Identification of prognostic genes for breast cancer related to systemic lupus erythematosus by integrated analysis and machine learning
Background: Systemic Lupus Erythematosus (SLE) is an autoimmune disease with multi-organ involvement, and some studies have found that SLE has a reduced risk of breast cancer (BRCA). So, we tried to find prognostic genes for BRCA related to SLE by integrated analysis and machine learning.Method: Fir...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liang, Xiaofeng [verfasserIn] Peng, Zhishen [verfasserIn] Lin, Zien [verfasserIn] Lin, Xiaobing [verfasserIn] Lin, Weiyi [verfasserIn] Deng, Ying [verfasserIn] Yang, Shujun [verfasserIn] Wei, Shanshan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Immunobiology - München : Elsevier, 1979, 228 |
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| Übergeordnetes Werk: |
volume:228 |
| DOI / URN: |
10.1016/j.imbio.2023.152730 |
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| Katalog-ID: |
ELV062255835 |
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| 245 | 1 | 0 | |a Identification of prognostic genes for breast cancer related to systemic lupus erythematosus by integrated analysis and machine learning |
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| 520 | |a Background: Systemic Lupus Erythematosus (SLE) is an autoimmune disease with multi-organ involvement, and some studies have found that SLE has a reduced risk of breast cancer (BRCA). So, we tried to find prognostic genes for BRCA related to SLE by integrated analysis and machine learning.Method: First, we downloaded 2 SLE datasets from Gene Expression Omnibus (GEO) and BRCA data from the Cancer Genome Atlas (TCGA). Subsequently, we performed differentially expressed genes (DEGs) and functional enrichment analysis by Metascape in SLE. Genes that were differentially expressed in both datasets were the validated DEGs. And after constructing PPI network, genes with nodes >30 were intersected with survival genes in BRCA to obtain candidate genes. Then, the candidate genes were validated by lasso regression in both training and validation sets to obtain prognostic genes. Afterwards, we investigated the diagnostic potential of prognostic genes for SLE and the predictive efficacy for BRCA prognosis. Moreover, GSEA analysis and immune infiltration were performed for SLE and BRCA. Finally, we constructed a prognostic gene-miRNAs network and did functional enrichment of the shared genes.Result: DEGs for SLE were mainly enriched with neutrophil degranulation and IFN pathways. After the lasso model of BRCA was established, IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE and had good predictive ability for the prognosis of BRCA. Prognostic genes had excellent diagnostic potential for SLE, with IFI35 and EIF2AK2 positively associated with SLE activity and IRF7 positively associated with IFI35. GSEA showed that both SLE and BRCA were associated with ubiquitinated degradation. Immune infiltrates suggest that plasma cells, dendritic cells (DC), neutrophils and monocyte were elevated in SLE. DC, NK and CD8+ T cells were elevated in the BRCA low-risk group. Finally, 5 shared miRNAs were confirmed, which were mainly enriched in the IFN pathway.Conclusion: IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE. IFN pathway played an important role in the etiology of SLE and the prognosis of BRCA. | ||
| 650 | 4 | |a Systemic lupus erythematosus | |
| 650 | 4 | |a Breast cancer | |
| 650 | 4 | |a Prognostic genes | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Immune infiltration | |
| 700 | 1 | |a Peng, Zhishen |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Zien |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Xiaobing |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Weiyi |e verfasserin |4 aut | |
| 700 | 1 | |a Deng, Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Shujun |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Shanshan |e verfasserin |4 aut | |
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10.1016/j.imbio.2023.152730 doi (DE-627)ELV062255835 (ELSEVIER)S0171-2985(23)04532-1 DE-627 ger DE-627 rda eng 570 610 VZ 42.00 bkl 44.45 bkl Liang, Xiaofeng verfasserin aut Identification of prognostic genes for breast cancer related to systemic lupus erythematosus by integrated analysis and machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Systemic Lupus Erythematosus (SLE) is an autoimmune disease with multi-organ involvement, and some studies have found that SLE has a reduced risk of breast cancer (BRCA). So, we tried to find prognostic genes for BRCA related to SLE by integrated analysis and machine learning.Method: First, we downloaded 2 SLE datasets from Gene Expression Omnibus (GEO) and BRCA data from the Cancer Genome Atlas (TCGA). Subsequently, we performed differentially expressed genes (DEGs) and functional enrichment analysis by Metascape in SLE. Genes that were differentially expressed in both