Flexible electronic-nose (E-nose) was constructed by assembling graphene oxide (GO) using different types of metal-ions (Mx+) with different ratio of GO to Mx+. Owing to the crosslinked networks, the Mx+ induced assembly of graphene films exhibited different porous structures. A chemiresistive sensor array was constructed by coating the GO-M hybrid films on PET substrate patterned with 8 interdigited electrodes, followed by in-situ reduction of GO to rGO with hydrazine vapor. Each of sensing elements on the sensor array showed cross-reactive response towards different types of gases at room temperature. Compared to bare rGO, incorporation of metal species into rGO significantly improved sensitivity owing to the additional interaction between metal species and gas analyte. Principle component analysis (PCA) showed that four types of exhaled breath (EB) biomarkers including acetone, isoprene, ammonia and hydrothion in sub-ppm concentrations can be well discriminated. To overcome the interference from humidity in EB, a protocol to collect and analyze EB gases was established and further validated by simulated EB samples. Finally, clinical EB samples collected from patients with lung cancer and healthy controls were analyzed. In a 106 cases study, healthy group can be accurately distinguished from lung cancer patients by linear discrimination analysis. With the assistance of artificial neural network, a sensitivity of 95.8% and specificity of 96.0% can be achieved in the diagnosis of lung cancer based on the E-nose. We also find that patients with renal failure can be distinguished through comparison of dynamic response curves between patient and healthy samples on some specific sensing elements. These results demonstrate the proposed E-nose will have great potential in noninvasive disease screening and personalized healthcare management.