CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题：Multi-omics analyses based on genes associated with oxidative stress and phospholipid metabolism revealed the intrinsic molecular characteristics of pancreatic cancer.
Multi-omics analyses based on genes associated with oxidative stress and phospholipid metabolism revealed the intrinsic molecular characteristics of pancreatic cancer.
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氧化应激（OS）会影响脂质代谢重编程，进而改变癌细胞的生物学活动。氧化应激和磷脂代谢（OSPM）如何影响胰腺癌（PC）预后，仍有待阐明。研究从既往发表的文献获取 35 份胰腺肿瘤样本、34 份癌旁样本和 31 份正常胰腺组织的代谢数据；泛癌样本来自癌症基因组图谱（TCGA）。
此外，还检索基因表达综合数据库（GEO）、国际癌症基因组联盟（ICGC）、ArrayExpress 和基因型-组织表达（GTEx）数据库，以获取更多 PC 和正常胰腺样本。研究比较 PC 与正常及癌旁组织中的代谢物，总结泛癌背景下 OSPM 关键基因的特征，并利用随机生存森林和多变量 Cox 回归构建 OSPM 相关特征模型。依据该特征模型将 PC 样本分为高风险和低风险亚组，进一步研究肿瘤免疫微环境失调。研究采用定量逆转录聚合酶链反应（qRT-PCR）检测 PC 与正常组织中特征基因的表达，并进一步验证其蛋白水平。在 PC 中，代谢组学研究显示磷脂代谢发生改变，转录组学研究则发现 OSPM 相关基因表达不同。
随后，研究将 930 份 PC 样本划分为预后不同的 3 种亚型，并开发了包含 8 个 OSPM 相关基因（SLC2A1、MMP14、TOP2A、MBOAT2、ANLN、ECT2、SLC22A3 和 FGD6）的 OSPM 特征模型。依据该模型划分的高、低风险亚组在预后、免疫检查点基因表达、免疫细胞浸润和肿瘤微环境方面均有差异。风险评分与 TIL、浆细胞样树突状细胞（pDC）、肥大细胞比例及 T 细胞共刺激呈负相关。研究验证了该特征中基因在 PC 与正常样本中的表达；与正常组织相比，SLC2A1、MMP14、TOP2A、MBOAT2、ANLN 和 SLC22A3 蛋白在 PC 样本中上调。整合代谢组学和转录组学数据后，本研究揭示了 PC 中 OSPM 的变化，并开发了 OSPM 相关特征模型，有助于预后评估和个体化治疗。
Oxidative stress (OS), which impacts lipid metabolic reprogramming, can affect the biological activities of cancer cells. How oxidative stress and phospholipid metabolism (OSPM) influence the prognosis of pancreatic cancer (PC) needs to be elucidated. The metabolic data of 35 pancreatic tumor samples, 34 para-carcinoma samples, and 31 normal pancreatic tissues were obtained from the previously published literature. Pan-cancer samples were obtained from The Cancer Genome Atlas (TCGA). And the Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), ArrayExpress, and the Genotype-Tissue Expression (GTEx) databases were searched for more PC and normal pancreatic samples. The metabolites in PC were compared with normal and para-carcinoma tissues. The characteristics of the key OSPM genes were summarized in pan-cancer. The random survival forest analysis and multivariate Cox regression analysis were utilized to construct an OSPM-related signature. Based on this signature, PC samples were divided into high- and low-risk subgroups. The dysregulations of the tumor immune microenvironment were further investigated. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was conducted to investigate the expression of genes in the signature in PC and normal tissues.
The protein levels of these genes were further demonstrated. In PC, metabolomic studies revealed the alteration of PM, while transcriptomic studies showed different expressions of OSPM-related genes. Then 930 PC samples were divided into three subtypes with different prognoses, and an OSPM-related signature including eight OSPM-related genes (i. e. , SLC2A1, MMP14, TOP2A, MBOAT2, ANLN, ECT2, SLC22A3, and FGD6) was developed. High- and low-risk subgroups divided by the signature showed different prognoses, expression levels of immune checkpoint genes, immune cell infiltration, and tumor microenvironment.
The risk score was negatively correlated with the proportion of TIL, pDC, Mast cell, and T cell co-stimulation. The expression levels of genes in the signature were verified in PC and normal samples. The protein levels of SLC2A1, MMP14, TOP2A, MBOAT2, ANLN, and SLC22A3 showed up-regulation in PC samples compared with normal tissues.
After integrating metabolomics and transcriptomics data, the alterations in OSPM in PC were investigated, and an OSPM-related signature was developed, which was helpful for the prognostic assessment and individualized treatment for PC.
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