Identification of potential biomarkers and analysis of prognostic values in head and neck squamous cell carcinoma by bioinformatics analysis

Pan-caner analysis identifies PSMA7 as a targets for amplification at 20q13.33 in tumorigenesis

The chromosome 20 long arm (20q) is one of the genomic hotspots where copy number alterations frequently occur in multiple types of tumors. However, it remains elusive which genes are implicated in 20q-related tumorigenesis. Here, by querying TCGA and GEO databases, we observed frequent copy number amplification at 20q and the chromosome subband 20q13.33 was amplificated in multiple cancers. Among those genes at 20q13.33, PSMA7 was found with the strongest correlation with cancers. Further analysis revealed that PSMA7 amplification was the most frequent genetic alteration event conferring adverse prognosis in various cancers. Consistent with the strong positive correlation between PSMA7 amplification and gene expression, elevated PSMA7 expression was observed in 20 of 33 types of cancers with a close link to adverse outcomes in certain tumors. In addition, PSMA7 was essential for the growth of almost 1095 cancer lines. Mechanistically, aberrant PSMA7 most probably influenced the proteasome and protease-related pathways to promote tumorigenesis and might be antagonized by several compounds, e.g., Docetaxel in relevant cancers. The current in-depth pan-cancer analysis refines our understanding of the crucial oncogenic role of copy number amplifications at PSMA7 loci at the novel chromosome amplicon 20q13.33 across different tumors.

Comprehensive Analysis Identifies Hyaluronan Mediated Motility Receptor and Cell Division Cycle 25C as Potential Prognostic Biomarkers in Head and Neck Squamous Cell Carcinoma

INTRODUCTION

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, but its pathogenic mechanisms remain unclear. This study aimed to identify the potential biomarkers underlying the diagnosis and treatment of HNSCC.

METHODS

Weighted gene co-expression network analysis (WGCNA) followed by pathway enrichment analysis, analysis of infiltrating immune cells, survival analysis, and methylation analysis were applied to identify the potential hub genes underlying the prognosis of HNSCC. The expression of hub genes was validated by immunofluorescence staining.

RESULTS

A total of 10,274 differentially expressed genes (DEGs) were identified. Through WGCNA, the yellow module (R2 = 0.33, P = 2e-14) was confirmed to be the most significantly associated with the histological grade of HNSCC, and the "Cell Cycle" proved to be the most enriched signaling pathway. Based on the results of survival analysis and immune cell infiltration, 10 hub genes (HMMR, CENPK, AURKA, CDC25C, FEN1, CKS1B, MAJIN, PCLAF, SPC25, and STAG3) were identified. Eight of these (excluding MAJIN and STAG3) were confirmed by performing survival analysis using another dataset (GSE41613). Further, we identified 4 methylation loci in 3 hub genes (cg15122828 and cg20554926 in HMMR, cg12519992 in CDC25C, and cg2655739 in KIAA0101/PCLAF) as being significantly related to survival. Finally, we demonstrated the high mRNA and protein expression of HMMR and CDC25C in HNSCC patients.

CONCLUSION

Two real hub genes (HMMR and CDC25C) and 3 methylation loci were identified that could potentially serve as prognostic and therapeutic targets for HNSCC, which is significant for studying the pathological mechanisms underlying HNSCC and for developing novel therapies for this disease.

Integrative analysis of gene expression and DNA methylation to identify biomarkers of non-genital warts induced by low-risk human papillomaviruses infection

Background

Human papillomaviruses have been shown to dysregulate the gene expression and DNA methylation profiles of their host cells over the course of infection. However, there is a lack of information on the impact of low-risk HPV infection and wart formation on host cell's expression and methylation patterns. Therefore, the objective of this study is to analyse the genome and methylome of common warts using an integrative approach.

Methods

In the present study, gene expression (GSE136347) and methylation (GSE213888) datasets of common warts were obtained from the GEO database. Identification of the differentially expressed and differentially methylated genes was carried out using the RnBeads R package and the edgeR Bioconductor package. Next, functional annotation of the identified genes was obtained using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Network construction and analyses of the gene-gene, protein-protein, and signaling interactions of the differentially expressed and differentially methylated genes was performed using the GeneMANIA web interface, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and the Signaling Network Open Resource 2.0 (SIGNOR 2.0), respectively. Lastly, significant hub genes were identified using the Cytoscape application CytoHubba.

Results

A total of 276 genes were identified as differentially expressed and differentially methylated in common warts, with 52% being upregulated and hypermethylated. Functional enrichment analysis identified extracellular components as the most enriched annotations, while network analyses identified ELN, ITGB1, TIMP1, MMP2, LGALS3, COL1A1 and ANPEP as significant hub genes.

