Identification of hub genes and establishment of a diagnostic model in tuberculosis infection
Abstract Tuberculosis (TB) poses significant challenges due to its high transmissibility within populations and intrinsic resistance to treatment, rendering it a formidable respiratory disease with a substantial susceptibility burden. This study was designed to identify new potential therapeutic tar...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Chunli [verfasserIn] Li, Xing [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2024 |
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| Übergeordnetes Werk: |
Enthalten in: AMB express - Springer Berlin Heidelberg, 2011, 14(2024), 1 vom: 13. Apr. |
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| Übergeordnetes Werk: |
volume:14 ; year:2024 ; number:1 ; day:13 ; month:04 |
| Links: |
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| DOI / URN: |
10.1186/s13568-024-01691-7 |
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| Katalog-ID: |
SPR055521010 |
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| 520 | |a Abstract Tuberculosis (TB) poses significant challenges due to its high transmissibility within populations and intrinsic resistance to treatment, rendering it a formidable respiratory disease with a substantial susceptibility burden. This study was designed to identify new potential therapeutic targets for TB and establish a diagnostic model. mRNA expression data for TB were from GEO database, followed by conducting differential expression analysis. The top 50 genes with differential expression were subjected to GO and KEGG enrichment analyses. To establish a PPI network, the STRING database was utilized, and hub genes were identified utilizing five algorithms (EPC, MCC, MNC, Radiality, and Stress) within the cytoHubba plugin of Cytoscape software. Furthermore, a hub gene co-expression network was constructed using the GeneMANIA database. Consistency clustering was performed on hub genes, and ssGSEA was utilized to analyze the extent of immune infiltration in different subgroups. LASSO analysis was employed to construct a diagnostic model, and ROC curves were used for validation. Through the analysis of GEO data, a total of 159 genes were identified as differentially expressed. Further, GO and KEGG enrichment analyses revealed that these genes were mainly enriched in viral defense, symbiotic defense, and innate immune response-related pathways. Hub genes, including DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15, were identified using cytoHubba analysis of the PPI network. The GeneMANIA analysis unmasked that the co-expression rate of hub genes was 81.55%, and the physical interaction rate was 12.27%. Consistency clustering divided TB patients into two subgroups, and ssGSEA revealed different degrees of immune infiltration in different subgroups. LASSO analysis identified IFIT1, IFIT2, IFIT3, IFIH1, RSAD2, OAS1, OAS2, and STAT1 as eight immune-related key genes, and a diagnostic model was constructed. The ROC curve demonstrated that the model exhibited excellent diagnostic performance. DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15 were hub genes in TB, and the diagnostic model based on eight immune-related key genes exhibited good diagnostic performance. | ||
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10.1186/s13568-024-01691-7 doi (DE-627)SPR055521010 (SPR)s13568-024-01691-7-e DE-627 ger DE-627 rakwb eng 570 VZ 58.30 bkl Liu, Chunli verfasserin aut Identification of hub genes and establishment of a diagnostic model in tuberculosis infection 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Tuberculosis (TB) poses significant challenges due to its high transmissibility within populations and intrinsic resistance to treatment, rendering it a formidable respiratory disease with a substantial susceptibility burden. This study was designed to identify new potential therapeutic targets for TB and establish a diagnostic model. mRNA expression data for TB were from GEO database, followed by conducting differential expression analysis. The top 50 genes with differential expression were subjected to GO and KEGG enrichment analyses. To establish a PPI network, the STRING database was utilized, and hub genes were identified utilizing five algorithms (EPC, MCC, MNC, Radiality, and Stress) within the cytoHubba plugin of Cytoscape software. Furthermore, a hub gene co-expression network was constructed using the GeneMANIA