Prediction and identification of immune genes related to the prognosis of patients with colon adenocarcinoma and its mechanisms

Chen, Sihan; GD, Cao; Wu, Wei; Lu, Yida; He, Xingyuan; Yang, Lei; Chen, Ke; Chen, Bin; Xiong, Maoming

doi:10.21203/rs.3.rs-26039/v1

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…The results of this research highlight that myeloid dendritic cells mainly results from lymphatic metastasis of COAD. Myeloid dendritic cells can eliminate inflammation by inhibiting the activity of macrophages, but their regulatory mechanism in colon cancer requires further study [ 34 ].…”

Section: Discussionmentioning

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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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%

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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“…Black module has relatively high Z summary score and low median rank, indicating high preservation. In the current study, all the four gene modules were considered to be conserved and selected for further 2020:08:51875:2:1:NEW 19 Nov 2020) Manuscript to be reviewed in colon adenocarcinoma (Chen et al 2020;Li et al 2020;Miao et al 2020;Sun et al 2020), and the role of INHBA in gastric cancer has been widely reported also. INHBA was highly expressed (Kaneda et al 2011;Seeruttun et al 2019;Zhang et al 2010) and aberrantly methylated (Zhang et al 2019) in gastric tumor samples, and high INHBA expression was associated with significantly poorer 5-year survival than low expression group (Katayama et al 2017;Wang et al 2012).…”

Section: Discussionmentioning

confidence: 99%

Peer Review #1 of "Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis (v0.3)"

2021

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Background: Gastric cancer is one of the most lethal tumors and is characterized by poor prognosis and lack of effective diagnostic or therapeutic biomarkers. The aim of this study was to find hub genes serving as biomarkers in gastric cancer diagnosis and therapy.Methods: GSE66229 from Gene Expression Omnibus (GEO) was used as training set. Genes bearing the top 25% standard deviations among all the samples in training set were performed to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. Then, hub genes were further screened by using the "least absolute shrinkage and selection operator" (LASSO) logistic regression. Finally, hub genes were validated in GS54129 dataset from GEO by supervised learning method artificial neural network (ANN) algorithm.Results: 12 modules with strong preservation were identified by using WGCNA methods in training set. Of which, five modules significantly related to gastric cancer were selected as clinically significant modules, and 713 candidate genes were identified from these four modules. Then,

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“…For example, KRAS proto-oncogene and TP53 tumor suppressor gene are related to the development and prognosis of colon cancer [ 5 , 6 ]. Likewise, INHBA plays an immunomodulatory role in colon cancer [ 7 ], and BRIP1 is related to the susceptibility of colon cancer [ 8 ]. At present, although radical resection combined with chemotherapy can improve the survival rate of colon cancer, the treatment results are still unsatisfactory [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analyses of correlation and survival reveal informative lncRNA prognostic signatures in colon cancer

et al. 2021

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Background Colon cancer is a commonly worldwide cancer with high morbidity and mortality. Long non-coding RNAs (lncRNAs) are involved in many biological processes and are closely related to the occurrence of colon cancer. Identification of the prognostic signatures of lncRNAs in colon cancer has great significance for its treatment. Methods We first identified the colon cancer-related mRNAs and lncRNAs according to the differential analysis methods using the expression data in TCGA. Then, we performed correlation analysis between the identified mRNAs and lncRNAs by integrating their expression values and secondary structure information to estimate the co-regulatory relationships between the cancer-related mRNAs and lncRNAs. Besides, the competing endogenous RNA regulation network based on co-regulatory relationships was constructed to reveal cancer-related regulatory patterns. Meanwhile, we used traditional regression analysis (univariate Cox analysis, random survival forest analysis, and lasso regression analysis) to screen the cancer-related lncRNAs. Finally, by combining the identified colon cancer-related lncRNAs according to the above analyses, we constructed a risk prognosis model for colon cancer through multivariate Cox analysis and also validated the model in the colon cancer dataset in TCGA cohorts. Results Six lncRNAs were found highly correlated with the overall survival of colon cancer patients, and a risk prognosis model based on them was constructed to predict the overall survival of colon cancer patients. In particular, EVX1-AS, ZNF667-AS1, CTC-428G20.6, and CTC-297N7.9 were first reported to be related to colon cancer by using our model, among which EVX1-AS and ZNF667-AS1 have been predicted to be related to colon cancer in LncRNADisease database. Conclusions This study identified the potential regulatory relationships between lncRNAs and mRNAs by integrating their expression values and secondary structure information and presented a significant 6-lncRNA risk prognosis model to predict the overall survival of colon cancer patients.

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Prediction and identification of immune genes related to the prognosis of patients with colon adenocarcinoma and its mechanisms

Cited by 3 publications

References 12 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

Peer Review #1 of "Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis (v0.3)"

Comprehensive analyses of correlation and survival reveal informative lncRNA prognostic signatures in colon cancer

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