Bee venom (BV), a type of defensive venom, has been confirmed to have favorable activities, such as anti-tumor, neuroprotective, anti-inflammatory, analgesic, anti-infectivity effects, etc. This study reviewed the recent progress on the pharmacological effects and mechanisms of BV and its main components against cancer, neurological disorders, inflammatory diseases, pain, microbial diseases, liver, kidney, lung and muscle injury, and other diseases in literature during the years 2018–2021. The related target proteins of BV and its main components against the diseases include Akt, mTOR, JNK, Wnt-5α, HIF-1α, NF-κB, JAK2, Nrf2, BDNF, Smad2/3, AMPK, and so on, which are referring to PI3K/Akt/mTOR, MAPK, Wnt/β-catenin, HIF-1α, NF-κB, JAK/STAT, Nrf2/HO-1, TrkB/CREB/BDNF, TGF-β/Smad2/3, and AMPK signaling pathways, etc. Further, with the reported targets, the potential effects and mechanisms on diseases were bioinformatically predicted via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, disease ontology semantic and enrichment (DOSE) and protein-protein interaction (PPI) analyses. This review provides new insights into the therapeutic effects and mechanisms of BV and its main components on diseases.

Purpose Gastric cancer is often detected in the late stages, due to which its mortality rate remains high. Early detection of gastric cancer could significantly improve the prognosis of patients since the survival rate of early gastric cancer after treatment exceeds 96%. This study aimed to analyze early gastric cancer (EGC) risk factors and construct a nomogram model to predict EGC patients. Methods A retrospective study was conducted on 589 patients, including 325 patients with EGC and 264 patients with benign gastric disease. Age, sex, neutrophil to lymphocyte ratio (NLR), creatinine, hypertension, diabetes and other clinical data were collected accordingly. A nomogram was then constructed using univariate analysis and multivariate analysis. Moreover, a correction curve and AUCs were utilized to determine the accuracy of our model. Results Our findings revealed that sex, age, NLR, creatinine, basophil, hypertension and diabetes were risk factors for EGC. A predictive nomogram model was constructed based on the above risk factors showing good consistency and accuracy (AUC = 0.77), the validation cohort showed good consistency(AUC = 0.776). Conclusion The nomogram model presented good reliability, and it will help clinicians to predict and diagnose EGC patients timely while avoiding unnecessary gastrectomy.