Bioinformatics-Based Identification of Tumor Microenvironment-Related Prognostic Genes in Pancreatic Cancer.
Bioinformatics-Based Identification of Tumor Microenvironment-Related Prognostic Genes in Pancreatic Cancer.
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基于生物信息学的胰腺癌肿瘤微环境相关预后基因的鉴定
DOI：
10.3389/fgene.2021.632803
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发表时间：
2021
影响因子：
3.7
通讯作者：
Huang K
中科院分区：
文献类型：
--
作者：
Chen S;Huang F;Chen S;Chen Y;Li J;Li Y;Lian G;Huang K
越来越多的证据表明，浸润在胰腺癌微环境中的免疫细胞和基质细胞显著影响肿瘤进展。然而，可靠的微环境相关的预后基因签名尚未建立。本研究旨在阐明胰腺癌中肿瘤微环境相关的预后基因。我们应用ESTIMATE算法将来自TCGA数据集的胰腺癌患者分类为高和低免疫/基质评分组，并确定其差异表达基因。然后，进行单变量和LASSO考克斯回归，以确定总体生存相关的差异表达基因（DEG）。采用多因素考克斯回归分析筛选独立的预后相关基因，构建风险评分模型。最后，通过Kaplan-Meier曲线、时间依赖的受试者操作特征和Harrell一致性指数对风险评分模型的性能进行评价。总生存分析表明，高免疫/基质评分组与预后不良密切相关。多因素考克斯回归分析显示TRPC 7、CXCL 10、CUX 2和COL 2A 1 4个基因的特征是独立的预后因素。随后，由这些基因构建的风险预测模型上级优于AJCC分期的时间依赖性受试者操作特征和Harrell的一致性指数，并且KRAS和TP 53突变与高风险评分密切相关。此外，CXCL 10主要由肿瘤相关巨噬细胞表达，其受体CXCR 3在单细胞水平上在T细胞中高度表达。这项研究全面调查了肿瘤微环境，并验证了胰腺癌的免疫/基质相关生物标志物。
Growing evidence has highlighted that the immune and stromal cells that infiltrate in pancreatic cancer microenvironment significantly influence tumor progression. However, reliable microenvironment-related prognostic gene signatures are yet to be established. The present study aimed to elucidate tumor microenvironment-related prognostic genes in pancreatic cancer. We applied the ESTIMATE algorithm to categorize patients with pancreatic cancer from TCGA dataset into high and low immune/stromal score groups and determined their differentially expressed genes. Then, univariate and LASSO Cox regression was performed to identify overall survival-related differentially expressed genes (DEGs). And multivariate Cox regression analysis was used to screen independent prognostic genes and construct a risk score model. Finally, the performance of the risk score model was evaluated by Kaplan-Meier curve, time-dependent receiver operating characteristic and Harrell’s concordance index. The overall survival analysis demonstrated that high immune/stromal score groups were closely associated with poor prognosis. The multivariate Cox regression analysis indicated that the signatures of four genes, including TRPC7, CXCL10, CUX2, and COL2A1, were independent prognostic factors. Subsequently, the risk prediction model constructed by those genes was superior to AJCC staging as evaluated by time-dependent receiver operating characteristic and Harrell’s concordance index, and both KRAS and TP53 mutations were closely associated with high risk scores. In addition, CXCL10 was predominantly expressed by tumor associated macrophages and its receptor CXCR3 was highly expressed in T cells at the single-cell level. This study comprehensively investigated the tumor microenvironment and verified immune/stromal-related biomarkers for pancreatic cancer.
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影响因子：
7.3
作者：
Gao J;Aksoy BA;Dogrusoz U;Dresdner G;Gross B;Sumer SO;Sun Y;Jacobsen A;Sinha R;Larsson E;Cerami E;Sander C;Schultz N
通讯作者：
Schultz N
胰腺导管腺癌条件培养基诱导的肿瘤驱动类巨噬细胞通过分泌IL-8促进肿瘤转移
影响因子：
4
作者：
Chen SJ;Lian GD;Li JJ;Zhang QB;Zeng LJ;Yang KG;Huang CM;Li YQ;Chen YT;Huang KH
通讯作者：
Huang KH
影响因子：
7.2
作者：
Lunardi S;Lim SY;Muschel RJ;Brunner TB
通讯作者：
Brunner TB
影响因子：
--
作者：
Pu, Ning;Chen, Qiangda;Wu, Wenchuan
通讯作者：
Wu, Wenchuan
影响因子：
25.7
作者：
Li, Chang Xian;Ling, Chang Chun;Man, Kwan
通讯作者：
Man, Kwan