An Integrated Approach Utilizing Single-Cell and Bulk RNA-Sequencing for the Identification of a Mitophagy-Associated Genes Signature: Implications for Prognostication and Therapeutic Stratification in Prostate Cancer
<b>Introduction</b>: Prostate cancer, notably prostate adenocarcinoma (PARD), has high incidence and mortality rates. Although typically resistant to immunotherapy, recent studies have found immune targets for prostate cancer. Stratifying patients by molecular subtypes may identify those...
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2025-01-01
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| author | Yuke Zhang Li Ding Zhijin Zhang Liliang Shen Yadong Guo Wentao Zhang Yang Yu Zhuoran Gu Ji Liu Aimaitiaji Kadier Jiang Geng Shiyu Mao Xudong Yao |
| author_facet | Yuke Zhang Li Ding Zhijin Zhang Liliang Shen Yadong Guo Wentao Zhang Yang Yu Zhuoran Gu Ji Liu Aimaitiaji Kadier Jiang Geng Shiyu Mao Xudong Yao |
| author_sort | Yuke Zhang |
| collection | DOAJ |
| description | <b>Introduction</b>: Prostate cancer, notably prostate adenocarcinoma (PARD), has high incidence and mortality rates. Although typically resistant to immunotherapy, recent studies have found immune targets for prostate cancer. Stratifying patients by molecular subtypes may identify those who could benefit from immunotherapy. <b>Methods</b>: We used single-cell and bulk RNA sequencing data from GEO and TCGA databases. We characterized the tumor microenvironment at the single-cell level, analyzing cell interactions and identifying fibroblasts linked to mitophagy. Target genes were narrowed down at the bulk transcriptome level to construct a PARD prognosis prediction nomogram. Unsupervised consensus clustering classified PARD into subtypes, analyzing differences in clinical features, immune infiltration, and immunotherapy. Furthermore, the cellular functions of the genes of interest were verified in vitro. <b>Results</b>: We identified ten cell types and 160 mitophagy-related single-cell differentially expressed genes (MR-scDEGs). Strong interactions were observed between fibroblasts, endothelial cells, CD8<sup>+</sup> T cells, and NK cells. Fibroblasts linked to mitophagy were divided into six subtypes. Intersection of DEGs from three bulk datasets with MR-scDEGs identified 26 key genes clustered into two subgroups. COX regression analysis identified seven prognostic key genes, enabling a prognostic nomogram model. High and low-risk groups showed significant differences in clinical features, immune infiltration, immunotherapy, and drug sensitivity. In prostate cancer cell lines, CAV1, PALLD, and ITGB8 are upregulated, while CLDN7 is downregulated. Knockdown of PALLD significantly inhibits the proliferation and colony-forming ability of PC3 and DU145 cells, suggesting the important roles of this gene in prostate cancer progression. <b>Conclusions</b>: This study analyzed mitophagy-related genes in PARD, predicting prognosis and aiding in subtype identification and immunotherapy response analysis. This approach offers new strategies for treating prostate cancer with specific molecular subtypes and helps develop potential biomarkers for personalized medicine strategies. |
| format | Article |
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| institution | DOAJ |
| issn | 2227-9059 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Biomedicines |
| spelling | doaj-art-a9a5e9cd736a4e93b4d1dc90ced0b18c2025-08-20T03:12:10ZengMDPI AGBiomedicines2227-90592025-01-0113231110.3390/biomedicines13020311An Integrated Approach Utilizing Single-Cell and Bulk RNA-Sequencing for the Identification of a Mitophagy-Associated Genes Signature: Implications for Prognostication and Therapeutic Stratification in Prostate CancerYuke Zhang0Li Ding1Zhijin Zhang2Liliang Shen3Yadong Guo4Wentao Zhang5Yang Yu6Zhuoran Gu7Ji Liu8Aimaitiaji Kadier9Jiang Geng10Shiyu Mao11Xudong Yao12Department of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, 301 Middle Yan Chang Road, Shanghai 200072, ChinaDepartment of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, 301 Middle Yan Chang Road, Shanghai 200072, ChinaDepartment of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, 301 Middle Yan Chang Road, Shanghai 200072, ChinaDepartment of Urology, Ningbo Yinzhou People’s Hospital, 251 Baizhang East Road, Ningbo 315100, ChinaDepartment of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, 301 Middle Yan Chang Road, Shanghai 200072, ChinaDepartment of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, 301 Middle Yan Chang Road, Shanghai 200072, ChinaDepartment of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, 301 Middle Yan Chang Road, Shanghai 200072, ChinaDepartment of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, 301 Middle Yan Chang Road, Shanghai 200072, ChinaDepartment of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, 301 Middle Yan Chang