Literature DB >> 31595030

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

Hailong Zheng1, Kai Song1, Yelin Fu1, Tianyi You1, Jing Yang1, Wenbing Guo1, Kai Wang1, Liangliang Jin1, Yunyan Gu1, Lishuang Qi1, Wenyuan Zhao2, Zheng Guo3,4,5.   

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.

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Year:  2019        PMID: 31595030     DOI: 10.1038/s41417-019-0139-1

Source DB:  PubMed          Journal:  Cancer Gene Ther        ISSN: 0929-1903            Impact factor:   5.987


  40 in total

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Review 4.  Tackling the widespread and critical impact of batch effects in high-throughput data.

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Journal:  Histopathology       Date:  2008-03       Impact factor: 5.087

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Authors:  Matthew Fleming; Sreelakshmi Ravula; Sergei F Tatishchev; Hanlin L Wang
Journal:  J Gastrointest Oncol       Date:  2012-09

10.  Integration of mRNA expression profile, copy number alterations, and microRNA expression levels in breast cancer to improve grade definition.

Authors:  Claudia Cava; Gloria Bertoli; Marilena Ripamonti; Giancarlo Mauri; Italo Zoppis; Pasquale Anthony Della Rosa; Maria Carla Gilardi; Isabella Castiglioni
Journal:  PLoS One       Date:  2014-05-27       Impact factor: 3.240

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  3 in total

1.  A novel qualitative signature based on lncRNA pairs for prognosis prediction in hepatocellular carcinoma.

Authors:  Xiaoyun Bu; Luyao Ma; Shuang Liu; Dongsheng Wen; Anna Kan; Yujie Xu; Xuanjia Lin; Ming Shi
Journal:  Cancer Cell Int       Date:  2022-02-22       Impact factor: 5.722

2.  StemSC: a cross-dataset human stemness index for single-cell samples.

Authors:  Hailong Zheng; Jiajing Xie; Kai Song; Jing Yang; Huiting Xiao; Jiashuai Zhang; Keru Li; Rongqiang Yuan; Yuting Zhao; Yunyan Gu; Wenyuan Zhao
Journal:  Stem Cell Res Ther       Date:  2022-03-21       Impact factor: 6.832

3.  Establishment of a 7-microRNA prognostic signature and identification of hub target genes in colorectal carcinoma.

Authors:  Shengying Jiang; Xiaoli Xie; Huiqing Jiang
Journal:  Transl Cancer Res       Date:  2022-02       Impact factor: 1.241

  3 in total

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