A comprehensive AI-driven analysis of large-scale omic datasets reveals novel dual-purpose targets for the treatment of cancer and aging.

As aging and tumorigenesis are tightly interconnected biological processes, targeting their common underlying driving pathways may induce dual-purpose anti-aging and anti-cancer effects. Our transcriptomic analyses of 16,740 healthy samples demonstrated tissue-specific age-associated gene expression, with most tumor suppressor genes downregulated during aging. Furthermore, a large-scale pan-cancer analysis of 11 solid tumor types (11,303 cases and 4431 control samples) revealed that many cellular processes, such as protein localization, DNA replication, DNA repair, cell cycle, and RNA metabolism, were upregulated in cancer but downregulated in healthy aging tissues, whereas pathways regulating cellular senescence were upregulated in both aging and cancer. Common cancer targets were identified by the AI-driven target discovery platform-PandaOmics. Age-associated cancer targets were selected and further classified into four groups based on their reported roles in lifespan. Among the 51 identified age-associated cancer targets with anti-aging experimental evidence, 22 were proposed as dual-purpose targets for anti-aging and anti-cancer treatment with the same therapeutic direction. Among age-associated cancer targets without known lifespan-regulating activity, 23 genes were selected based on predicted dual-purpose properties. Knockdown of histone demethylase KDM1A, one of these unexplored candidates, significantly extended lifespan in Caenorhabditis elegans. Given KDM1A's anti-cancer activities reported in both preclinical and clinical studies, our findings propose KDM1A as a promising dual-purpose target. This is the first study utilizing an innovative AI-driven approach to identify dual-purpose target candidates for anti-aging and anti-cancer treatment, supporting the value of AI-assisted target identification for drug discovery.

Biomedical generative pre-trained based transformer language model for age-related disease target discovery.

Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets using a large language model (LLM). We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction. Our study demonstrates that pre-training of the LLM model with task-specific texts improves its performance. Applying the developed pipeline, we retrieved prospective aging and age-related disease targets and showed that these proteins are in correspondence with the database data. Moreover, we propose CCR5 and PTH as potential novel dual-purpose anti-aging and disease targets which were not previously identified as age-related but were highly ranked in our approach. Overall, our work highlights the high potential of transformer models in novel target prediction and provides a roadmap for future integration of AI approaches for addressing the intricate challenges presented in the biomedical field.

Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics - An AI-Enabled Biological Target Discovery Platform.

Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease with ill-defined pathogenesis, calling for urgent developments of new therapeutic regimens. Herein, we applied PandaOmics, an AI-driven target discovery platform, to analyze the expression profiles of central nervous system (CNS) samples (237 cases; 91 controls) from public datasets, and direct iPSC-derived motor neurons (diMNs) (135 cases; 31 controls) from Answer ALS. Seventeen high-confidence and eleven novel therapeutic targets were identified and will be released onto ALS.AI (http://als.ai/). Among the proposed targets screened in the c9ALS Drosophila model, we verified 8 unreported genes (KCNB2, KCNS3, ADRA2B, NR3C1, P2RY14, PPP3CB, PTPRC, and RARA) whose suppression strongly rescues eye neurodegeneration. Dysregulated pathways identified from CNS and diMN data characterize different stages of disease development. Altogether, our study provides new insights into ALS pathophysiology and demonstrates how AI speeds up the target discovery process, and opens up new opportunities for therapeutic interventions.

Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine.

Aging biology is a promising and burgeoning research area that can yield dual-purpose pathways and protein targets that may impact multiple diseases, while retarding or possibly even reversing age-associated processes. One widely used approach to classify a multiplicity of mechanisms driving the aging process is the hallmarks of aging. In addition to the classic nine hallmarks of aging, processes such as extracellular matrix stiffness, chronic inflammation and activation of retrotransposons are also often considered, given their strong association with aging. In this study, we used a variety of target identification and prioritization techniques offered by the AI-powered PandaOmics platform, to propose a list of promising novel aging-associated targets that may be used for drug discovery. We also propose a list of more classical targets that may be used for drug repurposing within each hallmark of aging. Most of the top targets generated by this comprehensive analysis play a role in inflammation and extracellular matrix stiffness, highlighting the relevance of these processes as therapeutic targets in aging and age-related diseases. Overall, our study reveals both high confidence and novel targets associated with multiple hallmarks of aging and demonstrates application of the PandaOmics platform to target discovery across multiple disease areas.

TPI1-reduced extracellular vesicles mediated by Rab20 downregulation promotes aerobic glycolysis to drive hepatocarcinogenesis.

