Prognostic Value of Immune-Related Genes in the Tumor Microenvironment of Bladder Cancer

Li, Faping; Teng, Haolin; Liu, Mingdi; Liu, Bin; Zhang, Difei; Xu, Zhixiang; Wang, Yishu; Zhou, Honglan

doi:10.3389/fonc.2020.01302

Cited by 38 publications

(37 citation statements)

References 48 publications

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“…Furthermore, the correlation between COL1A1 and the B7-CD28 ligand-receptor family was analyzed based on TCGA database, indicating there was a close positive correlation between COL1A1 and CD276, which was consistent with the prognostic value of COL1A1. Previous study shown that COL1A1 was correlated positively with the tumor infiltration levels of CD4+ T cells and macrophages in Bladder Cancer (Li et al, 2020). CD276, expressed in multiple tumor lines, tumor-infiltrating dendritic cells, and macrophages, can inhibit T-cell activation and autoimmunity (Son, Kwon & Cho, 2019).…”

Section: Discussionmentioning

confidence: 95%

COL1A1 is a prognostic biomarker and correlated with immune infiltrates in lung cancer

Geng

Shen

et al. 2021

PeerJ

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Objective Lung cancer (LC) is one of the top ten malignant tumors and the first leading cause of cancer-related death among both men and women worldwide. It is imperative to identify immune-related biomarkers for early LC diagnosis and treatment. Methods Three Gene Expression Omnibus (GEO) datasets were selected to acquire the differentially expressed genes(DEGs) between LC and normal lung samples through GEO2R tools of NCBI. To identify hub genes, the DEGs were performed functional enrichment analysis, the protein–protein interaction (PPI) network construction, and Lasso regression. Then, a nomogram was constructed to predict the prognosis of patients with carcinoma based on hub genes. We further evaluated the influence of COL1A1 on clinical prognosis using GSE3141, GSE31210, and TCGA database. Also, the correlations between COL1A1 and cancer immune infiltrates and the B7-CD28 family was investigated via TIMER and GEPIA. Further analysis of immunohistochemistry shown that the COL1A1 expression level is positively correlated with CD276 expression level. Results By difference analysis, there were 340 DEGs between LC and normal lung samples. Then, we picked out seven hub genes, which were identified as components of the risk signature to divide LC into low and high-risk groups. Among them, the expression of COL1A1 is highly correlated with overall survival(OS) and progression-free survival (PFS) (p < 0.05). Importantly, there is a moderate to strong positive relationships between COL1A1 expression level and infiltration level of CD4+ T cells, Macrophage, Neutrophil, and Dendritic cell, as well as CD276 expression level. Conclusion These findings suggest that COL1A1 is correlated with prognosis and immune infiltrating levels, including CD4+ T cells, Macrophage, Neutrophil, and Dendritic cell, as well as CD276 expression level, indicating COL1A1 can be a potential immunity-related biomarker and therapeutic target in LC.

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Section: Discussionmentioning

confidence: 95%

COL1A1 is a prognostic biomarker and correlated with immune infiltrates in lung cancer

Geng

Shen

et al. 2021

PeerJ

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“…Targeted inhibition of ANXA1 can reduce the function of Tregs and shrink breast tumors [ 46 ]. COL4A2 expression is positively correlated with the presence of macrophage and dendritic cell infiltration in cervical-cell cancer, while COL1A1 is positively correlated with tumor infiltration levels of macrophages and CD4 + T cells in bladder cancer [ 47 , 48 ].…”

Section: Discussionmentioning

confidence: 99%

Construction of a Prognostic Gene Signature Associated with Immune Infiltration in Glioma: A Comprehensive Analysis Based on the CGGA

