Decoding long non­coding RNAs: Friends and foes in cancer development (Review).

Cancer remains a formidable adversary, challenging medical advancements with its dismal prognosis, low cure rates and high mortality rates. Within this intricate landscape, long non­coding RNAs (lncRNAs) emerge as pivotal players, orchestrating proliferation and migration of cancer cells. Harnessing the potential of lncRNAs as therapeutic targets and prognostic markers holds immense promise. The present comprehensive review delved into the molecular mechanisms underlying the involvement of lncRNAs in the onset and progression of the top five types of cancer. By meticulously examining lncRNAs across diverse types of cancer, it also uncovered their distinctive roles, highlighting their exclusive oncogenic effects or tumor suppressor properties. Notably, certain lncRNAs demonstrate diverse functions across different cancers, confounding the conventional understanding of their roles. Furthermore, the present study identified lncRNAs exhibiting aberrant expression patterns in numerous types of cancer, presenting them as potential indicators for cancer screening and diagnosis. Conversely, a subset of lncRNAs manifests tissue­specific expression, hinting at their specialized nature and untapped significance in diagnosing and treating specific types of cancer. The present comprehensive review not only shed light on the intricate network of lncRNAs but also paved the way for further research and clinical applications. The unraveled molecular mechanisms offer a promising avenue for targeted therapeutics and personalized medicine, combating cancer proliferation, invasion and metastasis.

Integrated approach to generate artificial samples with low tumor fraction for somatic variant calling benchmarking.

BACKGROUND: High-throughput sequencing (HTS) has become the gold standard approach for variant analysis in cancer research. However, somatic variants may occur at low fractions due to contamination from normal cells or tumor heterogeneity; this poses a significant challenge for standard HTS analysis pipelines. The problem is exacerbated in scenarios with minimal tumor DNA, such as circulating tumor DNA in plasma. Assessing sensitivity and detection of HTS approaches in such cases is paramount, but time-consuming and expensive: specialized experimental protocols and a sufficient quantity of samples are required for processing and analysis. To overcome these limitations, we propose a new computational approach specifically designed for the generation of artificial datasets suitable for this task, simulating ultra-deep targeted sequencing data with low-fraction variants and demonstrating their effectiveness in benchmarking low-fraction variant calling. RESULTS: Our approach enables the generation of artificial raw reads that mimic real data without relying on pre-existing data by using NEAT, a fine-grained read simulator that generates artificial datasets using models learned from multiple different datasets. Then, it incorporates low-fraction variants to simulate somatic mutations in samples with minimal tumor DNA content. To prove the suitability of the created artificial datasets for low-fraction variant calling benchmarking, we used them as ground truth to evaluate the performance of widely-used variant calling algorithms: they allowed us to define tuned parameter values of major variant callers, considerably improving their detection of very low-fraction variants. CONCLUSIONS: Our findings highlight both the pivotal role of our approach in creating adequate artificial datasets with low tumor fraction, facilitating rapid prototyping and benchmarking of algorithms for such dataset type, as well as the important need of advancing low-fraction variant calling techniques.

A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.

BACKGROUND: RNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene expression measurements extracted from cancer patients. One challenge of current cancer predictors is that they often have suboptimal performance estimates when integrating molecular datasets generated from different labs. Often, the quality of the data is variable, procured differently, and contains unwanted noise hampering the ability of a predictive model to extract useful information. Data preprocessing methods can be applied in attempts to reduce these systematic variations and harmonize the datasets before they are used to build a machine learning model for resolving tissue of origins. RESULTS: We aimed to investigate the impact of data preprocessing steps-focusing on normalization, batch effect correction, and data scaling-through trial and comparison. Our goal was to improve the cross-study predictions of tissue of origin for common cancers on large-scale RNA-Seq datasets derived from thousands of patients and over a dozen tumor types. The results showed that the choice of data preprocessing operations affected the performance of the associated classifier models constructed for tissue of origin predictions in cancer. CONCLUSION: By using TCGA as a training set and applying data preprocessing methods, we demonstrated that batch effect correction improved performance measured by weighted F1-score in resolving tissue of origin against an independent GTEx test dataset. On the other hand, the use of data preprocessing operations worsened classification performance when the independent test dataset was aggregated from separate studies in ICGC and GEO. Therefore, based on our findings with these publicly available large-scale RNA-Seq datasets, the application of data preprocessing techniques to a machine learning pipeline is not always appropriate.