datasets were the validated DEGs. And after constructing PPI network, genes with nodes >30 were intersected with survival genes in BRCA to obtain candidate genes. Then, the candidate genes were validated by lasso regression in both training and validation sets to obtain prognostic genes. Afterwards, we investigated the diagnostic potential of prognostic genes for SLE and the predictive efficacy for BRCA prognosis. Moreover, GSEA analysis and immune infiltration were performed for SLE and BRCA. Finally, we constructed a prognostic gene-miRNAs network and did functional enrichment of the shared genes.Result: DEGs for SLE were mainly enriched with neutrophil degranulation and IFN pathways. After the lasso model of BRCA was established, IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE and had good predictive ability for the prognosis of BRCA. Prognostic genes had excellent diagnostic potential for SLE, with IFI35 and EIF2AK2 positively associated with SLE activity and IRF7 positively associated with IFI35. GSEA showed that both SLE and BRCA were associated with ubiquitinated degradation. Immune infiltrates suggest that plasma cells, dendritic cells (DC), neutrophils and monocyte were elevated in SLE. DC, NK and CD8+ T cells were elevated in the BRCA low-risk group. Finally, 5 shared miRNAs were confirmed, which were mainly enriched in the IFN pathway.Conclusion: IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE. IFN pathway played an important role in the etiology of SLE and the prognosis of BRCA. Systemic lupus erythematosus Breast cancer Prognostic genes Machine learning Immune infiltration Peng, Zhishen verfasserin aut Lin, Zien verfasserin aut Lin, Xiaobing verfasserin aut Lin, Weiyi verfasserin aut Deng, Ying verfasserin aut Yang, Shujun verfasserin aut Wei, Shanshan verfasserin aut Enthalten in Immunobiology München : Elsevier, 1979 228 Online-Ressource (DE-627)335415989 (DE-600)2060227-3 (DE-576)096290919 1878-3279 nnns volume:228 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_168 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4251 GBV_ILN_4323 42.00 Biologie: Allgemeines VZ 44.45 Immunologie VZ AR 228 |
| spelling |
10.1016/j.imbio.2023.152730 doi (DE-627)ELV062255835 (ELSEVIER)S0171-2985(23)04532-1 DE-627 ger DE-627 rda eng 570 610 VZ 42.00 bkl 44.45 bkl Liang, Xiaofeng verfasserin aut Identification of prognostic genes for breast cancer related to systemic lupus erythematosus by integrated analysis and machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Systemic Lupus Erythematosus (SLE) is an autoimmune disease with multi-organ involvement, and some studies have found that SLE has a reduced risk of breast cancer (BRCA). So, we tried to find prognostic genes for BRCA related to SLE by integrated analysis and machine learning.Method: First, we downloaded 2 SLE datasets from Gene Expression Omnibus (GEO) and BRCA data from the Cancer Genome Atlas (TCGA). Subsequently, we performed differentially expressed genes (DEGs) and functional enrichment analysis by Metascape in SLE. Genes that were differentially expressed in both datasets were the validated DEGs. And after constructing PPI network, genes with nodes >30 were intersected with survival genes in BRCA to obtain candidate genes. Then, the candidate genes were validated by lasso regression in both training and validation sets to obtain prognostic genes. Afterwards, we investigated the diagnostic potential of prognostic genes for SLE and the predictive efficacy for BRCA prognosis. Moreover, GSEA analysis and immune infiltration were performed for SLE and BRCA. Finally, we constructed a prognostic gene-miRNAs network and did functional enrichment of the shared genes.Result: DEGs for SLE were mainly enriched with neutrophil degranulation and IFN pathways. After the lasso model of BRCA was established, IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE and had good predictive ability for the prognosis of BRCA. Prognostic genes had excellent diagnostic potential for SLE, with IFI35 and EIF2AK2 positively associated with SLE activity and IRF7 positively associated with IFI35. GSEA showed that both SLE and BRCA were associated with ubiquitinated degradation. Immune infiltrates suggest that plasma cells, dendritic cells (DC), neutrophils and monocyte were elevated in SLE. DC, NK and CD8+ T cells were elevated in the BRCA low-risk group. Finally, 5 shared miRNAs were confirmed, which were mainly enriched in the IFN pathway.Conclusion: IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE. IFN pathway played an important role in the etiology of SLE and the prognosis of BRCA. Systemic lupus erythematosus Breast cancer Prognostic genes Machine learning Immune infiltration Peng, Zhishen verfasserin aut Lin, Zien verfasserin aut Lin, Xiaobing verfasserin aut Lin, Weiyi verfasserin aut Deng, Ying verfasserin aut Yang, Shujun verfasserin aut Wei, Shanshan verfasserin aut Enthalten in Immunobiology München : Elsevier, 1979 228 Online-Ressource (DE-627)335415989 (DE-600)2060227-3 (DE-576)096290919 1878-3279 nnns volume:228 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_168 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4251 GBV_ILN_4323 42.00 Biologie: Allgemeines VZ 44.45 Immunologie VZ AR 228 |
