Nudt21 Controls Cell Fate by Connecting Alternative Polyadenylation to Chromatin Signaling.

Cell fate transitions involve rapid gene expression changes and global chromatin remodeling, yet the underlying regulatory pathways remain incompletely understood. Here, we identified the RNA-processing factor Nudt21 as a novel regulator of cell fate change using transcription-factor-induced reprogramming as a screening assay. Suppression of Nudt21 enhanced the generation of induced pluripotent stem cells, facilitated transdifferentiation into trophoblast stem cells, and impaired differentiation of myeloid precursors and embryonic stem cells, suggesting a broader role for Nudt21 in cell fate change. We show that Nudt21 directs differential polyadenylation of over 1,500 transcripts in cells acquiring pluripotency, although only a fraction changed protein levels. Remarkably, these proteins were strongly enriched for chromatin regulators, and their suppression neutralized the effect of Nudt21 during reprogramming. Collectively, our data uncover Nudt21 as a novel post-transcriptional regulator of cell fate and establish a direct, previously unappreciated link between alternative polyadenylation and chromatin signaling.

The rise of baobab trees in Madagascar.

The baobab trees (genus Adansonia) have attracted tremendous attention because of their striking shape and distinctive relationships with fauna1. These spectacular trees have also influenced human culture, inspiring innumerable arts, folklore and traditions. Here we sequenced genomes of all eight extant baobab species and argue that Madagascar should be considered the centre of origin for the extant lineages, a key issue in their evolutionary history2,3. Integrated genomic and ecological analyses revealed the reticulate evolution of baobabs, which eventually led to the species diversity seen today. Past population dynamics of Malagasy baobabs may have been influenced by both interspecific competition and the geological history of the island, especially changes in local sea levels. We propose that further attention should be paid to the conservation status of Malagasy baobabs, especially of Adansonia suarezensis and Adansonia grandidieri, and that intensive monitoring of populations of Adansonia za is required, given its propensity for negatively impacting the critically endangered Adansonia perrieri.

TRIM21 induces selective autophagic degradation of c-Myc and sensitizes regorafenib therapy in colorectal cancer.

Kirsten rat sarcoma virus (KRAS) mutation is associated with malignant tumor transformation and drug resistance. However, the development of clinically effective targeted therapies for KRAS-mutant cancer has proven to be a formidable challenge. Here, we report that tripartite motif-containing protein 21 (TRIM21) functions as a target of extracellular signal-regulated kinase 2 (ERK2) in KRAS-mutant colorectal cancer (CRC), contributing to regorafenib therapy resistance. Mechanistically, TRIM21 directly interacts with and ubiquitinates v-myc avian myelocytomatosis viral oncogene homolog (c-Myc) at lysine 148 (K148) via K63-linkage, enabling c-Myc to be targeted to the autophagy machinery for degradation, ultimately resulting in the downregulation of enolase 2 expression and inhibition of glycolysis. However, mutant KRAS (KRAS/MT)-driven mitogen-activated protein kinase (MAPK) signaling leads to the phosphorylation of TRIM21 (p-TRIM21) at Threonine 396 (T396) by ERK2, disrupting the interaction between TRIM21 and c-Myc and thereby preventing c-Myc from targeting autophagy for degradation. This enhances glycolysis and contributes to regorafenib resistance. Clinically, high p-TRIM21 (T396) is associated with an unfavorable prognosis. Targeting TRIM21 to disrupt KRAS/MT-driven phosphorylation using the antidepressant vilazodone shows potential for enhancing the efficacy of regorafenib in treating KRAS-mutant CRC in preclinical models. These findings are instrumental for KRAS-mutant CRC treatment aiming at activating TRIM21-mediated selective autophagic degradation of c-Myc.

Retinoic acid is dispensable for meiotic initiation but required for spermiogenesis in the mammalian testis.

Retinoic acid (RA) is the proposed mammalian 'meiosis inducing substance'. However, evidence for this role comes from studies in the fetal ovary, where germ cell differentiation and meiotic initiation are temporally inseparable. In the postnatal testis, these events are separated by more than 1 week. Exploiting this difference, we discovered that, although RA is required for spermatogonial differentiation, it is dispensable for the subsequent initiation, progression and completion of meiosis. Indeed, in the absence of RA, the meiotic transcriptome program in both differentiating spermatogonia and spermatocytes entering meiosis was largely unaffected. Instead, transcripts encoding factors required during spermiogenesis were aberrant during preleptonema, and the subsequent spermatid morphogenesis program was disrupted such that no sperm were produced. Taken together, these data reveal a RA-independent model for male meiotic initiation.

