Exploring the role of Mir204/211 in HNSCC by the combination of bioinformatic analysis of ceRNA and transcription factor regulation

Identification of a novel ceRNA network related to prognosis and immunity in HNSCC based on integrated bioinformatic investigation

Head and neck squamous cell carcinoma (HNSCC) is characterized by an immunosuppression environment and necessitates the development of new immunotherapy response predictors. The study aimed to build a prognosis-related competing endogenous RNA (ceRNA) network based on immune-related genes (IRGs) and analyze its immunological signatures. Differentially expressed IRGs were identified by bioinformatics analysis with Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and ImmPort databases. Finally, via upstream prognosis-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) prediction and co-expression analysis, we built an immune-related ceRNA network (LINC00052/hsa-miR-148a-3p/PLAU) related to HNSCC patient prognosis. CIBERSORT analysis demonstrated that there were substantial differences in 11 infiltrating immune cells in HNSCC, and PLAU was closely correlated with 10 type cells, including T cells CD8+ (R =  - 0.329), T cells follicular helper (R = - 0.342) and macrophage M0 (R = 0.278). Methylation and Tumor Immune Dysfunction and Exclusion (TIDE) analyses revealed that PLAU upregulation was most likely caused by hypomethylation and that high PLAU expression may be associated with tumor immune evasion in HNSCC, respectively.

Genome-wide identification and functional analysis of circRNAs in Trichophyton rubrum conidial and mycelial stages

Background

Circular RNAs (circRNAs) are a group of noncoding RNAs that participate in gene expression regulation in various pathways. The essential roles of circRNAs have been revealed in many species. However, knowledge of circRNAs in fungi is still not comprehensive.

Results

Trichophyton rubrum (T. rubrum) is considered a model organism of human pathogenic filamentous fungi and dermatophytes. In this study, we performed a genome-wide investigation of circRNAs in T. rubrum based on high-throughput sequencing and ultimately identified 4254 circRNAs. Most of these circRNAs were specific to the conidial or mycelial stage, revealing a developmental stage-specific expression pattern. In addition, 940 circRNAs were significantly differentially expressed between the conidial and mycelial stages. PCR experiments conducted on seven randomly selected differentially expressed (DE-) circRNAs confirmed the circularized structures and relative expression levels of these circRNAs. Based on their genome locations, most circRNAs originated from intergenic regions, unlike those in plants and animals. Furthermore, we constructed circRNA-miRNA-mRNA regulatory networks that included 661 DE-circRNAs targeting 140 miRNAs and further regulating 2753 mRNAs. The relative expression levels of two randomly selected circRNA-miRNA-mRNA axes were investigated by qRT-PCR, and the competing endogenous RNA (ceRNA) network theory was validated. Functional enrichment analysis of the target genes suggested that they were significantly involved in posttranscriptional processes and protein synthesis as well as some small-molecule metabolism processes. CircRNAs are relatively more conserved in closely related dermatophytes but rarely conserved in distantly related species. Tru_circ07138_001 is a highly conserved circRNA that was conserved in all ten dermatophytes analyzed in our study and three distantly related species. Its host gene TERG_07138 was also highly conserved in two of these distantly related species Gallus gallus and Caenorhabditis elegans. The specific role of this circRNA deserves further exploration.

Conclusions

Our study is the first to provide a global profile of circRNAs in T. rubrum as well as dermatophytes. These results could serve as valuable resources for research on circRNA regulatory mechanisms in fungi and reveal new insights for further investigation of the physical characteristics of these significant human fungal pathogens.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08184-y.

