Identification of Genes Related to 5-Fluorouracil Based Chemotherapy for Colorectal Cancer.
Identification of Genes Related to 5-Fluorouracil Based Chemotherapy for Colorectal Cancer.
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DOI：
10.3389/fimmu.2022.887048
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发表时间：
2022
影响因子：
7.3
通讯作者：
Sui, Xinbing
中科院分区：
文献类型：
--
作者：
Huang, Xingxing;Ke, Kun;Jin, Weiwei;Zhu, Qianru;Zhu, Qicong;Mei, Ruyi;Zhang, Ruonan;Yu, Shuxian;Shou, Lan;Sun, Xueni;Feng, Jiao;Duan, Ting;Mou, Yiping;Xie, Tian;Wu, Qibiao;Sui, Xinbing
结直肠癌(CRC)是最常见的恶性肿瘤之一，其发病率和死亡率呈逐年上升趋势。长期以来，5-氟尿嘧啶(5-FU)一直被用作结直肠癌患者的标准一线治疗方案。5-FU为主的化疗方案虽然对晚期结直肠癌有效，但由此产生的耐药性仍然是导致结直肠癌患者预后不良的关键问题。因此，迫切需要寻找新的生物标志物来预测5-FU为主的化疗的疗效。从基因表达总览(GEO)和癌症基因组图谱(TCGA)检索CRC样本。免疫相关基因从ImmPort数据库中检索。结直肠癌的单细胞测序结果通过ArrayExpress数据库获得。筛选5-FU耐药相关基因，用R包进行验证。使用估计算法对肿瘤微环境(TME)进行评估。通过KEGG和GO分析，探讨肿瘤微环境中耐药反应患者和敏感反应患者的生物信号通路。用pRophetic算法预测5-FU的敏感性。GSEA和GSVA分析挖掘RBP7基因的生物信号通路。9个免疫相关基因被鉴定为与5-FU耐药和结直肠癌患者不良无病生存(DFS)相关，这些基因的特征是在DFS预后模型中形成的。4个免疫相关基因与结直肠癌患者的5-FU耐药和总生存期(OS)相关。这些基因的特征是建立了一种OS预后模型。5-FU耐药和5-FU敏感的结直肠癌患者的评估分数有显著差异。以5-FU为基础的化疗耐药和敏感患者在肿瘤微环境中表现出不同的GO和KEGG浓集。RBP7作为肿瘤免疫微环境(TIME)相关基因，有可能预测结直肠癌患者的化疗耐药和预后不良。GSEA分析显示，在结直肠癌患者中，RBP7高表达和低表达之间存在多重信号差异。化疗耐药组(RBP7High)和化疗敏感组(RBP7Low)患者的缺氧和肿瘤坏死因子α信号转导途径在化疗耐药组(RBP7High)和化疗敏感组(RbP7Low)之间存在显著差异。单细胞RNA-seq结果表明，RBP7主要分布于内皮柄细胞、内皮尖细胞和髓系细胞。免疫相关基因有望成为预测结直肠癌化疗耐药的潜在预后生物标志物。RBP7可能作为肿瘤微环境调节剂诱导5-FU耐药，从而影响结直肠癌患者的预后。
Colorectal cancer (CRC) is one of the most common malignancies and its incidence and mortality are increasing yearly. 5-Fluorouracil (5-FU) has long been used as a standard first-line treatment for CRC patients. Although 5-FU-based chemotherapy is effective for advanced CRC, the consequent resistance remains a key problem and causes the poor prognosis of CRC patients. Thus, there is an urgent need to identify new biomarkers to predict the response to 5-FU-based chemotherapy. CRC samples were retrieved from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). The immune-related genes were retrieved from the ImmPort database. Single-cell sequencing results from colorectal cancer were obtained by the ArrayExpress database. 5-FU resistance-related genes were filtered and validated by R packages. ESTIMATE algorithms were used to assess the tumor microenvironment (TME). KEGG and GO analysis were performed to explore the biological signaling pathway for resistant-response patients and sensitive-response patients in the tumor microenvironment. pRRophetic algorithms were used to predict 5-FU sensitivity. GSEA and GSVA analysis was performed to excavate the biological signaling pathway of the RBP7 gene. Nine immune-related genes were identified to be associated with 5-FU resistance and poor disease-free survival (DFS) of CRC patients and the signature of these genes was developed in a DFS-prognostic model. Four immune-related genes were determined to be associated with 5-FU resistance and overall survival (OS) of CRC patients. The signature of these genes was developed an OS-prognostic model. ESTIMATE scores showed a significant difference between 5-FU resistant and 5-FU sensitive CRC patients. Resistant-response patients and sensitive-response patients to 5-FU based chemotherapy showed different GO and KEGG enrichment on the tumor microenvironment. RBP7, as a tumor immune microenvironment (TIME) related gene, was found to have the potential of predicting chemotherapy resistance and poor prognosis of CRC patients. GSEA analysis showed multiple signaling differences between the high and low expression of RBP7 in CRC patients. Hypoxia and TNFα signaling via NFκB gene sets were significantly different between chemotherapy resistant (RBP7High) and chemotherapy sensitive (RBP7Low) patients. Single-cell RNA-seq suggested RBP7 was centrally distributed in endothelial stalk cells, endothelial tip cells, and myeloid cells. Immune-related genes will hopefully be potential prognostic biomarkers to predict chemotherapy resistance for CRC. RBP7 may function as a tumor microenvironment regulator to induce 5-FU resistance, thereby affecting the prognosis of CRC patients.
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影响因子：
3.8
作者：
Kunicka T;Prochazka P;Krus I;Bendova P;Protivova M;Susova S;Hlavac V;Liska V;Novak P;Schneiderova M;Pitule P;Bruha J;Vycital O;Vodicka P;Soucek P
通讯作者：
Soucek P
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
DOI：
10.1080/10409238.2020.1768207
发表时间：
2020-04
影响因子：
6.5
作者：
Blaner WS;Brun PJ;Calderon RM;Golczak M
通讯作者：
Golczak M
影响因子：
8.8
作者：
通讯作者：
--
影响因子：
82.9
作者：
Binnewies M;Roberts EW;Kersten K;Chan V;Fearon DF;Merad M;Coussens LM;Gabrilovich DI;Ostrand-Rosenberg S;Hedrick CC;Vonderheide RH;Pittet MJ;Jain RK;Zou W;Howcroft TK;Woodhouse EC;Weinberg RA;Krummel MF
通讯作者：
Krummel MF