Identification and validation of oxeiptosis-associated lncRNAs and prognosis-related signature genes to predict the immune status in uterine corpus endometrial carcinoma

Novel Insights into the Links between N6-Methyladenosine and Regulated Cell Death in Musculoskeletal Diseases

Musculoskeletal diseases (MSDs), including osteoarthritis (OA), osteosarcoma (OS), multiple myeloma (MM), intervertebral disc degeneration (IDD), osteoporosis (OP), and rheumatoid arthritis (RA), present noteworthy obstacles associated with pain, disability, and impaired quality of life on a global scale. In recent years, it has become increasingly apparent that N6-methyladenosine (m6A) is a key regulator in the expression of genes in a multitude of biological processes. m6A is composed of 0.1-0.4% adenylate residues, especially at the beginning of 3'-UTR near the translation stop codon. The m6A regulator can be classified into three types, namely the "writer", "reader", and "eraser". Studies have shown that the epigenetic modulation of m6A influences mRNA processing, nuclear export, translation, and splicing. Regulated cell death (RCD) is the autonomous and orderly death of cells under genetic control to maintain the stability of the internal environment. Moreover, distorted RCDs are widely used to influence the course of various diseases and receiving increasing attention from researchers. In the past few years, increasing evidence has indicated that m6A can regulate gene expression and thus influence different RCD processes, which has a central role in the etiology and evolution of MSDs. The RCDs currently confirmed to be associated with m6A are autophagy-dependent cell death, apoptosis, necroptosis, pyroptosis, ferroptosis, immunogenic cell death, NETotic cell death and oxeiptosis. The m6A-RCD axis can regulate the inflammatory response in chondrocytes and the invasive and migratory of MM cells to bone remodeling capacity, thereby influencing the development of MSDs. This review gives a complete overview of the regulatory functions on the m6A-RCD axis across muscle, bone, and cartilage. In addition, we also discuss recent advances in the control of RCD by m6A-targeted factors and explore the clinical application prospects of therapies targeting the m6A-RCD in MSD prevention and treatment. These may provide new ideas and directions for understanding the pathophysiological mechanism of MSDs and the clinical prevention and treatment of these diseases.

A concise review of the regulatory, diagnostic, and prognostic implications of HOXB-AS3 in tumors

Recent studies have reported that HOXB-AS3 (HOXB Cluster Antisense RNA 3) is an intriguing molecule with dual functionality as a long noncoding RNA (lncRNA) and putative coding peptide in tumorigenesis and progression. The significant expression alterations of HOXB-AS3 were detected in diverse cancer types and closely correlated with clinical stage and patient survival. Furthermore, HOXB-AS3 was involved in a spectrum of biological processes in solid tumors and hematological malignancies, such as stemness, lipid metabolism, migration, invasion, and tumor growth. This review comprehensively analyzes its clinical relevance for diagnosis and prognosis across human tumors and summarizes its functional role and regulatory mechanisms in different malignant tumors, including liver cancer, acute myeloid leukemia, ovarian cancer, lung cancer, endometrial carcinoma, colon cancer, and oral squamous cell carcinoma. Overall, HOXB-AS3 emerges as a promising biomarker and novel therapeutic target in multiple human tumors.

Unlocking the predictive potential of long non-coding RNAs: a machine learning approach for precise cancer patient prognosis

The intricate web of cancer biology is governed by the active participation of long non-coding RNAs (lncRNAs), playing crucial roles in cancer cells' proliferation, migration, and drug resistance. Pioneering research driven by machine learning algorithms has unveiled the profound ability of specific combinations of lncRNAs to predict the prognosis of cancer patients. These findings highlight the transformative potential of lncRNAs as powerful therapeutic targets and prognostic markers. In this comprehensive review, we meticulously examined the landscape of lncRNAs in predicting the prognosis of the top five cancers and other malignancies, aiming to provide a compelling reference for future research endeavours. Leveraging the power of machine learning techniques, we explored the predictive capabilities of diverse lncRNA combinations, revealing their unprecedented potential to accurately determine patient outcomes.