database. Consistency clustering was performed on hub genes, and ssGSEA was utilized to analyze the extent of immune infiltration in different subgroups. LASSO analysis was employed to construct a diagnostic model, and ROC curves were used for validation. Through the analysis of GEO data, a total of 159 genes were identified as differentially expressed. Further, GO and KEGG enrichment analyses revealed that these genes were mainly enriched in viral defense, symbiotic defense, and innate immune response-related pathways. Hub genes, including DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15, were identified using cytoHubba analysis of the PPI network. The GeneMANIA analysis unmasked that the co-expression rate of hub genes was 81.55%, and the physical interaction rate was 12.27%. Consistency clustering divided TB patients into two subgroups, and ssGSEA revealed different degrees of immune infiltration in different subgroups. LASSO analysis identified IFIT1, IFIT2, IFIT3, IFIH1, RSAD2, OAS1, OAS2, and STAT1 as eight immune-related key genes, and a diagnostic model was constructed. The ROC curve demonstrated that the model exhibited excellent diagnostic performance. DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15 were hub genes in TB, and the diagnostic model based on eight immune-related key genes exhibited good diagnostic performance. Tuberculosis (dpeaa)DE-He213 PPI (dpeaa)DE-He213 Diagnostic model (dpeaa)DE-He213 Immune Infiltration (dpeaa)DE-He213 Biomarkers (dpeaa)DE-He213 Li, Xing verfasserin aut Enthalten in AMB express Springer Berlin Heidelberg, 2011 14(2024), 1 vom: 13. Apr. (DE-627)665672926 (DE-600)2621432-5 2191-0855 nnns volume:14 year:2024 number:1 day:13 month:04 https://dx.doi.org/10.1186/s13568-024-01691-7 X:VERLAG 0 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 58.30 VZ AR 14 2024 1 13 04 |
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10.1186/s13568-024-01691-7 doi (DE-627)SPR055521010 (SPR)s13568-024-01691-7-e DE-627 ger DE-627 rakwb eng 570 VZ 58.30 bkl Liu, Chunli verfasserin aut Identification of hub genes and establishment of a diagnostic model in tuberculosis infection 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Tuberculosis (TB) poses significant challenges due to its high transmissibility within populations and intrinsic resistance to treatment, rendering it a formidable respiratory disease with a substantial susceptibility burden. This study was designed to identify new potential therapeutic targets for TB and establish a diagnostic model. mRNA expression data for TB were from GEO database, followed by conducting differential expression analysis. The top 50 genes with differential expression were subjected to GO and KEGG enrichment analyses. To establish a PPI network, the STRING database was utilized, and hub genes were identified utilizing five algorithms (EPC, MCC, MNC, Radiality, and Stress) within the cytoHubba plugin of Cytoscape software. Furthermore, a hub gene co-expression network was constructed using the GeneMANIA database. Consistency clustering was performed on hub genes, and ssGSEA was utilized to analyze the extent of immune infiltration in different subgroups. LASSO analysis was employed to construct a diagnostic model, and ROC curves were used for validation. Through the analysis of GEO data, a total of 159 genes were identified as differentially expressed. Further, GO and KEGG enrichment analyses revealed that these genes were mainly enriched in viral defense, symbiotic defense, and innate immune response-related pathways. Hub genes, including DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15, were identified using cytoHubba analysis of the PPI network. The GeneMANIA analysis unmasked that the co-expression rate of hub genes was 81.55%, and the physical interaction rate was 12.27%. Consistency clustering divided TB patients into two subgroups, and ssGSEA revealed different degrees of immune infiltration in different subgroups. LASSO analysis identified IFIT1, IFIT2, IFIT3, IFIH1, RSAD2, OAS1, OAS2, and STAT1 as eight immune-related key genes, and a diagnostic model was constructed. The ROC curve demonstrated that the model exhibited excellent diagnostic performance. DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15 were hub genes in TB, and the diagnostic model based on eight immune-related key genes exhibited good diagnostic performance. Tuberculosis (dpeaa)DE-He213 PPI (dpeaa)DE-He213 Diagnostic model (dpeaa)DE-He213 Immune Infiltration (dpeaa)DE-He213 Biomarkers (dpeaa)DE-He213 Li, Xing verfasserin aut Enthalten in AMB express Springer Berlin Heidelberg, 2011 14(2024), 1 vom: 13. Apr. (DE-627)665672926 (DE-600)2621432-5 2191-0855 nnns volume:14 year:2024 number:1 day:13 month:04 https://dx.doi.org/10.1186/s13568-024-01691-7 X:VERLAG 0 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 58.30 VZ AR 14 2024 1 13 04 |
