Biomarkers are important in the study of tumor processes for early detection and precise treatment. The biomarkers that have been previously detected are not useful for clinical application for primary colorectal carcinoma (PCRC). The aim of this study was to explore clinically valuable biomarkers of PCRC based on integrated bioinformatic analysis. Gene expression data were acquired from the GSE41258 dataset, and the differentially expressed genes were determined between PCRC and normal colorectal samples. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were implemented via Gene Set Enrichment Analysis. A protein-protein interaction (PPI) network was constructed. The significant modules and hub genes were screened and identified in the PPI network. A total of 202 DEGs were identified, including 58 upregulated and 144 downregulated genes in PCRC samples compared to those in normal colorectal samples. Enrichment analysis demonstrated that the gene sets enriched in PCRC were significantly related to bicarbonate transport, regulation of sodium ion transport, potassium ion homeostasis, regulation of telomere maintenance, and other processes. A total of 10 hub genes was identified by cytoHubba: PYY, CXCL3, CXCL11, CXCL8, CXCL12, CCL20, MMP3, P2RY14, NPY1R, and CXCL1. The hub genes, such as NPY1R, P2RY14, and CXCL12, and the electrolyte disequilibrium resulting from the differential expression of genes, especially bicarbonate imbalance, may provide novel insights and evidence for the future diagnosis and targeted therapy of PCRC.

Background: Postoperative pain significantly impacts patients’ quality of life and recovery. Although acute pain services (APS) have been implemented in many hospitals worldwide, no study has directly compared the efficacy of different APS subtypes in managing acute postoperative pain. Objective: This network meta-analysis aimed to evaluate the effectiveness of various APS models in alleviating postoperative pain in adults undergoing surgery. Methods: Four English-language databases (PubMed, Web of Science, Embase, and Cochrane Library) and three Chinese-language databases (CNKI, WANFANG, and SinoMed) were searched to identify randomized controlled trials (RCTs) that compared the efficacy of different pain management models for postoperative pain in adult patients. Statistical analyses were conducted using R version 4.4.2 and Stata version 18. Results: A total of 38 studies were included in this network meta-analysis. All APS subtypes demonstrated superior pain relief compared to the traditional ward doctor-nurse model. These included nurse-based anesthesiologist specialist-supervised APS (NBASS-APS; SMD: −1.99, 99% CI: −2.55, −1.43), nurse-based anesthesiologist-supervised APS (NBAS-APS; SMD: −1.44, 99% CI: −2.18, −0.70), pain management multidisciplinary team (PMDT; SMD: −1.31, 99% CI: −1.74, −0.87), and conventional APS (C-APS; SMD: −0.83, 99% CI: −1.43, −0.24). Surface under the cumulative ranking (SUCRA) analysis identified NBASS-APS as having the highest probability of achieving optimal pain relief (98.0%), followed by NBAS-APS (65.9%), PMDT (58.0%), C-APS (28.1%), and the traditional model (0.1%). Conclusion: APS models are significantly more effective than the traditional ward doctor-nurse model in relieving postoperative pain, with NBASS-APS emerging as the most promising approach, followed by NBAS-APS, PMDT, and C-APS.