Roles of post-translational modifications of UHRF1 in cancer.

UHRF1 as a member of RING-finger type E3 ubiquitin ligases family, is an epigenetic regulator with five structural domains. It has been involved in the regulation of a series of biological functions, such as DNA replication, DNA methylation, and DNA damage repair. Additionally, aberrant overexpression of UHRF1 has been observed in over ten cancer types, indicating that UHRF1 is a typical oncogene. The overexpression of UHRF1 repressed the transcription of such tumor-suppressor genes as CDKN2A, BRCA1, and CDH1 through DNMT1-mediated DNA methylation. In addition to the upstream transcription factors regulating gene transcription, post-translational modifications (PTMs) also contribute to abnormal overexpression of UHRF1 in cancerous tissues. The types of PTM include phosphorylation, acetylation, methylationand ubiquitination, which regulate protein stability, histone methyltransferase activity, intracellular localization and the interaction with binding partners. Recently, several novel PTM types of UHRF1 have been reported, but the detailed mechanisms remain unclear. This comprehensive review summarized the types of UHRF1 PTMs, as well as their biological functions. A deep understanding of these crucial mechanisms of UHRF1 is pivotal for the development of novel UHRF1-targeted anti-cancer therapeutic strategies in the future.

Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning.

BACKGROUND: The prediction of drug sensitivity plays a crucial role in improving the therapeutic effect of drugs. However, testing the effectiveness of drugs is challenging due to the complex mechanism of drug reactions and the lack of interpretability in most machine learning and deep learning methods. Therefore, it is imperative to establish an interpretable model that receives various cell line and drug feature data to learn drug response mechanisms and achieve stable predictions between available datasets. RESULTS: This study proposes a new and interpretable deep learning model, DrugGene, which integrates gene expression, gene mutation, gene copy number variation of cancer cells, and chemical characteristics of anticancer drugs to predict their sensitivity. This model comprises two different branches of neural networks, where the first involves a hierarchical structure of biological subsystems that uses the biological processes of human cells to form a visual neural network (VNN) and an interpretable deep neural network for human cancer cells. DrugGene receives genotype input from the cell line and detects changes in the subsystem states. We also employ a traditional artificial neural network (ANN) to capture the chemical structural features of drugs. DrugGene generates final drug response predictions by combining VNN and ANN and integrating their outputs into a fully connected layer. The experimental results using drug sensitivity data extracted from the Cancer Drug Sensitivity Genome Database and the Cancer Treatment Response Portal v2 reveal that the proposed model is better than existing prediction methods. Therefore, our model achieves higher accuracy, learns the reaction mechanisms between anticancer drugs and cell lines from various features, and interprets the model's predicted results. CONCLUSIONS: Our method utilizes biological pathways to construct neural networks, which can use genotypes to monitor changes in the state of network subsystems, thereby interpreting the prediction results in the model and achieving satisfactory prediction accuracy. This will help explore new directions in cancer treatment. More available code resources can be downloaded for free from GitHub ( https://github.com/pangweixiong/DrugGene ).

Pragmatic nationwide master observational trial based on genomic alterations in advanced solid tumors: KOrean Precision Medicine Networking Group Study of MOlecular profiling guided therapy based on genomic alterations in advanced Solid tumors (KOSMOS)-II study protocol KCSG AL-22-09.

