• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

用于确定结直肠腺癌分级的定性转录特征。

A qualitative transcriptional signature for determining the grade of colorectal adenocarcinoma.

机构信息

Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.

Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.

出版信息

Cancer Gene Ther. 2020 Sep;27(9):680-690. doi: 10.1038/s41417-019-0139-1. Epub 2019 Oct 9.

DOI:10.1038/s41417-019-0139-1
PMID:31595030
Abstract

Histological grading (HG) is an important prognostic factor of colorectal adenocarcinoma (CRAC): the high-grade CRAC patients have poorer prognosis after tumor resection. Especially, the high-grade stage II CRAC patients are recommended to receive adjuvant chemotherapy. Due to the subjective nature of HG assessment, it is difficult to achieve consistency among pathologists, which brings patients uncertain grading outcomes and inappropriate treatments. We developed a qualitative transcriptional signature based on the within-sample relative expression orderings (REOs) of gene pairs to discriminate high-grade and low-grade CRAC. Using the stage II-III CRAC samples, we detected gene pairs with stable REOs in the high-grade samples and reversal stable REOs in the low-grade samples, and retained the gene pairs whose specific REO patterns were significantly associated with the disease-free survival of patients by univariate Cox regression model. Then, we used a forward-backward searching procedure to extract gene pairs with the highest concordance index as the final grading signature. Finally, 9 gene pairs (9-GPS) were developed to divide CRAC patients into high-grade and low-grade groups. With the signature, there were more differential expression characteristics between reclassified high-grade and low-grade groups. Significant difference of prognosis between the classified two group patients could be seen in four independent datasets. Additionally, genomic analyses showed that the classified high-grade groups were characterized by hypermutation while classified low-grade groups were characterized by frequent copy number alternations. In conclusion, the 9-GPS can provide an objective and robust grading assessment for CRAC patients, which could assist clinical treatment decision.

摘要

组织学分级(HG)是结直肠腺癌(CRAC)的一个重要预后因素:高级别 CRAC 患者在肿瘤切除后预后较差。特别是,高级别 II 期 CRAC 患者建议接受辅助化疗。由于 HG 评估的主观性,病理学家之间难以达成一致,这给患者带来不确定的分级结果和不适当的治疗。我们基于基因对的样本内相对表达顺序(REO)开发了一种定性转录特征,以区分高低级别 CRAC。使用 II 期-III 期 CRAC 样本,我们检测到在高级别样本中具有稳定 REO 的基因对和在低级别样本中具有反转稳定 REO 的基因对,并通过单变量 Cox 回归模型保留与患者无病生存显著相关的基因对的特定 REO 模式。然后,我们使用前向-后向搜索过程提取具有最高一致性指数的基因对作为最终分级特征。最终,开发了 9 个基因对(9-GPS)将 CRAC 患者分为高低级别组。使用该特征,重新分类的高级别和低级别组之间有更多的差异表达特征。在四个独立的数据集可以看到分类后两组患者预后的显著差异。此外,基因组分析表明,分类的高级别组以超突变为特征,而分类的低级别组以频繁的拷贝数改变为特征。总之,9-GPS 可为 CRAC 患者提供客观而稳健的分级评估,有助于临床治疗决策。

相似文献

1
A qualitative transcriptional signature for determining the grade of colorectal adenocarcinoma.用于确定结直肠腺癌分级的定性转录特征。
Cancer Gene Ther. 2020 Sep;27(9):680-690. doi: 10.1038/s41417-019-0139-1. Epub 2019 Oct 9.
2
A qualitative transcriptional signature to reclassify histological grade of ER-positive breast cancer patients.一种用于重新分类 ER 阳性乳腺癌患者组织学分级的转录特征。
BMC Genomics. 2020 Apr 6;21(1):283. doi: 10.1186/s12864-020-6659-0.
3
A Qualitative Transcriptional Signature for Predicting Prognosis and Response to Bevacizumab in Metastatic Colorectal Cancer.用于预测转移性结直肠癌患者贝伐珠单抗预后和反应的转录特征分析
Mol Cancer Ther. 2020 Jul;19(7):1497-1505. doi: 10.1158/1535-7163.MCT-19-0864. Epub 2020 May 5.
4
A qualitative transcriptional signature for predicting the biochemical recurrence risk of prostate cancer patients after radical prostatectomy.一种用于预测前列腺癌患者根治性前列腺切除术后生化复发风险的转录特征。
Prostate. 2020 Apr;80(5):376-387. doi: 10.1002/pros.23952. Epub 2020 Jan 21.
5
A qualitative transcriptional signature of recurrence risk for stages II-III gastric cancer patients after surgical resection.手术切除后 II-III 期胃癌患者复发风险的定性转录特征。
J Gastroenterol Hepatol. 2021 Sep;36(9):2501-2512. doi: 10.1111/jgh.15439. Epub 2021 Feb 18.
6
Individualized predictive signatures for 5-fluorouracil-based chemotherapy in right- and left-sided colon cancer.右半结肠癌和左半结肠癌中基于氟尿嘧啶的化疗的个体化预测标志物。
Cancer Sci. 2018 Jun;109(6):1939-1948. doi: 10.1111/cas.13622. Epub 2018 May 23.
7
A qualitative transcriptional prognostic signature for patients with stage I-II pancreatic ductal adenocarcinoma.用于 I-II 期胰腺导管腺癌患者的定性转录预后特征。
Transl Res. 2020 May;219:30-44. doi: 10.1016/j.trsl.2020.02.004. Epub 2020 Feb 13.
8
A qualitative transcriptional signature to reclassify estrogen receptor status of breast cancer patients.一种重新分类乳腺癌患者雌激素受体状态的定性转录特征。
Breast Cancer Res Treat. 2018 Jul;170(2):271-277. doi: 10.1007/s10549-018-4758-2. Epub 2018 Mar 23.
9
Gene expression profiling-derived immunohistochemistry signature with high prognostic value in colorectal carcinoma.基于基因表达谱的免疫组织化学特征在结直肠癌中具有高预后价值。
Gut. 2014 Sep;63(9):1457-67. doi: 10.1136/gutjnl-2013-305475. Epub 2013 Oct 30.
10
CT-based Radiomics Signature to Discriminate High-grade From Low-grade Colorectal Adenocarcinoma.基于 CT 的放射组学特征鉴别高低级别结直肠腺癌
Acad Radiol. 2018 Oct;25(10):1285-1297. doi: 10.1016/j.acra.2018.01.020. Epub 2018 Mar 2.

