{"doi":"10.20944/preprints202502.0965.v1","title":"Characterizing Plasma-based Metabolomic Signatures for Metastasis in Non-Small Cell Lung Cancer","abstract":"Background/Objectives: The current staging of non-small cell lung cancer (NSCLC) relies on conventional imaging, which lacks sensitivity to detect micrometa-static disease. Functional assessment of NSCLC progression may provide independent information to enhance prediction of metastatic risk.. The objective of this study was to determine if we could identify a metabolomic signature predictive of metastasis in pa-tients with NSCLC treated with definitive radiation. Methods: Plasma samples were collected prospectively from patients enrolled in a clinical trial with non-metastatic NSCLC treated with definitive radiation. Metabolites were extracted and mass spec-trometry-based analysis was performed using a flow injection electrospray (FIE) Fourier transform ion cyclotron resonance (FTICR) mass spectrometry (MS) method. Early metastasis was defined as metastasis within 1 year of radiation treatment. Results: The study cohort included 28 patients. FIE-FITCR produced highly reproducible profiles in technical replicates A total of 48 metabolic features were identified to be different in patients with early metastasis compared to patients without early metastasis (all ad-justed p values < 0.05, Welch’s t-test), including glycerophospholipids, sphingolipids, and fatty acyls. In follow up samples collected after initiation of chemotherapy and radiation treatment, a total of 154 metabolic features were significantly altered in patients who developed early metastasis compared to those who did not. Conclusions: We identified several distinct changes in the metabolic profiles of patients with NSCLC who developed metastatic disease within 1 year of definitive radiation. These findings highlight the potential of metabolomic profiling as a predictive tool for assessing met-astatic risk in NSCLC.","journal":"Preprints.org","year":2025,"id":562135,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.95,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":301519,"name":"Yanlong Zhu","orcid":"0000-0003-4909-7336","position":1,"is_corresponding":false},{"id":275525,"name":"Sean J. McIlwain","orcid":"0000-0002-3820-8400","position":2,"is_corresponding":false},{"id":1464540,"name":"Haotian Deng","orcid":"0009-0001-4412-8722","position":3,"is_corresponding":false},{"id":332688,"name":"Allan R. Brasier","orcid":"0000-0002-5012-4090","position":4,"is_corresponding":false},{"id":256465,"name":"Ying Ge","orcid":"0000-0001-5211-6812","position":5,"is_corresponding":false},{"id":380536,"name":"Michelle E. Kimple","orcid":"0000-0003-0869-9699","position":6,"is_corresponding":false},{"id":392525,"name":"Andrew M. Baschnagel","orcid":"0000-0002-7929-0105","position":7,"is_corresponding":false},{"id":9456,"name":"M. Liu","orcid":"0000-0003-0056-7296","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:56:05.550545Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}