Introduction to the Gene Expression Analysis

Identification and preliminary analysis of hub genes associated with bladder cancer progression by comprehensive bioinformatics analysis

Bladder cancer (BC) is a crisis to human health. It is necessary to understand the molecular mechanisms of the development and progression of BC to determine treatment options. Publicly available expression data were obtained from TCGA and GEO databases to spot differentially expressed genes (DEGs) between cancer and normal bladder tissues. Weighted co-expression networks were constructed, and Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Associations in hub genes, immune infiltration, and immune therapy were evaluated separately. Protein-protein interaction (PPI) networks for the genes identified in the normal and tumor groups were launched. 3461 DEGs in the TCGA dataset and 1069 DEGs in the GSE dataset were identified, including 87 overlapping genes between cancer and normal bladder groups. Hub genes in the tumor group were mainly enriched for cell proliferation, while hub genes in the normal group were related to the synthesis and secretion of neurotransmitters. Based on survival analysis, CDH19, RELN, PLP1, and TRIB3 were considerably associated with prognosis (P < 0.05). CDH19, RELN, PLP1, and TRIB3 may play important roles in the development of BC and are potential biomarkers in therapy and prognosis.

Genome-wide identification of the heat shock transcription factor gene family in two kiwifruit species

High temperatures have a significant impact on plant growth and metabolism. In recent years, the fruit industry has faced a serious threat due to high-temperature stress on fruit plants caused by global warming. In the present study, we explored the molecular regulatory mechanisms that contribute to high-temperature tolerance in kiwifruit. A total of 36 Hsf genes were identified in the A. chinensis (Ac) genome, while 41 Hsf genes were found in the A. eriantha (Ae) genome. Phylogenetic analysis revealed the clustering of kiwifruit Hsfs into three distinct groups (groups A, B, and C). Synteny analysis indicated that the expansion of the Hsf gene family in the Ac and Ae genomes was primarily driven by whole genome duplication (WGD). Analysis of the gene expression profiles revealed a close relationship between the expression levels of Hsf genes and various plant tissues and stress treatments throughout fruit ripening. Subcellular localization analysis demonstrated that GFP-AcHsfA2a/AcHsfA7b and AcHsfA2a/AcHsfA7b -GFP were localized in the nucleus, while GFP-AcHsfA2a was also observed in the cytoplasm of Arabidopsis protoplasts. The results of real-time quantitative polymerase chain reaction (RT-qPCR) and dual-luciferase reporter assay revealed that the majority of Hsf genes, especially AcHsfA2a, were expressed under high-temperature conditions. In conclusion, our findings establish a theoretical foundation for analyzing the potential role of Hsfs in high-temperature stress tolerance in kiwifruit. This study also offers valuable information to aid plant breeders in the development of heat-stress-resistant plant materials.

Recent progress in biomarker-based diagnostics of Helicobacter pylori, gastric cancer-causing bacteria

The progression of any disease and its outcomes depend on the complicated interaction between pathogens, host and environmental factors. Thus, complete knowledge of bacterial toxins involved in pathogenesis is necessary to develop diagnostic methods and alternative therapies, including vaccines. This review summarizes recently employed biomarkers to diagnose the presence of Helicobacter pylori bacteria. The authors review distinct types of disease-associated biomarkers such as urease, DNA, miRNA, aptamers and bacteriophages that can be utilized as targets to detect Helicobacter pylori and, moreover, gastric cancer in its early stage. A detailed explanation is also given in the context of the recent utilization of these biomarkers in the development of a highly specific and sensitive biosensing platform.

Exploring the molecular mechanism of linagliptin in osteosarcoma cell lines for anti-cancer activity

BACKGROUND

Finding new applications for widely used current drugs is a fast and effective technique for discovering new anticancer chemicals. Osteosarcoma (OS), the most prevalent form of bone cancer, has several side effects that significantly lower patients' quality of life. This study aims to systematically examine the anti-cancer activity of linagliptin (LG) in the osteosarcoma cell line Saos-2.

METHODS

MTT assays and flow cytometry were used to assess cell viability and apoptosis, respectively. qPCR array experiments were carried out to determine target gene expressions and explain the molecular mechanism of LG's action.

RESULTS

Linagliptin treatment significantly decreased the viability of Saos-2 cells and hFOB1.19 cells (p < 0.001). The treatment also induced increased apoptotic effects in both Saos-2 cells (p < 0.001) and hFOB1.19 cells (p < 0.05). qPCR assays were conducted to assess cancer pathway analysis after applying specific quantities of LG to Saos-2 and hFOB1.19 cells.

CONCLUSION

The findings of this study demonstrate that LG inhibits the proliferation of Saos-2 cells and induces cell death. LG supports cell death by suppressing the expression of specific genes involved in cancer pathways.

Evaluation of dental pulp stem cells behavior after odontogenic differentiation induction by three different bioactive materials on two different scaffolds

BACKGROUND

To study the odontogenic potential of dental pulp stem cells (DPSCs) after induction with three different bioactive materials: activa bioactive (base/liner) (AB), TheraCal LC (TC), and mineral trioxide aggregate (MTA), when combined with two different types of scaffolds.

METHODS

DPSCs were isolated from freshly extracted premolars of young orthodontic patients, cultured, expanded to passage 4 (P), and characterized by flow cytometric analysis. DPSCs were seeded onto two scaffolds in contact with different materials (AB, TC, and MTA). The first scaffold contained polycaprolactone-nano-chitosan and synthetic hydroxyapatite (PCL-NC-HA), whereas the second scaffold contained polycaprolactone-nano-chitosan and synthetic Mg-substituted hydroxyapatite (PCL-NC-Mg-HA). DPSC viability and proliferation were evaluated at various time points. To assess odontoblastic differentiation, gene expression analysis of dentin sialophosphoprotein (DSPP) by quantitative real-time polymerase chain reaction (qRT-PCR) and morphological changes in cells were performed using inverted microscope phase contrast images and scanning electron microscopy. The fold-change in DSPP between subgroups was compared using a one-way ANOVA. Tukey's test was used to compare the fold-change in DSPP between the two subgroups in multiple comparisons, and P was set at p < 0.05.

RESULTS

DSPP expression was significantly higher in the PCL-NC-Mg-HA group than in the PCL-NC-HA group, and scanning electron microscopy revealed a strong attachment of odontoblast-like cells to the scaffold that had a stronger odontogenic differentiation effect on DPSCs than the scaffold that did not contain magnesium. MTA has a significantly higher odontogenic differentiation effect on cultured DPSCs than AB or TC does. The combination of scaffolds and bioactive materials improves DPSCs induction in odontoblast-like cells.

CONCLUSIONS

The PCL-NC-Mg-HA scaffold showed better odontogenic differentiation effects on cultured DPSCs. Compared to AB and TC, MTA is the most effective bioactive material for inducing the odontogenic differentiation of cultured DPSCs.

Precision oncology comes of age

ABSTRACT

Each cancer has a unique fingerprint, and precision oncology can be used to more effectively fight malignancies. The identified genes, expressed RNA, and proteins expressed in patients' cancers are now used routinely to predict prognosis and inform treatment recommendations. This article describes how malignancies develop and some of the targeted drugs that can be used against them.

Spatiotemporal dissection of tumor microenvironment via in situ sensing and monitoring in tumor-on-a-chip

Real-time monitoring in the tumor microenvironment provides critical insights of cancer progression and mechanistic understanding of responses to cancer treatments. However, clinical challenges and significant questions remain regarding assessment of limited clinical tissue samples, establishment of validated, controllable pre-clinical cancer models, monitoring of static versus dynamic markers, and the translation of insights gained from in vitro tumor microenvironments to systematic investigation and understanding in clinical practice. State-of-art tumor-on-a-chip strategies will be reviewed herein, and emerging real-time sensing and monitoring platforms for on-chip analysis of tumor microenvironment will also be examined. The integration of the sensors with tumor-on-a-chip platforms to provide spatiotemporal information of the tumor microenvironment and the associated challenges will be further evaluated. Though optimal integrated systems for in situ monitoring are still in evolution, great promises lie ahead that will open new paradigm for rapid, comprehensive analysis of cancer development and assist clinicians with powerful tools to guide the diagnosis, prognosis and treatment course in cancer.