Gong

Liu

Xiong

et al. 2021

Journal of Oncology

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Background. Tumor microenvironment (TME) is closely related to the progression of glioma and the therapeutic effect of drugs on this cancer. The aim of this study was to develop a signature associated with the tumor immune microenvironment using machine learning. Methods. We downloaded the transcriptomic and clinical data of glioma patients from the Chinese Glioma Genome Atlas (CGGA) databases (mRNAseq_693). The single-sample Gene Set Enrichment Analysis (ssGSEA) database was used to quantify the relative abundance of immune cells. We divided patients into two different infiltration groups via unsupervised clustering analysis of immune cells and then selected differentially expressed genes (DEGs) between the two groups. Survival-related genes were determined using Cox regression analysis. We next randomly divided patients into a training set and a testing set at a ratio of 7 : 3. By integrating the DEGs into least absolute shrinkage and selection operator (LASSO) regression analysis in the training set, we were able to construct a 15-gene signature, which was validated in the testing and total sets. We further validated the signature in the mRNAseq_325 dataset of CGGA. Results. We identified 74 DEGs associated with tumor immune infiltration, 70 of which were significantly associated with overall survival (OS). An immune-related gene signature was established, consisting of 15 key genes: adenosine triphosphate (ATP)-binding cassette subfamily C member 3 (ABCC3), collagen type IV alpha 1 chain (COL4A1), podoplanin (PDPN), annexin A1 (ANXA1), COL4A2, insulin-like growth factor binding protein 2 (IGFBP2), serpin family A member 3 (SERPINA3), CXXC-type zinc finger protein 11 (CXXC11), junctophilin 3 (JPH3), secretogranin III (SCG3), secreted protein acidic and rich in cysteine (SPARC)-related modular calcium-binding protein 1 (SMOC1), Cluster of Differentiation 14 (CD14), COL1A1, S100 calcium-binding protein A4 (S100A4), and transforming growth factor beta 1 (TGF-β1). The OS of patients in the high-risk group was worse than that of patients in the low-risk group. GSEA showed that interleukin-6 (IL-6)/Janus kinase (JAK)/signal transducer and activator of transcription (STAT3) signaling, interferon gamma (IFN-γ) response, angiogenesis, and coagulation were more highly enriched in the high-risk group and that oxidative phosphorylation was more highly enriched in the low-risk group. Conclusion. We constructed a stable gene signature associated with immune infiltration to predict the survival rates of glioma patients.

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“…The interactions between numerous components of TME may result in different tumor growth patterns and prognosis, but the precise mechanisms remain to be elucidated and thoroughly investigated. Li et al recently found that three immune-related genes ( COL1A1 , COMP , and SERPINE2 ) are significantly correlated in both TME and tumor prognosis, thus revealing the possible effects and pathways of microenvironment on tumor prognosis ( 22 ). Huo et al analyzed the scores of 22 immune cells in tumor tissues using gene expression data from 816 HNSCC patients and developed a TME score-based prognostic risk model accordingly.…”

Section: Discussionmentioning

confidence: 99%

CCR4, CCR8, and P2RY14 as Prognostic Factors in Head and Neck Squamous Cell Carcinoma Are Involved in the Remodeling of the Tumor Microenvironment

Meng

Qin

et al. 2021

Front. Oncol.

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The tumor microenvironment (TME) plays a critical role in the initiation and progression of cancer. However, the specific mechanism of its regulation in head and neck squamous cell carcinoma (HNSCC) remains unclear. In this study, we first applied the ESTIMATE method to calculate the immune and stromal scores in patients’ tumor tissues from The Cancer Genome Atlas (TCGA) database. GSE41613, GSE30784, and GSE37991 data sets from the Gene Expression Omnibus (GEO) database were recruited for further validation. Differentially expressed genes (DEGs) were identified and then analyzed by Cox regression analysis and protein-protein interaction (PPI) network construction. DEGs significantly associated with prognosis and TME will be identified as hub genes. These genes were also validated at the protein level by immunohistochemical analysis of 10 pairs of primary tumor tissues and the adjacent normal tissues from our institution. The relationship between hub genes expression and immune cell fraction estimated by CIBERSORT software was also examined. 275 DEGs were significantly associated with TME. CCR4, CCR8, and P2RY14 have then identified as hub genes by intersection Cox and PPI analysis. Further investigation revealed that the expression of CCR4, CCR8, and P2RY14 was negatively correlated with clinicopathological characteristics (clinical stage, T stage) and positively associated with survival in HNSCC patients, especially in male patients. The expression of CCR8 and P2RY14 was lower in males than in females. CCR8 and P2RY14 were differentially expressed in tumor tissues than normal tissues, and the results were validated at the protein level by immunohistochemistry experiments. Gene set enrichment analysis (GSEA) showed that the high expression groups’ hub genes were mainly enriched for immune-related activities. In the low-expression groups, genes were primarily enriched in metabolic pathways. CIBERSORT results showed that the expression of these genes was all negatively correlated with the fraction of memory B cells and positively correlated with the fraction of the other four cells, including naive B cells, resting T cells CD4 memory, T cells follicular helper, and T cells regulatory (Tregs). The results suggest that CCR4, CCR8, and P2RY14 may be responsible for maintaining the immune dominance of TME, thus leading to a better prognosis.

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Prognostic Value of Immune-Related Genes in the Tumor Microenvironment of Bladder Cancer

Cited by 38 publications

References 48 publications

COL1A1 is a prognostic biomarker and correlated with immune infiltrates in lung cancer

COL1A1 is a prognostic biomarker and correlated with immune infiltrates in lung cancer

Construction of a Prognostic Gene Signature Associated with Immune Infiltration in Glioma: A Comprehensive Analysis Based on the CGGA

CCR4, CCR8, and P2RY14 as Prognostic Factors in Head and Neck Squamous Cell Carcinoma Are Involved in the Remodeling of the Tumor Microenvironment

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