Posterior circulation ischemic stroke: radiomics-based machine learning approach to identify onset time from magnetic resonance imaging.

PURPOSE: Posterior circulation ischemic stroke (PCIS) possesses unique features. However, previous studies have primarily or exclusively relied on anterior circulation stroke cases to build machine learning (ML) models for predicting onset time. To date, there is no research reporting the effectiveness and stability of ML in identifying PCIS onset time. We aimed to build diffusion-weighted imaging-based ML models to identify the onset time of PCIS patients. METHODS: Consecutive PCIS patients within 24 h of definite symptom onset were included (112 in the training set and 49 in the independent test set). Images were processed as follows: volume of interest segmentation, image feature extraction, and feature selection. Five ML models, naïve Bayes, logistic regression, tree ensemble, k-nearest neighbor, and random forest, were built based on the training set to estimate the stroke onset time (binary classification: ≤ 4.5 h or > 4.5 h). Relative standard deviations (RSD), receiver operating characteristic (ROC) curves, and the calibration plot was performed to evaluate the stability and performance of the five models. RESULTS: The random forest model had the best performance in the test set, with the highest area under the curve (AUC, 0.840; 95% CI: 0.706, 0.974). This model also achieved the highest accuracy, sensitivity, specificity, positive predictive value, and negative predictive value (83.7%, 64.3%, 91.4%, 75.0%, and 86.5%, respectively). Furthermore, the model had high stability (RSD = 0.0094). CONCLUSION: The PCIS case-based ML model was effective for estimating the symptom onset time and achieved considerably high specificity and stability.

Deciphering the Immune Microenvironment at the Forefront of Tumor Aggressiveness by Constructing a Regulatory Network with Single-Cell and Spatial Transcriptomic Data.

The heterogeneity and intricate cellular architecture of complex cellular ecosystems play a crucial role in the progression and therapeutic response of cancer. Understanding the regulatory relationships of malignant cells at the invasive front of the tumor microenvironment (TME) is important to explore the heterogeneity of the TME and its role in disease progression. In this study, we inferred malignant cells at the invasion front by analyzing single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data of ER-positive (ER+) breast cancer patients. In addition, we developed a software pipeline for constructing intercellular gene regulatory networks (IGRNs), which help to reduce errors generated by single-cell communication analysis and increase the confidence of selected cell communication signals. Based on the constructed IGRN between malignant cells at the invasive front of the TME and the immune cells of ER+ breast cancer patients, we found that a high expression of the transcription factors FOXA1 and EZH2 played a key role in driving tumor progression. Meanwhile, elevated levels of their downstream target genes (ESR1 and CDKN1A) were associated with poor prognosis of breast cancer patients. This study demonstrates a bioinformatics workflow of combining scRNA-seq and ST data; in addition, the study provides the software pipelines for constructing IGRNs automatically (cIGRN). This strategy will help decipher cancer progression by revealing bidirectional signaling between invasive frontline malignant tumor cells and immune cells, and the selected signaling molecules in the regulatory network may serve as biomarkers for mechanism studies or therapeutic targets.

Progress of disinfection catalysts in advanced oxidation processes, mechanisms and synergistic antibiotic degradation.

Human diseases caused by pathogenic microorganisms make people pay more attention to disinfection. Meanwhile, antibiotics can cause microbial resistance and increase the difficulty of disease treatment, resulting in risk of triggering a vicious circle. Advanced oxidation process (AOPs) has been widely studied in the field of synergistic treatment of the two contaminates. This paper reviews the application of catalytic materials and their modification strategies in the context of AOPs for disinfection and antibiotic degradation. It also delves into the mechanisms of disinfection such as the pathways for microbial inactivation and the related influencing factors, which are essential for understanding the pivotal role of catalytic materials in disinfection principles by AOPs. More importantly, the exploratory research on the combined use of AOPs for disinfection and antibiotic degradation is discussed, and the potential and prospects in this field is highlighted. Finally, the limitations and challenges associated with the application of AOPs in disinfection and antibiotic degradation are summarized. It aims to provide a starting point for future research efforts to facilitate the widespread use of advanced oxidation processes in the field of public health.