Activation of multiple proto-oncogenic tyrosine kinases in breast cancer via loss of the PTPN12 phosphatase.

Among breast cancers, triple-negative breast cancer (TNBC) is the most poorly understood and is refractory to current targeted therapies. Using a genetic screen, we identify the PTPN12 tyrosine phosphatase as a tumor suppressor in TNBC. PTPN12 potently suppresses mammary epithelial cell proliferation and transformation. PTPN12 is frequently compromised in human TNBCs, and we identify an upstream tumor-suppressor network that posttranscriptionally controls PTPN12. PTPN12 suppresses transformation by interacting with and inhibiting multiple oncogenic tyrosine kinases, including HER2 and EGFR. The tumorigenic and metastatic potential of PTPN12-deficient TNBC cells is severely impaired upon restoration of PTPN12 function or combined inhibition of PTPN12-regulated tyrosine kinases, suggesting that TNBCs are dependent on the proto-oncogenic tyrosine kinases constrained by PTPN12. Collectively, these data identify PTPN12 as a commonly inactivated tumor suppressor and provide a rationale for combinatorially targeting proto-oncogenic tyrosine kinases in TNBC and other cancers based on their profile of tyrosine-phosphatase activity.

GAABind: a geometry-aware attention-based network for accurate protein-ligand binding pose and binding affinity prediction.

Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket-ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset; the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the severe acute respiratory syndrome coronavirus 2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.

PPICT: an integrated deep neural network for predicting inter-protein PTM cross-talk.

Post-translational modifications (PTMs) fine-tune various signaling pathways not only by the modification of a single residue, but also by the interplay of different modifications on residue pairs within or between proteins, defined as PTM cross-talk. As a challenging question, less attention has been given to PTM dynamics underlying cross-talk residue pairs and structural information underlying protein-protein interaction (PPI) graph, limiting the progress in this PTM functional research. Here we propose a novel integrated deep neural network PPICT (Predictor for PTM Inter-protein Cross-Talk), which predicts PTM cross-talk by combining protein sequence-structure-dynamics information and structural information for PPI graph. We find that cross-talk events preferentially occur among residues with high co-evolution and high potential in allosteric regulation. To make full use of the complex associations between protein evolutionary and biophysical features, and protein pair features, a heterogeneous feature combination net is introduced in the final prediction of PPICT. The comprehensive test results show that the proposed PPICT method significantly improves the prediction performance with an AUC value of 0.869, outperforming the existing state-of-the-art methods. Additionally, the PPICT method can capture the potential PTM cross-talks involved in the functional regulatory PTMs on modifying enzymes and their catalyzed PTM substrates. Therefore, PPICT represents an effective tool for identifying PTM cross-talk between proteins at the proteome level and highlights the hints for cross-talk between different signal pathways introduced by PTMs.

Development of Injectable Thermosensitive Nanocomposite Hydrogel for Ratiometric Drug Delivery to Treat Drug Resistant Chondrosarcoma In Vivo.

Chondrosarcoma(CS), a prevalent primary malignant bone tumor, frequently exhibits chemotherapy resistance attributed to upregulated anti-apoptosis pathways such as the Bcl-2 family. In this manuscript, a new strategy is presented to augment chemosensitivity and mitigate systemic toxicity by harnessing a nano-enabled drug delivery hydrogel platform. The platform utilizes "PLGA-PEG-PLGA", an amphiphilic triblock copolymer combining hydrophilic polyethylene glycol (PEG) and hydrophobic polylactide glycolide (PLGA) blocks, renowned for its properties conducive to crafting a biodegradable, temperature-sensitive hydrogel. This platform is tailored to encapsulate a ratiometrically designed dual-loaded liposomes containing a first-line chemo option for CS, Doxorubicin (Dox), plus a calculated amount of small molecule inhibitor for anti-apoptotic Bcl-2 pathway, ABT-737. In vitro and in vivo evaluations demonstrate successful Bcl-2 suppression, resulting in the restoration of Dox sensitivity, evident through impeded tumor growth and amplified necrosis rates at the tumor site. This delivery system showcases remarkable thermal responsiveness, injectability, and biodegradability, all finely aligned with the clinical demands of CS treatment. Collectively, this study introduces a transformative avenue for tackling drug resistance in CS chemotherapy, offering significant clinical potential.

