SMTRI: A deep learning-based web service for predicting small molecules that target miRNA-mRNA interactions.

Mature microRNAs (miRNAs) are short, single-stranded RNAs that bind to target mRNAs and induce translational repression and gene silencing. Many miRNAs discovered in animals have been implicated in diseases and have recently been pursued as therapeutic targets. However, conventional pharmacological screening for candidate small-molecule drugs can be time-consuming and labor-intensive. Therefore, developing a computational program to assist mature miRNA-targeted drug discovery in silico is desirable. Our previous work (https://doi.org/10.1002/advs.201903451) revealed that the unique functional loops formed during Argonaute-mediated miRNA-mRNA interactions have stable structural characteristics and may serve as potential targets for small-molecule drug discovery. Developing drugs specifically targeting disease-related mature miRNAs and their target mRNAs would avoid affecting unrelated ones. Here, we present SMTRI, a convolutional neural network-based approach for efficiently predicting small molecules that target RNA secondary structural motifs formed by interactions between miRNAs and their target mRNAs. Measured on three additional testing sets, SMTRI outperformed state-of-the-art algorithms by 12.9%-30.3% in AUC and 2.0%-18.4% in accuracy. Moreover, four case studies on the published experimentally validated RNA-targeted small molecules also revealed the reliability of SMTRI.

Molecular classification of geriatric breast cancer displays distinct senescent subgroups of prognostic significance.

Breast cancer in the elderly presents distinct biological characteristics and clinical treatment responses compared with cancer in younger patients. Comprehensive Geriatric Assessment is recommended for evaluating treatment efficacy in elderly cancer patients based on physiological classification. However, research on molecular classification in older cancer patients remains insufficient. In this study, we identified two subgroups with distinct senescent clusters among geriatric breast cancer patients through multi-omics analysis. Using various machine learning algorithms, we developed a comprehensive scoring model called "Sene_Signature," which more accurately distinguished elderly breast cancer patients compared with existing methods and better predicted their prognosis. The Sene_Signature was correlated with tumor immune cell infiltration, as supported by single-cell transcriptomics, RNA sequencing, and pathological data. Furthermore, we observed increased drug responsiveness in patients with a high Sene_Signature to treatments targeting the epidermal growth factor receptor and cell-cycle pathways. We also established a user-friendly web platform to assist investigators in assessing Sene_Signature scores and predicting treatment responses for elderly breast cancer patients. In conclusion, we developed a novel model for evaluating prognosis and therapeutic responses, providing a potential molecular classification that assists in the pre-treatment assessment of geriatric breast cancer.

Identification of a histone deacetylase inhibitor as a therapeutic candidate for congenital central hypoventilation syndrome.

Congenital central hypoventilation syndrome (CCHS), a rare genetic disease caused by heterozygous PHOX2B mutations, is characterized by life-threatening breathing deficiencies. PHOX2B is a transcription factor required for the specification of the autonomic nervous system, which contains, in particular, brainstem respiratory centers. In CCHS, PHOX2B mutations lead to cytoplasmic PHOX2B protein aggregations, thus compromising its transcriptional capability. Currently, the only available treatment for CCHS is assisted mechanical ventilation. Therefore, identifying molecules with alleviating effects on CCHS-related breathing impairments is of primary importance. A transcriptomic analysis of cells transfected with different PHOX2B constructs was used to identify compounds of interest with the CMap tool. Using fluorescence microscopy and luciferase assay, the selected molecules were further tested in vitro for their ability to restore the nuclear location and function of PHOX2B. Finally, an electrophysiological approach was used to investigate ex vivo the effects of the most promising molecule on respiratory activities of PHOX2B-mutant mouse isolated brainstem. The histone deacetylase inhibitor SAHA was found to have low toxicity in vitro, to restore the proper location and function of PHOX2B protein, and to improve respiratory rhythm-related parameters ex vivo. Thus, our results identify SAHA as a promising agent to treat CCHS-associated breathing deficiencies.

The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.

Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry.

Construction of a T m-value prediction model and molecular dynamics study of AmNA-containing gapmer antisense oligonucleotide.