| allfields_unstemmed |
10.1186/s13568-024-01691-7 doi (DE-627)SPR055521010 (SPR)s13568-024-01691-7-e DE-627 ger DE-627 rakwb eng 570 VZ 58.30 bkl Liu, Chunli verfasserin aut Identification of hub genes and establishment of a diagnostic model in tuberculosis infection 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Tuberculosis (TB) poses significant challenges due to its high transmissibility within populations and intrinsic resistance to treatment, rendering it a formidable respiratory disease with a substantial susceptibility burden. This study was designed to identify new potential therapeutic targets for TB and establish a diagnostic model. mRNA expression data for TB were from GEO database, followed by conducting differential expression analysis. The top 50 genes with differential expression were subjected to GO and KEGG enrichment analyses. To establish a PPI network, the STRING database was utilized, and hub genes were identified utilizing five algorithms (EPC, MCC, MNC, Radiality, and Stress) within the cytoHubba plugin of Cytoscape software. Furthermore, a hub gene co-expression network was constructed using the GeneMANIA database. Consistency clustering was performed on hub genes, and ssGSEA was utilized to analyze the extent of immune infiltration in different subgroups. LASSO analysis was employed to construct a diagnostic model, and ROC curves were used for validation. Through the analysis of GEO data, a total of 159 genes were identified as differentially expressed. Further, GO and KEGG enrichment analyses revealed that these genes were mainly enriched in viral defense, symbiotic defense, and innate immune response-related pathways. Hub genes, including DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15, were identified using cytoHubba analysis of the PPI network. The GeneMANIA analysis unmasked that the co-expression rate of hub genes was 81.55%, and the physical interaction rate was 12.27%. Consistency clustering divided TB patients into two subgroups, and ssGSEA revealed different degrees of immune infiltration in different subgroups. LASSO analysis identified IFIT1, IFIT2, IFIT3, IFIH1, RSAD2, OAS1, OAS2, and STAT1 as eight immune-related key genes, and a diagnostic model was constructed. The ROC curve demonstrated that the model exhibited excellent diagnostic performance. DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15 were hub genes in TB, and the diagnostic model based on eight immune-related key genes exhibited good diagnostic performance. Tuberculosis (dpeaa)DE-He213 PPI (dpeaa)DE-He213 Diagnostic model (dpeaa)DE-He213 Immune Infiltration (dpeaa)DE-He213 Biomarkers (dpeaa)DE-He213 Li, Xing verfasserin aut Enthalten in AMB express Springer Berlin Heidelberg, 2011 14(2024), 1 vom: 13. Apr. (DE-627)665672926 (DE-600)2621432-5 2191-0855 nnns volume:14 year:2024 number:1 day:13 month:04 https://dx.doi.org/10.1186/s13568-024-01691-7 X:VERLAG 0 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 58.30 VZ AR 14 2024 1 13 04 |
| allfieldsGer |
10.1186/s13568-024-01691-7 doi (DE-627)SPR055521010 (SPR)s13568-024-01691-7-e DE-627 ger DE-627 rakwb eng 570 VZ 58.30 bkl Liu, Chunli verfasserin aut Identification of hub genes and establishment of a diagnostic model in tuberculosis infection 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Tuberculosis (TB) poses significant challenges due to its high transmissibility within populations and intrinsic resistance to treatment, rendering it a formidable respiratory disease with a substantial susceptibility burden. This study was designed to identify new potential therapeutic targets for TB and establish a diagnostic model. mRNA expression data for TB were from GEO database, followed by conducting differential expression analysis. The top 50 genes with differential expression were subjected to GO and KEGG enrichment analyses. To establish a PPI network, the STRING database was utilized, and hub genes were identified utilizing five algorithms (EPC, MCC, MNC, Radiality, and Stress) within the cytoHubba plugin of Cytoscape