Identification and validation of immune cells and hub genes alterations in recurrent implantation failure: A GEO data mining study

Introduction: Recurrent implantation failure (RIF) is a distressing problem in assisted reproductive technology (ART). Immunity plays a vital role in recurrent implantation failure (RIF) occurrence and development, but its underlying mechanism still needs to be fully elucidated. Through bioinformatics analysis, this study aims to identify the RIF-associated immune cell types and immune-related genes. Methods: The differentially expressed genes (DEGs) were screened based on RIF-associated Gene Expression Omnibus (GEO) datasets. Then, the enrichment analysis and protein-protein interaction (PPI) analysis were conducted with the DEGs. The RIF-associated immune cell types were clarified by combining single sample gene set enrichment analysis (ssGSEA) and CIBERSORT. Differentially expressed immune cell types-related modules were identified by weighted gene co-expression network analysis (WGCNA) and local maximal quasi-clique merger (lmQCM) analysis. The overlapping genes between DEGs and genes contained by modules mentioned above were delineated as candidate hub genes and validated in another two external datasets. Finally, the microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) that interacted with hub genes were predicted, and the competing endogenous RNA (ceRNA) regulatory network was structured. Results: In the present study, we collected 324 DEGs between RIF and the control group, which functions were mainly enriched in immune-related signaling pathways. Regarding differential cell types, the RIF group had a higher proportion of activated memory CD4 T cells and a lower proportion of γδ T cells in the endometrial tissue. Finally, three immune-related hub genes (ALOX5AP, SLC7A7, and PTGS2) were identified and verified to effectively discriminate RIF from control individuals with a specificity rate of 90.8% and a sensitivity rate of 90.8%. In addition, we constructed a key ceRNA network that is expected to mediate molecular mechanisms in RIF. Conclusion: Our study identified the intricate correlation between immune cell types and RIF and provided new immune-related hub genes that offer promising diagnostic and therapeutic targets for RIF.

A systematic review and in silico study of potential genetic markers implicated in cases of overactive bladder

OBJECTIVE

The contribution of genetic factors to the presence of an overactive bladder is recognized. This study aimed to (1) assemble and synthesize available data from studies assessing differential gene expression in patients with overactive bladder vs controls without overactive bladder and (2) determine possible correlations and functional pathways between genes.

DATA SOURCES

We searched PubMed, Ovid or Medline, and Wiley Cochrane Central Register of Controlled Trials databases between January 1, 2000, and December 15, 2021.

STUDY ELIGIBILITY CRITERIA

Studies were included if gene expression was detected and quantified using molecular approaches performed on human bladder tissue specimens directly and excluded if the gene expression analysis was carried out from blood and urine specimens alone.

METHODS

A systematic review was completed to identify publications that reported differently expressed gene candidates among patients with overactive bladder vs healthy individuals. Gene networking connections and pathway analysis were performed employing Metascape software, where inputs were identified from our systematic review of differentially expressed genes in overactive bladder.

RESULTS

A total of 9 studies were included in the final analysis and 11 genes were identified as being up-regulated (purinergic receptor P2X 2 [P2RX2], smoothelin [SMTN], growth-associated protein 43 [GAP43], transient receptor potential cation channel subfamily M member 8 [TRPM8], cadherin 11 [CDH1], gap junction protein gamma 1 [GJC1], cholinergic receptor muscarinic 2 [CHRM2], cholinergic receptor muscarinic 3 [CHRM3], and transient receptor potential cation channel subfamily V member 4 [TRPV4]) or down-regulated (purinergic receptor P2X 2 [P2RX3] and purinergic receptor P2X 5 [P2RX5]) in patients with overactive bladder. Gene network analysis showed that genes are involved in chemical synaptic transmission, smooth muscle contraction, blood circulation, and response to temperature stimulus. Network analysis demonstrated a significant genetic interaction between TRPV4, TRPM8, P2RX3, and PR2X2 genes.

CONCLUSION

Outcomes of this systematic review highlighted potential biomarkers for treatment efficacy and have laid the groundwork for developing future gene therapies for overactive bladder in clinical settings.

Lipocalin-2 participates in sepsis-induced myocardial injury by mediating lipid accumulation and mitochondrial dysfunction

BACKGROUND

Sepsis-induced cardiomyopathy (SIC) is one major cause of death for sepsis but lacks timely diagnosis and specific treatment due to unclear mechanisms. Lipocalin-2 (LCN-2) is a key regulator of lipid metabolism which has been recently proved closely related to sepsis, however, the relationship between LCN-2 and septic myocardial injury remains unknown. We aim to explore the role of LCN-2 in the pathological progress of SIC based on clinical and laboratory evidence.

METHODS

Consecutive patients admitted to the intensive care unit (ICU) from August 2021 to April 2022 fulfilling the criteria of severe sepsis were included. The level of LCN-2 in plasma was assayed and analyzed with clinical characteristics. Biostatistical analysis was performed for further identification and pathway enrichment. Mouse model for SIC was thereafter established, in which plasma and tissue LCN-2 levels were tested. RNA sequencing was used for verification and to reveal the possible mechanism. Mitochondrial function and intracellular lipid levels were assayed to further assess the biological effects of targeting LCN-2 in cardiomyocytes with small interference RNAs (siRNAs).

RESULTS

The level of LCN-2 in plasma was markedly higher in patients with severe sepsis and was associated with higher cardiac biomarkers and lower LVEF. In the in vivo experiment, circulating LCN-2 from plasma was found to increase in SIC mice. A higher level of LCN-2 transcription in myocardial tissue was also found in SIC and showed a clear time relationship. RNA sequencing analysis showed the level of LCN-2 was associated with several gene-sets relevant to mitochondrial function and lipid metabolism-associated pathways. The suppression of LCN-2 protected mitochondrial morphology and limited the production of ROS, as well as restored the mitochondrial membrane potential damaged by LPS. Neutral lipid staining showed prominent lipid accumulation in LPS group, which was alleviated by the treatment of siLCN2.

CONCLUSION

The level of LCN-2 is significantly increased in SIC at both circulating and tissue levels, which is correlated with the severity of myocardial injury indicators, and may work as an early and great predictor of SIC. LCN-2 probably participates in the process of septic myocardial injury through mediating lipid accumulation and affecting mitochondrial function.

A comprehensive survey on computational learning methods for analysis of gene expression data

Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.

Integrative weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer

Chronic obstructive pulmonary disease (COPD) is a multifactorial progressive airflow obstruction in the lungs, accounting for high morbidity and mortality across the world. This study aims to identify potential COPD blood-based biomarkers by analyzing the dysregulated gene expression patterns in blood and lung tissues with the help of robust computational approaches. The microarray gene expression datasets from blood (136 COPD and 6 controls) and lung tissues (16 COPD and 19 controls) were analyzed to detect shared differentially expressed genes (DEGs). Then these DEGs were used to construct COPD protein network-clusters and functionally enrich them against gene ontology annotation terms. The hub genes in the COPD network clusters were then queried in GWAS catalog and in several cancer expression databases to explore their pathogenic roles in lung cancers. The comparison of blood and lung tissue datasets revealed 63 shared DEGs. Of these DEGs, 12 COPD hub gene-network clusters (SREK1, TMEM67, IRAK2, MECOM, ASB4, C1QTNF2, CDC42BPA, DPF3, DET1, CCDC74B, KHK, and DDX3Y) connected to dysregulations of protein degradation, inflammatory cytokine production, airway remodeling, and immune cell activity were prioritized with the help of protein interactome and functional enrichment analysis. Interestingly, IRAK2 and MECOM hub genes from these COPD network clusters are known for their involvement in different pulmonary diseases. Additional COPD hub genes like SREK1, TMEM67, CDC42BPA, DPF3, and ASB4 were identified as prognostic markers in lung cancer, which is reported in 1% of COPD patients. This study identified 12 gene network- clusters as potential blood based genetic biomarkers for COPD diagnosis and prognosis.

Identification of a predictive gene signature related to pyroptosis for the prognosis of cutaneous melanoma

Pyroptosis is a form of inflammatory programmed cell death. However, because of no specific molecular biomarker, pyroptosis has not been considered as a novel therapeutic method to treat cutaneous melanoma (CM). Here, we identified pyroptosis genes that associate with the prognosis of CM patients and constructed an effective model for the prognostic prediction of CM patients. To identify genes related to pyroptosis that are differentially expressed in CM, we obtained gene expression data of CM patients and normal skin tissues from the Cancer Genome Atlas and the Genotype-Tissue Expression databases, and used another cohort obtained from Gene Expression Omnibus database for validation. Three genes (BST2, GBP5, and AIM2) that were associated with prognosis were found and incorporated into our prognostic model. Furthermore, we divided the patients into 2 groups: a high-risk group and a low-risk group. Functional analyses indicated that our model was correlated with patient survival and cancer growth. Multivariate and univariate Cox regressions revealed that the constructed model could serve as an independent prognostic factor for CM patients. Meanwhile, compared with other clinical characteristics, our model significantly improved the diagnostic accuracy. Gene function analysis revealed that pyroptosis genes BST2, GBP5, and AIM2 were differentially expressed in CM patients and positively associated with patient prognosis. Finally, a risk score was used to generate nomograms that displayed favorable discriminatory abilities for CM. In summary, our model could significantly predict the prognosis of CM patients and be used for the development of CM therapy.

Identification and Validation of Candidate Gene Module Along With Immune Cells Infiltration Patterns in Atherosclerosis Progression to Plaque Rupture via Transcriptome Analysis

Objective

To explore the differentially expressed genes (DEGs) along with infiltrating immune cells landscape and their potential mechanisms in the progression of atherosclerosis from onset to plaque rupture.

