鉴定和描述不可逆性牙髓炎中与铁死亡相关的 ceRNA 网络。

Identification and characterization of the ferroptosis-related ceRNA network in irreversible pulpitis.

机构信息

Department of Endodontics, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, China.

Department of Stomatology, Nanfang Hospital, Southern Medical University, Guangzhou, China.

出版信息

Front Immunol. 2023 May 19;14:1198053. doi: 10.3389/fimmu.2023.1198053. eCollection 2023.

DOI:10.3389/fimmu.2023.1198053

PMID:37275855

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10235459/

Abstract

BACKGROUND

The role of ferroptosis in irreversible pulpitis (IP) remains unclear. The competing endogenous RNA (ceRNA) theory that has been widely investigated is rarely used studied in IP. Hub lncRNAs selected from a ceRNA network may provide a novel hypothesis for the interaction of ferroptosis and IP.

METHODS

Differentially expressed genes (DEGs) were intersected with 484 ferroptosis markers to identify differentially expressed ferroptosis-related genes (DE-FRGs). Functional analysis and protein-protein interaction (PPI) networks were constructed to reveal the functions of DE-FRGs. Then, coexpression analyses were conducted between DE-FRGs and DElncRNAs to define ferroptosis-related DElncRNAs (FR-DElncRNAs). Predictions of DE-FRG- and FR-DElncRNA-related miRNAs were obtained, and members of both groups were selected. Additionally, two ceRNA networks consisting of FR-DElncRNAs, miRNAs and DE-FRGs from upregulated and downregulated groups were built. Finally, the hub lncRNAs of the ceRNA networks were used for immuno-infiltration analysis and qPCR verification.

RESULTS

According to the results of PCA and clustering analysis, 5 inflamed and 5 healthy pulp tissue samples were selected for analysis. The intersection of DEGs with 484 ferroptosis marker genes identified 72 DE-FRGs. The response to stimulus, cellular process, signaling, localization, and biological regulation pathways related to DE-FRGs were enriched. In total, 161 downregulated and 40 upregulated FR-DElncRNAs were chosen by coexpression analysis for further investigation. The MultimiR package and starBase were used to predict miRNAs of DE-FRGs and FR-DElncRNAs, respectively. The upregulated ceRNA network contained 2 FR-DElncRNAs (↑), 19 miRNAs (↓) and 22 DE-FRGs (↑). The downregulated network contained 44 FR-DElncRNAs (↓), 251 miRNAs (↑) and 10 DE-FRGs (↓). Six hub lncRNAs were identified based on the MCC method (LUCAT1 and AC106897.1 ↑; LINC00943, AL583810.1, AC068888.1, and AC125257.1↓). In addition, strong relationships between hub lncRNAs and immune cells were shown by immune infiltration analysis. Finally, validated by qPCR assays of the pulp tissue of IP patients, the expression levels in clinical samples were consistent with the microarray data.

CONCLUSION

Two ceRNA networks were comprehensively constructed, and 6 hub lncRNAs were identified. These genes provide novel insights into the relationship between ferroptosis and IP. Intriguingly, the LINC00943/hsa-miR-29a-3p/PDK4 axis was deemed to be the key node in this network.

摘要

背景

铁死亡在不可逆性牙髓炎（IP）中的作用尚不清楚。竞争内源性 RNA（ceRNA）理论已被广泛研究，但在 IP 中很少被研究。从 ceRNA 网络中选择的关键长非编码 RNA（lncRNA）可能为铁死亡和 IP 的相互作用提供新的假设。

方法

差异表达基因（DEGs）与 484 个铁死亡标志物相交，以鉴定差异表达的铁死亡相关基因（DE-FRGs）。构建功能分析和蛋白质-蛋白质相互作用（PPI）网络，以揭示 DE-FRGs 的功能。然后，对 DE-FRGs 和差异表达 lncRNA（DElncRNA）进行共表达分析，以定义铁死亡相关的差异表达 lncRNA（FR-DElncRNA）。获得 DE-FRG 和 FR-DElncRNA 相关 miRNA 的预测，并选择两组的成员。此外，构建了由上调和下调组的 FR-DElncRNA、miRNA 和 DE-FRGs 组成的两个 ceRNA 网络。最后，对 ceRNA 网络的关键 lncRNA 进行免疫浸润分析和 qPCR 验证。

结果

根据 PCA 和聚类分析的结果，选择了 5 个炎症和 5 个健康牙髓组织样本进行分析。DEGs 与 484 个铁死亡标志物基因的交集鉴定出 72 个 DE-FRGs。与 DE-FRGs 相关的途径有对刺激的反应、细胞过程、信号转导、定位和生物调节。总共选择了 161 个下调和 40 个上调的 FR-DElncRNA 进行进一步研究。MultimiR 包和 starBase 分别用于预测 DE-FRGs 和 FR-DElncRNAs 的 miRNAs。上调的 ceRNA 网络包含 2 个 FR-DElncRNA（↑）、19 个 miRNA（↓）和 22 个 DE-FRGs（↑）。下调的网络包含 44 个 FR-DElncRNA（↓）、251 个 miRNA（↑）和 10 个 DE-FRGs（↓）。基于 MCC 方法确定了 6 个关键 lncRNA（LUCAT1 和 AC106897.1↑；LINC00943、AL583810.1、AC068888.1、AC125257.1↓）。此外，通过对 IP 患者牙髓组织的免疫浸润分析显示，关键 lncRNA 与免疫细胞之间存在强烈关系。最后，通过对 IP 患者牙髓组织的 qPCR 检测验证，临床样本中的表达水平与微阵列数据一致。