BACKGROUND: Next-generation sequencing (NGS) has been introduced to many Korean institutions to support molecular diagnostics in cancer since 2017, when it became eligible for reimbursement by the National Health Insurance Service. However, the uptake of molecularly guided treatment (MGT) based on NGS results has been limited because of stringent regulations regarding prescriptions outside of approved indications, a lack of clinical trial opportunities, and limited access to molecular tumor boards (MTB) at most institutions. The KOSMOS-II study was designed to demonstrate the feasibility and effectiveness of MGT, informed by MTBs, using a nationwide precision medicine platform. METHODS: The KOSMOS-II trial is a large-scale nationwide master observational study. It involves a framework for screening patients with metastatic solid tumors for actionable genetic alterations based on local NGS testing. It recommends MGT through a remote and centralized MTB meeting held biweekly. MGT can include one of the following options: Tier 1, the therapeutic use of investigational drugs targeting genetic alterations such as ALK, EGFR, ERBB2, BRAF, FH, ROS1, and RET, or those with high tumor mutational burden; Tier 2, comprising drugs with approved indications or those permitted for treatment outside of the indications approved by the Health Insurance Review and Assessment Service of Korea; Tier 3, involving clinical trials matching the genetic alterations recommended by the MTB. Given the anticipated proportion of patients receiving MGT in the range of 50% ± 3.25%, this study aims to enroll 1,000 patients. Patients must have progressed to one or more lines of therapy and undergone NGS before enrollment. DISCUSSION: This pragmatic master protocol provides a mass-screening platform for rare genetic alterations and high-quality real-world data. Collateral clinical trials, translational studies, and clinico-genomic databases will contribute to generating evidence for drug repositioning and the development of new biomarkers. TRIAL REGISTRATION: NCT05525858.

Nuclear mTOR Signaling Orchestrates Transcriptional Programs Underlying Cellular Growth and Metabolism.

mTOR is a central regulator of cell growth and metabolism in response to mitogenic and nutrient signals. Notably, mTOR is not only found in the cytoplasm but also in the nucleus. This review highlights direct involvement of nuclear mTOR in regulating transcription factors, orchestrating epigenetic modifications, and facilitating chromatin remodeling. These effects intricately modulate gene expression programs associated with growth and metabolic processes. Furthermore, the review underscores the importance of nuclear mTOR in mediating the interplay between metabolism and epigenetic modifications. By integrating its functions in nutrient signaling and gene expression related to growth and metabolism, nuclear mTOR emerges as a central hub governing cellular homeostasis, malignant transformation, and cancer progression. Better understanding of nuclear mTOR signaling has the potential to lead to novel therapies against cancer and other growth-related diseases.

Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data.

In recent years, researchers have proven the effectiveness and speediness of machine learning-based cancer diagnosis models. However, it is difficult to explain the results generated by machine learning models, especially ones that utilized complex high-dimensional data like RNA sequencing data. In this study, we propose the binarilization technique as a novel way to treat RNA sequencing data and used it to construct explainable cancer prediction models. We tested our proposed data processing technique on five different models, namely neural network, random forest, xgboost, support vector machine, and decision tree, using four cancer datasets collected from the National Cancer Institute Genomic Data Commons. Since our datasets are imbalanced, we evaluated the performance of all models using metrics designed for imbalance performance like geometric mean, Matthews correlation coefficient, F-Measure, and area under the receiver operating characteristic curve. Our approach showed comparative performance while relying on less features. Additionally, we demonstrated that data binarilization offers higher explainability by revealing how each feature affects the prediction. These results demonstrate the potential of data binarilization technique in improving the performance and explainability of RNA sequencing based cancer prediction models.

One gene to rule them all - clinical perspectives of a potent suppressor of cytokine signaling - SOCS1.