software. Furthermore, a hub gene co-expression network was constructed using the GeneMANIA database. Consistency clustering was performed on hub genes, and ssGSEA was utilized to analyze the extent of immune infiltration in different subgroups. LASSO analysis was employed to construct a diagnostic model, and ROC curves were used for validation. Through the analysis of GEO data, a total of 159 genes were identified as differentially expressed. Further, GO and KEGG enrichment analyses revealed that these genes were mainly enriched in viral defense, symbiotic defense, and innate immune response-related pathways. Hub genes, including DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15, were identified using cytoHubba analysis of the PPI network. The GeneMANIA analysis unmasked that the co-expression rate of hub genes was 81.55%, and the physical interaction rate was 12.27%. Consistency clustering divided TB patients into two subgroups, and ssGSEA revealed different degrees of immune infiltration in different subgroups. LASSO analysis identified IFIT1, IFIT2, IFIT3, IFIH1, RSAD2, OAS1, OAS2, and STAT1 as eight immune-related key genes, and a diagnostic model was constructed. The ROC curve demonstrated that the model exhibited excellent diagnostic performance. DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15 were hub genes in TB, and the diagnostic model based on eight immune-related key genes exhibited good diagnostic performance. Tuberculosis (dpeaa)DE-He213 PPI (dpeaa)DE-He213 Diagnostic model (dpeaa)DE-He213 Immune Infiltration (dpeaa)DE-He213 Biomarkers (dpeaa)DE-He213 Li, Xing verfasserin aut Enthalten in AMB express Springer Berlin Heidelberg, 2011 14(2024), 1 vom: 13. Apr. (DE-627)665672926 (DE-600)2621432-5 2191-0855 nnns volume:14 year:2024 number:1 day:13 month:04 https://dx.doi.org/10.1186/s13568-024-01691-7 X:VERLAG 0 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 58.30 VZ AR 14 2024 1 13 04 |
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Identification of hub genes and establishment of a diagnostic model in tuberculosis infection |
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Abstract Tuberculosis (TB) poses significant challenges due to its high transmissibility within populations and intrinsic resistance to treatment, rendering it a formidable respiratory disease with a substantial susceptibility burden. This study was designed to identify new potential therapeutic targets for TB and establish a diagnostic model. mRNA expression data for TB were from GEO database, followed by conducting differential expression analysis. The top 50 genes with differential expression were subjected to GO and KEGG enrichment analyses. To establish a PPI network, the STRING database was utilized, and hub genes were identified utilizing five algorithms (EPC, MCC, MNC, Radiality, and Stress) within the cytoHubba plugin of Cytoscape software. Furthermore, a hub gene co-expression network was constructed using the GeneMANIA database. Consistency clustering was performed on hub genes, and ssGSEA was utilized to analyze the extent of immune infiltration in different subgroups. LASSO analysis was employed to construct a diagnostic model, and ROC curves were used for validation. Through the analysis of GEO data, a total of 159 genes were identified as differentially expressed. Further, GO and KEGG enrichment analyses revealed that these genes were mainly enriched in viral defense, symbiotic defense, and innate immune response-related pathways. Hub genes, including DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15, were identified using cytoHubba analysis of the PPI network. The GeneMANIA analysis unmasked that the co-expression rate of hub genes was 81.55%, and the physical interaction rate was 12.27%. Consistency clustering divided TB patients into two subgroups, and ssGSEA revealed different degrees of immune infiltration in different subgroups. LASSO analysis identified IFIT1, IFIT2, IFIT3, IFIH1, RSAD2, OAS1, OAS2, and STAT1 as eight immune-related key genes, and a diagnostic model was constructed. The ROC curve demonstrated that the model exhibited excellent diagnostic performance. DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15 were hub genes in TB, and the diagnostic model based on eight immune-related key genes exhibited good diagnostic performance. © The Author(s) 2024 |
