Identification of AKI signatures and classification patterns in ccRCC based on machine learning
BackgroundAcute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI). In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI. In addition, the interaction between AKI and...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-05-01
|
| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2023.1195678/full |
| _version_ | 1827942075416444928 |
|---|---|
| author | Li Wang Fei Peng Zhen Hua Li Yu Fei Deng Meng Na Ruan Zhi Guo Mao Lin Li |
| author_facet | Li Wang Fei Peng Zhen Hua Li Yu Fei Deng Meng Na Ruan Zhi Guo Mao Lin Li |
| author_sort | Li Wang |
| collection | DOAJ |
| description | BackgroundAcute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI). In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI. In addition, the interaction between AKI and clear cell renal cell carcinoma (ccRCC) remain elusive.MethodsFour public AKI datasets (GSE126805, GSE139061, GSE30718, and GSE90861) treated as discovery datasets and one (GSE43974) treated as a validation dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AKI and normal kidney tissues were identified using the R package limma. Four machine learning algorithms were used to identify the novel AKI biomarkers. The correlations between the seven biomarkers and immune cells or their components were calculated using the R package ggcor. Furthermore, two distinct ccRCC subtypes with different prognoses and immune characteristics were identified and verified using seven novel biomarkers.ResultsSeven robust AKI signatures were identified using the four machine learning methods. The immune infiltration analysis revealed that the numbers of activated CD4 T cells, CD56dim natural killer cells, eosinophils, mast cells, memory B cells, natural killer T cells, neutrophils, T follicular helper cells, and type 1 T helper cells were significantly higher in the AKI cluster. The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set. In addition, the calibration plot demonstrated few errors between the predicted and actual values. In a separate analysis, the immune components and cellular differences between the two ccRCC subtypes based on their AKI signatures were compared. Patients in the CS1 had better overall survival, progression-free survival, drug sensitivity, and survival probability.ConclusionOur study identified seven distinct AKI-related biomarkers based on four machine learning methods and proposed a nomogram for stratified AKI risk prediction. We also confirmed that AKI signatures were valuable for predicting ccRCC prognosis. The current work not only sheds light on the early prediction of AKI, but also provides new insights into the correlation between AKI and ccRCC. |
| first_indexed | 2024-03-13T09:53:13Z |
| format | Article |
| id | doaj.art-15682c627c6b4bf892a213634070e8ae |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-03-13T09:53:13Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-15682c627c6b4bf892a213634070e8ae2023-05-24T05:12:01ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2023-05-011010.3389/fmed.2023.11956781195678Identification of AKI signatures and classification patterns in ccRCC based on machine learningLi Wang0Fei Peng1Zhen Hua Li2Yu Fei Deng3Meng Na Ruan4Zhi Guo Mao5Lin Li6Department of Nephrology, Changzheng Hospital, Naval Medical University, Shanghai, ChinaDepartment of Cardiology, Jinshan Hospital of Fudan University, Shanghai, ChinaDepartment of Cardiology, Changzheng Hospital, Naval Medical University, Shanghai, ChinaDepartment of Nephrology, Changzheng Hospital, Naval Medical University, Shanghai, ChinaDepartment of Nephrology, Changzheng Hospital, Naval Medical University, Shanghai, ChinaDepartment of Nephrology, Changzheng Hospital, Naval Medical University, Shanghai, ChinaDepartment of Nephrology, Changzheng Hospital, Naval Medical University, Shanghai, ChinaBackgroundAcute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI). In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI. In addition, the interaction between AKI and clear cell renal cell carcinoma (ccRCC) remain elusive.MethodsFour public AKI datasets (GSE126805, GSE139061, GSE30718, and GSE90861) treated as discovery datasets and one (GSE43974) treated as a validation dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AKI and normal kidney tissues were identified using the R package limma. Four machine learning algorithms were used to identify the novel AKI biomarkers. The correlations between the seven biomarkers and immune cells or their components were calculated using the R package ggcor. Furthermore, two distinct ccRCC subtypes with different prognoses and immune characteristics were identified and verified using seven novel biomarkers.ResultsSeven robust AKI signatures were identified using the four machine learning methods. The immune infiltration analysis revealed that the numbers of activated CD4 T cells, CD56dim natural killer cells, eosinophils, mast cells, memory B cells, natural killer T cells, neutrophils, T follicular helper cells, and type 1 T helper cells were significantly higher in the AKI cluster. The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set. In addition, the calibration plot demonstrated few errors between the predicted and actual values. In a separate analysis, the immune components and cellular differences between the two ccRCC subtypes based on their AKI signatures were compared. Patients in the CS1 had better overall survival, progression-free survival, drug sensitivity, and survival probability.ConclusionOur study identified seven distinct AKI-related biomarkers based on four machine learning methods and proposed a nomogram for stratified AKI risk prediction. We also confirmed that AKI signatures were valuable for predicting ccRCC prognosis. The current work not only sheds light on the early prediction of AKI, but also provides new insights into the correlation between AKI and ccRCC.https://www.frontiersin.org/articles/10.3389/fmed.2023.1195678/fullacute kidney injurymachine learningmolecular subtypesimmunityclear cell renal cell carcinoma |
| spellingShingle | Li Wang Fei Peng Zhen Hua Li Yu Fei Deng Meng Na Ruan Zhi Guo Mao Lin Li Identification of AKI signatures and classification patterns in ccRCC based on machine learning Frontiers in Medicine acute kidney injury machine learning molecular subtypes immunity clear cell renal cell carcinoma |
| title | Identification of AKI signatures and classification patterns in ccRCC based on machine learning |
| title_full | Identification of AKI signatures and classification patterns in ccRCC based on machine learning |
| title_fullStr | Identification of AKI signatures and classification patterns in ccRCC based on machine learning |
| title_full_unstemmed | Identification of AKI signatures and classification patterns in ccRCC based on machine learning |
| title_short | Identification of AKI signatures and classification patterns in ccRCC based on machine learning |
| title_sort | identification of aki signatures and classification patterns in ccrcc based on machine learning |
| topic | acute kidney injury machine learning molecular subtypes immunity clear cell renal cell carcinoma |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2023.1195678/full |
| work_keys_str_mv | AT liwang identificationofakisignaturesandclassificationpatternsinccrccbasedonmachinelearning AT feipeng identificationofakisignaturesandclassificationpatternsinccrccbasedonmachinelearning AT zhenhuali identificationofakisignaturesandclassificationpatternsinccrccbasedonmachinelearning AT yufeideng identificationofakisignaturesandclassificationpatternsinccrccbasedonmachinelearning AT mengnaruan identificationofakisignaturesandclassificationpatternsinccrccbasedonmachinelearning AT zhiguomao identificationofakisignaturesandclassificationpatternsinccrccbasedonmachinelearning AT linli identificationofakisignaturesandclassificationpatternsinccrccbasedonmachinelearning |