Assessment and prognostic significance of a serum cytokine panel in diffuse large B­cell lymphoma.

The objective of the present study was to assess the levels of circulating cytokines in patients with diffuse large B-cell lymphoma (DLBCL), and to examine the associations between the cytokine levels, clinicopathological manifestations and patient prognosis. The study enrolled 49 patients with DLBCL, 11 patients with chronic lymphocytic leukemia/small lymphocytic lymphoma and 67 healthy controls from Zhejiang Provincial People's Hospital (Hangzhou, China) between January 2017 and January 2020. The serum levels of interleukin (IL)-2, IL-4, IL-6, IL-10, IL-17, tumor necrosis factor (TNF)-α and interferon (IFN)-γ were measured using flow cytometry. The IL-6, IL-10 and IFN-γ levels were significantly raised in patients with DLBCL compared with those in the healthy controls (P<0.05). The levels of IL-10 were significantly higher in patients with raised levels of circulating lactate dehydrogenase (P<0.05), while increases in both IL-6 and IL-10 were associated with raised C-reactive protein (CRP) levels, with IL-6 levels positively associated with those of serum CRP (P<0.01; r=0.66). Additionally, International Prognostic Index (IPI) risk stratification of patients with DLBCL was strongly associated with circulating IL-6 and IL-10 levels. Raised IL-6, IL-10 and TNF-α levels were linked with worse short-term treatment efficacies (P<0.05). Moreover, the accuracy of the model predicting short-term treatment response in patients with DLBCL, obtained using the support vector machine algorithm, was 81.63%. It was also found that raised serum IL-6 and IL-10 levels, together with reduced levels of IL-17, were associated with survival of <1 year in patients with DLBCL (P<0.05), although no significant link was found between cytokine levels and long-term overall survival. In conclusion, the serum levels of IL-6, IL-10, IL-17, TNF-α and IFN-γ can potentially serve as biological indicators of DLBCL tumor immune status, and combined application with the IPI score can be a robust prognostic indicator in patients with DLBCL.

Breaking the barriers of data scarcity in drug-target affinity prediction.

Accurate prediction of drug-target affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive, resulting in limited data availability that poses challenges for deep learning approaches. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. To overcome this challenge, we present the Semi-Supervised Multi-task training (SSM) framework for DTA prediction, which incorporates three simple yet highly effective strategies: (1) A multi-task training approach that combines DTA prediction with masked language modeling using paired drug-target data. (2) A semi-supervised training method that leverages large-scale unpaired molecules and proteins to enhance drug and target representations. This approach differs from previous methods that only employed molecules or proteins in pre-training. (3) The integration of a lightweight cross-attention module to improve the interaction between drugs and targets, further enhancing prediction accuracy. Through extensive experiments on benchmark datasets such as BindingDB, DAVIS and KIBA, we demonstrate the superior performance of our framework. Additionally, we conduct case studies on specific drug-target binding activities, virtual screening experiments, drug feature visualizations and real-world applications, all of which showcase the significant potential of our work. In conclusion, our proposed SSM-DTA framework addresses the data limitation challenge in DTA prediction and yields promising results, paving the way for more efficient and accurate drug discovery processes.

R2-DDI: relation-aware feature refinement for drug-drug interaction prediction.

Precisely predicting the drug-drug interaction (DDI) is an important application and host research topic in drug discovery, especially for avoiding the adverse effect when using drug combination treatment for patients. Nowadays, machine learning and deep learning methods have achieved great success in DDI prediction. However, we notice that most of the works ignore the importance of the relation type when building the DDI prediction models. In this work, we propose a novel R$^2$-DDI framework, which introduces a relation-aware feature refinement module for drug representation learning. The relation feature is integrated into drug representation and refined in the framework. With the refinement features, we also incorporate the consistency training method to regularize the multi-branch predictions for better generalization. Through extensive experiments and studies, we demonstrate our R$^2$-DDI approach can significantly improve the DDI prediction performance over multiple real-world datasets and settings, and our method shows better generalization ability with the help of the feature refinement design.

Discovering drug-target interaction knowledge from biomedical literature.

MOTIVATION: The interaction between drugs and targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedical domain, automatically discovering DTI knowledge from biomedical literature, which are usually triplets about drugs, targets and their interaction, becomes an urgent demand in the industry. Existing methods of discovering biological knowledge are mainly extractive approaches that often require detailed annotations (e.g. all mentions of biological entities, relations between every two entity mentions, etc.). However, it is difficult and costly to obtain sufficient annotations due to the requirement of expert knowledge from biomedical domains. RESULTS: To overcome these difficulties, we explore an end-to-end solution for this task by using generative approaches. We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations. Further, we propose a semi-supervised method, which leverages the aforementioned end-to-end model to filter unlabeled literature and label them. Experimental results show that our method significantly outperforms extractive baselines on DTI discovery. We also create a dataset, KD-DTI, to advance this task and release it to the community. AVAILABILITY AND IMPLEMENTATION: Our code and data are available at https://github.com/bert-nmt/BERT-DTI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SPRoBERTa: protein embedding learning with local fragment modeling.

