Identification of a novel prognostic gene signature in pleural mesothelioma: a study based on The Cancer Genome Atlas database and experimental validation

Our plain-English summary of Identification of a novel prognostic gene signature in pleural mesothelioma: a study based on The Cancer Genome Atlas database and experimental validation, by X. Wang, Y. Yang, W. Yang, et al (Translational Cancer Research, 2025).

The study

• Authors: X. Wang, Y. Yang, W. Yang, X. Yang, J. Li, Y. Lin, Z. Li, J. Li, W. Xiong
• Journal: Translational Cancer Research
• Published: 2025;14(5):2981-2998
• Cited by: 2 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Background: Early detection and prognostic prediction are crucial in improving the survival of patients with pleural mesothelioma (PM). Therefore, this study aimed to develop a gene prognostic risk model for PM patients based on The Cancer Genome Atlas (TCGA) database analysis and experimental validations.

Methods: Obtaining gene expression data and clinical information of PM from the TCGA database, the dataset was divided into a training set and a testing set. Univariate Cox regression analysis, robust testing, and multivariate Cox regression analysis were performed on the training set to establish a prognostic risk model. Risk scores were calculated for each patient, and the dataset was stratified into high- and low-risk groups. The predictive efficacy and accuracy of the model were evaluated using Kaplan-Meier survival curves and receiver operating characteristic (ROC) curves. The messenger RNA (mRNA) expression levels of genes in the prognostic model in clinical samples and PM cell lines were detected by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Gene expression validation in the prognostic model was conducted using samples from the TCGA and the Genotype-Tissue Expression (GTEx) project databases. The University of ALabama at birmingham CANcer data analysis portal (UALCAN) database was utilized to explore the expression patterns of genes in the prognostic model. Finally, gene set enrichment analysis (GSEA) was performed on genes in the prognostic model to explore their potential biological functions and signaling pathways.

Results: A prognostic risk assessment model consisting of three genes, ubiquitin like with PHD and ring finger domain 1 (UHRF1), kinesin family member 4A (KIF4A), and never in mitosis gene A-related kinase 2 (NEK2) was constructed. The risk score of the prognostic model is calculated as follows: risk score = Expression level of UHRF1 × 1.4525 - Expression level of KIF4A × 1.3270 + Expression level of NEK2 × 1.4167. Patients were further stratified into high- and low-risk groups at this optimal cutoff point. Kaplan-Meier curves demonstrate that, compared to patients in the high-risk group, those in the low-risk group exhibited significantly prolonged overall survival. Visualization of the model through a forest plot revealed a Log-Rank P<0.0001 for the entire model, indicating its potential as an independent prognostic marker for PM. The mRNA expression levels of three genes in the prognostic model significantly elevated in tumor samples and PM cell lines than in non-tumorigenic tissues and cell lines as detected by qRT-PCR. Additionally, these genes exhibited significant differences in expression among PM patients of different stages, tumor subtypes, ages, and metastatic statuses. The overexpressed group of these three genes was significantly enriched in pathways such as DNA replication, mRNA surveillance pathway, nuclear transport, ribosome biogenesis in eukaryotes, and spliceosome pathways.

Conclusions: Three prognostic marker genes (UHRF1, KIF4A, and NEK2) as a gene cluster may serve as prognostic marker genes in PM.

