Background <p>Symptom burden in primary brain tumor patients varies, emphasizing the need for comprehensive understanding to improve patient care. This study aims to identify distinct symptom clusters among brain tumor patients in Shanghai, China, using Latent Profile Analysis (LPA) to guide personalized diagnosis, treatment, and supportive care.</p> Methods <p>A longitudinal study was conducted among 161 patients with primary brain tumors in Shanghai. Participants completed the MD Anderson Symptom Inventory Brain Tumor Module (MDASI-BT) at three intervals: the day of admission (T1), three days after surgery (T2), and two weeks after surgery (T3). Latent Profile Analysis (LPA) was used to identify subgroups with unique symptom patterns.</p> Results <p>Six distinct subgroups were identified (entropy = 0.964), ranging from low-burden to persistently severe patterns. Subgroup membership was partially associated with age, tumor grade, and diagnosis. These subgroups were: transient postoperative burden group, stable symptom with cognitive emergence group, distress-predominant, low burden group, elderly–high grade, persistently severe group, nausea-dominant recovery group, and distress-plus-nausea, younger urban group.</p> Conclusion <p>Our findings reveal substantial heterogeneity in perioperative symptom experiences among brain tumor patients. Identifying subgroups with high and persistent symptom burden may help clinicians target interventions such as enhanced education, proactive monitoring, rehabilitation, psychological support, and antiemetic management. This subgroup-based approach may improve quality of life, reduce morbidity, and guide precision supportive care in neuro-oncology.</p>