Conclusions

To the best knowledge of the authors, this is the first integrative study to be carried out on non-genital warts induced by low-risk HPV types. Future studies are required to re-validate the findings in larger populations using alternative approaches.

Identification of key genes with prognostic value in gastric cancer by bioinformatics analysis

Background: Gastric cancer (GC) is a digestive system tumor with high morbidity and mortality. It is urgently required to identify genes to elucidate the underlying molecular mechanisms. The aim of this study is to identify the key genes which may affect the prognosis of GC patients and be a therapeutic strategy for GC patients by bioinformatic analysis.

Methods: The significant prognostic differentially expressed genes (DEGs) were screened out from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. The protein–protein interaction (PPI) network was established by STRING and screening key genes by MCODE and CytoNCA plug-ins in Cytoscape. Functional enrichment analysis, construction of a prognostic risk model, and nomograms verify key genes as potential therapeutic targets.

Results: In total, 997 genes and 805 genes were related to the prognosis of GC in the GSE84437 and TCGA datasets, respectively. We define the 128 genes shared by the two datasets as prognostic DEGs (P-DEGs). Then, the first four genes (MYLK, MYL9, LUM, and CAV1) with great node importance in the PPI network of P-DEGs were identified as key genes. Independent prognostic risk analysis found that patients with high key gene expression had a poor prognosis, excluding their age, gender, and TNM stage. GO and KEGG enrichment analyses showed that key genes may exert influence through the PI3K-Akt pathway, in which extracellular matrix organization and focal adhesion may play important roles in key genes influencing the prognosis of GC patients.

Conclusion: We found that MYLK, MYL9, LUM, and CAV1 are potential and reliable prognostic key genes that affect the invasion and migration of gastric cancer.

Nine-gene signature and nomogram for predicting survival in patients with head and neck squamous cell carcinoma

Background: Head and neck squamous cell carcinomas (HNSCCs) are derived from the mucosal linings of the upper aerodigestive tract, salivary glands, thyroid, oropharynx, larynx, and hypopharynx. The present study aimed to identify the novel genes and pathways underlying HNSCC. Despite the advances in HNSCC research, diagnosis, and treatment, its incidence continues to rise, and the mortality of advanced HNSCC is expected to increase by 50%. Therefore, there is an urgent need for effective biomarkers to predict HNSCC patients’ prognosis and provide guidance to the personalized treatment.

Methods: Both HNSCC clinical and gene expression data were abstracted from The Cancer Genome Atlas (TCGA) database. Intersecting analysis was adopted between the gene expression matrix of HNSCC patients from TCGA database to extract TME-related genes. Differential gene expression analysis between HNSCC tissue samples and normal tissue samples was performed by R software. Then, HNSCC patients were categorized into clusters 1 and 2 via NMF. Next, TME-related prognosis genes (p < 0.05) were analyzed by univariate Cox regression analysis, LASSO Cox regression analysis, and multivariate Cox regression analysis. Finally, nine genes were selected to construct a prognostic risk model and a prognostic gene signature. We also established a nomogram using relevant clinical parameters and a risk score. The Kaplan–Meier curve, survival analysis, time-dependent receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and the concordance index (C-index) were carried out to assess the accuracy of the prognostic risk model and nomogram. Potential molecular mechanisms were revealed by gene set enrichment analysis (GSEA). Additionally, gene correlation analysis and immune cell correlation analysis were conducted for further enriching our results.

Results: A novel HNSCC prognostic model was established based on the nine genes (GTSE1, LRRN4CL, CRYAB, SHOX2, ASNS, KRT23, ANGPT2, HOXA9, and CARD11). The value of area under the ROC curves (AUCs) (0.769, 0.841, and 0.816) in TCGA whole set showed that the model effectively predicted the 1-, 3-, and 5-year overall survival (OS). Results of the Cox regression assessment confirmed the nine-gene signature as a reliable independent prognostic factor in HNSCC patients. The prognostic nomogram developed using multivariate Cox regression analysis showed a superior C-index over other clinical signatures. Also, the calibration curve had a high level of concordance between estimated OS and the observed OS. This showed that its clinical net can precisely estimate the one-, three-, and five-year OS in HNSCC patients. The gene set enrichment analysis (GSEA) to some extent revealed the immune- and tumor-linked cascades.

Conclusion: In conclusion, the TME-related nine-gene signature and nomogram can effectively improve the estimation of prognosis in patients with HNSCC.