Methods

In this study, three atherosclerosis-related microarray datasets were downloaded from the NCBI-GEO database. The gene set enrichment analysis (GSEA) was performed for interpreting the biological insights of gene expression data. The CIBERSORTx algorithm was applied to infer the relative proportions of infiltrating immune cells of the atherosclerotic samples. DEGs of the datasets were screened using R. The protein interaction network was constructed via STRING. The cluster genes were analyzed by the Cytoscape software. Gene ontology (GO) enrichment was performed via geneontology.org. The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and receiver operating characteristics (ROC) analyses were performed to build machine learning models for differentiating atherosclerosis status. The Pearson correlation analysis was carried out to illustrate the relationship between cluster genes and immune cells. The expression levels of the cluster genes were validated in two external cohorts. Transcriptional factors and drug-gene interaction analysis were performed to investigate the promising targets for atherosclerosis intervention.

Results

Pathways related to immunoinflammatory responses were identified according to GSEA analysis, and the detailed fractions infiltrating immune cells were compared between the early and advanced atherosclerosis. Additionally, we identified 170 DEGs in atherosclerosis progression (|log2FC|≥1 and adjusted p < 0.05). They were mainly enriched in GO terms relating to inflammatory response and innate immune response. A cluster of nine genes, such as ITGB2, C1QC, LY86, CTSS, C1QA, CSF1R, LAPTM5, VSIG4, and CD163, were found to be significant, and their correlations with infiltrating immune cells were calculated. The cluster genes were also validated to be upregulated in two external cohorts. Moreover, C1QA and ITGB2 may exert pathogenic functions in the entire process of atherogenesis.

Conclusions

We reanalyzed the transcriptomic signature of atherosclerosis development from onset to plaque rupture along with the landscape of the immune cell, as well as revealed new insights and specific prospective DEGs for the investigation of disease-associated dynamic molecular processes and their regulations with immune cells.

Identification of Hub Genes Associated With the Development of Stomach Adenocarcinoma by Integrated Bioinformatics Analysis

Objective

This study was conducted in order to gain a better understanding of the molecular mechanisms of stomach adenocarcinoma (STAD), which is necessary to predict the prognosis of STAD and develop novel gene therapy strategies.

Methods

In this study, the gene expression profile of GSE118916 in the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas Program (TCGA) was used to explore the differential co-expression genes of STAD and normal tissues.

Results

A total of 407 STAD samples were collected, consisting of 375 from stomach adenocarcinoma tissues and 32 from normal tissues, as well as RNA-seq count data for 19,600 genes. Forty-two differentially expressed genes were screened by weighted gene co-expression network analysis (WGCNA) and differentially expressed gene analysis. According to the functional annotation analysis of the clusterProfiler R package, these genes were analyzed for GO function enrichment, digestion (biological process), tube bottom material membrane (cell component), and oxidoreductase activity (molecular function). The KEGG pathway was enriched in gastric acid secretion and chemical carcinogenesis. In addition, Cytoscape's cytoHubba plug-in was used to identify seven hub genes (EWSR1, ESR1, CLTC, PCMT1, TP53, HUWE1, and HDAC1) in a protein-protein interaction (PPI) network consisting of 7 nodes and 11 edges. Compared with normal tissues, CLTC and TP53 genes were upregulated in stomach adenocarcinoma (P < 0.05). TP53 was expressed differently in stages II and IV, EWSR1 was expressed differently in stages II and III, and ESR1 was expressed differently in stages I-III. Among the seven hub genes, Kaplan-Meier analysis and TCGG showed that the expression levels of HDAC1 and CLTC were significantly correlated with OS in patients with stomach adenocarcinoma (P < 0.05). GEPIA2 analysis showed that ESR1 expression was closely correlated with OS and DFS in gastric adenocarcinoma (P < 0.05). Then, the expression of the genes and their correlations were revealed by the R2 Platform (http://r2.amc.nl). Finally, we collected 18 pairs of gastric mucosal tissues from normal people and cancer tissues from patients with stomach adenocarcinoma. The expression levels of the above seven hub genes and their relative protein expression were detected by RT-PCR and immunohistochemistry (IHC). The results showed that the gene and protein expression levels in stomach adenocarcinoma tissues were increased than those in the normal group.

Conclusion

In summary, we believe that the identified hub genes were related to the occurrence of stomach adenocarcinoma, especially the expression of ESR1, HDAC1, and CLTC genes, which are related to the prognosis and overall survival of patients and may become the potential for the future diagnosis and treatment of STAD.

Identification of Hub Genes in Colorectal Adenocarcinoma by Integrated Bioinformatics

An improved understanding of the molecular mechanism of colorectal adenocarcinoma is necessary to predict the prognosis and develop new target gene therapy strategies. This study aims to identify hub genes associated with colorectal adenocarcinoma and further analyze their prognostic significance. In this study, The Cancer Genome Atlas (TCGA) COAD-READ database and the gene expression profiles of GSE25070 from the Gene Expression Omnibus were collected to explore the differentially expressed genes between colorectal adenocarcinoma and normal tissues. The weighted gene co-expression network analysis (WGCNA) and differential expression analysis identified 82 differentially co-expressed genes in the collected datasets. Enrichment analysis was applied to explore the regulated signaling pathway in colorectal adenocarcinoma. In addition, 10 hub genes were identified in the protein–protein interaction (PPI) network by using the cytoHubba plug-in of Cytoscape, where five genes were further proven to be significantly related to the survival rate. Compared with normal tissues, the expressions of the five genes were both downregulated in the GSE110224 dataset. Subsequently, the expression of the five hub genes was confirmed by the Human Protein Atlas database. Finally, we used Cox regression analysis to identify genes associated with prognosis, and a 3-gene signature (CLCA1–CLCA4–GUCA2A) was constructed to predict the prognosis of patients with colorectal cancer. In conclusion, our study revealed that the five hub genes and CLCA1–CLCA4–GUCA2A signature are highly correlated with the development of colorectal adenocarcinoma and can serve as promising prognosis factors to predict the overall survival rate of patients.

GEOexplorer: a webserver for gene expression analysis and visualisation

Gene Expression Omnibus (GEO) is a database repository hosting a substantial proportion of publicly available high throughput gene expression data. Gene expression analysis is a powerful tool to gain insight into the mechanisms and processes underlying the biological and phenotypic differences between sample groups. Despite the wide availability of gene expression datasets, their access, analysis, and integration are not trivial and require specific expertise and programming proficiency. We developed the GEOexplorer webserver to allow scientists to access, integrate and analyse gene expression datasets without requiring programming proficiency. Via its user-friendly graphic interface, users can easily apply GEOexplorer to perform interactive and reproducible gene expression analysis of microarray and RNA-seq datasets, while producing a wealth of interactive visualisations to facilitate data exploration and interpretation, and generating a range of publication ready figures. The webserver allows users to search and retrieve datasets from GEO as well as to upload user-generated data and combine and harmonise two datasets to perform joint analyses. GEOexplorer, available at https://geoexplorer.rosalind.kcl.ac.uk, provides a solution for performing interactive and reproducible analyses of microarray and RNA-seq gene expression data, empowering life scientists to perform exploratory data analysis and differential gene expression analysis on-the-fly without informatics proficiency.

Identification of Hub Genes Associated With Non-alcoholic Steatohepatitis Using Integrated Bioinformatics Analysis

Background and aims: As a major cause of liver disease worldwide, non-alcoholic fatty liver disease (NAFLD) comprises non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH). Due to the high prevalence and poor prognosis of NASH, it is critical to understand its mechanisms. However, the etiology and mechanisms remain largely unknown. In addition, the gold standard for the diagnosis of NASH is liver biopsy, which is an invasive procedure. Therefore, there is a pressing need to develop noninvasive tests for NASH diagnosis. The goal of the study is to discover key genes involved in NASH development and investigate their value as noninvasive biomarkers.

Methods: The Gene Expression Omnibus (GEO) database was used to obtain two datasets encompassing NASH patients and healthy controls. We used weighted gene co-expression network analysis (WGCNA) and differential expression analysis in order to investigate the association between gene sets and clinical features, as well as to discover co-expression modules. A protein-protein interaction (PPI) network was created to extract hub genes. The results were validated using another publicly available dataset and mice treated with a high-fat diet (HFD) and carbon tetrachloride (CCl4).

Results: A total of 24 differentially co-expressed genes were selected by WGCNA and differential expression analysis. KEGG analysis indicated most of them were enriched in the focal adhesion pathway. GO analysis showed these genes were mainly enriched in circadian rhythm, aging, angiogenesis and response to drug (biological process), endoplasmic reticulum lumen (cellular component), and protein binding (molecular function). As a result, eight genes (JUN, SERPINE1, GINS2, TYMS, HMMR, IGFBP2, BIRC3, TNFRSF12A) were identified as hub genes. Finally, three genes were found significantly changed in both the validation dataset and the mouse model.

Conclusion: Our research discovered genes that have the potential to mediate the process of NASH and might be useful diagnostic biomarkers for the disorder.

Identification of IMPA2 as the hub gene associated with colorectal cancer and liver metastasis by integrated bioinformatics analysis

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IMPA2 was selected as the hub gene associated with CRC and liver metastasis by integrated bioinformatics analysis.

