Abstract
BACKGROUND: Metastatic adenocarcinoma of unknown primary site (MACUP) is the most common cancer of unknown primary site, and shows worse prognosis. Prediction of its tumor site origin attracts a growing attention. However, the site determined by gene expression profiling does not have a significant impact on the survival. Some other special method might need to be found out. METHODS: We reviewed 1011 MACUP patients diagnosed by pathological examination and immunohistochemistry based on the Surveillance, Epidemiology, and End Results (SEER) database during 2010-2016. Kaplan-Meier curves and Cox proportional hazard model were analyzed to compare the survival. Logistic regression models and relevant nomograms were performed to predicting the probability of the primary site which including digestive system, respiratory system, and female breast. The validation and clinical utility of models were measured with relevant statistical approaches. RESULTS: About 324 (32.1%), 299 (29.6%), and 203 (20.1%) of MACUP patients were identified as the primary sites of digestive system, respiratory system, and female breast, respectively. Patients derived from digestive system and respiratory system showed poorer survival than these with other sites. Digestive system was significantly associated with liver (Odds ratio =13.21 [95% confidence interval =8.48-21.02]) or lung (2.36 [1.40-3.97]) metastasis, while respiratory system was linked to brain (11.68 [6.68-21.26]) or lymph node (3.39 [2.26-5.13]) metastasis. Patients identified as female breast were prone to occur bone metastasis (5.85 [3.68-9.45]). Logistic regression nomograms were developed to help clinicians intuitively predict the probabilities of tumor site origin with 0.867, 0.824, and 0.753 of the C-index, respectively. Decision curve analysis and clinical impact curves both revealed the clinical effectiveness. CONCLUSIONS: We profiled different tumor site origin of MACUP patients and established prediction models. These features might be significant for clinicians to improve the probabilities of predicting the primary sites, and to decide subsequent treatment strategy.