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Sphingolipids in prostate cancer prognosis: integrating single-cell and bulk sequencing.
Zhou, Shan; Sun, Li; Mao, Fei; Chen, Jing.
Afiliación
  • Zhou S; Department of Ultrasound, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian City 223300, People’s Republic of China.
  • Sun L; Department of Ultrasound, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian City 223300, People’s Republic of China.
  • Mao F; Department of Urology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian City 223300, People’s Republic of China.
  • Chen J; Department of Urology, The Affiliated Huaian No. 1 People’s Hospital of Xuzhou Medical University, Huaian City 223300, People’s Republic of China.
Aging (Albany NY) ; 16(9): 8031-8043, 2024 05 06.
Article en En | MEDLINE | ID: mdl-38713159
ABSTRACT

BACKGROUND:

Stratifying patient risk and exploring the tumor microenvironment are critical endeavors in prostate cancer research, essential for advancing our understanding and management of this disease.

METHODS:

Single-cell sequencing data for prostate cancer were sourced from the pradcellatlas website, while bulk transcriptome data were obtained from the TCGA database. Dimensionality reduction cluster analysis was employed to investigate heterogeneity in single-cell sequencing data. Gene set enrichment analysis, utilizing GO and KEGG pathways, was conducted to explore functional aspects. Weighted gene coexpression network analysis (WGCNA) identified key gene modules. Prognostic models were developed using Cox regression and LASSO regression techniques, implemented in R software. Validation of key gene expression levels was performed via PCR assays.

RESULTS:

Through integrative analysis of single-cell and bulk transcriptome data, key genes implicated in prostate cancer pathogenesis were identified. A prognostic model focused on sphingolipid metabolism (SRSR) was constructed, comprising five genes "FUS," "MARK3," "CHTOP," "ILF3," and "ARIH2." This model effectively stratified patients into high-risk and low-risk groups, with the high-risk cohort exhibiting significantly poorer prognoses. Furthermore, distinct differences in the immune microenvironment were observed between these groups. Validation of key gene expression, exemplified by ILF3, was confirmed through PCR analysis.

CONCLUSION:

This study contributes to our understanding of the role of sphingolipid metabolism in prostate cancer diagnosis and treatment. The identified prognostic model holds promise for improving risk stratification and patient outcomes in clinical settings.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Esfingolípidos / Análisis de la Célula Individual Límite: Humans / Male Idioma: En Revista: Aging (Albany NY) Asunto de la revista: GERIATRIA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Esfingolípidos / Análisis de la Célula Individual Límite: Humans / Male Idioma: En Revista: Aging (Albany NY) Asunto de la revista: GERIATRIA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos