Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera

Abstract

Differential diagnosis of pulmonary nodules detected by computed tomography (CT) remains a challenge in clinical practice. Here, we characterize the global metabolomes of 480 serum samples including healthy controls, benign pulmonary nodules, and stage I lung adenocarcinoma. The adenocarcinoma demonstrates a distinct metabolomic signature, whereas benign nodules and healthy controls share major similarities in metabolomic profiles. A panel of 27 metabolites is identified in the discovery cohort (n = 306) to distinguish between benign and malignant nodules. The discriminant model achieves an AUC of 0.915 and 0.945 in the internal validation (n = 104) and external validation cohort (n = 111), respectively. Pathway analysis reveals elevation in glycolytic metabolites associated with decreased tryptophan in serum of lung adenocarcinoma vs benign nodules and healthy controls, and demonstrates that uptake of tryptophan promotes glycolysis in lung cancer cells. Our study highlights the value of the serum metabolite biomarkers in risk assessment of pulmonary nodules detected by CT screening.

Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis

“…LASSO is widely used in scenarios with high-dimensional datasets, like metabolomics with small n (n as the sample number) and large p (p as the m/z feature number) (Xiao et al, 2022 ; Yao et al, 2023a ). LASSO mitigated this risk of overfitting by applying an L 1 -penalty to select the most relevant m/z features for classification, thus enhancing the robustness and generalizability of the constructed model (Tibshirani et al, 2010 ; Zou and Hastie, 2005 ).…”

“…Regarding genes and proteins, the upstream molecules in the pathways, several biomarkers ( e.g ., cell-free DNA and CA-125) are developed for EC diagnosis and showed an AUC of ~0.60–0.90 with biochemical reaction-based signal amplification assay (Cicchillitti et al, 2017 ; Jia et al, 2013 ; Knific et al, 2017 ; Li et al, 2018 ; Martinez-Garcia et al, 2018 ; Torres et al, 2013 ). In parallel, metabolite biomarkers provide a direct snapshot of the disease phenotype (Buergel et al, 2022 ; Yao et al, 2023b ; Zhang et al, 2023 ). Moreover, metabolic reprogramming is recognized as a hallmark of malignancy and the reprogrammed metabolic activities can be exploited for diagnostic purposes (Faubert et al, 2020 ; Lopez-Otin et al, 2023 ).…”

“…Emerging blood-based biomarker assays are promising for large-scale cancer diagnostics, focusing on genes, proteins, and metabolites (Cheng et al, 2019 ; El-Deiry et al, 2019 ; Yao et al, 2023b ). Particularly, metabolite biomarkers offer a more direct snapshot of disease phenotype than gene and protein biomarkers (Buergel et al, 2022 ; Yang et al, 2022 ).…”

Ultrasensitive PD-L1-Expressing Exosome Immunosensors Based on a Chemiluminescent Nickel–Cobalt Hydroxide Nanoflower for Diagnosis and Classification of Lung Adenocarcinoma

“…Non-small cell lung cancer accounts for more than 80% of all lung cancers and is one of the leading causes of cancer-related death. − Lung adenocarcinoma (LUAD) is the most common histological type of non-small cell lung cancer, which is divided into precursor glandular lesions, minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC), according to the 2021 World Health Organization (WHO) updated classification of LUAD. , Studies have shown that pathologically suspected precursor glandular lesions or MIA require close follow-up or limited resection (segmental or wedge resection), while lobectomy is considered the standard surgical treatment for IAC. , Currently, low-dose computed tomography (LDCT) is the primary tool for detecting nodules and screening LUAD. − However, the classification of LUAD identified in LDCT images often depends on the radiologist’s clinical experience, leading to potential misclassifications. While artificial intelligence algorithms have recently been developed to assist radiologists in analyzing LDCT results, challenges persist in accurately categorizing LUAD, particularly in distinguishing between patients with MIA and those with IAC.…”