| allfields_unstemmed |
10.1016/j.imbio.2023.152730 doi (DE-627)ELV062255835 (ELSEVIER)S0171-2985(23)04532-1 DE-627 ger DE-627 rda eng 570 610 VZ 42.00 bkl 44.45 bkl Liang, Xiaofeng verfasserin aut Identification of prognostic genes for breast cancer related to systemic lupus erythematosus by integrated analysis and machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Systemic Lupus Erythematosus (SLE) is an autoimmune disease with multi-organ involvement, and some studies have found that SLE has a reduced risk of breast cancer (BRCA). So, we tried to find prognostic genes for BRCA related to SLE by integrated analysis and machine learning.Method: First, we downloaded 2 SLE datasets from Gene Expression Omnibus (GEO) and BRCA data from the Cancer Genome Atlas (TCGA). Subsequently, we performed differentially expressed genes (DEGs) and functional enrichment analysis by Metascape in SLE. Genes that were differentially expressed in both datasets were the validated DEGs. And after constructing PPI network, genes with nodes >30 were intersected with survival genes in BRCA to obtain candidate genes. Then, the candidate genes were validated by lasso regression in both training and validation sets to obtain prognostic genes. Afterwards, we investigated the diagnostic potential of prognostic genes for SLE and the predictive efficacy for BRCA prognosis. Moreover, GSEA analysis and immune infiltration were performed for SLE and BRCA. Finally, we constructed a prognostic gene-miRNAs network and did functional enrichment of the shared genes.Result: DEGs for SLE were mainly enriched with neutrophil degranulation and IFN pathways. After the lasso model of BRCA was established, IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE and had good predictive ability for the prognosis of BRCA. Prognostic genes had excellent diagnostic potential for SLE, with IFI35 and EIF2AK2 positively associated with SLE activity and IRF7 positively associated with IFI35. GSEA showed that both SLE and BRCA were associated with ubiquitinated degradation. Immune infiltrates suggest that plasma cells, dendritic cells (DC), neutrophils and monocyte were elevated in SLE. DC, NK and CD8+ T cells were elevated in the BRCA low-risk group. Finally, 5 shared miRNAs were confirmed, which were mainly enriched in the IFN pathway.Conclusion: IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE. IFN pathway played an important role in the etiology of SLE and the prognosis of BRCA. Systemic lupus erythematosus Breast cancer Prognostic genes Machine learning Immune infiltration Peng, Zhishen verfasserin aut Lin, Zien verfasserin aut Lin, Xiaobing verfasserin aut Lin, Weiyi verfasserin aut Deng, Ying verfasserin aut Yang, Shujun verfasserin aut Wei, Shanshan verfasserin aut Enthalten in Immunobiology München : Elsevier, 1979 228 Online-Ressource (DE-627)335415989 (DE-600)2060227-3 (DE-576)096290919 1878-3279 nnns volume:228 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_168 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4251 GBV_ILN_4323 42.00 Biologie: Allgemeines VZ 44.45 Immunologie VZ AR 228 |
| allfieldsGer |
10.1016/j.imbio.2023.152730 doi (DE-627)ELV062255835 (ELSEVIER)S0171-2985(23)04532-1 DE-627 ger DE-627 rda eng 570 610 VZ 42.00 bkl 44.45 bkl Liang, Xiaofeng verfasserin aut Identification of prognostic genes for breast cancer related to systemic lupus erythematosus by integrated analysis and machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Systemic Lupus Erythematosus (SLE) is an autoimmune disease with multi-organ involvement, and some studies have found that SLE has a reduced risk of breast cancer (BRCA). So, we tried to find prognostic genes for BRCA related to SLE by integrated analysis and machine learning.Method: First, we downloaded 2 SLE datasets from Gene Expression Omnibus (GEO) and BRCA data from the Cancer Genome Atlas (TCGA). Subsequently, we performed differentially expressed genes (DEGs) and functional enrichment analysis by Metascape in SLE. Genes that were differentially expressed in both datasets were the validated DEGs. And after constructing PPI network, genes with nodes >30 were intersected with survival genes in BRCA to obtain candidate genes. Then, the candidate genes were validated by lasso regression in both training and validation sets to obtain prognostic genes. Afterwards, we investigated the diagnostic potential of prognostic genes for SLE and the predictive efficacy for BRCA prognosis. Moreover, GSEA analysis and immune infiltration were performed for SLE and BRCA. Finally, we constructed a prognostic gene-miRNAs network and did functional enrichment of the shared genes.Result: DEGs for SLE were mainly enriched