi Sacco Hospital, University of Milan, Milan, Italy
| | - Roberta Caiazzo
- Pediatric Infectious Disease Unit, Luigi Sacco Hospital, University of Milan, Milan, Italy
| | - Crescenzo Coppola
- Pediatric Infectious Disease Unit, Luigi Sacco Hospital, University of Milan, Milan, Italy
| | - Daniela David
- Pediatric Infectious Disease Unit, Luigi Sacco Hospital, University of Milan, Milan, Italy
| | - Raffaella Di Tonno
- Pediatric Infectious Disease Unit, Luigi Sacco Hospital, University of Milan, Milan, Italy
| | - Vania Giacomet
- Pediatric Infectious Disease Unit, Luigi Sacco Hospital, University of Milan, Milan, Italy
| |
Collapse
|
5
|
Song D, Yang Q, Li L, Wei Y, Zhang C, Du H, Ren G, Li H. Novel prognostic biomarker TBC1D1 is associated with immunotherapy resistance in gliomas. Front Immunol 2024; 15:1372113. [PMID: 38529286 PMCID: PMC10961388 DOI: 10.3389/fimmu.2024.1372113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 02/28/2024] [Indexed: 03/27/2024] Open
Abstract
Background Glioma, an aggressive brain tumor, poses a challenge in understanding the mechanisms of treatment resistance, despite promising results from immunotherapy. Methods We identified genes associated with immunotherapy resistance through an analysis of The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) databases. Subsequently, qRT-PCR and western blot analyses were conducted to measure the mRNA and protein levels of TBC1 Domain Family Member 1 (TBC1D1), respectively. Additionally, Gene Set Enrichment Analysis (GSEA) was employed to reveal relevant signaling pathways, and the expression of TBC1D1 in immune cells was analyzed using single-cell RNA sequencing (scRNA-seq) data from GEO database. Tumor Immune Dysfunction and Exclusion (TIDE) database was utilized to assess T-cell function, while Tumor Immunotherapy Gene Expression Resource (TIGER) database was employed to evaluate immunotherapy resistance in relation to TBC1D1. Furthermore, the predictive performance of molecules on prognosis was assessed using Kaplan-Meier plots, nomograms, and ROC curves. Results The levels of TBC1D1 were significantly elevated in tumor tissue from glioma patients. Furthermore, high TBC1D1 expression was observed in macrophages compared to other cells, which negatively impacted T cell function, impaired immunotherapy response, promoted treatment tolerance, and led to poor prognosis. Inhibition of TBC1D1 was found to potentially synergistically enhance the efficacy of immunotherapy and prolong the survival of cancer patients with gliomas. Conclusion Heightened expression of TBC1D1 may facilitate an immunosuppressive microenvironment and predict a poor prognosis. Blocking TBC1D1 could minimize immunotherapy resistance in cancer patients with gliomas.
Collapse
Affiliation(s)
- Daqiang Song
- Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Pharmacy, Chongqing Medical University, Chongqing, China
| | - Qian Yang
- Clinical Molecular Medicine Testing Center, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Liuying Li
- Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuxian Wei
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chong Zhang
- Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Ultrasound, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Huimin Du
- Department of Oncology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Guosheng Ren
- Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongzhong Li
- Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| |
Collapse
|
6
|
Yang Y, Zhang G. Lysosome-Related Diagnostic Biomarkers for Pediatric Sepsis Integrated by Machine Learning. J Inflamm Res 2023; 16:5575-5583. [PMID: 38034045 PMCID: PMC10685105 DOI: 10.2147/jir.s437110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/21/2023] [Indexed: 12/02/2023] Open
Abstract
Background There is currently no biomarker that can reliably identify sepsis, despite recent scientific advancements. We systematically evaluated the value of lysosomal genes for the diagnosis of pediatric sepsis. Methods Three datasets (GSE13904, GSE26378, and GSE26440) were obtained from the gene expression omnibus (GEO) database. LASSO regression analysis and random forest analysis were employed for screening pivotal genes to construct a diagnostic model between the differentially expressed genes (DEGs) and lysosomal genes. The efficacy of the diagnostic model for pediatric sepsis identification in the three datasets was validated through receiver operating characteristic curve (ROC) analysis. Furthermore, a total of 30 normal samples and 35 pediatric sepsis samples were gathered to detect the expression levels of crucial genes and assess the diagnostic model's efficacy in diagnosing pediatric sepsis in real clinical samples through real-time quantitative PCR (qRT-PCR). Results Among the 83 differentially expressed genes (DEGs) related to lysosomes, four key genes (STOM, VNN1, SORT1, and RETN) were identified to develop a diagnostic model for pediatric sepsis. The expression levels of these four key genes were consistently higher in the sepsis group compared to the normal group across all three cohorts. The diagnostic model exhibited excellent diagnostic performance, as evidenced by area under the curve (AUC) values of 1, 0.971, and 0.989. Notably, the diagnostic model also demonstrated strong diagnostic ability with an AUC of 0.917 when applied to the 65 clinical samples, surpassing the efficacy of conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%). Conclusion A four-gene diagnostic model of lysosomal function was devised and validated, aiming to accurately detect pediatric sepsis cases and propose potential target genes for lysosomal intervention in affected children.
