Key modules and hub genes identified by coexpression network analysis for revealing novel biomarkers for larynx squamous cell carcinoma

Cell Cycle-Related Gene SPC24: A Novel Potential Diagnostic and Prognostic Biomarker for Laryngeal Squamous Cell Cancer

Laryngeal squamous cell cancer (LSCC) is a common malignant tumor with a high degree of malignancy, and its etiology remains unclear. Therefore, screening potential biomarkers is necessary to facilitate the treatment and diagnosis of LSCC. Robust rank aggregation (RRA) analysis was used to integrate two gene expression datasets of LSCC patients from the Gene Expression Omnibus (GEO) database and identify differentially expressed genes (DEGs) between LSCC and nonneoplastic tissues. A gene coexpression network was constructed using weighted gene correlation network analysis (WGCNA) to explore potential associations between the module genes and clinical features of LSCC. Combining differential gene expression analysis and survival analysis, we screened potential hub genes, including CDK1, SPC24, HOXB7, and SELENBP1. Subsequently, western blotting and immunohistochemistry were used to test the protein levels in clinical specimens to verify our findings. Finally, four candidate diagnostic and prognostic biomarkers (CDK1, SPC24, HOXB7, and SELENBP1) were identified. We propose, for the first time, that SPC24 is a gene that may associate with LSCC malignancy and is a novel therapeutic target. These findings may provide important mechanistic insight of LSCC.

Identification of Novel Prognostic Markers Associated With Laryngeal Squamous Cell Carcinoma Using Comprehensive Analysis

Background

Laryngeal squamous cell carcinoma (LSCC) is a leading malignant cancer of the head and neck. Patients with LSCC, in which the cancer has infiltrated and metastasized, have a poor prognosis. Therefore, there is an urgent need to identify more potential targets for drugs and biomarkers for early diagnosis.

Methods

RNA sequence data from LSCC and patients’ clinical traits were obtained from the Gene Expression Omnibus (GEO) (GSE142083) and The Cancer Genome Atlas (TCGA) database. Differentially expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify hub genes. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, prognostic value analysis, receiver operating characteristic (ROC) curve analysis, gene mutation analysis, tumor-infiltrating immune cell abundance profile estimation, gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA) were performed. Single-gene RNA sequencing data were obtained from the GSE150321 dataset. Cell proliferation and viability were confirmed by the CCK-8 assay and real-time PCR.

Results

A total of 701 DEGs, including 329 upregulated and 372 downregulated genes, were screened in the GSE142083 dataset. Using WGCNA, three modules were identified to be closely related to LSCC. After intersecting the DEGs and performing univariate and multivariate Cox analyses, a novel prognostic model based on three genes (SLC35C1, HOXB7, and TEDC2) for LSCC was established. Interfering TEDC2 expression inhibited tumor cell proliferation and migration.

Conclusions

Our results show that SLC35C1, HOXB7, and TEDC2 have the potential to become new therapeutic targets and prognostic biomarkers for LSCC.

Identification of Key Modules and Hub Genes Involved in Esophageal Squamous Cell Carcinoma Tumorigenesis Using WCGNA

Introduction:

The mechanistic basis for the development of esophageal squamous cell carcinoma (ESCC) remains poorly understood. The goal of the present study was thus to characterize mRNA and long noncoding RNA (lncRNA) expression profiles associated with ESCC in order to identify key hub genes associated with the pathogenesis of this cancer.

Materials and Methods:

The GSE26866 and GSE45670 datasets from the Gene Expression Omnibus (GEO) database were used to conduct a weighted gene co-expression network analysis (WGCNA), after which Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Cytoscape was additionally used to construct lncRNA-mRNA networks, after which hub genes were identified and validated through the assessment of TCGA datasets and clinical samples.

Results:

Two gene modules were found to be closely linked to ESCC tumorigenesis. These genes were enriched in cell cycle, MAPK signaling, JAK-STAT signaling, pyrimidine metabolism, arachidonic acid metabolism, and P53 signaling pathway activity, all of which are directly linked with the development of cancer. In total, we identified and validated 9 hub genes associated with ESCC (DDX18, DNMT1, NCAPG, WDHD1, PRR11, VOPP1, ZKSCAN5, LC35C2, and PHACTR2).

Conclusion:

In summary, we identified key gene modules and hub genes associated with ESCC development, and we constructed a lncRNA-mRNA network pertaining to this cancer type. These results provide a foundation for future research regarding the mechanistic basis of ESCC.

LUM is the hub gene of advanced fibrosis in nonalcoholic fatty liver disease patients

INTRODUCTION

Advanced fibrosis in nonalcoholic fatty liver disease (NAFLD) is associated with a poor prognosis. The genetic factors contributing to fibrosis in NAFLD have been described. However, the genetic mechanism and hub genes of advanced fibrosis have not been elucidated to date. In this study, we performed a weighted gene coexpression network analysis (WGCNA) to identify the hub genes related to advanced fibrosis in NAFLD.