Well understanding protein function and structure in computational biology helps in the understanding of human beings. To face the limited proteins that are annotated structurally and functionally, the scientific community embraces the self-supervised pre-training methods from large amounts of unlabeled protein sequences for protein embedding learning. However, the protein is usually represented by individual amino acids with limited vocabulary size (e.g. 20 type proteins), without considering the strong local semantics existing in protein sequences. In this work, we propose a novel pre-training modeling approach SPRoBERTa. We first present an unsupervised protein tokenizer to learn protein representations with local fragment pattern. Then, a novel framework for deep pre-training model is introduced to learn protein embeddings. After pre-training, our method can be easily fine-tuned for different protein tasks, including amino acid-level prediction task (e.g. secondary structure prediction), amino acid pair-level prediction task (e.g. contact prediction) and also protein-level prediction task (remote homology prediction, protein function prediction). Experiments show that our approach achieves significant improvements in all tasks and outperforms the previous methods. We also provide detailed ablation studies and analysis for our protein tokenizer and training framework.

Back translation for molecule generation.

MOTIVATION: Molecule generation, which is to generate new molecules, is an important problem in bioinformatics. Typical tasks include generating molecules with given properties, molecular property improvement (i.e. improving specific properties of an input molecule), retrosynthesis (i.e. predicting the molecules that can be used to synthesize a target molecule), etc. Recently, deep-learning-based methods received more attention for molecule generation. The labeled data of bioinformatics is usually costly to obtain, but there are millions of unlabeled molecules. Inspired by the success of sequence generation in natural language processing with unlabeled data, we would like to explore an effective way of using unlabeled molecules for molecule generation. RESULTS: We propose a new method, back translation for molecule generation, which is a simple yet effective semisupervised method. Let X be the source domain, which is the collection of properties, the molecules to be optimized, etc. Let Y be the target domain which is the collection of molecules. In particular, given a main task which is about to learn a mapping from the source domain X to the target domain Y, we first train a reversed model g for the Y to X mapping. After that, we use g to back translate the unlabeled data in Y to X and obtain more synthetic data. Finally, we combine the synthetic data with the labeled data and train a model for the main task. We conduct experiments on molecular property improvement and retrosynthesis, and we achieve state-of-the-art results on four molecule generation tasks and one retrosynthesis benchmark, USPTO-50k. AVAILABILITY AND IMPLEMENTATION: Our code and data are available at https://github.com/fyabc/BT4MolGen. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Current Progress in Investigating Mature T- and NK-Cell Lymphoma Gene Aberrations by Next-Generation Sequencing (NGS).

Despite efforts to abrogate the severe threat to life posed by the profound malignancy of mature natural killer/T-cell lymphoma (NKTCL), therapeutic advances still require further investigation of its inherent regulatory biochemical processes. Next-generation sequencing (NGS) is an increasingly developing gene detection technique, which has been widely used in lymphoma genetic research in recent years. Targeted therapy based on the above studies has also generated a series of advances, making genetic mutation a new research hotspot in lymphoma. Advances in NKTCL-related gene mutations are reviewed in this paper.

Oncolytic viruses as a promising therapeutic strategy for hematological malignancies.

The incidence of hematological malignancies such as multiple myeloma, leukemia, and lymphoma has increased over time. Although bone marrow transplantation, immunotherapy and chemotherapy have led to significant improvements in efficacy, poor prognosis in elderly patients, recurrence and high mortality among hematological malignancies remain major challenges, and innovative therapeutic strategies should be explored. Besides directly lyse tumor cells, oncolytic viruses can activate immune responses or be engineered to express therapeutic factors to increase antitumor efficacy, and have gradually been recognized as an appealing approach for fighting cancers. An increasing number of studies have applied oncolytic viruses in hematological malignancies and made progress. In particular, strategies combining immunotherapy and oncolytic virotherapy are emerging. Various phase I clinical trials of oncolytic reovirus with lenalidomide or programmed death 1(PD-1) immune checkpoint inhibitors in multiple myeloma are ongoing. Moreover, preclinical studies of combinations with chimeric antigen receptor T (CAR-T) cells are underway. Thus, oncolytic virotherapy is expected to be a promising approach to cure hematological malignancies. This review summarizes progress in oncolytic virus research in hematological malignancies. After briefly reviewing the development and oncolytic mechanism of oncolytic viruses, we focus on delivery methods of oncolytic viruses, especially systemic delivery that is suitable for hematological tumors. We then discuss the main types of oncolytic viruses applied for hematological malignancies and related clinical trials. In addition, we present several ways to improve the antitumor efficacy of oncolytic viruses. Finally, we discuss current challenges and provide suggestions for future studies.

