METHODS: We undertook a rigorous quantification of FRGs in PAAD samples, sourcing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. These repositories also provided extensive metadata, encompassing mesenchymal stemness index (mRNAsi), genomic mutations, copy number variations (CNV), tumor mutational burden (TMB), and other clinical attributes. A predictive model was constructed utilizing Lasso regression analysis, and a co-expression study was executed to elucidate the complex interconnections between FRGs and other gene sets.
RESULTS: Intriguingly, FRGs were substantially upregulated in the high-risk cohort, even in the absence of clinically manifest symptoms, emphasizing their utility as prognostic biomarkers. Gene set enrichment analysis (GSEA) revealed significant enrichment of immune and tumor-related pathways in this high-risk demographic. Striking heterogeneities in immune function and N6-methyladenosine (m6A) RNA modification were observed between the low- and high-risk groups. Our analysis further implicated a cohort of genes-including LINC01559, C11orf86, SERPINB5, DSG3, MSLN, EREG, FAM83A, CXCL5, LY6D, and PSCA-as cardinal mediators in PAAD pathogenesis. A convergence of our predictive model with an analysis of CNVs, single nucleotide polymorphisms (SNPs), and drug sensitivities, revealed an intricate relationship with the FRGs.
CONCLUSIONS: Our findings accentuate the salient role of FRGs as critical modulators in the pathogenesis and progression of PAAD. Importantly, our composite prognostic framework offers invaluable insights into PAAD clinical trajectory. Moreover, the complex crosstalk between FRGs and immune cell landscapes in the tumor microenvironment (TME) may elucidate prospective therapeutic strategies. The clinical translational utility of these insights, however, requires further in-depth empirical exploration. Accordingly, the FRG signature introduces a compelling new avenue for risk stratification and targeted therapeutic interventions in this devastating malignancy.
方法:我们对PAAD样品中的FRGs进行了严格的定量,数据来自癌症基因组图谱(TCGA)和基因表达综合(GEO)数据库。这些存储库还提供了大量的元数据,包括间充质干性指数(mRNAsi),基因组突变,拷贝数变异(CNV),肿瘤突变负荷(TMB),和其他临床属性。利用Lasso回归分析构建了预测模型,并进行了共表达研究以阐明FRGs与其他基因集之间的复杂互连。
结果:有趣的是,在高风险队列中,FRGs显著上调,即使没有临床表现,强调它们作为预后生物标志物的效用。基因集富集分析(GSEA)显示,在这种高危人群中,免疫和肿瘤相关途径的显着富集。在低和高风险组之间观察到免疫功能和N6-甲基腺苷(m6A)RNA修饰的惊人异质性。我们的分析进一步涉及一组基因,包括LINC01559,C11orf86,SERPINB5,DSG3,MSLN,EREG,FAM83A,CXCL5,LY6D,和PSCA-作为PAAD发病机制的主要介质。我们的预测模型与CNV分析的收敛性,单核苷酸多态性(SNPs),和药物敏感性,揭示了与FRG的复杂关系。
结论:我们的发现强调了FRGs作为PAAD发病机制和进展中关键调节剂的显著作用。重要的是,我们的复合预后框架为PAAD临床轨迹提供了宝贵的见解.此外,肿瘤微环境(TME)中FRGs与免疫细胞景观之间复杂的串扰可能阐明前瞻性治疗策略.这些见解的临床转化效用,然而,需要进一步深入的实证探索。因此,FRG签名为这种破坏性恶性肿瘤的风险分层和有针对性的治疗干预提供了一条令人信服的新途径.