Targeted multiplex validation of CSF proteomic biomarkers: implications for differentiation of PCNSL from tumor-free controls and other brain tumors.

Introduction: Primary central nervous system lymphoma (PCNSL) is a rare type of non-Hodgkin's lymphoma that affects brain parenchyma, eyes, cerebrospinal fluid, and spinal cord. Diagnosing PCNSL can be challenging because imaging studies often show similar patterns as other brain tumors, and stereotactic brain lesion biopsy conformation is invasive and not always possible. This study aimed to validate a previous proteomic profiling (PMID: 32610669) of cerebrospinal fluid (CSF) and develop a CSF-based proteomic panel for accurate PCNSL diagnosis and differentiation. Methods: CSF samples were collected from patients of 30 PCNSL, 30 other brain tumors, and 31 tumor-free/benign controls. Liquid chromatography tandem-mass spectrometry targeted proteomics analysis was used to establish CSF-based proteomic panels. Results: Final proteomic panels were selected and optimized to diagnose PCNSL from tumor-free controls or other brain tumor lesions with an area under the curve (AUC) of 0.873 (95%CI: 0.723-0.948) and 0.937 (95%CI: 0.807- 0.985), respectively. Pathways analysis showed diagnosis panel features were significantly enriched in pathways related to extracellular matrices-receptor interaction, focal adhesion, and PI3K-Akt signaling, while prion disease, mineral absorption and HIF-1 signaling were significantly enriched with differentiation panel features. Discussion: This study suggests an accurate clinical test panel for PCNSL diagnosis and differentiation with CSF-based proteomic signatures, which may help overcome the challenges of current diagnostic methods and improve patient outcomes.

Global metabolomics revealed deviations from the metabolic aging clock in colorectal cancer patients.

Background: Markers of aging hold promise in the context of colorectal cancer (CRC) care. Utilizing high-resolution metabolomic profiling, we can unveil distinctive age-related patterns that have the potential to predict early CRC development. Our study aims to unearth a panel of aging markers and delve into the metabolomic alterations associated with aging and CRC. Methods: We assembled a serum cohort comprising 5,649 individuals, consisting of 3,002 healthy volunteers, 715 patients diagnosed with colorectal advanced precancerous lesions (APL), and 1,932 CRC patients, to perform a comprehensive metabolomic analysis. Results: We successfully identified unique age-associated patterns across 42 metabolic pathways. Moreover, we established a metabolic aging clock, comprising 9 key metabolites, using an elastic net regularized regression model that accurately estimates chronological age. Notably, we observed significant chronological disparities among the healthy population, APL patients, and CRC patients. By combining the analysis of circulative carcinoembryonic antigen levels with the categorization of individuals into the "hypo" metabolic aging subgroup, our blood test demonstrates the ability to detect APL and CRC with positive predictive values of 68.4% (64.3%, 72.2%) and 21.4% (17.8%, 25.9%), respectively. Conclusions: This innovative approach utilizing our metabolic aging clock holds significant promise for accurately assessing biological age and enhancing our capacity to detect APL and CRC.