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The expression of IMPA2 was significantly decreased in CRC and liver metastasis samples, and low IMPA2 expression was correlated with poor prognosis.

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The potential mechanisms of IMPA2-mediated CRC and metastasis may be associated with lipid metabolism and epithelial mesenchymal transition (EMT).

Background and Objectives

Colorectal cancer (CRC) is one of the most common malignant tumors worldwide with high incidence and mortality rate, while colorectal liver metastasis (CRLM) is one of the major causes of cancer-related deaths. Therefore, the present study aims to identify the hub gene associated with CRC carcinogenesis and liver metastasis, and then explore its diagnostic and prognostic value as well as the potential regulation mechanism.

Methods

The overlapping differential co-expression genes among CRC, CRLM, and normal tissues were explored on the GSE49355 and GSE81582 datasets from the Gene Expression Omnibus (GEO) database by integrated bioinformatics analysis. Then, the hub prognostic genes were selected from the overlapping genes by univariate Cox proportional hazard analysis and online database Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Subsequently, the clinical value of the hub genes was evaluated in the TCGA and GSE39582 cohorts. Finally, the underlying mechanisms of the hub gene regulating CRC carcinogenesis and metastasis were explored by Gene function annotation and DNA methylation analysis.

Results

Inositol mono-phosphatase 2 (IMPA2) was identified as the hub gene associated with CRC carcinogenesis and liver metastasis. IMPA2 had an excellent diagnostic efficiency, and its expression was significantly decreased in CRC and liver metastasis samples, being positively correlated with poor prognosis. Moreover, its low expression was associated with AJCC stage III+IV, T4, N1+2, and M1. In addition, our results revealed that the potential mechanisms used by IMPA2 to mediate CRC carcinogenesis and metastasis could be associated with lipid metabolism and epithelial mesenchymal transition (EMT). Finally, IMPA2 expression could be regulated by DNA methylation.

Conclusions

IMPA2 was identified and reported for the first time as a hub gene biomarker in the diagnosis and prognosis of CRC, which could regulate CRC carcinogenesis and liver metastasis through the regulation of lipid metabolism, EMT, and DNA methylation.

CGN Correlates With the Prognosis and Tumor Immune Microenvironment in Clear Cell Renal Cell Carcinoma

This study aimed to screen and verify the important prognostic genes related to clear cell renal cell carcinoma (ccRCC) and further analyze their relationship with the immune microenvironment. Gene expression profiles from the TCGA-KIRC, GSE46699, GSE36895, and GSE16449 datasets were utilized to explore differentially co-expressed genes in ccRCC. We screened 124 differentially co-expressed genes using a weighted gene co-expression network and differential gene expression analyses. Univariate and multivariate Cox survival analyses revealed that the expressions of genes CGN, FECH, UCHL1, and WT1 were independently related to the overall survival of ccRCC patients. Kaplan–Meier survival analysis was performed, and CGN was found to have the strongest correlation with the prognosis of ccRCC patients and was consequently selected for further analyses and experimental verification. The results showed that NK cell activation, resting dendritic cells, resting monocytes, and resting mast cells were positively correlated with CGN expression; CD4+ memory activated T cells, regulatory T cells, and M0 macrophages were negatively correlated with CGN expression. Finally, using western blotting and reverse transcription polymerase chain reaction, we verified that the CGN protein level was down-regulated in ccRCC samples, which was consistent with the mRNA levels. CGN was thus identified as diagnosis and prognosis biomarker for ccRCC and is related to the immune microenvironment.

SPINK5 is a Prognostic Biomarker Associated With the Progression and Prognosis of Laryngeal Squamous Cell Carcinoma Identified by Weighted Gene Co-Expression Network Analysis

Laryngeal squamous cell carcinoma (LSCC) is one of the most common types of head and neck squamous cell carcinomas (HNSCC) and is the second most prevalent malignancy occurring in the head and neck or respiratory tract, with a high incidence and mortality rate. Survival is limited for patients with LSCC. To identify more biomarkers associated with the prognosis of patients with LSCC, using bioinformatics analysis, this study used The Cancer Genome Atlas (TCGA) LSCC dataset and gene expression profiles of GSE59102 from the Gene Expression Omnibus (GEO). Eighty-one differentially co-expressed genes were identified by weighted gene co-expression network analysis (WGCNA). Next, 10 hub genes (PPL, KRT78, CRNN, PTK7, SCEL, AGRN, SPINK5, AIF1L, EMP1, and PPP1R3C) were screened from a protein-protein interaction (PPI) network. Based on survival analysis, SPINK5 was significantly correlated with survival time in LSCC patients. After verification in the TCGA and HPA databases, SPINK5 was selected as a prognostic biomarker. Finally, the GSEA results showed that downregulation of SPINK5 gene expression may promote tumorigenesis and the development of cancers by the “BASAL CELL CARCINOMA” pathway, and it has been implicated in disrupting DNA damage and repair pathways. Collectively, SPINK5 may serve as a potential prognostic biomarker in LSCC.

Digital multiplexed analysis of circular RNAs in FFPE and fresh non‐small cell lung cancer specimens

Although many studies highlight the implication of circular RNAs (circRNAs) in carcinogenesis and tumor progression, their potential as cancer biomarkers has not yet been fully explored in the clinic due to the limitations of current quantification methods. Here, we report the use of the nCounter platform as a valid technology for the analysis of circRNA expression patterns in non‐small cell lung cancer (NSCLC) specimens. Under this context, our custom‐made circRNA panel was able to detect circRNA expression both in NSCLC cells and formalin‐fixed paraffin‐embedded (FFPE) tissues. CircFUT8 was overexpressed in NSCLC, contrasting with circEPB41L2, circBNC2, and circSOX13 downregulation even at the early stages of the disease. Machine learning (ML) approaches from different paradigms allowed discrimination of NSCLC from nontumor controls (NTCs) with an 8‐circRNA signature. An additional 4‐circRNA signature was able to classify early‐stage NSCLC samples from NTC, reaching a maximum area under the ROC curve (AUC) of 0.981. Our results not only present two circRNA signatures with diagnosis potential but also introduce nCounter processing following ML as a feasible protocol for the study and development of circRNA signatures for NSCLC.

Identification and Validation of Hub Genes Associated with Bladder Cancer by Integrated Bioinformatics and Experimental Assays

Bladder cancer (BC) is the most common malignant tumor of the urinary system and is associated with high morbidity and mortality; however, the molecular mechanism underlying its occurrence is not clear. In this study, the gene expression profile and related clinical information of GSE13507 were downloaded from the Gene Expression Omnibus (GEO) database. RNA sequencing (RNA-seq) expression data and related clinical information were retrieved from The Cancer Genome Atlas (TCGA) database. Overlapping genes were identified by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). Then, we carried out functional enrichment analysis to understand the potential biological functions of these co-expressed genes. Finally, we performed a protein-protein interaction (PPI) analysis combined with survival analysis. Using the CytoHubba plug-in of Cytoscape, TROAP, CENPF, PRC1, AURKB, CCNB2, CDC20, TTK, CEP55, ASPM, and CDCA8 were identified as candidate central genes. According to the survival analysis, the high expression of TTK was related to the poor overall survival (OS) of patients with BC. TTK may also affect the bladder tumor microenvironment (TME) by affecting the number of immune cells. The expression level of TTK was verified by immunohistochemistry (IHC) and real-time quantitative polymerase chain reaction (RT-qPCR), and the tumor-promoting effect of TTK in BC cells was confirmed in vitro. Our results also identified the MSC-AS1/hsa-miR-664b-3p/TTK regulatory axis, which may also play an important role in the progression of BC, but further research is needed to verify this result. In summary, our results provide a new idea for accurate early diagnosis, clinical treatment, and prognosis of BC.

Design and Delivery of SINEUP: A New Modular Tool to Increase Protein Translation

SINEUP is a new class of long non-coding RNAs (lncRNAs) which contain an inverted Short Interspersed Nuclear Element (SINE) B2 element (invSINEB2) necessary to specifically upregulate target gene translation. Originally identified in the mouse AS-Uchl1 (antisense Ubiquitin carboxyl-terminal esterase L1) locus, natural SINEUP molecules are oriented head to head to their sense protein coding, target gene (Uchl1, in this example). Peculiarly, SINEUP is able to augment, in a specific and controlled way, the expression of the target protein, with no alteration of target mRNA levels. SINEUP is characterized by a modular structure with the Binding Domain (BD) providing specificity to the target transcript and an effector domain (ED)-containing the invSINEB2 element-able to promote the loading to the heavy polysomes of the target mRNA. Since the understanding of its modular structure in the endogenous AS-Uchl1 ncRNA, synthetic SINEUP molecules have been developed by creating a specific BD for the gene of interest and placing it upstream the invSINEB2 ED. Synthetic SINEUP is thus a novel molecular tool that potentially may be used for any industrial or biomedical application to enhance protein production, also as possible therapeutic strategy in haploinsufficiency-driven disorders.Here, we describe a detailed protocol to (1) design a specific BD directed to a gene of interest and (2) assemble and clone it with the ED to obtain a functional SINEUP molecule. Then, we provide guidelines to efficiently deliver SINEUP into mammalian cells and evaluate its ability to effectively upregulate target protein translation.

