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Motivation: Lung cancer is one of the leading causes for cancer-relateddeath, with a five-year survival rate of 18%. It is a priority for us tounderstand the underlying mechanisms that affect the implementation andeffectiveness of lung cancer therapeutics. In this study, we combine the powerof Bioinformatics and Systems Biology to comprehensively uncover functional andsignaling pathways of drug treatment using bioinformatics inference andmultiscale modeling of both scRNA-seq data and proteomics data. The innovativeand cross-disciplinary approach can be further applied to other computationalstudies in tumorigenesis and oncotherapy. Results: A time series of lungadenocarcinoma-derived A549 cells after DEX treatment were analysed. (1) Wefirst discovered the differentially expressed genes in those lung cancer cells.Then through the interrogation of their regulatory network, we identified keyhub genes including TGF-\b{eta}, MYC, and SMAD3 varied underlie DEX treatment.Further enrichment analysis revealed the TGF-\b{eta} signaling pathway as thetop enriched term. Those genes involved in the TGF-\b{eta} pathway and theircrosstalk with the ERBB pathway presented a strong survival prognosis inclinical lung cancer samples. (2) Based on biological validation and furthercuration, a multiscale model of tumor regulation centered on bothTGF-\b{eta}-induced and ERBB-amplified signaling pathways was developed tocharacterize the dynamics effects of DEX therapy on lung cancer cells. Oursimulation results were well matched to available data of SMAD2, FOXO3,TGF\b{eta}1, and TGF\b{eta}R1 over the time course. Moreover, we providedpredictions of different doses to illustrate the trend and therapeuticpotential of DEX treatment.

Uncovering the dynamic effects of DEX treatment on lung cancer by  integrating bioinformatic inference and multiscale modeling of scRNA-seq and  proteomics data