Amelioration of Atherosclerosis by lycopene is linked to the modulation of gut microbiota dysbiosis and related gut-heart axis activation in high-fat diet-fed ApoE-/- mice.

BACKGROUND: Interplay between gut microbiota and heart, termed "gut-heart" axis, has a crucial role in the pathogenesis of atherosclerosis. Our previous study showed that lycopene possesses anti-inflammatory and anti-atherosclerotic effects, but its link to the gut microbiota is poorly understood. Herein, we surmised that lycopene could regulate the gut microbiota, exert anti-atherosclerotic effect by regulating the "gut-heart" axis. METHODS: Male ApoE-/- mice were fed a high-fat diet (HFD) with or without lycopene (0.1% w/w) for 19 weeks. Gut microbiota was analyzed by 16 S rRNA sequencing, the protein levels of zonula occludens-1 (ZO-1), occludin, toll-like receptor 4 (TLR4) and phospho-nuclear factor-κB (NF-κB) p65 were measured by Western blotting, the levels of serum inflammatory factors including monocyte chemotactic protein 1 (MCP-1), tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), and IL-6 were assayed using ELISA kits. Also, the concentrations of serum lipopolysaccharide (LPS), D-lactic acid (D-LA) and diamine peroxidase (DAO) were measured through ELISA method. RESULTS: The aortic sinus sections revealed that lycopene supplementation significantly reduced the extent of atherosclerotic lesions and inhibited atherosclerosis development caused by HFD. The analysis of gut microbiota showed that lycopene reduced the ratio of Firmicutes/Bacteroides and increased the relative abundance of Verrucomicrobia, Akkermansia and Alloprevotella, which were related to elevated intestinal barrier function and reduced inflammation. Moreover, lycopene up-regulated the expression of intestinal ZO-1 and occludin and decreased serum LPS, D-LA and DAO levels. In addition, lycopene inhibited the expression of TLR4 and phospho-NF-κB p65 in aortic sinus plaque, serum MCP-1, TNF-α, IL-1ß, and IL-6 levels were also lowered by lycopene treatment. CONCLUSIONS: Our results indicated the protective effect of lycopene against atherosclerosis induced by HFD and further revealed that its mechanism might be its prebiotic effect on maintaining gut microbiota homeostasis and improving intestinal barrier function, consequently reducing serum LPS-triggered inflammatory response in the heart.

Bilateral Pectoralis Major Muscle Flaps in Treating Deep Sternal Wound Infection following CABG in Diabetic Patients: Two Case Reports.

Deep sternal wound infection (DSWI) is a life-threatening complication after cardiac operations, especially after coronary artery bypass grafting (CABG) in diabetic patients. Bilateral pectoralis major muscle flaps have been performed to treat DSWI. Two diabetic patients suffering from DSWI after CABG were treated by bilateral pectoralis major muscle flaps in our hospital. Both patients were discharged with full recovery. Satisfactory results can be obtained with bilateral pectoralis major muscle flaps following tissue debridement and drainage. This procedure should be performed when DSWI occurs in diabetic patients after CABG.

The Conformational Transitions and Dynamics of Burkholderia cepacia Lipase Regulated by Water-Oil Interfaces.

Structural dynamics and conformational transitions are crucial for the activities of enzymes. As one of the most widely used industrial biocatalysts, lipase could be activated by the water-oil interfaces. The interface activations were believed to be dominated by the close-to-open transitions of the lid subdomains. However, the detailed mechanism and the roles of structure transitions are still under debate. In this study, the dynamic structures and conformational transitions of Burkholderia cepacia lipase (LipA) were investigated by combining all-atom molecular dynamics simulations, enhanced sampling simulation, and spectrophotometric assay experiments. The conformational transitions between the lid-open and lid-closed states of LipA in aqueous solution are directly observed by the computational simulation methods. The interactions between the hydrophobic residues on the two lid-subdomains are the driven forces for the LipA closing. Meanwhile, the hydrophobic environment provided by the oil interfaces would separate the interactions between the lid-subdomains and promote the structure opening of LipA. Moreover, our studies demonstrate the opening of the lids structure is insufficient to initiate the interfacial activation, providing explanations for the inability of interfacial activation of many lipases with lid structures.