Collapse
Affiliation(s)
- Yang Yang
- Department of Nuclear Medicine, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, People’s Hospital of Henan University, Zhengzhou, Henan, 450003, People’s Republic of China
| | - Genhao Zhang
- Department of Blood Transfusion, Zhengzhou University First Affiliated Hospital, Zhengzhou, Henan, 450052, People’s Republic of China
| |
Collapse
|
7
|
Zhang WY, Chen ZH, An XX, Li H, Zhang HL, Wu SJ, Guo YQ, Zhang K, Zeng CL, Fang XM. Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric sepsis by integrating bioinformatics and machine learning. World J Pediatr 2023; 19:1094-1103. [PMID: 37115484 PMCID: PMC10533616 DOI: 10.1007/s12519-023-00717-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 03/10/2023] [Indexed: 04/29/2023]
Abstract
BACKGROUND Pediatric sepsis is a complicated condition characterized by life-threatening organ failure resulting from a dysregulated host response to infection in children. It is associated with high rates of morbidity and mortality, and rapid detection and administration of antimicrobials have been emphasized. The objective of this study was to evaluate the diagnostic biomarkers of pediatric sepsis and the function of immune cell infiltration in the development of this illness. METHODS Three gene expression datasets were available from the Gene Expression Omnibus collection. First, the differentially expressed genes (DEGs) were found with the use of the R program, and then gene set enrichment analysis was carried out. Subsequently, the DEGs were combined with the major module genes chosen using the weighted gene co-expression network. The hub genes were identified by the use of three machine-learning algorithms: random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator. The receiver operating characteristic curve and nomogram model were used to verify the discrimination and efficacy of the hub genes. In addition, the inflammatory and immune status of pediatric sepsis was assessed using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). The relationship between the diagnostic markers and infiltrating immune cells was further studied. RESULTS Overall, after overlapping key module genes and DEGs, we detected 402 overlapping genes. As pediatric sepsis diagnostic indicators, CYSTM1 (AUC = 0.988), MMP8 (AUC = 0.973), and CD177 (AUC = 0.986) were investigated and demonstrated statistically significant differences (P < 0.05) and diagnostic efficacy in the validation set. As indicated by the immune cell infiltration analysis, multiple immune cells may be involved in the development of pediatric sepsis. Additionally, all diagnostic characteristics may correlate with immune cells to varying degrees. CONCLUSIONS The candidate hub genes (CD177, CYSTM1, and MMP8) were identified, and the nomogram was constructed for pediatric sepsis diagnosis. Our study could provide potential peripheral blood diagnostic candidate genes for pediatric sepsis patients.
Collapse
Affiliation(s)
- Wen-Yuan Zhang
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China
| | - Zhong-Hua Chen
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China
- Department of Anesthesiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, 312000, China
| | | | - Hui Li
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China
| | - Hua-Lin Zhang
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China
| | - Shui-Jing Wu
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China
| | - Yu-Qian Guo
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China
| | - Kai Zhang
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China
| | - Cong-Li Zeng
- Department of Anesthesiology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Xiang-Ming Fang
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China.
