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IntroductionThe prediction of progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is an important clinical challenge. This study aimed to identify the independent risk factors and develop a nomogram model that can predict progression from MCI to AD.MethodsData of 141 patients with MCI were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We set a follow-up time of 72 months and defined patients as stable MCI (sMCI) or progressive MCI (pMCI) according to whether or not the progression of MCI to AD occurred. We identified and screened independent risk factors by utilizing weighted gene co-expression network analysis (WGCNA), where we obtained 14,893 genes after data preprocessing and selected the soft threshold β = 7 at an R2 of 0.85 to achieve a scale-free network. A total of 14 modules were discovered, with the midnightblue module having a strong association with the prognosis of MCI. Using machine learning strategies, which included the least absolute selection and shrinkage operator and support vector machine-recursive feature elimination; and the Cox proportional-hazards model, which included univariate and multivariable analyses, we identified and screened independent risk factors. Subsequently, we developed a nomogram model for predicting the progression from MCI to AD. The performance of our nomogram was evaluated by the C-index, calibration curve, and decision curve analysis (DCA). Bioinformatics analysis and immune infiltration analysis were conducted to clarify the function of early B cell factor 1 (EBF1).ResultsFirst, the results showed that 40 differentially expressed genes (DEGs) related to the prognosis of MCI were generated by weighted gene co-expression network analysis. Second, five hub variables were obtained through the abovementioned machine learning strategies. Third, a low Montreal Cognitive Assessment (MoCA) score [hazard ratio (HR): 4.258, 95% confidence interval (CI): 1.994–9.091] and low EBF1 expression (hazard ratio: 3.454, 95% confidence interval: 1.813–6.579) were identified as the independent risk factors through the Cox proportional-hazards regression analysis. Finally, we developed a nomogram model including the MoCA score, EBF1, and potential confounders (age and gender). By evaluating our nomogram model and validating it in both internal and external validation sets, we demonstrated that our nomogram model exhibits excellent predictive performance. Through the Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes Genomes (KEGG) functional enrichment analysis, and immune infiltration analysis, we found that the role of EBF1 in MCI was closely related to B cells.ConclusionEBF1, as a B cell-specific transcription factor, may be a key target for predicting progression from MCI to AD. Our nomogram model was able to provide personalized risk factors for the progression from MCI to AD after evaluation and validation.