The discovery of Suppressor of Cytokine Signaling 1 (SOCS1) in 1997 marked a significant milestone in understanding the regulation of Janus kinase/Signal transducer and activator of transcription (JAK/STAT) signaling pathways. Subsequent research deciphered its cellular functions, and recent insights into SOCS1 deficiencies in humans underscored its critical role in immune regulation. In humans, SOCS-haploinsufficiency (SOCS1-HI) presents a diverse clinical spectrum, encompassing autoimmune diseases, infection susceptibility, and cancer. Variability in disease manifestation, even within families sharing the same genetic variant, raises questions about clinical penetrance and the need for individualized treatments. Current therapeutic strategies include JAK inhibition, with promising results in controlling inflammation in SOCS1-HI patients. Hematopoietic stem cell transplantation and gene therapy emerge as promising avenues for curative treatments. The evolving landscape of SOCS1 research, emphasizes the need for a nuanced understanding of genetic variants and their functional consequences.

An extensive analysis of the prognostic and immune role of FOXO1 in various types of cancer.

Forkhead Box O1 (FOXO1) has been reported to play important roles in many tumors. However, FOXO1 has not been studied in pan-cancer. The purpose of this study was to reveal the roles of FOXO1 in pan-cancer (33 cancers in this study). Through multiple public platforms, a pan-cancer analysis of FOXO1 was conducted to obtained FOXO1 expression profiles in various tumors to explore the relationship between FOXO1 expression and prognosis of these tumors and to disclose the potential mechanism of FOXO1 in these tumors. FOXO1 was associated with the prognosis of multiple tumors, especially LGG (low grade glioma), OV (ovarian carcinoma), and KIRC (kidney renal clear cell carcinoma). FOXO1 might play the role of an oncogenic gene in LGG and OV, while playing the role of a cancer suppressor gene in KIRC. FOXO1 expression had a significant correlation with the infiltration of some immune cells in LGG, OV, and KIRC. By combining FOXO1 expression and immune cell infiltration, we found that FOXO1 might influence the overall survival of LGG through the infiltration of myeloid dendritic cells or CD4+ T cells. Functional enrichment analysis and gene set enrichment analysis showed that FOXO1 might play roles in tumors through immunoregulatory interactions between a lymphoid and a non-lymphoid cell, TGF-beta signaling pathway, and transcriptional misregulation in cancer. FOXO1 was associated with the prognosis of multiple tumors, especially LGG, OV, and KIRC. In these tumors, FOXO1 might play its role via the regulation of the immune microenvironment.

A collective strategy to promote the dissemination of single cancer cells.

The transition from collective to single-cell invasion in metastatic tumors has been regarded as the consequence of oncogenic drivers in concert with extracellular triggers received from the tumor microenvironment. In this issue, Yoon and colleagues (https://doi.org/10.1083/jcb.202308080) have identified an epigenetic program by which collective niches release laminin-332 and thereby cause the detachment and invasion of fully individualized tumor cells.

Human pan-cancer analysis of the predictive biomarker for the CDKN3.

BACKGROUND: Cell cycle protein-dependent kinase inhibitor protein 3 (CDKN3), as a member of the protein kinase family, has been demonstrated to exhibit oncogenic properties in several tumors. However, there are no pan-carcinogenic analyses for CDKN3. METHODS: Using bioinformatics tools such as The Cancer Genome Atlas (TCGA) and the UCSC Xena database, a comprehensive pan-cancer analysis of CDKN3 was conducted. The inverstigation encompassed the examination of CDKN3 function actoss 33 different kinds of tumors, as well as the exploration of gene expressions, survival prognosis status, clinical significance, DNA methylation, immune infiltration, and associated signal pathways. RESULTS: CDKN3 was significantly upregulated in most of tumors and correlated with overall survival (OS) of patients. Methylation levels of CDKN3 differed significantly between tumors and normal tissues. In addition, infiltration of CD4 + T cells, cancer-associated fibroblasts, macrophages, and endothelial cells were associated with CDKN3 expression in various tumors. Mechanistically, CDKN3 was associated with P53, PI3K-AKT, cell cycle checkpoints, mitotic spindle checkpoint, and chromosome maintenance. CONCLUSION: Our pan-cancer analysis conducted in the study provides a comprehensive understanding of the involvement of CDKN3 gene in tumorigenesis. The findings suggest that targeting CDKN3 may potentially lead to novel therapeutic strategies for the treatment of tumors.