New Clues to Prognostic Biomarkers of Four Hematological Malignancies

Globally, one out of every two reported cases of hematologic malignancies (HMs) results in death. Each year approximately 1.24 million cases of HMs are recorded, of which 58% become fatal. Early detection remains critical in the management and treatment of HMs. However, this is thwarted by the inadequate number of reliable biomarkers. In this study, we mined public databases for RNA-seq data on four common HMs intending to identify novel biomarkers that could serve as HM management and treatment targets. A standard RNA-seq analysis pipeline was strictly adhered to in identifying differentially expressed genes (DEGs) with DESeq2, limma+voom and edgeR. We further performed gene enrichment analysis, protein-protein interaction (PPI) network analysis, survival analysis and tumor immune infiltration level detection on the genes using G:Profiler, Cytoscape and STRING, GEPIA tool and TIMER, respectively. A total of 2,136 highly-ranked DEGs were identified in HM vs. non-HM samples. Gene ontology and pathway enrichment analyses revealed the DEGs to be mainly enriched in steroid biosynthesis (5.075×10-4), cholesterol biosynthesis (2.525×10-8), protein binding (3.308×10-18), catalytic activity (2.158×10-10) and biogenesis (5.929×10-8). The PPI network resulted in 60 hub genes which were verified with data from TCGA, MET500, CPTAC and GTEx projects. Survival analyses with clinical data from TCGA showed that high expression of SRSF1, SRSF6, UBE2Z and PCF11, and low expression of HECW2 were correlated with poor prognosis in HMs. In summary, our study unraveled essential genes that could serve as potential biomarkers for prognosis and may serve as drug targets for HM management.

In vivo generation of bone marrow from embryonic stem cells in interspecies chimeras

Generation of bone marrow (BM) from embryonic stem cells (ESCs) promises to accelerate the development of future cell therapies for life-threatening disorders. However, such approach is limited by technical challenges to produce a mixture of functional BM progenitor cells able to replace all hematopoietic cell lineages. Herein, we used blastocyst complementation to simultaneously produce BM cell lineages from mouse ESCs in a rat. Based on fluorescence-activated cell sorting analysis and single-cell RNA sequencing, mouse ESCs differentiated into multiple hematopoietic and stromal cell types that were indistinguishable from normal mouse BM cells based on gene expression signatures and cell surface markers. Receptor-ligand interactions identified Cxcl12-Cxcr4, Lama2-Itga6, App-Itga6, Comp-Cd47, Col1a1-Cd44, and App-Il18rap as major signaling pathways between hematopoietic progenitors and stromal cells. Multiple hematopoietic progenitors, including hematopoietic stem cells (HSCs) in mouse-rat chimeras derived more efficiently from mouse ESCs, whereas chondrocytes predominantly derived from rat cells. In the dorsal aorta and fetal liver of mouse-rat chimeras, mouse HSCs emerged and expanded faster compared to endogenous rat cells. Sequential BM transplantation of ESC-derived cells from mouse-rat chimeras rescued lethally irradiated syngeneic mice and demonstrated long-term reconstitution potential of donor HSCs. Altogether, a fully functional BM was generated from mouse ESCs using rat embryos as 'bioreactors'.

Integrin alpha 6 as a stemness driver is a novel promising target for HPV (+) head and neck squamous cell carcinoma

Although the incidence rates of head and neck squamous cell carcinoma (HNSCC) associated with human papilloma virus (HPV) infection have recently been on the rise, the underlying mechanism of its tumorigenesis remains largely unknown. Here, we investigated whether HNSCC cells with high expression of integrin alpha 6 (ITGα6), one of the HPV receptors, have a preference during HPV infection. In addition, we examined the gain or loss of function of the ITGα6 gene in HPV + ve HNSCC cells, as well as its prognostic value in patients with HNSCC. HPV pseudovirus was found to be more infective, with HNSCC cells featuring an overexpressed ITGα6 gene compared to the control cells. Overexpression and suppression of ITGα6 respectively increases and decreases stemness phenotypes of HPV + ve HNSCC cells. Furthermore, ITGα6 can regulate stemness by partially mediating AKT pathway in HPV + ve HNSCC cells. Finally, patients with HPV + ve HNSCC had a poor prognosis in cases of elevated ITGα6 expression; however, the expression levels of ITGα6 did not influence the survival rates of HPV-negative HNSCC patients. In conclusion, ITGα6 can serve as a potential therapeutic target for HPV + ve HNSCC cancer-like stem cells (CSCs).

Synthetic Evaluation of MicroRNA-1-3p Expression in Head and Neck Squamous Cell Carcinoma Based on Microarray Chips and MicroRNA Sequencing

Background

MicroRNA-1-3p (miR-1-3p) exerts significant regulation in various tumor cells, but its molecular mechanisms in head and neck squamous cell carcinoma (HNSCC) are still ill defined. This study is aimed at detecting the expression of miR-1-3p in HNSCC and at determining its significant regulatory pathways.