RNase H-dependent antisense oligonucleotides (gapmer ASOs) represent a class of nucleic acid therapeutics that bind to target RNA to facilitate RNase H-mediated RNA cleavage, thereby regulating the expression of disease-associated proteins. Integrating artificial nucleic acids into gapmer ASOs enhances their therapeutic efficacy. Among these, amido-bridged nucleic acid (AmNA) stands out for its potential to confer high affinity and stability to ASOs. However, a significant challenge in the design of gapmer ASOs incorporating artificial nucleic acids, such as AmNA, is the accurate prediction of their melting temperature (T m ) values. The T m is a critical parameter for designing effective gapmer ASOs to ensure proper functioning. However, predicting accurate T m values for oligonucleotides containing artificial nucleic acids remains problematic. We developed a T m prediction model using a library of AmNA-containing ASOs to address this issue. We measured the T m values of 157 oligonucleotides through differential scanning calorimetry, enabling the construction of an accurate prediction model. Additionally, molecular dynamics simulations were used to elucidate the molecular mechanisms by which AmNA modifications elevate T m , thereby informing the design strategies of gapmer ASOs.

Single-cell analysis of the survival mechanisms of fratricidal CAR-T targeting of T cell malignancies.

Chimeric antigen receptor T (CAR-T) cell therapy targeting T cell tumors still faces many challenges, one of which is its fratricide due to the target gene expressed on CAR-T cells. Despite this, these CAR-T cells can be expanded in vitro by extending the culture time and effectively eliminating malignant T cells. However, the mechanisms underlying CAR-T cell survival in cell subpopulations, the molecules involved, and their regulation are still unknown. We performed single-cell transcriptome profiling to investigate the fratricidal CAR-T products (CD26 CAR-Ts and CD44v6 CAR-Ts) targeting T cells, taking CD19 CAR-Ts targeting B cells from the same donor as a control. Compared with CD19 CAR-Ts, fratricidal CAR-T cells exhibit no unique cell subpopulation, but have more exhausted T cells, fewer cytotoxic T cells, and more T cell receptor (TCR) clonal amplification. Furthermore, we observed that fratricidal CAR-T cell survival was accompanied by target gene expression. Gene expression results suggest that fratricidal CAR-T cells may downregulate their human leukocyte antigen (HLA) molecules to evade T cell recognition. Single-cell regulatory network analysis and suppression experiments revealed that exhaustion mediated by critical regulatory factors may contribute to fratricidal CAR-T cell survival. Together, these data provide valuable and first-time insights into the survival of fratricidal CAR-T cells.

Differential network analysis reveals the key role of the ECM-receptor pathway in α-particle-induced malignant transformation.

Space particle radiation is a major environmental factor in spaceflight, and it is known to cause body damage and even trigger cancer, but with unknown molecular etiologies. To examine these causes, we developed a systems biology approach by focusing on the co-expression network analysis of transcriptomics profiles obtained from single high-dose (SE) and multiple low-dose (ME) α-particle radiation exposures of BEAS-2B human bronchial epithelial cells. First, the differential network and pathway analysis based on the global network and the core modules showed that genes in the ME group had higher enrichment for the extracellular matrix (ECM)-receptor interaction pathway. Then, collagen gene COL1A1 was screened as an important gene in the ME group assessed by network parameters and an expression study of lung adenocarcinoma samples. COL1A1 was found to promote the emergence of the neoplastic characteristics of BEAS-2B cells by both in vitro experimental analyses and in vivo immunohistochemical staining. These findings suggested that the degree of malignant transformation of cells in the ME group was greater than that of the SE, which may be caused by the dysregulation of the ECM-receptor pathway.

ASOptimizer: Optimizing antisense oligonucleotides through deep learning for IDO1 gene regulation.