Research progress on morphology and mechanism of programmed cell death.

Programmed cell death (PCD) is a basic process of life that is closely related to the growth, development, aging and disease of organisms and is one of the hotspots of life science research today. PCD is a kind of genetic control, autonomous and orderly important cell death that involves the activation, expression, and regulation of a series of genes. In recent years, with the deepening of research in this field, new mechanisms of multiple PCD pathways have been revealed. This article reviews and summarizes the multiple PCD pathways that have been discovered, analyses and compares the morphological characteristics and biomarkers of different types of PCD, and briefly discusses the role of various types of PCD in the diagnosis and treatment of different diseases, especially malignant tumors.

Chemotherapy-induced PTEN-L secretion promotes the selection of PTEN-deficient tumor cells.

BACKGROUND: PTEN loss has been identified in various tumor types and is linked to unfavorable clinical outcomes. In addition to PTEN mutation, multiple mechanisms contribute to PTEN loss during tumor development. However, the natural selection process of PTEN-deficient tumor cells remains unclear. Here, we aimed at further elucidating the role of PTEN-L in tumor progression. METHODS: PTEN knockout cell lines were generated using CRISPR/Cas9 technology. Ni-NTA affinity column chromatography was employed for PTEN-L purification. Tumor cell metastasis was evaluated in murine models and observed using the IVIS Spectrum Imaging System. RNA-sequencing, western blotting, PCR, flow cytometry, and cell proliferation assays were employed to investigate tumor cell dormancy and related mechanisms. RESULTS: The chemotherapeutic drugs, cisplatin, paclitaxel, and doxorubicin, induced tumor cells to secrete PTEN-long (PTEN-L), which shields PTEN-deficient tumor cells from chemotherapy-induced apoptosis better than it shields PTEN-intact cells. Further investigation revealed that PTEN-L treatment induced dormancy in PTEN-null tumor cells, characterized by an increase in p16 and p27 levels, cell-cycle arrest, reduced cell proliferation, and enhanced DNA repair. Furthermore, PTEN-L treatment selectively promoted the accumulation and growth of PTEN-null tumor cells in the lungs of C57BL/6J mice, while evading immune surveillance. Mechanistically, PTEN-L induced dormancy in PTEN-null tumor cells by activating the p38 signaling pathway. Addition of a p38 inhibitor effectively reversed dormancy and growth of PTEN-deficient tumor cells in the lungs. We also demonstrated that PTEN expression played a pivotal role in determining the outcome of PTEN-L-mediated antitumor therapy. CONCLUSIONS: In summary, PTEN-L was identified as a potent inducer of dormancy in PTEN-deficient tumor cells, which increased their efficient selection within the tumor microenvironment.

Unveiling a novel fusion gene enhances CAR T cell therapy for solid tumors.

The efficacy of Adoptive Cell Transfer Therapy (ACT) in combating hematological tumors has been well-documented, yet its application to solid tumors faces formidable hurdles, chief among them being the suboptimal therapeutic response and the immunosuppressive milieu within the tumor microenvironment (TME). Recently, Garcia, J. et al. present compelling findings shedding light on potential breakthroughs in this domain. Their investigation reveals the pronounced augmentation of anti-tumor activity in CAR T cells through the introduction of a T cell neoplasm fusion gene, CARD11-PIK3R3. The incorporation of this gene into engineered T cell therapy holds promise as a formidable tool in the arsenal of cancer immunotherapy. The innovative strategy outlined not only mitigates the requirement for high doses of CAR T cells but also enhances tumor control while exhibiting encouraging safety profiles. The exploration of the CARD11-PIK3R3 fusion gene represents an advancement in our approach to bolstering the anti-tumor efficacy of immunotherapeutic interventions. Nonetheless, the imperative for further inquiry to ascertain its transfection efficiency and long-term safety cannot be overstated. Nevertheless, this seminal investigation offers a beacon of hope in surmounting the formidable treatment impediments posed by solid tumors, paving the way for a transformative era in cancer therapeutics.