Identification of hub genes and construction of an mRNA-miRNA-lncRNA network of gastric carcinoma using integrated bioinformatics analysis

Background

Gastric carcinoma (GC) is one of the most common cancer globally. Despite its worldwide decline in incidence and mortality over the past decades, gastric cancer still has a poor prognosis. However, the key regulators driving this process and their exact mechanisms have not been thoroughly studied. This study aimed to identify hub genes to improve the prognostic prediction of GC and construct a messenger RNA-microRNA-long non-coding RNA(mRNA-miRNA-lncRNA) regulatory network.

Methods

The GSE66229 dataset, from the Gene Expression Omnibus (GEO) database, and The Cancer Genome Atlas (TCGA) database were used for the bioinformatic analysis. Differential gene expression analysis methods and Weighted Gene Co-expression Network Analysis (WGCNA) were used to identify a common set of differentially co-expressed genes in GC. The genes were validated using samples from TCGA database and further validation using the online tools GEPIA database and Kaplan-Meier(KM) plotter database. Gene set enrichment analysis(GSEA) was used to identify hub genes related to signaling pathways in GC. The RNAInter database and Cytoscape software were used to construct an mRNA-miRNA-lncRNA network.

Results

A total of 12 genes were identified as the common set of differentially co-expressed genes in GC. After verification of these genes, 3 hub genes, namely CTHRC1, FNDC1, and INHBA, were found to be upregulated in tumor and associated with poor GC patient survival. In addition, an mRNA-miRNA-lncRNA regulatory network was established, which included 12 lncRNAs, 5 miRNAs, and the 3 hub genes.

Conclusions

In summary, the identification of these hub genes and the establishment of the mRNA-miRNA-lncRNA regulatory network provide new insights into the underlying mechanisms of gastric carcinogenesis. In addition, the identified hub genes, CTHRC1, FNDC1, and INHBA, may serve as novel prognostic biomarkers and therapeutic targets.

Identification of Hub Genes Associated With Tuberculous Pleurisy by Integrated Bioinformatics Analysis

Improving the understanding of the molecular mechanism of tuberculous pleurisy is required to develop diagnosis and new therapy strategies of targeted genes. The purpose of this study is to identify important genes related to tuberculous pleurisy. In this study, the expression profile obtained by sequencing the surgically resected pleural tissue was used to explore the differentially co-expressed genes between tuberculous pleurisy tissue and normal tissue. 29 differentially co-expressed genes were screened by weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis methods. According to the functional annotation analysis of R clusterProfiler software package, these genes are mainly enriched in nucleotide−sugar biosynthetic process (biological process), ficolin−1−rich granule lumen (cell component), and electron transfer activity (molecular function). In addition, in the protein-protein interaction (PPI) network, 20 hub genes of DEGs and WCGNA genes were identified using the CytoHubba plug-in of Cytoscape. In the end, RPL17 was identified as a gene that can be the biomarker of tuberculous pleurisy. At the same time, there are seven genes that may have relationship with the disease (UBA7, NDUFB8, UQCRFS1, JUNB, PSMC4, PHPT1, and MAPK11).

Identifying the hub genes and immune infiltration related to pyroptosis in rheumatoid arthritis

Rheumatoid arthritis (RA) is one of the most common autoimmune joint disorders globally, but its pathophysiological mechanisms have not been thoroughly investigated. Pyroptosis significantly correlates with programmed cell death. However, targeting pyroptosis has not been considered as a therapeutic strategy in RA due to a lack of systematic studies on validated biomarkers. The present study aimed to identify hub pyroptosis biomarkers and immune infiltration in RA. The gene expression profiles of synovial tissues were obtained from the Gene Expression Omnibus (GEO) database to identify differentially expressed pyroptosis genes (DEPGs). Meanwhile, the CIBERSORT algorithm was used to explore the association between immune infiltration and RA. Consequently, two hub DEPGs (EGFR and JUN) were identified as critical genes in RA. Through gene ontology and pathway enrichment analysis. EGFR and JUN were found to be primarily involved in the ErbB signaling pathway, PD-1 checkpoint pathway, GnRH signaling pathway, etc. Furthermore, for immune infiltration analysis, the pyroptosis genes EGFR and JUN were closely connected with four and one immune cell types, respectively. Overall, this study presents a novel method to identify hub DEPGs and their correlation with immune infiltration, which may provide novel perspectives into the diagnosis and treatment of patients with RA.

Natural SINEUP RNAs in Autism Spectrum Disorders: RAB11B-AS1 Dysregulation in a Neuronal CHD8 Suppression Model Leads to RAB11B Protein Increase

CHD8 represents one of the highest confidence genetic risk factors implied in Autism Spectrum Disorders, with most mutations leading to CHD8 haploinsufficiency and the insurgence of specific phenotypes, such as macrocephaly, facial dysmorphisms, intellectual disability, and gastrointestinal complaints. While extensive studies have been conducted on the possible consequences of CHD8 suppression and protein coding RNAs dysregulation during neuronal development, the effects of transcriptional changes of long non-coding RNAs (lncRNAs) remain unclear. In this study, we focused on a peculiar class of natural antisense lncRNAs, SINEUPs, that enhance translation of a target mRNA through the activity of two RNA domains, an embedded transposable element sequence and an antisense region. By looking at dysregulated transcripts following CHD8 knock down (KD), we first identified RAB11B-AS1 as a potential SINEUP RNA for its domain configuration. Then we demonstrated that such lncRNA is able to increase endogenous RAB11B protein amounts without affecting its transcriptional levels. RAB11B has a pivotal role in vesicular trafficking, and mutations on this gene correlate with intellectual disability and microcephaly. Thus, our study discloses an additional layer of molecular regulation which is altered by CHD8 suppression. This represents the first experimental confirmation that naturally occurring SINEUP could be involved in ASD pathogenesis and underscores the importance of dysregulation of functional lncRNAs in neurodevelopment.

The current and future applications of in situ hybridization technologies in anatomical pathology

INTRODUCTION

In situ hybridization (ISH) plays an important role in the field of molecular diagnostics, especially in an anatomical pathology laboratory. ISH is a technique that can detect the targeted DNA or RNA sequences in tissue sections from frozen or fixed materials with labeled DNA or RNA probes. Radioactive and non-radioactive probes are the two major probes that can be used to label the targeted nucleic acids.

AREAS COVERED

Two decades after the Human Genome Project, ISH has not only simply been applied to identify the chromosomal location of a human gene but has also been extensively applied to gene expressions studies and utilized for clinical diagnosis, especially for the determination of biomarkers for breast and ovarian cancers - human epidermal growth factor receptor 2. Duchenne muscular dystrophy, Cri-du-chat syndrome, Angelman syndrome, PraderWilli syndrome, cystic fibrosis, and trisomy are diseases that can also be detected by ISH. In this review, the basic principles, historical development, advantages and disadvantages, enhancement in reporting molecules and probes, advancement in detection methods, in situ PCR, clinical applications and novel applications of ISH will be discussed.

EXPERT OPINION

With the advancement in ISH technologies and appropriate training, diagnosis can be improved in Anatomical Pathology.

LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer

The aim of this study is to identify potential biomarkers for early diagnosis of gynecologic cancer in order to improve survival. Cervical cancer (CC) and endometrial cancer (EC) are the most common malignant tumors of gynecologic cancer among women in the world. As the underlying molecular mechanisms in both cervical and endometrial cancer remain unclear, a comprehensive and systematic bioinformatics analysis is required. In our study, gene expression profiles of GSE9750, GES7803, GES63514, GES17025, GES115810, and GES36389 downloaded from Gene Expression Omnibus (GEO) were utilized to analyze differential gene expression between cancer and normal tissues. A total of 78 differentially expressed genes (DEGs) common to CC and EC were identified to perform the functional enrichment analyses, including gene ontology and pathway analysis. KEGG pathway analysis of 78 DEGs indicated that three main types of pathway participate in the mechanism of gynecologic cancer such as drug metabolism, signal transduction, and tumorigenesis and development. Furthermore, 20 diagnostic signatures were confirmed using the least absolute shrink and selection operator (LASSO) regression with 10-fold cross validation. Finally, we used the GEPIA2 online tool to verify the expression of 20 genes selected by the LASSO regression model. Among them, the expression of PAMR1 and SLC24A3 in tumor tissues was downregulated significantly compared to the normal tissue, and found to be statistically significant in survival rates between the CC and EC of patients (p < 0.05). The two genes have their function: (1.) PAMR1 is a tumor suppressor gene, and many studies have proven that overexpression of the gene markedly suppresses cell growth, especially in breast cancer and polycystic ovary syndrome; (2.) SLC24A3 is a sodium–calcium regulator of cells, and high SLC24A3 levels are associated with poor prognosis. In our study, the gene signatures can be used to predict CC and EC prognosis, which could provide novel clinical evidence to serve as a potential biomarker for future diagnosis and treatment.

Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis

Background

Osteoarthritis (OA) is the most common chronic degenerative joint disorder globally that is characterized by synovitis, cartilage degeneration, joint space stenosis, and sub-cartilage bone hyperplasia. However, the pathophysiologic mechanisms of OA have not been thoroughly investigated.

Methods

In this study, we conducted various bioinformatics analyses to identify hub biomarkers and immune infiltration in OA. The gene expression profiles of synovial tissues from 29 healthy controls and 36 OA samples were obtained from the gene expression omnibus database to identify differentially expressed genes (DEGs). The CIBERSORT algorithm was used to explore the association between immune infiltration and arthritis.

Results

Eighteen hub DEGs were identified as critical biomarkers for OA. Through gene ontology and pathway enrichment analyses, it was found that these DEGs were primarily involved in PI3K-Akt signaling pathway and Rap1 signaling pathway. Furthermore, immune infiltration analysis revealed differences in immune infiltration between patients with OA and healthy controls. The hub gene ZNF160 was closely related to immune cells, especially mast cell activation in OA.

Conclusion

Overall, this study presented a novel method to identify hub DEGs and their correlation with immune infiltration, which may provide novel insights into the diagnosis and treatment of patients with OA.

Identification of key genes for HNSCC from public databases using bioinformatics analysis

Background

The cause and underlying molecular mechanisms of head and neck squamous cell carcinoma (HNSCC) are unclear. Our study aims to identify the key genes associated with HNSCC and reveal potential biomarkers.

Methods

In this study, the expression profile dataset GSE83519 of the Gene Expression Omnibus database and the RNA sequencing dataset of HNSCC of The Cancer Genome Atlas were included for analysis. Sixteen differentially expressed genes were screened from these two datasets using R software. Gene Expression Profiling Interactive Analysis 2 (GEPIA2) was then adopted for survival analysis, and finally, three key genes related to the overall survival of HNSCC patients were identified. Furthermore, we verified these three genes using the Oncomine database and from real-time PCR and immunohistochemistry results from HNSCC tissues.

Results

The expression data of 44 samples from GSE83519 and 545 samples from TCGA-HNSC were collected. Using bioinformatics, the two databases were integrated, and 16 DEGs were screened out. Gene Ontology (GO) enrichment analysis showed that the biological functions of DEGs focused primarily on the apical plasma membrane and regulation of anoikis. Kyoto Encyclopedia of Genes and Genomes (KEGG) signalling pathway analysis showed that these DEGs were mainly involved in drug metabolism-cytochrome P450 and serotonergic synapses. Survival analysis identified three key genes, CEACAM5, CEACAM6 and CLCA4, that were closely related to HNSCC prognosis. The Oncomine database, qRT–PCR and IHC verified that all 3 key genes were downregulated in most HNSCC tissues compared to adjacent normal tissues.

Conclusions

This study indicates that integrated bioinformatics analyses play an important role in screening for differentially expressed genes and pathways in HNSCC, helping us better understand the biomarkers and molecular mechanism of HNSCC.

Long non-coding RNAs in oncourology

For several decades, research in tumor biology has focused on the involvement of genes encoding a protein. Only recently has it been discovered that a whole class of molecules called non-coding RNAs (ncRNAs) play a key regulatory role in health and disease. Long noncoding RNAs (lncRNAs) are a group of noncoding RNAs longer than 200 nucleotides. It has been found that lncRNAs play a fundamental role in the biology of many types of tumors, including tumors of the genitourinary system. As a result, hundreds of clinical trials dedicated to oncourology have begun, using lncRNA as new biomarkers or treatments. Identifying new specific biomarkers, in the form of lncRNAs, will increase the ability to differentiate the tumor and other processes, determine the localization and extent of the tumor, and the ability to predict the course of the disease, and plan treatment. Therapy of tumors, especially malignant ones, is also a difficult task. When surgery and chemotherapy fail, radiation therapy becomes the treatment choice. Therefore, the possibility that lncRNAs could represent innovative therapeutic agents or targets is an exciting idea. However, the possibility of their use in modern clinical practice is limited, and this is associated with several problems at the pre-, analytical and post-analytical stages. Another problem in the study of lncRNAs is the large number and variety of their functions in tumors. Therefore, solving technological problems in lncRNAs study in oncourology may open up new possibilities for lncRNAs use in modern clinical practice.

Comprehensive Bioinformatics Analysis Identifies POLR2I as a Key Gene in the Pathogenesis of Hypertensive Nephropathy

Hypertensive nephropathy (HN), mainly caused by chronic hypertension, is one of the major causes of end-stage renal disease. However, the pathogenesis of HN remains unclarified, and there is an urgent need for improved treatments. Gene expression profiles for HN and normal tissue were obtained from the Gene Expression Omnibus database. A total of 229 differentially co-expressed genes were identified by weighted gene co-expression network analysis and differential gene expression analysis. These genes were used to construct protein–protein interaction networks to search for hub genes. Following validation in an independent external dataset and in a clinical database, POLR2I, one of the hub genes, was identified as a key gene related to the pathogenesis of HN. The expression level of POLR2I is upregulated in HN, and the up-regulation of POLR2I is positively correlated with renal function in HN. Finally, we verified the protein levels of POLR2I in vivo to confirm the accuracy of our analysis. In conclusion, our study identified POLR2I as a key gene related to the pathogenesis of HN, providing new insights into the molecular mechanisms underlying HN.

Identification of potential hub genes associated with skin wound healing based on time course bioinformatic analyses

Background

The skin is the largest organ of the body and has multiple functions. Wounds remain a significant healthcare problem due to the large number of traumatic and pathophysiological conditions patients suffer.

Methods

Gene expression profiles of 37 biopsies collected from patients undergoing split-thickness skin grafts at five different time points were downloaded from two datasets (GSE28914 and GSE50425) in the Gene Expression Omnibus (GEO) database. Principal component analysis (PCA) was applied to classify samples into different phases. Subsequently, differentially expressed genes (DEGs) analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional enrichment analyses were performed, and protein–protein interaction (PPI) networks created for each phase. Furthermore, based on the results of the PPI, hub genes in each phase were identified by molecular complex detection combined with the ClueGO algorithm.

Results

Using principal component analysis, the collected samples were divided into four phases, namely intact phase, acute wound phase, inflammatory and proliferation phase, and remodeling phase. Intact samples were used as control group. In the acute wound phase, a total of 1 upregulated and 100 downregulated DEGs were identified. Tyrosinase (TYR), tyrosinase Related Protein 1 (TYRP1) and dopachrome tautomerase (DCT) were considered as hub genes and enriched in tyrosine metabolism which dominate the process of melanogenesis. In the inflammatory and proliferation phase, a total of 85 upregulated and 164 downregulated DEGs were identified. CHEK1, CCNB1 and CDK1 were considered as hub genes and enriched in cell cycle and P53 signaling pathway. In the remodeling phase, a total of 121 upregulated and 49 downregulated DEGs were identified. COL4A1, COL4A2, and COL6A1 were considered as hub genes and enriched in protein digestion and absorption, and ECM-receptor interaction.

Conclusion

This comprehensive bioinformatic re-analysis of GEO data provides new insights into the molecular pathogenesis of wound healing and the potential identification of therapeutic targets for the treatment of wounds.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12893-021-01298-w.

Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

Background

Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists.

Results

In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases.

Conclusions

We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00336-1.

The Role of Extracellular Vesicles as Shuttles of RNA and Their Clinical Significance as Biomarkers in Hepatocellular Carcinoma

Extracellular vesicles (EVs) have attracted interest as mediators of intercellular communication following the discovery that EVs contain RNA molecules, including non-coding RNA (ncRNA). Growing evidence for the enrichment of peculiar RNA species in specific EV subtypes has been demonstrated. ncRNAs, transferred from donor cells to recipient cells, confer to EVs the feature to regulate the expression of genes involved in differentiation, proliferation, apoptosis, and other biological processes. These multiple actions require accuracy in the isolation of RNA content from EVs and the methodologies used play a relevant role. In liver, EVs play a crucial role in regulating cell-cell communications and several pathophysiological events in the heterogeneous liver class of cells via horizontal transfer of their cargo. This review aims to discuss the rising role of EVs and their ncRNAs content in regulating specific aspects of hepatocellular carcinoma development, including tumorigenesis, angiogenesis, and tumor metastasis. We analyze the progress in EV-ncRNAs' potential clinical applications as important diagnostic and prognostic biomarkers for liver conditions.

Identification of Hub Genes Associated With Melanoma Development by Comprehensive Bioinformatics Analysis

Introduction

This study aimed to identify important genes associated with melanoma to further develop new target gene therapies and analyze their significance concerning prognosis.

Materials and methods

Gene expression data for melanoma and normal tissue were downloaded from three databases. Differentially co-expressed genes were identified by WGCNA and DEGs analysis. These genes were subjected to GO, and KEGG enrichment analysis and construction of the PPI visualized with Cytoscape and screened for the top 10 Hub genes using CytoHubba. We validated the Hub gene's protein levels with an immunohistochemical assay to confirm the accuracy of our analysis.