Multiple microRNA signature panel as promising potential for diagnosis and prognosis of head and neck cancer

MicroRNAs are small non-coding RNA that regulate gene expressions of human body. To date, numerous studies have reported that microRNAs possess great diagnostic and prognostic power in head and neck cancer and had governed a lot of attention. The factor for the successfulness of miRNAs in these aspects is due to cancer being fundamentally tied to genetic changes, which are regulated by these miRNAs. Head and neck cancer, leading the world record for cancer as number sixth, is caused by multiple risk factors such as tobacco consumption, alcohol consumption, dietary factors, ethnicity, family history, and human papilloma virus. It derives at locations such as oral cavity, pharynx, larynx, paranasal sinus and salivary gland and have high rate of mortality with high recurrence rate. Besides, head and neck cancer is also usually having poor prognosis due to its asymptomatic nature. However, this diagnostic and prognostic power can be further improved by using multiple panels of miRNA as a signature or even combined with TNM staging system to obtain even more remarkable results. This is due to multiple factors such as tumour heterogeneity and components of the tumour which may affect the composition of miRNAs. This review covers the examples of such miRNA signatures, compare their diagnostic and prognostic powers, discuss some controversial roles of unreported miRNAs, and the molecular mechanisms of the miRNAs in gene targeting and pathways.

A ceRNA-associated risk model predicts the poor prognosis for head and neck squamous cell carcinoma patients

Head and neck squamous cell carcinoma (HNSCC) is one of the most malignant cancers with poor prognosis worldwide. Emerging evidence indicates that competing endogenous RNAs (ceRNAs) are involved in various diseases, however, the regulatory mechanisms of ceRNAs underlying HNSCC remain unclear. In this study, we retrieved differentially expressed long non-coding RNAs (DElncRNAs), messenger RNAs (DEmRNAs) and microRANs (DEmiRNAs) from The Cancer Genome Atlas database and constructed a ceRNA-based risk model in HNSCC by integrated bioinformatics approaches. Functional enrichment analyses showed that DEmRNAs might be involved in extracellular matrix related biological processes, and protein–protein interaction network further selected out prognostic genes, including MYL1 and ACTN2. Importantly, co-expressed RNAs identified by weighted co-expression gene network analysis constructed the ceRNA networks. Moreover, AC114730.3, AC136375.3, LAT and RYR3 were highly correlated to overall survival of HNSCC by Kaplan–Meier method and univariate Cox regression analysis, which were subsequently implemented multivariate Cox regression analysis to build the risk model. Our study provides a deeper understanding of ceRNAs on the regulatory mechanisms, which will facilitate the expansion of the roles on the ceRNAs in the tumorigenesis, development and treatment of HNSCC.

Comprehensive analysis of competitive endogenous RNAs network reveals potential prognostic lncRNAs in gastric cancer

Long non-coding RNAs (lncRNAs) are key regulators of a range of human diseases, including various cancers, with multiple previous studies having explored lncRNA dysregulation in the context of gastric cancer (GC). The present study sought to expand upon these previous results by downloading lncRNA, mRNA, and microRNA (miRNA) expression profiles derived from 180 GC tissues and 24 normal control tissues within the Cancer Genome Atlas (TCGA) database. These datasets were then interrogated to identify GC-related differentially expressed (DE) RNAs (|fold change| ≥ 2, FDR< 0.01), leading to the identification of 1946 DE lncRNAs, 123 DE miRNAs, and 3159 DE mRNAs. These results were then used to generate a putative GC-related competitive endogenous RNA (ceRNA) network composed of 131 lncRNAs, 9 miRNAs, and 78 mRNAs. Subsequent survival analyses based upon this network revealed 17 of these lncRNAs to be significantly associated with GC patient survival (P < 0.05). Further multivariable Cox regression and lasso analyses allowed for the construction of an 8-lncRNA risk score that was able to effectively predict GC patient survival with good discriminative ability. The Kaplan-Meier Plotter database further confirmed that network hub genes that were related to these 8 lncRNAs were associated with GC patient prognosis (P < 0.05). As the ceRNA network in the present study was constructed with a focus on both disease stage and differential gene expression, it represents a key resource that will offer valuable insights into the mechanistic roles of ceRNA pathways in GC development and progression.