Rab GTPases are major mediators that ensure the proper spatiotemporal regulation of intracellular trafficking. Functional impairment and altered expression of Rab proteins have been revealed in various human cancers. There is an emerging evidence about the role of Rab proteins in the biogenesis of extracellular vesicles (EVs). In hepatocellular carcinoma (HCC), using RNA sequencing comparing expression profiles of adjacent non-tumorous tissues and HCC, Rab20 is identified to be the most frequently downregulated Rab member in HCC. Functionally, restoration of Rab20 in metastatic HCC cells results in the release of EVs with a diminished activity to promote cell growth, motility and metastasis. Conversely, EVs released from normal liver cells with Rab20 knockdown loses suppressive effect on HCC cell growth and motility. Proteomic profiling revealed the level of triosephosphate isomerase 1 (TPI1), a glycolytic enzyme, in EVs to be positively associated with Rab20 expression of the releasing cells. TPI1 targeted to be expressed in EVs released by Rab20 knockdown cells compromises the oncogenic activity of EVs. Besides, EVs released by TPI1 knockdown cells recapitulates the promoting effect of EVs derived from HCC cells with Rab20 underexpression. Aerobic glycolysis is beneficial to the survival and proliferation of tumour cells. Here, we observed that the enhanced cell growth and motility are driven by the enhanced aerobic glycolysis induced by EVs with reduced TPI1. The addition of glycolytic inhibitor blocks the promoting effect of EVs with reduced TPI1. Taken together, our study provides a mechanistic link among tumour cell-derived EVs and glucose metabolism in HCC with Rab20 deregulation.

Tumour extracellular vesicle-derived Complement Factor H promotes tumorigenesis and metastasis by inhibiting complement-dependent cytotoxicity of tumour cells.

The complement system is involved in the immunosurveillance of pathogens and tumour cells. Proteomic profiling revealed that extracellular vesicles (EVs) released by metastatic hepatocellular carcinoma (HCC) cells contained a significant number of complement proteins. Complement Factor H (CFH), an abundant soluble serum protein that inhibits the alternative complement pathway, was found to be highly expressed in EVs of metastatic HCC cell lines. Here, we investigated the functional role of EV-CFH and explored the therapeutic efficacy of targeting EV-CFH with an anti-CFH antibody in HCC. The results showed that EVs that are enriched in CFH promoted HCC cell growth, migration, invasiveness and enhanced liver tumour formation in mice. EV-CFH also promoted metastasis, which was significantly abrogated when treated with an anti-CFH antibody. These findings demonstrate an unexplored function of EV-CFH in protecting HCC cells by evading complement attack, thereby facilitating tumorigenesis and metastasis. Lastly, we demonstrated the therapeutic efficacy of an anti-CFH antibody in suppressing tumour formation in a syngeneic mouse model. This study suggests a new therapeutic strategy for HCC, by inhibiting EV-CFH with a tumour specific anti-CFH antibody.

Galectin-1 promotes hepatocellular carcinoma and the combined therapeutic effect of OTX008 galectin-1 inhibitor and sorafenib in tumor cells.

BACKGROUND: Galectins are beta-galactose specific binding proteins. In human cancers, including hepatocellular carcinoma (HCC), galectin-1 (Gal-1) is often found to be overexpressed. In order to combat the dismal diagnosis and death rates of HCC, gene silencing and targeted inhibition of Gal-1 was investigated for its improved therapeutic potential. METHODS: Cellular and secretory Gal-1 levels were analyzed using HCC clinical samples. The study of Gal-1 was carried by both knockdown and overexpression approaches. The stable clones were tested by in vitro assays and in vivo experiments. Mass spectrometry was used to identify downstream targets of Gal-1. The upstream regulator of Gal-1, microRNA-22 (miR-22) was characterized by functional assays. The therapeutic effect of inhibiting Gal-1 was also analyzed. RESULTS: Gal-1 overexpression was observed in HCC and correlated with aggressive clinicopathological features and poorer survival. The loss of Gal-1 resulted in hindered cell migration, invasion and anchorage independent growth. This was also observed in the animal models, in that when Gal-1 was knocked down, there were fewer lung metastases. Proteomic profiling of control and Gal-1 knockdown cells identified that the level of retention in endoplasmic reticulum 1 (RER1) was suppressed when Gal-1 level was reduced. The cell motility of Gal-1 knockdown cells was enhanced upon the rescue of RER1 expression. In HCC tissues, Gal-1 and RER1 expressions displayed a significant positive correlation. The upstream regulator of Gal-1, miR-22 was observed to be underexpressed in HCC tissues and negatively correlated with Gal-1. Silencing of miR-22 resulted in the upregulation of Gal-1 and enhanced cell growth, migration and invasion. However, such enhancement was abolished in cells treated with OTX008, an inhibitor of Gal-1. Combinational treatment of OTX008 and sorafenib significantly reduced tumor growth and size. CONCLUSIONS: Gal-1 overexpression was detected in HCC and this played a role in promoting tumorigenic processes and metastasis. The function of Gal-1 was found to be mediated through RER1. The correlations between miR-22, Gal-1 and RER1 expressions demonstrated the importance of miR-22 regulation on Gal-1/RER1 oncogenic activity. Lastly, the combinational treatment of OTX008 and sorafenib proved to be an improved therapeutic option compared to when administering sorafenib alone.