An Integrated Machine Learning Framework Identifies Prognostic Gene Pair Biomarkers Associated with Programmed Cell Death Modalities in Clear Cell Renal Cell Carcinoma
Background: Clear cell renal cell carcinoma (ccRCC) is a common and lethal urological malignancy for which there are no effective personalized therapeutic strategies. Programmed cell death (PCD) patterns have emerged as critical determinants of clinical prognosis and immunotherapy responses. However...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IMR Press
2024-03-01
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| Series: | Frontiers in Bioscience-Landmark |
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| Online Access: | https://www.imrpress.com/journal/FBL/29/3/10.31083/j.fbl2903121 |
| _version_ | 1827304560008364032 |
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| author | Bohong Chen Mingguo Zhou Li Guo Haoxiang Huang Xinyue Sun Zihe Peng Dapeng Wu Wei Chen |
| author_facet | Bohong Chen Mingguo Zhou Li Guo Haoxiang Huang Xinyue Sun Zihe Peng Dapeng Wu Wei Chen |
| author_sort | Bohong Chen |
| collection | DOAJ |
| description | Background: Clear cell renal cell carcinoma (ccRCC) is a common and lethal urological malignancy for which there are no effective personalized therapeutic strategies. Programmed cell death (PCD) patterns have emerged as critical determinants of clinical prognosis and immunotherapy responses. However, the actual clinical relevance of PCD processes in ccRCC is still poorly understood. Methods: We screened for PCD-related gene pairs through single-sample gene set enrichment analysis (ssGSEA), consensus cluster analysis, and univariate Cox regression analysis. A novel machine learning framework incorporating 12 algorithms and 113 unique combinations were used to develop the cell death-related gene pair score (CDRGPS). Additionally, a radiomic score (Rad_Score) derived from computed tomography (CT) image features was used to classify the CDRGPS status as high or low. Finally, we conclusively verified the function of PRSS23 in ccRCC. Results: The CDRGPS was developed through an integrated machine learning approach that leveraged 113 algorithm combinations. CDRGPS represents an independent prognostic biomarker for overall survival and demonstrated consistent performance between training and external validation cohorts. Moreover, CDRGPS showed better prognostic accuracy compared to seven previously published cell death-related signatures. In addition, patients classified as high-risk by CDRGPS exhibited increased responsiveness to tyrosine kinase inhibitors (TKIs), mammalian Target of Rapamycin (mTOR) inhibitors, and immunotherapy. The Rad_Score demonstrated excellent discrimination for predicting high versus low CDRGPS status, with an area under the curve (AUC) value of 0.813 in the Cancer Imaging Archive (TCIA) database. PRSS23 was identified as a significant factor in the metastasis and immune response of ccRCC, thereby validating experimental in vitro results. Conclusions: CDRGPS is a robust and non-invasive tool that has the potential to improve clinical outcomes and enable personalized medicine in ccRCC patients. |
| first_indexed | 2024-04-24T17:39:01Z |
| format | Article |
| id | doaj.art-e90e5f18d1d5408ebfc6f534ce311a26 |
| institution | Directory Open Access Journal |
| issn | 2768-6701 |
| language | English |
| last_indexed | 2024-04-24T17:39:01Z |
| publishDate | 2024-03-01 |
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| series | Frontiers in Bioscience-Landmark |
| spelling | doaj.art-e90e5f18d1d5408ebfc6f534ce311a262024-03-28T02:27:55ZengIMR PressFrontiers in Bioscience-Landmark2768-67012024-03-0129312110.31083/j.fbl2903121S2768-6701(24)01246-2An Integrated Machine Learning Framework Identifies Prognostic Gene Pair Biomarkers Associated with Programmed Cell Death Modalities in Clear Cell Renal Cell CarcinomaBohong Chen0Mingguo Zhou1Li Guo2Haoxiang Huang3Xinyue Sun4Zihe Peng5Dapeng Wu6Wei Chen7Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, ChinaDepartment of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, ChinaDepartment of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, ChinaDepartment of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, ChinaDepartment of Neurology, The