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INTRODUCTION

Cancer-associated fibroblasts (CAFs) are a diverse group of cells that significantly contribute to reshaping the tumor microenvironment (TME), and no research has systematically explored the molecular landscapes of senescence related CAFs (senes CAF) in NB.

METHODS

We utilized pan-cancer single cell and spatial transcriptomics analysis to identify the subpopulation of senes CAFs via senescence related genes, exploring its spatial distribution characteristics. Harnessing the maker genes with prognostic significance, we delineated the molecular landscapes of senes CAFs in bulk-seq data. We established the senes CAFs related signature (SCRS) by amalgamating 12 and 10 distinct machine learning (ML) algorithms to precisely diagnose stage 4 NB and to predict prognosis in NB. Based on risk scores calculated by prognostic SCRS, patients were categorized into high and low risk groups according to median risk score. We conducted comprehensive analysis between two risk groups, in terms of clinical applications, immune microenvironment, somatic mutations, immunotherapy, chemotherapy and single cell level. Ultimately, we explore the biological function of the hub gene JAK1 in pan-cancer multi-omics landscape.

RESULTS

Through integrated analysis of pan-cancer spatial and single-cell transcriptomics data, we identified distinct functional subgroups of CAFs and characterized their spatial distribution patterns. With marker genes of senes CAF and leave-one-out cross-validation, we selected RF algorithm to establish diagnostic SCRS, and SuperPC algorithm to develop prognostic SCRS. SCRS demonstrated a stable predictive capability, outperforming the previously published NB signatures and clinic variables. We stratified NB patients into high and low risk group, which showed the low-risk group with a superior survival outcome, an abundant immune infiltration, a different mutation landscape, and an enhanced sensitivity to immunotherapy. Single cell analysis reveals biologically cellular variations underlying model genes of SCRS. Spatial transcriptomics delineated the molecular variant expressions of hub gene JAK1 in malignant cells across cancers, while immunohistochemistry validated the differential protein levels of JAK1 in NB.

CONCLUSION

Based on multi-omics analysis and ML algorithms, we successfully developed the SCRS to enable accurate diagnosis and prognostic stratification in NB, which shed light on molecular landscapes of senes CAF and clinical utilization of SCRS.

引言

癌症相关成纤维细胞（CAFs）是一类多样的细胞，对重塑肿瘤微环境（TME）有显著贡献，且尚无研究系统地探索神经母细胞瘤（NB）中衰老相关CAFs（senes CAF）的分子图谱。

方法

我们利用泛癌单细胞和空间转录组学分析，通过衰老相关基因鉴定senes CAFs亚群，探索其空间分布特征。利用具有预后意义的标记基因，我们在批量测序数据中描绘了senes CAFs的分子图谱。我们通过合并12种和10种不同的机器学习（ML）算法建立了senes CAFs相关特征（SCRS），以精确诊断4期NB并预测NB的预后。根据预后SCRS计算的风险评分，患者根据中位风险评分分为高风险组和低风险组。我们在临床应用、免疫微环境、体细胞突变、免疫治疗、化疗和单细胞水平方面对两个风险组进行了综合分析。最终，我们在泛癌多组学图谱中探索了枢纽基因JAK1的生物学功能。

结果

通过对泛癌空间和单细胞转录组学数据的综合分析，我们鉴定了CAFs的不同功能亚群，并表征了它们的空间分布模式。利用senes CAF的标记基因和留一法交叉验证，我们选择随机森林（RF）算法建立诊断SCRS，并选择SuperPC算法开发预后SCRS。SCRS显示出稳定的预测能力，优于先前发表的NB特征和临床变量。我们将NB患者分为高风险组和低风险组，低风险组显示出更好的生存结果、丰富的免疫浸润﹑不同的突变图谱以及对免疫治疗的增强敏感性。单细胞分析揭示了SCRS模型基因背后的生物学细胞差异。空间转录组学描绘了跨癌症的恶性细胞中枢纽基因JAK1的分子变异表达，而免疫组化验证了NB中JAK1的差异蛋白水平。