Recent studies have highlighted the effectiveness of using antisense oligonucleotides (ASOs) for cellular RNA regulation, including targets that are considered undruggable; however, manually designing optimal ASO sequences can be labor intensive and time consuming, which potentially limits their broader application. To address this challenge, we introduce a platform, the ASOptimizer, a deep-learning-based framework that efficiently designs ASOs at a low cost. This platform not only selects the most efficient mRNA target sites but also optimizes the chemical modifications for enhanced performance. Indoleamine 2,3-dioxygenase 1 (IDO1) promotes cancer survival by depleting tryptophan and producing kynurenine, leading to immunosuppression through the aryl-hydrocarbon receptor (Ahr) pathway within the tumor microenvironment. We used ASOptimizer to identify ASOs that target IDO1 mRNA as potential cancer therapeutics. Our methodology consists of two stages: sequence engineering and chemical engineering. During the sequence-engineering stage, we optimized and predicted ASO sequences that could target IDO1 mRNA efficiently. In the chemical-engineering stage, we further refined these ASOs to enhance their inhibitory activity while reducing their potential cytotoxicity. In conclusion, our research demonstrates the potential of ASOptimizer for identifying ASOs with improved efficacy and safety.

Fusion of multi-source relationships and topology to infer lncRNA-protein interactions.

Long non-coding RNAs (lncRNAs) are important factors involved in biological regulatory networks. Accurately predicting lncRNA-protein interactions (LPIs) is vital for clarifying lncRNA's functions and pathogenic mechanisms. Existing deep learning models have yet to yield satisfactory results in LPI prediction. Recently, graph autoencoders (GAEs) have seen rapid development, excelling in tasks like link prediction and node classification. We employed GAE technology for LPI prediction, devising the FMSRT-LPI model based on path masking and degree regression strategies and thereby achieving satisfactory outcomes. This represents the first known integration of path masking and degree regression strategies into the GAE framework for potential LPI inference. The effectiveness of our FMSRT-LPI model primarily relies on four key aspects. First, within the GAE framework, our model integrates multi-source relationships of lncRNAs and proteins with LPN's topological data. Second, the implemented masking strategy efficiently identifies LPN's key paths, reconstructs the network, and reduces the impact of redundant or incorrect data. Third, the integrated degree decoder balances degree and structural information, enhancing node representation. Fourth, the PolyLoss function we introduced is more appropriate for LPI prediction tasks. The results on multiple public datasets further demonstrate our model's potential in LPI prediction.

SAP30, an oncogenic driver of progression, poor survival, and drug resistance in neuroblastoma.

Neuroblastoma is the most devastating extracranial solid malignancy in children. Despite an intense treatment regimen, the prognosis for high-risk neuroblastoma patients remains poor, with less than 40% survival. So far, MYCN amplification status is considered the most prognostic factor but corresponds to only ∼25% of neuroblastoma patients. Therefore, it is essential to identify a better prognosis and therapy response marker in neuroblastoma patients. We applied robust bioinformatic data mining tools, such as weighted gene co-expression network analysis, cisTarget, and single-cell regulatory network inference and clustering on two neuroblastoma patient datasets. We found Sin3A-associated protein 30 (SAP30), a driver transcription factor positively associated with high-risk, progression, stage 4, and poor survival in neuroblastoma patient cohorts. Tumors of high-risk neuroblastoma patients and relapse-specific patient-derived xenografts showed higher SAP30 levels. The advanced pharmacogenomic analysis and CRISPR-Cas9 screens indicated that SAP30 essentiality is associated with cisplatin resistance and further showed higher levels in cisplatin-resistant patient-derived xenograft tumor cell lines. Silencing of SAP30 induced cell death in vitro and led to a reduced tumor burden and size in vivo. Altogether, these results indicate that SAP30 is a better prognostic and cisplatin-resistance marker and thus a potential drug target in high-risk neuroblastoma.

Single-cell transcriptome profiling implicates the psychological stress-induced disruption of spermatogenesis.

