Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer–target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer–target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.

Connective tissue growth factor (CTGF) is a key signaling and regulatory molecule involved in different biological processes, such as cell proliferation, angiogenesis, and wound healing, as well as multiple pathologies, such as tumor development and tissue fibrosis. Although the underlying mechanisms of CTGF remain incompletely understood, a commonly accepted theory is that the interactions between different protein domains in CTGF and other various regulatory proteins and ligands contribute to its variety of functions. Here, we highlight the structure of each domain of CTGF and its biology functions in physiological conditions. We further summarized main diseases that are deeply influenced by CTGF domains and the potential targets of these diseases. Finally, we address the advantages and disadvantages of current drugs targeting CTGF and provide the perspective for the drug discovery of the next generation of CTGF inhibitors based on aptamers.

Clinical studies in a range of cancers have detected elevated levels of the Wnt antagonist Dickkopf-1 (DKK1) in the serum or tumors of patients, and this was frequently associated with a poor prognosis. Our analysis of DKK1 gene profile using data from TCGA also proves the high expression of DKK1 in 14 types of cancers. Numerous preclinical studies have demonstrated the cancer-promoting effects of DKK1 in both in vitro cell models and in vivo animal models. Furthermore, DKK1 showed the ability to modulate immune cell activities as well as the immunosuppressive cancer microenvironment. Expression level of DKK1 is positively correlated with infiltrating levels of myeloid-derived suppressor cells (MDSCs) in 20 types of cancers, while negatively associated with CD8⁺ T cells in 4 of these 20 cancer types. Emerging experimental evidence indicates that DKK1 has been involved in T cell differentiation and induction of cancer evasion of immune surveillance by accumulating MDSCs. Consequently, DKK1 has become a promising target for cancer immunotherapy, and the mechanisms of DKK1 affecting cancers and immune cells have received great attention. This review introduces the rapidly growing body of literature revealing the cancer-promoting and immune regulatory activities of DKK1. In addition, this review also predicts that by understanding the interaction between different domains of DKK1 through computational modeling and functional studies, the underlying functional mechanism of DKK1 could be further elucidated, thus facilitating the development of anti-DKK1 drugs with more promising efficacy in cancer immunotherapy.

Aptamers are promising therapeutic and diagnostic agents for various diseases due to their high affinity and specificity against target proteins. Structural determination in combination with multiple biochemical and biophysical methods could help to explore the interacting mechanism between aptamers and their targets. Regrettably, structural studies for aptamer–target interactions are still the bottleneck in this field, which are facing various difficulties. In this review, we first reviewed the methods for resolving structures of aptamer–protein complexes and for analyzing the interactions between aptamers and target proteins. We summarized the general features of the interacting nucleotides and residues involved in the interactions between aptamers and proteins. Challenges and perspectives in current methodologies were discussed. Approaches for determining the binding affinity between aptamers and target proteins as well as modification strategies for stabilizing the binding affinity of aptamers to target proteins were also reviewed. The review could help to understand how aptamers interact with their targets and how alterations such as chemical modifications in the structures affect the affinity and function of aptamers, which could facilitate the optimization and translation of aptamers-based theranostics.

Immunotherapy, especially anti-PD-1, is becoming a pillar of modern muscle-invasive bladder cancer (MIBC) treatment. However, the objective response rates (ORR) are relatively low due to the lack of precise biomarkers to select patients. Herein, the molecular subtype, tumor mutation burden (TMB), and CD8+ T cells were calculated by the gene expression and mutation profiles of MIBC patients. MIBC immunotypes were constructed using clustering analysis based on tumor mutation burden, CD8+ T cells, and molecular subtypes. Mutated genes, enriched functional KEGG pathways and GO terms, and co-expressed network-specific hub genes have been identified. We demonstrated that ORR of immunotype A patients identified by molecular subtype, CD8+ T cells, and TMB is about 36% predictable. PIK3CA, RB1, FGFR3, KMT2C, MACF1, RYR2, and EP300 are differentially mutated among three immunotypes. Pathways such as ECM-receptor interaction, PI3K-Akt signaling pathway, and TGF-beta signaling pathway are top-ranked in enrichment analysis. Low expression of ACTA2 was associated with the MIBC survival benefit. The current study constructs a model that could identify suitable MIBC patients for immunotherapy, and it is an important step forward to the personalized treatment of bladder cancers.

Basal and luminal subtypes of muscle-invasive bladder cancer (MIBC) have distinct molecular profiles and heterogeneous clinical behaviors. The interactions between mRNAs and lncRNAs, which might be regulated by miRNAs, have crucial roles in many cancers. However, the miRNA-dependent crosstalk between lncRNA and mRNA in specific MIBC subtypes still remains unclear. In this study, we first classified MIBC into two conservative subtypes using miRNA, mRNA and lncRNA expression data derived from The Cancer Genome Atlas. Then we investigated subtype-related biological pathways and evaluated the subtype classification performance using Decision Trees, Random Forest and eXtreme Gradient Boosting (XGBoost). At last, we explored potential miRNA-mediated lncRNA-mRNA crosstalks based on co-expression analysis. Our results show that: (1) the luminal subtype is primarily characterized by upregulation of metabolism-related pathways while the basal subtype is predominantly characterized by upregulation of epithelial-mesenchymal transition, metastasis, and immune system process-related pathways; (2) the XGBoost prediction model is consistently robust for classification of the molecular subtypes of MIBC across four datasets (The area under the ROC curve > 0.9); (3) the expression levels of the molecules in the miR-200c and miR141-mediated lncRNA-mRNA crosstalks differ considerably between the two subtypes and have close relationships with the prognosis of MIBC. The miR-200c and miR-141-dependent mRNA-lncRNA crosstalks might be of great significance in tumorigenesis and tumor progression and may serve as the novel prognostic predictors and classification markers of MIBC subtypes.