Molecular Insights on the Conformational Transitions and Activity Regulation of the c-Met Kinase Induced by Ligand Binding.

The tyrosine-protein kinase Met (c-Met) is an important signaling molecule involved in cellular growth and division. The dysregulation of c-Met may induce many fatal diseases, including non-small cell lung cancer, gastrointestinal cancers, hepatocellular carcinoma, etc. The activation of the c-Met kinase is dominant by the structure and dynamics of many important functional motifs, which are regulated by adenosine triphosphate (ATP) binding. c-Met inhibitors bind to the ATP-binding site or the allosteric pocket to compete with ATP molecules or alter the conformation of the function-related domains. Nevertheless, the mechanisms of ligand binding to c-Met are still unclear, especially the regulation of the functional motifs by different inhibitors. These greatly impede the development of novel drugs to overcome the drug tolerance to the currently marketed c-Met inhibitors. In this study, we used enhanced sampling technology to study the binding and regulation of two specific c-Met inhibitors. The results show that the two ligands adopt different binding processes even though with similar binding affinity. More importantly, our results uncovered different protein conformational features and the correlated motions of functional motifs regulated by the inhibitors, providing the structural basis for the functional suppression of the protein kinases.

Single-Cell RNA Sequencing Analysis of Gene Regulatory Network Changes in the Development of Lung Adenocarcinoma.

Lung cancer is a highly heterogeneous disease. Cancer cells and other cells within the tumor microenvironment interact to determine disease progression, as well as response to or escape from treatment. Understanding the regulatory relationship between cancer cells and their tumor microenvironment in lung adenocarcinoma is of great significance for exploring the heterogeneity of the tumor microenvironment and its role in the genesis and development of lung adenocarcinoma. This work uses public single-cell transcriptome data (distant normal, nLung; early LUAD, tLung; advanced LUAD, tL/B), to draft a cell map of lung adenocarcinoma from onset to progression, and provide a cell-cell communication view of lung adenocarcinoma in the different disease stages. Based on the analysis of cell populations, it was found that the proportion of macrophages was significantly reduced in the development of lung adenocarcinoma, and patients with lower proportions of macrophages exhibited poor prognosis. We therefore constructed a process to screen an intercellular gene regulatory network that reduces any error generated by single cell communication analysis and increases the credibility of selected cell communication signals. Based on the key regulatory signals in the macrophage-tumor cell regulatory network, we performed a pseudotime analysis of the macrophages and found that signal molecules (TIMP1, VEGFA, SPP1) are highly expressed in immunosuppression-associated macrophages. These molecules were also validated using an independent dataset and were significantly associated with poor prognosis. Our study provides an effective method for screening the key regulatory signals in the tumor microenvironment and the selected signal molecules may serve as a reference to guide the development of diagnostic biomarkers for risk stratification and therapeutic targets for lung adenocarcinoma.

MRI-based radiomics model and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular carcinoma.

BACKGROUND: Prediction of locoregional treatment response is important for further therapeutic strategy in patients with hepatocellular carcinoma. This study aimed to investigate the role of MRI-based radiomics and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular carcinoma. METHODS: The initial postoperative MRI after locoregional treatment in 100 patients with hepatocellular carcinoma was retrospectively analysed. The outcome was evaluated according to mRECIST at 6 months. We delineated the tumour volume of interest on arterial phase, portal venous phase and T2WI. The radiomics features were selected by using the independent sample t test or nonparametric Mann‒Whitney U test and the least absolute shrinkage and selection operator. The clinical variables were selected by using univariate analysis and multivariate analysis. The radiomics model and combined model were constructed via multivariate logistic regression analysis. A nomogram was constructed that incorporated the Rad score and selected clinical variables. RESULTS: Fifty patients had an objective response, and fifty patients had a nonresponse. Nine radiomics features in the arterial phase were selected, but none of the portal venous phase or T2WI radiomics features were predictive of the treatment response. The best radiomics model showed an AUC of 0.833. Two clinical variables (hCRP and therapy method) were selected. The AUC of the combined model was 0.867. There was no significant difference in the AUC between the combined model and the best radiomics model (P = 0.573). Decision curve analysis demonstrated the nomogram has satisfactory predictive value. CONCLUSIONS: MRI-based radiomics analysis may serve as a promising and noninvasive tool to predict outcome of locoregional treatment in HCC patients, which will facilitate the individualized follow-up and further therapeutic strategies guidance.