HOPS/TMUB1 Enhances Apoptosis in TP53 Mutation-Independent Setting in Human Cancers.

TP53 mutations are prevalent in various cancers, yet the complexity of apoptotic pathway deregulation suggests the involvement of additional factors. HOPS/TMUB1 is known to extend the half-life of p53 under normal and stress conditions, implying a regulatory function. This study investigates, for the first time, the potential modulatory role of the ubiquitin-like-protein HOPS/TMUB1 in p53-mutants. A comprehensive analysis of apoptosis in the most frequent p53-mutants, R175, R248, and R273, in SKBR3, MIA PaCa2, and H1975 cells indicates that the overexpression of HOPS induces apoptosis at least equivalent to that caused by DNA damage. Immunoprecipitation assays confirm HOPS binding to p53-mutant forms. The interaction of HOPS/TMUB1 with p53-mutants strengthens its effect on the apoptotic cascade, showing a context-dependent gain or loss of function. Gene expression analysis of the MYC and TP63 genes shows that H1975 exhibit a gain-of-function profile, while SKBR3 promote apoptosis in a TP63-dependent manner. The TCGA data further corroborate HOPS/TMUB1's positive correlation with apoptotic genes BAX, BBC3, and NOXA1, underscoring its relevance in patient samples. Notably, singular TP53 mutations inadequately explain pathway dysregulation, emphasizing the need to explore additional contributing factors. These findings illuminate the intricate interplay among TP53 mutations, HOPS/TMUB1, and apoptotic pathways, providing valuable insights for targeted cancer interventions.

Tumor Antigens beyond the Human Exome.

With the advent of immunotherapeutics, a new era in the combat against cancer has begun. Particularly promising are neo-epitope-targeted therapies as the expression of neo-antigens is tumor-specific. In turn, this allows the selective targeting and killing of cancer cells whilst healthy cells remain largely unaffected. So far, many advances have been made in the development of treatment options which are tailored to the individual neo-epitope repertoire. The next big step is the achievement of efficacious "off-the-shelf" immunotherapies. For this, shared neo-epitopes propose an optimal target. Given the tremendous potential, a thorough understanding of the underlying mechanisms which lead to the formation of neo-antigens is of fundamental importance. Here, we review the various processes which result in the formation of neo-epitopes. Broadly, the origin of neo-epitopes can be categorized into three groups: canonical, noncanonical, and viral neo-epitopes. For the canonical neo-antigens that arise in direct consequence of somatic mutations, we summarize past and recent findings. Beyond that, our main focus is put on the discussion of noncanonical and viral neo-epitopes as we believe that targeting those provides an encouraging perspective to shape the future of cancer immunotherapeutics.

Clinical and Biologic Correlates of ADORA2A Transcriptomic Expression in Cancer.

