Ovarian cancer is considered a silent killer due to the lack of clear symptoms and efficient diagnostic tools that often lead to late diagnoses. Over recent years, the impelling need for proficient biomarkers has led researchers to consider metabolomics, an emerging omics science that deals with analyses of the entire set of small-molecules (≤1.5 kDa) present in biological systems. Metabolomics profiles, as a mirror of tumor–host interactions, have been found to be useful for the analysis and identification of specific cancer phenotypes. Cancer may cause significant metabolic alterations to sustain its growth, and metabolomics may highlight this, making it possible to detect cancer in an early phase of development. In the last decade, metabolomics has been widely applied to identify different metabolic signatures to improve ovarian cancer diagnosis. The aim of this review is to update the current status of the metabolomics research for the discovery of new diagnostic metabolomic biomarkers for ovarian cancer. The most promising metabolic alterations are discussed in view of their potential biological implications, underlying the issues that limit their effective clinical translation into ovarian cancer diagnostic tools.

Soft tissue sarcomas (STS) are a group of rare and heterogeneous cancers with few diagnostic or prognostic biomarkers. This metabolomics study aimed to identify new serum prognostic biomarkers to improve the prediction of overall survival in patients with metastatic STS. The study enrolled 24 patients treated with the same trabectedin regimen. The baseline serum metabolomics profile, targeted to 68 metabolites encompassing amino acids and bile acids pathways, was quantified by liquid chromatography-tandem mass spectrometry. Correlations between individual metabolomics profiles and overall survival were examined and a risk model to predict survival was built by Cox multivariate regression. The median overall survival of the studied patients was 13.0 months (95% CI, 5.6–23.5). Among all the metabolites investigated, only citrulline and histidine correlated significantly with overall survival. The best Cox risk prediction model obtained integrating metabolomics and clinical data, included citrulline, hemoglobin and patients’ performance status score. It allowed to distinguish patients into a high-risk group with a low median overall survival of 2.1 months and a low- to moderate-risk group with a median overall survival of 19.1 months (p < 0.0001). The results of this metabolomics translation study indicate that citrulline, an amino acid belonging to the arginine metabolism, represents an important metabolic signature that may contribute to explain the high inter-patients overall survival variability of STS patients. The risk prediction model based on baseline serum citrulline, hemoglobin and performance status may represent a new prognostic tool for the early classification of patients with metastatic STS, according to their overall survival expectancy.

Over the last decades, the study of cancer metabolism has returned to the forefront of cancer research and challenged the role of genetics in the understanding of cancer development. One of the major impulses of this new trend came from the discovery of oncometabolites, metabolic intermediates whose abnormal cellular accumulation triggers oncogenic signalling and tumorigenesis. These findings have led to reconsideration and support for the long-forgotten hypothesis of Warburg of altered metabolism as oncogenic driver of cancer and started a novel paradigm whereby mitochondrial metabolites play a pivotal role in malignant transformation. In this review, we describe the evolution of the cancer metabolism research from a historical perspective up to the oncometabolites discovery that spawned the new vision of cancer as a metabolic disease. The oncometabolites’ mechanisms of cellular transformation and their contribution to the development of new targeted cancer therapies together with their drawbacks are further reviewed and discussed.

The epidermal growth factor receptor inhibitor (EGFRIs) treatments are commonly associated with the development of adverse skin effects. This study aims to investigate the lipid composition change in sebum during cetuximab-based treatment in an attempt to identify specific metabolic signatures useful in predicting the occurrence of severe skin toxicity. Sebum from 30 metastatic colorectal cancer (mCRC) patients was collected at three time points during the targeted therapy by the application of Sebutape® on the forehead, and the major lipid classes were analyzed and quantified by 1H-NMR. Univariate analysis was performed to reveal significant alterations among patients in sebum production as well as lipid composition and over the course of cetuximab therapy. A transient but significant decrease in sebum production associated with a reduction in the relative content of triglycerides (TG) and squalene (SQ) was found to be induced by cetuximab administration. The reduction of these two lipid classes was also found to be associated with the severity of skin rash experienced by patients. The results of this study indicate that cetuximab-based treatment can reduce sebum gland activity, leading to an overall decrease in sebum production and the induction of specific modifications to its composition. The extent of the loss of skin barrier function may be important for determining the severity of skin toxicity development.

Radical hemithoracic radiotherapy (RHRT) represents an advanced therapeutic option able to improve overall survival of malignant pleural mesothelioma patients. This study aims to investigate the systemic effects of this radiotherapy modality on the serum metabolome and their potential implications in determining the individual clinical outcome. Nineteen patients undergoing RHRT at the dose of 50 Gy in 25 fractions were enrolled. Serum targeted metabolomics profiles were investigated at baseline and the end of radiotherapy by liquid chromatography and tandem mass spectrometry. Univariate and multivariate OPLS-DA analyses were applied to study the serum metabolomics changes induced by RHRT while PLS regression analysis to evaluate the association between such changes and overall survival. RHRT was found to affect almost all investigated metabolites classes, in particular, the amino acids citrulline and taurine, the C14, C18:1 and C18:2 acyl-carnitines as well as the unsaturated long chain phosphatidylcholines PC ae 42:5, PC ae 44:5 and PC ae 44:6 were significantly decreased. The enrichment analysis showed arginine metabolism and the polyamine biosynthesis as the most perturbed pathways. Moreover, specific metabolic changes encompassing the amino acids and acyl-carnitines resulted in association with the clinical outcome accounting for about 60% of the interpatients overall survival variability. This study highlighted that RHRT can induce profound systemic metabolic effects some of which may have a significant prognostic value. The integration of metabolomics in the clinical assessment of the malignant pleural mesothelioma could be useful to better identify the patients who can achieve the best benefit from the RHRT treatment.