Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO Database Article Swipe
Meng Dou
,
Chenguang Ding
,
Bingxuan Zheng
,
Ge Deng
,
Kun Zhu
,
Cuixiang Xu
,
Wujun Xue
,
Xiaoming Ding
,
Jin Zheng
,
Puxun Tian
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2022.859693
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2022.859693
Objective We aimed to identify feature immune-related genes that correlated with graft rejection and to develop a prognostic model based on immune-related genes in kidney transplantation. Methods Gene expression profiles were obtained from the GEO database. The GSE36059 dataset was used as a discovery cohort. Then, differential expression analysis and a machine learning method were performed to select feature immune-related genes. After that, univariate and multivariate Cox regression analyses were used to identify prognosis-related genes. A novel Riskscore model was built based on the results of multivariate regression. The levels of these feature genes were also confirmed in an independent single-cell dataset and other GEO datasets. Results 15 immune-related genes were expressed differently between non-rejection and rejection kidney allografts. Those differentially expressed immune-related genes (DE-IRGs) were mainly associated with immune-related biological processes and pathways. Subsequently, a 5-immune-gene signature was constructed and showed favorable predictive results in the GSE21374 dataset. Recipients were divided into the high-risk and low-risk groups according to the median value of RiskScore. The GO and KEGG analysis indicated that the differentially expressed genes (DEGs) between high-risk and low-risk groups were mainly involved in inflammatory pathways, chemokine-related pathways, and rejection-related pathways. Immune infiltration analysis demonstrated that RiskScore was potentially related to immune infiltration. Kaplan-Meier survival analysis suggested that recipients in the high-risk group had poor graft survival. AUC values of 1- and 3-year graft survival were 0.804 and 0.793, respectively. Conclusion Our data suggest that this immune-related prognostic model had good sensitivity and specificity in predicting the 1- and 3-year kidney graft survival and might act as a useful tool for predicting kidney graft loss.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fimmu.2022.859693
- https://www.frontiersin.org/articles/10.3389/fimmu.2022.859693/pdf
- OA Status
- gold
- Cited By
- 8
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4214679365
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4214679365Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fimmu.2022.859693Digital Object Identifier
- Title
-
Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO DatabaseWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-25Full publication date if available
- Authors
-
Meng Dou, Chenguang Ding, Bingxuan Zheng, Ge Deng, Kun Zhu, Cuixiang Xu, Wujun Xue, Xiaoming Ding, Jin Zheng, Puxun TianList of authors in order
- Landing page
-
https://doi.org/10.3389/fimmu.2022.859693Publisher landing page
- PDF URL
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https://www.frontiersin.org/articles/10.3389/fimmu.2022.859693/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fimmu.2022.859693/pdfDirect OA link when available
- Concepts
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Immune system, KEGG, Proportional hazards model, Gene, Survival analysis, Multivariate statistics, Biology, Chemokine, Immunology, Oncology, Medicine, Gene expression, Transcriptome, Internal medicine, Genetics, Machine learning, Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
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2024: 4, 2023: 4Per-year citation counts (last 5 years)
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43Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| countries_distinct_count | 1 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I4210121771 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile.value | 0.81468408 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |