Targeting JMJD1C to selectively disrupt tumor Treg cell fitness enhances antitumor immunity.

Regulatory T (Treg) cells are critical for immune tolerance but also form a barrier to antitumor immunity. As therapeutic strategies involving Treg cell depletion are limited by concurrent autoimmune disorders, identification of intratumoral Treg cell-specific regulatory mechanisms is needed for selective targeting. Epigenetic modulators can be targeted with small compounds, but intratumoral Treg cell-specific epigenetic regulators have been unexplored. Here, we show that JMJD1C, a histone demethylase upregulated by cytokines in the tumor microenvironment, is essential for tumor Treg cell fitness but dispensable for systemic immune homeostasis. JMJD1C deletion enhanced AKT signals in a manner dependent on histone H3 lysine 9 dimethylation (H3K9me2) demethylase and STAT3 signals independently of H3K9me2 demethylase, leading to robust interferon-γ production and tumor Treg cell fragility. We have also developed an oral JMJD1C inhibitor that suppresses tumor growth by targeting intratumoral Treg cells. Overall, this study identifies JMJD1C as an epigenetic hub that can integrate signals to establish tumor Treg cell fitness, and we present a specific JMJD1C inhibitor that can target tumor Treg cells without affecting systemic immune homeostasis.

Cytoplasmic DNA sensing by KU complex in aged CD4+ T cell potentiates T cell activation and aging-related autoimmune inflammation.

Aging is associated with DNA accumulation and increased homeostatic proliferation of circulating T cells. Although these attributes are associated with aging-related autoimmunity, their direct contributions remain unclear. Conventionally, KU complex, the regulatory subunit of DNA-dependent protein kinase (DNA-PK), together with the catalytic subunit of DNA-PK (DNA-PKcs), mediates DNA damage repair in the nucleus. Here, we found KU complex abundantly expressed in the cytoplasm, where it recognized accumulated cytoplasmic DNA in aged human and mouse CD4+ T cells. This process enhanced T cell activation and pathology of experimental autoimmune encephalomyelitis (EAE) in aged mice. Mechanistically, KU-mediated DNA sensing facilitated DNA-PKcs recruitment and phosphorylation of the kinase ZAK. This activated AKT and mTOR pathways, promoting CD4+ T cell proliferation and activation. We developed a specific ZAK inhibitor, which dampened EAE pathology in aged mice. Overall, these findings demonstrate a KU-mediated cytoplasmic DNA-sensing pathway in CD4+ T cells that potentiates aging-related autoimmunity.

TG468: a text graph convolutional network for predicting clinical response to immune checkpoint inhibitor therapy.

Enhancing cancer treatment efficacy remains a significant challenge in human health. Immunotherapy has witnessed considerable success in recent years as a treatment for tumors. However, due to the heterogeneity of diseases, only a fraction of patients exhibit a positive response to immune checkpoint inhibitor (ICI) therapy. Various single-gene-based biomarkers and tumor mutational burden (TMB) have been proposed for predicting clinical responses to ICI; however, their predictive ability is limited. We propose the utilization of the Text Graph Convolutional Network (GCN) method to comprehensively assess the impact of multiple genes, aiming to improve the predictive capability for ICI response. We developed TG468, a Text GCN model framing drug response prediction as a text classification task. By combining natural language processing (NLP) and graph neural network techniques, TG468 effectively handles sparse and high-dimensional exome sequencing data. As a result, TG468 can distinguish survival time for patients who received ICI therapy and outperforms single gene biomarkers, TMB and some classical machine learning models. Additionally, TG468's prediction results facilitate the identification of immune status differences among specific patient types in the Cancer Genome Atlas dataset, providing a rationale for the model's predictions. Our approach represents a pioneering use of a GCN model to analyze exome data in patients undergoing ICI therapy and offers inspiration for future research using NLP technology to analyze exome sequencing data.

KinomeMETA: a web platform for kinome-wide polypharmacology profiling with meta-learning.