with neutrophil degranulation and IFN pathways. After the lasso model of BRCA was established, IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE and had good predictive ability for the prognosis of BRCA. Prognostic genes had excellent diagnostic potential for SLE, with IFI35 and EIF2AK2 positively associated with SLE activity and IRF7 positively associated with IFI35. GSEA showed that both SLE and BRCA were associated with ubiquitinated degradation. Immune infiltrates suggest that plasma cells, dendritic cells (DC), neutrophils and monocyte were elevated in SLE. DC, NK and CD8+ T cells were elevated in the BRCA low-risk group. Finally, 5 shared miRNAs were confirmed, which were mainly enriched in the IFN pathway.Conclusion: IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE. IFN pathway played an important role in the etiology of SLE and the prognosis of BRCA. Systemic lupus erythematosus Breast cancer Prognostic genes Machine learning Immune infiltration Peng, Zhishen verfasserin aut Lin, Zien verfasserin aut Lin, Xiaobing verfasserin aut Lin, Weiyi verfasserin aut Deng, Ying verfasserin aut Yang, Shujun verfasserin aut Wei, Shanshan verfasserin aut Enthalten in Immunobiology München : Elsevier, 1979 228 Online-Ressource (DE-627)335415989 (DE-600)2060227-3 (DE-576)096290919 1878-3279 nnns volume:228 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_168 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4251 GBV_ILN_4323 42.00 Biologie: Allgemeines VZ 44.45 Immunologie VZ AR 228 |
| allfieldsSound |
10.1016/j.imbio.2023.152730 doi (DE-627)ELV062255835 (ELSEVIER)S0171-2985(23)04532-1 DE-627 ger DE-627 rda eng 570 610 VZ 42.00 bkl 44.45 bkl Liang, Xiaofeng verfasserin aut Identification of prognostic genes for breast cancer related to systemic lupus erythematosus by integrated analysis and machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Systemic Lupus Erythematosus (SLE) is an autoimmune disease with multi-organ involvement, and some studies have found that SLE has a reduced risk of breast cancer (BRCA). So, we tried to find prognostic genes for BRCA related to SLE by integrated analysis and machine learning.Method: First, we downloaded 2 SLE datasets from Gene Expression Omnibus (GEO) and BRCA data from the Cancer Genome Atlas (TCGA). Subsequently, we performed differentially expressed genes (DEGs) and functional enrichment analysis by Metascape in SLE. Genes that were differentially expressed in both datasets were the validated DEGs. And after constructing PPI network, genes with nodes >30 were intersected with survival genes in BRCA to obtain candidate genes. Then, the candidate genes were validated by lasso regression in both training and validation sets to obtain prognostic genes. Afterwards, we investigated the diagnostic potential of prognostic genes for SLE and the predictive efficacy for BRCA prognosis. Moreover, GSEA analysis and immune infiltration were performed for SLE and BRCA. Finally, we constructed a prognostic gene-miRNAs network and did functional enrichment of the shared genes.Result: DEGs for SLE were mainly enriched with neutrophil degranulation and IFN pathways. After the lasso model of BRCA was established, IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE and had good predictive ability for the prognosis of BRCA. Prognostic genes had excellent diagnostic potential for SLE, with IFI35 and EIF2AK2 positively associated with SLE activity and IRF7 positively associated with IFI35. GSEA showed that both SLE and BRCA were associated with ubiquitinated degradation. Immune infiltrates suggest that plasma cells, dendritic cells (DC), neutrophils and monocyte were elevated in SLE. DC, NK and CD8+ T cells were elevated in the BRCA low-risk group. Finally, 5 shared miRNAs were confirmed, which were mainly enriched in the IFN pathway.Conclusion: IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE. IFN pathway played an important role in the etiology of SLE and the prognosis of BRCA. Systemic lupus erythematosus Breast cancer Prognostic genes Machine learning Immune infiltration Peng, Zhishen verfasserin aut Lin, Zien verfasserin aut Lin, Xiaobing verfasserin aut Lin, Weiyi verfasserin aut Deng, Ying verfasserin aut Yang, Shujun verfasserin aut Wei, Shanshan verfasserin aut Enthalten in Immunobiology München : Elsevier, 1979 228 Online-Ressource (DE-627)335415989 (DE-600)2060227-3 (DE-576)096290919 1878-3279 nnns volume:228 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_168 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4251 GBV_ILN_4323 42.00 Biologie: Allgemeines VZ 44.45 Immunologie VZ AR 228 |
| language |
English |
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Enthalten in Immunobiology 228 volume:228 |
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identification of prognostic genes for breast cancer related to systemic lupus erythematosus by integrated analysis and machine learning |
