The prognostic value of plasma complement factor B (CFB) in thyroid carcinoma

Wu, Pu; Shi, Jinyuan; Sun, Wei; Zhang, Hao

doi:10.1080/21655979.2021.2005745

Cited by 11 publications

(7 citation statements)

References 44 publications

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“…The model involved CFB, MAL2, PSME2, MRPL13, HMGB3, DCTPP1, SHCBP1, SLC35A2, and EVA1B, of which CFB, PSME2, and EVA1B were used as cancer prognostic protective factors, and the remaining genes were used as prognostic risk factors. CFB is stably upregulated in various cancer tissues, and in studies of adenocarcinoma, this gene has been shown to alleviate cancer progression by activating cellular immune responses, consistent with the trend of this study in predicting progression of breast cancer [35]. PSME2 has been less studied in cancer, and reports indicate that this gene is a typical poor prognostic marker in renal cell carcinoma and promotes malignant tumor progression by inhibiting autophagy [36].…”

Section: Discussionsupporting

confidence: 84%

Construction and Validation of a Prognostic Model Based on mRNAsi-Related Genes in Breast Cancer

Zhao

Lin

2022

Computational and Mathematical Methods in Medicine

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Background. Breast cancer is a big threat to the women across the world with substantial morbidity and mortality. The pressing matter of our study is to establish a prognostic gene model for breast cancer based on mRNAsi for predicting patient’s prognostic survival. Methods. From The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we downloaded the expression profiles of genes in breast cancer. On the basis of one-class logistic regression (OCLR) machine learning algorithm, mRNAsi of samples was calculated. Kaplan-Meier (K-M) and Kruskal-Wallis (K-W) tests were utilized for the assessment of the connection between mRNAsi and clinicopathological variables of the samples. As for the analysis on the correlation between mRNAsi and immune infiltration, ESTIMATE combined with Spearman test was employed. The weighted gene coexpression network analysis (WGCNA) network was established by utilizing the differentially expressed genes in breast cancer, and the target module with the most significant correlation with mRNAsi was screened. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to figure out the biological functions of the target module. As for the construction of the prognostic model, univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were performed on genes in the module. The single sample gene set enrichment analysis (ssGSEA) and tumor mutational burden were employed for the analysis on immune infiltration and gene mutations in the high- and low-risk groups. As for the analysis on whether this model had the prognostic value, the nomogram and calibration curves of risk scores and clinical characteristics were drawn. Results. Nine mRNAsi-related genes (CFB, MAL2, PSME2, MRPL13, HMGB3, DCTPP1, SHCBP1, SLC35A2, and EVA1B) comprised the prognostic model. According to the results of ssGSEA and gene mutation analysis, differences were shown in immune cell infiltration and gene mutation frequency between the high- and low-risk groups. Conclusion. Nine mRNAsi-related genes screened in our research can be considered as the biomarkers to predict breast cancer patients’ prognoses, and this model has a potential relationship with individual somatic gene mutations and immune regulation. This study can offer new insight into the development of diagnostic and clinical treatment strategies for breast cancer.

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Section: Discussionsupporting

confidence: 84%

Construction and Validation of a Prognostic Model Based on mRNAsi-Related Genes in Breast Cancer

Zhao

Lin

2022

Computational and Mathematical Methods in Medicine

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“…There is not always a direct correlation between gene alteration and the invasiveness of TC, whereas the altered gene expression may affect the TME to influence the tumor angiogenesis and dissemination [ 6 ]. It has been well-recognized that the TME is beneficial for many stages of tumor development from occurrence to metastasis, and it largely affects tumor treatment and the clinical outcome [ 38 ]. However, current studies concerning TME of TC are contradictory.…”

Section: Discussionmentioning

confidence: 99%

GATA binding protein 1 recruits histone deacetylase 2 to the promoter region of nuclear receptor binding protein 2 to affect the tumor microenvironment and malignancy of thyroid carcinoma