Kinase-targeted inhibitors hold promise for new therapeutic options, with multi-target inhibitors offering the potential for broader efficacy while minimizing polypharmacology risks. However, comprehensive experimental profiling of kinome-wide activity is expensive, and existing computational approaches often lack scalability or accuracy for understudied kinases. We introduce KinomeMETA, an artificial intelligence (AI)-powered web platform that significantly expands the predictive range with scalability for predicting the polypharmacological effects of small molecules across the kinome. By leveraging a novel meta-learning algorithm, KinomeMETA efficiently utilizes sparse activity data, enabling rapid generalization to new kinase tasks even with limited information. This significantly expands the repertoire of accurately predictable kinases to 661 wild-type and clinically-relevant mutant kinases, far exceeding existing methods. Additionally, KinomeMETA empowers users to customize models with their proprietary data for specific research needs. Case studies demonstrate its ability to discover new active compounds by quickly adapting to small dataset. Overall, KinomeMETA offers enhanced kinome virtual profiling capabilities and is positioned as a powerful tool for developing new kinase inhibitors and advancing kinase research. The KinomeMETA server is freely accessible without registration at https://kinomemeta.alphama.com.cn/.

KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling.

Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.

The emergence of machine learning force fields in drug design.

In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.

An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph.

Identifying the potential compound-protein interactions (CPIs) plays an essential role in drug development. The computational approaches for CPI prediction can reduce time and costs of experimental methods and have benefited from the continuously improved graph representation learning. However, most of the network-based methods use heterogeneous graphs, which is challenging due to their complex structures and heterogeneous attributes. Therefore, in this work, we transformed the compound-protein heterogeneous graph to a homogeneous graph by integrating the ligand-based protein representations and overall similarity associations. We then proposed an Inductive Graph AggrEgator-based framework, named CPI-IGAE, for CPI prediction. CPI-IGAE learns the low-dimensional representations of compounds and proteins from the homogeneous graph in an end-to-end manner. The results show that CPI-IGAE performs better than some state-of-the-art methods. Further ablation study and visualization of embeddings reveal the advantages of the model architecture and its role in feature extraction, and some of the top ranked CPIs by CPI-IGAE have been validated by a review of recent literature. The data and source codes are available at https://github.com/wanxiaozhe/CPI-IGAE.

Evaluation of bladder filling effects on the dose distribution during radiotherapy for cervical cancer based on daily CT images.

PURPOSE: This study aimed to assess the effects of bladder filling during cervical cancer radiotherapy on target volume and organs at risk (OARs) dose based on daily computed tomography (daily-CT) images and provide bladder-volume-based dose prediction models. METHODS: Nineteen patients (475 daily-CTs) comprised the study group, and five patients comprised the validation set (25 daily-CTs). Target volumes and OARs were delineated on daily-CT images and the treatment plan was recalculated accordingly. The deviation from the planning bladder volume (DVB), the correlation between DVB and clinical (CTV)/planning (PTV) target volume in terms of prescribed dose coverage, and the relationship of small bowel volume and bladder dose with the ratio of bladder volume (RVB) were analyzed. RESULTS: In all cases, the prescribed dose coverage in the CTV was >95% when DVB was <200 cm3 , whereas that in the PTV was >95% when RVB was <160%. The ratio of bladder V45 Gy to the planning bladder V45 Gy (RBV45 ) exhibited a negative linear relationship with RVB (RBV45  = -0.18*RVB + 120.8; R2  = 0.80). Moreover, the ratio of small bowel volume to planning small bowel volume (RVS) exhibited a negative linear relationship with RVB (RVS = -1.06*RVB +217.59; R2  = 0.41). The validation set results showed that the linear model predicted well the effects of bladder volume changes on target volume coverage and bladder dose. CONCLUSIONS: This study assessed dosimetry and volume effects of bladder filling on target and OARs based on daily-CT images. We established a quantitative relationship between these parameters, providing dose prediction models for cervical cancer radiotherapy.

Impact of hysterectomy on cardiovascular disease and different subtypes: a meta-analysis.

PURPOSE: To explore the association between hysterectomy and cardiovascular disease (CVD). METHODS: Observational studies from eight databases and reference lists of relevant studies were systematically searched up to January 2021. Pooled relative risks (RRs) and 95% CIs were calculated by the random-effects model. Subgroup analyses and meta-regressions were performed to explore potential sources of heterogeneity. Small-study effects were estimated by Egger's test and funnel plot. RESULTS: Seventeen studies from 14 articles were included in our meta-analysis. Hysterectomy was positively related to CVD (1.16; 95% CI 1.03-1.30; sample size: 1,233,495). For the subtype, ischemic heart disease, the pooled RR (95% CI) for hysterectomy versus non-hysterectomy was 1.20 (95% CI 1.08-1.35; sample size: 1,210,504), and the pooled HR (95% CI) was 1.15 (95% CI 1.03-1.29; sample size: 1,181,924). In the association between ischemic heart disease and hysterectomy with ovarian preservation (1.33; 95% CI 1.05-1.68; sample size: 29,058) or hysterectomy with oophorectomy (1.31; 95% CI 1.02-1.67; sample size: 23,257), significant positive associations were observed. A significant association was also found with having hysterectomy before 50 years (1.19; 95% CI 1.01-1.41; sample size: 515,297), but not with after 50 years (1.16; 95% CI 0.87-1.54; sample size: 398,383). In addition, hysterectomy was positively related with hypertension (1.31; 95% CI 1.10-1.56; sample size: 47,831). CONCLUSION: Hysterectomy was related to the increased risks of ischemic heart disease and hypertension. However, the evidence is not strong.

The Wnt/ß-Catenin Pathway Regulated Cytokines for Pathological Neuropathic Pain in Chronic Compression of Dorsal Root Ganglion Model.

Neuropathic pain is one of the important challenges in the clinic. Although a lot of research has been done on neuropathic pain (NP), the molecular mechanism is still elusive. We aimed to investigate whether the Wnt/ß-catenin pathway was involved in NP caused by sustaining dorsal root ganglion (DRG) compression with the chronic compression of dorsal root ganglion model (CCD). Our RNA sequencing results showed that several genes related to the Wnt pathway have changed in DRG and spinal cord dorsal horn (SCDH) after CCD surgery. Therefore, we detected the activation of the Wnt/ß-catenin pathway in DRG and SCDH and found active ß-catenin significantly upregulated in DRG and SCDH 1 day after CCD surgery and peaked on days 7-14. Immunofluorescence results also confirmed nuclear translocalization of active ß-catenin in DRG and SCDH. Additionally, rats had obvious mechanical induced pain after CCD surgery and the pain was significantly alleviated after the application of the Wnt/ß-catenin pathway inhibitor XAV939. Furthermore, we found that the levels of proinflammatory factors tumor necrosis factor-α (TNF-α) and interleukin-18 (IL-18) were significantly elevated in CCD rat serum, while the levels of them were correspondingly decreased after the Wnt/ß-catenin pathway being inhibited. The results of Spearman correlation coefficient analysis showed that the levels of TNF-α and IL-18 were negatively correlated with the mechanical withdrawal thresholds (MWT) after CCD surgery. Collectively, our findings suggest that the Wnt/ß-catenin pathway plays a critical role in the pathogenesis of NP and may be an effective target for the treatment of NP.

KinomeX: a web application for predicting kinome-wide polypharmacology effect of small molecules.

MOTIVATION: The large-scale kinome-wide virtual profiling for small molecules is a daunting task by experimental and traditional in silico drug design approaches. Recent advances in deep learning algorithms have brought about new opportunities in promoting this process. RESULTS: KinomeX is an online platform to predict kinome-wide polypharmacology effect of small molecules based solely on their chemical structures. The prediction is made by a multi-task deep neural network model trained with over 140 000 bioactivity data points for 391 kinases. Extensive computational and experimental validations have been performed. Overall, KinomeX enables users to create a comprehensive kinome interaction network for designing novel chemical modulators, and is of practical value on exploring the previously less studied or untargeted kinases. AVAILABILITY AND IMPLEMENTATION: KinomeX is available at: https://kinome.dddc.ac.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

White Matter High Signals Interfere with Noncontrast Computed Tomography in the Early Identification of Cerebral Infarction.

BACKGROUND: We developed an image patch classification-based method to detect early ischemic stroke. The accuracy of this method was >75%. We aimed to analyze patients' image data to identify interference factors that would affect its accuracy. METHODS: We conducted a retrospective analysis of 162 patients who were hospitalized with acute ischemic stroke. Factors related to the noncontrast computed tomography (ncCT) determination results were analyzed according to the patient's sex, age, clinical symptoms, cerebral infarction volume, cerebral infarction location, and whether or not the white matter high (WMH) signal was combined. RESULTS: The volume of cerebral infarction was positively correlated with the predicted results. The correct percentages of patients with volumes >1 and <1 mL were 59.18 and 83.19%, respectively, and the difference was statistically significant (p = 0.001). The correct percentage of the internal capsule region (47.1%) was significantly lower than that of the other groups (p = 0.011). The correct percentage of lateral ventricular paraventricular infarction was significantly lower than that of non-lateral ventricle patients (70.8 vs. 85.7%). In patients with lateral ventricular paraventricular infarction, if the WMH was combined, the correct percentage will decreased further as the Fazekas level increased. The correct percentage of lateral ventricle infarction combined with Fazekas 3 was 40.0%, which was statistically significant compared with the patient having Fazekas 0 with lateral ventricular infarction (p = 0.01). CONCLUSIONS: WMH had a similar computed tomography appearance to cerebral infarction and could interfere with the prediction of the cerebral infarction region by ncCT. This result provides a reference for clinicians to choose imaging methods for identifying acute cerebral infarction areas.

