Complex diseases generally result from dysfunction of relevant module molecules, which dynamically change with time and conditions, rather than that of an individual molecule. In this study, a novel network construction algorithm for identifying early warning network signals (namely, IEWNS) is proposed for improving the performance of early diagnosis. To this end, we theoretically derived a network-based dynamic criterion, namely, the relationship of variation (RV), to construct dynamic networks. RV infers correlation t statistics to measure dynamic changes in molecular relationships during the process of disease development. Based on the dynamic networks constructed by IEWNS, early warning network signals used to represent the occurrence of complex disease deterioration can be defined without human intervention. IEWNS was employed to perform a comprehensive analysis of gene expression profiles of lung adenocarcinoma (LUAD) from The Cancer Genome Atlas database and the Gene Expression Omnibus database. The experimental results suggest that the network biomarker selected by IEWNS can facilitate a better understanding of pathogenetic mechanisms and help to achieve effective early diagnosis of LUAD. In conclusion, IEWNS provides novel insight into the pathogenic mechanisms of complex diseases and helps to define prospective biomarkers for assessing disease deterioration.