Results

A total of 435 differentially co-expressed genes were obtained. Survival curves showed that high expression of FOXM1,\ EXO1, KIF20A, TPX2, and CDC20 in melanoma patients with 5 of the top 10 hub genes was associated with reduced overall survival (OS). Immunohistochemistry showed that all five genes were expressed at higher protein levels in melanoma than in paracancerous tissues.

Conclusion

FOXM1, EXO1, KIF20A, TPX2, and CDC20 are prognosis-associated core genes of melanoma, and their high expression correlates with the low prognosis of melanoma patients and can be used as biomarkers for melanoma diagnosis, treatment, and prognosis prediction.

RNA Flow Cytometry for the Study of T Cell Metabolism

T cells undergo activation and differentiation programs along a continuum of states that can be tracked through flow cytometry using a combination of surface and intracellular markers. Such dynamic behavior is the result of transcriptional and post-transcriptional events, initiated and sustained by the activation of specific transcription factors and by epigenetic remodeling. These signaling pathways are tightly integrated with metabolic routes in a bidirectional manner: on the one hand, T cell receptors and costimulatory molecules activate metabolic reprogramming; on the other hand, metabolites modify T cell transcriptional programs and functions. Flow cytometry represents an invaluable tool to analyze the integration of phenotypical, functional, metabolic and transcriptional features, at the single cell level in heterogeneous T cell populations, and from complex microenvironments, with potential clinical application in monitoring the efficacy of cancer immunotherapy. Here, we review the most recent advances in flow cytometry-based analysis of gene expression, in combination with indicators of mitochondrial activity, with the aim of revealing and characterizing major metabolic pathways in T cells.

Rapid identification of magnesium ascorbyl phosphate utilizing phosphatase through a chromogenic change-coupled activity assay

In this study, we report a chromogenic reaction between magnesium ascorbyl phosphate (MAP) and ferric chloride to generate a Brown-Red clathrate, while the Treated MAP by phosphatases forms Colorless (BRTC) product with ferric chloride. The BRTC was indicative of phosphatase activity-mediated excision of phosphorous group from MAP and utilized to screen phosphatases from bacterial cell lysates. From ten tested strains, BRTC was observed in the cell lysate of Salmonella enterica subsp. enterica serovar Cerro 87. BRTC was again employed to track phosphatase activity of the resuspensions of the ammonium sulfate graded precipitations of the cell lysate. Two phosphatases, PhoN and YcdX, were identified by LC-MS/MS analysis in the protein fraction giving most obvious BRTC phenotype and validated by examination of in vitro activity of the purified proteins. KEY POINTS: • BRTC is labelling-free, naked-eye visible, and independent of any facilities. • BRTC can directly screen phosphatases from microbial cell lysates. • Using BRTC system, two phosphatases were identified in Salmonella enterica subsp. enterica serovar Cerro 87.

Expression and prognostic value of E2F3 transcription factor in non-small cell lung cancer

E2F transcription factor 3 (E2F3) plays a vital role in the development of various types of cancer. To verify whether E2F3 is a suitable biomarker for the prognosis of lung cancer, bioinformatics analysis was performed to determine the differential expression level of E2F3 in lung cancer and the surrounding non-tumor tissues, and the results were confirmed in a NSCLC cell line and a tissue microarray (TMA). The relevance of E2F3 in non-small cell lung cancer (NSCLC) was investigated in 19 studies from the Oncomine database and confirmed in The Cancer Genome Atlas database. In the lung cancer cell line A549, the inhibition of E2F3 mRNA expression level led to decreased tumor cell viability and cell migration, which was determined by a Cell Counting Kit-8 and wound healing assays, respectively. Immunohistochemistry analyses of E2F3, Bcl-2, Bax and caspase-3 were performed in the NSCLC TMA (n=50). The assessment of TMA detected the increase of E2F3 protein expression level in the tumor tissues, as compared with that in the non-tumor tissues, which was also correlated with the increase in expression of Bcl-2 in tumors. Analysis of the clinical data from patients with NSCLC revealed that the overexpression of E2F3 was associated with early lymphatic spreading, and poor patient survival time. The OncomiR website was used to predict the E2F3 upstream microRNAs and determine their prognostic value in patients with NSCLC. The results from the present study revealed that E2F3 was overexpressed at both the transcriptional and translational levels in NSCLC tissues, as compared with that in non-tumor tissues. The overexpression of E2F3 was associated with the upregulation of the anti-apoptotic factor, Bcl-2, which may contribute to uncontrolled tumor growth. Thus, E2F3 was shown to have important oncogenic properties in the development of NSCLC, and it may become a potential biomarker for patients with NSCLC.

Bioinformatics Analysis Identifies IL6ST as a Potential Tumor Suppressor Gene for Triple-Negative Breast Cancer

Improved insight into the molecular mechanisms of triple-negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study was to identify genes significantly associated with TNBC and further analyze their prognostic significance. The Cancer Genome Atlas (TCGA) TNBC database and gene expression profiles of GSE76275 from Gene Expression Omnibus (GEO) were used to explore differentially co-expressed genes in TNBC compared with those in normal tissues and non-TNBC breast cancer tissues. Differential gene expression and weighted gene co-expression network analyses identified 24 differentially co-expressed genes. Functional annotation suggested that these genes were primarily enriched in processes such as metabolism, membrane, and protein binding. The protein-protein interaction (PPI) network further identified ten hub genes, five of which (MAPT, CBS, SOX11, IL6ST, and MEX3A) were confirmed to be differentially expressed in an independent dataset (GSE38959). Moreover, CBS and MEX3A expression was upregulated, whereas IL6ST expression was downregulated in TNBC tissues compared to that in other breast cancer subtypes. Furthermore, lower expression of IL6ST was associated with worse overall survival in patients with TNBC. Thus, IL6ST might play an important role in TNBC progression and could serve as a tumor suppressor gene for diagnosis and treatment.

Identifying the Immunological Gene Signatures of Immune Cell Subtypes

The immune system is a complicated defensive system that comprises multiple functional cells and molecules acting against endogenous and exogenous pathogenic factors. Identifying immune cell subtypes and recognizing their unique immunological functions are difficult because of the complicated cellular components and immunological functions of the immune system. With the development of transcriptomics and high-throughput sequencing, the gene expression profiling of immune cells can provide a new strategy to explore the immune cell subtyping. On the basis of the new profiling data of mouse immune cell gene expression from the Immunological Genome Project (ImmGen), a novel computational pipeline was applied to identify different immune cell subtypes, including αβ T cells, B cells, γδ T cells, and innate lymphocytes. First, the profiling data was analyzed by a powerful feature selection method, Monte-Carlo Feature Selection, resulting in a feature list and some informative features. For the list, the two-stage incremental feature selection method, incorporating random forest as the classification algorithm, was applied to extract essential gene signatures and build an efficient classifier. On the other hand, a rule learning scheme was applied on the informative features to construct quantitative expression rules. A group of gene signatures was found as qualitatively related to the biological processes of four immune cell subtypes. The quantitative expression rules can efficiently cluster immune cells. This work provides a novel computational tool for immune cell quantitative subtyping and biomarker recognition.

Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform

Extracellular vesicles (EVs) are double-layered phospholipid membrane vesicles that are released by most cells and can mediate intercellular communication through their RNA cargo. In this study, we tested if the NanoString nCounter platform can be used for the analysis of EV-mRNA. We developed and optimized a methodology for EV enrichment, EV-RNA extraction and nCounter analysis. Then, we demonstrated the validity of our workflow by analyzing EV-RNA profiles from the plasma of 19 cancer patients and 10 controls and developing a gene signature to differentiate cancer versus control samples. TRI reagent outperformed automated RNA extraction and, although lower plasma input is feasible, 500 μL provided highest total counts and number of transcripts detected. A 10-cycle pre-amplification followed by DNase treatment yielded reproducible mRNA target detection. However, appropriate probe design to prevent genomic DNA binding is preferred. A gene signature, created using a bioinformatic algorithm, was able to distinguish between control and cancer EV-mRNA profiles with an area under the ROC curve of 0.99. Hence, the nCounter platform can be used to detect mRNA targets and develop gene signatures from plasma-derived EVs.

