Multi-Omics Analysis Reveals the Role of Changes in Platelet Activation Pathways in the Survival Outcome of Patients with Esophageal Squamous Cell Carcinoma.

Objective: The objective of this study was to integrate metabolomics and transcriptomics data to identify key diagnostic and prognostic markers for esophageal squamous cell carcinoma (ESCC). Plasma samples were collected from 85 ESCC patients at different stages and 50 healthy volunteers for non-targeted metabolomic analysis. Methods: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was employed for non-targeted metabolomic analysis. Subsequently, we integrated the metabolomic data with transcriptomic data from the Gene Expression Omnibus (GEO) and prognosis data from The Cancer Genome Atlas Program (TCGA) to perform pathway analysis. Our focus was on pathways that involve both metabolites and upstream genes, as they often exhibit higher accuracy. Results: Through the integration of metabolomics and transcriptomics, we identified significant alterations in the platelet activation pathway in ESCC. This pathway involves the participation of both metabolites and genes, making it a more accurate reflection of pathological changes associated with the disease. Notably, metabolite arachidonic acid (AA) and chemokine receptor type 2(CXCR2) were significantly downregulated in ESCC, while genes collagen type I alpha 1(COL1A1), collagen type I alpha 2(COL1A2), collagen type III alpha 1(COL3A1), type 3 inositol 1,4,5-trisphosphate receptor (ITPR3), and insulin-like growth factor II mRNA binding protein 3(IGF2BP3) were significantly upregulated, indicating the presence of tumor-induced platelet activation in ESCC. Further analysis of prognosis data revealed that high expression of COL1A1, IGF2BP3, and ITPR3 was associated with a favorable prognosis for ESCC, while high CXCR2 expression was linked to an adverse prognosis. In addition, we combined COL1A1, ITPR3, IGF2BP3, CXCR2, and AA to form a diagnostic biomarker panel. The receiver operating characteristic curve (ROC) demonstrated excellent diagnostic capability (AUC=0.987). Conclusion: Our study underscores the significant role of platelet activation pathways and related genes in the diagnosis and prognosis of ESCC patients. These findings offer promising insights for improving the clinical management of ESCC.

The markers and risk stratification model of intracranial aneurysm instability in a large Chinese cohort.

Intracranial aneurysm is the leading cause of nontraumatic subarachnoid hemorrhage. Evaluating the unstable (rupture and growth) risk of aneurysms is helpful to guild decision-making for unruptured intracranial aneurysms (UIA). This study aimed to develop a model for risk stratification of UIA instability. The UIA patients from two prospective, longitudinal multicenter Chinese cohorts recruited from January 2017 to January 2022 were set as the derivation cohort and validation cohort. The primary endpoint was UIA instability, comprising aneurysm rupture, growth, or morphology change, during a 2-year follow-up. Intracranial aneurysm samples and corresponding serums from 20 patients were also collected. Metabolomics and cytokine profiling analysis were performed on the derivation cohort (758 single-UIA patients harboring 676 stable UIAs and 82 unstable UIAs). Oleic acid (OA), arachidonic acid (AA), interleukin 1ß (IL-1ß), and tumor necrosis factor-α (TNF-α) were significantly dysregulated between stable and unstable UIAs. OA and AA exhibited the same dysregulated trends in serums and aneurysm tissues. The feature selection process demonstrated size ratio, irregular shape, OA, AA, IL-1ß, and TNF-α as features of UIA instability. A machine-learning stratification model (instability classifier) was constructed based on radiological features and biomarkers, with high accuracy to evaluate UIA instability risk (area under curve (AUC), 0.94). Within the validation cohort (492 single-UIA patients harboring 414 stable UIAs and 78 unstable UIAs), the instability classifier performed well to evaluate the risk of UIA instability (AUC, 0.89). Supplementation of OA and pharmacological inhibition of IL-1ß and TNF-α could prevent intracranial aneurysms from rupturing in rat models. This study revealed the markers of UIA instability and provided a risk stratification model, which may guide treatment decision-making for UIAs.

Chronic kidney disease-induced muscle atrophy: Molecular mechanisms and promising therapies.

Chronic kidney disease (CKD) is a high-risk chronic catabolic disease due to its high morbidity and mortality. CKD is accompanied by many complications, leading to a poor quality of life, and serious complications may even threaten the life of CKD patients. Muscle atrophy is a common complication of CKD. Muscle atrophy and sarcopenia in CKD patients have complex pathways that are related to multiple mechanisms and related factors. This review not only discusses the mechanisms by which inflammation, oxidative stress, mitochondrial dysfunction promote CKD-induced muscle atrophy but also explores other CKD-related complications, such as metabolic acidosis, vitamin D deficiency, anorexia, and excess angiotensin II, as well as other related factors that play a role in CKD muscle atrophy, such as insulin resistance, hormones, hemodialysis, uremic toxins, intestinal flora imbalance, and miRNA. We highlight potential treatments and drugs that can effectively treat CKD-induced muscle atrophy in terms of complication treatment, nutritional supplementation, physical exercise, and drug intervention, thereby helping to improve the prognosis and quality of life of CKD patients.