The Construction and Analysis of ceRNA Network and Patterns of Immune Infiltration in Colon Adenocarcinoma Metastasis

Chang, Zhengyan; Huang, Runzhi; Fu, Wanting; Li, Jiehan; Ji, Guo; Huang, Jinglei; Shi, Weijun; Yin, Huabin; Wang, Weifeng; Meng, Tong; Huang, Zongqiang; Wei, Qing; Qin, Huanlong

doi:10.3389/fcell.2020.00688

Cited by 36 publications

(29 citation statements)

References 73 publications

(74 reference statements)

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“…Subsequently, the CIBERSORT algorithm performed deconvolution analysis on the immune microenvironment to assess the proportion of the immune cells in AD. What is more, previous research confirmed the effectiveness of the CIBERSORT technique (Dai et al, 2018;Mao et al, 2018;Chang et al, 2020). Besides, the immune cell infiltration scoring model was built by LASSO regression based on 22 immune cells.…”

Section: Discussionmentioning

confidence: 73%

Discovery and Validation of Key Biomarkers Based on Immune Infiltrates in Alzheimer’s Disease

Liu

Pan

2021

Front. Genet.

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BackgroundAs the most common neurodegenerative disease, Alzheimer’s disease (AD) leads to progressive loss of cognition and memory. Presently, the underlying pathogenic genes of AD patients remain elusive, and effective disease-modifying therapy is not available. This study explored novel biomarkers that can affect diagnosis and treatment in AD based on immune infiltration.MethodsThe gene expression profiles of 139 AD cases and 134 normal controls were obtained from the NCBI GEO public database. We applied the computational method CIBERSORT to bulk gene expression profiles of AD to quantify 22 subsets of immune cells. Besides, based on the use of the Least Absolute Shrinkage Selection Operator (LASSO), this study also applied SVM-RFE analysis to screen key genes. GO-based semantic similarity and logistic regression model analyses were applied to explore hub genes further.ResultsThere was a remarkable significance in the infiltration of immune cells between the subgroups. The proportions for monocytes, M0 macrophages, and dendritic cells in the AD group were significantly higher than those in the normal group, while the proportion of some cells was lower than that of the normal group, such as NK cell resting, T-cell CD4 naive, T-cell CD4 memory activation, and eosinophils. Additionally, seven genes (ABCA2, CREBRF, CD72, CETN2, KCNG1, NDUFA2, and RPL36AL) were identified as hub genes. Then we performed the analysis of immune factor correlation, gene set enrichment analysis (GSEA), and GO based on seven hub genes. The AUC of ROC prediction model in test and validation sets were 0.845 and 0.839, respectively. Eventually, the mRNA expression analysis of ABCA2, NDUFA2, CREBRF, and CD72 revealed significant differences among the seven hub genes and then was confirmed by RT-PCR.ConclusionA model based on immune cell infiltration might be used to forecast AD patients’ diagnosis, and it provided a new perspective for AD treatment targets.

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Section: Discussionmentioning

confidence: 73%

Discovery and Validation of Key Biomarkers Based on Immune Infiltrates in Alzheimer’s Disease

Liu

Pan

2021

Front. Genet.

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“…Previous studies have performed hypoxia-related analyses for certain types of cancer, while constructing prognostic signatures, including hepatocellular carcinoma (Jiang et al, 2020), gastric cancer (Chen et al, 2020), and melanoma (Shou et al, 2020). In terms of COAD itself, many previous studies have also proposed transcriptome signatures associated with COAD prognosis through bioinformatics analysis (Chang et al, 2020;Di et al, 2020;Luo et al, 2020). Currently, there are several hypoxia-related signatures that can predict clinical outcomes of colorectal cancer patients (Lee et al, 2019;Zou et al, 2019;Yang et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Hypoxia Constructing the Prognostic Model of Colorectal Adenocarcinoma and Related to the Immune Microenvironment

Zhang

Yang

Peng

et al. 2021

Front. Cell Dev. Biol.

