Dataset Information

Identification of HMMR as a prognostic biomarker for patients with lung adenocarcinoma via integrated bioinformatics analysis.

ABSTRACT:

Background

Lung adenocarcinoma (LUAD) is the most prevalent tumor in lung carcinoma cases and threatens human life seriously worldwide. Here we attempt to identify a prognostic biomarker and potential therapeutic target for LUAD patients.

Methods

Differentially expressed genes (DEGs) shared by GSE18842, GSE75037, GSE101929 and GSE19188 profiles were determined and used for protein-protein interaction analysis, enrichment analysis and clinical correlation analysis to search for the core gene, whose expression was further validated in multiple databases and LUAD cells (A549 and PC-9) by quantitative real-time PCR (qRT-PCR) and western blot analyses. Its prognostic value was estimated using the Kaplan-Meier method, meta-analysis and Cox regression analysis based on the Cancer Genome Atlas (TCGA) dataset and co-expression analysis was conducted using the Oncomine database. Gene Set Enrichment Analysis (GSEA) was performed to illuminate the potential functions of the core gene.

Results

A total of 115 shared DEGs were found, of which 24 DEGs were identified as candidate hub genes with potential functions associated with cell cycle and FOXM1 transcription factor network. Among these candidates, HMMR was identified as the core gene, which was highly expressed in LUAD as verified by multiple datasets and cell samples. Besides, high HMMR expression was found to independently predict poor survival in patients with LUAD. Co-expression analysis showed that HMMR was closely related to FOXM1 and was mainly involved in cell cycle as suggested by GSEA.

Conclusion

HMMR might be served as an independent prognostic biomarker for LUAD patients, which needs further validation in subsequent studies.

SUBMITTER: Li Z

PROVIDER: S-EPMC8710063 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe objectives of this study were to identify hub genes and biological pathways involved in lung adenocarcinoma (LUAD) via bioinformatics analysis, and investigate potential therapeutic targets.AimTo determine reliable prognostic biomarkers for early diagnosis and treatment of LUAD.MethodsTo identify potential therapeutic targets for LUAD, two microarray datasets derived from the Gene Expression Omnibus (GEO) database were analyzed, GSE3116959 and GSE118370. Differentially expressed genes (DEGs) in LUAD and normal tissues were identified using the GEO2R tool. The Hiplot database was then used to generate a volcanic map of the DEGs. Weighted gene co-expression network analysis was conducted to cluster the genes in GSE116959 and GSE118370 into different modules, and identify immune genes shared between them. A protein-protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes database, then the CytoNCA and CytoHubba components of Cytoscape software were used to visualize the genes. Hub genes with high scores and co-expression were identified, and the Database for Annotation, Visualization and Integrated Discovery was used to perform enrichment analysis of these genes. The diagnostic and prognostic values of the hub genes were calculated using receiver operating characteristic curves and Kaplan-Meier survival analysis, and gene-set enrichment analysis was conducted. The University of Alabama at Birmingham Cancer data analysis portal was used to analyze relationships between the hub genes and normal specimens, as well as their expression during tumor progression. Lastly, validation of protein expression was conducted on the identified hub genes via the Human Protein Atlas database.ResultsThree hub genes with high connectivity were identified; cellular retinoic acid binding protein 2 (CRABP2), matrix metallopeptidase 12 (MMP12), and DNA topoisomerase II alpha (TOP2A). High expression of these genes was associated with a poor LUAD prognosis, and the genes exhibited high diagnostic value.ConclusionExpression levels of CRABP2, MMP12, and TOP2A in LUAD were higher than those in normal lung tissue. This observation has diagnostic value, and is linked to poor LUAD prognosis. These genes may be biomarkers and therapeutic targets in LUAD, but further research is warranted to investigate their usefulness in these respects.

Project description:BackgroundLung cancer has the highest cancer-related mortality worldwide. Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer (NSCLC). Chromatin licensing and DNA replication factor 1 (CDT1), a key regulator of cell cycle control and replication in eukaryotic cells, has been implicated in various cancer-related processes. Given its significant role in cancer, the focus on CDT1 in this study is justified as it holds promise as a potential biomarker or therapeutic target for cancer treatment. However, its prognostic value in lung adenocarcinoma (LUAD) remains unclear.MethodsBioinformatics analysis was conducted using data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were utilized to predict biological processes and signaling pathways, respectively. The LinkedOmics database was employed to identify differentially expressed genes (DEGs) associated with CDT1. Nomograms and Kaplan-Meier plots were generated to assess the survival rates of patients with lung adenocarcinoma (LUAD). To determine the RNA and protein expression levels of CDT1 in LUAD and adjacent normal tissues, quantitative polymerase chain reaction (qPCR) and immunohistochemistry techniques were employed, respectively.ResultsCDT1 was upregulated in the vast majority of cancer tissues, based on pan-cancer analysis in TCGA and GEO datasets, as to lung cancer, the level of CDT1 expression was much higher in LUAD tissue than in healthy lung tissue. Our clinical data supported these findings. In our study, we used a specific cutoff value to dichotomize the patient samples into high and low CDT1 expression groups. The Kaplan-Meier survival curve revealed poor survival rates in CDT1 high expression group than the low expression group. To determine if CDT1 expression was an independent risk factor in LUAD patients, univariate and multivariate Cox regression analyses were performed. The result showed that CDT1 was a potential novel prognosis factor for LUAD patients, whose prognosis was poorer when CDT1 expression was higher. Based on functional enrichment analysis, highly expressed DEGs of CDT1-high patients were predicted to be involved in the cell cycle. According to our analysis of immune infiltration, CDT1 exhibited a strong correlation with specific immune cell subsets and was found to be a significant predictor of poor survival in patients with LUAD.ConclusionsOur research found that CDT1 was upregulated in LUAD and that high CDT1 expression predicted poor prognosis. We comprehensively and systematically analyzed the expression level in the datasets as well as in our own clinical samples, we also evaluated the prognostic and diagnostic value of CDT1, and finally, the potential mechanisms of CDT1 in the progression of LUAD. These results suggested that CDT1 may be a prognostic marker and therapeutic target for LUAD.