KRAS-driven non-small cell lung cancer (NSCLC) patients have no effective targeted treatment. In this study, we aimed to investigate targeting epidermal growth factor receptor (EGFR) as a therapeutic approach in KRAS-driven lung cancer cells. We show that ablation of EGFR significantly suppressed tumor growth in KRAS-dependent cells and induced significantly higher expression of CX chemokine receptor 7 (CXCR7) and activation of MAPK (ERK1/2). Conversely, rescue of EGFR led to CXCR7 downregulation in EGFR−/− cells. Dual EGFR and CXCR7 inhibition led to substantial reduction of MAPK (pERK) and synergistic inhibition of cell growth. Analysis of two additional EGFR knockout NSCLC cell lines using CRISPR/Cas9 revealed genotype dependency of CXCR7 expression. In addition, treatment of different cells with gefitinib increased CXCR7 expression in EGFRwt but decreased it in EGFRmut cells. CXCR7 protein expression was detected in all NSCLC patient samples, with higher levels in adenocarcinoma as compared to squamous cell lung carcinoma and healthy control cases. In conclusion, EGFR and CXCR7 have a crucial interaction in NSCLC, and dual inhibition may be a potential therapeutic option for NSCLC patients.
BackgroundArtificial intelligence (AI) discrimination models using single radioactive variables in recognition algorithms of lung nodules cannot predict lung cancer accurately. Hence, we developed a clinical model that combines AI with blood test variables to predict lung cancer.MethodsBetween 2018 and 2021, 584 individuals (358 patients with lung cancer and 226 individuals with lung nodules other than cancer as control) were enrolled prospectively. Machine learning algorithms including lasso regression and random forest (RF) were used to select variables from blood test data, Logistic regression analysis was used to reconfirm the features to build the nomogram model. The predictive performance was assessed by performing the receiver operating characteristic (ROC) curve analysis as well as calibration, clinical decision and impact curves. A cohort of 48 patients was used to independently validate the model. The subgroup application was analyzed by pathological diagnosis.FindingsA total of 584 patients were enrolled (358 lung cancers, 61.30%,226 patients for the control group) to establish the model. The integrated model identified eight potential factors including carcinoembryonic antigen (CEA), AI score, Pro-Gastrin Releasing Peptide (ProGRP), cytokeratin 19 fragment antigen21-1(CYFRA211), squamous cell carcinoma antigen(SCC), indirect bilirubin(IBIL), activated partial thromboplastin time(APTT) and age. The area under the curve (AUC) of the nomogram was 0.907 (95% CI, 0.881-0.929). The decision and clinical impact curves showed good predictive accuracy of the model. An AUC of 0.844 (95% CI, 0.710 - 0.932) was obtained for the external validation group.ConclusionThe nomogram model integrating AI and clinical data can accurately predict lung cancer, especially for the squamous cell carcinoma subtype.
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