| |
Collapse
|
8
|
Wang X, Wang Z, Guo Z, Wang Z, Chen F, Wang Z. Exploring the Role of Different Cell-Death-Related Genes in Sepsis Diagnosis Using a Machine Learning Algorithm. Int J Mol Sci 2023; 24:14720. [PMID: 37834169 PMCID: PMC10572834 DOI: 10.3390/ijms241914720] [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: 07/31/2023] [Revised: 09/22/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
Sepsis, a disease caused by severe infection, has a high mortality rate. At present, there is a lack of reliable algorithmic models for biomarker mining and diagnostic model construction for sepsis. Programmed cell death (PCD) has been shown to play a vital role in disease occurrence and progression, and different PCD-related genes have the potential to be targeted for the treatment of sepsis. In this paper, we analyzed PCD-related genes in sepsis. Implicated PCD processes include apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, and alkaliptosis. We screened for diagnostic-related genes and constructed models for diagnosing sepsis using multiple machine-learning models. In addition, the immune landscape of sepsis was analyzed based on the diagnosis-related genes that were obtained. In this paper, 10 diagnosis-related genes were screened for using machine learning algorithms, and diagnostic models were constructed. The diagnostic model was validated in the internal and external test sets, and the Area Under Curve (AUC) reached 0.7951 in the internal test set and 0.9627 in the external test set. Furthermore, we verified the diagnostic gene via a qPCR experiment. The diagnostic-related genes and diagnostic genes obtained in this paper can be utilized as a reference for clinical sepsis diagnosis. The results of this study can act as a reference for the clinical diagnosis of sepsis and for target discovery for potential therapeutic drugs.
Collapse
Affiliation(s)
- Xuesong Wang
- School of Clinical Medicine, Tsinghua University, Beijing 100190, China; (X.W.); (Z.W.); (Z.W.); (F.C.)
- Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 100084, China;
| | - Ziyi Wang
- School of Clinical Medicine, Tsinghua University, Beijing 100190, China; (X.W.); (Z.W.); (Z.W.); (F.C.)
| | - Zhe Guo
- Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 100084, China;
| | - Ziwen Wang
- School of Clinical Medicine, Tsinghua University, Beijing 100190, China; (X.W.); (Z.W.); (Z.W.); (F.C.)
| | - Feng Chen
- School of Clinical Medicine, Tsinghua University, Beijing 100190, China; (X.W.); (Z.W.); (Z.W.); (F.C.)
| | - Zhong Wang
- School of Clinical Medicine, Tsinghua University, Beijing 100190, China; (X.W.); (Z.W.); (Z.W.); (F.C.)
- Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 100084, China;
| |
Collapse
|
9
|
Chen Y, Liu A, Liu H, Cai G, Lu N, Chen J. Identification and validation of the diagnostic signature associated with immune microenvironment of acute kidney injury based on ferroptosis-related genes through integrated bioinformatics analysis and machine learning. Front Cell Dev Biol 2023; 11:1210714. [PMID: 37576602 PMCID: PMC10413118 DOI: 10.3389/fcell.2023.1210714] [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: 04/23/2023] [Accepted: 07/14/2023] [Indexed: 08/15/2023] Open
Abstract
Background: Acute kidney injury (AKI) is a common and severe disease, which poses a global health burden with high morbidity and mortality. In recent years, ferroptosis has been recognized as being deeply related to Acute kidney injury. Our aim is to develop a diagnostic signature for Acute kidney injury based on ferroptosis-related genes (FRGs) through integrated bioinformatics analysis and machine learning. Methods: Our previously uploaded mouse Acute kidney injury dataset GSE192883 and another dataset, GSE153625, were downloaded to identify commonly expressed differentially expressed genes (coDEGs) through bioinformatic analysis. The FRGs were then overlapped with the coDEGs to identify differentially expressed FRGs (deFRGs). Immune cell infiltration was used to investigate immune cell dysregulation in Acute kidney injury. Functional enrichment analysis and protein-protein interaction network analysis were applied to identify candidate hub genes for Acute kidney injury. Then, receiver operator characteristic curve analysis and machine learning analysis (Lasso) were used to screen for diagnostic markers in two human datasets. Finally, these potential biomarkers were validated by quantitative real-time PCR in an Acute kidney injury model and across multiple datasets. Results: A total of 885 coDEGs and 33 deFRGs were commonly identified as differentially expressed in both GSE192883 and GSE153625 datasets. In cluster 1 of the coDEGs PPI network, we found a group of 20 genes clustered together with deFRGs, resulting in a total of 48 upregulated hub genes being identified. After ROC analysis, we discovered that 25 hub genes had an area under the curve (AUC) greater than 0.7; Lcn2, Plin2, and Atf3 all had AUCs over than this threshold in both human datasets GSE217427 and GSE139061. Through Lasso analysis, four hub genes (Lcn2, Atf3, Pir, and Mcm3) were screened for building a nomogram and evaluating diagnostic value. Finally, the expression of these four genes was validated in Acute kidney injury datasets and laboratory investigations, revealing that they may serve as ideal ferroptosis markers for Acute kidney injury. Conclusion: Four hub genes (Lcn2, Atf3, Pir, and Mcm3) were identified. After verification, the signature's versatility was confirmed and a nomogram model based on these four genes effectively distinguished Acute kidney injury samples. Our findings provide critical insight into the progression of Acute kidney injury and can guide individualized diagnosis and treatment.
