The FoxP1 gene regulates lung function, production of matrix metalloproteinases and inflammatory mediators, and viability of lung epithelia

circFOXP1: a potential diagnostic and therapeutic target in human diseases

Circular RNA (circRNA) are a unique class of non-coding RNAs characterized by their covalently closed loop structures, which grant them properties such as stability and conservation. Among these, circFOXP1 has been implicated in various diseases, including cancers, respiratory, skeletal, and cardiovascular disorders. This review systematically examines circFOXP1's role in disease progression, highlighting its involvement in critical biological processes, including cell proliferation, invasion, apoptosis, and autophagy. Mechanistically, circFOXP1 functions through miRNA sponging, protein interactions, and modulation of key signaling pathways such as Wnt and PI3K/AKT. We discuss its potential as a diagnostic and therapeutic target. Our analysis also identifies key unresolved questions, such as the precise regulatory networks involving circFOXP1 and its translation potential, offering pathways for future research.

Association of Fetal Lung Development Disorders with Adult Diseases: A Comprehensive Review

Fetal lung development is a crucial and complex process that lays the groundwork for postnatal respiratory health. However, disruptions in this delicate developmental journey can lead to fetal lung development disorders, impacting neonatal outcomes and potentially influencing health outcomes well into adulthood. Recent research has shed light on the intriguing association between fetal lung development disorders and the development of adult diseases. Understanding these links can provide valuable insights into the developmental origins of health and disease, paving the way for targeted preventive measures and clinical interventions. This review article aims to comprehensively explore the association of fetal lung development disorders with adult diseases. We delve into the stages of fetal lung development, examining key factors influencing fetal lung maturation. Subsequently, we investigate specific fetal lung development disorders, such as respiratory distress syndrome (RDS), bronchopulmonary dysplasia (BPD), congenital diaphragmatic hernia (CDH), and other abnormalities. Furthermore, we explore the potential mechanisms underlying these associations, considering the role of epigenetic modifications, transgenerational effects, and intrauterine environmental factors. Additionally, we examine the epidemiological evidence and clinical findings linking fetal lung development disorders to adult respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), and other respiratory ailments. This review provides valuable insights for healthcare professionals and researchers, guiding future investigations and shaping strategies for preventive interventions and long-term care.

Acute myeloid leukemia with unreported translocation (x; 3) (q24; p13): A case report

Key Clinical Message

Novel and rare chromosomal aberrations in AML are important to understand, particularly if associated with tumorigenesis and how they contribute to prognostic risk. It is important that acute leukemia be treated right away. Herein, novel (x; 3) (q24; p13) is described.

Abstract

Acute myeloid leukemia (AML) is a cancer of the blood and bone marrow. It is the most common type of acute leukemia in adults. This type of cancer usually gets worse quickly if it is not treated. Here, we report an unusual case of AML with an unreported translocation associated with AML.

Identification of Potential Biomarkers for Group I Pulmonary Hypertension Based on Machine Learning and Bioinformatics Analysis

A number of processes and pathways have been reported in the development of Group I pulmonary hypertension (Group I PAH); however, novel biomarkers need to be identified for a better diagnosis and management. We employed a robust rank aggregation (RRA) algorithm to shortlist the key differentially expressed genes (DEGs) between Group I PAH patients and controls. An optimal diagnostic model was obtained by comparing seven machine learning algorithms and was verified in an independent dataset. The functional roles of key DEGs and biomarkers were analyzed using various in silico methods. Finally, the biomarkers and a set of key candidates were experimentally validated using patient samples and a cell line model. A total of 48 key DEGs with preferable diagnostic value were identified. A gradient boosting decision tree algorithm was utilized to build a diagnostic model with three biomarkers, PBRM1, CA1, and TXLNG. An immune-cell infiltration analysis revealed significant differences in the relative abundances of seven immune cells between controls and PAH patients and a correlation with the biomarkers. Experimental validation confirmed the upregulation of the three biomarkers in Group I PAH patients. In conclusion, machine learning and a bioinformatics analysis along with experimental techniques identified PBRM1, CA1, and TXLNG as potential biomarkers for Group I PAH.