Proteomic and Biochemical Analysis of Extracellular Vesicles Isolated from Blood Serum of Patients with Melanoma

Proteomic and metabolomic profiles of plasma-derived Extracellular Vesicles differentiate melanoma patients from healthy controls

BACKGROUND

Plasma-derived Extracellular Vesicles (EVs) have been suggested as novel biomarkers in melanoma, due to their ability to reflect the cell of origin and ease of collection. This study aimed to identify novel EV biomarkers that can discriminate between disease stages. This was achieved by characterising the plasma-derived EVs of patients with melanoma, and comparing their proteomic and metabolomic profile to those from healthy controls.

METHODS

EVs were isolated from the plasma of 36 patients with melanoma and 13 healthy controls using Size Exclusion Chromatography. Proteomic and Metabolomic Analyses were performed, and machine learning algorithms were used to identify potential proteins and metabolites to differentiate the plasma-derived EVs from melanoma patients of different disease stages.

RESULTS

The concentration and size of the EV population isolated was similar between groups. Proteins (APOC4, PRG4, PLG, TNC, VWF and SERPIND1) and metabolites (lyso PC a C18:2, PC ae C44:3) previously associated with melanoma pathogenesis were identified as relevant in differentiating between disease stages.

CONCLUSION

The results further support the continued investigation of circulating plasma-derived EVs as biomarkers in melanoma. Furthermore, the potential of combined proteo-metabolomic signatures for differentiation between disease stages may provide valuable insights into early detection, prognosis, and personalised treatment strategies.

Extracellular vesicles proteins for early cancer diagnosis: From omics to biomarkers

Extracellular vesicles (EVs) are a promising source of early biomarkers for cancer diagnosis. They are enriched with diverse molecular content, such as proteins, DNA, mRNA, miRNA, lipids, and metabolites. EV proteins have been widely investigated as potential biomarkers since they reflect specific patient conditions. However, although many markers have been validated and confirmed using external cohorts of patients and different analytical approaches, no EV protein markers are approved for diagnostic use. This review presents the primary strategies adopted using mass spectrometry and immune-based techniques to identify and validate EV protein biomarkers. We report and discuss recent scientific research focusing on cancer biomarker discovery through EVs, emphasizing their significant potential for the tempestive diagnosis of several cancer typologies. Finally, recent advancements in the standardization of EV isolation and quantitation through the development of easy-to-use and high-throughput kits for sample preparation-that should make protein EV biomarkers more reliable and accessible-are presented. The data reported here showed that there are still several challenges to be addressed before a protein vesicle marker becomes an essential tool in diagnosing cancer.

Serum Extracellular Vesicles Cargo Approach in Bitches with Mammary Tumors

This study investigated serum extracellular vesicles (EVs) in bitches with mammary neoplasms, in order to understand their size, shape, and concentration, as well as their association with tumor malignancy. Thirty bitches were categorized into control (n = 10), mammary tumor grades I and II (GI, n = 13), and grade III (GII, n = 7). Serum was separated from blood collected during mastectomy, and EVs were isolated using size exclusion chromatography. The analysis revealed no significant differences in EV concentrations among groups, with similar concentrations for control, GI, and GII. Ninety-one proteins were identified in EV-enriched samples, with six showing varied abundance across groups. Notably, keratin 18 was highly abundant in GI, while sushi domain-containing protein, EvC ciliary subunit 2, and the joining chain of multimeric IgM and IgA were increased in GII. Additionally, protocadherin 17 and albumin were upregulated in both GI and GII. ROC curves identified potential biomarkers for differentiating tumor grades. Enrichment pathway analysis revealed AFP gene upregulation in the GI. Mass spectrometry proteomics data were deposited in Mendeley Data. The study provides valuable insights into serum EV characterization in bitches, suggesting keratin 18 and protocadherin 17 as potential biomarkers for canine mammary neoplasia, with implications for future diagnostic and therapeutic strategies.

Skin Cancer Metabolic Profile Assessed by Different Analytical Platforms

Skin cancer, including malignant melanoma (MM) and keratinocyte carcinoma (KC), historically named non-melanoma skin cancers (NMSC), represents the most common type of cancer among the white skin population. Despite decades of clinical research, the incidence rate of melanoma is increasing globally. Therefore, a better understanding of disease pathogenesis and resistance mechanisms is considered vital to accomplish early diagnosis and satisfactory control. The "Omics" field has recently gained attention, as it can help in identifying and exploring metabolites and metabolic pathways that assist cancer cells in proliferation, which can be further utilized to improve the diagnosis and treatment of skin cancer. Although skin tissues contain diverse metabolic enzymes, it remains challenging to fully characterize these metabolites. Metabolomics is a powerful omics technique that allows us to measure and compare a vast array of metabolites in a biological sample. This technology enables us to study the dermal metabolic effects and get a clear explanation of the pathogenesis of skin diseases. The purpose of this literature review is to illustrate how metabolomics technology can be used to evaluate the metabolic profile of human skin cancer, using a variety of analytical platforms including gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and nuclear magnetic resonance (NMR). Data collection has not been based on any analytical method.