Identification of m6A-Related Biomarkers Associated with Prognosis of Colorectal Cancer

Zhang, Zhiyong; Zhang, Xin

doi:10.12659/msm.932370

Cited by 13 publications

(7 citation statements)

References 38 publications

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“…24 UBE2H was identified as a m6A-related hub gene closely related to the clinicopathology and prognosis of CRC using a prognostic signature model. 25 In concordance with our findings, Cejas et al also found that CNOT3 overexpression in colon tissues was associated with worse prognosis outcomes of CRC. 26 Our study firstly developed a prognostic risk score based on 18 m6A-related gene expression signatures that could be used as an index to predict the OS of CRC patients, and further validated its predictive performance in two independent external datasets.…”

Section: Discussionsupporting

confidence: 93%

N6-Methylandenosine-Related Gene Expression Signatures for Predicting the Overall Survival and Immune Responses of Patients With Colorectal Cancer

Wang

Sun

et al. 2022

SSRN Journal

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Section: Discussionsupporting

confidence: 93%

N6-Methylandenosine-Related Gene Expression Signatures for Predicting the Overall Survival and Immune Responses of Patients With Colorectal Cancer

Wang

Sun

et al. 2022

SSRN Journal

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“…Previously, the use of bioinformatics methods to explore differentially expressed genes has made significant progress in pancreatic cancer ( Xu et al, 2020 ), gastric cancer ( Cheng et al, 2020 ; Gu et al, 2021 ; Yuan et al, 2021 ), colorectal cancer ( Cheng et al, 2021 ; Zhang and Zhang, 2021 ), prostate cancer ( Rode et al, 2021 ) and other diseases ( Ao et al, 2021 ; Yu et al, 2021b ; Laudisi et al, 2021 ; Zhang et al, 2021 ; Zhao et al, 2021 ). The occurrence and development of breast cancer is a process involving and synergistic action of multiple genes in multiple stages ( Kretschmer et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway Network

Zhang

Jiang

Gao

et al. 2022

Front. Cell Dev. Biol.

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Background: Breast cancer is the second largest cancer in the world, the incidence of breast cancer continues to rise worldwide, and women’s health is seriously threatened. Therefore, it is very important to explore the characteristic changes of breast cancer from the gene level, including the screening of differentially expressed genes and the identification of diagnostic markers.Methods: The gene expression profiles of breast cancer were obtained from the TCGA database. The edgeR R software package was used to screen the differentially expressed genes between breast cancer patients and normal samples. The function and pathway enrichment analysis of these genes revealed significant enrichment of functions and pathways. Next, download these pathways from KEGG website, extract the gene interaction relations, construct the KEGG pathway gene interaction network. The potential diagnostic markers of breast cancer were obtained by combining the differentially expressed genes with the key genes in the network. Finally, these markers were used to construct the diagnostic prediction model of breast cancer, and the predictive ability of the model and the diagnostic ability of the markers were verified by internal and external data.Results: 1060 differentially expressed genes were identified between breast cancer patients and normal controls. Enrichment analysis revealed 28 significantly enriched pathways (p < 0.05). They were downloaded from KEGG website, and the gene interaction relations were extracted to construct the gene interaction network of KEGG pathway, which contained 1277 nodes and 7345 edges. The key nodes with a degree greater than 30 were extracted from the network, containing 154 genes. These 154 key genes shared 23 genes with differentially expressed genes, which serve as potential diagnostic markers for breast cancer. The 23 genes were used as features to construct the SVM classification model, and the model had good predictive ability in both the training dataset and the validation dataset (AUC = 0.960 and 0.907, respectively).Conclusion: This study showed that the difference of gene expression level is important for the diagnosis of breast cancer, and identified 23 breast cancer diagnostic markers, which provides valuable information for clinical diagnosis and basic treatment experiments.

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“…Aberrant m 6 A modifications are closely related to the onset and progression of various cancers, including CRC ( Liu et al, 2018 ; Gong et al, 2019 ; Wang et al, 2020 ; Wang et al, 2021 ; Zhang and Zhang., 2021 ). Nowadays, lncRNAs are reported to act essential roles in the development of tumors, especially in epigenetic regulation, protein interaction, and RNA metabolism.…”

Section: Discussionmentioning

confidence: 99%

Potential Prognostic Value of a Seven m6A-Related LncRNAs Signature and the Correlative Immune Infiltration in Colon Adenocarcinoma

Chai

Zou

et al. 2021

Front. Genet.

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Long non-coding RNAs (lncRNAs) and their N6-methyladenosine (m6A) modifications play an essential role in tumorigenesis and cancer progression. This study was designed to explore the value of m6A-related lncRNAs in prognosis and therapeutic applications of immune infiltration of colon adenocarcinoma (COAD). We downloaded the COAD gene expression and clinical data from The Cancer Genome Atlas project. By co-expression analysis, Lasso Cox regression analysis, and univariate and multivariate Cox regression, we constructed an independent prognostic signature of seven m6A-related lncRNAs. The prognostic lncRNAs were divided into two clusters by consistent clustering analysis, as well as into two groups of low–high risk based on the signature. Then we identified the relationship between the different groups with clinical features and immune cell infiltration. Cluster 2 had a higher risk score with a lower survival rate. The risk score was higher in groups with advanced clinical features, such as stage III–IV, N1-3, and M1. The expression of AC156455.1 was increased in tumor tissues and cluster 2, and the lncRNA ZEB1−AS1 was notably higher in the high-risk group. Five types of immune cells showed differences in two clusters, and most were upregulated in type 2. The expression of memory B cells was positively correlated with the risk score. The prognostic model was verified by the Gene Expression Omnibus (GEO) dataset. Besides, we found that the expression of these seven lncRNAs in tumor tissues was significantly higher than that in normal tissues, which verified the feasibility of the model. Thus, the signature of seven m6A-related lncRNAs can independently predict the prognosis of COAD. This signature is also closely associated with immune cell infiltration, and new therapeutic targets can be explored from this field.

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Identification of m6A-Related Biomarkers Associated with Prognosis of Colorectal Cancer

Cited by 13 publications

References 38 publications

N6-Methylandenosine-Related Gene Expression Signatures for Predicting the Overall Survival and Immune Responses of Patients With Colorectal Cancer

N6-Methylandenosine-Related Gene Expression Signatures for Predicting the Overall Survival and Immune Responses of Patients With Colorectal Cancer

Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway Network

Potential Prognostic Value of a Seven m6A-Related LncRNAs Signature and the Correlative Immune Infiltration in Colon Adenocarcinoma

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