Prognostic Model of Colorectal Cancer Constructed by Eight Immune-Related Genes

Wen, Shuting; He, Lina; Zhong, Zhuotai; Mi, Hong; Liu, Fengbin

doi:10.3389/fmolb.2020.604252

Cited by 21 publications

(18 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The AUC at 1 year OS was compared between the IRGs signature and three published IRGs signatures, which included an 8-gene signature (Wen IRGSig), a 6-gene signature (ZhangIRGSig), and a 5-gene signature (Zhu IRGSig) in the same TCGA cohort ( Zhang et al, 2020b ; Wen et al, 2020 ; Zhu et al, 2020 ). The AUC of the IRGs signature at 1-year OS was 0.774, which was the highest of that of Wen IRGSig (0.640), ZhangIRGSig (0.720), and Zhu IRGSig (0.620) ( Figure 7 ).…”

Section: Resultsmentioning

confidence: 99%

A Prognostic Model Using Immune-Related Genes for Colorectal Cancer

Wei

Zhang

Liu

et al. 2022

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

There is evidence suggesting that immune genes play pivotal roles in the development and progression of colorectal cancer (CRC). Colorectal carcinoma patient data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) were randomly classified into a training set, a test set, and an external validation set. Differentially expressed gene (DEG) analyses, univariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) were used to identify survival-associated immune genes and develop a prognosis model. Receiver operating characteristic (ROC) analysis and principal component analysis (PCA) were used to evaluate the discrimination of the risk models. The model genes predicted were verified using the Human Protein Atlas (HPA) databases, colorectal cell lines, and fresh CRC and adjacent tissues. To understand the relationship between IRGs and immune invasion and the TME, we analyzed the content of immune cells and scored the TME using CIBERSORT and ESTIMATE algorithms. Finally, we predicted the potential sensitive chemotherapeutic drugs in different risk score groups by the Genomics of Drug Sensitivity in Cancer (GDSC). A total of 491 IRGs were screened, and 14 IRGs were identified to be significantly related to overall survival (OS) and applied to construct an immune-related gene (IRG) prognostic signature (IRGSig) for CRC patients. Calibration plots showed that nomograms have powerful predictive ability. PCA and ROC analysis further verified the predictive value of this fourteen-gene prognostic model in three independent databases. Furthermore, we discovered that the tumor microenvironment changed significantly during the tumor development process, from early to middle to late stage, which may be an essential factor for tumor deterioration. Finally, we selected six commonly used chemotherapeutic drugs that have the potential to be useful in the treatment of CRC. Altogether, immune genes were used to construct a prognosis model for CRC patients, and a variety of methods were used to test the accuracy of this model. In addition, we explored the immune mechanisms of CRC through immune cell infiltration and TME in CRC. Furthermore, we assessed the therapeutic sensitivity of many commonly used chemotherapeutic medicines in individuals with varying risk factors. Finally, the immune risk model and immune mechanism of CRC were thoroughly investigated in this paper.

show abstract

Section: Resultsmentioning

confidence: 99%

A Prognostic Model Using Immune-Related Genes for Colorectal Cancer

Wei

Zhang

Liu

et al. 2022

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

show abstract

“…Many previous studies have reported that DMGs and DEGs can be used as prognostic and diagnostic biomarkers for CRC [11,[28][29][30]. A previous study also reported that MDGs can assist with determining the prognosis of CRC patients [21].…”

Section: Discussionmentioning

confidence: 99%

Identification of methylation driven biomarkers for diagnosis and prognosis in colorectal cancer by Integrative Analysis of TCGA, GTEx, and GEO database

Cao¹,

Li²,

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: This work investigates the use of methylation driven biomarkers for diagnosis and prognosis in colorectal cancer (CRC) by mining DNA methylation and gene expression data from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), and the Gene Expression Omnibus (GEO). Methods: The differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were screened using mRNA expression and DNA methylation data from TCGA, respectively. The methylation driven genes (MDGs) of CRC were further identified using the MethylMix R package. Subsequently, the MDGs were analyzed with Random Forest (RF), support vector machine (SVM), and logistic regression (LR) algorithms to establish diagnosis prediction models as independent indicators using mRNA expression data from TCGA and GTEx. The RF algorithm was determined to be the most suitable and used to construct the diagnostic model with the combined MDGs, which was then validated by GSE39582 from GEO. Prognostic biomarkers were used to establish the risk score model, which was generated by univariate and multivariate Cox regression analyses. Moreover, we constructed and validated a nomogram that integrated the risk score and clinical information, including age, gender, and tumor stage. Results: 9 out of 10 MDGs performed well as independent diagnostic predictors, and STK33 and EPHX4 were also found to be associated with overall survival (OS). The results of the nomogram suggest that it is a better predictive model for prognosis than the risk score model. Conclusion: Our findings suggest that the identified MDGs could be biomarkers for diagnosis and prognosis of CRC.

show abstract

“…Recent studies highlight the role of the microbiota composition in intestinal inflammation in mice and humans, which has been linked to colorectal cancer 56 . There have been many publications and studies on the risk prediction model for the prognosis of colon cancer based on immune genes 57 – 62 . The abnormal metabolism of tumor glucose and lipids is an important part of tumor metabolic reprogramming, which is closely related to tumor occurrence, development, and prognois 50 .…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of a metabolic risk model predicting prognosis of colon cancer

Zuo

Liu

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Metabolic genes have played a significant role in tumor development and prognosis. In this study, we constructed a metabolic risk model to predict the prognosis of colon cancer based on The Cancer Genome Atlas (TCGA) and validated the model by Gene Expression Omnibus (GEO). We extracted 753 metabolic genes and identified 139 differentially expressed genes (DEGs) from TCGA database. Then we conducted univariate cox regression analysis and Least Absolute Shrinkage and Selection Operator Cox regression analysis to identify prognosis-related genes and construct the metabolic risk model. An eleven-gene prognostic model was constructed after 1000 resamples. The gene signature has been proved to have an excellent ability to predict prognosis by Kaplan–Meier analysis, time-dependent receiver operating characteristic, risk score, univariate and multivariate cox regression analysis based on TCGA. Then we validated the model by Kaplan–Meier analysis and risk score based on GEO database. Finally, we performed a weighted gene co-expression network analysis and protein–protein interaction network on DEGs, and Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology enrichment analyses were conducted. The results of functional analyses showed that most significantly enriched pathways focused on metabolism, especially glucose and lipid metabolism pathways.

show abstract

Prognostic Model of Colorectal Cancer Constructed by Eight Immune-Related Genes

Cited by 21 publications

References 53 publications

A Prognostic Model Using Immune-Related Genes for Colorectal Cancer

A Prognostic Model Using Immune-Related Genes for Colorectal Cancer

Identification of methylation driven biomarkers for diagnosis and prognosis in colorectal cancer by Integrative Analysis of TCGA, GTEx, and GEO database

Construction and validation of a metabolic risk model predicting prognosis of colon cancer

Contact Info

Product

Resources

About