Identification of differentially expressed genes and signaling pathways with Candida infection by bioinformatics analysis
Abstract Background Opportunistic Candida species causes severe infections when the human immune system is weakened, leading to high mortality. Methods In our study, bioinformatics analysis was used to study the high-throughput sequencing data of samples infected with four kinds of Candida species....
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| Format: | Article |
| Language: | English |
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BMC
2022-03-01
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| Series: | European Journal of Medical Research |
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| Online Access: | https://doi.org/10.1186/s40001-022-00651-w |
| _version_ | 1811335580042133504 |
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| author | Guo-Dong Zhu Li-Min Xie Jian-Wen Su Xun-Jie Cao Xin Yin Ya-Ping Li Yuan-Mei Gao Xu-Guang Guo |
| author_facet | Guo-Dong Zhu Li-Min Xie Jian-Wen Su Xun-Jie Cao Xin Yin Ya-Ping Li Yuan-Mei Gao Xu-Guang Guo |
| author_sort | Guo-Dong Zhu |
| collection | DOAJ |
| description | Abstract Background Opportunistic Candida species causes severe infections when the human immune system is weakened, leading to high mortality. Methods In our study, bioinformatics analysis was used to study the high-throughput sequencing data of samples infected with four kinds of Candida species. And the hub genes were obtained by statistical analysis. Results A total of 547, 422, 415 and 405 differentially expressed genes (DEGs) of Candida albicans, Candida glabrata, Candida parapsilosis and Candida tropicalis groups were obtained, respectively. A total of 216 DEGs were obtained after taking intersections of DEGs from the four groups. A protein–protein interaction (PPI) network was established using these 216 genes. The top 10 hub genes (FOSB, EGR1, JUNB, ATF3, EGR2, NR4A1, NR4A2, DUSP1, BTG2, and EGR3) were acquired through calculation by the cytoHubba plug-in in Cytoscape software. Validated by the sequencing data of peripheral blood, JUNB, ATF3 and EGR2 genes were significant statistical significance. Conclusions In conclusion, our study demonstrated the potential pathogenic genes in Candida species and their underlying mechanisms by bioinformatic analysis methods. Further, after statistical validation, JUNB, ATF3 and EGR2 genes were attained, which may be used as potential biomarkers with Candida species infection. |
| first_indexed | 2024-04-13T17:26:20Z |
| format | Article |
| id | doaj.art-c31cecc3e7d6430ba59e61502eb5e418 |
| institution | Directory Open Access Journal |
| issn | 2047-783X |
| language | English |
| last_indexed | 2024-04-13T17:26:20Z |
| publishDate | 2022-03-01 |
| publisher | BMC |
| record_format | Article |
| series | European Journal of Medical Research |
| spelling | doaj.art-c31cecc3e7d6430ba59e61502eb5e4182022-12-22T02:37:46ZengBMCEuropean Journal of Medical Research2047-783X2022-03-0127111110.1186/s40001-022-00651-wIdentification of differentially expressed genes and signaling pathways with Candida infection by bioinformatics analysisGuo-Dong Zhu0Li-Min Xie1Jian-Wen Su2Xun-Jie Cao3Xin Yin4Ya-Ping Li5Yuan-Mei Gao6Xu-Guang Guo7Department of Oncology, Guangzhou Geriatric HospitalDepartment of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Pediatrics, The Pediatrics School of Guangzhou Medical UniversityDepartment of Clinical Medicine, The Second Clinical School of Guangzhou Medical UniversityDepartment of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical UniversityAbstract Background Opportunistic Candida species causes severe infections when the human immune system is weakened, leading to high mortality. Methods In our study, bioinformatics analysis was used to study the high-throughput sequencing data of samples infected with four kinds of Candida species. And the hub genes were obtained by statistical analysis. Results A total of 547, 422, 415 and 405 differentially expressed genes (DEGs) of Candida albicans, Candida glabrata, Candida parapsilosis and Candida tropicalis groups were obtained, respectively. A total of 216 DEGs were obtained after taking intersections of DEGs from the four groups. A protein–protein interaction (PPI) network was established using these 216 genes. The top 10 hub genes (FOSB, EGR1, JUNB, ATF3, EGR2, NR4A1, NR4A2, DUSP1, BTG2, and EGR3) were acquired through calculation by the cytoHubba plug-in in Cytoscape software. Validated by the sequencing data of peripheral blood, JUNB, ATF3 and EGR2 genes were significant statistical significance. Conclusions In conclusion, our study demonstrated the potential pathogenic genes in Candida species and their underlying mechanisms by bioinformatic analysis methods. Further, after statistical validation, JUNB, ATF3 and EGR2 genes were attained, which may be used as potential biomarkers with Candida species infection.https://doi.org/10.1186/s40001-022-00651-wCandidaHigh-throughput sequencingDifferentially expressed genesSignaling pathwaysBioinformatics analysis |
| spellingShingle | Guo-Dong Zhu Li-Min Xie Jian-Wen Su Xun-Jie Cao Xin Yin Ya-Ping Li Yuan-Mei Gao Xu-Guang Guo Identification of differentially expressed genes and signaling pathways with Candida infection by bioinformatics analysis European Journal of Medical Research Candida High-throughput sequencing Differentially expressed genes Signaling pathways Bioinformatics analysis |
| title | Identification of differentially expressed genes and signaling pathways with Candida infection by bioinformatics analysis |
| title_full | Identification of differentially expressed genes and signaling pathways with Candida infection by bioinformatics analysis |
| title_fullStr | Identification of differentially expressed genes and signaling pathways with Candida infection by bioinformatics analysis |
| title_full_unstemmed | Identification of differentially expressed genes and signaling pathways with Candida infection by bioinformatics analysis |
| title_short | Identification of differentially expressed genes and signaling pathways with Candida infection by bioinformatics analysis |
| title_sort | identification of differentially expressed genes and signaling pathways with candida infection by bioinformatics analysis |
| topic | Candida High-throughput sequencing Differentially expressed genes Signaling pathways Bioinformatics analysis |
| url | https://doi.org/10.1186/s40001-022-00651-w |
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