引用本文的文献

1
Predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer with two step feature selection and ensemble learning.利用两步特征选择和集成学习预测局部晚期直肠癌新辅助放化疗的病理反应
Sci Rep. 2025 Mar 22;15(1):9936. doi: 10.1038/s41598-025-94337-y.
2
T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma.基于T2加权成像的放射组学-临床机器学习模型用于预测结直肠癌的分化程度。
World J Gastrointest Oncol. 2024 Mar 15;16(3):819-832. doi: 10.4251/wjgo.v16.i3.819.
3
Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma.

本文引用的文献

1
The inhibitory effects of COL1A2 on colorectal cancer cell proliferation, migration, and invasion.COL1A2对结肠癌细胞增殖、迁移和侵袭的抑制作用。
J Cancer. 2018 Jul 30;9(16):2953-2962. doi: 10.7150/jca.25542. eCollection 2018.
2
Transcriptional signatures for coupled predictions of stage II and III colorectal cancer metastasis and fluorouracil-based adjuvant chemotherapy benefit.转录特征可预测 II 期和 III 期结直肠癌转移和氟尿嘧啶为基础的辅助化疗获益情况。
FASEB J. 2019 Jan;33(1):151-162. doi: 10.1096/fj.201800222RRR. Epub 2018 Jun 29.
3
Rectal cancer and the pathologist.
基于机器学习的肝细胞癌早期诊断预测因子的研究进展。
Sci Rep. 2024 Mar 4;14(1):5274. doi: 10.1038/s41598-024-51265-7.
4
Identifying individualized prognostic signature and unraveling the molecular mechanism of recurrence in early-onset colorectal cancer.识别个体化预后特征并揭示早发性结直肠癌复发的分子机制。
Eur J Med Res. 2023 Nov 20;28(1):533. doi: 10.1186/s40001-023-01491-y.
5
StemSC: a cross-dataset human stemness index for single-cell samples.StemSC:单细胞样本的跨数据集人类干性指数。
Stem Cell Res Ther. 2022 Mar 21;13(1):115. doi: 10.1186/s13287-022-02803-5.
6
Establishment of a 7-microRNA prognostic signature and identification of hub target genes in colorectal carcinoma.7种微小RNA预后特征的建立及结直肠癌中关键靶基因的鉴定
Transl Cancer Res. 2022 Feb;11(2):367-381. doi: 10.21037/tcr-21-1992.
7
A novel qualitative signature based on lncRNA pairs for prognosis prediction in hepatocellular carcinoma.一种基于lncRNA对的新型定性特征用于肝细胞癌预后预测
Cancer Cell Int. 2022 Feb 22;22(1):95. doi: 10.1186/s12935-022-02507-z.
直肠癌与病理学家。
Minerva Chir. 2018 Dec;73(6):534-547. doi: 10.23736/S0026-4733.18.07739-8. Epub 2018 Apr 13.
4
An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics.TCGA 泛癌临床数据资源整合,推动高质量生存预后分析。
Cell. 2018 Apr 5;173(2):400-416.e11. doi: 10.1016/j.cell.2018.02.052.
5
Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer.定量或定性转录诊断特征?以结直肠癌为例。
BMC Genomics. 2018 Jan 29;19(1):99. doi: 10.1186/s12864-018-4446-y.
6
Towards understanding the mechanisms of actions of carcinoembryonic antigen-related cell adhesion molecule 6 in cancer progression.关于了解癌胚抗原相关细胞粘附分子6在癌症进展中的作用机制
Cancer Sci. 2018 Jan;109(1):33-42. doi: 10.1111/cas.13437. Epub 2018 Jan 2.
7
Cytokine Signaling in Tumor Progression.肿瘤进展中的细胞因子信号传导
Immune Netw. 2017 Aug;17(4):214-227. doi: 10.4110/in.2017.17.4.214. Epub 2017 Aug 9.
8
Circumvent the uncertainty in the applications of transcriptional signatures to tumor tissues sampled from different tumor sites.规避转录特征在取自不同肿瘤部位的肿瘤组织应用中的不确定性。
Oncotarget. 2017 May 2;8(18):30265-30275. doi: 10.18632/oncotarget.15754.
9
Robust transcriptional tumor signatures applicable to both formalin-fixed paraffin-embedded and fresh-frozen samples.适用于福尔马林固定石蜡包埋样本和新鲜冷冻样本的强大转录肿瘤特征。
Oncotarget. 2017 Jan 24;8(4):6652-6662. doi: 10.18632/oncotarget.14257.
10
Crosstalk between CCL7 and CCR3 promotes metastasis of colon cancer cells via ERK-JNK signaling pathways.CCL7与CCR3之间的相互作用通过ERK-JNK信号通路促进结肠癌细胞的转移。
Oncotarget. 2016 Jun 14;7(24):36842-36853. doi: 10.18632/oncotarget.9209.