Excessive serine from the bone marrow microenvironment impairs megakaryopoiesis and thrombopoiesis in Multiple Myeloma.

Thrombocytopenia is a major complication in a subset of patients with multiple myeloma (MM). However, little is known about its development and significance during MM. Here, we show thrombocytopenia is linked to poor prognosis in MM. In addition, we identify serine, which is released from MM cells into the bone marrow microenvironment, as a key metabolic factor that suppresses megakaryopoiesis and thrombopoiesis. The impact of excessive serine on thrombocytopenia is mainly mediated through the suppression of megakaryocyte (MK) differentiation. Extrinsic serine is transported into MKs through SLC38A1 and downregulates SVIL via SAM-mediated tri-methylation of H3K9, ultimately leading to the impairment of megakaryopoiesis. Inhibition of serine utilization or treatment with TPO enhances megakaryopoiesis and thrombopoiesis and suppresses MM progression. Together, we identify serine as a key metabolic regulator of thrombocytopenia, unveil molecular mechanisms governing MM progression, and provide potential therapeutic strategies for treating MM patients by targeting thrombocytopenia.

Free Energy Calculations Using the Movable Type Method with Molecular Dynamics Driven Protein-Ligand Sampling.

Fast and accurate biomolecular free energy estimation has been a significant interest for decades, and with recent advances in computer hardware, interest in new method development in this field has even grown. Thorough configurational state sampling using molecular dynamics (MD) simulations has long been applied to the estimation of the free energy change corresponding to the receptor-ligand complexing process. However, performing large-scale simulation is still a computational burden for the high-throughput hit screening. Among molecular modeling tools, docking and scoring methods are widely used during the early stages of the drug discovery process in that they can rapidly generate discrete receptor-ligand binding modes and their individual binding affinities. Unfortunately, the lack of thorough conformational sampling in docking and scoring protocols leads to difficulty discovering global minimum binding modes on a complicated energy landscape. The Movable Type (MT) method is a novel absolute binding free energy approach which has demonstrated itself to be robust across a wide range of targets and ligands. Traditionally, the MT method is used with protein-ligand binding modes generated with rigid-receptor or flexible-receptor (induced fit) docking protocols; however, these protocols are by their nature less likely to be effective with more highly flexible targets or with those situations in which binding involves multiple step pathways. In these situations, more thorough samplings are required to better explain the free energy of binding. Therefore, to explore the prediction capability and computational efficiency of the MT method when using more thorough protein-ligand conformational sampling protocols, in the present work, we introduced a series of binding mode modeling protocols ranging from conventional docking routines to single-trajectory conventional molecular dynamics (cMD) and parallel Monte Carlo molecular dynamics (MCMD). Through validation against several structurally and mechanistically diverse protein-ligand test sets, we explore the performance of the MT method as a virtual screening tool to work with the docking protocols and as an MD simulation-based binding free energy tool.

[Predicting tumor drug sensitivity with multi-omics data].

