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Background and aims: With the advent and implementation of high-sensitivity cardiac troponin assays, differentiation of patients with distinct types of myocardial injuries, including acute thrombotic myocardial infarction (TMI), acute non-thrombotic myocardial injury (nTMi), and chronic coronary atherosclerotic disease (cCAD), is of pressing clinical importance. Thermal liquid biopsy (TLB) emerges as a valuable diagnostic tool, relying on identifying thermally induced conformational changes of biomolecules in blood plasma. While TLB has proven useful in detecting and monitoring several cancers and autoimmune diseases, its application in cardiovascular diseases remains unexplored. In this proof-of-concept study, we sought to determine and characterize TLB profiles in patients with TMI, nTMi, and cCAD at multiple acute-phase time points (T 0 h, T 2 h, T 4 h, T 24 h, T 48 h) as well as a follow-up time point (Tfu) when the patient was in a stable state. Methods: TLB profiles were collected for 115 patients (60 with TMI, 35 with nTMi, and 20 with cCAD) who underwent coronary angiography at the event presentation and had subsequent follow-up. Medical history, physical, electrocardiographic, histological, biochemical, and angiographic data were gathered through medical records, standardized patient interviews, and core laboratory measurements. Results: Distinctive signatures were noted in the median TLB profiles across the three patient types. TLB profiles for TMI and nTMi patients exhibited gradual changes from T0 to Tfu, with significant differences during the acute and quiescent phases. During the quiescent phase, all three patient types demonstrated similar TLB signatures. An unsupervised clustering analysis revealed a unique TLB signature for the patients with TMI. TLB metrics generated from specific features of TLB profiles were tested for differences between patient groups. The first moment temperature (TFM) metric distinguished all three groups at time of presentation (T0). In addition, 13 other TLB-derived metrics were shown to have distinct distributions between patients with TMI and those with cCAD. Conclusion: Our findings demonstrated the use of TLB as a sensitive and data-rich technique to be explored in cardiovascular diseases, thus providing valuable insight into acute myocardial injury events.

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Atherosclerosis (AS) is the pathologic basis of various cardiovascular and cerebrovascular events, with a high degree of heterogeneity among different arterial beds. However, mechanistic differences between arterial beds remain unexplored. The aim of this study was to explore key genes and potential mechanistic differences between AS in different arterial beds through bioinformatics analysis. Carotid atherosclerosis (CAS), femoral atherosclerosis (FAS), infrapopliteal atherosclerosis (IPAS), abdominal aortic atherosclerosis (AAS), and AS-specific differentially expressed genes (DEGs) were screened from the GSE100927 and GSE57691 datasets. Immune infiltration analysis was used to identify AS immune cell infiltration differences. Unsupervised cluster analysis of AS samples from different regions based on macrophage polarization gene expression profiles. Weighted gene co-expression network analysis (WGCNA) was performed to identify the most relevant module genes with AS. Hub genes were then screened by LASSO regression, SVM-REF, and single-gene differential analysis, and a nomogram was constructed to predict the risk of AS development. The results showed that differential expression analysis identified 5, 4, 121, and 62 CAS, FAS, IPAS, AAS-specific DEGs, and 42 AS-common DEGs, respectively. Immune infiltration analysis demonstrated that the degree of macrophage and mast cell enrichment differed significantly in different regions of AS. The CAS, FAS, IPAS, and AAS could be distinguished into two different biologically functional and stable molecular clusters based on macrophage polarization gene expression profiles, especially for cardiomyopathy and glycolipid metabolic processes. Hub genes for 6 AS (ADAP2, CSF3R, FABP5, ITGAX, MYOC, and SPP1), 4 IPAS (CLECL1, DIO2, F2RL2, and GUCY1A2), and 3 AAS (RPL21, RPL26, and RPL10A) were obtained based on module gene, gender stratification, machine learning algorithms, and single-gene difference analysis, respectively, and these genes were effective in differentiating between different regions of AS. This study demonstrates that there are similarities and heterogeneities in the pathogenesis of AS between different arterial beds.

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In lung adenocarcinoma (LUAD), tumor antigens and immune phenotypes are important for cancer immunotherapy. This study aims to identify potential tumor antigens and immune subtypes for LUAD. In this study, the gene expression profiles and related clinical data of LUAD patients were collected from the TCGA and the GEO database. Then, we first identified four genes with copy number variation and mutation related to the survival of LUAD patients, in which FAM117A, INPP5J, and SLC25A42 were screened as potential tumor antigens. The expressions of these genes were significantly correlated with the infiltration of B cells CD4+ T cells and dendritic cells using TIMER and CIBERSORT algorithms. LUAD patients were divided into three immune clusters: C1(immune-desert), C2(immune-active), and C3(inflamed) using the Non-negative matrix factorization algorithm by using survival-related immune genes. The C2 cluster showed favorable overall survival compared to C1 and C3 clusters in both TCGA and two GEO LUAD cohorts. Different immune cell infiltration patterns, immune-associated molecular characteristics, and drug sensitivity were found among the three clusters. Moreover, different positions in the immune landscape map exhibited different prognostic characteristics using dimensionality reduction, providing further evidence of the immune clusters. The Weighted Gene Co-Expression Network Analysis was used to identify the co-expression modules of these immune genes. the three subtypes were significantly positively correlated with the turquoise module gene list, indicating a good prognosis with high scores. We hope that the identified tumor antigens and immune subtypes can be used for immunotherapy and prognosis in LUAD patients.