Collapse
Affiliation(s)
- Yalei Chen
- Department of Critical Care Medicine, Capital Medical University Electric Power Teaching Hospital/State Grid Beijing Electric Power Hospital, Beijing, China
| | - Anqi Liu
- Department of Critical Care Medicine, Capital Medical University Electric Power Teaching Hospital/State Grid Beijing Electric Power Hospital, Beijing, China
| | - Hunan Liu
- Department of Critical Care Medicine, Capital Medical University Electric Power Teaching Hospital/State Grid Beijing Electric Power Hospital, Beijing, China
| | - Guangyan Cai
- State Key Laboratory of Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Kidney Diseases, Nephrology Institute of the Chinese People’s Liberation Army, Beijing, China
| | - Nianfang Lu
- Department of Critical Care Medicine, Capital Medical University Electric Power Teaching Hospital/State Grid Beijing Electric Power Hospital, Beijing, China
| | - Jianwen Chen
- State Key Laboratory of Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Kidney Diseases, Nephrology Institute of the Chinese People’s Liberation Army, Beijing, China
| |
Collapse
|
10
|
Jiang H, Ren Y, Yu J, Hu S, Zhang J. Analysis of lactate metabolism-related genes and their association with immune infiltration in septic shock via bioinformatics method. Front Genet 2023; 14:1223243. [PMID: 37564869 PMCID: PMC10410269 DOI: 10.3389/fgene.2023.1223243] [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: 05/15/2023] [Accepted: 07/17/2023] [Indexed: 08/12/2023] Open
Abstract
Background: Lactate, as an essential clinical evaluation index of septic shock, is crucial in the incidence and progression of septic shock. This study aims to investigate the differential expression, regulatory relationship, clinical diagnostic efficacy, and immune infiltration of lactate metabolism-related genes (LMGs) in septic shock. Methods: Two sepsis shock datasets (GSE26440 and GSE131761) were screened from the GEO database, and the common differentially expressed genes (DEGs) of the two datasets were screened out. LMGs were selected from the GeneCards database, and lactate metabolism-related DEGs (LMDEGs) were determined by integrating DEGs and LMGs. Protein-protein interaction networks, mRNA-miRNA, mRNA-RBP, and mRNA-TF interaction networks were constructed using STRING, miRDB, ENCORI, and CHIPBase databases, respectively. Receiver operating characteristic (ROC) curves were constructed for each of the LMDEGs to evaluate the diagnostic efficacy of the expression changes in relation to septic shock. Finally, immune infiltration analysis was performed using ssGSEA and CIBERSORT. Results: This study identified 10 LMDEGs, including LDHB, STAT3, LDHA, GSR, FOXM1, PDP1, GCDH, GCKR, ABCC1, and CDKN3. Enrichment analysis revealed that DEGs were significantly enriched in pathways such as pyruvate metabolism, hypoxia pathway, and immune-inflammatory pathways. PPI networks based on LMDEGs, as well as 148 pairs of mRNA-miRNA interactions, 243 pairs of mRNA-RBP interactions, and 119 pairs of mRNA-TF interactions were established. ROC curves of eight LMDEGs (LDHA, GSR, STAT3, CDKN3, FOXM1, GCKR, PDP1, and LDHB) with consistent expression patterns in two datasets had an area under the curve (AUC) ranging from 0.662 to 0.889. The results of ssGSEA and CIBERSORT both showed significant differences in the infiltration of various immune cells, including CD8 T cells, T regulatory cells, and natural killer cells, and LMDEGs such as STAT3, LDHB, LDHA, PDP1, GSR, FOXM1, and CDKN3 were significantly associated with various immune cells. Conclusion: The LMDEGs are significantly associated with the immune-inflammatory response in septic shock and have a certain diagnostic accuracy for septic shock.