Exosomes Derived from CXCR4-Overexpressing BMSC Promoted Activation of Microvascular Endothelial Cells in Cerebral Ischemia/Reperfusion Injury.

Background: Ischemic stroke is a severe acute cerebrovascular disease which can be improved with neuroprotective therapies at an early stage. However, due to the lack of effective neuroprotective drugs, most stroke patients have varying degrees of long-term disability. In the present study, we investigated the role of exosomes derived from CXCR4-overexpressing BMSCs in restoring vascular function and neural repair after ischemic cerebral infarction. Methods: BMSCs were transfected with lentivirus encoded by CXCR4 (BMSCCXCR4). Exosomes derived from BMSCCXCR4 (ExoCXCR4) were isolated and characterized by transmission electron microscopy and dynamic light scattering. Western blot and qPCR were used to analyze the expression of CXCR4 in BMSCs and exosomes. The acute middle cerebral artery occlusion (MCAO) model was prepared, ExoCXCR4 were injected into the rats, and behavioral changes were analyzed. The role of ExoCXCR4 in promoting the proliferation and tube formation for angiogenesis and protecting brain endothelial cells was determined in vitro. Results: Compared with the control groups, the ExoCXCR4 group showed a significantly lower mNSS score at 7 d, 14 d, and 21 d after ischemia/reperfusion (P < 0.05). The bEnd.3 cells in the ExoCXCR4 group have stronger proliferation ability than other groups (P < 0.05), while the CXCR4 inhibitor can reduce this effect. Exosomes control (ExoCon) can significantly promote the migration of bEnd.3 cells (P < 0.05), while there was no significant difference between the ExoCXCR4 and ExoCon groups (P > 0.05). ExoCXCR4 can further promote the proliferation and tube formation for the angiogenesis of the endothelium compared with ExoCon group (P < 0.05). In addition, cobalt chloride (COCl2) can increase the expression of ß-catenin and Wnt-3, while ExoCon can reduce the expression of these proteins (P < 0.05). ExoCXCR4 can further attenuate the activation of Wnt-3a/ß-catenin pathway (P < 0.05). Conclusions: In ischemia/reperfusion injury, ExoCXCR4 promoted the proliferation and tube formation of microvascular endothelial cells and play an antiapoptotic role via the Wnt-3a/ß-catenin pathway.

15-hydroxy-6α,12-epoxy-7ß,10αH,11ßH-spiroax-4-ene-12-one sensitizes rectal tumor cells to anti-PD1 treatment through agonism of CD11b.

PURPOSE: Although immunotherapies have resulted in durable clinical responses, not all tumor types have seen substantial benefit. Extensive recruitment and accumulation of immunosuppressive myeloid cells into the tumor tissues has been postulated as a major mechanism of resistance to immunotherapies. Strategies targeting on single immunosuppressive cell type, in combination with checkpoint inhibitors, have resulted in promising outcomes in animal studies. However, compensatory actions by untargeted cells may limit the therapeutic efficacy. CD11b is highly expressed on the myeloid cell surface with an important role in their trafficking and cellular functions.In this study, we demonstrated that activation of CD11b with 15-hydroxy-6α,12-epoxy-7ß,10αH,11ßH-spiroax-4-ene-12-one (HESEO) enhanced the therapeutic efficacy of anti-PD1 treatment in the tumor model. MATERIALS AND METHODS: A syngeneic rectal tumor model was established. Different types of cells from the peripheral blood and tumor tissues were analyzed by flow cytometry. Real-time PCR was used to detect the gene expression. Therapeutic effects of HESEO combining with anti-PD1 antibody were assessed by the tumor model. RESULTS: Our data demonstrated that HESEO repolarized tumor-associated macrophages and reduced the number of tumor-infiltrating immunosuppressive myeloid cells. We further demonstrated that HESEO and immunotherapy combination promoted tumor growth control in a syngeneic tumor model. CONCLUSIONS: Our results showed that HESEO improved anti-tumor T cell immunity and rendered anti-PD1 treatment effective in unresponsive tumor models, providing proof of concept for a new combination strategy involving molecular agonism of CD11b to bypass the limitations of current clinical strategies to overcome resistance to immunotherapies.

