Low expression of HIF1AN accompanied by less immune infiltration is associated with poor prognosis in breast cancer.

Background: Hypoxia-inducible factor 1-alpha (HIF-1α) stability and transcriptional action are reduced by the hypoxia-inducible factor 1-alpha subunit suppressor (HIF1AN). Its inappropriate expression is associated with the development of cancer and immune control. It is yet unknown how HIF1AN, clinical outcomes, and immune involvement in breast cancer (BC) are related. Methods: Using the GEPIA, UALCAN, TIMER, Kaplan-Meier plotter, and TISIDB datasets, a thorough analysis of HIF1AN differential expression, medical prognosis, and the relationship between HIF1AN and tumor-infiltrating immune cells in BC was conducted. Quantitative real-time PCR (qRT-PCR) analysis of BC cells were used for external validation. Results: The findings revealed that, as compared to standard specimens, BC cells had significantly lower levels of HIF1AN expression. Good overall survival (OS) for BC was associated with higher HIF1AN expression. Additionally, in BC, the expression of HIF1AN was closely associated with the chemokines and immune cell infiltration, including neutrophils, macrophages, T helper cells, B cells, Tregs, monocytes, dendritic cells, and NK cells. A high correlation between HIF1AN expression and several immunological indicators of T-cell exhaustion was particularly revealed by the bioinformatic study. Conclusions: HIF1AN is a predictive indicator for breast tumors, and it is useful for predicting survival rates.

Integrated analysis of the functions and clinical implications of exosome circRNAs in colorectal cancer.

Background: Exosome circRNAs (Exo-circRNAs) regulate cancer progression and intercellular crosstalk in the tumor microenvironment. However, their biological functions and potential clinical importance in colorectal cancer (CRC) remain unknown. Methods: We used exoRBase 2.0 data to identify significant differentially expressed Exo-circRNAs (Exo-DEcircRNAs) in CRC patients and healthy individuals. The least absolute shrinkage and selector operation algorithm, support vector machine-recursive feature elimination, and multivariate Cox regression analyses were used to select candidate Exo-circRNAs and constructed a diagnostic model. Quantitative reverse transcription-polymerase chain reaction analysis was performed to confirm the expression of Exo-circRNAs in the serum samples of patients. Furthermore, we constructed an exosome circRNA-miRNA-mRNA network for CRC. Upregulated target mRNAs in the exosome competing endogenous RNA (Exo-ceRNA) network were used for functional and pathway enrichment analyses. We identified 22 immune cell types in CRC patients using CIBERSORT. Correlation analysis revealed the relationship between Exo-ceRNA networks and immune-infiltrating cells. The relationship between target mRNAs and immunotherapeutic response was also explored. Finally, using the Kaplan-Meier survival curve, a prognostic upregulated target mRNA was screened. We constructed a survival-related Exo-ceRNA subnetwork and explored the correlation between the Exo-ceRNA subnetwork and immune-infiltrating cells. Results: The constructed diagnostic model had a high area under the curve (AUC) value in both the training and validation sets (AUC = 0.744 and AUC = 0.741, respectively). qRT-PCR confirmed that the Exo-circRNAs were differentially expressed in CRC serum samples. We constructed Exo-ceRNA networks based on the interactions among seven upregulated Exo-DEcircRNAs, eight differentially expressed miRNAs, and twenty-two differentially expressed mRNAs in CRC. Functional enrichment analysis revealed that the upregulated target mRNAs were significantly enriched in cytoskeletal motor activity and the PI3K-Akt signaling pathway. Co-expression analysis showed a significant correlation between the Exo-ceRNA networks and immune cells. The significant correlation was observed between target mRNAs and the immunotherapeutic response. Additionally, based on the prognostic upregulated target gene (RGS2), we constructed a survival-related Exo-ceRNA subnetwork (Exosome hsa_circ_0050334-hsa_miR_182_5p-RGS2). CIBERSORT results revealed that the Exo-ceRNA subnetwork correlated with M2 macrophages (P = 4.6e-07, R = 0.31). Conclusions: Our study identified an Exo-diagnostic model, established Exo-ceRNA networks, and explored the correlation between Exo-ceRNA networks and immune cell infiltration in CRC. These findings elucidated the biological functions of Exo-circRNAs and their potential clinical implications.

Identification of Hub Genes and Biological Pathways in Inclusion Body Myositis Using Bioinformatics Analysis.

