An Integrated Analysis of mRNAs and miRNAs Microarray Profiles to Screen miRNA Signatures Involved in Nasopharyngeal Carcinoma

MicroRNA-452: a double-edged sword in multiple human cancers

MicroRNAs (miRNAs) are small, noncoding RNAs with important functions in development, cell differentiation, and regulation of cell cycle and apoptosis. MiRNA expression is deregulated in various pathological processes including tumorigenesis and cancer progression through various mechanisms including amplification or deletion of miRNA genes, mutations, and epigenetic silencing and defects in the miRNA biogenesis machinery. Several studies have now shown abnormal miRNA profiles and proved their involvement in the initiation and progression of cancer. Since miR-452 has diverse roles (as suppressor or oncogene) in different cellular processes including epithelial-mesenchymal transition (EMT), proliferation, migration, and invasion, in this review we highlight a brief overview of the biological function and regulatory mechanism of miR-452 and its involvement as a potential biomarker for diagnosis and treatment of various cancer types.

Identification of Key Genes in Nasopharyngeal Carcinoma Based on Bioinformatics Analysis

Objective

This study aimed to identify key genes associated with the pathogenesis of nasopharyngeal carcinoma (NPC) by bioinformatics analysis.

Methods

Datasets (GSE13597 and GSE34573) were screened and downloaded from the comprehensive gene expression database (GEO). GEO2R online tool was adopted to analyze microarray data GSE13597 and GSE34573 related to NPC. Volcano plot was generated using Bioconductor in R software. "Pheatmap" was used to draw heatmaps based on the top 10 regulated genes of GSE13597 and GSE34573. GO and KEGG analyses were conducted via online tool DAVID. We uploaded the DEGs of NPC to STRING software and then used Cytoscape software to draw PPI network of DEGs.

Results

216 DEGs were obtained in GSE13597 between patient and control group (111 up-regulated DEGs and 105 down-regulated DEGs). 1101 DEGs were obtained in GSE34573 (470 up-regulated DEGs and 641 down-regulated DEGs). 63 common differential genes were screened named co-DEGs in the two datasets. These DEGs were mainly associated with defense response to bacterium, cell-matrix adhesion, chemokine-mediated signaling pathway, tissue homeostasis, humoral immune response, cilium movement, cilium organization, cilium assembly, and epithelial cilium movement. KEGG pathway enrichment analysis showed that DEGs were mainly involved in viral protein interaction with cytokine and cytokine receptor, salivary secretion, p53 signaling pathway, IL-17 signaling pathway, cell cycle, PI3K-Akt signaling pathway, and ECM-receptor interaction. We identified seven hub genes, including FN1, MMP-10, MUC1, KIF23, CDK1, MUC5B, and MUC5AC.

Conclusions

Seven hub genes, including FN1, MMP-10, MUC1, KIF23, CDK1, MUC5B, and MUC5AC, might be therapeutic potential biomarkers of NPC.

ITGA9: Potential Biomarkers and Therapeutic Targets in Different Tumors

Abstract:

Integrins are a class of a cell surface adhesion molecule which composed of α subunit (ITGA) and β subunit (ITGB). They belong to heterodimer transmembrane glycoproteins. Its main function in organisms is as the receptor of cell adhesion molecules (CAMs) and extracellular matrix (ECM). According to the current research integration analysis, integrin α9 (ITGA9) is one of the integrin subunits, and there are few studies on ITGA9 among integrins. ITGA9 can improve cell migration and regulate various cellular biological functions, such as tumor cell proliferation, adhesion, invasion, and angiogenesis. But its abnormal expression mechanism in cancer and its specific role in tumor growth and metastasis are still unknown to a great extent. This review reveals the role of ITGA9 in the complex pathogenesis of many tumors and cancers, providing a new direction for the treatment of tumors and cancers. Relevant studies were retrieved and collected through the PubMed system. After determining ITGA9 as the research object, we found the close relationship between ITGA9 and tumorigenesis through the analysis of the research articles on ITGA9 in the PubMed system in the last 15 years, and further determined the references mainly based on the influencing factors of the articles. Thus, the role of ITGA9 in tumor and cancer genesis, proliferation, and metastasis was reviewed and analyzed. ITGA9 is an integrin subunit, which has been proved to be abnormally expressed in many tumors. After sorting and analyzing the research data, it was found that the abnormal expression of ITGA9 in a variety of tumors, including glioblastoma, rhabdomyosarcoma, melanoma, hepatocellular carcinoma, nasopharyngeal carcinoma, multiple myeloma, non-small cell lung cancer, and prostate cancer, was closely related to the proliferation, metastasis, adhesion, and angiogenesis of tumor cells. These results suggest that ITGA9 plays an important role in the occurrence and development of tumors. The integrin subunit ITGA9 may serve as a biomarker for the diagnosis of tumors and a potential therapeutic target for anti-tumor therapies.

Integrative Pan-Cancer Analysis of KIF15 Reveals Its Diagnosis and Prognosis Value in Nasopharyngeal Carcinoma

Background

KIF15 plays a vital role in many biological processes and has been reported to influence the occurrence and development of certain human cancers. However, there are few systematic evaluations on the role of KIF15 in human cancers, and the role of KIF15 in the diagnosis and prognosis of nasopharyngeal carcinoma (NPC) also remains unexplored. Therefore, this study aimed to conduct a pan-cancer analysis of KIF15 and evaluate its diagnostic and prognostic potential in NPC.