The prediction of tumor drug sensitivity plays an important role in clinically guiding patients' medication. In this paper, a multi-omics data-based cancer drug sensitivity prediction model was constructed by Stacking ensemble learning method. The data including gene expression, mutation, copy number variation and drug sensitivity value (IC50) of 198 drugs were downloaded from the GDSC database. Multiple feature selection methods were applied for dimensionality reduction. Six primary learners and one secondary learner were integrated into modeling by Stacking method. The model was validated with 5-fold cross-validation. In the prediction results, 36.4% of drug models' AUCs were greater than 0.9, 49.0% of drug models' AUCs were between 0.8-0.9, and the lowest drug model's AUC was 0.682. The multi-omics model for drug sensitivity prediction based on Stacking method is better than the known single-omics or multi-omics model in terms of accuracy and stability. The model based on multi-omics data is better than the single-omics data in predicting drug sensitivity. Function annotation and enrichment analysis of feature genes revealed the potential resistance mechanism of tumors to sorafenib, providing the model interpretability from a biological perspective, and demonstrated the model's potential applicability in clinical medication guidance.

The Conformational Transition Pathways and Hidden Intermediates in DFG-Flip Process of c-Met Kinase Revealed by Metadynamics Simulations.

Protein kinases intrinsically translate their conformations between active and inactive states, which is key to their enzymatic activities. The conformational flipping of the three-residue conservative motif, Asp-Phe-Gly (DFG), is crucial for many kinases' biological functions. Obtaining a detailed demonstration of the DFG flipping process and its corresponding dynamical and thermodynamical features could broaden our understanding of kinases' conformation-activity relationship. In this study, we employed metadynamics simulation, a widely used enhanced sampling technique, to analyze the conformational transition pathways of the DFG flipping for the c-Met kinase. The corresponding free energy landscape suggested two distinct transition pathways between the "DFG-in" and "DFG-out" states of the DFG-flip from c-Met. On the basis of the orientation direction of the F1223 residue, we correspondingly named the two pathways the "DFG-up" path, featuring forming a commonly discovered "DFG-up" transition state, and the "DFG-down" path, a unique transition pathway with F1223 rotating along the opposite direction away from the hydrophobic cavity. The free energies along the two pathways were then calculated using the Path Collective Variable (PCV) metadynamics simulation. The simulation results showed that, though having similar free energy barriers, the free energy cuve for the DFG-down path suggested a two-step conformational transition mechanism, while that for the DFG-up path showed the one-step transition feature. The c-Met DFG flipping mechanism and the new intermediate state discovered in this work could provide a deeper understanding of the conformation-activity relationship for c-Met and, possibly, reveal a new conformational state as the drug target for c-Met and other similar kinases.

Blocking glycine utilization inhibits multiple myeloma progression by disrupting glutathione balance.

Metabolites in the tumor microenvironment are a critical factor for tumor progression. However, the lack of knowledge about the metabolic profile in the bone marrow (BM) microenvironment of multiple myeloma (MM) limits our understanding of MM progression. Here, we show that the glycine concentration in the BM microenvironment is elevated due to bone collagen degradation mediated by MM cell-secreted matrix metallopeptidase 13 (MMP13), while the elevated glycine level is linked to MM progression. MM cells utilize the channel protein solute carrier family 6 member 9 (SLC6A9) to absorb extrinsic glycine subsequently involved in the synthesis of glutathione (GSH) and purines. Inhibiting glycine utilization via SLC6A9 knockdown or the treatment with betaine suppresses MM cell proliferation and enhances the effects of bortezomib on MM cells. Together, we identify glycine as a key metabolic regulator of MM, unveil molecular mechanisms governing MM progression, and provide a promising therapeutic strategy for MM treatment.

GeneXpert MTB/RIF combined with conventional methods for tuberculosis in Shanghai Regional Medical Center: a retrospective diagnostic study.