Methods

Data were obtained from the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Oncomine, ArrayExpress, Sequence Read Archive (SRA) databases, and additional literature. Expression values of miR-1-3p in HNSCC were analyzed comprehensively. The R language software was employed to screen differentially expressed genes, and bioinformatics assessment was performed. One sequence dataset (HNSCC: n = 484; noncancer: n = 44) and 18 chip datasets (HNSCC: n = 656; noncancer: n = 199) were obtained.

Results

The expression of miR-1-3p in HNSCC was visibly decreased in compare with noncancerous tissues. There were distinct differences in tumor state (P = 0.0417), pathological stage (P = 0.0058), and T stage (P = 0.0044). Comprehensive analysis of sequence and chip data also indicated that miR-1-3p was lowly expressed in HNSCC. The diagnostic performance of miR-1-3p in HNSCC is reflected in the sensitivity and specificity of the collection, etc. Bioinformatics analysis showed the possible biological process, cellular component, molecular function, and KEGG pathways of miR-1-3p in HNSCC. And ITGB4 was a possible target of miR-1-3p.

Conclusions

miR-1-3p's low expression may facilitate tumorigenesis and evolution in HNSCC through signaling pathways. ITGB4 may be a key gene in targeting pathways but still needs verification through in vitro experiments.

Multifaceted Roles of Long Non-coding RNAs in Head and Neck Cancer

The majority of RNA transcripts are non-coding RNA (ncRNA) transcripts with lengths exceeding 200 nucleotides that are not translated into protein. Unlike microRNAs (miRNAs), long ncRNAs (lncRNAs) are not confined to a single mechanism of action but have a large and diverse role in biological processes as they can function as transcription regulators, decoys, scaffolds, and enhancer RNAs. Currently, many lncRNA molecules are under investigation for their role in tumorigenesis, metastasis, and prognosis in different types of cancer. This review not only summarizes the characteristics and functions of lncRNAs but also discusses the therapeutic implications and applications of lncRNAs with roles associated with head and neck cancer. Our aim is to pinpoint the potential way to perturb specific lncRNAs for future therapeutic use.

Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis

Gastric cancer (GC) is one of the most common malignancies of the digestive system with few genetic markers for its early detection and prevention. In this study, differentially expressed genes (DEGs) were analyzed using GEO2R from GSE54129 and GSE13911 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, STRING, and Cytoscape. Finally, we performed survival analysis of key genes through the Kaplan-Meier plotter. A total of 1034 DEGs were identified in GC. GO and KEGG results showed that DEGs mainly enriched in plasma membrane, cell adhesion, and PI3K-Akt signaling pathway. Subsequently, the PPI network with 44 nodes and 333 edges was constructed, and 18 candidate genes in the network were focused on by centrality analysis and module analysis. Furthermore, data showed that high expressions of fibronectin 1(FN1), the tissue inhibitor of metalloproteinases 1 (TIMP1), secreted phosphoprotein 1 (SPP1), apolipoprotein E (APOE), and versican (VCAN) were related to poor overall survivals in GC patients. In summary, this study suggests that FN1, TIMP1, SPP1, APOE, and VCAN may act as the key genes in GC.

Paired like homeodomain 1 and SAM and SH3 domain-containing 1 in the progression and prognosis of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is an aggressive malignancy with high morbidity and mortality rates. In spite of numerous advancements have been made in therapeutic methods, the prognosis of HNSCC patients remains poor. Therefore, investigation of crucial genes during HNSCC tumorigenesis which could be exploited as biomarkers and therapeutic targets is greatly needed. In this study, original data of four independent datasets was downloaded from the Gene Expression Omnibus database and analyzed through R language to screen out differentially expressed genes. Paired like homeodomain 1 and SAM and SH3 domain-containing 1 were selected to be further explored through multiple online databases. Quantitative real-time polymerase chain reaction analysis and immunohistochemistry assay were adopted to validate the downregulation of paired like homeodomain 1 and SAM and SH3 domain-containing 1 in HNSCC and statistical analysis indicated their close associations with patient prognosis. In vitro experiments demonstrated the inhibitory effect of paired like homeodomain 1 and SAM and SH3 domain-containing 1 on HNSCC progression. Overall, we identified the aberrant downregulation of paired like homeodomain 1 and SAM and SH3 domain-containing 1 in HNSCC and suggested the potential of utilizing them as therapeutic targets or efficient biomarkers for diagnosis and prognosis evaluation. Our findings may provide novel evidences for the development of new strategies for HNSCC treatment.

Screening key lncRNAs with diagnostic and prognostic value for head and neck squamous cell carcinoma based on machine learning and mRNA-lncRNA co-expression network analysis

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is the seventh most common type of cancer around the world. The aim of this study was to seek the long non-coding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of HNSCC.