Methods

The expression pattern, prognostic value, molecular function, tumor mutation burden, microsatellite instability, and immune cell infiltration of KIF15 were examined based on public databases. Next, the diagnostic value of KIF15 in NPC was analyzed using the Gene Expression Omnibus (GEO) database and immunohistochemistry (IHC). Kaplan–Meier curves, Cox regression analyses, and nomograms were used to evaluate the effects of KIF15 expression on NPC prognosis. Finally, the effect of KIF15 on NPC was explored by in vitro experiments.

Results

The expression of KIF15 was significantly upregulated in 20 out of 33 cancer types compared to adjacent normal tissue. Kyoto Encyclopedia of Genes and Genomes enrichment (KEGG) analysis showed that KIF15 could participate in several cancer-related pathways. The increased expression level of KIF15 was correlated with worse clinical outcomes in many types of human cancers. Additionally, KIF15 expression was related to cancer infiltration of immune cells, tumor mutation burden, and microsatellite instability. In the analysis of NPC, KIF15 was significantly upregulated based on the GEO database and immunohistochemistry. A high expression of KIF15 was negatively associated with the prognosis of patients with NPC. A nomogram model integrating clinical characteristics and KIF15 expression was established, and it showed good predictive ability with an area under the curve value of 0.73. KIF15 knockdown significantly inhibited NPC cell proliferation and migration.

Conclusions

Our findings revealed the important and functional role of KIF15 as an oncogene in pan-cancer. Moreover, high expression of KIF15 was found in NPC tissues, and was correlated with poor prognosis in NPC. KIF15 may serve as a potential therapeutic target in NPC treatment.

miRNAs derived from circulating small extracellular vesicles as diagnostic biomarkers for nasopharyngeal carcinoma

The microRNAs (miRNAs) in circulating small extracellular vesicles (sEVs) have been suggested as potential biomarkers in cancer diagnosis. This study was designed to evaluate the circulating sEV‐derived miRNAs as biomarkers for the diagnosis of nasopharyngeal carcinoma (NPC). We compared the miRNA profiles in plasma‐derived sEVs between 16 patients with NPC and 5 healthy controls (HCs). A distinct set of miRNAs that were differentially expressed between patients with NPC and HCs was determined by means of integrative bioinformatics approaches. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis revealed that the target genes of the differentially expressed miRNAs (DEMs) were mainly involved in cancer‐associated signaling pathways. Seven representative DEMs were selected and further validated in an additional 60 patients with NPC and 40 HCs using quantitative reverse‐transcription PCR analysis (qRT‐PCR). Receiver operating characteristic (ROC) curve analysis was used to assess the accuracy of the sEV‐miRNA‐based model for diagnosis. The 3 miRNA‐based model, comprising miR‐134‐5p, miR‐205‐5p, and miR‐409‐3p, showed good discriminating power with an area under the curve (AUC) value of 0.88 in the training set and 0.91 in the validation set. Furthermore, the diagnostic model had an excellent classification ability to distinguish patients with NPC at different clinical stages or Epstein‐Barr virus infection status from HCs. In conclusion, our findings indicated that sEV‐derived miRNA levels were altered in the plasma of patients with NPC in comparison with those in HCs. The model based on the 3 sEV‐derived miRNAs could potentially act as an alternative or complementary approach for diagnosing NPC.

This study was designed to evaluate circulating sEV‐derived miRNAs as biomarkers for the diagnosis of nasopharyngeal carcinoma (NPC). Our findings indicated that sEV‐derived miRNA levels were altered in the plasma of patients with NPC compared with those in HCs. The model based on the 3 sEV‐derived miRNAs could potentially act as an alternative or complementary approach for diagnosing NPC.

Identification of miRNA-Mediated Subpathways as Prostate Cancer Biomarkers Based on Topological Inference in a Machine Learning Process Using Integrated Gene and miRNA Expression Data

Accurately identifying classification biomarkers for distinguishing between normal and cancer samples is challenging. Additionally, the reproducibility of single-molecule biomarkers is limited by the existence of heterogeneous patient subgroups and differences in the sequencing techniques used to collect patient data. In this study, we developed a method to identify robust biomarkers (i.e., miRNA-mediated subpathways) associated with prostate cancer based on normal prostate samples and cancer samples from a dataset from The Cancer Genome Atlas (TCGA; n = 546) and datasets from the Gene Expression Omnibus (GEO) database (n = 139 and n = 90, with the latter being a cell line dataset). We also obtained 10 other cancer datasets to evaluate the performance of the method. We propose a multi-omics data integration strategy for identifying classification biomarkers using a machine learning method that involves reassigning topological weights to the genes using a directed random walk (DRW)-based method. A global directed pathway network (GDPN) was constructed based on the significantly differentially expressed target genes of the significantly differentially expressed miRNAs, which allowed us to identify the robust biomarkers in the form of miRNA-mediated subpathways (miRNAs). The activity value of each miRNA-mediated subpathway was calculated by integrating multiple types of data, which included the expression of the miRNA and the miRNAs’ target genes and GDPN topological information. Finally, we identified the high-frequency miRNA-mediated subpathways involved in prostate cancer using a support vector machine (SVM) model. The results demonstrated that we obtained robust biomarkers of prostate cancer, which could classify prostate cancer and normal samples. Our method outperformed seven other methods, and many of the identified biomarkers were associated with known clinical treatments.