C1orf106, an innate immunity activator, is amplified in breast cancer and is required for basal-like/luminal progenitor fate decision

Ma, Ji; Liu, Cheng; Yang, Decao; Song, Jianguo; Zhang, Jing; Wang, Tianzhuo; Wang, Mengyuan; Xu, Weizhi; Li, Xueying; Ding, Shigang; Zhan, Jun; Zhang, Hongquan

doi:10.1007/s11427-019-9570-y

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…Downregulations of C1orf106 occur in PCs compared to prostate tissues, PCs with TMPRSS2-ERG fusion compared to those without the fusion, and mPCs compared to primary PCs ( Figures 3A-5). C1orf106 plays a role in activating innate immunity in breast cancer [65], suggesting that its downregulation contributes to the evasion of immune checkpoints during PC initiation and progression. In view of the emerging roles of escaping immune surveillance in all stages of tumorigenesis, the potential contributions of C1orf106 to this aspect of PC certainly warrants further investigation in future.…”

Section: Discussionmentioning

confidence: 99%

Differential Expression of a Panel of Ten CNTN1-Associated Genes during Prostate Cancer Progression and the Predictive Properties of the Panel towards Prostate Cancer Relapse

Chow

Kapoor

et al. 2021

Genes

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Contactin 1 (CNTN1) is a new oncogenic protein of prostate cancer (PC); its impact on PC remains incompletely understood. We observed CNTN1 upregulation in LNCaP cell-derived castration-resistant PCs (CRPC) and CNTN1-mediated enhancement of LNCaP cell proliferation. CNTN1 overexpression in LNCaP cells resulted in enrichment of the CREIGHTON_ENDOCRINE_THERAPY_RESISTANCE_3 gene set that facilitates endocrine resistance in breast cancer. The leading-edge (LE) genes (n = 10) of this enrichment consist of four genes with limited knowledge on PC and six genes novel to PC. These LE genes display differential expression during PC initiation, metastatic progression, and CRPC development, and they predict PC relapse following curative therapies at hazard ratio (HR) 2.72, 95% confidence interval (CI) 1.96–3.77, and p = 1.77 × 10-9 in The Cancer Genome Atlas (TCGA) PanCancer cohort (n = 492) and HR 2.72, 95% CI 1.84–4.01, and p = 4.99 × 10−7 in Memorial Sloan Kettering Cancer Center (MSKCC) cohort (n = 140). The LE gene panel classifies high-, moderate-, and low-risk of PC relapse in both cohorts. Additionally, the gene panel robustly predicts poor overall survival in clear cell renal cell carcinoma (ccRCC, p = 1.13 × 10−11), consistent with ccRCC and PC both being urogenital cancers. Collectively, we report multiple CNTN1-related genes relevant to PC and their biomarker values in predicting PC relapse.

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Section: Discussionmentioning

confidence: 99%

Differential Expression of a Panel of Ten CNTN1-Associated Genes during Prostate Cancer Progression and the Predictive Properties of the Panel towards Prostate Cancer Relapse

Chow

Kapoor

et al. 2021

Genes

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“…Thus, targeted screening for WFDC2 protein inhibition might benefits for the treatment of ovarian cancer. INAVA gene, also called C1orf106, has been recently reported essential for optimal MAPK and NF-κB activation in primary human cells by recruiting 14-3-3τ [40,41]. And inflammatory bowel disease (IBD)-associated polymorphisms in IN-AVA is revealed to play important roles in intestinal immune homeostasis by regulating pattern recognition receptor (PRR)-mediated signaling transduction and bacterial clearance.…”

Section: Discussionmentioning

confidence: 99%

Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer

Zhao¹,

Zhang³

et al. 2020

Bioscience Reports

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Background: Ovarian cancer causes high mortality rate worldwide, and despite numerous attempts, the outcome for patients with ovarian cancer are still not well improved. Microarray based gene expressional analysis provides with valuable information for discriminating functional genes in ovarian cancer development and progression. However, due to the differences in experimental design, the results varied significantly across individual datasets. Methods: In the present study, the data of gene expression in ovarian cancer was downloaded from Gene Expression Omnibus and 16 studies were included. A meta-analysis based gene expression analysis was performed to identify differentially expressed genes (DEGs). The most differentially expressed genes in our meta-analysis were selected for gene expression and gene function validation. Results: A total of 972 DEGs with P-valueCD24, SOX17, WFDC2, EPCAM, INAVA, and down-regulated AOX1 were revealed to be with consistent expressional patterns in clinical patient samples of ovarian cancer. Gene functional analysis demonstrated that up-regulated WFDC2 and INAVA promoted ovarian cancer cell migration, WFDC2 enhanced cell proliferation, while down-regulated AOX1 was functional in inducing cell apoptosis of ovarian cancer. Conclusion: Our study shed light on the molecular mechanisms underlying the development of ovarian cancer, and facilitated the understanding of novel diagnostic and therapeutic targets in ovarian cancer.

