Based on the molecular expression level, this paper compares lncRNA and mRNA expressions respectively in peripheral blood samples of the patients after SCI with NP and without NP, and screens disease-related biomarkers related to NP after SCI in peripheral blood samples of patients. The expression spectrum of 25 human peripheral blood samples (12 samples of refractory NP patients after SCI) was downloaded and data were normalized. Screening of GO annotations significantly associated with significant differentially expressed mRNAs and significant involvement of the KEGG pathway. The WGCNA algorithm was used to screen for modules and RNAs that were significantly associated with disease characterization. A co-expression network was constructed to extract the genes involved in the disease pathway from the co-expression network, construct a network of SCI pain-related pathways, and screen important disease-related biomarkers. Quantitative real-time PCR was used to detect the mRNA expression of hub genes. Data were normalized and re-annotated by detection of platform information, resulting in a total of 289 lncRNA and 18197 mRNAs. Screening resulted in 338 significant differentially expressed RNAs that met the threshold requirements. Differentially expressed RNAs were significantly enriched with the brown and magenta modules. Six KEGG signaling pathways were screened in the co-expression network, and three KEGG pathways with direct neuropathic pain were identified. The expression levels of E2F1, MAX, MITF, CTNNA1, and ADORA2B in the disease group were all significantly upregulated (p < 0.01). Compared with the normal group, the expression of OXTR was upregulated. We speculate that there are 7 genes and 2 lncRNAs directly involved in the pain pathway: E2F1, MAX, MITF, CTNNA1, ADORA2B, GRIK3, OXTR, LINC01119, and LINC02447. These molecules may be important for NP after SCI.

中文翻译：

---

筛选与脊髓损伤（SCI）后神经病理性疼痛（NP）相关的疾病相关生物标志物

本文基于分子表达水平，分别比较 SCI 后伴 NP 和不伴 NP 患者外周血标本中 lncRNA 和 mRNA 的表达量，筛选 SCI 后患者外周血标本中与 NP 相关的疾病相关生物标志物。下载25份人外周血样本（12份SCI后难治性NP患者样本）的表达谱，并对数据进行标准化。GO注释的筛选与显着差异表达的mRNA和KEGG通路的显着参与显着相关。WGCNA算法用于筛选与疾病特征显着相关的模块和RNA。构建共表达网络，从共表达网络中提取疾病通路涉及的基因，构建SCI疼痛相关通路网络，筛选重要的疾病相关生物标志物。采用实时定量PCR检测hub基因的mRNA表达量。通过检测平台信息对数据进行标准化和重新注释，总共得到289个lncRNA和18197个mRNA。筛选得出 338 个显着差异表达的 RNA，满足阈值要求。棕色和洋红色模块显着富集了差异表达的 RNA。在共表达网络中筛选了6条KEGG信号通路，确定了3条与神经病理性疼痛直接相关的KEGG通路。疾病组中E2F1、MAX、MITF、CTNNA1和ADORA2B的表达水平均显着上调（p < 0.01）。与正常组相比,OXTR表达上调。我们推测有7个基因和2个lncRNA直接参与疼痛通路：E2F1、MAX、MITF、CTNNA1、ADORA2B、GRIK3、OXTR、LINC01119和LINC02447。这些分子可能对 SCI 后的 NP 很重要。