e15028 Background: Multi-cancer early detection (MCED) tests have emerged as promising tools for reducing cancer-related healthcare costs and mortality. As metabolome is closely linked to the phenotype of bio-individual, metabolomics analysis enables the high throughput investigating of the pathophysiological conditions. Based on metabolomics and machine learning, we developed the Multiple Cancer Target (MCTarg) analysis for multiple cancer screening and diagnosis by a single blood draw. Methods: A total of 1153 individuals (172 healthy participants, 623 patients with lung cancer, gastric cancer, and colorectal cancer, and 358 these organ-related benign disease patients) were enrolled from three independent clinical sites in China. Plasma samples were collected and measured by mass spectrometry (MS)-based platforms (LC-MS polar and lipid). MCTarg was developed with three machine learning modules for three scenarios (community screening for non-healthy individuals, clinical diagnosis for classifying malignant and benign diseases, and tracing tumor of origin). Results: MCTarg exhibited an Area Under the Curve (AUC) of 0.96 with 92.82% sensitivity at 80% specificity to differentiate the non-healthy individuals from healthy controls, and an AUC of 0.86 with 75.41% sensitivity at 79.66% specificity in discriminating multi-cancer from benign diseases. Notably, the classification accuracy for distinguishing between malignant and benign conditions in the colorectal organ was reached 90.74%, with a sensitivity of 83.87% and a specificity of 100%. Furthermore, the overall accuracy for detecting the cancer origin reached 84.78% with an average sensitivity and specificity of 78.08% and 90.7%, respectively. Conclusions: The outstanding results indicated that blood-metabolites-based MCTarg is poised to be a cost-effective, precise, and versatile tool for diagnosing multi-cancer and discriminating tumor of origin. Its potential applications encompass health checkups and supplementary diagnostic scenarios. More deadly cancer types will be extended and the performance of MCTarg will be validated by prospective and population-scale cohorts with longitudinal follow-up.

Traditional tea grading depends on subjective sensory assessment, lacking objectivity. This study employed comprehensive metabolomic analysis and sensory evaluation on 161 tea samples to identify potential grading-related metabolites. Amino acids, lipids and organic acids predominantly determined the superior quality of green, yellow, and white teas, respectively, while flavonoids adversely affected their quality. The identified metabolic markers were effectively utilized for predicting tea sensory scores. Specifically, methionine derivatives and glutamate dictated green tea's sweet and umami taste. Glycerophospholipids (PCs and PEs) were positively correlated with appearance, aroma, and taste in yellow tea, while glycerolipids (MGDGs, SQDGs, and DGDGs) had negative visual associations. Fumaric acid and succinic acid bolstered fresh and umami flavor white tea. These findings suggest metabolomics holds promise for precise tea grading, using identified compounds as potential quality markers. Moreover, the health benefits of premium teas were emphasized by key metabolites like NAD, pantothenic acid, and fumaric acid.