Prognostic implications of cell division cycle protein 45 expression in hepatocellular carcinoma.

BACKGROUND: The overall prognosis of hepatocellular carcinoma (HCC) is poor and novel prognostic biomarkers might better monitor the progression of HCC. Cell division cycle protein 45 (CDC45) plays a key role in DNA replication and considered to be involved in tumorigenesis. This study investigated CDC45 expression in tumour tissues and defined its prognostic value in HCC patients. METHODS: We used immunohistochemistry (IHC) staining to examine the expression of CDC45 in tumour tissue specimens and compare them with adjacent normal tissue specimens using a constructed tissue microarray (TMA) and analyzed how clinical features are related to HCC prognosis. Functional enrichment analyses were used to describe significantly involved hallmark pathways of differentially expressed genes (DEGs, which were screened out according to the high or low expression of CDC45 in tumour tissues). RESULTS: Our findings showed that the proteome expression of CDC45 was evidently downregulated in HCC tissues compared with matched normal tissues (P < 0.0001). Although we did not find any differences in terms of vascular invasion, metastasis, lymphatic infiltration, or Edmondson grade between patients with high and low CDC45 expression, low CDC45 expression was significantly correlated with microvascular invasion (P = 0.046). Multivariate analysis indicated that CDC45 expression (P = 0.035) was an independent prognostic factor for the overall survival (OS) rate of HCC patients. Patients with CDC45 expression was positively correlated with OS rates among HCC patients (P < 0.05). Functional annotations indicated that CDC45 is involved in the most significant pathways, including the cell cycle, DNA replication, chemical carcinogenesis and drug metabolism-cytochrome P450 pathways. DISCUSSION: Our findings showed that low proteomic level of CDC45 was associated with a poor prognosis in HCC patients, indicating that CDC45 might be a novel prognostic marker.

Beclin1­armed oncolytic Vaccinia virus enhances the therapeutic efficacy of R­CHOP against lymphoma in vitro and in vivo.

Non­Hodgkin lymphoma (NHL) is a form of lymphoid malignancy, with diffuse large B cell lymphoma (DLBCL) being the most common NHL isoform. Approximately half of patients with DLBCL are successfully cured via first­line Rituximab, Cyclophosphamide, Epirubicin, Vindesine, Prednisolone (R­CHOP) treatment. However, 30­40% of patients with DLBCL ultimately suffer from treatment­refractory or relapsed disease. These patients often suffer from high mortality rates owing to a lack of suitable therapeutic options, and all patients are at a high risk of serious treatment­associated dose­dependent toxicity. As such, it is essential to develop novel treatments for NHL that are less toxic and more efficacious. Oncolytic Vaccinia virus (OVV) has shown promise as a means of treating numerous types of cancer. Gene therapy strategies further enhance OVV­based therapy by improving tumor cell recognition and immune evasion. Beclin1 is an autophagy­associated gene that, when upregulated, induces excess autophagy and cell death. The present study aimed to develop an OVV­Beclin1 therapy capable of inducing autophagic tumor cell death. OVV­Beclin1 was able to efficiently kill NHL cells and to increase the sensitivity of these cells to R­CHOP, thereby decreasing the dose­dependent toxic side effects associated with this chemotherapeutic regimen. The combination of OVV­Beclin1 and R­CHOP also significantly improved tumor growth inhibition and survival in a BALB/c murine model system owing to the synergistic induction of autophagic cell death. Together, these findings suggest that OVV­Beclin1 infection can induce significant autophagic cell death in NHL, highlighting this as a novel means of inducing tumor cell death via a mechanism that is distinct from apoptosis and necrosis.

Involvement of mGluR5 and TRPV1 in visceral nociception in a rat model of uterine cervical distension.

Metabotropic glutamate receptor 5 (mGluR5) and transient receptor potential vanilloid subtype 1 (TRPV1) have been shown to play critical roles in the transduction and modulation of cutaneous nociception in the central nervous system. However, little is known regarding the possible involvement of mGluR5 and TRPV1 in regulating visceral nociception from the uterine cervix. In this study, we used a rat model of uterine cervical distension to examine the effects of noxious stimuli to the uterine cervix on expression of spinal mGluR5 and TRPV1. Our findings included the following: (1) uterine cervical distension resulted in a stimulus-dependent increase in electromyographic, spinal c-Fos signal, and expression of mGluR5 and TRPV1 in the spinal cord; (2) intrathecal administration of the mGluR5 antagonist 2-methyl-6-(phenylethynyl)-pyri-dine significantly reduced the increased TRPV1 and c-Fos expression induced by uterine cervical distension; (3) the TRPV1 inhibitor SB-366791 inhibited increased spinal c-Fos expression but had no effect on the expression of mGluR5 in response to uterine cervical distension. Our findings indicate that the spinal mGluR5-TRPV1 pathway modulates nociceptive transmission in uterine cervical distension-induced pathological visceral pain.