[Bone Marrow Mesenchymal Stem Cell Exosomes Promote Brain Microvascular Endothelial Cell Proliferation and Migration in Rats].

OBJECTIVE: To study the effect of bone marrow mesenchyml stem cell (BMSC) exosomes (Exo) on the proliferation and migration of brain microvascular endothelial cells in rats. METHODS: BMSCs were extracted from rats and identified. The BMSCs were co-cultured with bEnd.3 cells in Transwell chamber for 24 h (BMSCs group). Extracted and identified the BMSCs exosomes (BMSC-Exo). Observed and qualitatively evaluated the cells' abilities on swallowing the BMSC-Exo under a fluorescence microscope. The optimal work concentration of BMSC-Exo was selected by detecting the cell vitality under different BMSC-Exo concentrations by CCK8 method. bEnd.3 cells were co-cultured with BMSC-Exo for 24 h (BMSC-Exo group). bEnd.3 cells cultured alone was set as control group. The proliferation and migration of bEnd.3 cells in the three groups were respectively detected by EDU and cell scratching experiment after 24 h of culture. RESULTS: Flow cytometry showed that P3 BMSCs were CD90 and CD29 positive and CD45 negative, with osteogenic differentiation and adipogenesis differentiation, indicating the extracted BMSCs high purity. The BMSC-Exo under transmission electron microscopy was round-shaped with a diameter of about 100 nm; NTA analysis found the diameter distribution of BMSC-Exo ranged from 50 to 600 nm, with a peak size of 150 nm. Immunofluorescence showed that the endothelial cells could swallow BMSC-Exo. CCK8 showed that supplement of 20 µg/mL BMSC-Exo had the best effect on cell proliferation. EDU results showed that BMSCs group and BMSC-Exo group could promote the proliferation of bEnd.3 cells compared with the control group (P<0.05), and there was no difference between BMSCs group and BMSC-Exo group (P>0.05). Cell scratch test showed that the cell mobility of the BMSC-Exo group was higher than that of the control group (P<0.05), but there was no significant difference between the BMSC-Exo group and the BMSCs group (P>0.05). CONCLUSION: BMSC-Exo can replace BMSCs in effectively promoting the proliferation and migration of cerebral microvascular endothelial cells, which provide a new potential treatment for angiogenesis after stroke.

Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies.

Over the past quarter of a century, there has been rapid development in structural biology, which now can provide solid evidence for understanding the functions of proteins. Concurrently, computational approaches with particular relevance to the chemical biology and drug design (CBDD) field have also incrementally and steadily improved. Today, these methods help elucidate detailed working mechanisms and accelerate the discovery of new chemical modulators of proteins. In recent years, integrating computational simulations and predictions with experimental validation has allowed for more effective explorations of the structure, function and modulation of important therapeutic targets. In this review, we summarize the main advancements in computational methodology development, which are then illustrated by several successful applications in CBDD. Finally, we conclude with a discussion of the current major challenges and future directions in the field.

Coffee consumption and the risk of cutaneous melanoma: a meta-analysis.

PURPOSE: Results from epidemiologic studies on coffee consumption and the risk of cutaneous melanoma are inconsistent. We conducted a meta-analysis to assess the associations between the consumption of total coffee, caffeinated coffee and decaffeinated coffee and the risk of cutaneous melanoma, respectively. METHODS: A literature search was performed in PubMed, Web of Science and EMBASE for relevant articles published up to August 2015. Pooled relative risks (RRs) with 95 % confidence intervals (CIs) were calculated with a random-effects model. Dose-response relationship was assessed by restricted cubic spline. RESULTS: Twelve studies involving 832,956 participants for total coffee consumption, 5 studies involving 717,151 participants for caffeinated coffee consumption and 6 studies involving 718,231 participants for decaffeinated coffee consumption were included in this meta-analysis. Compared with the lowest level of consumption, the pooled RRs were 0.80 (95 % CI 0.69-0.93, I (2) = 53.5 %), 0.85 (95 % CI 0.71-1.01, I (2) = 65.0 %) and 0.92 (95 % CI 0.81-1.05, I (2) = 0.0 %) for the consumption of total coffee, caffeinated coffee and decaffeinated coffee, respectively. In subgroup analysis by study design, the pooled RRs in cohort studies and case-control studies were 0.83 (95 % CI 0.72-0.97) and 0.74 (95 % CI 0.51-1.07) for total coffee consumption, respectively. Dose-response analysis suggested cutaneous melanoma risk decreased by 3 % [0.97 (0.93-1.00)] and 4 % [0.96 (0.92-1.01)] for 1 cup/day increment of total coffee and caffeinated coffee consumption, respectively. CONCLUSIONS: This meta-analysis suggests that coffee consumption may reduce the risk of cutaneous melanoma.

