IFI30 as a prognostic biomarker and correlation with immune infiltrates in glioma

Jiang, Wei; Zheng, Fei-Fei; Yao, Taotao; Gong, Fang; Zheng, Wenjie; Yao, Ninghua

doi:10.21037/atm-21-5569

Cited by 10 publications

(9 citation statements)

References 31 publications

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“…4b ) TGM2 correlated genes were ranked based on their Z-score and mapped using the TCGA SKCM dataset in LinkedOmics database [ 27 ]. Interestingly, many genes known to be associated with the activation of immune-related cancerous processes, response to immunotherapy, and tumor immune infiltration were found to be associated with TGM2 expression like C3AR1 ( r = 0.5712, p = 2.956e-10) [ 28 ], LAIR1 ( r = 0.5612, p = 7.015e-10) [ 29 ], FCER1G ( r = 0.5588, p = 0,8553e-10) [ 30 ], SLAMF8 ( r = 0.5562, p = 1.066e-10) [ 31 ], IFI30 ( r = 0.5472, p = 2.228e-10) [ 32 ], TYROBP ( r = 0.5462, p = 2.416e-09) [ 33 ], and SIGLEC-9 ( r = 0.5442, p = 2.829e-09) [ 34 ], which were found in the top ten TGM2 positively correlated ones through two-sided Pearson’s ( R ) coefficient analysis (Fig. S 2 ).…”

Section: Resultsmentioning

confidence: 99%

Promising prognostic value of Transglutaminase type 2 and its correlation with tumor-infiltrating immune cells in skin cutaneous melanoma

et al. 2022

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Tissue Transglutaminases (TGs) are crosslinking enzymes with pleiotropic functions that have been linked to the development and progression of numerous cancers, with a recent focus on their ability to remodel the tumor microenvironment. Although several pieces of evidence demonstrated their importance in the regulation of the major signaling pathways that control oncogenesis, the correlation between TGs with clinical and pathological features remains controversial and to be further explored. Moreover, an assessment of the TGs alterations together with a functional analysis associated with clinical features and prognostic values are still lacking and would help to understand these intricacies, particularly in human cancers. In the present study, we processed data from numerous public datasets to investigate TGs distribution and prognostic signature in cancer patients. Here, we found that skin cutaneous melanoma (SKCM) shows the highest abundance of TGs mutations among the other human cancers. Interestingly, among all the TGs, TG2 is the only member whose expression is associated with a better overall survival in SKCM, although its expression increases with the worsening of the tumor phenotype. Our analysis revealed a strong positive association between TG2 expression and anti-tumoral immune response, which would explain the relationship between high mRNA levels and better overall survival. Our data suggest that TG2 may be presented as a new promising immune biomarker of prognosis in SKCM, which may contribute to identifying patients who would benefit the most from adjuvant immunotherapy.

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Section: Resultsmentioning

confidence: 99%

Promising prognostic value of Transglutaminase type 2 and its correlation with tumor-infiltrating immune cells in skin cutaneous melanoma

et al. 2022

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“…More follow-up studies will be performed to verify the prognostic role of these genes in patients with GBM. In fact, studies have reported that IFI30 and HLA-DMA could be related to immune response process [ 24 , 42 ] which play tumor-regulatory roles in GBM [ 43 ]. The association between IFI30 or HLA-DMA and GBM immune microenvironment will be the topic of further research.…”

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in glioblastoma using bioinformatics analysis