A Comparative Transcriptomic Analysis of Human Placental Trophoblasts in Response to Pathogenic and Probiotic Enterococcus faecalis Interaction

With the ability to cross placental barriers in their hosts, strains of Gram-positive Enterococcus faecalis can exhibit either beneficial or harmful properties. However, the mechanisms underlying these effects have yet to be determined. A comparative transcriptomic analysis of human placental trophoblasts in response to pathogenic or probiotic E. faecalis was performed in order to investigate the molecular basis of different traits. Results indicated that both E. faecalis Symbioflor 1 and V583 could pass through the placental barrier in vitro with similar levels of invasion ability. In total, 2353 (1369 upregulated and 984 downregulated) and 2351 (1233 upregulated and 1118 downregulated) DEGs were identified in Symbioflor 1 and V583, respectively. Furthermore, 1074 (671 upregulated and 403 downregulated) and 1072 (535 upregulated and 537 downregulated) DEGs were only identified in Symbioflor 1 and V583 treatment groups, respectively. KEGG analysis showed that 6 and 9 signaling pathways were associated with interactions between Symbioflor 1 and V583. GO analysis revealed that these DEGs were mainly related to cellular and metabolic processes and biological regulation. However, 28 and 44 DEGs were classified into terms associated with placental and embryonic development in Symbioflor 1 and V583 treatment groups, respectively. Notably, 9 and 25 unique DEGs were identified only in Symbioflor 1 and V583 treatment groups, respectively. A large proportion of transcriptional responses differed when compared between pathogenic and probiotic E. faecalis interaction, and several unique DEGs and signal pathways were identified in the two different groups. These data enhance our understanding of how different traits can be affected by pathogenic and probiotic E. faecalis and the mechanisms underlying these effects.

Identification of Hub Genes Associated With Development and Microenvironment of Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis and Differential Gene Expression Analysis

A further understanding of the molecular mechanism of hepatocellular carcinoma (HCC) is necessary to predict a patient's prognosis and develop new targeted gene drugs. This study aims to identify essential genes related to HCC. We used the Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis to analyze the gene expression profile of GSE45114 in the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas database (TCGA). A total of 37 overlapping genes were extracted from four groups of results. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were performed on the 37 overlapping genes. Then, we used the STRING database to map the protein interaction (PPI) network of 37 overlapping genes. Ten hub genes were screened according to the Maximal Clique Centrality (MCC) score using the Cytohubba plugin of Cytoscape (including FOS, EGR1, EPHA2, DUSP1, IGFBP3, SOCS2, ID1, DUSP6, MT1G, and MT1H). Most hub genes show a significant association with immune infiltration types and tumor stemness of microenvironment in HCC. According to Univariate Cox regression analysis and Kaplan-Meier survival estimation, SOCS2 was positively correlated with overall survival (OS), and IGFBP3 was negatively correlated with OS. Moreover, the expression of IGFBP3 increased with the increase of the clinical stage, while the expression of SOCS2 decreased with the increase of the clinical stage. In conclusion, our findings suggest that SOCS2 and IGFBP3 may play an essential role in the development of HCC and may serve as a potential biomarker for future diagnosis and treatment.

Systematic review of gene expression studies in people with Lewy body dementia

OBJECTIVES

Lewy body dementia (LBD) is the second most prevalent neurodegenerative dementia and it causes more morbidity and mortality than Alzheimer's disease. Several genetic associations of LBD have been reported and their functional implications remain uncertain. Hence, we aimed to do a systematic review of all gene expression studies that investigated people with LBD for improving our understanding of LBD molecular pathology and for facilitating discovery of novel biomarkers and therapeutic targets for LBD.

METHODS

We systematically reviewed five online databases (PROSPERO protocol: CRD42017080647) and assessed the functional implications of all reported differentially expressed genes (DEGs) using Ingenuity Pathway Analyses.

RESULTS

We screened 3,809 articles and identified 31 eligible studies. In that, 1,242 statistically significant (p < 0.05) DEGs including 70 microRNAs have been reported in people with LBD. Expression levels of alternatively spliced transcripts of SNCA, SNCB, PRKN, APP, RELA, and ATXN2 significantly differ in LBD. Several mitochondrial genes and genes involved in ubiquitin proteasome system and autophagy-lysosomal pathway were significantly downregulated in LBD. Evidence supporting chronic neuroinflammation in LBD was inconsistent. Our functional analyses highlighted the importance of ribonucleic acid (RNA)-mediated gene silencing, neuregulin signalling, and neurotrophic factors in the molecular pathology of LBD.

CONCLUSIONS

α-synuclein aggregation, mitochondrial dysfunction, defects in molecular networks clearing misfolded proteins, and RNA-mediated gene silencing contribute to neurodegeneration in LBD. Larger longitudinal transcriptomic studies investigating biological fluids of people living with LBD are needed for molecular subtyping and staging of LBD. Diagnostic biomarker potential and therapeutic promise of identified DEGs warrant further research.

Expression of mitochondrial protein genes encoded by nuclear and mitochondrial genomes correlate with energy metabolism in dairy cattle

Background

Mutations in the mitochondrial genome have been implicated in mitochondrial disease, often characterized by impaired cellular energy metabolism. Cellular energy metabolism in mitochondria involves mitochondrial proteins (MP) from both the nuclear (NuMP) and mitochondrial (MtMP) genomes. The expression of MP genes in tissues may be tissue specific to meet varying specific energy demands across the tissues. Currently, the characteristics of MP gene expression in tissues of dairy cattle are not well understood. In this study, we profile the expression of MP genes in 29 adult and six foetal tissues in dairy cattle using RNA sequencing and gene expression analyses: particularly differential gene expression and co-expression network analyses.

Results

MP genes were differentially expressed (DE; over-expressed or under-expressed) across tissues in cattle. All 29 tissues showed DE NuMP genes in varying proportions of over-expression and under-expression. On the other hand, DE of MtMP genes was observed in < 50% of tissues and notably MtMP genes within a tissue was either all over-expressed or all under-expressed. A high proportion of NuMP (up to 60%) and MtMP (up to 100%) genes were over-expressed in tissues with expected high metabolic demand; heart, skeletal muscles and tongue, and under-expressed (up to 45% of NuMP, 77% of MtMP genes) in tissues with expected low metabolic rates; leukocytes, thymus, and lymph nodes. These tissues also invariably had the expression of all MtMP genes in the direction of dominant NuMP genes expression. The NuMP and MtMP genes were highly co-expressed across tissues and co-expression of genes in a cluster were non-random and functionally enriched for energy generation pathway. The differential gene expression and co-expression patterns were validated in independent cow and sheep datasets.

Conclusions

The results of this study support the concept that there are biological interaction of MP genes from the mitochondrial and nuclear genomes given their over-expression in tissues with high energy demand and co-expression in tissues. This highlights the importance of considering MP genes from both genomes in future studies related to mitochondrial functions and traits related to energy metabolism.

Peripheral mechanisms of arthritic pain: A proposal to leverage large animals for in vitro studies

Pain arising from musculoskeletal disorders such as arthritis is one of the leading causes of disability. Whereas the past 20-years has seen an increase in targeted therapies for rheumatoid arthritis (RA), other arthritis conditions, especially osteoarthritis, remain poorly treated. Although modulation of central pain pathways occurs in chronic arthritis, multiple lines of evidence indicate that peripherally driven pain is important in arthritic pain. To understand the peripheral mechanisms of arthritic pain, various in vitro and in vivo models have been developed, largely in rodents. Although rodent models provide numerous advantages for studying arthritis pathogenesis and treatment, the anatomy and biomechanics of rodent joints differ considerably to those of humans. By contrast, the anatomy and biomechanics of joints in larger animals, such as dogs, show greater similarity to human joints and thus studying them can provide novel insight for arthritis research. The purpose of this article is firstly to review models of arthritis and behavioral outcomes commonly used in large animals. Secondly, we review the existing in vitro models and assays used to study arthritic pain, primarily in rodents, and discuss the potential for adopting these strategies, as well as likely limitations, in large animals. We believe that exploring peripheral mechanisms of arthritic pain in vitro in large animals has the potential to reduce the veterinary burden of arthritis in commonly afflicted species like dogs, as well as to improve translatability of pain research into the clinic.

Identification of Hub Genes Associated With Development of Head and Neck Squamous Cell Carcinoma by Integrated Bioinformatics Analysis

Improved insight into the molecular mechanisms of head and neck squamous cell carcinoma (HNSCC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify significant genes associated with HNSCC and to further analyze its prognostic significance. In our study, the cancer genome atlas (TCGA) HNSCC database and the gene expression profiles of GSE6631 from the Gene Expression Omnibus (GEO) were used to explore the differential co-expression genes in HNSCC compared with normal tissues. A total of 29 differential co-expression genes were screened out by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. As suggested in functional annotation analysis using the R clusterProfiler package, these genes were mainly enriched in epidermis development and differentiation (biological process), apical plasma membrane and cell-cell junction (cellular component), and enzyme inhibitor activity (molecular function). Furthermore, in a protein-protein interaction (PPI) network containing 21 nodes and 25 edges, the ten hub genes (S100A8, S100A9, IL1RN, CSTA, ANXA1, KRT4, TGM3, SCEL, PPL, and PSCA) were identified using the CytoHubba plugin of Cytoscape. The expression of the ten hub genes were all downregulated in HNSCC tissues compared with normal tissues. Based on survival analysis, the lower expression of CSTA was associated with worse overall survival (OS) in patients with HNSCC. Finally, the protein level of CSTA, which was validated by the Human Protein Atlas (HPA) database, was down-regulated consistently with mRNA levels in head and neck cancer samples. In summary, our study demonstrated that a survival-related gene is highly correlated with head and neck cancer development. Thus, CSTA may play important roles in the progression of head and neck cancer and serve as a potential biomarker for future diagnosis and treatment.