METHODS

Base on TCGA dataset, the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified between HNSCC and normal tissue. The machine learning and survival analysis were performed to estimate the potential diagnostic and prognostic value of lncRNAs for HNSCC. We also build the co-expression network and functional annotation. The expression of selected candidate mRNAs and lncRNAs were validated by Quantitative real time polymerase chain reaction (qRT-PCR).

RESULTS

A total of 3363 DEmRNAs (1822 down-regulated and 1541 up-regulated mRNAs) and 32 DElncRNAs (13 down-regulated and 19 up-regulated lncRNAs) between HNSCC and normal tissue were obtained. A total of 13 lncRNAs (IL12A.AS1, RP11.159F24.6, RP11.863P13.3, LINC00941, FOXCUT, RNF144A.AS1, RP11.218E20.3, HCG22, HAGLROS, LINC01615, RP11.351J23.1, AC024592.9 and MIR9.3HG) were defined as optimal diagnostic lncRNAs biomarkers for HNSCC. The area under curve (AUC) of the support vector machine (SVM) model, decision tree model and random forests model and were 0.983, 0.842 and 0.983, and the specificity and sensitivity of the three model were 95.5% and 96.2%, 77.3% and 97.6% and 93.2% and 97.8%, respectively. Among them, AC024592.9, LINC00941, LINC01615 and MIR9-3HG was not only an optimal diagnostic lncRNAs biomarkers, but also related to survival time. The focal adhesion, ECM-receptor interaction, pathways in cancer and cytokine-cytokine receptor interaction were four significantly enriched pathways in DEmRNAs co-expressed with the identified optimal diagnostic lncRNAs. But for most of the selected DEmRNAs and DElncRNAs, the expression was consistent with our integrated analysis results, including LINC00941, LINC01615, FOXCUT, TGA6 and MMP13.

CONCLUSION

AC024592.9, LINC00941, LINC01615 and MIR9-3HG was not only an optimal diagnostic lncRNAs biomarkers, but also were a prognostic lncRNAs biomarkers.

Identification of SPRR3 as a novel diagnostic/prognostic biomarker for oral squamous cell carcinoma via RNA sequencing and bioinformatic analyses

Oral squamous cell carcinoma (OSCC) has always been one of the most aggressive and invasive cancers among oral and maxillofacial malignancies. As the morbidity and mortality of the disease have increased year by year, the search for a promising diagnostic and prognostic biomarker for the disease is becoming increasingly urgent. Tumorous and adjacent tissues were collected from three OSCC sufferers and we obtained 229 differentially expressed genes (DEGs) between tumor and normal tissues via high-throughput RNA sequence. Function and pathway enrichment analyses for DEGs were conducted to find a correlation between tumorigenesis status and DEGs. Protein interaction network and molecular complex detection (MCODE) were constructed to detect core modules. Two modules were enriched in MCODE. The diagnostic and prognostic values of the candidate genes were analyzed, which provided evidence for the candidate genes as new tumor markers. Small Proline Rich Protein 3 (SPRR3), a potential tumor marker that may be useful for the diagnosis of OSCC, was screened out. The survival analysis showed that SPRR3 under expression predicted the poor prognosis of OSCC patients. Further experiments have also shown that the expression of SPRR3 decreased as the malignancy of OSCC increased. Therefore, we believe that SPRR3 could be used as a novel diagnostic and prognostic tumor marker.

Molecular mechanisms and prognostic markers in head and neck squamous cell carcinoma: a bioinformatic analysis

The purpose of the present study was to enhance understanding of the molecular mechanisms underpinning head and neck squamous cell carcinoma (HNSCC). Microarray datasets were obtained from the gene expression omnibus database. By a bioinformatics method, 109 differentially expressed genes were identified between the two mRNA datasets, and these genes were classified primarily into biological process, molecular function, or cellular component. In the protein-protein interaction network analysis, top 20 hub genes were identified, and five (SERPINE1, SERPINH1, SPP1, PLAU and MMP1) of them were associated with the prognosis of HNSCC patients. Immunohistochemistry result also showed that the expression of the proteins encoded by these five genes were significantly upregulated in HNSCC, matching the bioinformatics analysis. Moreover, 28 differentially expressed miRNAs were also identified, with miR-196a and miR-1 being most upregulated and downregulated respectively. Our results provide potential biomarkers for HNSCC and may improve understanding of the molecular mechanisms underlying HNSCC.

Comprehensive analysis of transcriptome data for identifying biomarkers and therapeutic targets in head and neck squamous cell carcinoma

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignancy worldwide. Accumulating evidences have highlighted the importance of transcriptome data during HNSCC tumorigenesis. The aim of this study was to identify significant genes as effective biomarkers for HNSCC and constructed miRNA-mRNA regulatory network for a more comprehensive understanding of the underlying molecular mechanisms.