Male infertility has emerged as a global issue, partly attributed to psychological stress. However, the cellular and molecular mechanisms underlying the adverse effects of psychological stress on male reproductive function remain elusive. We created a psychologically stressed model using terrified-sound and profiled the testes from stressed and control rats using single-cell RNA sequencing. Comparative and comprehensive transcriptome analyses of 11,744 testicular cells depicted the cellular landscape of spermatogenesis and revealed significant molecular alterations of spermatogenesis suffering from psychological stress. At the cellular level, stressed rats exhibited delayed spermatogenesis at the spermatogonia and pachytene phases, resulting in reduced sperm production. Additionally, psychological stress rewired cellular interactions among germ cells, negatively impacting reproductive development. Molecularly, we observed the down-regulation of anti-oxidation-related genes and up-regulation of genes promoting reactive oxygen species (ROS) generation in the stress group. These alterations led to elevated ROS levels in testes, affecting the expression of key regulators such as ATF2 and STAR, which caused reproductive damage through apoptosis or inhibition of testosterone synthesis. Overall, our study aimed to uncover the cellular and molecular mechanisms by which psychological stress disrupts spermatogenesis, offering insights into the mechanisms of psychological stress-induced male infertility in other species and promises in potential therapeutic targets.

Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine learning algorithms.

Endometrial cancer (EC), the second most common malignancy in the female reproductive system, has garnered increasing attention for its genomic heterogeneity, but understanding of its metabolic characteristics is still poor. We explored metabolic dysfunctions in EC through a comprehensive multi-omics analysis (RNA-seq datasets from The Cancer Genome Atlas [TCGA], Cancer Cell Line Encyclopedia [CCLE], and GEO datasets; the Clinical Proteomic Tumor Analysis Consortium [CPTAC] proteomics; CCLE metabolomics) to develop useful molecular targets for precision therapy. Unsupervised consensus clustering was performed to categorize EC patients into three metabolism-pathway-based subgroups (MPSs). These MPS subgroups had distinct clinical prognoses, transcriptomic and genomic alterations, immune microenvironment landscape, and unique patterns of chemotherapy sensitivity. Moreover, the MPS2 subgroup had a better response to immunotherapy. Finally, three machine learning algorithms (LASSO, random forest, and stepwise multivariate Cox regression) were used for developing a prognostic metagene signature based on metabolic molecules. Thus, a 13-hub gene-based classifier was constructed to predict patients' MPS subtypes, offering a more accessible and practical approach. This metabolism-based classification system can enhance prognostic predictions and guide clinical strategies for immunotherapy and metabolism-targeted therapy in EC.

The mRNACalc webserver accounts for the N1-methylpseudouridine hypochromicity to enable precise nucleoside-modified mRNA quantification.

Nucleoside-modified messenger RNA (mRNA) technologies necessarily incorporate N1-methylpseudouridine into the mRNA molecules to prevent the over-stimulation of cytoplasmic RNA sensors. Despite this modification, mRNA concentrations remain mostly determined through the measurement of UV absorbance at 260 nm wavelength (A260). Herein, we report that the N1-methylpseudouridine absorbs approximately 40% less UV light at 260 nm than uridine, and its incorporation into mRNAs leads to the under-estimation of nucleoside-modified mRNA concentrations, with 5%-15% error, in an mRNA-sequence-dependent manner. We therefore examined the RNA quantification methods and developed the mRNACalc webserver. It accounts for the molar absorption coefficient of modified nucleotides at 260 nm wavelength, the RNA composition of the mRNA, and the A260 of the mRNA sample to enable accurate quantification of nucleoside-modified mRNAs.

MHGTMDA: Molecular heterogeneous graph transformer based on biological entity graph for miRNA-disease associations prediction.

MicroRNAs (miRNAs) play a crucial role in the prevention, prognosis, diagnosis, and treatment of complex diseases. Existing computational methods primarily focus on biologically relevant molecules directly associated with miRNA or disease, overlooking the fact that the human body is a highly complex system where miRNA or disease may indirectly correlate with various types of biomolecules. To address this, we propose a novel prediction model named MHGTMDA (miRNA and disease association prediction using heterogeneous graph transformer based on molecular heterogeneous graph). MHGTMDA integrates biological entity relationships of eight biomolecules, constructing a relatively comprehensive heterogeneous biological entity graph. MHGTMDA serves as a powerful molecular heterogeneity map transformer, capturing structural elements and properties of miRNAs and diseases, revealing potential associations. In a 5-fold cross-validation study, MHGTMDA achieved an area under the receiver operating characteristic curve of 0.9569, surpassing state-of-the-art methods by at least 3%. Feature ablation experiments suggest that considering features among multiple biomolecules is more effective in uncovering miRNA-disease correlations. Furthermore, we conducted differential expression analyses on breast cancer and lung cancer, using MHGTMDA to further validate differentially expressed miRNAs. The results demonstrate MHGTMDA's capability to identify novel MDAs.

