The Construction and Analysis of ceRNA Network and Patterns of Immune Infiltration in Mesothelioma With Bone Metastasis

Our plain-English summary of The Construction and Analysis of ceRNA Network and Patterns of Immune Infiltration in Mesothelioma With Bone Metastasis, by R. Huang, J. Wu, Z. Zheng, et al (Frontiers in Bioengineering and Biotechnology, 2019).

The study

• Authors: R. Huang, J. Wu, Z. Zheng, G. Wang, D. Song, P. Yan, H. Yin, P. Hu, X. Zhu, H. Wang, Q. Lv, T. Meng, Z. Huang, J. Zhang
• Journal: Frontiers in Bioengineering and Biotechnology
• Published: 2019;7:257
• Cited by: 24 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Background: Mesothelioma is a rare and aggressive tumor. Bone metastasis often occurs in the later stages of this disease along with poor quality of life. Thus, it is important to explore the tumorigenesis and bone metastasis mechanism of invasive mesothelioma. For this purpose, we established two nomograms based on tumor-infiltrating immune cells and ceRNA networks to describe the molecular immunity and the clinical prediction of mesothelioma patients with bone metastasis. Method: The expression profiles of mRNAs, lncRNAs, and miRNAs of 87 primary mesotheliomas were obtained from the TCGA database; there were four patients with bone metastasis and 83 patients without. We constructed a ceRNAs network based on the differentially expressed RNAs between mesothelioma and bone metastasis. CIBERSORT was used to distinguish 22 immune cell types from the tumor transcriptomes. Kaplan-Meier survival analysis and the Cox proportional hazards model were used to evaluate the prognostic value of each factor. Prognosis-associated immune cells and ceRNAs were applied to establish prediction nomograms. The receiver operating characteristic curves (ROC) and calibration curves were utilized to assess the discrimination and accuracy of the nomogram. Results: Differential analysis revealed that 20 lncRNAs, 15 miRNAs, and 230 mRNAs were significantly different in mesothelioma samples vs. bone metastasis samples. We constructed the ceRNA network to include 10 protein-coding mRNAs, 8 lncRNAs, and 10 miRNAs. Nine of 28 ceRNAs were found to be significant in the Kaplan-Meier analysis. Out of the 22 cell types, the fraction of dendritic cells resting (P = 0.018) was significantly different between the bone metastasis group and the non-bone metastasis group. The ROC and the calibration curves, based on ceRNA networks and tumor-infiltrating immune cells, respectively, suggested acceptable accuracy (AUC of 3-year survival: 0.827, AUC of 5-year survival: 0.840; AUC of 3-year survival: 0.730; AUC of 5-year survival: 0.753). Notably, based on the co-expression patterns between ceRNAs and Immune cells, we found that the hsa-miR-582-5p, CASP9, dendritic cells resting, ANIX2, T cells CD8, and T cells CD4 memory resting might be associated with the mesothelioma bone metastasis. Conclusion: Based on ceRNA networks and patterns of immune infiltration, our study provided a valid bioinformatics basis in order to explore the molecular mechanism and predict the possibility of mesothelioma bone metastasis.

Copyright © 2019 Huang, Wu, Zheng, Wang, Song, Yan, Yin, Hu, Zhu, Wang, Lv, Meng, Huang and Zhang.

In plain English

This was a bioinformatics study that analysed publicly available gene-activity data from The Cancer Genome Atlas (TCGA). It did not study asbestos exposure. The authors used data on 87 primary mesothelioma samples: 83 from patients without bone metastasis and four from patients with bone metastasis. Comparing the two groups, they found 20 lncRNAs, 15 miRNAs and 230 mRNAs that differed significantly. From these they built a network of 28 interacting RNAs, nine of which were significantly linked to survival. Of 22 immune cell types estimated in the tumors, the fraction of resting dendritic cells differed significantly between the groups (P = 0.018). The authors built two survival-prediction tools (nomograms), one from the RNA network and one from immune cells. For 3- and 5-year survival, their AUC values were 0.827 and 0.840 for the RNA tool and 0.730 and 0.753 for the immune tool. The authors concluded that their work provided a bioinformatics basis for exploring and predicting mesothelioma bone metastasis. They called it a correlation study, not a mechanism study, and noted that all data came from Western countries.

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 (correlation) analysis of publicly available tumor gene-expression data from the TCGA database, with checks against other public databases
• Who was studied: 87 primary mesothelioma samples from TCGA: 83 from patients without bone metastasis and 4 from patients with bone metastasis. The article does not state the tumor site (pleural or peritoneal), and the dates are not given.
• Exposure: No asbestos or other exposure was studied. Asbestos is mentioned only in the introduction, as background from other studies. The comparison was between tumors with and without bone metastasis.
• Main results:
  • 20 lncRNAs, 15 miRNAs and 230 mRNAs differed significantly between the bone metastasis and non-bone metastasis samples
  • The constructed RNA (ceRNA) network included 28 genes, and 9 of the 28 were significant in the Kaplan–Meier survival analysis
  • Of 22 immune cell types, the fraction of resting dendritic cells differed significantly between the bone metastasis group and the non-bone metastasis group (P = 0.018)
  • The survival nomogram based on the RNA network had an AUC of 0.827 for 3-year survival and 0.840 for 5-year survival
  • The survival nomogram based on immune cells had an AUC of 0.730 for 3-year survival and 0.753 for 5-year survival
  • The fraction of resting CD4 memory T cells differed significantly among the four tumor stages (P = 0.027)
  • The fraction of resting CD4 memory T cells was negatively associated with CD8 T cells (R = −0.62, P < 0.001)
• The authors' conclusion: The authors concluded that, based on the RNA networks and immune infiltration patterns, their study provided a bioinformatics basis for exploring the molecular mechanism of mesothelioma bone metastasis and predicting its possibility
• Limitations the authors state:
  • All data came from Western countries, so the authors cautioned against applying the conclusion to patients from Asian countries
  • Because public datasets were limited, the clinical and pathological factors analysed were not comprehensive, which may bias the analysis
  • The study cohort was small; the authors used other databases to try to reduce the resulting bias
  • It was a correlation study, not a study of biological mechanisms; the authors planned laboratory experiments to validate the findings

Topics

Medical terms in this study, explained in our Lexicon: Mesothelioma, Metastasis, Quality of Life, Carcinogenesis, Prognosis.

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