First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, ChinaDepartment of Urology, The Second Affiliated Hospital of Xi'an Jiaotong University, 710004 Xi'an, Shaanxi, ChinaDepartment of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, ChinaDepartment of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, ChinaBackground: Clear cell renal cell carcinoma (ccRCC) is a common and lethal urological malignancy for which there are no effective personalized therapeutic strategies. Programmed cell death (PCD) patterns have emerged as critical determinants of clinical prognosis and immunotherapy responses. However, the actual clinical relevance of PCD processes in ccRCC is still poorly understood. Methods: We screened for PCD-related gene pairs through single-sample gene set enrichment analysis (ssGSEA), consensus cluster analysis, and univariate Cox regression analysis. A novel machine learning framework incorporating 12 algorithms and 113 unique combinations were used to develop the cell death-related gene pair score (CDRGPS). Additionally, a radiomic score (Rad_Score) derived from computed tomography (CT) image features was used to classify the CDRGPS status as high or low. Finally, we conclusively verified the function of PRSS23 in ccRCC. Results: The CDRGPS was developed through an integrated machine learning approach that leveraged 113 algorithm combinations. CDRGPS represents an independent prognostic biomarker for overall survival and demonstrated consistent performance between training and external validation cohorts. Moreover, CDRGPS showed better prognostic accuracy compared to seven previously published cell death-related signatures. In addition, patients classified as high-risk by CDRGPS exhibited increased responsiveness to tyrosine kinase inhibitors (TKIs), mammalian Target of Rapamycin (mTOR) inhibitors, and immunotherapy. The Rad_Score demonstrated excellent discrimination for predicting high versus low CDRGPS status, with an area under the curve (AUC) value of 0.813 in the Cancer Imaging Archive (TCIA) database. PRSS23 was identified as a significant factor in the metastasis and immune response of ccRCC, thereby validating experimental in vitro results. Conclusions: CDRGPS is a robust and non-invasive tool that has the potential to improve clinical outcomes and enable personalized medicine in ccRCC patients.https://www.imrpress.com/journal/FBL/29/3/10.31083/j.fbl2903121programmed cell deathclear cell renal cell carcinomamachine learningsingle-cell rna-seqradiomicsprss23 |
| spellingShingle | Bohong Chen Mingguo Zhou Li Guo Haoxiang Huang Xinyue Sun Zihe Peng Dapeng Wu Wei Chen An Integrated Machine Learning Framework Identifies Prognostic Gene Pair Biomarkers Associated with Programmed Cell Death Modalities in Clear Cell Renal Cell Carcinoma Frontiers in Bioscience-Landmark programmed cell death clear cell renal cell carcinoma machine learning single-cell rna-seq radiomics prss23 |
| title | An Integrated Machine Learning Framework Identifies Prognostic Gene Pair Biomarkers Associated with Programmed Cell Death Modalities in Clear Cell Renal Cell Carcinoma |
| title_full | An Integrated Machine Learning Framework Identifies Prognostic Gene Pair Biomarkers Associated with Programmed Cell Death Modalities in Clear Cell Renal Cell Carcinoma |
| title_fullStr | An Integrated Machine Learning Framework Identifies Prognostic Gene Pair Biomarkers Associated with Programmed Cell Death Modalities in Clear Cell Renal Cell Carcinoma |
| title_full_unstemmed | An Integrated Machine Learning Framework Identifies Prognostic Gene Pair Biomarkers Associated with Programmed Cell Death Modalities in Clear Cell Renal Cell Carcinoma |
| title_short | An Integrated Machine Learning Framework Identifies Prognostic Gene Pair Biomarkers Associated with Programmed Cell Death Modalities in Clear Cell Renal Cell Carcinoma |
| title_sort | integrated machine learning framework identifies prognostic gene pair biomarkers associated with programmed cell death modalities in clear cell renal cell carcinoma |
| topic | programmed cell death clear cell renal cell carcinoma machine learning single-cell rna-seq radiomics prss23 |
| url | https://www.imrpress.com/journal/FBL/29/3/10.31083/j.fbl2903121 |
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