Visualization, benchmarking and characterization of nested single-cell heterogeneity as dynamic forest mixtures.

A major topic of debate in developmental biology centers on whether development is continuous, discontinuous, or a mixture of both. Pseudo-time trajectory models, optimal for visualizing cellular progression, model cell transitions as continuous state manifolds and do not explicitly model real-time, complex, heterogeneous systems and are challenging for benchmarking with temporal models. We present a data-driven framework that addresses these limitations with temporal single-cell data collected at discrete time points as inputs and a mixture of dependent minimum spanning trees (MSTs) as outputs, denoted as dynamic spanning forest mixtures (DSFMix). DSFMix uses decision-tree models to select genes that account for variations in multimodality, skewness and time. The genes are subsequently used to build the forest using tree agglomerative hierarchical clustering and dynamic branch cutting. We first motivate the use of forest-based algorithms compared to single-tree approaches for visualizing and characterizing developmental processes. We next benchmark DSFMix to pseudo-time and temporal approaches in terms of feature selection, time correlation, and network similarity. Finally, we demonstrate how DSFMix can be used to visualize, compare and characterize complex relationships during biological processes such as epithelial-mesenchymal transition, spermatogenesis, stem cell pluripotency, early transcriptional response from hormones and immune response to coronavirus disease. Our results indicate that the expression of genes during normal development exhibits a high proportion of non-uniformly distributed profiles that are mostly right-skewed and multimodal; the latter being a characteristic of major steady states during development. Our study also identifies and validates gene signatures driving complex dynamic processes during somatic or germline differentiation.

EBD: an eye biomarker database.

MOTIVATION: Many ophthalmic disease biomarkers have been identified through comprehensive multiomics profiling, and hold significant potential in advancing the diagnosis, prognosis, and management of diseases. Meanwhile, the eye itself serves as a natural biomarker for several systemic diseases including neurological, renal, and cardiovascular systems. We aimed to collect and standardize this eye biomarkers information and construct the eye biomarker database (EBD) to provide ophthalmologists with a platform to search, analyze, and download these eye biomarker data. RESULTS: In this study, we present the EBD , a world-first online compilation comprising 889 biomarkers for 26 ocular diseases and 939 eye biomarkers for 181 systemic diseases. The EBD also includes the information of 78 "nonbiomarkers"-the objects that have been proven cannot be biomarkers. Biological function and network analysis were conducted for these ocular disease biomarkers, and several hub pathways and common network topology characteristics were newly identified, which may promote future ocular disease biomarker discovery and characterizes the landscape of biomarkers for eye diseases at the pathway and network level. The EBD is expected to yield broader utility among developmental biologists and clinical scientists in and outside of the eye field by assisting in the identification of biomarkers linked to eye disorders and related systemic diseases. AVAILABILITY AND IMPLEMENTATION: EBD is available at http://www.eyeseeworld.com/ebd/index.html.

The horizontal gene transfer of perchlorate reduction genomic island in three bacteria from an ecological niche.

Three new strains of dissimilatory perchlorate-reducing bacteria (DPRB), QD19-16, QD1-5, and P3-1, were isolated from an active sludge. Phylogenetic trees based on 16S rRNA genes indicated that QD19-16, QD1-5, and P3-1 belonged to Brucella, Acidovorax, and Citrobacter, respectively, expanding the distribution of DPRB in the Proteobacteria. The three strains were gram-negative and facultative anaerobes with rod-shaped cells without flagella, which were 1.0-1.6 µm long and 0.5-0.6 µm wide. The three DPRB strains utilized similar broad spectrum of electron donors and acceptors and demonstrated a similar capability to reduce perchlorate within 6 days. The enzyme activity of perchlorate reductase in QD19-16 toward chlorate was higher than that toward perchlorate. The high sequence similarity of the perchlorate reductase operon and chlorite dismutase genes in the perchlorate reduction genomic islands (PRI) of the three strains implied that they were monophyletic origin from a common ancestral PRI. Two transposase genes (tnp1 and tnp2) were found in the PRIs of strain QD19-16 and QD1-5, but were absent in the strain P3-1 PRI. The presence of fragments of IR sequences in the P3-1 PRI suggested that P3-1 PRI had previously contained these two tnp genes. Therefore, it is plausible to suggest that a common ancestral PRI transferred across the strains Brucella sp. QD19-16, Acidovorax sp. QD1-5, and Citrobacter sp. P3-1 through horizontal gene transfer, facilitated by transposases. These results provided a direct evidence of horizontal gene transfer of PRI that could jump across phylogenetically unrelated bacteria through transposase. KEY POINTS: • Three new DPRB strains can effectively remove high concentration of perchlorate. • The PRIs of three DPRB strains are acquired from a single ancestral PRI. • PRIs are incorporated into different bacteria genome through HGT by transposase.

