Introduction: Despite rapid advances in molecular biology, personalized molecular therapy remains a clinical challenge for endometrial cancer due to its complex and heterogeneous tumor microenvironment.Based on clinical findings, AIB1 is a marker molecule for poor prognosis in endometrial cancer and may serve as a potential therapeutic target. Moreover, it is well known that aerobic glycolysis plays an important role in tumour energy metabolism. It has been previously reported in various hormone-related tumour studies that AIB1 affects glycolysis and promotes tumour development. However, the link between AIB1 and aerobic glycolysis in estrogen-dependent endometrial cancer remains unclear.Methods: We used two endometrial cancer cell lines to validate the high expression of target genes and the effect on the proliferative and invasive capacity of the tumours and verified the pattern of interactions and epigenetic modifications by CHIP and CO-IP techniques. Finally, the conclusions were validated on homozygous miceResults: In this study, we investigated the transcriptional co-activation functions of AIB1, including its acetylation by PCAF, binding to the c-myc transcription factor, and recruitment of glycolysis-related gene promoters.Discussion: Our findings provide new clues that perturbation of normal homeostatic levels of AIB1 is linked with endometrial cancer. These findings suggest that targeting AIB1-mediated regulation of aerobic glycolysis may offer a novel therapeutic approach for endometrial cancer with high AIB1 expression, opening new avenues for personalized diagnostics and treatment strategies in this disease.

Endometrial cancer (EC) incidence and the associated tumour burden have increased globally. To build a molecular expression prognostic model based on the tumour microenvironment to guide personalised treatment using a machine learning approach. Two datasets were reviewed, including a training cohort (n = 698) and a testing cohort (n = 151). All patients underwent hysterectomy ± adnexectomy ± lymph nodes dissection between December 2014 and June 2020 at the PLA General Hospital First Medical Center and received necessary and regular follow‐up. We developed novel models using R software to predict factors that affect survival, such as progression‐free survival and overall survival. Then, the model was optimised by evaluating the prediction efficiency in multiple dimensions. Eight hundred and forty‐nine patients with EC were included in the study. Survival‐related influences on EC patients were identified by univariate analysis and cox regression equations. In addition, a nomogram was visualised in conjunction with demographic characteristics and the above meaningful clinicopathological variables. Ultimately, through a comprehensive assessment, a random forest model (RF16) was developed for complementing the findings of the molecular classification of EC. The RF16 not only specifically characterises tumour molecules, but also enhances the generalizability of the model by replacing gene sequencing with immunohistochemistry. This study showed that the machine learning model (RF16) is low‐cost, efficient, and clinically valuable in guiding treatment for EC patients.