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Current cancer detection methods are hindered by invasiveness and limited sensitivity. In this study, we present a novel diagnostic approach that uses sensor cells expressing insect\u2010derived olfactory receptors (ORs), which respond to volatile organic compounds (VOCs) in urine samples. The response of these sensor cells to urinary VOCs is detected via luminescence emission, generating time\u2010series luminescence data. We screened a library of 483 ORs to identify CRC\u2010discriminative ORs, using urine samples collected from 75 CRC patients and 75 non\u2010cancer controls. After identifying the ORs that could distinguish between urine from CRC patients and urine from non\u2010cancer controls, we applied machine learning to analyze the time\u2010series luminescence data, enabling the creation of a diagnostic model with 80% sensitivity and an ROC\u2010AUC of 0.84. 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