Functional requirement of alternative splicing in epithelial-mesenchymal transition of pancreatic circulating tumor.

Circulating tumor cells (CTCs) that undergo epithelial-to-mesenchymal transition (EMT) can provide valuable information regarding metastasis and potential therapies. However, current studies on the EMT overlook alternative splicing. Here, we used single-cell full-length transcriptome data and mRNA sequencing of CTCs to identify stage-specific alternative splicing of partial EMT and mesenchymal states during pancreatic cancer metastasis. We classified definitive tumor and normal epithelial cells via genetic aberrations and demonstrated dynamic changes in the epithelial-mesenchymal continuum in both epithelial cancer cells and CTCs. We provide the landscape of alternative splicing in CTCs at different stages of EMT, uncovering cell-type-specific splicing patterns and splicing events in cell surface proteins suitable for therapies. We show that MBNL1 governs cell fate through alternative splicing independently of changes in gene expression and affects the splicing pattern during EMT. We found a high frequency of events that contained multiple premature termination codons and were enriched with C and G nucleotides in close proximity, which influence the likelihood of stop codon readthrough and expand the range of potential therapeutic targets. Our study provides insights into the EMT transcriptome's dynamic changes and identifies potential diagnostic and therapeutic targets in pancreatic cancer.

Global characterization of RNA editing in genetic regulation of multiple ovarian cancer subtypes.

RNA editing plays an extensive role in the initiation and progression of cancer. However, the overall profile and molecular functions of RNA editing in different ovarian cancer subtypes have not been fully characterized and elucidated. Here, we conducted a study on RNA editing in four cohorts of ovarian cancer subtypes through large-scale parallel reporting and bioinformatics analysis. Our findings revealed that RNA editing patterns exhibit subtype-specific characteristics within cancer subtypes. The expression pattern of ADAR and the number of differential editing sites varied under different conditions. CCOC and EOC exhibited significant editing deficiency, whereas HGSC and MOC displayed significant editing excess. The sites within the turquoise module of the coedited network also revealed their correlation with ovarian cancer. In addition, we identified an average of over 40,000 cis-edQTLs in the four subtypes. Finally, we explored the association between RNA editing and drug response, uncovering several potentially effective editing-drug pairs (EDP) and suggesting the conceivable utility of RNA editing sites as therapeutic targets for cancer treatment. Overall, our comprehensive study has identified and characterized RNA editing events in various subtypes of ovarian cancer, providing a new perspective for ovarian cancer research and facilitating the development of medical interventions and treatments.

EPRIM: An approach of identifying cancer immune-related epigenetic regulators.

Epigenetic regulation contributes to the dysregulation of gene expression involved in cancer biology. Nevertheless, the roles of epigenetic regulators (ERs) in tumor immunity and immune response remain basically unclear. Here, we developed the epigenetic regulator in immunology (EPRIM) approach to identify immune-related ERs and comprehensively dissected the ER regulation in tumor immune response across 33 cancers. The identified immune-related ERs were related to immune infiltration and could stratify cancer patients into two risk groups in multiple independent datasets. These patient groups were characterized by distinct immune functions, immune infiltrates, driver gene mutations, and prognoses. Furthermore, we constructed an immune ER-based signature and highlighted its potential utility in predicting clinical benefit from immunotherapy and selecting therapeutic agents. Taken together, our identification and evaluation of immune-related ERs highlight the usefulness of EPRIM for the understanding of ERs in immune regulation and the clinical relevance in evaluation of cancer patient prognosis and response to immune checkpoint blockade therapy.

Joint masking and self-supervised strategies for inferring small molecule-miRNA associations.