Collapse
Affiliation(s)
- Huimin Jiang
- Emergency Intensive Care Unit, Ningxiang People’s Hospital Affiliated to Hunan University of Chinese Medicine, Changsha, China
| | - Yun Ren
- Emergency Department, Ningxiang People’s Hospital Affiliated to Hunan University of Chinese Medicine, Changsha, China
| | - Jiale Yu
- Emergency Department, Ningxiang People’s Hospital Affiliated to Hunan University of Chinese Medicine, Changsha, China
| | - Sheng Hu
- Emergency Department, Ningxiang People’s Hospital Affiliated to Hunan University of Chinese Medicine, Changsha, China
| | - Jihui Zhang
- Emergency Intensive Care Unit, Ningxiang People’s Hospital Affiliated to Hunan University of Chinese Medicine, Changsha, China
- Emergency Department, Ningxiang People’s Hospital Affiliated to Hunan University of Chinese Medicine, Changsha, China
| |
Collapse
|
11
|
Zhang X, Wang X, Wang S, Zhang Y, Wang Z, Yang Q, Wang S, Cao R, Yu B, Zheng Y, Dang Y. Machine learning algorithms assisted identification of post-stroke depression associated biological features. Front Neurosci 2023; 17:1146620. [PMID: 36968495 PMCID: PMC10030717 DOI: 10.3389/fnins.2023.1146620] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
ObjectivesPost-stroke depression (PSD) is a common and serious psychiatric complication which hinders functional recovery and social participation of stroke patients. Stroke is characterized by dynamic changes in metabolism and hemodynamics, however, there is still a lack of metabolism-associated effective and reliable diagnostic markers and therapeutic targets for PSD. Our study was dedicated to the discovery of metabolism related diagnostic and therapeutic biomarkers for PSD.MethodsExpression profiles of GSE140275, GSE122709, and GSE180470 were obtained from GEO database. Differentially expressed genes (DEGs) were detected in GSE140275 and GSE122709. Functional enrichment analysis was performed for DEGs in GSE140275. Weighted gene co-expression network analysis (WGCNA) was constructed in GSE122709 to identify key module genes. Moreover, correlation analysis was performed to obtain metabolism related genes. Interaction analysis of key module genes, metabolism related genes, and DEGs in GSE122709 was performed to obtain candidate hub genes. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify signature genes. Expression of signature genes was validated in GSE140275, GSE122709, and GSE180470. Gene set enrichment analysis (GSEA) was applied on signature genes. Based on signature genes, a nomogram model was constructed in our PSD cohort (27 PSD patients vs. 54 controls). ROC curves were performed for the estimation of its diagnostic value. Finally, correlation analysis between expression of signature genes and several clinical traits was performed.ResultsFunctional enrichment analysis indicated that DEGs in GSE140275 enriched in metabolism pathway. A total of 8,188 metabolism associated genes were identified by correlation analysis. WGCNA analysis was constructed to obtain 3,471 key module genes. A total of 557 candidate hub genes were identified by interaction analysis. Furthermore, two signature genes (SDHD and FERMT3) were selected using LASSO and random forest analysis. GSEA analysis found that two signature genes had major roles in depression. Subsequently, PSD cohort was collected for constructing a PSD diagnosis. Nomogram model showed good reliability and validity. AUC values of receiver operating characteristic (ROC) curve of SDHD and FERMT3 were 0.896 and 0.964. ROC curves showed that two signature genes played a significant role in diagnosis of PSD. Correlation analysis found that SDHD (r = 0.653, P < 0.001) and FERM3 (r = 0.728, P < 0.001) were positively related to the Hamilton Depression Rating Scale 17-item (HAMD) score.ConclusionA total of 557 metabolism associated candidate hub genes were obtained by interaction with DEGs in GSE122709, key modules genes, and metabolism related genes. Based on machine learning algorithms, two signature genes (SDHD and FERMT3) were identified, they were proved to be valuable therapeutic and diagnostic biomarkers for PSD. Early diagnosis and prevention of PSD were made possible by our findings.
Collapse
Affiliation(s)
- Xintong Zhang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xiangyu Wang
- Department of Rehabilitation Medicine, The Affiliated Lianyungang Oriental Hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, China
| | - Shuwei Wang
- Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Yingjie Zhang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zeyu Wang
- Department of Rehabilitation Medicine, Shanghai Ruijin Rehabilitation Hospital, Shanghai, China
| | - Qingyan Yang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Song Wang
- Department of Neurological Rehabilitation, Wuxi Yihe Rehabilitation Hospital, Wuxi, Jiangsu, China
| | - Risheng Cao
- Department of Science and Technology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Risheng Cao,
| | - Binbin Yu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Binbin Yu,
| | - Yu Zheng
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Yu Zheng,
| | - Yini Dang
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- *Correspondence: Yini Dang,
| |
Collapse
|