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To study the effect of computerized tomography (CT) images based on deep learning algorithms on the diagnosis of pulmonary nodules and the effect of radiofrequency ablation (RFA), the U-shaped fully convolutional neural network (FCNN) (U-Net) was enhanced. The convolutional neural network (CNN) algorithm was compared with the U-Net algorithm, and segmentation performances were analyzed. Then, it was applied to the CT image diagnosis of 110 lung cancer patients admitted to hospital. The patients in the observation group (55 cases) were diagnosed based on the improved U-Net algorithm, while those in the control group (55 cases) were diagnosed by traditional methods and then treated with RFA. The Dice coefficient (0.8753) and intersection over union (IOU) (0.8788) obtained by the proposed algorithm were remarkably higher than the Dice coefficient (0.7212) and IOU (0.7231) obtained by the CNN algorithm, and the differences were considerable ( P < 0.05 ). The boundary of the pulmonary nodule can be segmented more accurately by the proposed algorithm, which had the segmentation result closest to the gold standard among the three algorithms. The diagnostic accuracy of the pulmonary nodule in the observation group (95.3%) was superior to that of the control group (90.7%). The long diameter, volume, and maximum area of the pulmonary nodule of the observation group were significantly higher than those of the control group, with substantial differences ( P < 0.05 ). Patients were reexamined after one, three, and six months of treatment, and 71 patients (64.55%) had complete remission, 32 patients (29.10%) had partial remission, 6 patients (5.45%) had stable disease, and 1 patient (0.90%) had disease progression. The remission rate (complete remission + partial remission) was 93.65%. The improved U-NET algorithm had good image segmentation performance and ideal segmentation effect. It can clearly display the shape of pulmonary nodules, locate the lesions, and accurately evaluate the therapeutic effect of RFA, which had clinical application value.

Background Long noncoding RNAs (lncRNAs) play oncogenic or tumor-suppressive roles in various cancers. However, the epigenetic modification of lncRNA and its cognate sense gene in lung cancer remain largely unknown.Methods : qRT-PCR and Western blot were conducted to detect the expressions of DDP10-AS1 and DPP10 expression in lung cancer cell lines and tissues. The impact of DDP10-AS1 on DPP10 expression, cell growth, invasion, apoptosis and in vivo tumor growth were investigated in lung cancer cells by Western blot, rescue experiments, colony formation, flow cytometry and xenograft animal experiment.Results A novel antisense lncRNA, DPP10-AS1, is found to be highly expressed in cancer tissues and the upregulation of DPP10-AS1 predicts poor prognosis in lung cancer patients. Notably, DPP10-AS1 promotes lung cancer cell growth, colony formation, cell cycle progression and represses apoptosis in lung cancer cells by upregulating DPP10 expression. Additionally, DPP10-AS1 facilitates lung tumor growth via upregulation of DPP10 protein in xenograft mouse model. Importantly, DPP10-AS1 positively regulates DPP10 gene expression and they are coordinately upregulated in lung cancer tissues. Mechanically, DPP10-AS1 associates with DPP10 mRNA but does not enhance DPP10 mRNA stability. Hypomethylation of DPP10-AS1 and DPP10 contributes to their coordinate upregulation in lung cancer.Conclusions These findings indicate that the upregulated antisense lncRNA DPP10-AS1 promotes lung cancer malignant processes and facilitates tumorigenesis by epigenetically regulating its cognate sense gene DPP10, and DPP10-AS1 may act as a candidate prognostic biomarker and a potential therapeutic target in lung cancer.

The resistance to cisplatin‐based chemotherapy is a common cause of poor prognosis in cancer patients. Cisplatin stimulation causes cyclin C translocating to mitochondria, and in turn induces mitochondrial fission. However, little is known about the role of cyclin C in mitochondrial dysfunction in cancer cells challenged with cisplatin. In the present commentary, we bring to the attention of readers the recent report by Jiang et al which revealed the importance of ubiquitylation and translocation of cyclin C in gastric cancer cells in response to cisplatin stimulation for mitochondrial stability. This finding provides new insights into exploring the novel mechanisms of chemoresistance and developing the new chemotherapy synergistic agents in the era of precision oncology.