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Li KM, Meng LF, Yang ZH, Hu WT. NUP155 and NDC1 interaction in NSCLC: a promising target for tumor progression. Front Pharmacol 2024; 15:1514367. [PMID: 39720592 PMCID: PMC11666513 DOI: 10.3389/fphar.2024.1514367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2024] [Accepted: 11/28/2024] [Indexed: 12/26/2024] Open
Abstract
Background NUP155 was reported to involve breast invasive carcinoma and hepatocellular carcinoma. We hypothesized that NUP155 and NDC1impacted the progression of NSCLC. Methods The dataset was analyzed to find differentially expressed genes. Functional enrichment analysis and Kaplan-Meier survival analysis were performed for differentially expressed genes. Western blot, Clone formation assay, Transwell assay and CCK-8 assay were performed to determine the performance and role of the target gene in NSCLC. Results The research found that the NUP family played a role in various diseases. Differential expression analysis and survival analysis were performed to identify 6 related-genes, including NUP155, NDC1, KPNA2, MAD2L1, NUP62CL, and POM121L2NUP155 and NDC1 could interact with NUP53, respectively. This effect was necessary to complete the assembly of the nuclear pore complex. Conclusion NUP155 interacted with NDC1 to complete the assembly of the nuclear pore complex, which promoted the development of NSCLC. Our study demonstrated that NUP155 was expected to be a potential target for the treatment of NSCLC.
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Affiliation(s)
| | | | | | - Wen-Tao Hu
- Department of Thoracic Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, China
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Deng L, Yang J, Zhang M, Zhu K, Jing M, Zhang Y, Zhang B, Han T, Zhou J. Whole-lesion iodine map histogram analysis versus single-slice spectral CT parameters for determining novel International Association for the Study of Lung Cancer grade of invasive non-mucinous pulmonary adenocarcinomas. Diagn Interv Imaging 2024; 105:165-173. [PMID: 38072730 DOI: 10.1016/j.diii.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/30/2023] [Accepted: 12/02/2023] [Indexed: 05/05/2024]
Abstract
PURPOSE The purpose of this study was to evaluate and compare the performances of whole-lesion iodine map histogram analysis to those of single-slice spectral computed tomography (CT) parameters in discriminating between low-to-moderate grade invasive non-mucinous pulmonary adenocarcinoma (INMA) and high-grade INMA according to the novel International Association for the Study of Lung Cancer grading system of INMA. MATERIALS AND METHODS Sixty-one patients with INMA (34 with low-to-moderate grade [i.e., grade I and grade II] and 27 with high grade [i.e., grade III]) were evaluated with spectral CT. There were 28 men and 33 women, with a mean age of 56.4 ± 10.5 (standard deviation) years (range: 29-78 years). The whole-lesion iodine map histogram parameters (mean, standard deviation, variance, skewness, kurtosis, entropy, and 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile) were measured for each INMA. In other sessions, by placing regions of interest at representative levels of the tumor and normalizing them, spectral CT parameters (iodine concentration and normalized iodine concentration) were obtained. Discriminating capabilities of spectral CT and histogram parameters were assessed and compared using area under the ROC curve (AUC) and logistic regression models. RESULTS The 1st, 10th, and 25th percentiles of the iodine map histogram analysis, and iodine concentration and normalized iodine concentration of single-slice spectral CT parameters were significantly different between high-grade and low-to-moderate grade INMAs (P < 0.001 to P = 0.002). The 1st percentile of histogram parameters (AUC, 0.84; 95% confidence interval [CI]: 0.73-0.92) and iodine concentration (AUC, 0.78; 95% CI: 0.66-0.88) from single-slice spectral CT parameters had the best performance for discriminating between high-grade and low-to-moderate grade INMAs. At ROC curve analysis no significant differences in AUC were found between histogram parameters (AUC = 0.86; 95% CI: 0.74-0.93) and spectral CT parameters (AUC = 0.81; 95% CI: 0.74-0.93) (P = 0.60). CONCLUSION Both whole-lesion iodine map histogram analysis and single-slice spectral CT parameters help discriminate between low-to-moderate grade and high-grade INMAs according to the novel International Association for the Study of Lung Cancer grading system, with no differences in diagnostic performances.
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Affiliation(s)
- Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Jingjing Yang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Mingtao Zhang
- Second Clinical School, Lanzhou University, Lanzhou 730000, China; Department of Orthopedics, Lanzhou University Second Hospital, 730000, China
| | - Kaibo Zhu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Yuting Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China.
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Liu R, Huang Y. CDC7 as a novel biomarker and druggable target in cancer. Clin Transl Oncol 2022; 24:1856-1864. [PMID: 35657477 DOI: 10.1007/s12094-022-02853-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 05/05/2022] [Indexed: 11/25/2022]
Abstract
Due to the bottlenecks encountered in traditional treatment for tumor, more effective drug targets need to be developed. Cell division cycle 7 kinase plays an important role in DNA replication, DNA repair and recombination signaling pathways. In this review, we first describe recent studies on the role of CDC7 in DNA replication in normal human tissues, and then we integrate new evidence focusing on the important role of CDC7 in replication stress tolerance of tumor cells and its impact on the prognosis of clinical oncology patients. Finally, we comb through the CDC7 inhibitors identified in recent studies as a reference for further research in clinical practice.
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Affiliation(s)
- Runze Liu
- National Center for International Research of Bio-Targeting Theranostics, Guangxi Key Laboratory of Bio-Targeting Theranostics, Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy, Guangxi Talent Highland of Bio-Targeting Theranostics, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Yong Huang
- National Center for International Research of Bio-Targeting Theranostics, Guangxi Key Laboratory of Bio-Targeting Theranostics, Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy, Guangxi Talent Highland of Bio-Targeting Theranostics, Guangxi Medical University, Nanning, 530021, Guangxi, China.
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