Methods

A total of four independent microarrays conducted on HNSCC samples were downloaded from the Gene Expression Omnibus (GEO) and analyzed through R software. FunRich was applied to predict potential transcription factors and targeted genes of miRNAs. Protein-protein interaction (PPI) network and miRNA-mRNA regulatory network were constructed in Cytoscape. Additionally, the database for annotation, visualization, and integrated discovery (DAVID) was utilized to perform GO and KEGG pathway enrichment analyses. Validation of gene expression levels was conducted by online databases and qPCR experiments.

Results

A total of 35 and 193 differentially expressed miRNAs (DEMs) and mRNAs (DEGs) were screened out by the limma package in R. The interactive network of the overlapping DEGs presented three significant modules and ten hub genes (FN1, MMP3, SPP1, STAT1, LOX, CXCL5, CXCL11, ISG15, IFIT3, and RSAD2). Predicted target genes of DEMs were visualized in Cytoscape and six miRNA-mRNA regulatory pairs were identified. Further validation demonstrated the upregulation of SLC16A1 and COL4A1 in HNSCC.

Conclusions

We performed an integrated and comprehensive bioinformatics analysis of miRNAs and mRNAs in HNSCC, contributing to explore the underlying regulatory mechanisms and to identify genetic biomarkers and therapeutic targets for HNSCC.

Identification of core biomarkers associated with pathogenesis and prognostic outcomes of laryngeal squamous-cell cancer using bioinformatics analysis

PURPOSE

Despite advances in the treatment of laryngeal squamous-cell carcinoma (LSCC), the survival rate of LSCC remains poor. Thereby, it is urgent to identify novel diagnostic and prognostic biomarkers for LSCC. The study aimed to identify potential core genes associated with the pathogenesis and prognosis of LSCC.

METHODS

Differentially expressed genes between LSCC and normal laryngeal tissue samples were screened by an integrated analysis of data from GEO and TCGA databases. Core genes related to the pathogenesis and prognosis of LSCC were identified by employing protein-protein interaction network and Cox proportional hazards model analyses.

RESULTS

Ten hub genes (AURKA, AURKB, CDC45, KIF2C, NDC80, EXO1, TYMS, RAD51AP1, ITGA3, and UBE2T) that might be highly related to the pathogenesis of LSCC were identified. An eight-gene prognostic signature consisted of ZG16B, STATH, RTN4R, MSRA, CBX8, SLC5A1, EFNB1 and CNTFR was constructed with a good performance in predicting overall survivals.

CONCLUSION

Our findings might shed some new light on the pathogenesis of LSCC and help identify new therapeutic targets of LSCC.

Transcriptomics and Epigenomics in head and neck cancer: available repositories and molecular signatures

For many years, head and neck squamous cell carcinoma (HNSCC) has been considered as a single entity. However, in the last decades HNSCC complexity and heterogeneity have been recognized. In parallel, high-throughput omics techniques had allowed picturing a larger spectrum of the behavior and characteristics of molecules in cancer and a large set of omics web-based tools and informative repository databases have been developed. The objective of the present review is to provide an overview on biological, prognostic and predictive molecular signatures in HNSCC. To contextualize the selected data, our literature survey includes a short summary of the main characteristics of omics data repositories and web-tools for data analyses. The timeframe of our analysis was fixed, encompassing papers published between January 2015 and January 2019. From more than 1000 papers evaluated, 61 omics studies were selected: 33 investigating mRNA signatures, 11 and 13 related to miRNA and other non-coding-RNA signatures and 4 analyzing DNA methylation signatures. More than half of identified signatures (36) had a prognostic value but only in 10 studies selection of a specific anatomical sub-site (8 oral cavity, 1 oropharynx and 1 both oral cavity and oropharynx) was performed. Noteworthy, although the sample size included in many studies was limited, about one-half of the retrieved studies reported an external validation on independent dataset(s), strengthening the relevance of the obtained data. Finally, we highlighted the development and exploitation of three gene-expression signatures, whose clinical impact on prognosis/prediction of treatment response could be high. Based on this overview on omics-related literature in HNSCC, we identified some limits and strengths. The major limits are represented by the low number of signatures associated to DNA methylation and to non-coding RNA (miRNA, lncRNA and piRNAs) and the availability of a single dataset with multiple omics on more than 500 HNSCC (i.e. TCGA). The major strengths rely on the integration of multiple datasets through meta-analysis approaches and on the growing integration among omics data obtained on the same cohort of patients. Moreover, new approaches based on artificial intelligence and informatic analyses are expected to be available in the next future.

