For decades, several attempts have been made to obtain a mimetic model for the study of metastasis, the reason of most of deaths caused by cancer, in order to solve the unknown phenomena surrounding this disease. To better understand this cellular dissemination process, more realistic models are needed that are capable of faithfully recreating the entire and essential tumor microenvironment (TME). Thus, new tools known as tumor‐on‐a‐chip and metastasis‐on‐a‐chip have been recently proposed. These tools incorporate microfluidic systems and small culture chambers where TME can be faithfully modeled thanks to 3D bioprinting. In this work, a literature review has been developed about the different phases of metastasis, the remaining unknowns and the use of new models to study this disease. The aim is to provide a global vision of the current panorama and the great potential that these systems have for in vitro translational research on the molecular basis of the pathology. In addition, these models will allow progress toward a personalized medicine, generating chips from patient samples that mimic the original tumor and the metastatic process to perform a precise pharmacological screening by establishing the most appropriate treatment protocol.

Engineered stone silicosis (ESS), primarily caused by inhaling respirable crystalline silica, poses a significant occupational health risk globally. ESS has no effective treatment and presents a rapid progression from simple silicosis (SS) to progressive massive fibrosis (PMF), with respiratory failure and death. Despite the use of diagnostic methods like chest x‐rays and high‐resolution computed tomography, early detection of silicosis remains challenging. Since routine blood tests have shown promise in detecting inflammatory markers associated with the disease, this study aims to assess whether routine blood biomarkers, coupled with machine learning techniques, can effectively differentiate between healthy individuals, subjects with SS, and PMF. To this end, 107 men diagnosed with silicosis, ex‐workers in the engineered stone (ES) sector, and 22 healthy male volunteers as controls not exposed to ES dust were recruited. Twenty‐one primary biochemical markers derived from peripheral blood extraction were obtained retrospectively from clinical hospital records. Relief‐F features selection technique was applied, and the resulting subset of 11 biomarkers was used to build five machine learning models, demonstrating high performance with sensitivities and specificities in the best case greater than 82% and 89%, respectively. The percentage of lymphocytes, the angiotensin‐converting enzyme, and lactate dehydrogenase indexes were revealed, among others, as blood biomarkers with significant cumulative importance for the machine learning models. Our study reveals that these biomarkers could detect a chronic inflammatory status and potentially serve as a supportive tool for the diagnosis, monitoring, and early detection of the progression of silicosis.

Engineered stone silicosis has become an occupational epidemic disease that progresses rapidly to progressive massive fibrosis with respiratory failure and death, and there is no effective treatment. Silica deposition in the lung triggers a series of inflammatory reactions with the participation of multiple cytokines and cellular mediators whose role in the development and progression of the disease is largely unknown. We hypothesized that differences in plasma cytokine levels exist between patients diagnosed with simple silicosis (SS) and patients diagnosed with progressive massive fibrosis (PMF). Plasma samples from 91 ES silicosis patients, diagnosed and classified by chest radiography and/or high-resolution computed tomography with SS (n = 53) and PMF (n = 38), were assayed by multiplex assays for levels of 34 cytokines. Additionally, a healthy volunteer control group (n = 22) was included. Plasma levels of a high number of cytokines were significantly higher in subjects with silicosis than in healthy control subjects. Moreover, the levels of IL-1RA, IL-8, IL-10, IL-16, IL-18, TNF-α, MIP-1α, G-CSF and VEGF were significantly elevated in PMF compared to SS patients. This study shows that plasma cytokine levels differ between healthy people and silicosis patients, and some of them are also significantly elevated in patients with PMF compared with patients with SS, which could indicate their involvement in the severity of the disease, be considered as biomarkers and could be explored as future therapeutic targets for the disease.

Silicosis caused by engineered stone (ES-silicosis) is an emerging worldwide issue characterized by inflammation and fibrosis in the lungs. To our knowledge, only a few reports have investigated leukocyte/lymphocyte subsets in ES-silicosis patients. The present study was designed to explore the proportions of the main lymphocyte subsets in ES-silicosis patients stratified into two groups, one with simple silicosis (SS) and the other with a more advanced state of the disease, defined as progressive massive fibrosis (PMF). The proportions of B (memory and plasmablasts) cells, T (helper, cytotoxic, regulatory) cells, and natural killer (NK) (regulatory and cytotoxic) cells were investigated by multiparameter flow cytometry in 91 ES-silicosis patients (53 SS patients and 38 PMF patients) and 22 healthy controls (HC). Although the total number of leukocytes did not differ between the groups studied, lymphopenia was observed in patients compared to healthy controls. Compared with those in healthy controls, the proportions of memory B cells, naïve helper T cells, and the CD4⁺/CD8⁺ T cells’ ratio in the peripheral blood of patients with silicosis were significantly decreased, while the percentages of plasma cells, memory helper T cells, and regulatory T cells were significantly increased. For the NK cell subsets, no significant differences were found between the groups studied. These results revealed altered cellular immune processes in the peripheral blood of patients with ES-silicosis and provided further insight into silicosis pathogenesis.

Background/Objectives: Silicosis caused by dust from engineered stone (ES) exposure is an emerging occupational lung disease that severely impacts respiratory health. This study aimed to analyze the association between cytokine profiles and disease severity and progression in patients with engineered stone silicosis (ESS) to assess their potential as biomarkers of progression and their usefulness to stratify risk. Methods: A longitudinal study was conducted with a seven-year follow-up (2017-2024) on 72 workers with simple silicosis (SS) or progressive massive fibrosis (PMF), all with a history of cutting, polishing, and finishing ES countertops. Data on lung function and levels of 27 cytokines were collected at four control points. Machine learning (ML) models were built to classify the disease stage and predict its progression. Results: 39% of patients with SS progressed to PMF. Significant differences in the expression of some cytokines were observed between ESS stages, suggesting a role in the evolution of the inflammatory process. Specifically, higher levels of IL-1RA, IL-8, IL-9, and IFN-$\gamma$ were found at checkpoint 1 in patients with PMF compared to SS. The longitudinal analysis revealed a significant relationship between IL-1RA and MCP-1$\alpha$ and disease duration with MCP-1$\alpha$ also being associated with time and disease grade. Machine learning (ML) models were built using the cytokines selected through a sequential backward feature selection. The Support Vector Machine model achieved an accuracy of 83% in classifying disease stage (SS, PMF), and of 77% in predicting the disease progression. Conclusions: The findings suggest that cytokines can be used as dynamic biomarkers to reflect underlying inflammatory processes and monitor disease evolution. The performance of ML algorithms to predict diagnostic status based on cytokine profiles highlights their clinical value in supporting early diagnosis, monitoring disease progression, and guiding clinical decisions.