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Identification of prognostic genes for breast cancer related to systemic lupus erythematosus by integrated analysis and machine learning |
| abstract |
Background: Systemic Lupus Erythematosus (SLE) is an autoimmune disease with multi-organ involvement, and some studies have found that SLE has a reduced risk of breast cancer (BRCA). So, we tried to find prognostic genes for BRCA related to SLE by integrated analysis and machine learning.Method: First, we downloaded 2 SLE datasets from Gene Expression Omnibus (GEO) and BRCA data from the Cancer Genome Atlas (TCGA). Subsequently, we performed differentially expressed genes (DEGs) and functional enrichment analysis by Metascape in SLE. Genes that were differentially expressed in both datasets were the validated DEGs. And after constructing PPI network, genes with nodes >30 were intersected with survival genes in BRCA to obtain candidate genes. Then, the candidate genes were validated by lasso regression in both training and validation sets to obtain prognostic genes. Afterwards, we investigated the diagnostic potential of prognostic genes for SLE and the predictive efficacy for BRCA prognosis. Moreover, GSEA analysis and immune infiltration were performed for SLE and BRCA. Finally, we constructed a prognostic gene-miRNAs network and did functional enrichment of the shared genes.Result: DEGs for SLE were mainly enriched with neutrophil degranulation and IFN pathways. After the lasso model of BRCA was established, IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE and had good predictive ability for the prognosis of BRCA. Prognostic genes had excellent diagnostic potential for SLE, with IFI35 and EIF2AK2 positively associated with SLE activity and IRF7 positively associated with IFI35. GSEA showed that both SLE and BRCA were associated with ubiquitinated degradation. Immune infiltrates suggest that plasma cells, dendritic cells (DC), neutrophils and monocyte were elevated in SLE. DC, NK and CD8+ T cells were elevated in the BRCA low-risk group. Finally, 5 shared miRNAs were confirmed, which were mainly enriched in the IFN pathway.Conclusion: IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE. IFN pathway played an important role in the etiology of SLE and the prognosis of BRCA. |
| abstractGer |
Background: Systemic Lupus Erythematosus (SLE) is an autoimmune disease with multi-organ involvement, and some studies have found that SLE has a reduced risk of breast cancer (BRCA). So, we tried to find prognostic genes for BRCA related to SLE by integrated analysis and machine learning.Method: First, we downloaded 2 SLE datasets from Gene Expression Omnibus (GEO) and BRCA data from the Cancer Genome Atlas (TCGA). Subsequently, we performed differentially expressed genes (DEGs) and functional enrichment analysis by Metascape in SLE. Genes that were differentially expressed in both datasets were the validated DEGs. And after constructing PPI network, genes with nodes >30 were intersected with survival genes in BRCA to obtain candidate genes. Then, the candidate genes were validated by lasso regression in both training and validation sets to obtain prognostic genes. Afterwards, we investigated the diagnostic potential of prognostic genes for SLE and the predictive efficacy for BRCA prognosis. Moreover, GSEA analysis and immune infiltration were performed for SLE and BRCA. Finally, we constructed a prognostic gene-miRNAs network and did functional enrichment of the shared genes.Result: DEGs for SLE were mainly enriched with neutrophil degranulation and IFN pathways. After the lasso model of BRCA was established, IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE and had good predictive ability for the prognosis of BRCA. Prognostic genes had excellent diagnostic potential for SLE, with IFI35 and EIF2AK2 positively associated with SLE activity and IRF7 positively associated with IFI35. GSEA showed that both SLE and BRCA were associated with ubiquitinated degradation. Immune infiltrates suggest that plasma cells, dendritic cells (DC), neutrophils and monocyte were elevated in SLE. DC, NK and CD8+ T cells were elevated in the BRCA low-risk group. Finally, 5 shared miRNAs were confirmed, which were mainly enriched in the IFN pathway.Conclusion: IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE. IFN pathway played an important role in the etiology of SLE and the prognosis of BRCA. |
| abstract_unstemmed |