Yin

Hao

et al. 2022

BioMed Eng OnLine

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Background Glioblastoma (GBM) is the most malignant grade of glioma. Highly aggressive characteristics of GBM and poor prognosis cause GBM-related deaths. The potential prognostic biomarkers remain to be demonstrated. This research builds up predictive gene targets of expression alterations in GBM utilizing bioinformatics analysis. Methods and results The microarray datasets (GSE15824 and GSE16011) associated with GBM were obtained from Gene Expression Omnibus (GEO) database to identify the differentially expressed genes (DEGs) between GBM and non-tumor tissues. In total, 719 DEGs were obtained and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for function enrichment analysis. Furthermore, we constructed protein–protein Interaction (PPI) network among DEGs utilizing Search Tool for the Retrieval of Interacting Genes (STRING) online tool and Cytoscape software. The DEGs of degree > 10 was selected as hub genes, including 73 upregulated genes and 21 downregulated genes. Moreover, MCODE application in Cytoscape software was employed to identify three key modules involved in GBM development and prognosis. Additionally, we used the Gene expression profiling and interactive analyses (GEPIA) online tool to further confirm four genes involving in poor prognosis of GBM patients, including interferon-gamma-inducible protein 30 (IFI30), major histocompatibility complex class II-DM alpha (HLA-DMA), Prolyl 4-hydroxylase beta polypeptide (P4HB) and reticulocalbin-1 (RCN1). Furthermore, the correlation analysis indicated that the expression of IFI30, an acknowledged biomarker in glioma, was positively correlated with HLA-DMA, P4HB and RCN1. RCN1 expression was positively correlated with P4HB and HLA-DMA. Moreover, qRT-PCR and immunohistochemistry analysis further validated the upregulation of four prognostic markers in GBM tissues. Conclusions Analysis of multiple datasets combined with global network information and experimental verification presents a successful approach to uncover the risk hub genes and prognostic markers of GBM. Our study identified four risk- and prognostic-related gene signatures, including IFI30, HLA-DMA, P4HB and RCN1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of GBM.

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“…Meanwhile, FCRL3 contributed to the activation of B cells, while suppressing the differentiation of plasma cells ( 36 ). IFI30 has been known as a prognosis factor for many cancers, such as glioma ( 37 , 38 ) and breast cancer ( 39 ). GILT, the protein encoded by IFI30, was shown to enhance the T cells mediated immune surveillance against tumor cells, implying its druggable potential in tumor therapy ( 40 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of CD8+ T cell infiltration-related genes and their prognostic values in cervical cancer

Shen

Wang²,

Li³

et al. 2022

Front. Oncol.

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Cervical cancer is a female-specific cancer with relatively high morbidity and mortality. As known to all, immune cell infiltrations in the cancer microenvironment are closely related to the cancer diagnosis and prognosis. Here we revealed that the CD8+ T cell infiltration was significantly upregulated in cervical cancer versus normal cervix uteri samples. Through univariate and multivariate cox analyses, we discovered that the CD8+ T cell infiltration was the only independent beneficial factor for the prognosis of cervical cancer. To explore the genes associated with the CD8+ T cell infiltration in cervical cancer, we performed the WGCNA analysis on the differentially expressed genes (DEGs) of cervical cancer versus normal cervix uteri tissues. As a result, 231 DEGs were found to be associated with CD8+ T cell infiltration in cervical cancer. Subsequently, with the Cytoscape analysis, we identified 105 hub genes out of the 231 DEGs. To further explore the genes that might be responsible for the prognosis of cervical cancer, we performed a univariate cox analysis followed by a LASSO assay on the 105 hub genes and located four genes (IGSF6, TLR10, FCRL3, and IFI30) finally. The four genes could be applied to the prediction of the prognosis of cervical cancer, and relatively higher expression of these four genes predicted a better prognosis. These findings contributed to our understanding of the prognostic values of CD8+ T cell infiltration and its associated genes in cervical cancer and thus might benefit future immune-related therapies.

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IFI30 as a prognostic biomarker and correlation with immune infiltrates in glioma

Cited by 10 publications

References 31 publications

Promising prognostic value of Transglutaminase type 2 and its correlation with tumor-infiltrating immune cells in skin cutaneous melanoma

Promising prognostic value of Transglutaminase type 2 and its correlation with tumor-infiltrating immune cells in skin cutaneous melanoma

Identification of novel prognostic targets in glioblastoma using bioinformatics analysis

Identification of CD8+ T cell infiltration-related genes and their prognostic values in cervical cancer

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