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The long non-coding RNA (lncRNA) Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) maintains the integrity of the intestinal epithelial barrier and regulates local inflammation. However, its influences on intestinal microbial communities and tissue susceptibility to cancer development remain unexplored. Here, we report that MALAT1 regulates host anti-microbial response gene expression and the composition of mucosal-associated microbial communities in a region-specific manner. In the APC mutant mouse model of intestine tumorigenesis, knocking out MALAT1 results in higher polyp counts in the small intestine and colon. Interestingly, intestine polyps that developed in the absence of MALAT1 were smaller in size. These findings highlight the unexpected bivalent role of MALAT1 in restricting and promoting cancer progression at different disease stages. Among the 30 MALAT1-targets shared by both the small intestine and colon, ZNF638 and SENP8 levels are predictive of colon adenoma patient overall survival and disease-free survival. Genomic assays further revealed that MALAT1 modulates intestinal target expression and splicing through both direct and indirect mechanisms. This study expands the role of lncRNAs in regulating intestine homeostasis, microbial communities, and cancer pathogenesis.

IntroductionThe diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS.MethodsIn this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients.ResultsThe ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care.DiscussionIn this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.

Recently, there has been renewed interest in metabolic therapy for cancer, particularly in amino acid deprivation by enzymes. L-asparaginase was approved for the treatment of acute lymphoblastic leukemia by the U.S. Food and Drug Administration. Arginine deiminase and recombinant human arginase have been developed into clinical trials as potential cancer therapeutic agents for the treatment of arginine-auxotrophic tumors. Moreover, other novel amino acid degrading enzymes, such as glutaminase, methionase, lysine oxidase, phenylalanine ammonia lyase, have been developed for the treatment of malignant cancers. One of the greatest obstacles faced by anticancer drugs is the development of drug resistance, which is reported to be associated with autophagy. Autophagy is an evolutionarily conserved catabolic process that is responsible for the degradation of dysfunctional proteins and organelles. There is a growing body of literature revealing that, in response to metabolism stress, autophagy could be induced by amino acid deprivation. The manipulation of autophagy in combination with amino acid degrading enzymes is actively being investigated as a potential therapeutic approach in preclinical studies. Importantly, shedding light on how autophagy fuels tumor metabolism during amino acid deprivation will enable more potential combinational therapeutic strategies. This study summarizes recent advances, discussing several potential anticancer enzymes, and highlighting the promising combined therapeutic strategy of amino acid degrading enzymes and autophagy modulators in tumors