Dataset Information

Identifying circRNA-miRNA-mRNA Regulatory Networks in Chemotherapy-Induced Peripheral Neuropathy.

ABSTRACT: Chemotherapy-induced peripheral neuropathy (CIPN) is a frequent and severe side effect of first-line chemotherapeutic agents. The association between circular RNAs (circRNAs) and CIPN remains unclear. In this study, CIPN models were constructed with Taxol, while 134 differentially expressed circRNAs, 353 differentially expressed long non-coding RNAs, and 86 differentially expressed messenger RNAs (mRNAs) were identified utilizing RNA sequencing. CircRNA-targeted microRNAs (miRNAs) were predicted using miRanda, and miRNA-targeted mRNAs were predicted using TargetScan and miRDB. The intersection of sequencing and mRNA prediction results was selected to establish the circRNA-miRNA-mRNA networks, which include 15 circRNAs, 18 miRNAs, and 11 mRNAs. Functional enrichment pathway analyses and immune infiltration analyses revealed that differentially expressed mRNAs were enriched in the immune system, especially in T cells, monocytes, and macrophages. Cdh1, Satb2, Fas, P2ry2, and Zfhx2 were further identified as hub genes and validated by RT-qPCR, correlating with macrophages, plasmacytoid dendritic cells, and central memory CD4 T cells in CIPN. Additionally, we predicted the associated diseases, 36 potential transcription factors (TFs), and 30 putative drugs for hub genes using the DisGeNET, TRRUST, and DGIdb databases, respectively. Our results indicated the crucial role of circRNAs, and the immune microenvironment played in CIPN, providing novel insights for further research.

SUBMITTER: Cao F

PROVIDER: S-EPMC10453290 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Identifying circRNA-miRNA-mRNA Regulatory Networks in Chemotherapy-Induced Peripheral Neuropathy.

Cao Fei F Wang Xintong X Ye Qingqing Q Yan Fang F Lu Weicheng W Xie Jingdun J Bi Bingtian B Wang Xudong X

Current issues in molecular biology 20230816 8

Chemotherapy-induced peripheral neuropathy (CIPN) is a frequent and severe side effect of first-line chemotherapeutic agents. The association between circular RNAs (circRNAs) and CIPN remains unclear. In this study, CIPN models were constructed with Taxol, while 134 differentially expressed circRNAs, 353 differentially expressed long non-coding RNAs, and 86 differentially expressed messenger RNAs (mRNAs) were identified utilizing RNA sequencing. CircRNA-targeted microRNAs (miRNAs) were predicted ...[more]

PMID: 37623249

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Project description:BackgroundMacrophage play a crucial role in regulating tumor progression. This study intended to investigate the circular RNA (circRNA) regulatory network associated with macrophage infiltration in hepatocellular carcinoma (HCC).MethodsThe immune cell fractions of HCC from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium were calculated by Estimation of the Proportion of Immune and Cancer cells algorithm. The differentially expressed mRNAs (DEmRNAs), microRNAs (DEmiRNAs) and circRNAs (DEcircRNAs) were identified from HCC and adjacent non-tumor cases of TCGA or Gene Expression Omnibus database. The DEmRNAs related to macrophage were selected by weighted gene co-expression network analysis and then utilized to generate the circRNA-miRNA-mRNA network. A hub circRNA regulatory network was established based on the co-expressed DEmiRNAs and DEmRNAs owning contrary correlation with the clinical characteristics, survival and macrophage infiltration level. A gene signature based on the DEmRNAs in hub network was also generated for further evaluation. The circRNA binding bite for miRNA was detected by luciferase assay.ResultsHigh macrophage fraction predicted good survival for HCC. A circRNA-miRNA-mRNA network was constructed by 27 macrophage related DEmRNAs, 21 DEmiRNAs, and 15 DEcircRNAs. Among this network, the expression of hsa-miR-139-5p was negatively correlated with CDCA8, KPNA2, PRC1 or TOP2A. Hsa-miR-139-5p low or targeted DEmRNA high expression was associated with low macrophage infiltration, high grade, advanced stage and poor prognosis of HCC. Additionally, the risk score generated by 4-DEmRNA signature could reflect the macrophage infiltration status and function as an independent prognostic factor for HCC. Finally, hsa_circ_0007456 acting on hsa-miR-139-5p related network was viewed as the hub circRNA regulatory network. Taken together, some circRNA regulatory networks may be associated with macrophage infiltration, which provides clues for mechanism study and therapeutic strategies of HCC.

Project description:BackgroundIdentifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes.ResultsIn this paper, we propose a method, weighted similarity network fusion (WSNF), to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs), transcription factors (TFs) and messenger RNAs (mRNAs) and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients) and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA) and glioblastoma multiforme (GBM) datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some miRNA-TF-mRNA sub-networks vary across different identified subtypes. In addition, pathway enrichment analyses show that the top pathways involving the most differentially expressed genes in each of the identified subtypes are different. The results would provide valuable information for understanding the mechanisms characterising different cancer subtypes and assist the design of treatment therapies. All datasets and the R scripts to reproduce the results are available online at the website: http://nugget.unisa.edu.au/Thuc/cancersubtypes/.

Project description:Background and purposeChemotherapy-induced peripheral neuropathy (CIPN) is perceived differently by patients and physicians, complicating its assessment. Current recommendations advocate combining clinical and patient-reported outcomes measures, but this approach can be challenging in patient care. This multicenter European study aims to bridge the gap between patients' perceptions and neurological impairments by aligning both perspectives to improve treatment decision-making.MethodsData were pooled from two prospective studies of subjects (n = 372) with established CIPN. Patient and physician views regarding CIPN were assessed using the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE), Total Neuropathy Scale-clinical version (TNSc) items, and the disease-specific quality of life - Chemotherapy-Induced Peripheral Neuropathy questionnaire (QLQ-CIPN20) from the European Organization for Research and Treatment of Cancer (EORTC). To identify inherent neurotoxic severity patterns, we employed hierarchical cluster analysis optimized with k-means clustering and internally validated by discriminant functional analysis.ResultsBoth NCI-CTCAE and TNSc demonstrated a significant difference in the distribution of severity grades in relation to QLQ-CIPN20 scores. However, a proportion of subjects with different neurotoxic severity grades exhibited overlapping QLQ-CIPN20 scores. We identified three distinct clusters classifying subjects as having severely impaired, intermediately impaired, and mildly impaired CIPN based on TNSc and QLQ-CIPN20 scores. No differences in demographics, cancer type distribution, or class of drug received were observed.ConclusionsOur results confirm the heterogeneity in CIPN perception between patients and physicians and identify three well-differentiated subgroups of patients delineated by degree of CIPN impairment based on scores derived from TNSc and QLQ-CIPN20. A more refined assessment of CIPN could potentially be achieved using the calculator tool derived from the cluster equations in this study. This tool, which facilitates individual patient classification, requires prospective validation.

Dataset Information

Identifying circRNA-miRNA-mRNA Regulatory Networks in Chemotherapy-Induced Peripheral Neuropathy.

Publications

Identifying circRNA-miRNA-mRNA Regulatory Networks in Chemotherapy-Induced Peripheral Neuropathy.

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