BACKGROUND: Inclusion body myositis (IBM) is a unique idiopathic inflammatory myopathy with unclear pathogenesis and poor prognosis. Although previous publications have identified some molecular biomarkers, the value of these biomarkers is unknown. OBJECTIVE: To identify hub genes and signaling pathways related to IBM for understanding the IBM-related mechanisms and providing guidance for therapy development. METHODS: Two microarray datasets (GSE3112 and GSE128470) were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R was used to detect differentially expressed genes (DEGs) between IBM and normal muscle tissues. The hub genes were determined using protein-protein interaction (PPI) network in Cytoscape. The specific signaling pathways and biological functions of IBM were identified using GO, KEGG, and GSEA enrichment analyses. Moreover, CIBERSORT was applied to estimate the expression level of 22 immune cell types in IBM and normal muscle tissue. The relationship between the immune cell types and hub genes was then explored. RESULTS: A total of 219 DEGs and 10 hub genes were identified. Enrichment analyses revealed that the chemokine signaling pathway, cellular response to interferon-gamma, and P53 pathway have crucial roles in IBM. Immune infiltration analyses showed that IBM was associated with high level of CD8 T cells, Tregs, and macrophages. Finally, five potential drugs were predicted for IBM patients through CMap (connectivity map) database. CONCLUSION: In this study, the underlying molecular mechanisms and immunological landscape of IBM were investigated, and thus may provide new directions for future research on IBM pathogenesis.

MXD3 as an Immunological and Prognostic Factor From Pancancer Analysis.

MAX dimerization protein 3 (MXD3), a transcriptional regulator of the MXD3 superfamily, is a part of the MYC-MAX-MXD network. However, its role in tumors has been reported in several cancers, such as B-cell acute lymphoblastic leukemia, medulloblastoma, neuroblastoma, and glioblastoma. Based on TCGA and GEO data, our first pancancer study of MXD3 confirmed the high expression of MXD3 in cancer tissues. Our results revealed that patients suffering from cancers with higher MXD3 expression had poor OS, DSS, DFI, and PFI. We further explored the methylation status of the MXD3 gene body and gene promoter in cancer. Patients with a higher MXD3 gene body have better OS, while the prognosis of patients with a high MXD3 promoter is more complex. We also verified the differential expression of three clinical phenotypes of MXD3: age, sex, and tumor stage, in a variety of tumors, suggesting a correlation between MXD3 and clinical characteristics. We explored the negative relationship between MXD3 and TMB and MSI in most types of cancer, indicating the poor prognosis of patients with high MXD3 expression. We further investigated the relationship between MXD3 and immune infiltrating cells and identified the relationship between MXD3 and immune genes, immunosuppressive genes, and antigen-presenting genes. All of the above findings established a solid relationship between MXD3 and the immune environment and immune cells. These results demonstrated that MXD3 might also be a potential immune factor. We also found a higher expression of MXD3 and promoter according to the increasing glioma WHO grade or histologic types. Glioma patients with high MXD3 or MXD3 promoter expression had poor survival. Finally, we used IHC to verify the higher expression of MXD3 in glioma samples compared to normal samples. Our study shows that MXD3, as a poor prognostic factor, plays a significant role in many cancers, especially glioma. Although more clinical evidence for MXD3 as a clinical therapeutic target and an immunotherapy site is needed, MXD3 can play an important guiding role in multiple clinical treatments, including immunotherapy and demethylation therapy.

Dermatomyositis: immunological landscape, biomarkers, and potential candidate drugs.

INTRODUCTION: Dermatomyositis (DM) is a rare inflammatory disease characterized by the invasion of the skin and muscles. Environmental, genetic, and immunological factors contribute to disease pathology. To date, no bioinformatics studies have been conducted on the potential pathogenic genes and immune cell infiltration in DM. Therefore, we aimed to identify differentially expressed genes (DEGs) and immune cells, as well as potential pathogenic genes and immune characteristics, which may be useful for the diagnosis and treatment of DM. METHOD: GSE1551, GSE5370, GSE39454, and GSE48280 from Gene Expression Omnibus were included in our study. Limma, ClusterProfiler, and Kyoto Encyclopedia of Genes and Genomes were used to identify DEGs, Gene Ontology (GO), and perform pathway analyses, respectively. Cytoscape was used to construct the protein-protein interaction (PPI) network. Small-molecule drugs were identified using a connectivity map (CMap), and the TIMER database was used to identify infiltrating cells. RESULTS: DEG analysis identified 12 downregulated and 163 upregulated genes. GO analysis showed that DEGs were enriched in immune-related pathways. Ten hub genes were identified from the PPI network. Additionally, CMap analysis showed that caffeic acid, sulfaphenazole, molindone, tiabendazole, and bacitracin were potential small-molecule drugs with therapeutic significance. We identified eight immune cells with differential infiltration in patients with DM and controls. Finally, we constructed a powerful diagnostic model based on memory B cells, M1, and M2 macrophages. CONCLUSIONS: This study explored the potential molecular mechanism and immunological landscape of DM and may guide future research and treatment of DM. KEY POINTS: • We explored the molecular mechanism and immunological landscape of dermatomyositis. • GO analysis showed that DEGs were enriched in immune-related pathways. • We predicted small-molecular drugs with potential therapeutic significance based on bioanalytical techniques. • We identified six immune cells with differential infiltration in patients with DM and controls.

Corrigendum: MXD3 as an Immunological and Prognostic Factor From Pancancer Analysis.

[This corrects the article DOI: 10.3389/fmolb.2021.702206.].