Identification of AUNIP as a candidate diagnostic and prognostic biomarker for oral squamous cell carcinoma

Background

Oral squamous cell carcinoma (OSCC) is one of the most common malignant tumors worldwide. Patients with poorly differentiated OSCC often exhibit a poor prognosis. AUNIP (Aurora Kinase A and Ninein Interacting Protein), also known as AIBp, plays a key role in cell cycle and DNA damage repair. However, the function of AUNIP in OSCC remains elusive.

Methods

The differentially expressed genes (DEGs) were obtained using R language. Receiver operating characteristic curve analysis was performed to identify diagnostic markers for OSCC. The effectiveness of AUNIP in diagnosing OSCC was evaluated by machine learning. AUNIP expression was analyzed in publicly available databases and clinical specimens. Bioinformatics analysis and in vitro experiments were conducted to explore biological functions and prognostic value of AUNIP in OSCC.

Findings

The gene integration analysis revealed 90 upregulated DEGs. One candidate biomarker, AUNIP, for the diagnosis of OSCC was detected, and its expression gradually increased along with malignant differentiation of OSCC. Bioinformatics analysis demonstrated that AUNIP could be associated with tumor microenvironment, human papillomavirus infection, and cell cycle in OSCC. The suppression of AUNIP inhibited OSCC cell proliferation and resulted in G0/G1 phase arrest in OSCC cells. The survival analysis showed that AUNIP overexpression predicted poor prognosis of OSCC patients.

Interpretation: AUNIP could serve as a candidate diagnostic and prognostic biomarker for OSCC and suppression of AUNIP may be a potential approach to preventing and treating OSCC.

Fund

Taishan Scholars Project in Shandong Province (ts201511106) and the National Natural Science Foundation of China (Nos. 61603218).

Bioinformatics-based discovery of PYGM and TNNC2 as potential biomarkers of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is an aggressive malignancy with high morbidity and mortality rates and ranks as the sixth most common cancer all over the world. Despite numerous advancements in therapeutic methods, the prognosis of HNSCC patients still remains poor. Therefore, there is an urgent need to have a better understanding of the molecular mechanisms underlying HNSCC progression and to identify essential genes that could serve as effective biomarkers and potential treatment targets. In the present study, original data of three independent datasets were downloaded from the Gene Expression Omnibus database (GEO) and R language was applied to screen out the differentially expressed genes (DEGs). PYGM and TNNC2 were finally selected from the overlapping DEGs of three datasets for further analyses. Transcriptional and survival data related to PYGM and TNNC2 was detected through multiple online databases such as Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), cBioportal, and UALCAN. Quantitative real-time polymerase chain reaction (qPCR) analysis was adopted for the validation of PYGM and TNNC2 mRNA level in HNSCC tissues and cell lines. Survival curves were plotted to evaluate the association of these two genes with HNSCC prognosis. It was demonstrated that PYGM and TNNC2 were significantly down-regulated in HNSCC and the aberrant expression of PYGM and TNNC2 were correlated with HNSCC prognosis, implying the potential of exploiting them as therapeutic targets for HNSCC treatment or potential biomarkers for diagnosis and prognosis.

Identification of SERPINE1, PLAU and ACTA1 as biomarkers of head and neck squamous cell carcinoma based on integrated bioinformatics analysis

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is the six leading cancer by incidence worldwide. The 5-year survival rate of HNSCC patients remains less than 65% due to lack of symptoms in the early stage. Hence, biomarkers which can improve detection of HNSCC should improve clinical outcome.

METHODS

Gene expression profiles (GSE6631, GSE58911) and the Cancer Genome Atlas (TCGA) HNSCC data were used for integrated bioinformatics analysis; the differentially expressed genes (DEGs) were then subjected to functional and pathway enrichment analysis, protein-protein interaction (PPI) network construction. Subsequently, module analysis of the PPI network was performed and overall survival (OS) analysis of hub genes in subnetwork was studied. Finally, immunohistochemistry was used to verify the selected markers.

RESULTS

A total of 52 up-regulated and 80 down-regulated DEGs were identified, which were mainly associated with ECM-receptor interaction and focal adhesion signaling pathways. Importantly, a set of prognostic signatures including SERPINE1, PLAU and ACTA1 were screened from DEGs, which could predict OS in HNSCC patients from TCGA cohort. Experiment of clinical samples further successfully validated that these three signature genes were aberrantly expressed in the oral epithelial dysplasia and HNSCC, and correlated with aggressiveness of HNSCC patients.

CONCLUSIONS

SERPINE1, PLAU and ACTA1 played important roles in regulating the initiation and progression of HNSCC, and could be identified as key biomarkers for precise diagnosis and prognosis of HNSCC, which will provide potential targets for clinical therapies.

