Fatty acid metabolism heterogeneity in prostate cancer unveils prognostic biomarkers and immune crosstalk

IF 0.9 Q4 GENETICS & HEREDITY

Gene Reports Pub Date : 2025-07-04 DOI:10.1016/j.genrep.2025.102280

Dan Yang , Hanghang Chen , Zhenqi Wang , Haihua Luo, Yong Jiang

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引用次数: 0

Abstract

Background

Fatty acid metabolism (FAM) is critical in prostate cancer (PCa), but few studies have explored its single-cell mechanisms. We analyzed single-cell and bulk transcriptome data from PCa patients to uncover FAM's role and identify potential therapeutic targets.

Methods

Single-cell RNA sequencing identified ELOVL5, SCD, and FADS2 as key FAM genes. FAM scores were calculated using the “AUCell” algorithm and a gene list from the molecular signatures database. FAM scores in each cell type were compared between benign and cancerous samples. In three significant cell types, cells were grouped into high- and low-FAM categories based on median scores, and differentially expressed genes (DEGs) between groups were identified. DEGs were analyzed in bulk RNA sequencing to identify prognostic genes, validated by RT-qPCR. Cell interactions were explored using the “CellChat” algorithm to identify FAM-related communications.

Results

Three key FAM genes—ELOVL5, SCD, and FADS2—were differentially expressed between benign and cancerous samples. High FAM scores were observed in cancer cells and monocytes but lower in T cells. Cancer cells with high FAM scores showed increased stemness and copy number variations. In T cells and monocytes, FAM scores correlated with differentiation. CD226 and NECTIN2 were key interactions in the high-FAM group. Twelve FAM-related genes were identified as prognostic factors, validated by RT-qPCR.

Conclusions

FAM levels are elevated in prostate adenocarcinomas, particularly in cancer cells, monocytes, and T cells. High FAM scores correlate with stemness and genetic instability in cancer cells, while indicating increased differentiation in monocytes and T cells. CD226 and NECTIN2 were key interactions in the high-FAM group, suggesting FAM's role in NECTIN2–CD226 interactions and its potential link to the NECTIN2–TIGIT immune checkpoint axis.

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前列腺癌的脂肪酸代谢异质性揭示了预后生物标志物和免疫串扰

脂肪酸代谢（FAM）在前列腺癌（PCa）中起着至关重要的作用，但很少有研究探讨其单细胞机制。我们分析了PCa患者的单细胞和大量转录组数据，以揭示FAM的作用并确定潜在的治疗靶点。方法单细胞RNA测序鉴定出ELOVL5、SCD和FADS2为FAM关键基因。FAM评分使用“AUCell”算法和来自分子特征数据库的基因列表计算。比较良性和癌变样本中每种细胞类型的FAM评分。在三种重要的细胞类型中，根据中位数得分将细胞分为高fam和低fam类别，并鉴定各组之间的差异表达基因（DEGs）。通过大量RNA测序分析deg以鉴定预后基因，并通过RT-qPCR验证。使用“CellChat”算法探索细胞相互作用，以识别fam相关的通信。结果3个关键FAM基因elovl5、SCD和fads2在良性和癌性样本中表达差异。在癌细胞和单核细胞中观察到较高的FAM评分，但在T细胞中观察到较低的FAM评分。FAM评分高的癌细胞表现出更高的干性和拷贝数变异。在T细胞和单核细胞中，FAM评分与分化相关。CD226和NECTIN2是高fam组的关键相互作用。12个fam相关基因被鉴定为预后因素，并通过RT-qPCR验证。结论sfam水平在前列腺腺癌中升高，尤其是在癌细胞、单核细胞和T细胞中。高FAM评分与癌细胞的干性和遗传不稳定性相关，同时表明单核细胞和T细胞的分化增加。CD226和NECTIN2是高FAM组的关键相互作用，提示FAM在NECTIN2 - CD226相互作用中的作用及其与NECTIN2 - tigit免疫检查点轴的潜在联系。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Gene Reports Biochemistry, Genetics and Molecular Biology-Genetics

CiteScore

3.30

自引率

7.70%

发文量

246

审稿时长

49 days

期刊介绍： Gene Reports publishes papers that focus on the regulation, expression, function and evolution of genes in all biological contexts, including all prokaryotic and eukaryotic organisms, as well as viruses. Gene Reports strives to be a very diverse journal and topics in all fields will be considered for publication. Although not limited to the following, some general topics include: DNA Organization, Replication & Evolution -Focus on genomic DNA (chromosomal organization, comparative genomics, DNA replication, DNA repair, mobile DNA, mitochondrial DNA, chloroplast DNA). Expression & Function - Focus on functional RNAs (microRNAs, tRNAs, rRNAs, mRNA splicing, alternative polyadenylation) Regulation - Focus on processes that mediate gene-read out (epigenetics, chromatin, histone code, transcription, translation, protein degradation). Cell Signaling - Focus on mechanisms that control information flow into the nucleus to control gene expression (kinase and phosphatase pathways controlled by extra-cellular ligands, Wnt, Notch, TGFbeta/BMPs, FGFs, IGFs etc.) Profiling of gene expression and genetic variation - Focus on high throughput approaches (e.g., DeepSeq, ChIP-Seq, Affymetrix microarrays, proteomics) that define gene regulatory circuitry, molecular pathways and protein/protein networks. Genetics - Focus on development in model organisms (e.g., mouse, frog, fruit fly, worm), human genetic variation, population genetics, as well as agricultural and veterinary genetics. Molecular Pathology & Regenerative Medicine - Focus on the deregulation of molecular processes in human diseases and mechanisms supporting regeneration of tissues through pluripotent or multipotent stem cells.