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Background: Hypoxia is a common phenomenon in solid tumors, which plays an important role in tumor proliferation, apoptosis, angiogenesis, invasion and metastasis, energy metabolism and chemoradiotherapy resistance. However, comprehensive analysis of hypoxia markers in colorectal adenocarcinoma (COAD) is still lacking. And there is a need for mechanism exploration and clinical application.Methods: The gene expression, mutation and clinical data of COAD were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. Tumor samples from TCGA were randomly divided into the training and internal validation groups, while tumor samples from GEO were used as the external validation group. Univariate COX—LASSO—multivariate COX method was applied to construct the prognostic model. We clustered all TCGA tumor samples into high, medium and low hypoxia groups, evaluated the correlation between hypoxia degree and immunoactivity, and explored the combined effect of mutation for common target genes and model riskscore on survival in COAD patients. Finally, we developed a dynamic nomograph App online for direct clinical application and carried out multiple validations of the prognostic model.Results: Our hypoxia-related prognostic model for COAD patients is accurate and has been successfully validated internally and externally. Single Sample Gene Set Enrichment Analysis (ssGSEA) and Gene Set Enrichment Analysis (GSEA) results suggest that for COAD patients with higher hypoxia, the stronger the associated immunosuppressive activity, providing a possible mechanism for the lower survival rate. Finally, the dynamic nomograph App online enhances the clinical translational significance of the study.Conclusion: In this study, an accurate prognostic model for COAD patients was established and validated. In addition, our innovative findings include correlations between hypoxia levels and immune activity, as well as an in-depth exploration of common target gene mutations.

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“…In multiple cancers, Treg cells are associated with poor prognosis ( 32 ). Meanwhile, M0 macrophages may be related with the distant metastasis and prognosis of COAD ( 33 ). Macrophages M1 and activated memory CD4 + T cells were highly expressed in the low-risk group.…”

Section: Discussionmentioning

confidence: 99%

Prognostic Immune-Related Analysis Based on Differentially Expressed Genes in Left- and Right-Sided Colon Adenocarcinoma

Guo

Deng

et al. 2021

Front. Oncol.

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BackgroundColon adenocarcinoma (COAD) can be divided into left-sided and right-sided COAD (LCCs and RCCs, respectively). They have unique characteristics in various biological aspects, particularly immune invasion and prognosis. The purpose of our study was to develop a prognostic risk scoring model (PRSM) based on differentially expressed immune-related genes (IRGs) between LCCs and RCCs, therefore the prognostic key IRGs could be identified.MethodsThe gene sets and clinical information of COAD patients were derived from TCGA and GEO databases. The comparison of differentially expressed genes (DEGs) of LCCs and RCCs were conducted with appliance of “Limma” analysis. The establishment about co-expression modules of DEGs related with immune score was conducted by weighted gene co-expression network analysis (WGCNA). Furthermore, we screened the module genes and completed construction of gene pairs. The analysis of the prognosis and the establishment of PRSM were performed with univariate- and lasso-Cox regression. We employed the PRSM in the model group and verification group for the purpose of risk group assignment and PRSM accuracy verification. Finally, the identification of the prognostic key IRGs was guaranteed by the adoption of functional enrichment, “DisNor” and protein-protein interaction (PPI).ResultsA total of 215 genes were screened out by differential expression analysis and WGCNA. A PRSM with 16 immune-related gene pairs (IRGPs) was established upon the genes pairing. Furthermore, we confirmed that the risk score was an independent factor for survival by univariate- and multivariate-Cox regression. The prognosis of high-risk group in model group (P < 0.001) and validation group (P = 0.014) was significantly worse than that in low-risk group. Treg cells (P < 0.001) and macrophage M0 (P = 0.015) were highly expressed in the high-risk group. The functional analysis indicated that there was significant up-regulation with regard of lymphocyte and cytokine related terms in low-risk group. Finally, we identified five prognostic key IRGs associated with better prognosis through PPI and prognostic analysis, including IL2RB, TRIM22, CIITA, CXCL13, and CXCR6.ConclusionThrough the analysis and screening of the DEGs between LCCs and RCCs, we constructed a PRSM which could predicate prognosis of LCCs and RCCs, and five prognostic key IRGs were identified as well. Therefore, the basis for identifying the benefits of immunotherapy and immunomodulatory was built.

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The Construction and Analysis of ceRNA Network and Patterns of Immune Infiltration in Colon Adenocarcinoma Metastasis

Cited by 36 publications

References 73 publications

Discovery and Validation of Key Biomarkers Based on Immune Infiltrates in Alzheimer’s Disease

Discovery and Validation of Key Biomarkers Based on Immune Infiltrates in Alzheimer’s Disease

Hypoxia Constructing the Prognostic Model of Colorectal Adenocarcinoma and Related to the Immune Microenvironment

Prognostic Immune-Related Analysis Based on Differentially Expressed Genes in Left- and Right-Sided Colon Adenocarcinoma

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