Identification of a five‐gene signature with prognostic value in colorectal cancer

Sun, Guangwei; Li, Yalun; Peng, Yangjie; Lu, Dapeng; Zhang, Fuqiang; Cui, Xueyang; Zhang, Qingyue; Li, Zhuang

doi:10.1002/jcp.27154

Cited by 25 publications

(23 citation statements)

References 34 publications

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“…In terms of predicting tumor-related prognosis, the accuracy of statistical models and the reduction in interference are superior to single genes or markers [ 17 ]. As high-throughput sequencing technology has developed and databases are increasingly developed, big data can be used to explore statistical models for prediction of tumor prognosis [ 5 ]. Expression data from a large number of genes in each sample can be conveniently processed using big data analysis [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

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Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

Chen

Cao

et al. 2020

Bioscience Reports

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Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, that is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA）was correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1) which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

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Section: Discussionmentioning

confidence: 99%

“…Increasing numbers of institutions have adopted multiple genes to build predictive models for disease diagnosis. With the development of high-throughput sequencing technology, an increasing network of public databases have been established whose genetic and clinical data can be exploited to build a prognostic model of related diseases [5].…”

Section: Introductionmentioning

confidence: 99%

Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

Chen

Cao

et al. 2020

Bioscience Reports

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“…Further research on these genes is required. It should be noted that SCG2 and PCOLCE2 have been predicted to be associated with the prognosis of CRC, but in-depth investigation on these two genes in CRC is rarely reported [ 42 , 43 ]. It is necessary to explore their roles in tumors, especially in CRC.…”

Section: Discussionmentioning

confidence: 99%

Genomics and prognosis analysis of epithelial-mesenchymal transition in colorectal cancer patients

et al. 2020

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Background The epithelial-mesenchymal transition (EMT) plays a pivotal role in various physiological processes, such as embryonic development, tissue morphogenesis, and wound healing. EMT also plays an important role in cancer invasion, metastasis, and chemoresistance. Additionally, EMT is partially responsible for chemoresistance in colorectal cancer (CRC). The aim of this research is to develop an EMT-based prognostic signature in CRC. Methods RNA-seq and microarray data, together with clinical information, were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. A total of 244 differentially expressed EMT-related genes (ERGs) were obtained by comparing the expression between normal and tumor tissues. An EMT-related signature of 11 genes was identified as crucially related to the overall survival (OS) of patients through univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO), and Cox regression analysis. Finally, we established a clinical nomogram to predict the survival possibility of CRC patients by integrating clinical characteristics and the EMT-related gene signature. Results Two hundred and forty-four differentially expressed ERGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that EMT-related signaling pathway genes were highly related to CRC. Kaplan-Meier analysis revealed that the 11-EMT signature could significantly distinguish high- and low-risk patients in both TCGA and GEO CRC cohorts. In addition, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusion We developed a novel EMT-related gene signature for the prognosis prediction of CRC patients, which could improve the individualized outcome prediction in CRC.

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“…Solute carrier family 12 member 5 (SLC12A5) contributes to disease progression and metastasis, and is an independent prognostic factor in CRC [19]. Additionally, the prognostic value of the gene signature for predicting prognosis of patients with CRC has been reported [17,18]. However, in advanced CRC, the predictive value of the gene signature is still unclear.…”

Section: Discussionmentioning

confidence: 99%

“…These studies show that single gene expression may have the potential value for predicting prognosis of patients with CRC. Some recent studies have reported the prognostic significance of the gene signature for predicting prognosis of CRC [17,18]. Unfortunately, these recent studies have some limitations, such as relatively small study population, lack of further validation cohort, or analysis of only one or a few genes.…”

Section: Introductionmentioning

confidence: 99%

Development of a 15-gene signature for predicting prognosis in advanced colorectal cancer

Xiao

2020

Bioengineered

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Advanced colorectal cancer (CRC) is a significant cause of cancer mortality, with a poor prognosis. Here, we identified a novel prognostic signature for predicting survival of advanced CRC. Advanced CRC data were used (training set: n = 267 and validation set: n = 264). The survival analyses were investigated. The functional analysis of the prognostic signature was examined. In this study, our 15-gene signature was established and was an independent prognostic factor of advanced CRC. Stratification analyses also showed that this signature was still powerful for survival prediction in each stratum of age, gender, stage, and metastasis status. In mechanism, our signature involved in DNA replication, DNA damage, and cell cycle. Therefore, our findings suggested that this 15-gene signature has prognostic and predictive value in advanced CRC, which could be further used in personalized therapy for advanced CRC. ARTICLE HISTORY

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Identification of a five‐gene signature with prognostic value in colorectal cancer

Cited by 25 publications

References 34 publications

Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

Genomics and prognosis analysis of epithelial-mesenchymal transition in colorectal cancer patients

Development of a 15-gene signature for predicting prognosis in advanced colorectal cancer

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