Establishment of a novel prognostic prediction model through bioinformatics analysis for prostate cancer based on ferroptosis-related genes and its application in immune cell infiltration.
Establishment of a novel prognostic prediction model through bioinformatics analysis for prostate cancer based on ferroptosis-related genes and its application in immune cell infiltration.
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DOI：
10.21037/tau-22-454
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发表时间：
2022-08
影响因子：
2
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中科院分区：
文献类型：
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铁下垂相关基因(FRGS)对前列腺癌(PCa)患者的生存和预后起着至关重要的作用。我们通过生物信息学分析建立铁下垂相关的总生存期(OS)和无病生存期(DFS)的预测模型，通过免疫细胞浸润(ICI)的特征来评估临床生存状况，为治疗监测提供信息。首先，从前人的研究中获得了268个FRG。根据癌症基因组图谱(TCGA)数据库鉴定差异表达的FRG，并通过基因本体论(GO)和京都基因和基因组百科全书(KEGG)进行FRG富集性分析。然后，我们进行了单变量、最小绝对收缩和选择算子(LASSO)和多变量COX回归分析，以建立OS和DFS相关的预后预测模型。进一步分析了该模型与临床病理特征的关系。随后，通过KEGG数据库获得了免疫细胞亚群的独特基因组特征。根据与铁性下垂相关的特定基因及其与ICI的关联，评估了不同风险组患者的免疫渗透情况。我们通过生物信息学分析构建了OS和DFS预后模型。T3-4期OS和DFS相关模型预测值高于T1-2期(P=0.0057，P<0.001)，N0期DFS模型预测值高于N1期(P=0.0136)。基于KEGG数据集的单样本基因集浓缩分析(SsGSEA)结果显示，高危组p53信号最丰富，而低危组最丰富的信号是内吞作用。高危组M2巨噬细胞(P=0.007)和中性粒细胞(P=0.024)明显增多，低危组CD_4激活的记忆T细胞显著积聚(P=0.017)。基于FRGS和ICI的OS和DFS相关模型为PCa患者的疾病状态评估创造了新的见解，可能有助于未来个性化和精准治疗的发展。
Ferroptosis-related genes (FRGs) play vital roles in survival and prognosis of prostate cancer (PCa) patients. We establish a ferroptosis-related prediction model through bioinformatics analysis for overall survival (OS) and disease-free survival (DFS), so as to evaluate the clinical survival status through the characteristics of immune cell infiltration (ICI), which could provide information for treatment monitoring. At first, 268 FRGs were obtained from previous studies. Differentially expressed FRGs were identified based on The Cancer Genome Atlas (TCGA) database, and FRG enrichment analysis was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We then performed univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to establish OS- and DFS-related prognostic prediction models. The association of the model and clinicopathological features was further analyzed. Subsequently, unique genomic signatures of immune cell subsets were obtained through the KEGG database. Based on specific genes associated with ferroptosis and their association with ICI, immune infiltration was assessed in patients in different risk groups. We constructed an OS- and an DFS-prognostic model through bioinformatics analysis. The predicted values of OS and DFS-related models were higher in T3–4 than in T1–2 (P=0.0057, P<0.001), and the predicted value of the DFS model in N0 stage was higher than that in N1 stage (P=0.0136). Results of Single-sample gene set enrichment analysis (ssGSEA) on the basis of the KEGG dataset showed p53 signaling being the most enriched signal in the high-risk group, while endocytosis was the most enriched signal in the low-risk group. M2 macrophages (P=0.007) and neutrophils (P=0.024) were enriched in the high-risk group, and CD4-activated memory T cells were significantly accumulated in the low-risk group (P=0.017). The OS- and DFS-related model based on FRGs and ICI create new insights into the disease state assessment of PCa patients., which may aid in the development of individualized and precise treatment in the future.
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影响因子：
64.5
作者：
Dixon SJ;Lemberg KM;Lamprecht MR;Skouta R;Zaitsev EM;Gleason CE;Patel DN;Bauer AJ;Cantley AM;Yang WS;Morrison B 3rd;Stockwell BR
通讯作者：
Stockwell BR
影响因子：
4.9
作者：
Fan J;Pan J;Zhang X;Chen Y;Zeng Y;Huang L;Ma D;Chen Z;Wu G;Fan W
通讯作者：
Fan W
影响因子：
5.6
作者：
Luo, Cheng;Chen, Junxing;Chen, Lingwu
通讯作者：
Chen, Lingwu
影响因子：
3.6
作者：
Hussein, Mahmoud-Rezk A.;Al-Assiri, Mana;Musalam, Adel O.
通讯作者：
Musalam, Adel O.
影响因子：
3.7
作者：
Liu G;Ma JY;Hu G;Jin H
通讯作者：
Jin H