<scp>G</scp>ene expression differences among different <scp>MSI</scp> statuses in colorectal cancer

Lei, Chen; Pan, Xiaoyong; Hu, Xiaohua; Zhang, Yuhang; Wang, Shaopeng; Huang, Tao; Cai, Yi

doi:10.1002/ijc.31554

Cited by 82 publications

(49 citation statements)

References 67 publications

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“…TMB and MSI are used as biomarkers to evaluate the therapeutic effect of PD-1 antibody and microsatellite instability is also one of the tumor progression [11,12]. We found that the expression level of CBLL1 was correlated with TMB in patients with BLCA, BRCA, COAD, LAML, LGG, LUAD, LUSC, SARC, STAD, THCA, THYM and UVM, and with MSI in patients with ACC, BRCA, CESC, COAD, DLBC, HNSC, PRAD, READ, SARC, STAD, TGCT, THCA and UCEC, which indicates that CBLL1 may be a biomarker of treatment and prognosis in patients with pan-cancer.…”

Section: Discussionmentioning

confidence: 99%

The Role of CBLL1 in the Prognosis and Immune Infiltration of Pan-Cancer

Guo

et al. 2020

Preprint

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ObjectiveThis study aims to explore the role of CBLL1 in pan-carcinoma and tumor immune infiltrates. MethodsDownload mRNA expression, mutation and clinical data in UCSC database, to analyze the relationship between CBLL1 expression and clinicopathological vlaue, and immune microenvironment in pan-cancer. CIBERSORT was used to analyze the relationship between CBLL1 expression and the infiltration of pan-carcinoma immune cells. The mRNA expression data of UCSC database were used to analyze the correlation between CBLL1 expression and pan-cancer immunomodulations, checkpoints and receptor molecules. ResultsThe levels of CBLL1 mRNA expression in pan-cancer tissues were abnormal. The level of CBLL1 is related to the age, race, clinical stage and treatment effect of patients with pan-carcinoma and associated with the prognosis of patients with KIRC, LUSC, THCA, THYM, MESO, PRAD, STAD, and UVM. Univariate COX regression analysis showed that expression of CBLL1 was a risk factor for poor prognosis in patients with KICH, KIRC, LAML, THYM, KIRC, PCPG, OV, PRAD, STAD, GBM and UVM. The expression level of CBLL1 was correlated with BLCA, BRCA, COAD, LAML, LGG, LUAD, LUSC, SARC, STAD, THCA, THYM and UVM tumor mutational burden, and with ACC, BRCA, CESC, COAD, DLBC, HNSC, PRAD, READ, SARC, STAD, TGCT, THCA and UCEC microsatellite instability. The expression level of CBLL1 was correlated with cancer stromal cells and immune cells. The expression of CBLL1 is related to pan-cancer immunomodulators, checkpoints and receptor molecules. ConclusionCBLL1 is abnormally expressed in patients with pan-carcinoma, which is expected to be a biomarker for prognosis, mutation and immune infiltration in patients with pan-carcinoma.

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Section: Discussionmentioning

confidence: 99%

The Role of CBLL1 in the Prognosis and Immune Infiltration of Pan-Cancer

Guo

et al. 2020

Preprint

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“…Therefore, we adopted a mutual information-based method, i.e., mRMR (Peng et al, 2005), which has been widely used in feature ranking (Niu et al, 2013; Zhao et al, 2013; Zhou et al, 2015; Zhang et al, 2016; Li and Huang, 2017; Liu et al, 2017). It considers both the relevance between features and sample labels and the redundancy among features and has been proven to be an effective feature selection method, especially for gene expression analysis (Qin et al, 2012; Zhang et al, 2014b, 2017, 2018; Zhang Y. et al, 2014; Li et al, 2015; Zhou et al, 2015; Wang et al, 2016; Song et al, 2017; Chen et al, 2018b). The method works like this: let us use Ω to denote all the 25,159 genes, Ω s to denote the selected gene set that includes m genes, and Ω g to denote the n genes that will be evaluated, and one of them will be selected.…”

Section: Methodsmentioning

confidence: 99%

Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods

Lan

Kong

et al. 2018

Front. Genet.

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Osteoarthritis (OA) is a complex disease that affects articular joints and may cause disability. The incidence of OA is extremely high. Most elderly people have the symptoms of osteoarthritis. The physiotherapy of OA is time consuming, and the chances of full recovery from OA are very minimal. The most effective way of fighting OA is early diagnosis and early intervention. Liquid biopsy has become a popular noninvasive test. To find the blood gene expression signature for OA, we reanalyzed the publicly available blood gene expression profiles of 106 patients with OA and 33 control samples using an automatic computational pipeline based on advanced feature selection methods. Finally, a compact 23-gene set was identified. On the basis of these 23 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. Its sensitivity (Sn), specificity (Sp), accuracy (ACC), and Mathew's correlation coefficient (MCC) were 0.991, 0.909, 0.971, and 0.920, respectively. Obviously, the performance needed to be validated in an independent large dataset, but the in-depth biological analysis of the 23 biomarkers showed great promise and suggested that mRNA surveillance pathway and multicellular organism growth played important roles in OA. Our results shed light on OA diagnosis through liquid biopsy.

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“…To do that, the minimum redundancy maximum relevance (mRMR) method [ 12 ] proposed by Peng et al was employed to analyze all the features and yield a feature list, named the mRMR feature list. This feature selection method has been applied to tackle various biological problems [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. In the method, the discriminated power of a feature f is reflected by the relevance between it and a target class c , which is measured from their mutual information ( MI ).…”

Section: Methodsmentioning

confidence: 99%

A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes

Lei

Zhang

et al. 2018

Genes

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Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on “qualitatively tissue-specific expressed genes” which are highly enriched in one or a group of tissues but paid less attention to “quantitatively tissue-specific expressed genes”, which are expressed in all or most tissues but with differential expression levels. In this study, we applied machine learning algorithms to build a computational method for identifying “quantitatively tissue-specific expressed genes” capable of distinguishing 25 human tissues from their expression patterns. Our results uncovered the expression of 432 genes as optimal features for tissue classification, which were obtained with a Matthews Correlation Coefficient (MCC) of more than 0.99 yielded by a support vector machine (SVM). This constructed model was superior to the SVM model using tissue enriched genes and yielded MCC of 0.985 on an independent test dataset, indicating its good generalization ability. These 432 genes were proven to be widely expressed in multiple tissues and a literature review of the top 23 genes found that most of them support their discriminating powers. As a complement to previous studies, our discovery of these quantitatively tissue-specific genes provides insights into the detailed understanding of tissue development and function.

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Gene expression differences among different MSI statuses in colorectal cancer

Cited by 82 publications

References 67 publications

The Role of CBLL1 in the Prognosis and Immune Infiltration of Pan-Cancer

The Role of CBLL1 in the Prognosis and Immune Infiltration of Pan-Cancer

Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods

A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes

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