Road, Shanghai 200072, ChinaDepartment of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, 301 Middle Yan Chang Road, Shanghai 200072, ChinaDepartment of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, 301 Middle Yan Chang Road, Shanghai 200072, ChinaDepartment of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, 301 Middle Yan Chang Road, Shanghai 200072, ChinaDepartment of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, 301 Middle Yan Chang Road, Shanghai 200072, China<b>Introduction</b>: Prostate cancer, notably prostate adenocarcinoma (PARD), has high incidence and mortality rates. Although typically resistant to immunotherapy, recent studies have found immune targets for prostate cancer. Stratifying patients by molecular subtypes may identify those who could benefit from immunotherapy. <b>Methods</b>: We used single-cell and bulk RNA sequencing data from GEO and TCGA databases. We characterized the tumor microenvironment at the single-cell level, analyzing cell interactions and identifying fibroblasts linked to mitophagy. Target genes were narrowed down at the bulk transcriptome level to construct a PARD prognosis prediction nomogram. Unsupervised consensus clustering classified PARD into subtypes, analyzing differences in clinical features, immune infiltration, and immunotherapy. Furthermore, the cellular functions of the genes of interest were verified in vitro. <b>Results</b>: We identified ten cell types and 160 mitophagy-related single-cell differentially expressed genes (MR-scDEGs). Strong interactions were observed between fibroblasts, endothelial cells, CD8<sup>+</sup> T cells, and NK cells. Fibroblasts linked to mitophagy were divided into six subtypes. Intersection of DEGs from three bulk datasets with MR-scDEGs identified 26 key genes clustered into two subgroups. COX regression analysis identified seven prognostic key genes, enabling a prognostic nomogram model. High and low-risk groups showed significant differences in clinical features, immune infiltration, immunotherapy, and drug sensitivity. In prostate cancer cell lines, CAV1, PALLD, and ITGB8 are upregulated, while CLDN7 is downregulated. Knockdown of PALLD significantly inhibits the proliferation and colony-forming ability of PC3 and DU145 cells, suggesting the important roles of this gene in prostate cancer progression. <b>Conclusions</b>: This study analyzed mitophagy-related genes in PARD, predicting prognosis and aiding in subtype identification and immunotherapy response analysis. This approach offers new strategies for treating prostate cancer with specific molecular subtypes and helps develop potential biomarkers for personalized medicine strategies.https://www.mdpi.com/2227-9059/13/2/311prostate cancersingle-cell RNA sequencingprognosisbiomarkermitophagy-related genes |
| spellingShingle | Yuke Zhang Li Ding Zhijin Zhang Liliang Shen Yadong Guo Wentao Zhang Yang Yu Zhuoran Gu Ji Liu Aimaitiaji Kadier Jiang Geng Shiyu Mao Xudong Yao An Integrated Approach Utilizing Single-Cell and Bulk RNA-Sequencing for the Identification of a Mitophagy-Associated Genes Signature: Implications for Prognostication and Therapeutic Stratification in Prostate Cancer Biomedicines prostate cancer single-cell RNA sequencing prognosis biomarker mitophagy-related genes |
| title | An Integrated Approach Utilizing Single-Cell and Bulk RNA-Sequencing for the Identification of a Mitophagy-Associated Genes Signature: Implications for Prognostication and Therapeutic Stratification in Prostate Cancer |
| title_full | An Integrated Approach Utilizing Single-Cell and Bulk RNA-Sequencing for the Identification of a Mitophagy-Associated Genes Signature: Implications for Prognostication and Therapeutic Stratification in Prostate Cancer |
| title_fullStr | An Integrated Approach Utilizing Single-Cell and Bulk RNA-Sequencing for the Identification of a Mitophagy-Associated Genes Signature: Implications for Prognostication and Therapeutic Stratification in Prostate Cancer |
| title_full_unstemmed | An Integrated Approach Utilizing Single-Cell and Bulk RNA-Sequencing for the Identification of a Mitophagy-Associated Genes Signature: Implications for Prognostication and Therapeutic Stratification in Prostate Cancer |
| title_short | An Integrated Approach Utilizing Single-Cell and Bulk RNA-Sequencing for the Identification of a Mitophagy-Associated Genes Signature: Implications for Prognostication and Therapeutic Stratification in Prostate Cancer |
| title_sort | integrated approach utilizing single cell and bulk rna sequencing for the identification of a mitophagy associated genes signature implications for prognostication and therapeutic stratification in prostate cancer |
| topic | prostate cancer single-cell RNA sequencing prognosis biomarker mitophagy-related genes |
| url | https://www.mdpi.com/2227-9059/13/2/311 |
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