Weighted gene co-expression network analysis and prognostic analysis identifies hub genes and the molecular mechanism related to head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a lethal disease with suboptimal survival outcomes. In this study, we aimed to find an independent prognostic factor of head and neck squamous cell carcinoma and investigate its effect on tumor cell proliferation, apoptosis, migration progress and cell cycle phase. Weighted gene co-expression network analysis (WGCNA) is an analysis method for mining module information in chip data through soft threshold. In this article, it was used to divide differential genes into different modules and determined the ten hub genes. Overall survival (OS) and disease-free survival (DFS) analyses as well as univariate and multivariate regression analyses were used to figure out HMGA2 as the independent prognostic factor. RT-qPCR and western blot results revealed the HMGA2 expression levels. Via colony formation, flow cytometry and wound healing assays, we tested the involvement of HMGA2 knockdown in corresponding cancer cell biological behaviors. HMGA2 level was up-regulated in HNSCC tissues and cell lines (SCC-25 and FaDu) in comparison with their normal counterparts. HMGA2 knockdown decreased cancer cell proliferation, promoted cell apoptosis, blocked cell cycle at G0/G1 phase, and inhibited cell migration. We regarded HMGA2 as a potential diagnostic and therapeutic target of HNSCC.

Analysis of lncRNA-Mediated ceRNA Crosstalk and Identification of Prognostic Signature in Head and Neck Squamous Cell Carcinoma

Long non-coding RNA (lncRNA) can act as ceRNA to regulate the expression of target genes by sponging miRNAs, and therefore plays an essential role in tumor initiation and progression. However, functional roles and regulatory mechanisms of lncRNAs as ceRNAs in head and neck squamous cell carcinoma (HNSCC) remain to be determined. We downloaded RNA sequence profiles from The Cancer Genome Atlas (TCGA) database, and identified the differential RNAs by bioinformatics. Then we analyzed the biological processes of differential expressed RNAs (DER), and established their interaction networks and pathway analysis to find out potential biological effects of these DERs. Besides, we also explored the relationship between the DERs and prognosis of HNSCC patients. We obtained 525 tumor samples and 44 paracancerous controls, and there were 1081 DElncRNAs, 1889 DEmRNAs, and 145 DEmiRNAs. GO and KEGG pathways analysis of these DEmRNAs were mainly involved in "Protein digestion and absorption," "Calcium signaling pathway," and "ECM-receptor interaction." The analysis of the ceRNA network identified 61 DElncRNAs as functional ceRNAs whose dysregulated expression may affect the expression of oncogenes/tumor suppressor genes. Furthermore, univariate and multivariate Cox regression analysis revealed that 4 DElncRNAs, 3 EDmiRNAs, and 6 DEmRNAs can predict survival with high accuracy. Survival analysis found that 4 lncRNAs was related to prognostic, including overexpressed RP11-366H4.1, HOTTIP, RP11-865I6.2, and RP11-275N1.1 patients had a worse survival. In conclusion, through constructing the ceRNA network in HNSCC patients, we identified key lncRNA-miRNA-mRNA network in HNSCC. All the DERs might participate in varieties of pathways in the initiation, progression, and invasion of HNSCC. Furthermore, some miRNAs (hsa-mir-99a, hsa-mir-337, and hsa-mir-137) and mRNAs (NOSTRIN, TIMP4, GRB14, HOXB9, CELSR3, and ADGRD2) may be the prognostic genes of HNSCC. This study provided a new target and theoretical basis for further research on molecular mechanisms and biomarkers.

Application of weighted gene co-expression network analysis to identify key modules and hub genes in oral squamous cell carcinoma tumorigenesis

Purpose

Oral squamous cell carcinoma (OSCC) is one of the most common malignant diseases worldwide, yet its molecular mechanisms are largely unknown. We aimed to construct gene co-expression networks to identify key modules and hub genes involved in the pathogenesis of OSCC.

Patients and methods

We used dataset GSE30784 to construct co-expression networks by weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Database for Annotation, Visualization and Integrated Discovery (DAVID). Hub genes were screened and validated by other datasets.

Results

Turquoise and brown modules were found to be the most significantly related to tumorigenesis. Functional enrichment analysis showed that the turquoise module was associated with cell–cell adhesion, extracellular matrix and collagen catabolic process. A total of 10 hub genes (MMP1, TNFRSF12A, PLAU, FSCN1, PDPN, KRT78, EVPL, GGT6, SMIM5 and CYSRT1) were identified and validated at transcriptional and translational levels. Their genetic alteration and survival analysis were also revealed.

Conclusion

We identified two modules and 10 hub genes, which were associated with the tumorigenesis of OSCC. The two modules provided references that will advance the understanding of mechanisms of tumorigenesis in OSCC. Moreover, the hub genes may serve as biomarkers and therapeutic targets for precise diagnosis and treatment of OSCC in the future.