Neonatal late-onset sepsis (NS) is a life-threatening condition driven by a dysregulated host response to infection, where neutrophil dysfunction plays a pivotal role. Pattern recognition receptors (PRRs), as the frontline sensors of the innate immune system, are increasingly recognized as key regulators of immune cell function, yet their specific connection to neutrophil biology in NS remains largely undefined. This study aimed to decode a novel PRR-neutrophil (PRRN)-associated molecular signature in NS. Bulk transcriptomic data from NS peripheral samples (GSE138712) were analyzed using Limma, WGCNA, and CIBERSORT to identify shared PRRN-related differentially expressed genes (DEGs). Hub genes were narrowed down via PPI network analysis. A diagnostic model was constructed using 132 machine learning algorithms on the training set (GSE69686, NS peripheral blood bulk profile) and validated on an independent set (GSE25504, NS peripheral blood bulk profile) based on PRRN-associated hub genes. SHAP analysis identified the central hub gene in training set. Single-cell RNA sequencing data of sepsis patient peripheral blood(GSE167363) were used to validate its cell-specific distribution, functional trajectory, and regulatory network at spatial and temporal manners via cutting-edge analytical framework. AI-based drug screening (DrugReflector) and molecular docking computationally nominated a candidate therapeutic compound for NS targeting PRRN-associated central hub genes. Finally, NS patient clinical samples were collected for the validation of expression patterns of hub genes. We identified PRRN-associated diagnostic and candidate therapeutic models for NS patients and nominated GSR as the PRRN-associated and up-regulated hub gene in NS pathogenesis. Single-cell analysis confirmed that GSR is predominantly expressed in neutrophils within the NS samples and is enriched in pathways related to innate immune response. This study systematically defines novel PRRN molecular co-expression patterns in NS, establishing GSR as a neutrophil-centric hub gene. These findings offer a potential diagnostic framework and an exploratory therapeutic target for NS patients, and the candidate compound requires further experimental validation.
Growing evidence highlights calcium signaling’s pivotal role in cancer progression. However, a prognostic model based on calcium signaling pathway-related genes (CSPRGs) in colon adenocarcinoma (COAD) remains unexplored. Transcriptomic data of COAD patients were obtained from TCGA, with GSE17536 and GSE17537 from GEO for validation. CSPRGs were retrieved from the KEGG database. Univariate, LASSO, and multivariate Cox regression analyses were utilized to establish a prognostic risk model. Additionally, correlations between calcium signaling pathway-related risk score (CSPRS) and tumor mutation burden (TMB), immune cell infiltration, and drug sensitivity were analyzed. Marker gene expression was evaluated via qRT-PCR, Western blot, CCK-8, colony formation, and Transwell assays; the effect of FLT1 on cellular function was also verified. A 15-CSPRG prognostic model (EGFR, ERBB4, FGF4, FLT1, FGF22, HTR2B, NGF, NOS2, ATP2A1, NTRK2, OXTR, P2RX5, RYR2, CACNA1D, and CACNA1F) was constructed. COAD patients were stratified by median CSPRS into high- and low-CSPRS groups. The low-CSPRS group showed significant enrichment of resting dendritic cells, activated CD4+ memory T cells, and neutrophils. The high-CSPRS group exhibited higher TMB. High-CSPRS patients were more sensitive to AZD8186, BMS-754,807, and PRT062607, while low-CSPRS patients responded better to Gallibiscoquinazole, Ulixertinib, and VX-11e. qRT-PCR and Western blot confirmed FLT1, ATP2A1, and OXTR upregulation in COAD cell lines. Functional assays demonstrated that FLT1 knockdown suppressed the malignant phenotype of COAD cells and reduced PD-L1 expression. Furthermore, FLT1 knockdown reduced the protein expression levels of ITPR1 and ATP2B4 and inhibited the phosphorylation of CaMKIIα at the Thr286 site. Our study established a risk model based on 15 CSPRGs, providing a theoretical foundation and clinical insights into COAD research and treatment.
Programmed cell death (PCD) is closely associated with the occurrence and progression of diabetic nephropathy (DN). This study aims to explore the diagnostic biomarkers of PCD in the DN and its underlying mechanisms. DN-related datasets were downloaded from the GEO database, and PCD-related genes were collected. PCD-related subtypes were identified through cluster analysis, and differentially expressed genes between subtypes were determined. Subtype-related genes were identified using WGCNA, and key genes were identified through multiple machine learning algorithms. The expression levels of the key genes were validated in several datasets, and their diagnostic values were assessed using ROC curves. The biological processes involving these genes were analyzed through KEGG, GO, and GSEA, and their associations with immune cells were examined. The expression levels of ARPC1B and IGFBP6 in the HK-2 cell line cultured in high-glucose medium were verified by PCR. Functional knockdown experiments were performed for ARPC1B to examine its association with epithelial-mesenchymal transition (EMT) related changes. By acquiring DN-related datasets from the GEO database and integrating multiple PCD-associated genes, we identified PCD subtypes (P1 and P2) through cluster analysis. Results revealed significant differences in PCD scores and immune cell infiltration levels between the two subtypes. WGCNA analysis revealed that the yellow module was closely associated with PCD subtypes. Further integration of multiple machine learning algorithms identified two potential genes, ARPC1B and IGFBP6. Additionally, functional analysis indicated that both genes participate in diverse biological processes and are associated with multiple immune cell types. PCR experiments confirmed that the mRNA expression levels of both ARPC1B and IGFBP6 were elevated in the HK-2 cell line cultured in conditions with high glucose. ARPC1B knockdown was associated with changes in EMT-related markers, providing preliminary functional evidence for its involvement in EMT-related processes. Integrative transcriptomic analyses prioritized ARPC1B and IGFBP6 as PCD-associated candidate biomarkers for DN. Preliminary functional evidence supports an association between ARPC1B and EMT-related changes.
