ABSTRACT
PURPOSE: To explore the specific role and regulatory mechanism of oxysterol binding protein like 5 (OSBPL5) in non-small cell lung cancer (NSCLC). METHODS AND RESULTS: Quantitative real-time polymerase chain reaction (qRT-PCR) analysis demonstrated that OSBPL5 expression was notably elevated in NSCLC tissues and cell lines, and Kaplan-Meier analysis manifested that high OSBPL5 expression was closely related to the poor prognosis of NSCLC patients. Besides, according to the results from western blot analysis, cell counting kit-8, EdU and Transwell assays, knockdown of OSBPL5 suppressed NSCLC cell proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) process. Additionally, by performing qRT-PCR analysis, luciferase reporter and RNA pull-down assays, we verified that OSBPL5 was a downstream target of miR-526b-3p and long noncoding RNA (lncRNA) LMCD1-AS1 served as a sponge for miR-526b-3p. Moreover, from rescue assays, we observed that OSBPL5 overexpression offset LMCD1-AS1 knockdown-mediated inhibition in cell proliferation, migration, invasion and EMT in NSCLC. CONCLUSIONS: This paper was the first to probe the molecular regulatory mechanism of OSBPL5 involving the LMCD1-AS1/miR-526b-3p axis in NSCLC and our results revealed that the LMCD1-AS1/miR-526b-3p/OSBPL5 axis facilitates NSCLC cell proliferation, migration, invasion and EMT, which may offer a novel therapeutic direction for NSCLC.
Subject(s)
Carcinoma, Non-Small-Cell Lung/genetics , Co-Repressor Proteins/genetics , LIM Domain Proteins/genetics , Lung Neoplasms/genetics , MicroRNAs/genetics , Receptors, Steroid/genetics , Cell Line, Tumor , Cell Movement/genetics , Cell Proliferation/genetics , Gene Expression Regulation, Neoplastic , HumansABSTRACT
Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language processing (NLP). Large language models typically consist of artificial neural networks with numerous parameters, trained on large amounts of unlabeled input using self-supervised or semi-supervised learning. However, their potential for solving bioinformatics problems may even exceed their proficiency in modeling human language. In this review, we will present a summary of the prominent large language models used in natural language processing, such as BERT and GPT, and focus on exploring the applications of large language models at different omics levels in bioinformatics, mainly including applications of large language models in genomics, transcriptomics, proteomics, drug discovery and single cell analysis. Finally, this review summarizes the potential and prospects of large language models in solving bioinformatic problems.