Multi-omics with Dynamic Network Biomarker Algorithm Prefigures Organ-Specific Metastasis of Lung Adenocarcinoma

Efficacious strategies for early detection of lung cancer metastasis are of significance for improving the survival of lung cancer patients. Here we show the marker genes and serum secretome foreshadowing the lung cancer site-specific metastasis through dynamic network biomarker (DNB) algorithm, utilizing two clinical cohorts of four major types of lung cancer distant metastases, with single-cell RNA sequencing (scRNA-seq) of primary lesions and liquid chromatography-mass spectrometry data of sera. Also, we locate the intermediate status of cancer cells, along with its gene signatures, in each metastatic state trajectory that cancer cells at this stage still have no specific organotropism. Furthermore, an integrated neural network model based on the filtered scRNA-seq data is successfully constructed and validated to predict the metastatic state trajectory of cancer cells. Overall, our study provides an insight to locate the pre-metastasis status of lung cancer and primarily examines its clinical application value, contributing to the early detection of lung cancer metastasis in a more feasible and efficacious way. Detecting lung cancer metastasis efficiently is crucial to improve survival. Here, the authors use single-cell RNA-sequencing and liquid chromatography mass spectrometry, analyzed by dynamic network biomarker algorithm and neural networks, to identify biomarkers of lung cancer site-specific metastasis from serum samples and primary lesions, allowing the prediction of metastatic sites and trajectories.