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Abstract Tuberculosis (TB) poses significant challenges due to its high transmissibility within populations and intrinsic resistance to treatment, rendering it a formidable respiratory disease with a substantial susceptibility burden. This study was designed to identify new potential therapeutic targets for TB and establish a diagnostic model. mRNA expression data for TB were from GEO database, followed by conducting differential expression analysis. The top 50 genes with differential expression were subjected to GO and KEGG enrichment analyses. To establish a PPI network, the STRING database was utilized, and hub genes were identified utilizing five algorithms (EPC, MCC, MNC, Radiality, and Stress) within the cytoHubba plugin of Cytoscape software. Furthermore, a hub gene co-expression network was constructed using the GeneMANIA database. Consistency clustering was performed on hub genes, and ssGSEA was utilized to analyze the extent of immune infiltration in different subgroups. LASSO analysis was employed to construct a diagnostic model, and ROC curves were used for validation. Through the analysis of GEO data, a total of 159 genes were identified as differentially expressed. Further, GO and KEGG enrichment analyses revealed that these genes were mainly enriched in viral defense, symbiotic defense, and innate immune response-related pathways. Hub genes, including DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15, were identified using cytoHubba analysis of the PPI network. The GeneMANIA analysis unmasked that the co-expression rate of hub genes was 81.55%, and the physical interaction rate was 12.27%. Consistency clustering divided TB patients into two subgroups, and ssGSEA revealed different degrees of immune infiltration in different subgroups. LASSO analysis identified IFIT1, IFIT2, IFIT3, IFIH1, RSAD2, OAS1, OAS2, and STAT1 as eight immune-related key genes, and a diagnostic model was constructed. The ROC curve demonstrated that the model exhibited excellent diagnostic performance. DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15 were hub genes in TB, and the diagnostic model based on eight immune-related key genes exhibited good diagnostic performance. © The Author(s) 2024 |
| abstract_unstemmed |
Abstract Tuberculosis (TB) poses significant challenges due to its high transmissibility within populations and intrinsic resistance to treatment, rendering it a formidable respiratory disease with a substantial susceptibility burden. This study was designed to identify new potential therapeutic targets for TB and establish a diagnostic model. mRNA expression data for TB were from GEO database, followed by conducting differential expression analysis. The top 50 genes with differential expression were subjected to GO and KEGG enrichment analyses. To establish a PPI network, the STRING database was utilized, and hub genes were identified utilizing five algorithms (EPC, MCC, MNC, Radiality, and Stress) within the cytoHubba plugin of Cytoscape software. Furthermore, a hub gene co-expression network was constructed using the GeneMANIA database. Consistency clustering was performed on hub genes, and ssGSEA was utilized to analyze the extent of immune infiltration in different subgroups. LASSO analysis was employed to construct a diagnostic model, and ROC curves were used for validation. Through the analysis of GEO data, a total of 159 genes were identified as differentially expressed. Further, GO and KEGG enrichment analyses revealed that these genes were mainly enriched in viral defense, symbiotic defense, and innate immune response-related pathways. Hub genes, including DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15, were identified using cytoHubba analysis of the PPI network. The GeneMANIA analysis unmasked that the co-expression rate of hub genes was 81.55%, and the physical interaction rate was 12.27%. Consistency clustering divided TB patients into two subgroups, and ssGSEA revealed different degrees of immune infiltration in different subgroups. LASSO analysis identified IFIT1, IFIT2, IFIT3, IFIH1, RSAD2, OAS1, OAS2, and STAT1 as eight immune-related key genes, and a diagnostic model was constructed. The ROC curve demonstrated that the model exhibited excellent diagnostic performance. DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15 were hub genes in TB, and the diagnostic model based on eight immune-related key genes exhibited good diagnostic performance. © The Author(s) 2024 |
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