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Lipids perform multiple biological functions and reflect the physiology and pathology of cells, tissues, and organs. Here, we sought to understand lipid content in relation to tumor pathology by characterizing phospholipids and sphingolipids in the orthotopic mouse glioma using MALDI MS imaging (MSI) and LC-MS/MS. Unsupervised clustering analysis of the MALDI-MSI data segmented the coronal tumoral brain section into 10 histopathologically salient regions. Heterogeneous decrease of the common saturated phosphatidylcholines (PCs) in the tumor was accompanied by the increase of analogous PCs with one or two additional fatty acyl double bonds and increased lyso-PCs. Polyunsaturated fatty acyl-PCs and ether PCs highlighted the striatal tumor margins, whereas the distributions of other PCs differentiated the cortical and striatal tumor parenchyma. We detected a reduction of SM d18:1/18:0 and the heterogeneous mild increase of SM d18:1/16:0 in the tumor, whereas ceramides accumulated only in a small patch deep in the tumoral parenchyma. LC-MS/MS analyses of phospholipids and sphingolipids complemented the MALDI-MSI observation, providing a snapshot of these lipids in the tumor. Finally, the proposed mechanisms responsible for the tumoral lipid changes were contrasted with our interrogation of gene expression in human glioma. Together, these lipidomic results unveil the aberrant and heterogeneous lipid metabolism in mouse glioma where multiple lipid-associated signaling pathways underline the tumor features, promote the survival, growth, proliferation, and invasion of different tumor cell populations, and implicate the management strategy of a multiple-target approach for glioma and related brain malignancies.

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We aimed to screen the drug metabolism-related subgroups of pancreatic adenocarcinoma (PAAD) and to study the prognosis, clinical features, immune infiltration, and gene mutation differences of different subtypes in PAAD patients. All 181 cases of PAAD samples and clinical characteristics data were downloaded from The Cancer Genome Atlas (TCGA). After matching the drug metabolism-related genes downloaded from PMID 33202946 with the TCGA dataset, the drug metabolism-related genes were initially obtained. Besides, univariate Cox regression analysis was used to screen the drug metabolism genes related to the prognosis of PAAD. Moreover, the construction of the protein-protein interaction (PPI) network and gene ontology were performed. The four subgroups of PAAD obtained from unsupervised clustering analysis were systematically analyzed, including prognostic, GSVA, immune infiltration, and gene mutation analysis. A total of 83 drug metabolism genes related to the prognosis of PAAD were obtained and enriched in 16 pathways. The PPI network was composed of 248 relationship pairs. Four subgroups that can identify different subtypes of PPAD were obtained, and there were significant differences in survival and clinical characteristics, mutation types, and immune infiltration abundance between subgroups. A total of 17 different pathways among the four subgroups involved in cell cycle, response to stimulants such as drugs, and transmembrane transport. In this study, the four subgroups related to the drug metabolism of PAAD were comprehensively analyzed, and the important role of drug metabolism-related genes in the immune infiltration and prognosis of PAAD were emphasized.

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OBJECTIVE: The malignant phenotypes of cancer are defined not only by its intrinsic tumor cells but also by the tumor-infiltrating immune cells activated and recruited to the cancer microenvironment. However, a comprehensive introduction of gastric cancer immune cell infiltration has not been identified so far. METHODS: In this study, we comprehensively analyzed the tumor-infiltrating immune cells abundance in gastric cancer for the first time by CIBERSORT. The meta-analysis, single-sample gene set enrichment analysis and hierarchical agglomerative clustering were used to measure and evaluate the respective proportions of 22 cell types of immune infiltration using normalized gene expression data. The fraction of tumor-infiltrating immune cells subpopulations was also evaluated to determine the associations with clinical features and molecular subtypes. RESULTS: Tumor-infiltrating immune cells are extensively involved in the pathogenesis and development of the gastric cancer. We discovered Tfh and activated CD4+ memory T cells were associated with poorer overall survival and Progression-free survival (PFS), but that naïve B cells were opposite for PFS. Unsupervised clustering analysis revealed there existed three tumor-infiltrating immune cells subgroups with distinct survival patterns. Specially, cluster 1 showed significantly better clinical outcome than other two clusters. CONCLUSIONS: Collectively, our data explored the differences of tumor-infiltrating immune cells in gastric cancer, and these variations were likely to be important clues for prognosis and management of its future clinical implementation.

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Aim: The study was conducted to understand how key determinants of the Patient Financial Eligibility Tool (PFET), a previously validated tool for assessing patients' ability to contribute to their medication costs, vary across countries. Materials & methods: A clustering analysis was conducted on economic data from 1404 patients from Thailand (n = 947), the UAE (n = 347) and Mexico (n = 110). Results: The analysis identified seven patient clusters, including globally wealthy or poor patients (14%/48%) and those with only selectively increased PFET economic indicators (38%), and revealed country-specific differences in the correlation between PFET metrics and patients' overall economic status. Conclusion: The PFET is a versatile tool that can be adapted to each country's economic context to assess patients' ability to contribute to their medication costs.

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