MATERIALS AND METHODS

The datasets GSE89632 and GSE31803 of NAFLD patients were selected from the Gene Expression Omnibus (GEO) database of NCBI and analyzed by WGCNA. The hub genes were selected in the GSE31803 dataset and verified in the GSE31803 dataset. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the dataset were also performed.

RESULTS

The gene LUM was identified as the hub gene in the datasets GSE89632 and GSE31803 according to three different algorithms (gene significance and module membership, the pathways of the genes, and protein expressed by the genes). The functional enrichment analysis shows that the identified module is related to the extracellular matrix, regulation of cell proliferation, and the inflammatory response. The metabolic pathway analysis identified metabolic pathways and focal adhesion as the most important pathways.

CONCLUSION

By a variety of methods, LUM was identified as the hub gene of advanced fibrosis in patients with NAFLD. Therefore, further research on the LUM gene is warranted.

PTPLAD2 and USP49 Involved in the Pathogenesis of Smoke-Induced COPD by Integrative Bioinformatics Analysis

Purpose

Chronic obstructive pulmonary disease (COPD) is a typical chronic disease, but its molecular pathogenesis remains unclear. This study aimed to investigate the expression of biomarkers during COPD development.

Methods

Markers significantly associated with COPD were screened using bioinformatics tools. qRT-PCR and Western blot were used to explore the expression of PTPLAD2 and USP49 in BEAS-2B cells. CCK-8 assay was used to determine the influence of PTPLAD2 and USP49 in BEAS-2B on cell proliferation.

Results

In this study, 86 DEGs were identified in GSE76925. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses suggested that the phosphoinositide 3-kinase-Akt signaling pathway, ECM-receptor interaction, mRNA process, and viral transcription were all involved in the development of COPD. In addition, 14 hub genes were identified by WGCNA. PTPLAD2 and USP49 shared DEGs and hub genes and their expression levels were significantly reduced after CSE-treatment in BEAS-2B cells.

Conclusion

Our results suggest that PTPLAD2 and USP49 may be useful biomarkers of COPD.

The PLCE1 rs2274223 variant is associated with the risk of laryngeal squamous cell carcinoma

Background: Laryngeal squamous cell carcinoma (LSCC) ranks second in the mortality rate in respiratory malignant tumors and has potential similarity in genomic alterations with the esophageal squamous cell carcinoma (ESCC). The PLCE1 rs2274223 variant is the most significant susceptibility loci identified in ESCC. Whether it is also associated with LSCC susceptibility is still unclear. Materials and Methods: A total of 331 LSCC patients and 349 healthy controls were recruited in this study. The PLCE1 rs2274223 variant was genotyped by using the Taqman SNP Genotyping Assay. Association between PLCE1 rs2274223 variant and LSCC risk was estimated by logistic regression analysis, which was performed using SAS software. Results: The PLCE1 rs2274223 variant was identified to be significantly associated with the susceptibility of LSCC in the additive model (OR = 1.40, 95% CI: 1.06-1.86, P=0.019). Compared with the wild-type (AA) carriers, the risk genotype (GG) carriers had a 2.8-fold risk of LSCC (95% CI: 1.13-7.06, P=0.026). Stratified analysis showed that the association between rs2274223 and LSCC risk was with higher significance in individuals above 60 (P = 0.027) males (P = 0.030) or non-smokers (P = 0.026). Conclusion: The PLCE1 rs2274223 variant was significantly associated with risk of LSCC, which may be a potential biomarker and therapeutic target for the LSCC.

Characterization of long non-coding RNA and messenger RNA profiles in laryngeal cancer by weighted gene co-expression network analysis

Laryngeal cancer (LC) is a malignant tumor in the head and neck region. It was recently elucidated that long non-coding RNAs (lncRNAs) participate in the pathogenesis of LC. However, the detailed mechanism of lncRNA in LC and whether long non-coding RNAs serve as effective biomarkers remains unclear. Ribonucleic acid (RNA) sequence data of LC and 11 patient clinical traits were extracted from The Cancer Genome Atlas (TCGA) database and analyzed by weighted gene co-expression network analysis (WGCNA). A total of 9 co-expression modules were identified. The co-expression Pink module significantly correlated with four clinical traits, including history of smoking, lymph node count, tumor status, and the success of follow-up treatment. Based on the co-expression Pink module, lncRNA-microRNA (miRNA)-messenger RNA (mRNA) and lncRNA-RNA binding protein-mRNA networks were constructed. We found that 8 lncRNAs significantly impacted overall survival (OS) in LC patients. These identified lncRNA and hub gene biomarkers were also validated in multiple LC cells in vitro via qPCR. Taken together, this study provided the framework of co-expression gene modules of LC and identified some important biomarkers in LC development and disease progression.