Establishment and bioinformatics analysis of a four-miRNA prognostic signature for pleural mesothelioma

Our plain-English summary of Establishment and bioinformatics analysis of a four-miRNA prognostic signature for pleural mesothelioma, by X. Yang, Y. Lin, B. Dong, et al (Journal of Cancer, 2024).

The study

• Authors: X. Yang, Y. Lin, B. Dong, B. Li, R. Liu, X. Wang, J. Li, X. Cheng, Z. Li, W. Xiong
• Journal: Journal of Cancer
• Published: 2024;15(20):6505-6520
• Cited by: 3 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Objective: Pleural mesothelioma (PM), an uncommon yet highly aggressive malignant neoplasm, has a very poor prognosis with a median survival of less than one year after diagnosis, morbidity and mortality due to PM are on the rise year by year worldwide. Our research aims to utilize molecular characteristics and microRNAs (miRNAs) as a breakthrough in predicting the survival of PM patients, hoping to find a molecular mechanism that can predict the survival of PM patients. Methods: The miRNA expression profiles and corresponding clinical information of patients with PM were obtained from The Cancer Genome Atlas (TCGA) database, a miRNA-based prognostic signature was developed using Cox regression analysis in the training cohort, which was validated in the testing cohort and complete cohort. The association between miRNA levels and survival outcomes was determined, the miRNAs in prognostic model were experimentally validated by quantitative real-time PCR (qRT-PCR) in cell lines. Target genes of prognostic miRNAs were identified using TargetScan, miRDB, and miRTarBase databases, biological function prediction of which was accomplished by GO and KEGG analysis. Gene Expression Omnibus (GEO) database was utilized for core targets recognition, immune infiltration and survival analysis were conducted to investigate the relationship between core targets and immune cells by bioinformatics analysis. Results: This miRNA-related prognostic risk model can effectively stratify patients into high-risk and low-risk groups, and have good sensitivity and specificity to assess the prognosis of patients with PM, which can also be used as an independent prognostic factor for overall survival (OS) prediction in patients with PM, the OS for patients in high-risk group was significantly poorer compared with patients in low-risk group. Moreover, all four miRNAs (hsa-miR-181a-2-3p, hsa-miR-491-5P, hsa-miR-503-5p, and hsa-miR-3934-5p) were found to be differentially expressed in PM cell lines as compared with normal cell line, GO and KEGG analysis revealed that target genes of miRNAs in prognostic model were involved in multiple tumor-associated signaling pathways and functions in PM, core miRNA targets also correlated with immune cell infiltration, indicating their potential role in PM initiation and progression. Conclusions: A robust four-miRNA prognostic signature with great performances in prediction of the OS for PM patients was developed in our study, providing new avenues for the prognostic predication of PM.

© The author(s).

In plain English

This study analyzed existing data rather than recruiting patients. The authors took microRNA (small regulatory RNA) and survival data for 84 patients with pleural mesothelioma from The Cancer Genome Atlas, a public database. They split the patients at random into a training group of 44 and a testing group of 40. Seven microRNAs were linked to survival in the training group; the authors built a risk score from four: hsa-miR-181a-2-3p, hsa-miR-491-5p, hsa-miR-503-5p and hsa-miR-3934-5p. In the training, testing and combined groups, patients with high risk scores had significantly poorer survival than those with low scores (p less than 0.001). The score's accuracy measure (AUC) was above 0.8 in all three groups. In laboratory tests, levels of the four microRNAs differed between two mesothelioma cell lines and one normal mesothelial cell line. Further database analyses linked two predicted target genes, KIF23 and VPS37B, to survival and immune cells. The authors concluded the model could serve as a prognostic biomarker. They noted it was validated only within one database and could not effectively distinguish high-risk early-stage patients. Asbestos exposure was not studied.

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 database data (TCGA, GEO, TIMER2.0, target-prediction databases) to build and internally validate a prognostic score, plus laboratory measurement of the microRNAs in cell lines
• Who was studied: 84 pleural mesothelioma patient samples with microRNA expression data from the TCGA-MESO dataset, randomly split into a training set (44) and a test set (40). Also two human mesothelioma cell lines (NCI-H2452, NCI-H28), one normal mesothelial cell line (MeT-5A), and three GEO gene-expression datasets (GSE2549, GSE42977, GSE51024). The article does not give where or when the patients were treated.
• Exposure: No asbestos or fiber exposure was analyzed. The factor studied was tumor microRNA expression levels in relation to overall survival.
• Main results:
  • In the training set (44 patients), univariate Cox analysis identified seven microRNAs that the text says were associated with prognosis at p less than 0.001.
  • Four microRNAs were kept in the multivariate model, with these hazard ratios (95% CI):
• hsa-miR-181a-2-3p: 1.756140 (1.103044 to 2.795924), p 0.017630
• hsa-miR-503-5p: 1.390078 (1.043388 to 1.851963), p 0.024440
• hsa-miR-491-5p: 0.609513 (0.440836 to 0.842732), p 0.002744
• hsa-miR-3934-5p: 0.681271 (0.460666 to 1.007521), p 0.054551, which is not below the study's p less than 0.05 threshold
  • In the training, testing and complete cohorts, patients in the high-risk group (split at the median risk score) had significantly poorer survival than the low-risk group (p less than 0.001).
  • The area under the ROC curve was more than 0.8 in all three sets.
  • In univariate and multivariate analysis with clinical characteristics, the risk score was a significant independent predictor for pleural mesothelioma patients (p less than 0.001).
  • By qRT-PCR, miR-181a-2-3p, hsa-miR-503-5p and hsa-miR-3934-5p were significantly higher, and hsa-miR-491-5p significantly lower, in the mesothelioma cell lines than in the normal mesothelial cell line.
  • Target prediction produced 172 microRNA-mRNA pairs. Of three genes shared with the GEO datasets, high expression of KIF23 and VPS37B predicted shorter overall survival (log-rank p less than 0.05).
  • In database analyses of immune cells:
• KIF23 expression correlated with infiltration of T cell CD4+ Th2 cells (Rho 0.762) and MDSCs (Rho 0.633).
• Activated NK cells were significantly reduced in the VPS37B-high expression group (p less than 0.05).
• The authors' conclusion: The authors concluded that their microRNA risk model distinguished high- and low-risk pleural mesothelioma patients with good sensitivity and specificity and can be used as a biomarker for predicting prognosis.
• Limitations the authors state:
  • The model cannot effectively distinguish high-risk patients at an early stage of pleural mesothelioma.
  • The model was validated only within the TCGA database, without external independent datasets.
  • Models specific to the different pathological subtypes of pleural mesothelioma were not developed.

Topics

Medical terms in this study, explained in our Lexicon: Pleural Mesothelioma, Neoplasm, Prognosis, Median Overall Survival, Overall Survival.

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• Identification of glycolysis genes signature for predicting prognosis in malignant pleural mesothelioma by bioinformatics and machine learning — Y. Xiao, W. Huang, et al. (2022)
• Plasmatic extracellular vesicle microRNAs in malignant pleural mesothelioma and asbestos-exposed subjects suggest a 2-miRNA signature as potential biomarker of disease — T. Cavalleri, L. Angelici, et al. (2017)
• Two Different Serum MiRNA Signatures Correlate with the Clinical Outcome and Histological Subtype in Pleural Malignant Mesothelioma Patients — M. Lamberti, R. Capasso, et al. (2015)
• An EBC/Plasma miRNA Signature Discriminates Lung Adenocarcinomas From Pleural Mesothelioma and Healthy Controls — A. Faversani, C. Favero, et al. (2021)

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