Inferring small molecule-miRNA associations (MMAs) is crucial for revealing the intricacies of biological processes and disease mechanisms. Deep learning, renowned for its exceptional speed and accuracy, is extensively used for predicting MMAs. However, given their heavy reliance on data, inaccuracies during data collection can make these methods susceptible to noise interference. To address this challenge, we introduce the joint masking and self-supervised (JMSS)-MMA model. This model synergizes graph autoencoders with a probability distribution-based masking strategy, effectively countering the impact of noisy data and enabling precise predictions of unknown MMAs. Operating in a self-supervised manner, it deeply encodes the relationship data of small molecules and miRNA through the graph autoencoder, delving into its latent information. Our masking strategy has successfully reduced data noise, enhancing prediction accuracy. To our knowledge, this is the pioneering integration of a masking strategy with graph autoencoders for MMA prediction. Furthermore, the JMSS-MMA model incorporates a node-degree-based decoder, deepening the understanding of the network's structure. Experiments on two mainstream datasets confirm the model's efficiency and precision, and ablation studies further attest to its robustness. We firmly believe that this model will revolutionize drug development, personalized medicine, and biomedical research.

Unveiling the mechanisms of nephrotoxicity caused by nephrotoxic compounds using toxicological network analysis.

Billions of people worldwide have experienced irreversible kidney injuries, which is mainly attributed to the complexity of drug-induced nephrotoxicity. Consequently, there is an urgent need for uncovering the mechanisms of nephrotoxicity caused by compounds. In the present study, a network-based methodology was applied to explore the mechanisms of nephrotoxicity induced by specific compounds. Initially, a total of 42 nephrotoxic compounds and 60 kinds of syndromes associated with nephrotoxicity were collected from public resources. Afterward, network localization and separation algorithms were used to map the targets of compounds and diseases into the human interactome. By doing so, 199 statistically significant nephrotoxic networks displaying the interaction between compound targets and disease genes were obtained, which played pivotal roles in compounds-induced nephrotoxicity. Subsequently, enrichment analysis pinpointed core Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways that highlight commonalities in nephrotoxicity induced by nephrotoxic compounds. It was found that nephrotoxic compounds primarily induce nephrotoxicity by mediating the advanced glycosylation end products-receptor for advanced glycosylation end products signaling pathway in diabetic complications, human cytomegalovirus infection, lipid and atherosclerosis, Kaposi sarcoma-associated herpesvirus infection, apoptosis, and the phosphatidylinositol 3-kinase-Akt pathways. These results provide valuable insights for preventing drug-induced nephrotoxicity. Furthermore, the approaches we used are also helpful in conducting research on other kinds of toxicities.

Systematic analysis of DNA methylation-mediated TF dysregulation on lncRNAs reveals critical roles in tumor immunity.

Emerging evidence suggests that DNA methylation affects transcriptional regulation and expression perturbations of long non-coding RNAs (lncRNAs) in cancer. However, a comprehensive investigation into the transcriptional control of DNA methylation-mediated dysregulation of transcription factors (TFs) on lncRNAs has been lacking. Here, we integrated the transcriptome, methylome, and regulatome across 21 human cancers and systematically identified the transcriptional regulation of DNA methylation-mediated TF dysregulations (DMTDs) on lncRNAs. Our findings reveal that TF regulation of lncRNAs is significantly impacted by DNA methylation. Comparative analysis of DMTDs on mRNAs revealed a conserved pattern of TFs involvement. Pan-cancer Methylation TFs (MethTFs) and Methylation LncRNAs (MethLncRNAs) were identified, and were found to be closely associated with cancer hallmarks and clinical features. In-depth analysis of co-expressed mRNAs with pan-cancer MethLncRNAs unveiled frequent disruptions in cancer immunity, particularly in the context of inflammatory response. Furthermore, we identified five immune-related network modules that contribute to immune cell infiltration in cancer. Immune-related subtypes were subsequently classified, characterized by high levels of immune cell infiltration, expression of immunomodulatory genes, and relevant immune cytolytic activity score, major histocompatibility complex score, response to chemotherapy, and prognosis. Our findings provide valuable insights into cancer immunity from the epigenetic and transcriptional regulation perspective.