构建并验证结直肠粘液腺癌患者的预后模型,发现新的预后相关基因FAM174B

IF 1.5 4区 医学 Q4 ONCOLOGY Translational cancer research Pub Date : 2024-10-31 Epub Date: 2024-10-29 DOI:10.21037/tcr-24-347
Xiangwen Tan, Qing Fang, Yunhua Xu, Shuxiang Li, Jinyi Yuan, Kunming Xu, Xiguang Chen, Guang Fu, Yarui Liu, Qiulin Huang, Xiuda Peng, Shuai Xiao
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Therefore, this study aims to identify potential biomarkers and construct a prognostic model to better predict patient outcomes in MAC.</p><p><strong>Methods: </strong>We conducted differential genes expression investigation, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO)-Cox regression model using RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) to pinpoint hub genes. Then, the hub genes were used to construct a prognostic model for MAC. Kaplan-Meier survival, receiver operating characteristic (ROC), and Cox regression analysis were used to assess the prognostic utility of the model. 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引用次数: 0

摘要

背景:黏液腺癌(MAC)是结直肠癌(CRC)的一种特殊组织学亚型,具有独特的医学、疾病相关和遗传学特征。与非特异性腺癌(AC)相比,MAC 的预后一般较差,但 MAC 的预后指标却很少见。因此,本研究旨在确定潜在的生物标志物并构建预后模型,以更好地预测 MAC 患者的预后:方法:我们利用癌症基因组图谱(TCGA)中的RNA测序(RNA-seq)数据进行了差异基因表达调查、加权基因共表达网络分析(WGCNA)和最小绝对收缩与选择算子(LASSO)-Cox回归模型,以确定枢纽基因。然后,利用这些中心基因构建了MAC的预后模型。卡普兰-梅耶生存率、接收器操作特征(ROC)和考克斯回归分析被用来评估该模型的预后效用。利用基因组富集分析(GSEA)检验了枢纽基因的潜在生物学功能:结果:通过差异基因表达分析、WGCNA和LASSO回归分析,确定了MAC和AC之间的四个枢纽基因,即FAM174B、CREB3L1、SPDEF和RAP1GAP。根据这四个枢纽基因构建的预后特征模型可将 MAC 分成低危和高危两组。与低风险组相比,高风险组的总生存期(OS)明显较低(P=0.007)。1年、3年和5年OS的曲线下面积(AUC)分别为0.61[95%置信区间(CI):0.73-0.49]、0.69(95% CI:0.76-0.63)和0.77(95% CI:0.83-0.71)。我们还发现,FAM174B的表达与MAC的OS密切相关(P=0.02)。此外,FAM174B的表达与MAC的粘蛋白型O-糖生物合成呈正相关。最后,研究表明 FAM174B 与粘液形成的关键分子 MUC5AC(P=0.004,r=0.33)、MUC5B(PMUC2)(PConclusions)呈正相关:我们开发并验证了预测 MAC 存活率的四基因预后模型。此外,我们还发现 FAM174B 可能与 MAC 中粘蛋白的产生有关。
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Construction and validation of prognostic model for colorectal mucinous adenocarcinoma patients and identification of a new prognosis related gene FAM174B.

Background: Mucinous adenocarcinoma (MAC) is a peculiar histological subtype of colorectal cancer (CRC) with distinct medical, disease-related, and genetic characteristics. The prognosis of MAC is generally poorer less favorable compared to non-specific adenocarcinoma (AC), but the prognostic indicator of MAC is rare. Therefore, this study aims to identify potential biomarkers and construct a prognostic model to better predict patient outcomes in MAC.

Methods: We conducted differential genes expression investigation, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO)-Cox regression model using RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) to pinpoint hub genes. Then, the hub genes were used to construct a prognostic model for MAC. Kaplan-Meier survival, receiver operating characteristic (ROC), and Cox regression analysis were used to assess the prognostic utility of the model. The potential biological function of the hub gene was examined using gene set enrichment analysis (GSEA).

Results: Four hub genes, FAM174B, CREB3L1, SPDEF, and RAP1GAP, were identified between MAC and AC by differential genes expression analysis, WGCNA, and LASSO regression analysis. The prognostic signature model was constructed based on these four hub genes, which could divide MAC into low- and high-risk groups. The overall survival (OS) was notably lower in the high-risk group compared to the low-risk group (P=0.007). The area under the curves (AUCs) for 1-, 3-, and 5-year OS were 0.61 [95% confidence interval (CI): 0.73-0.49], 0.69 (95% CI: 0.76-0.63), and 0.77 (95% CI: 0.83-0.71), respectively. We also found that FAM174B expression was closely related to the OS of MAC (P=0.02). Further, the expression of FAM174B was positively correlated with MAC's Mucin type O-glycan biosynthesis. Finally, it was indicated that FAM174B was positively correlated with the critical molecules of mucus formation, MUC5AC (P=0.004, r=0.33), MUC5B (P<0.001, r=0.43), and MUC2 (P<0.001, r=0.39).

Conclusions: We have developed and validated a four-gene prognostic model to predict the survival of MAC. Additionally, we found that FAM174B might correlate with mucin production in MAC.

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期刊介绍: Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.
期刊最新文献
Construction and validation of prognostic model for colorectal mucinous adenocarcinoma patients and identification of a new prognosis related gene FAM174B. Erratum: Identification of a ferroptosis-related gene signature for the prognosis of pediatric neuroblastoma. Establishment and validation of a prediction model for gastric cancer with perineural invasion based on preoperative inflammatory markers. Establishment and verification of a prognostic immune cell signature-based model for breast cancer overall survival. Exosomal AHSG in ovarian cancer ascites inhibits malignant progression of ovarian cancer by p53/FAK/Src signaling.
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