Dataset Information

Identifying M1 Macrophage-Related Genes Through a Co-expression Network to Construct a Four-Gene Risk-Scoring Model for Predicting Thyroid Cancer Prognosis.

ABSTRACT: Macrophages are key innate immune cells in the tumor microenvironment that regulate primary tumor growth, vascularization, metastatic spread and response to therapies. Macrophages can polarize into two different states (M1 and M2) with distinct phenotypes and functions. To investigate the known tumoricidal effects of M1 macrophages, we obtained RNA expression profiles and clinical data from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA). The proportions of immune cells in tumor samples were assessed using CIBERSORT, and weighted gene co-expression network analysis (WGCNA) was used to identify M1 macrophage-related modules. Univariate Cox analysis and LASSO-Cox regression analysis were performed, and four genes (SPP1, DHRS3, SLC11A1, and CFB) with significant differential expression were selected through GEPIA. These four genes can be considered hub genes. The four-gene risk-scoring model may be an independent prognostic factor for THCA patients. The validation cohort and the entire cohort confirmed the results. Univariate and multivariate Cox analysis was performed to identify independent prognostic factors for THCA. Finally, a prognostic nomogram was built based on the entire cohort, and the nomogram combining the risk score and clinical prognostic factors was superior to the nomogram with individual clinical prognostic factors in predicting overall survival. Time-dependent ROC curves and DCA confirmed that the combined nomogram is useful. Gene set enrichment analysis (GSEA) was used to elucidate the potential molecular functions of the high-risk group. Our study identified four genes associated with M1 macrophages and established a prognostic nomogram that predicts overall survival for patients with THCA, which may help determine clinical treatment options for different patients.

SUBMITTER: Zhuang G

PROVIDER: S-EPMC7658400 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Identifying M1 Macrophage-Related Genes Through a Co-expression Network to Construct a Four-Gene Risk-Scoring Model for Predicting Thyroid Cancer Prognosis.

Zhuang Gaojian G Zeng Yu Y Tang Qun Q He Qian Q Luo Guoqing G

Frontiers in genetics 20201029

Macrophages are key innate immune cells in the tumor microenvironment that regulate primary tumor growth, vascularization, metastatic spread and response to therapies. Macrophages can polarize into two different states (M1 and M2) with distinct phenotypes and functions. To investigate the known tumoricidal effects of M1 macrophages, we obtained RNA expression profiles and clinical data from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA). The proportions of immune cells in tumor samples were ...[more]

PMID: 33193731

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Project description:The prognostic prediction of hepatocellular carcinoma (HCC) is still challenging. Immune cells play a crucial role in tumor initiation, progression, and drug resistance. However, prognostic value of immune-related genes in HCC remains to be further clarified. In this study, the mRNA expression profiles and corresponding clinical information of HCC patients were downloaded from public databases. Then, we estimated the abundance of immune cells and identified the differentially infiltrated and prognostic immune cells. The weighted gene co-expression network analysis (WGCNA) was performed to identify immune-related genes in TCGA cohort and GEO cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to establish a risk-scoring model in the TCGA cohort. HCC patients from the GSE14520 datasets were utilized for risk model validation. Our results found that high level of dendritic cell (DC) infiltration was associated with poor prognosis. Over half of the DC-related genes (58.2%) were robustly differentially expressed between HCC and normal specimens in the TCGA cohort. 17 differentially expressed genes (DEGs) were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. A 12-gene risk-scoring model was established to evaluate the prognosis of HCC. The high-risk group exhibits significantly lower OS rate of HCC patients than the low-risk group. The risk-scoring model shows benign predictive capacity in both GEO dataset and TCGA dataset. The 12-gene risk-scoring model may independently perform prognostic value for HCC patients. Receiver operating characteristic (ROC) curve analysis of the risk-scoring model in GEO cohort and TCGA cohort performed well in predicting OS. Taken together, the 12-gene risk-scoring model could provide prognostic and potentially predictive information for HCC. SDC3, NCF2, BTN3A3, and WARS were noticed as a novel prognostic factor for HCC.

Project description:Macrophage polarization is increasingly recognised as an important pathogenetic factor in inflammatory and neoplastic diseases. Proinflammatory M1 macrophages promote T helper (Th) 1 responses and show tumoricidal activity. M2 macrophages contribute to tissue repair and promote Th2 responses. CD68 and CD163 are used to identify macrophages in tissue sections. However, characterisation of polarised macrophages in situ has remained difficult. Macrophage polarisation is regulated by transcription factors, pSTAT1 and RBP-J for M1, and CMAF for M2. We reasoned that double-labelling immunohistochemistry for the detection of macrophage markers together with transcription factors may be suitable to characterise macrophage polarisation in situ. To test this hypothesis, we have studied conditions associated with Th1- and Th2-predominant immune responses: infectious mononucleosis and Crohn's disease for Th1 and allergic nasal polyps, oxyuriasis, wound healing and foreign body granulomas for predominant Th2 response. In all situations, CD163+ cells usually outnumbered CD68+ cells. Moreover, CD163+ cells, usually considered as M2 macrophages, co-expressing pSTAT1 and RBP-J were found in all conditions examined. The numbers of putative M1 macrophages were higher in Th1- than in Th2-associated diseases, while more M2 macrophages were seen in Th2- than in Th1 related disorders. In most Th1-related diseases, the balance of M1 over M2 cells was shifted towards M1 cells, while the reverse was observed for Th2-related conditions. Hierarchical cluster analysis revealed two distinct clusters: cluster I included Th1 diseases together with cases with high numbers of CD163+pSTAT1+, CD68+pSTAT1+, CD163+RBP-J+ and CD68+RBP-J+ macrophages; cluster II comprised Th2 conditions together with cases displaying high numbers of CD163+CMAF+ and CD68+CMAF+ macrophages. These results suggest that the detection of pSTAT1, RBP-J, and CMAF in the context of CD68 or CD163 expression is a suitable tool for the characterisation of macrophage polarisation in situ. Furthermore, CD163 cannot be considered a reliable M2 marker when used on its own.

Dataset Information

Identifying M1 Macrophage-Related Genes Through a Co-expression Network to Construct a Four-Gene Risk-Scoring Model for Predicting Thyroid Cancer Prognosis.

Publications

Identifying M1 Macrophage-Related Genes Through a Co-expression Network to Construct a Four-Gene Risk-Scoring Model for Predicting Thyroid Cancer Prognosis.

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