INO80 governs superenhancer-mediated oncogenic transcription and tumor growth in melanoma.

Superenhancers (SEs) are large genomic regions with a high density of enhancer marks. In cancer, SEs are found near oncogenes and dictate cancer gene expression. However, how oncogenic SEs are regulated remains poorly understood. Here, we show that INO80, a chromatin remodeling complex, is required for SE-mediated oncogenic transcription and tumor growth in melanoma. The expression of Ino80, the SWI/SNF ATPase, is elevated in melanoma cells and patient melanomas compared with normal melanocytes and benign nevi. Furthermore, Ino80 silencing selectively inhibits melanoma cell proliferation, anchorage-independent growth, tumorigenesis, and tumor maintenance in mouse xenografts. Mechanistically, Ino80 occupies >90% of SEs, and its occupancy is dependent on transcription factors such as MITF and Sox9. Ino80 binding reduces nucleosome occupancy and facilitates Mediator recruitment, thus promoting oncogenic transcription. Consistently, genes co-occupied by Ino80 and Med1 are selectively expressed in melanomas compared with melanocytes. Together, our results reveal an essential role of INO80-dependent chromatin remodeling in SE function and suggest a novel strategy for disrupting SEs in cancer treatment.

Drug Repurposing for COVID-19 by Constructing a Comorbidity Network with Central Nervous System Disorders.

In the post-COVID-19 era, treatment options for potential SARS-CoV-2 outbreaks remain limited. An increased incidence of central nervous system (CNS) disorders has been observed in long-term COVID-19 patients. Understanding the shared molecular mechanisms between these conditions may provide new insights for developing effective therapies. This study developed an integrative drug-repurposing framework for COVID-19, leveraging comorbidity data with CNS disorders, network-based modular analysis, and dynamic perturbation analysis to identify potential drug targets and candidates against SARS-CoV-2. We constructed a comorbidity network based on the literature and data collection, including COVID-19-related proteins and genes associated with Alzheimer's disease, Parkinson's disease, multiple sclerosis, and autism spectrum disorder. Functional module detection and annotation identified a module primarily involved in protein synthesis as a key target module, utilizing connectivity map drug perturbation data. Through the construction of a weighted drug-target network and dynamic network-based drug-repurposing analysis, ubiquitin-carboxy-terminal hydrolase L1 emerged as a potential drug target. Molecular dynamics simulations suggested pregnenolone and BRD-K87426499 as two drug candidates for COVID-19. This study introduces a dynamic-perturbation-network-based drug-repurposing approach to identify COVID-19 drug targets and candidates by incorporating the comorbidity conditions of CNS disorders.

FLCMC: Federated Learning Approach for Chinese Medicinal Text Classification.

Addressing the privacy protection and data sharing issues in Chinese medical texts, this paper introduces a federated learning approach named FLCMC for Chinese medical text classification. The paper first discusses the data heterogeneity issue in federated language modeling. Then, it proposes two perturbed federated learning algorithms, FedPA and FedPAP, based on the self-attention mechanism. In these algorithms, the self-attention mechanism is incorporated within the model aggregation module, while a perturbation term, which measures the differences between the client and the server, is added to the local update module along with a customized PAdam optimizer. Secondly, to enable a fair comparison of algorithms' performance, existing federated algorithms are improved by integrating a customized Adam optimizer. Through experiments, this paper first conducts experimental analyses on hyperparameters, data heterogeneity, and validity on synthetic datasets, which proves that the proposed federated learning algorithm has significant advantages in classification performance and convergence stability when dealing with heterogeneous data. Then, the algorithm is applied to Chinese medical text datasets to verify its effectiveness on real datasets. The comparative analysis of algorithm performance and communication efficiency shows that the algorithm exhibits strong generalization ability on deep learning models for Chinese medical texts. As for the synthetic dataset, upon comparing with comparison algorithms FedAvg, FedProx, FedAtt, and their improved versions, the experimental results show that for data with general heterogeneity, both FedPA and FedPAP show significantly more accurate and stable convergence behavior. On the real Chinese medical dataset of doctor-patient conversations, IMCS-V2, with logistic regression and long short-term memory network as training models, the experiment results show that in comparison to the above three comparison algorithms and their improved versions, FedPA and FedPAP both possess the best accuracy performance and display significantly more stable and accurate convergence behavior, proving that the method in this paper has better classification effects for Chinese medical texts.

