Background Neuropathic pain (NP) caused by a lesion or disease of the somatosensory nervous system is a common chronic pain condition that has a major impact on quality of life. However, NP pathogenesis remains unclear. The purpose of this study was to identify differentially expressed genes (DEGs) and specific and meaningful gene targets for the diagnosis and treatment of NP. Methods Data from rat spinal nerve ligations and the sham group were downloaded from the Gene Expression Omnibus (GEO) database. Based on the single-sample gene set enrichment analysis (ssGSEA) method, 29 immune gene sets were identified in each sample, and these samples were correlated with the immune infiltration phenotype. LASSO regression modeling was used to screen key genes to identify diagnostic gene markers. According to GSEA and GSVA, NP is concentrated in a large number of immune-related pathways and genes. Additionally, we used the DGIdb database and correlation test to construct gene-drug and transcription factor interaction networks for differentially expressed genes relevant to NP-related ferroptosis. We used WGCNA to identify gene co-expression modules of NP, and explored the relationship between gene networks and phenotypes. Finally, we crossed core genes with diagnostic markers and analyzed gene correlation with molecular subtypes and immune cells. Results We identified 224 DEGs, including 191 upregulated genes and 33 downregulated genes. APC co-stimulation, CCR, cytolytic activity, humid-promoting, neutrophils, NK cells, and RGS4, CXCL2, DRD4 and other 7 genes related to ferroptosis were involved in NP development. Key genes of RGS4 and HIF−1 signaling pathway were screened. Conclusion This study contributes to our understanding of the neuroimmune mechanism of neuropathic pain, provides a reference for NP biomarkers and drug targets. Ferroptosis may be the next research direction to explore NP mechanism.

Objective. To screen the differentially expressed miRNAs (DEMs) and the differentially expressed gene mRNAs (DEGs) in lung adenocarcinoma (LUAD) from the TCGA database and to explore the relationship between miRNAs and the prognosis of lung adenocarcinoma and their biological functions. Methods. The RNA-seq and miRNA-seq data of lung adenocarcinoma samples were downloaded from the TCGA database for analysis, and the R program was used to screen for differentially expressed miRNAs and mRNAs. Then, the molecular functions, biological processes, cellular components, and signaling pathways involved in the occurrence and development of LUAD were analyzed using the functional accumulation analysis software of GSEA. The relationship between the integrated differentially expressed RNAs was analyzed by miRcode, TargetScan, and miRTarbase databases, and the miRNA-mRNA network was constructed. Result. A total of 516 differentially expressed miRNAs and 5464 differentially expressed mRNAs were identified in LUAD. The GSEA enrichment analysis showed that miRNAs and mRNAs were mainly enriched in extracellular structure organization, external encapsulating structure organization, extracellular matrix organization, and gated channel activity. They were mainly involved in neuroactive ligand-receptor interaction signaling pathway. Some miRNAs and mRNAs in clustering modules were found to be associated with the prognosis of LUAD. Four targeting networks consisting of 22 miRNAs and 531 mRNAs were constructed. Conclusion. The miRNA and mRNA related to the prognosis of LUAD were screened out, which provided a valuable preliminary basis for the follow-upin-depth clinical research and basic experimental research of LUAD.