Homozygous deletion (HD) of CDKN2A and CDKN2B (CDKN2A/BHD) is the most frequent copy‐number variation (CNV) in lung adenocarcinoma (LUAD). CDKN2A/BHD has been associated with poor outcomes in LUAD; however, the mechanisms of its prognostic effect remain unknown. We analyzed genome, transcriptome, and clinical data from 517 patients with LUAD from the Cancer Genome Atlas (TCGA) and from 788 primary LUAD tumor and matched control samples from the MSK‐IMPACT clinical cohort. CDKN2A/BHD was present in 19.1% of the TCGA‐LUAD cohort and in 5.7% of the MSK‐IMPACT cohort. CDKN2A/BHD patients had shorter disease‐free survival and overall survival compared with CDKN2A/BWT individuals in both cohorts. Differences in clinical features did not influence the outcomes in the CDKN2A/BHD population. Mutation analyses showed that overall tumor mutational burden and mutations in classical drivers such as EGFR and RB1 were not associated with CDKN2A/BHD. In contrast, homozygous deletion of type I interferons (IFN‐IHD) frequently co‐occurred with CDKN2A/BHD. CDKN2A/B and IFN‐I are co‐located in the same p21.3 region of chromosome 9. The co‐occurrence of CDKN2A/BHD and IFN‐IHD was not related to whole‐genome doubling, chromosome instability, or aneuploidy. Patients with co‐occurring CDKN2A/BHD and IFN‐IHD had shorter disease‐free survival and overall survival compared with CDKN2A/BWT patients. CDKN2A/BHDIFN‐IHD had downregulated several key immune response pathways, suggesting that poor prognosis in CDKN2A/BHD LUAD could potentially be attributed to an immunosuppressive tumor microenvironment as a result of IFN‐I depletion.
This study was aimed at two image segmentation methods of three-dimensional (3D) U-shaped network (U-Net) and multilevel boundary sensing residual U-shaped network (RUNet) and their application values on the auxiliary diagnosis of lung cancer. In this study, on the basis of the 3D U-Net segmentation method, the multilevel boundary sensing RUNet was worked out after optimization. 92 patients with lung cancer were selected, and their clinical data were counted; meanwhile, the lung nodule detection was performed to obtain the segmentation effect under 3D U-Net. The accuracy of 3D U-Net and multilevel boundary sensing RUNet was compared on lung magnetic resonance imaging (MRI) after lung nodule segmentation. Patients with benign lung tumors were taken as controls; the blood immune biochemical indicators progastrin-releasing peptide (pro-CRP), carcinoembryonic antigen (CEA), and neuron-specific enolase (NSE) in patients with malignant lung tumors were analyzed. It was found that the accuracy, sensitivity, and specificity were all greater than 90% under the algorithm-based MRI of benign and malignant tumor patients. Based on the imaging signs for the MRI image of lung nodules, the segmentation effect of the RUNet was clearer than that of the 3D U-Net. In addition, serum levels of pro-CRP, NSE, and CAE in patients with benign lung tumors were 28.9 pg/mL, 12.5 ng/mL, and 10.8 ng/mL, respectively, which were lower than 175.6 pg/mL, 33.6 ng/mL, and 31.9 ng/mL in patients with malignant lung tumors significantly ( P < 0.05 ). Thus, the RUNet image segmentation method was better than the 3D U-Net. The pro-CRP, CEA, and NSE could be used as diagnostic indicators for malignant lung tumors.
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