Phylogenomic Analyses of Alismatales Shed Light into Adaptations to Aquatic Environments.

Land plants first evolved from freshwater algae, and flowering plants returned to water as early as the Cretaceous and multiple times subsequently. Alismatales is the largest clade of aquatic angiosperms including all marine angiosperms, as well as terrestrial plants. We used Alismatales to explore plant adaptations to aquatic environments by analyzing a data set that included 95 samples (89 Alismatales species) covering four genomes and 91 transcriptomes (59 generated in this study). To provide a basis for investigating adaptations, we assessed phylogenetic conflict and whole-genome duplication (WGD) events in Alismatales. We recovered a relationship for the three main clades in Alismatales as (Tofieldiaceae, Araceae) + core Alismatids. We also found phylogenetic conflict among the three main clades that was best explained by incomplete lineage sorting and introgression. Overall, we identified 18 putative WGD events across Alismatales. One of them occurred at the most recent common ancestor of core Alismatids, and three occurred at seagrass lineages. We also found that lineage and life-form were both important for different evolutionary patterns for the genes related to freshwater and marine adaptation. For example, several light- or ethylene-related genes were lost in the seagrass Zosteraceae, but are present in other seagrasses and freshwater species. Stomata-related genes were lost in both submersed freshwater species and seagrasses. Nicotianamine synthase genes, which are important in iron intake, expanded in both submersed freshwater species and seagrasses. Our results advance the understanding of the adaptation to aquatic environments and WGDs using phylogenomics.

Preclinical development and evaluation of nanobody-based CD70-specific CAR T cells for the treatment of acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) treatment remains challenging. CD70 was reported as a promising AML-specific antigen. Preclinically, CAR T-cell with single-chain-variable fragment (scFv) or truncated CD27 targeting CD70 has been reported to treat AML. However, various disadvantages including spontaneous exhaustion, proteinase-mediated loss of functional receptors, and high immunogenicity, limited its further application to clinical settings. Alternatively, the single-variable domain on heavy chain (VHH), also known as nanobodies, with comparable binding ability and specificity, provides an optional solution. METHOD: We generated CD70 knocked-out novel nanobody-based anti-CD70-CAR T-cells (nb70CAR-T) with two different VHHs for antigen detection. Next, we detected the CD70 expression on primary AML blasts by flow cytometry and associated the efficacy of nb70CAR-T with the target antigen density. Finally, epigenetic modulators were investigated to regulate the CD70 expression on AML cells to promote the functionality of nb70CAR-T. RESULTS: Our nb70CAR-T exhibited expected tumoricidal functionality against CD70-expressed cell lines and primary AML blasts. However, CD70 expression in primary AML blasts was not consistently high and nb70CAR-T potently respond to an estimated 40.4% of AML patients when the CD70 expression level was over a threshold of 1.6 (MFI ratio). Epigenetic modulators, Decitabine and Chidamide can up-regulate CD70 expression on AML cells, enhancing the treatment efficacy of nb70CAR-T. CONCLUSION: CD70 expression in AML blasts was not fully supportive of its role in AML targeted therapy as reported. The combinational use of Chidamide and Decitabine with nb70CAR-T could provide a new potential for the treatment of AML.

Iris lactea var. chinensis plant drought tolerance depends on the response of proline metabolism, transcription factors, transporters and the ROS-scavenging system.

