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lemeng Zhang



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    MA10 - Assessing and Managing Supportive Care Needs (ID 215)

    • Event: WCLC 2020
    • Type: Mini Oral
    • Track: Palliative and Supportive Care
    • Presentations: 1
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      MA10.05 - Distress Screening in Lung Cancer Patients using a Distress Thermometer: A Meta-Analysis (ID 2432)

      11:45 - 12:45  |  Presenting Author(s): lemeng Zhang

      • Abstract
      • Slides

      Introduction

      Distress thermometer (DT) has been widely recommended for distress screening in cancer patients due to its simple procedure, high sensitivity, and specificity. A meta-analysis was conducted to statistically summarize the prevalence of distress in lung cancer patients using DT screenings.

      Methods

      Effective measures were presented with a prevalence or risk difference with a 95% confidence interval (CI). A heterogeneity test was assessed using the Q test and the I2 statistics helped to decide whether to use the random effects model or fixed effects model.

      Results

      Ten eligible studies, including a total of 1,082 patients were included in this analysis. The pooled prevalence of distress in patients with lung cancer was 0.4904 [95% CI (0.4151, 0.5660)]. The subgroup analysis revealed that the distress prevalence was significantly different (P < 0.05) across North America, Europe, and China with values of 0.5333 [95% CI (0.4522, 0.6137)], 0.4381 [95% CI (0.3157, 0.5643)], 0.3857 [95% CI (0.3389, 0.4341)], respectively. However, the distress prevalence in terms of DT threshold, published year, and sample size were with no significant differences. Moreover, the distress prevalence was statistically significant between males and females (P < 0.05), yet there were no significant differences with histology and previous treatment. Additionally, no significant publication bias was identified by Begg's funnel plot and Egger's test.

      Conclusion

      Overall, the distress prevalence was exceedingly high, with tremendous clinical challenges for lung cancer patients. Routine distress screening and evaluation by DT may be necessary to develop proper interventions and to improve oncology management.

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    P54 - Tumor Biology and Systems Biology - Basic and Translational Science - Carcinogenesis (ID 191)

    • Event: WCLC 2020
    • Type: Posters
    • Track: Tumor Biology and Systems Biology - Basic and Translational Science
    • Presentations: 1
    • Moderators:
    • Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall
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      P54.03 - Multiple Microarray Analyses Identify Key Genes Associated with the Development of NSCLC from COPD (ID 2431)

      00:00 - 00:00  |  Presenting Author(s): lemeng Zhang

      • Abstract

      Introduction

      Chronic obstructive pulmonary disease (COPD) is an independent risk factor of non-small cell lung cancer (NSCLC). This study aimed to analyze the key genes and potential molecular mechanisms that are involved in the development from COPD to NSCLC.

      Methods

      Expression profiles of COPD and NSCLC in GSE106899, GSE12472, and GSE12428 were downloaded from the Gene Expression Omnibus (GEO) database, followed by identification of the differentially expressed genes (DEGs) between COPD and NSCLC. Based on the identified DEGs, functional pathway enrichment and lung carcinogenesis-related networks analyses were performed and further visualized with Cytoscape software. Then, principal component analysis (PCA), cluster analysis, and support vector machines (SVM) verified the ability of the top modular genes to distinguish COPD from NSCLC. Additionally, the corrections between these key genes and clinical staging of NSCLC were studied using the UALCAN and HPA websites. Finally, a prognostic risk model was constructed based on multivariate Cox regression analysis. Kaplan-Meier survival curves of the top modular genes on the training and verification sets were generated.

      Results

      A total of 2350, 1914, and 1850 DEGs were obtained from GSE106899, GSE12472, and GSE12428 datasets, respectively. Following analysis of protein-protein interaction networks, the identified modular gene signatures containing H2AFX, MCM2, MCM3, MCM7, POLD1, and RPA1 were identified as markers for discrimination between COPD and NSCLC. The modular gene signatures were mainly enriched in the processes of DNA replication, cell cycle, mismatch repair, and others. Besides, the expression levels of these genes were significantly higher in NSCLC than in COPD, which was further verified by the immunohistochemistry. In addition, the high expression levels of H2AFX, MCM2, MCM7, and POLD1 correlate with poor prognosis of lung adenocarcinoma (LUAD). The Cox regression prognostic risk model showed the similar results and the predictive ability of this model is independent of other clinical variables.

      Conclusion

      This study revealed several key modules that closely relate to NSCLC with underlying disease COPD, which provide a deeper understanding of the potential mechanisms underlying the malignant development from COPD to NSCLC. This study provides valuable prognostic factors in high-risk lung cancer patients with COPD.