Multiple myeloma (MM) is a hematologic malignancy characterized by aberrant expansion of monoclonal plasma cells with high mortality and severe complications due to the lack of early diagnosis and timely treatment. Circulating miRNAs have shown potential in the diagnosis of MM with inconsistent results, which remains to be fully assessed. Here we updated a meta-analysis with relative studies and essays published in English before Jan 31, 2021. After steps of screening, 32 studies from 11 articles that included a total of 627 MM patients and 314 healthy controls were collected. All data were analyzed by REVMAN 5.3 and Stata MP 16, and the quality of included literatures was estimated by Diagnostic Accuracy Study 2 (QUADAS-2). The pooled area under the curve (AUC) shown in summary receiver operating characteristic (SROC) analyses of circulating miRNAs was 0.87 (95%CI, 0.81–0.89), and the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 0.79, 0.86, 5, 0.27, 22, respectively. Meta-regression and subgroup analysis exhibited that “miRNA cluster”, patient “detailed stage or Ig isotype” accounted for a considerable proportion of heterogeneity, revealing the importance of study design and patient inclusion in diagnostic trials; thus standardized recommendations were proposed for further studies. In addition, the performance of the circulating miRNAs included in MM prognosis and treatment response prediction was summarized, indicating that they could serve as valuable biomarkers, which would expand their clinical application greatly.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=234297, PROSPERO, identifier (CRD42021234297).
Recent years have witnessed a growing body of evidence suggesting that platelets are involved in several stages of the metastatic process via direct or indirect interactions with cancer cells, contributing to the progression of neoplastic malignancies. Cancer cells can dynamically exchange components with platelets in and out of blood vessels, and directly phagocytose platelets to hijack their proteome, transcriptome, and secretome, or be remotely regulated by metabolites or microparticles released by platelets, resulting in phenotypic, genetic, and functional modifications. Moreover, platelet interactions with stromal and immune cells in the tumor microenvironment lead to alterations in their components, including the ribonucleic acid (RNA) profile, and complicate the impact of platelets on cancers. A deeper understanding of the roles of platelets and their RNAs in cancer will contribute to the development of anticancer strategies and the optimization of clinical management. Encouragingly, advances in high-throughput sequencing, bioinformatics data analysis, and machine learning have allowed scientists to explore the potential of platelet RNAs for cancer diagnosis, prognosis, and guiding treatment. However, the clinical application of this technique remains controversial and requires larger, multicenter studies with standardized protocols. Here, we integrate the latest evidence to provide a broader insight into the role of platelets in cancer progression and management, and propose standardized recommendations for the clinical utility of platelet RNAs to facilitate translation and benefit patients.
Objectives To explore the value of a logistic regression model based on haematological parameters for the early diagnosis of silicosis by comparing the differences in haematological parameters between silicosis patients and healthy physical examiners.Methods A total of 390 individuals, including 195 silicosis patients and 195 normal participants were included in the training cohort. Then, 65 silicosis patients and 65 healthy individuals were enrolled in the validation cohort. Whole blood samples were collected from all participants, and hematological indicator characteristics were determined. Features with statistical significance in the univariate analysis of the training cohort and reported significant features were included in the logistic regression analysis to determine the independent factors influencing the diagnosis of silicosis and to construct a logistic diagnostic model. A receiver operating characteristic (ROC) curve was plotted to evaluate the accuracy of the model in diagnosing silicosis.Results In the training cohort, several hematological indicators were significantly different in silicosis patients, including Hematocrit(HCT), Hemoglobin(HGB), Mean corpuscular volume(MCV), Red Blood Cell Count(RBC), White blood cell count (WBC), Mon#, Mon%, Neu#, Neu%, Red blood cell distribution width coefficient of variation(RDW_CV), C-reactive protein(CRP), Hydroxybutyrate dehydrogenase (HBDH), Lactate dehydrogenase(LDH), Prothrombin time(PT), International normalized ratio(INR), Fibrinogen(FIB), and D-Dimer(DD) levels, all with statistical significance (P < 0.05). The silicosis diagnostic model performed well in the training cohort (Area Under Curve, AUC = 0.943) and had high diagnostic sensitivity (83.1%) and specificity (92.3%). The diagnostic model also effectively distinguished between silicosis patients and the control cohort in the validation cohort (AUC = 0.936).Conclusions This study confirmed that Age, CRP, LDH, Macro%, and INR were independent factors influencing the diagnosis of silicosis, and the logistic regression model based on these indicators could provide a reliable basis for predicting silicosis diagnosis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.