BACKGROUND: Iris lactea var. chinensis, a perennial herbaceous species, is widely distributed and has good drought tolerance traits. However, there is little information in public databases concerning this herb, so it is difficult to understand the mechanism underlying its drought tolerance. RESULTS: In this study, we used Illumina sequencing technology to conduct an RNA sequencing (RNA-seq) analysis of I. lactea var. chinensis plants under water-stressed conditions and rehydration to explore the potential mechanisms involved in plant drought tolerance. The resulting de novo assembled transcriptome revealed 126,979 unigenes, of which 44,247 were successfully annotated. Among these, 1187 differentially expressed genes (DEGs) were identified from a comparison of the water-stressed treatment and the control (CK) treatment (T/CK); there were 481 upregulated genes and 706 downregulated genes. Additionally, 275 DEGs were identified in the comparison of the rehydration treatment and the water-stressed treatment (R/T). Based on Quantitative Real-time Polymerase Chain Reaction (qRT-PCR) analysis, the expression levels of eight randomly selected unigenes were consistent with the transcriptomic data under water-stressed and rehydration treatment, as well as in the CK. According to Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, proline metabolism-related DEGs, including those involved in the 'proline catabolic process', the 'proline metabolic process', and 'arginine and proline metabolism', may play important roles in plant drought tolerance. Additionally, these DEGs encoded 43 transcription factors (TFs), 46 transporters, and 22 reactive oxygen species (ROS)-scavenging system-related proteins. Biochemical analysis and histochemical detection showed that proline and ROS were accumulated under water-stressed conditions, which is consistent with the result of the transcriptomic analysis. CONCLUSIONS: In summary, our transcriptomic data revealed that the drought tolerance of I. lactea var. chinensis depends on proline metabolism, the action of TFs and transporters, and a strong ROS-scavenging system. The related genes found in this study could help us understand the mechanisms underlying the drought tolerance of I. lactea var. chinensis.

MiR-200/183 family-mediated module biomarker for gastric cancer progression: an AI-assisted bioinformatics method with experimental functional survey.

BACKGROUND: Gastric cancer (GC) is a major cancer burden throughout the world with a high mortality rate. The performance of current predictive and prognostic factors is still limited. Integrated analysis is required for accurate cancer progression predictive biomarker and prognostic biomarkers that help to guide therapy. METHODS: An AI-assisted bioinformatics method that combines transcriptomic data and microRNA regulations were used to identify a key miRNA-mediated network module in GC progression. To reveal the module's function, we performed the gene expression analysis in 20 clinical samples by qRT-PCR, prognosis analysis by multi-variable Cox regression model, progression prediction by support vector machine, and in vitro studies to elaborate the roles in GC cells migration and invasion. RESULTS: A robust microRNA regulated network module was identified to characterize GC progression, which consisted of seven miR-200/183 family members, five mRNAs and two long non-coding RNAs H19 and CLLU1. Their expression patterns and expression correlation patterns were consistent in public dataset and our cohort. Our findings suggest a two-fold biological potential of the module: GC patients with high-risk score exhibited a poor prognosis (p-value < 0.05) and the model achieved AUCs of 0.90 to predict GC progression in our cohort. In vitro cellular analyses shown that the module could influence the invasion and migration of GC cells. CONCLUSIONS: Our strategy which combines AI-assisted bioinformatics method with experimental and clinical validation suggested that the miR-200/183 family-mediated network module as a "pluripotent module", which could be potential marker for GC progression.

A novel two-step administration of XPO-1 inhibitor may enhance the effect of anti-BCMA CAR-T in relapsed/refractory extramedullary multiple myeloma.

BACKGROUND: Extramedullary disease usually implies a dismal outcome in relapsed/refractory multiple myeloma patients, and requires novel treatment approaches. We designed a trial using Selinexor, a nuclear export protein 1 inhibitor, together with anti-B cell maturation antigen (BCMA) chimeric antigen receptor (CAR)-T cell product CT103A to treat these patients, and describe the first two cases in this report. METHODS: Selinexor was administered with a novel two-step schedule in bridging therapy and in maintenance. The clinical responses and adverse events were recorded after CAR-T infusion and Selinexor administration. In vitro analysis of the influence of Selinexor on CAR-T cell function was performed using myeloma cell lines. RESULTS: After infusion, both patients achieved stringent complete remission (sCR), and were maintained in sCR at data-cutoff, with survival over 13 and 10 months, respectively. Neither immune effector cell-associated neurotoxicity syndrome nor over grade 2 cytokine release syndrome was observed. Meanwhile, the patients showed good tolerance to the combination. In addition, we demonstrated that low dose of Selinexor could upregulate the expression of BCMA on plasma cell lines and subsequently enhance the function of CAR-T cell in vitro. CONCLUSIONS: The combination of Selinexor and CT103A exerts preliminary synergistic effect, and can be developed as a promising strategy for relapsed/refractory extramedullary myeloma.