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对[具体内容缺失]、[具体内容缺失]和[具体内容缺失]进行差异DNA甲基化分析,为台湾地区结直肠癌预后预测提供了新见解。

Differential DNA methylation analysis of , , and provides novel insights into colorectal cancer prognosis prediction in Taiwan.

作者信息

Su Jing-Quan, Lai Pin-Yu, Hu Pei-Hsuan, Hu Je-Ming, Chang Pi-Kai, Chen Chao-Yang, Wu Jia-Jheng, Lin Yu-Jyun, Sun Chien-An, Yang Tsan, Hsu Chih-Hsiung, Lin Hua-Ching, Chou Yu-Ching

机构信息

School of Medicine, National Defense Medical Center, Taipei 114, Taiwan.

School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.

出版信息

World J Gastroenterol. 2022 Feb 28;28(8):825-839. doi: 10.3748/wjg.v28.i8.825.

DOI:10.3748/wjg.v28.i8.825
PMID:35317099
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8900576/
Abstract

BACKGROUND

Patients with colorectal cancer (CRC) undergo surgery, as well as perioperative chemoradiation or adjuvant chemotherapy primarily based on the tumor-node- metastasis (TNM) cancer staging system. However, treatment responses and prognostic outcomes of patients within the same stage vary markedly. The potential use of novel biomarkers can improve prognostication and shared decision making before implementation into certain therapies.

AIM

To investigate whether , , and methylation status could be associated with CRC prognosis.

METHODS

We conducted a Taiwanese cohort study involving 208 patients with CRC recruited from Tri-Service General Hospital and applied the candidate gene approach to identify three genes involved in oncogenesis pathways. A methylation-specific polymerase chain reaction (MS-PCR) and EpiTYPER DNA methylation analysis were employed to detect methylation status and to quantify the methylation level of candidate genes in tumor tissue and adjacent normal tissue from participants. We evaluated , , and methylation as predictors of prognosis, including recurrence-free survival (RFS), progression-free survival (PFS), and overall survival (OS), using a Cox regression model and Kaplan-Meier analysis.

RESULTS

We revealed various outcomes related to methylation and prognosis. Significantly shorter PFS and OS were associated with the CpG_3+CpG_7 hypermethylation of from tumor tissue compared with CpG_3+CpG_7 hypomethylation [hazard ratio (HR) = 2.24, 95% confidence interval (CI) = 1.03-4.85 for PFS, HR = 2.56 and 95%CI = 1.08-6.04 for OS]. By contrast, a significantly longer RFS was associated with CpG_2 and CpG_13 hypermethylation of from normal tissue compared with CpG_2 and CpG_13 hypomethylation [HR (95%CI) = 0.15 (0.03-0.71) for CpG_2 and 0.20 (0.04-0.97) for CpG_13]. The relationship between the methylation status of and the prognosis of CRC did not reach statistical significance.

CONCLUSION

Our study found that CpG_3+CpG_7 hypermethylation of from tumor tissue was associated with significantly shorter PFS and OS compared with CpG_3+CpG_7 hypomethylation. CpG_2 and CpG_13 hypermethylation of from normal tissue was associated with a significantly longer RFS compared with CpG_2 and CpG_13 hypomethylation. These methylation-related biomarkers which have implications for CRC prognosis prediction may aid physicians in clinical decision-making.

摘要

背景

结直肠癌(CRC)患者主要依据肿瘤-淋巴结-转移(TNM)癌症分期系统接受手术以及围手术期放化疗或辅助化疗。然而,处于同一分期的患者其治疗反应和预后结果差异显著。新型生物标志物的潜在应用可改善预后评估,并在实施某些治疗前助力共同决策。

目的

探讨[具体基因1]、[具体基因2]和[具体基因3]的甲基化状态是否与CRC预后相关。

方法

我们开展了一项台湾队列研究,纳入了从三军总医院招募的208例CRC患者,并采用候选基因方法来识别参与肿瘤发生途径的三个基因。运用甲基化特异性聚合酶链反应(MS-PCR)和EpiTYPER DNA甲基化分析来检测甲基化状态,并对参与者肿瘤组织及相邻正常组织中候选基因的甲基化水平进行定量。我们使用Cox回归模型和Kaplan-Meier分析,评估[具体基因1]、[具体基因2]和[具体基因3]的甲基化作为预后预测指标的情况,包括无复发生存期(RFS)、无进展生存期(PFS)和总生存期(OS)。

结果

我们揭示了与甲基化和预后相关的各种结果。与CpG_3 + CpG_7低甲基化相比,肿瘤组织中[具体基因1]的CpG_3 + CpG_7高甲基化与显著更短的PFS和OS相关[PFS的风险比(HR)= 2.24,95%置信区间(CI)= 1.03 - 4.85,OS的HR = 2.56,95%CI = 1.08 - 6.04]。相比之下,与CpG_2和CpG_13低甲基化相比,正常组织中[具体基因2]的CpG_2和CpG_13高甲基化与显著更长的RFS相关[CpG_2的HR(95%CI)= 0.15(0.03 - 0.71),CpG_13的HR = 0.20(0.04 - 0.97)]。[具体基因3]的甲基化状态与CRC预后之间的关系未达到统计学显著性。

结论

我们的研究发现,与CpG_3 + CpG_7低甲基化相比,肿瘤组织中[具体基因1]的CpG_3 + CpG_7高甲基化与显著更短的PFS和OS相关。与CpG_2和CpG_13低甲基化相比,正常组织中[具体基因2]的CpG_2和CpG_13高甲基化与显著更长的RFS相关。这些与甲基化相关的生物标志物对CRC预后预测具有重要意义,可能有助于医生进行临床决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f571/8900576/131761a0a7f6/WJG-28-825-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f571/8900576/b1b0640ad2e8/WJG-28-825-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f571/8900576/5ce05755cda4/WJG-28-825-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f571/8900576/da080c6638ad/WJG-28-825-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f571/8900576/e40019d1635a/WJG-28-825-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f571/8900576/131761a0a7f6/WJG-28-825-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f571/8900576/b1b0640ad2e8/WJG-28-825-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f571/8900576/5ce05755cda4/WJG-28-825-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f571/8900576/da080c6638ad/WJG-28-825-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f571/8900576/e40019d1635a/WJG-28-825-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f571/8900576/131761a0a7f6/WJG-28-825-g005.jpg

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