Identification of sepsis-associated encephalopathy biomarkers through machine learning and bioinformatics approaches
Abstract Sepsis-associated encephalopathy (SAE) is common in septic patients, characterized by acute and long-term cognitive impairment, and is associated with higher mortality. This study aimed to identify SAE-related biomarkers and evaluate their diagnostic potential. We analyzed three SAE-related...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-82885-8 |
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| _version_ | 1841559426628059136 |
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| author | Jingchao Lei Jia Zhai Jing Qi Chuanzheng Sun |
| author_facet | Jingchao Lei Jia Zhai Jing Qi Chuanzheng Sun |
| author_sort | Jingchao Lei |
| collection | DOAJ |
| description | Abstract Sepsis-associated encephalopathy (SAE) is common in septic patients, characterized by acute and long-term cognitive impairment, and is associated with higher mortality. This study aimed to identify SAE-related biomarkers and evaluate their diagnostic potential. We analyzed three SAE-related sequencing datasets, using two as training sets and one as a validation set. Weighted Gene Co-expression Network Analysis and four machine learning methods—Elastic Net regression, LASSO, random forest, and XGBoost—were employed, dentifying 18 biomarkers with significant expression changes. External validation and in vitro experiments confirmed the differential expression of these biomarkers. These findings provide insights into SAE pathogenesis and suggest potential therapeutic targets. |
| format | Article |
| id | doaj-art-4c36df375f37409ea098f02f8664030c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4c36df375f37409ea098f02f8664030c2025-01-05T12:29:02ZengNature PortfolioScientific Reports2045-23222024-12-0114111010.1038/s41598-024-82885-8Identification of sepsis-associated encephalopathy biomarkers through machine learning and bioinformatics approachesJingchao Lei0Jia Zhai1Jing Qi2Chuanzheng Sun3Department of Emergency, The Third Xiangya Hospital of Central South UniversityDepartment of Emergency, The Third Xiangya Hospital of Central South UniversityDepartment of Emergency, The Third Xiangya Hospital of Central South UniversityDepartment of Emergency, The Third Xiangya Hospital of Central South UniversityAbstract Sepsis-associated encephalopathy (SAE) is common in septic patients, characterized by acute and long-term cognitive impairment, and is associated with higher mortality. This study aimed to identify SAE-related biomarkers and evaluate their diagnostic potential. We analyzed three SAE-related sequencing datasets, using two as training sets and one as a validation set. Weighted Gene Co-expression Network Analysis and four machine learning methods—Elastic Net regression, LASSO, random forest, and XGBoost—were employed, dentifying 18 biomarkers with significant expression changes. External validation and in vitro experiments confirmed the differential expression of these biomarkers. These findings provide insights into SAE pathogenesis and suggest potential therapeutic targets.https://doi.org/10.1038/s41598-024-82885-8 |
| spellingShingle | Jingchao Lei Jia Zhai Jing Qi Chuanzheng Sun Identification of sepsis-associated encephalopathy biomarkers through machine learning and bioinformatics approaches Scientific Reports |
| title | Identification of sepsis-associated encephalopathy biomarkers through machine learning and bioinformatics approaches |
| title_full | Identification of sepsis-associated encephalopathy biomarkers through machine learning and bioinformatics approaches |
| title_fullStr | Identification of sepsis-associated encephalopathy biomarkers through machine learning and bioinformatics approaches |
| title_full_unstemmed | Identification of sepsis-associated encephalopathy biomarkers through machine learning and bioinformatics approaches |
| title_short | Identification of sepsis-associated encephalopathy biomarkers through machine learning and bioinformatics approaches |
| title_sort | identification of sepsis associated encephalopathy biomarkers through machine learning and bioinformatics approaches |
| url | https://doi.org/10.1038/s41598-024-82885-8 |
| work_keys_str_mv | AT jingchaolei identificationofsepsisassociatedencephalopathybiomarkersthroughmachinelearningandbioinformaticsapproaches AT jiazhai identificationofsepsisassociatedencephalopathybiomarkersthroughmachinelearningandbioinformaticsapproaches AT jingqi identificationofsepsisassociatedencephalopathybiomarkersthroughmachinelearningandbioinformaticsapproaches AT chuanzhengsun identificationofsepsisassociatedencephalopathybiomarkersthroughmachinelearningandbioinformaticsapproaches |