Background: At present, the diagnosis of tuberculosis (TB) is still challenging, and improving the efficiency of diagnosis can help prevent and control TB. This retrospective clinical study aimed to assess the diagnostic efficiency of GeneXpert MTB/RIF for pulmonary TB. Methods: A total of 620 newly-diagnosed patients who visited the pulmonary clinic of Shanghai Tongren Hospital between 2018 and 2021 were enrolled in the study. All 620 patients had acid-fast Bacilli (AFB) identified by Ziehl Neelsen staining (ZNS) test, BECTEC MGIT 960 liquid culture (LC), and GeneXpert MTB/RIF assay (GX). A total of 53 patients also underwent interferon-γ release assay (IGRA). The diagnostic efficacy of ZNS, LC, GX alone or in combination in pulmonary TB was evaluated, with clinical diagnosis as the gold standard. Moreover, the IGRA for pulmonary TB diagnosis was preliminarily assessed. Results: Eventually, 185 cases were clinically confirmed (which included 36 etiologically negative cases) in the total enrolled 620 first-diagnosed patients. Overall, the 3 methods ZNS, LC, and GX showed sensitivities of 55.68%, 64.32%, and 68.64%, specificities of 98.39%, 95.40%, and 99.08%, positive predictive values (PPV) of 93.64%, 85.61%, and 96.95%, and negative predictive values (NPV) of 83.92%, 86.28%, and 88.14%, respectively. The GX method showed the highest specificity and PPV for a solitary single method, with 99.08% and 96.95%, respectively. Regarding pairwise combination methods, all showed superior sensitivity to a single test, reaching a maximum of 80.00%. Among them, the LC + GX combination showed both the highest sensitivity (80.00%) and NPV (91.78%), and the corresponding area under the receiver operating characteristic curve (0.875) was the largest. Among the 53 patients who underwent IGRA testing, 42 were positive (including 4 etiologically negative cases), and 11 were negative. The overall sensitivity of IGRA for diagnosing pulmonary TB was 90.00%, specificity was 27.27%, PPV was 42.86%, and NPV was 81.82%. Conclusions: The GX method shows promise as a first-line diagnostic method for pulmonary TB. Furthermore, the sensitivity was significantly improved when combined with LC. This combination will screen out some etiologically negative patients plus IGRA, so their combination is recommended for practice optimization.

ProGeo-Neo v2.0: A One-Stop Software for Neoantigen Prediction and Filtering Based on the Proteogenomics Strategy.

A proteogenomics-based neoantigen prediction pipeline, namely ProGeo-neo, was previously developed by our team to predict neoantigens, allowing the identification of class-I major histocompatibility complex (MHC) binding peptides based on single-nucleotide variation (SNV) mutations. To improve it, we here present an updated pipeline, i.e., ProGeo-neo v2.0, in which a one-stop software solution was proposed to identify neoantigens based on the paired tumor-normal whole genome sequencing (WGS)/whole exome sequencing (WES) data in FASTQ format. Preferably, in ProGeo-neo v2.0, several new features are provided. In addition to the identification of MHC-I neoantigens, the new version supports the prediction of MHC class II-restricted neoantigens, i.e., peptides up to 30-mer in length. Moreover, the source of neoantigens has been expanded, allowing more candidate neoantigens to be identified, such as in-frame insertion-deletion (indels) mutations, frameshift mutations, and gene fusion analysis. In addition, we propose two more efficient screening approaches, including an in-group authentic neoantigen peptides database and two more stringent thresholds. The range of candidate peptides was effectively narrowed down to those that are more likely to elicit an immune response, providing a more meaningful reference for subsequent experimental validation. Compared to ProGeo-neo, the ProGeo-neo v2.0 performed well based on the same dataset, including updated functionality and improved accuracy.

Single-Cell RNA-seq Analysis Reveals Dysregulated Cell-Cell Interactions in a Tumor Microenvironment Related to HCC Development.

The heterogeneity of tumor microenvironment (TME) of hepatocellular carcinoma (HCC) may relate to cell-cell interaction event (CCE) dysregulation and would affect therapeutic responses and clinical outcomes. To reveal the differentiation of CCEs in the liver tissue from healthy donors (HD) to HCC, scRNA-seq data of ~62000 cells from HD, paracancerous nontumor tissue (NT), and HCC were analyzed. The microenvironmental CCE landscape was constructed. Dysregulated cell types and changed molecular functions were identified with CCE alterations in HCC. Dysregulated CCEs which function as pivotal roles in tumorigenesis and development of HCC included SPP1-CD44, MIF-TNFRSF14, and VEGFA-NRP1. A CCE-based immune regulatory network was extracted to illustrate the mechanism of TME dysregulation. A prognostic signature based on CCE genes was identified and validated in independent datasets. Our study provided insights into the characteristics of the cross-talk between tumor cells and microenvironment in HCC and established a workflow strategy for CCE analyses based on scRNA-seq data.

dbPepNeo2.0: A Database for Human Tumor Neoantigen Peptides From Mass Spectrometry and TCR Recognition.

