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L. Xu

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    Poster Display session (Friday) (ID 65)

    • Event: ELCC 2018
    • Type: Poster Display session
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 4/13/2018, 12:30 - 13:00, Hall 1
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      43P - A network-based signature to predict the survival of non-smoking lung adenocarcinoma (ID 388)

      12:30 - 13:00  |  Author(s): L. Xu

      • Abstract

      A substantial increase in the number of non-smoking lung adenocarcinoma (LAC) patients draws extensively attention in the past decades. Effective biomarkers are needed to identify high-risk patients to guide the therapy. Here, we provided a network-based signature to predict the survival of non-smoking LAC.

      Gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Significant gene co-expression networks and hub genes were identified by Weighted Gene Co-expression Network Analysis (WGCNA). Potential mechanisms and pathways of co-expression networks were analyzed by Gene Ontology (GO). The predictive signature was constructed by penalized Cox regression analysis and tested in two independent datasets.

      Two distinct co-expression modules were significantly correlated with non-smoking status across four GEO datasets. GO revealed that nuclear division and cell cycle pathways were main mechanisms of the blue module and that genes in the turquoise module were involved in lymphocyte activation and cell adhesion pathways. Seventeen genes were selected from hub genes at an optimal lambda value and built the prognostic signature. The prognostic signature distinguished the survival of non-smoking LAC (training: hazard ratio (HR) = 3.696, 95%confident interval (CI): 2.025–6.748, p < 0.001; testing: HR = 2.9, 95%CI:1.322–6.789, p = 0.006; HR = 2.78, 95%CI:1.658–6.654, p = 0.022) and had moderate predictive abilities in the training and validation datasets.

      The prognostic signature is a promising predictor of non-smoking LAC patients, which might benefit to clinical practice and precision therapeutic management.

      Clinical trial identification:

      Legal entity responsible for the study:
      Lin Xu

      National Natural Science Foundation of China (Nos. 81472702, 81501977 and 81672294)

      All authors have declared no conflicts of interest.