BackgroundCancer remains a leading cause of mortality worldwide. A non-invasive screening solution was required for early diagnosis of cancer. Multi-cancer early detection (MCED) tests have been considered to address the challenge by simultaneously identifying multiple types of cancer within a single test using minimally invasive blood samples. However, a multi-cancer screening strategy utilizing urine-based metabolomics has not yet been developed.MethodsWe enrolled 911 cancer patients with 548 lung cancer (LC), 177 with gastric cancer (GC), and 186 with colorectal cancer (CRC), alongside 563 individuals with non-cancerous benign diseases and 229 healthy controls (HC) and investigated the metabolic profiles of urine samples. Participants were randomly allocated to discovery and validation cohorts. The discovery cohort was used for identifying multi-cancer and tissue-specific signatures to build the cancer screening and tumor origin prediction models, while the validation cohort was employed for assessing the performance of these models.ResultsWe identified and annotated a total of 360 metabolites from the urine samples. Using the LASSO regression algorithm, 18 metabolites were characterized as urinary metabolic biomarkers and exhibited excellent discriminative performance between cancer patients and HC with AUC of 0.96 in the validation cohort. In comparison with the performance of traditional tumor markers CEA, the screening model performed higher sensitivity across the cancer stages, with a particularly increase in sensitivity among early-stage cancer patients. Moreover, the screening model also exhibited in high classification of cancers from non-cancerous group, comprising with HC and benign disease participants. Furthermore, two non-overlapping metabolic panels were selected to differentiate LC from Non-LC and GC from CRC with the AUC values of 0.87 and 0.83 in validation cohorts, respectively. Additionally, the model accurately predicted the origin of three lethal cancers: lung, gastric, and colorectal, with an overall accuracy of 0.75. The AUC values for LC, GC, and CRC were 0.88, 0.88, and 0.80, respectively.DiscussionOur study demonstrates the potential of urine-based metabolomics for multi-cancer early detection. The approach offers non-invasive cancer screening, promising widespread implementation in population-based programs for early detection and improved outcomes. Further validation and expansion are needed for broader clinical applicability.

BACKGROUND AND AIMS:Two of the most lethal gastrointestinal (GI) cancers, gastric cancer (GC) and colon cancer (CC), are ranked in the top five cancers that cause deaths worldwide. Most GI cancer deaths can be reduced by earlier detection and more appropriate medical treatment. Unlike the current "gold standard" techniques, non-invasive and highly sensitive screening tests are required for GI cancer diagnosis. Here, we explored the potential of metabolomics for GI cancer detection and the classification of tissue-of-origin, and even the prognosis management.METHODS:Plasma samples from 37 gastric cancer (GC), 17 colon cancer (CC), and 27 non-cancer (NC) patients were prepared for metabolomics and lipidomics analysis by three MS-based platforms. Univariate, multivariate, and clustering analyses were used for selecting significant metabolic features. ROC curve analysis was based on a series of different binary classifications as well as the true-positive rate (sensitivity) and the false-positive rate (1-specificity).RESULTS:GI cancers exhibited obvious metabolic perturbation compared with benign diseases. The differentiated metabolites of gastric cancer (GC) and colon cancer (CC) were targeted to same pathways but with different degrees of cellular metabolism reprogramming. The cancer-specific metabolites distinguished the malignant and benign, and classified the cancer types. We also applied this test to before- and after-surgery samples, wherein surgical resection significantly altered the blood-metabolic patterns. There were 15 metabolites significantly altered in GC and CC patients who underwent surgical treatment, and partly returned to normal conditions.CONCLUSION:Blood-based metabolomics analysis is an efficient strategy for GI cancer screening, especially for malignant and benign diagnoses. The cancer-specific metabolic patterns process the potential for classifying tissue-of-origin in multi-cancer screening. Besides, the circulating metabolites for prognosis management of GI cancer is a promising area of research.

As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLineTM and UlibMS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.

为探明烟叶醇化过程中代谢物和脂质的变化规律,本研究采用气相色谱-质谱联用仪(GC-MS)和液相色谱-质谱联用仪(LC-MS)相结合的方法对不同产地烟叶醇化过程中的化学成分进行了代谢组学和脂质组学分析.结果表明,醇化后的烟叶中糖类、氨基酸、脂质和核苷酸等初级代谢产物的相对含量减少,黄酮类、苯衍生物、酚类和萜类化合物等次级代谢产物则增加;进一步研究发现,上述初级代谢产物的变化随醇化时间(1～7年)的增加而递减,而次级代谢产物呈现逐年递增的趋势.同一品种云烟87(YY87)在不同产地(广东和湖南)之间化学成分差异较大,主要为氨基酸和膜脂,而醇化后差异减小.综上所述,代谢组学和脂质组学方法能有效区分醇化和非醇化烟叶,并且能揭示不同醇化程度烟叶的特征及不同产地对烟叶化学成分的影响.