Jiang

Chen

et al. 2022

Bioengineered

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The tumor microenvironment (TME) and activated angiogenesis in thyroid carcinoma (TC) are critical for tumor growth and metastasis. Nuclear receptor binding protein 2 ( NRBP2 ) has been suggested as a tumor suppressor. This study examines the function of NRBP2 in the progression of TC and the regulatory mechanism. By analyzing bioinformatic tools including GSE165724 dataset and the Cancer Genome Atlas system, we predicted NRBP2 as a poorly expressed gene in TC. Decreased NRBP2 expression was detected in TC tumor tissues and cells. Poor expression of NRBP2 was linked to unfavorable prognosis of patients. GATA binding protein 1 ( GATA1 ) was found as a negative regulator of NRBP2 . It recruited histone deacetylase2 ( HDAC2 ) to the NRBP2 promoter to trigger histone deacetylation. NRBP2 overexpression suppressed growth of TC cells, and it reduced expression of TME markers, M2 polarization of macrophages, and angiogenesis in TC. Similar results were reproduced in vivo in nude mice. However, the anti-oncogenic roles of NRBP2 were blocked after further overexpression of GATA1 or HDAC2 . In summary, this study demonstrates that GATA1 recruits HDAC2 to the NRBP2 promoter and enhances the TME and angiogenesis in TC cells.

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“…Immune cells have critical effects on tumor microenvironment [ 24 ]. Plasma complement factor B expression was linearly associated with macrophage M1 cells in the tumor microenvironment of thyroid carcinoma [ 25 ]. In hepatocellular carcinoma, resting mast cells in the tumor microenvironment were adversely linked with PD-L1 mRNA expression [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

ADGRD1 as a Potential Prognostic and Immunological Biomarker in Non-Small-Cell Lung Cancer

Tian

et al. 2022

BioMed Research International

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ADGRD1 (GPR133), an adhesion G protein-coupled receptor (GPCR), has been linked to cancer. However, the prognostic value and regulatory function within non-small-cell lung cancer (NSCLC) is still unclear. This work adopted various bioinformatics methods, including publicly available databases as well as real-time PCR (RT-PCR), for detecting ADGRD1 expression level and investigating the correlation between ADGRD1 expression level and prognosis, tumor mutational burden (TMB), microsatellite instability (MSI), immune infiltrating cells, immune-related genes, and targeted regulation mechanisms in NSCLC. According to the results, ADGRD1 expression decreased within NSCLC, which might be the factor predicting prognosis of NSCLC. Meanwhile, ADGRD1 showed significant correlation with TMB and MSI, respectively, as well as immune cell infiltrating levels in lung adenocarcinoma (LUAD), which were primarily linked to macrophage M1, mast cell resting, T cell CD4 memory activated, and T cell CD4 memory resting and were associated with mast cell activated and mast cell resting in lung squamous cell carcinoma (LUSC). The most promising upstream regulation pathways of ADGRD1 were likely miR-142-5p, miR-93-5p, and miR-17-5p, which were overexpressed and associated with poor prognosis in NSCLC. ADGRD1 and immune-related genes correlated with ADGRD1 were shown to be enriched in “positive regulation of leukocyte activation,” “external side of plasma membrane,” “receptor ligand activity,” and “cytokine-cytokine receptor interaction” pathways. ADGRD1 expression and regulation may be critical in determining NSCLC prognosis.

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The prognostic value of plasma complement factor B (CFB) in thyroid carcinoma

Cited by 11 publications

References 44 publications

Construction and Validation of a Prognostic Model Based on mRNAsi-Related Genes in Breast Cancer

Construction and Validation of a Prognostic Model Based on mRNAsi-Related Genes in Breast Cancer

GATA binding protein 1 recruits histone deacetylase 2 to the promoter region of nuclear receptor binding protein 2 to affect the tumor microenvironment and malignancy of thyroid carcinoma

ADGRD1 as a Potential Prognostic and Immunological Biomarker in Non-Small-Cell Lung Cancer

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