ADORA2A (adenosine A2a receptor) and ADORA2B propagate immunoregulatory signals, including restricting both innate and adaptive immunity, though recent data also suggest a tumor suppressor effect in certain settings. We evaluated the RNA expression from 514 tumors in a clinical-grade laboratory; 489 patients with advanced/metastatic disease had clinical outcome correlates. Transcript expression was standardized to internal housekeeping genes and ranked (0-100 scale) relative to 735 specimens from 35 different cancer types. Transcript abundance rank values were defined as "low/moderate" (0-74) or "high" (75-100) percentile RNA expression ranks. Overall, 20.8% of tumors had high ADORA2A (≥75 percentile RNA rank). The greatest proportion of high ADORA2A expressors was found in neuroendocrine and breast cancers and sarcomas, whereas the lowest was found in colorectal and ovarian cancers, albeit with patient-to-patient variability. In multivariable logistic regression analysis, there was a significant positive correlation between high ADORA2A RNA expression and a high expression of the immune checkpoint-related molecules PD-1 (p = 0.015), VISTA (p ≤ 0.001), CD38 (p = 0.031), and CD39 (p ≤ 0.001). In 217 immunotherapy-treated patients, high ADORA2A did not correlate significantly with progression-free (p = 0.51) or overall survival (OS) (p = 0.09) from the initiation of the checkpoint blockade. However, high versus not-high ADORA2A transcript expression correlated with longer OS from the time of advanced/metastatic disease (N = 489 patients; (HR 0.69 (95% CI 0.51-0.95) (p = 0.02)). Therefore, high ADORA2A transcript levels may be a favorable prognostic factor, unrelated to immunotherapy. Importantly, ascertaining co-expression patterns of ADORA2A with PD-1 and VISTA in individual tumors as a basis for the precision co-targeting of ADORA2A and these other checkpoint-related molecules warrants investigation in clinical trials.

FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response.

Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia-reperfusion injury and other pathological cell death. The regulation of ferroptosis mainly focuses on three pathways: system Xc-/GPX4 axis, lipid peroxidation and iron metabolism. The genes involved in these processes were divided into driver, suppressor and marker. Importantly, small molecules or drugs that mediate the expression of these genes are often good treatments in the clinic. Herein, a newly developed database, named 'FERREG', is documented to (i) providing the data of ferroptosis-related regulation of diseases occurrence, progression and drug response; (ii) explicitly describing the molecular mechanisms underlying each regulation; and (iii) fully referencing the collected data by cross-linking them to available databases. Collectively, FERREG contains 51 targets, 718 regulators, 445 ferroptosis-related drugs and 158 ferroptosis-related disease responses. FERREG can be accessed at https://idrblab.org/ferreg/.

DIRMC: a database of immunotherapy-related molecular characteristics.

Cancer immunotherapy has brought about a revolutionary breakthrough in the field of cancer treatment. Immunotherapy has changed the treatment landscape for a variety of solid and hematologic malignancies. To assist researchers in efficiently uncovering valuable information related to cancer immunotherapy, we have presented a manually curated comprehensive database called DIRMC, which focuses on molecular features involved in cancer immunotherapy. All the content was collected manually from published literature, authoritative clinical trial data submitted by clinicians, some databases for drug target prediction such as DrugBank, and some experimentally confirmed high-throughput data sets for the characterization of immune-related molecular interactions in cancer, such as a curated database of T-cell receptor sequences with known antigen specificity (VDJdb), a pathology-associated TCR database (McPAS-TCR) et al. By constructing a fully connected functional network, ranging from cancer-related gene mutations to target genes to translated target proteins to protein regions or sites that may specifically affect protein function, we aim to comprehensively characterize molecular features related to cancer immunotherapy. We have developed the scoring criteria to assess the reliability of each MHC-peptide-T-cell receptor (TCR) interaction item to provide a reference for users. The database provides a user-friendly interface to browse and retrieve data by genes, target proteins, diseases and more. DIRMC also provides a download and submission page for researchers to access data of interest for further investigation or submit new interactions related to cancer immunotherapy targets. Furthermore, DIRMC provides a graphical interface to help users predict the binding affinity between their own peptide of interest and MHC or TCR. This database will provide researchers with a one-stop resource to understand cancer immunotherapy-related targets as well as data on MHC-peptide-TCR interactions. It aims to offer reliable molecular characteristics support for both the analysis of the current status of cancer immunotherapy and the development of new immunotherapy. DIRMC is available at http://www.dirmc.tech/. Database URL: http://www.dirmc.tech/.