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“…The above studies indicated that the SNPs in the C1orf106 gene may affect CD4 + T-cell polarization through methylation. Overexpression of C1orf106 was associated with invasive breast cancer and its poor prognosis and could be used as a novel marker to predict the aggressiveness and prognosis of breast cancer ( 38 ). In addition, 2 SNPs (rs442905 and rs59457695) in the C1orf106 gene and protein expression levels could be used to predict the therapeutic effect of infliximab in patients with Crohn’s disease ( 39 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of Biomarkers Associated With CD4+ T-Cell Infiltration With Gene Coexpression Network in Dermatomyositis

Huang

Zhang

et al. 2022

Front. Immunol.

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BackgroundDermatomyositis is an autoimmune disease characterized by damage to the skin and muscles. CD4+ T cells are of crucial importance in the occurrence and development of dermatomyositis (DM). However, there are few bioinformatics studies on potential pathogenic genes and immune cell infiltration of DM. Therefore, this study intended to explore CD4+ T-cell infiltration–associated key genes in DM and construct a new model to predict the level of CD4+ T-cell infiltration in DM.MethodsGSE46239, GSE142807, GSE1551, and GSE193276 datasets were downloaded. The WGCNA and CIBERSORT algorithms were performed to identify the most correlated gene module with CD4+ T cells. Matascape was used for GO enrichment and KEGG pathway analysis of the key gene module. LASSO regression analysis was used to identify the key genes and construct the prediction model. The correlation between the key genes and CD4+ T-cell infiltration was investigated. GSEA was performed to research the underlying signaling pathways of the key genes. The key gene-correlated transcription factors were identified through the RcisTarget and Gene-motif rankings databases. The miRcode and DIANA-LncBase databases were used to build the lncRNA-miRNA-mRNA network.ResultsIn the brown module, 5 key genes (chromosome 1 open reading frame 106 (C1orf106), component of oligomeric Golgi complex 8 (COG8), envoplakin (EVPL), GTPases of immunity-associated protein family member 6 (GIMAP6), and interferon-alpha inducible protein 6 (IFI6)) highly associated with CD4+ T-cell infiltration were identified. The prediction model was constructed and showed better predictive performance in the training set, and this satisfactory model performance was validated in another skin biopsy dataset and a muscle biopsy dataset. The expression levels of the key genes promoted the CD4+ T-cell infiltration. GSEA results revealed that the key genes were remarkably enriched in many immunity-associated pathways, such as JAK/STAT signaling pathway. The cisbp_M2205, transcription factor-binding site, was enriched in C1orf106, EVPL, and IF16. Finally, 3,835 lncRNAs and 52 miRNAs significantly correlated with key genes were used to build a ceRNA network.ConclusionThe C1orf106, COG8, EVPL, GIMAP6, and IFI6 genes are associated with CD4+ T-cell infiltration. The prediction model constructed based on the 5 key genes may better predict the level of CD4+ T-cell infiltration in damaged muscle and lesional skin of DM. These key genes could be recognized as potential biomarkers and immunotherapeutic targets of DM.

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C1orf106, an innate immunity activator, is amplified in breast cancer and is required for basal-like/luminal progenitor fate decision

Cited by 11 publications

References 33 publications

Differential Expression of a Panel of Ten CNTN1-Associated Genes during Prostate Cancer Progression and the Predictive Properties of the Panel towards Prostate Cancer Relapse

Differential Expression of a Panel of Ten CNTN1-Associated Genes during Prostate Cancer Progression and the Predictive Properties of the Panel towards Prostate Cancer Relapse

Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer

Identification of Biomarkers Associated With CD4+ T-Cell Infiltration With Gene Coexpression Network in Dermatomyositis

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