LncRNA PVT1 facilitates the growth and metastasis of colorectal cancer by sponging with miR-3619-5p to regulate TRIM29 expression.

BACKGROUND: Colorectal cancer (CRC) is the second most common cause of cancer-related death worldwide. Long noncoding RNA (lncRNA) is involved in many malignant tumors. This study aimed to clarify the role of the lncRNA plasmacytoma variant translocation 1 (PVT1) in CRC growth and metastasis. METHODS: Differentially expressed lncRNAs in CRC were analyzed using the Cancer Genome Atlas. Gene expression profiling interactive analysis and a comprehensive resource for lncRNAs from cancer arrays databases were used to analyze lncRNA PVT1 expression and CRC prognosis, respectively. Cell counting kit-8, wound healing, colony formation, Transwell, and immunofluorescence assays were used to evaluate CRC cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT), respectively. Tumor growth and metastasis models were used to explore the PVT1 effect on the growth and metastasis of CRC in vivo. RESULTS: PVT1 was highly expressed in CRC, associated with a poor prognosis of CRC, and showed good diagnostic value. Transfection of sh-PVT1 or pcDNA3.1-PVT1 reduced or increased the proliferation, wound healing rate, colony formation, invasion, and EMT of CRC cells. PVT1 and miR-3619-5p were co-expressed in CRC cytoplasm, and PVT1 acted as a competitive endogenous RNA (ceRNA) by sponging miR-3619-5p to up-regulate tripartite motif containing 29 (TRIM29) expression. MiR-3619-5p overexpression and TRIM29 knockdown reduced proliferation, wound healing rate, invasion, and EMT of CRC cells. However, simultaneous PVT1 and miR-3619-5p overexpression or knockdown of miR-3619-5p and TRIM29 knockdown rescued the malignant phenotype of CRC cells. CONCLUSIONS: We first clarified the ceRNA mechanism of PVT1 in CRC, which induced growth and metastasis by sponging with miR-3619-5p to regulate TRIM29.

Multi-Omics Insights into Disulfidptosis-Related Genes Reveal RPN1 as a Therapeutic Target for Liver Cancer.

Disulfidptosis, a newly identified mode of programmed cell death, is yet to be comprehensively elucidated with respect to its multi-omics characteristics in tumors, specific pathogenic mechanisms, and antitumor functions in liver cancer. This study included 10,327 tumor and normal tissue samples from 33 cancer types. In-depth analyses using various bioinformatics tools revealed widespread dysregulation of disulfidptosis-related genes (DRGs) in pan-cancer and significant associations with prognosis, genetic variations, tumor stemness, methylation levels, and drug sensitivity. Univariate and multivariate Cox regression and LASSO regression were used to screen and construct prognosis-related hub DRGs and predictive models in the context of liver cancer. Subsequently, single cell analysis was conducted to investigate the subcellular localization of RPN1, a hub DRG, in various solid tumors. Western blotting was performed to validate the expression of RPN1 at both cellular and tissue levels. Additionally, functional experiments, including CCK8, EdU, clone, and transwell assays, indicated that RPN1 knockdown promoted the proliferative and invasive capacities of liver cancer cells. Therefore, this study elucidated the multi-omics characteristics of DRGs in pan-cancer and established a prognostic model for liver cancer. Additionally, this study revealed the molecular functions of RPN1 in liver cancer, suggesting its potential as a therapeutic target for this disease.

Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery.

Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning. TranSiGen analyzes basal cell gene expression and molecular structures to reconstruct chemical-induced transcriptional profiles with high accuracy. By capturing both cellular and compound information, TranSiGen-derived representations demonstrate efficacy in diverse downstream tasks like ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. Notably, in vitro validation of TranSiGen's application in pancreatic cancer drug discovery highlights its potential for identifying effective compounds. We envisage that integrating TranSiGen into the drug discovery and mechanism research holds significant promise for advancing biomedicine.