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Organoids, recognized as invaluable models in tumor and stem cell research, assume a pivotal role in the meticulous analysis of diverse datasets pertaining to their growth dynamics, drug screening processes and related phenomena. However, the manual scrutiny and conventional statistical methodologies employed in handling organoid data often grapple with challenges such as diminished precision and efficiency, heightened complexity, escalated human resource requirements, and a degree of subjectivity. Acknowledging the remarkable efficacy of artificial intelligence (AI) in the realms of biology and medicine, the incorporation of AI into organoid research stands poised to enhance the objectivity, precision and expediency of analyses. This integration empowers organoids to more effectively fulfill objectives such as disease modeling, drug screening and precision medicine. Notably, significant strides have been made in AI-driven analyses of organoid image data. The amalgamation of deep learning into image analysis facilitates a more meticulous delineation of the microstructural intricacies and nuanced changes within organoids, achieving a level of accuracy akin to that of experts. This not only elevates the precision of organoid morphology and growth recognition, but also contributes to substantial time and cost savings in research endeavors. Furthermore, the infusion of AI technology has yielded breakthroughs in the processing of organoid omics data, resulting in heightened efficiency in data processing and the identification of latent gene expression patterns. This furnishes novel tools for comprehending cellular development and unraveling the intricate mechanisms underlying various diseases. In addition to image data, AI techniques applied to diverse organoid datasets, encompassing electrical signals and spectra, have realized an unbiased classification of organoid types and states, embarking on a comprehensive journey towards characterizing organoids holistically. In the pivotal domain of drug screening for organoids, AI emerges as a stalwart companion, providing robust support for real-time process monitoring and result prediction. Leveraging high-content microscopy images and sophisticated deep learning models, researchers can dynamically monitor organoid responses to drugs, effecting non-invasive detection of drug impacts and amplifying the precision and efficiency of drug screening processes. Despite the significant strides made by AI in organoid research, challenges persist, encompassing hurdles in data acquisition, constraints in sample quality and quantity, and quandaries associated with model interpretability. Overcoming these challenges necessitates dedicated future research efforts aimed at enhancing data consistency, fortifying model interpretability, and exploring methodologies for the seamless fusion of multimodal data. Such endeavors are poised to usher in a more comprehensive and dependable application of AI in organoid research. In summation, the integration of AI technology introduces unparalleled opportunities to organoid research, resulting in noteworthy advancements. Nevertheless, interdisciplinary research and collaborative efforts remain imperative to navigate challenges and propel the more profound integration of AI into organoid research. The future holds promise for AI to assume an even more prominent role in advancing organoid research toward clinical translation and precision medicine.

Lung adenocarcinoma (LUAD) is one of the most aggressive types of lung cancer. The prognosis of LUAD patients remains poor, and the overall efficacy of gemcitabine-based chemotherapy is still unsatisfactory. Long noncoding RNAs (lncRNAs) play important roles in several cancer types by interacting with multiple proteins, RNA, and DNA. However, the relationship between lncRNA dysregulation and gemcitabine resistance in LUAD has not been fully elucidated. In this study, lncRNA CYTOR expression and its association with the prognosis of LUAD patients are assessed by quantitative RT-PCR and Kaplan-Meier survival analysis. In vitro and in vivo functional studies are conducted to evaluate the biological functions of CYTOR in LUAD. The underlying mechanism regarding the tumor-promoting effects of CYTOR is explored using RNA immunoprecipitation, biotin-labelled RNA pulldown, luciferase reporter assays, and western blot analysis. We identify that CYTOR is an oncogenic lncRNA and is apparently upregulated in LUAD by analysing TCGA-LUAD data. High CYTOR expression is a poor prognostic factor for LUAD. Functional studies reveal that CYTOR confers LUAD cells with stronger resistance to gemcitabine treatment and upregulates the expression levels of epithelial-mesenchymal transition (EMT)-related proteins. Mechanically, CYTOR acts as a competitive endogenous RNA (ceRNA) to absorb miR-125a-5p, weakens the antitumor function of miR-125a-5p, and ultimately upregulates ANLN and RRM2 expressions. Taken together, this study explains the mechanism of lncRNA in the gemcitabine resistance of LUAD and formulates a theoretical framework for the in depth study of LUAD.