Progenesis QI (PQI) is a multiplatform bioinformatics tool that facilitates the identification workflow for metabolomics experiments. PQI uses fragmentation data provided by MassBank of North America (MoNA) libraries, among others, for metabolite annotation. However, PQI does not officially support MoNA libraries and other libraries based on structure-data files (.sdf). This paper describes the development and application of a software named MoNA to Progenesis QI Library Converter, allowing PQI and MoNA by correcting the fragmentation data of the library for Progenesis readability. We evaluated several public experimental datasets, including human plasma, plant extracts, cultured cells, bacteria, rat serum, and rat hippocampus. The results showed that it is mandatory to proceed with file conversion of each library to allow PQI to access fragmentation information from .msp (main spectra profile) files. This step is highly recommended to improve the identification level of the metabolites.

Background: Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data. Methods: Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC (n = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively. Results: The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance. Conclusion: We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.

This study aimed to characterize the composition of lipids in the red blood cells (RBCs) of adolescent swimmers and correlate this lipidome with the aerobic performance of the athletes. Five experimental assessments were performed by 37 adolescent swimmers. During the first session, the athletes went to the laboratory facility for venous blood sampling. The critical velocity protocol was conducted over the 4 subsequent days to measure aerobic performance (CV), comprising maximal efforts over distances of 100, 200, 400, and 800 m in a swimming pool. RBCs were obtained and extracted for analysis using the liquid chromatography—high resolution mass spectrometry untargeted approach. A total of 2146 ions were detected in the RBCs, of which 119 were identified. The enrichment pathway analysis indicated intermediary lipids in the glycerophospholipid, glycerolipid, sphingolipid, linoleic acid, and alpha-linolenic metabolisms, as well as pentose and glucuronate interconversions. A significant impact of the intermediary lipids was observed for the glycerophospholipid metabolism, including phosphatidylethanolamine (PE), phosphatidylcholine (PC), 1-acyl-sn-glycero-3-phosphocholine, sn-glycerol 3-phosphate, and phosphatidic acid. Inverse and significant associations were observed for PE 18:2/18:3 (r = −0.39; p = 0.015), PC 18:3/20:0 (r = −0.33; p = 0.041), and phosphatidic acid 18:0/0:0 (r = −0.47; p = 0.003) with aerobic performance. Swimmers who exhibited higher levels of aerobic performance also had the lowest abundance of PE, PC, and phosphatidic acid.

Papillary thyroid carcinoma (PTC) is among the most common endocrine malignancies worldwide, and although generally associated with a favorable prognosis, a subset of patients develops aggressive disease with higher recurrence risk. This highlights the need for improved molecular characterization. Data integration approaches combined with computational methods offer new opportunities to refine diagnosis and uncover disease mechanisms. This study aims to integrate omics data and apply machine learning (ML) to identify clinically relevant biomarkers in papillary thyroid carcinoma. We selected 11 genes from the differentially expressed genes (DEGs)–LASSO intersection approach. Genes were validated using an independent external dataset (AUC = 91%, Sens. = 92%, Spec. = 97%, and Acc. = 95%). DEGs were integrated with metabolomics data from the literature, enabling the construction of a metabolite–gene interaction network, highlighting norepinephrine, arachidonic acid, and glutamic acid as representative metabolites, while the main genes were SLC6A14, ADK, ATIC, NT5E, and AR. We identified potential drug–gene interactions and performed survival analysis to assess the relevance of the possible biomarkers. This novel pipeline combining integration and machine learning provides new insights into thyroid cancer biology and identifies promising diagnostic markers, supporting advances in precision medicine.

Prostate cancer (PCa) exhibits marked biological heterogeneity, resulting in substantial differences in disease recurrence and clinical outcomes and highlighting the need for improved molecular risk stratification. In this study, we developed a machine learning–driven prognostic framework to identify transcriptomic features associated with disease-free survival (DFS) and stratify patients according to recurrence risk. Differentially expressed genes between prostate cancer and normal prostate tissue were initially screened, and their prognostic relevance was evaluated in the TCGA-PRAD cohort. A prognostic signature was subsequently developed using LASSO-Cox and multivariate Cox regression, generating a patient-level risk score for recurrence stratification. The final prognostic model comprised 33 genes and effectively separated patients into high- and low-risk groups, with significantly different DFS outcomes. The model demonstrated good prognostic discrimination, as indicated by the concordance index and time-dependent area under the receiver operating characteristic curve (AUC), and significantly separated patients into high- and low-risk groups. Random Survival Forest analysis further supported the prognostic relevance of the selected features, while SHAP analysis identified the relative contribution of individual genes to patient-level risk prediction. Drug–gene interaction analysis further identified a subset of the 33 signature genes targeted by FDA-approved drugs, providing hypothesis-generating information regarding potential therapeutic relevance. The results support the potential of the 33-gene signature for molecular risk stratification and recurrence prediction in PCa. However, the model was developed and evaluated within the TCGA-PRAD cohort and requires independent external and prospective validation before clinical translation.