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In plain English

This was a computer-based (bioinformatics) and laboratory study of pleural mesothelioma. It did not examine asbestos exposure. The authors started with gene activity and clinical data for 87 pleural mesothelioma samples in The Cancer Genome Atlas. After samples missing survival data and duplicates were removed, the remaining samples were split into training (43) and testing (42) sets. Screening narrowed 2,753 survival-linked genes to three, UHRF1, KIF4A and NEK2, which were combined into a risk score. Low-risk patients survived significantly longer than high-risk patients in the training set (P<0.001), testing set (P=0.006) and complete set (P<0.001). In the final model, KIF4A alone was not statistically significant (P=0.051). Laboratory tests found higher levels of all three genes in four mesothelioma cell lines than in a non-tumorigenic mesothelial cell line. Levels were also higher in 41 tumor samples than in paired non-tumor tissue from patients at a hospital in Dali, China. The authors concluded that the three genes may serve as prognostic markers. They noted that the model could not effectively pick out high-risk patients among early-stage cases and that more clinical samples are needed to validate it.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Bioinformatics analysis of public gene-expression databases (TCGA, GTEx, UALCAN) to build and test a gene-based survival (prognostic) model, with laboratory checks of gene expression (qRT-PCR) in cell lines and patient tissue samples
• Who was studied: 87 pleural mesothelioma samples with clinical follow-up data were downloaded from The Cancer Genome Atlas. After samples missing survival status or survival time and duplicate samples were removed, the remaining samples were split into a training set of 43 and a testing set of 42. Laboratory checks used 41 pleural mesothelioma tissues and their paired non-tumor tissues from patients who had surgery at The First Affiliated Hospital of Dali University, China. They also used four human mesothelioma cell lines (NCI-H2452, NCI-H28, NCI-H2052, MTSO-211H) and one mesothelial cell line (Met5A). Normal lung tissue data came from the GTEx database.
• Exposure: No asbestos exposure was studied. The factor examined was tumor expression (activity) of genes, ending in a three-gene model of UHRF1, KIF4A and NEK2.
• Main results:
  • In the training set, univariate Cox regression identified 2,753 genes associated with overall survival (P<0.05). A robustness test reduced these to 7 genes, and multivariable analysis kept 3 genes.
  • The risk score was calculated as: expression of UHRF1 × 1.4525 minus expression of KIF4A × 1.3270 plus expression of NEK2 × 1.4167
  • ROC analysis of the three-gene risk score gave an area under the curve of 0.91. The optimal cutoff was 1.149.
  • Training set: the cutoff split patients into high-risk (n=18) and low-risk (n=25) groups, and survival differed significantly between them (P<0.001)
  • Testing set: patients were split into high-risk (n=20) and low-risk (n=22) groups. High-risk patients had significantly shorter survival (P=0.006).
  • Complete dataset: patients were split into high-risk (n=38) and low-risk (n=47) groups. Low-risk patients had significantly longer survival (P<0.001).
  • In the forest plot of the training set (N=43, 33 events), the figure labels the values as odds ratios. These were UHRF1 4.27 (95% CI 1.86–9.8, P<0.001), NEK2 4.12 (1.14–14.9, P=0.03) and KIF4A 0.27 (0.07–1.0, P=0.051). KIF4A was not statistically significant. The concordance index was 0.78.
  • qRT-PCR found UHRF1, KIF4A and NEK2 expression significantly higher in the four mesothelioma cell lines than in the Met5A mesothelial cell line. Expression was also significantly higher in the 41 pleural mesothelioma tissues than in their paired non-tumor tissues.
  • In 374 samples from the TCGA and GTEx databases, all three genes were expressed at higher levels in pleural mesothelioma tumor tissue than in normal lung tissue (P<0.001)
  • In UALCAN data, UHRF1 expression differed between biphasic and epithelioid subtypes (P=0.003), between Stage 1 and Stage 2 (P=0.045), and between N0 and N1 lymph node status (P=0.02). KIF4A differed between biphasic and epithelioid subtypes (P=0.006) and between age groups 41–60 and 81–100 years (P=0.02). NEK2 differed between age groups 41–60 and 81–100 years (P=0.008). The 81–100 year group contained 2 patients.
  • In gene set enrichment analysis, samples with high expression of each of the three genes were significantly enriched in pathways including DNA replication, the Fanconi anemia pathway, mRNA surveillance, nucleocytoplasmic transport, ribosome biogenesis and the spliceosome
• The authors' conclusion: The authors concluded that the three genes UHRF1, KIF4A and NEK2, taken together as a gene cluster, may serve as prognostic marker genes in pleural mesothelioma
• Limitations the authors state:
  • The model fails to effectively distinguish high-risk patients among early-stage pleural mesothelioma cases, so it cannot guide more intensive treatment or shorter review intervals for them
  • More clinical samples are needed to validate the model

Topics

Medical terms in this study, explained in our Lexicon: Pleural Mesothelioma, Kaplan-Meier Curve, Overall Survival.

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