Neoantigens are widely reported to induce T-cell response and lead to tumor regression, indicating a promising potential to immunotherapy. Previously, we constructed an open-access database, i.e., dbPepNeo, providing a systematic resource for human tumor neoantigens to storage and query. In order to expand data volume and application scope, we updated dbPepNeo to version 2.0 (http://www.biostatistics.online/dbPepNeo2). Here, we provide about 801 high-confidence (HC) neoantigens (increased by 170%) and 842,289 low-confidence (LC) HLA immunopeptidomes (increased by 107%). Notably, 55 class II HC neoantigens and 630 neoantigen-reactive T-cell receptor-ß (TCRß) sequences were firstly included. Besides, two new analytical tools are developed, DeepCNN-Ineo and BLASTdb. DeepCNN-Ineo predicts the immunogenicity of class I neoantigens, and BLASTdb performs local alignments to look for sequence similarities in dbPepNeo2.0. Meanwhile, the web features and interface have been greatly improved and enhanced.

ToPP: Tumor online prognostic analysis platform for prognostic feature selection and clinical patient subgroup selection.

Patients with cancer with different molecular characterization and subtypes result in different response to anticancer therapeutics and survival. To identify features that are associated with prognosis is essential to precision medicine by providing clues for target identification, drug discovery. Here, we developed a tumor online prognostic analysis platform (ToPP) which integrated eight multi-omics features and clinical data from 68 cancer projects. It provides multiple approaches for customized prognostic studies, including 1) Prognostic analysis based on multi-omics features and clinical characteristics; 2) Automatic construction of prognostic model; 3) Pancancer prognostic analysis in multi-omics data; 4) Explore the impact of different levels of feature combinations on patient prognosis; 5) More sophisticated prognostic analysis according to regulatory network. ToPP provides a comprehensive source and easy-to-use interface for tumor prognosis research, with one-stop service of multi-omics, subtyping, and online prognostic modeling. The web server is freely available at http://www.biostatistics.online/topp/index.php.

Cellular Interaction Analysis Characterizing Immunosuppressive Microenvironment Functions in MM Tumorigenesis From Precursor Stages.

Cell-cell interaction event (CCEs) dysregulation may relate to the heterogeneity of the tumor microenvironment (TME) and would affect therapeutic responses and clinical outcomes. To reveal the alteration of the immune microenvironment in bone marrow from a healthy state to multiple myeloma (MM), scRNA-seq data of the four states, including healthy state normal bone marrow (NBM) and three disease states (MGUS, SMM, and MM), were collected for analysis. With immune microenvironment reconstruction, the cell types, including NK cells, CD8+ T cells, and CD4+ T cells, with a higher percentage in disease states were associated with prognosis of MM patients. Furthermore, CCEs were annotated and dysregulated CCEs were identified. The number of CCEs were significantly changed between disease states and NBM. The dysregulated CCEs participated in regulation of immune cell proliferation and immune response, such as MIF-TNFRSF14 interacted between early B cells and CD8+ T cells. Moreover, CCE genes related to drug response, including bortezomib and melphalan, provide candidate therapeutic markers for MM treatment. Furthermore, MM patients were separated into three risk groups based on the CCE prognostic signature. Immunoregulation-related differentiation and activation of CD4+ T cells corresponded to the progression status with moderate risk. These results provide a comprehensive understanding of the critical role of intercellular communication in the immune microenvironment over the evolution of premalignant MM, which is related to the tumorigenesis and progression of MM, which moreover, suggests a way of potential target selection for clinical intervention.