Background: Systemic Lupus Erythematosus (SLE) is an autoimmune disease with multi-organ involvement, and some studies have found that SLE has a reduced risk of breast cancer (BRCA). So, we tried to find prognostic genes for BRCA related to SLE by integrated analysis and machine learning.Method: First, we downloaded 2 SLE datasets from Gene Expression Omnibus (GEO) and BRCA data from the Cancer Genome Atlas (TCGA). Subsequently, we performed differentially expressed genes (DEGs) and functional enrichment analysis by Metascape in SLE. Genes that were differentially expressed in both datasets were the validated DEGs. And after constructing PPI network, genes with nodes >30 were intersected with survival genes in BRCA to obtain candidate genes. Then, the candidate genes were validated by lasso regression in both training and validation sets to obtain prognostic genes. Afterwards, we investigated the diagnostic potential of prognostic genes for SLE and the predictive efficacy for BRCA prognosis. Moreover, GSEA analysis and immune infiltration were performed for SLE and BRCA. Finally, we constructed a prognostic gene-miRNAs network and did functional enrichment of the shared genes.Result: DEGs for SLE were mainly enriched with neutrophil degranulation and IFN pathways. After the lasso model of BRCA was established, IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE and had good predictive ability for the prognosis of BRCA. Prognostic genes had excellent diagnostic potential for SLE, with IFI35 and EIF2AK2 positively associated with SLE activity and IRF7 positively associated with IFI35. GSEA showed that both SLE and BRCA were associated with ubiquitinated degradation. Immune infiltrates suggest that plasma cells, dendritic cells (DC), neutrophils and monocyte were elevated in SLE. DC, NK and CD8+ T cells were elevated in the BRCA low-risk group. Finally, 5 shared miRNAs were confirmed, which were mainly enriched in the IFN pathway.Conclusion: IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE. IFN pathway played an important role in the etiology of SLE and the prognosis of BRCA. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV062255835</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231124093049.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230827s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.imbio.2023.152730</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV062255835</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0171-2985(23)04532-1</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.45</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liang, Xiaofeng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Identification of prognostic genes for breast cancer related to systemic lupus erythematosus by integrated analysis and machine learning</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background: Systemic Lupus Erythematosus (SLE) is an autoimmune disease with multi-organ involvement, and some studies have found that SLE has a reduced risk of breast cancer (BRCA). So, we tried to find prognostic genes for BRCA related to SLE by integrated analysis and machine learning.Method: First, we downloaded 2 SLE datasets from Gene Expression Omnibus (GEO) and BRCA data from the Cancer Genome Atlas (TCGA). Subsequently, we performed differentially expressed genes (DEGs) and functional enrichment analysis by Metascape in SLE. Genes that were differentially expressed in both datasets were the validated DEGs. And after constructing PPI network, genes with nodes >30 were intersected with survival genes in BRCA to obtain candidate genes. Then, the candidate genes were validated by lasso regression in both training and validation sets to obtain prognostic genes. Afterwards, we investigated the diagnostic potential of prognostic genes for SLE and the predictive efficacy for BRCA prognosis. Moreover, GSEA analysis and immune infiltration were performed for SLE and BRCA. Finally, we constructed a prognostic gene-miRNAs network and did functional enrichment of the shared genes.Result: DEGs for SLE were mainly enriched with neutrophil degranulation and IFN pathways. After the lasso model of BRCA was established, IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE and had good predictive ability for the prognosis of BRCA. Prognostic genes had excellent diagnostic potential for SLE, with IFI35 and EIF2AK2 positively associated with SLE activity and IRF7 positively associated with IFI35. GSEA showed that both SLE and BRCA were associated with ubiquitinated degradation. Immune infiltrates suggest that plasma cells, dendritic cells (DC), neutrophils and monocyte were elevated in SLE. DC, NK and CD8+ T cells were elevated in the BRCA low-risk group. Finally, 5 shared miRNAs were confirmed, which were mainly enriched in the IFN pathway.Conclusion: IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE. IFN pathway played an important role in the etiology of SLE and the prognosis of BRCA.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Systemic lupus erythematosus</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Breast cancer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prognostic genes</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Immune infiltration</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Peng, Zhishen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lin, Zien</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lin, 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