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Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.

Abstract Delta compression, as a complementary technique for data deduplication, has gained widespread attention in network storage systems. It can eliminate redundant data between non‐duplicate but similar chunks that cannot be identified by data deduplication. The network transmission overhead between servers and clients can be greatly reduced by using data deduplication and delta compression techniques. Resemblance detection is a technique that identifies similar chunks for post‐deduplication delta compression in network storage systems. The existing resemblance detection approaches fail to detect similar chunks with arbitrary similarity by setting a similarity threshold, which can be suboptimal. In this paper, the authors propose Chunk2vec, a resemblance detection scheme for delta compression that utilizes deep learning techniques and Approximate Nearest Neighbour Search technique to detect similar chunks with any given similarity range. Chunk2vec uses a deep neural network, Sentence‐BERT, to extract an approximate feature vector for each chunk while preserving its similarity with other chunks. The experimental results on five real‐world datasets indicate that Chunk2vec improves the accuracy of resemblance detection for delta compression and achieves higher compression ratio than the state‐of‐the‐art resemblance detection technique.

Neutrophil extracellular traps (NETs) released from neutrophils are related to cancer progression. However, the relationship between the therapeutic effects of immune checkpoint inhibitors (ICIs) such as anti-PD-1 and anti-PD-L1 antibodies and plasma NET concentration in patients with non-small cell lung cancer (NSCLC) is poorly understood. In this study, concentrations of citrullinated histone H3 (CitH3), a surrogate marker of NETs, in plasma before/after treatment were examined in patients with advanced or recurrent NSCLC undergoing ICI treatment (n = 185). The clinical significances of NET levels before/after treatment and posttreatment changes were statistically evaluated. As a result, multivariate Cox analysis showed that high NET levels before treatment were statistically significant predictors of unfavorable overall survival (OS; p < 0.001, HR 1.702, 95% CI 1.356–2.137) and progression-free survival (PFS; p < 0.001, HR 1.566, 95% CI 1.323–1.855). The Kaplan-Meier curves showed significant separation between the high- and low-NET groups in OS (p = 0.002) and PFS (p < 0.001). Additionally, high NET levels after treatment were also significantly associated with worse OS (p < 0.001) and PFS (p < 0.001) by multivariate Cox analysis. Notably, the pretreatment NET levels were significantly correlated with the plasma levels of NET-related inflammatory cytokines, such as IL-6 and IL-8, and with NET-related gene expression and immune-suppressive profile in peripheral blood mononuclear cells. Our findings suggest that NETs released from activated neutrophils might reduce the clinical efficacy of ICIs in patients with NSCLC.

Introduction: Clinical roles of plasma IL-6 levels have been reported in patients with various cancers, including non-small cell lung cancer (NSCLC), treated with immune checkpoint inhibitors (ICIs). However, the roles of other IL-6 signaling components, soluble IL-6 receptor (sIL-6R) and soluble gp130 (sgp130), in the plasma have not been elucidated.Methods: Blood was collected from 106 patients with NSCLC before initiation of ICI treatment (anti-PD-1 or anti-PD-L1 antibody). Plasma levels of IL-6, sIL-6R, sgp130, and their complexes were assessed by Cox regression hazard model to evaluate their clinical significance. The clinical role of IL-6 or IL-6R genetic polymorphisms was also analyzed.Results: Cox regression analysis showed that higher plasma IL-6 levels significantly predicted unfavorable overall survival (OS; hazard ratio [HR] 1.34, 95% confidence interval [CI] 1.05–1.68, p = 0.012) in NSCLC patients treated with ICIs. However, plasma sIL-6R and sgp130 levels showed no prognostic significance (p = 0.882 and p = 0.934, respectively). In addition, the estimated concentrations of binary IL-6:sIL-6R and ternary IL-6:sIL-6R:sgp130 complexes and their ratios (binary/ternary complex) were not significantly associated with OS (p = 0.647, p = 0.727, and p = 0.273, respectively). Furthermore, the genetic polymorphisms of IL-6 (−634G>C) and IL-6R (48892A>C) showed no clinical role by Kaplan-Meier survival analysis (p = 0.908 and p = 0.639, respectively).Discussion: These findings demonstrated the clinical significance of plasma levels of IL-6, but not of other IL-6 signaling components, sIL-6R and sgp130, suggesting that classical IL-6 signaling, but not trans-signaling, may be related to anti-tumor immune responses in cancer patients treated with ICIs.

Abstract Background circular RNAs (circRNAs) have been considered novel biomarker candidates for human cancers, such as triple-negative breast cancer (TNBC). circ_0001006 was identified as a differentially expressed circRNA in metastatic breast cancer, but its significance and function in TNBC were unclear. The significance of circ_0001006 in TNBC was assessed and exploring its potential molecular mechanism to provide a therapeutic target for TNBC. Results circ_0001006 showed significant upregulation in TNBC and close association with patients’ histological grade, Ki67 level, and TNM stage. Upregulated circ_0001006 could predict a worse prognosis and high risk of TNBC patients. In TNBC cells, silencing circ_0001006 suppressed cell proliferation, migration, and invasion. In mechanism, circ_0001006 could negatively regulate miR-424-5p, which mediated the inhibition of cellular processes by circ_0001006 knockdown. Conclusions Upregulated circ_0001006 in TNBC served as a poor prognosis predictor and tumor promoter via negatively regulating miR-424-5p.

Introduction: This study aimed to identify immune mediators, including cytokines, chemokines, and growth factors, in the plasma for predicting treatment efficacy and immune-related adverse events (irAEs) in advanced urothelial carcinoma (aUC) treated with immune checkpoint inhibitors (ICIs).Methods: We enrolled 57 patients with aUC who were treated with the anti-programmed cell death protein 1 (PD-1) antibody pembrolizumab after the failure of platinum-based chemotherapy between February 2018 and December 2020. Plasma levels of 73 soluble immune mediators were measured before and 6 weeks after initiating pembrolizumab therapy. The association of estimated soluble immune mediators with clinical outcomes, including overall survival (OS), progression-free survival (PFS), anti-tumor responses, and irAEs, were statistically evaluated.Results: In the multivariate analysis, levels of 18 factors at baseline and 12 factors during treatment were significantly associated with OS. Regarding PFS, baseline levels of 17 factors were significantly associated with PFS. Higher levels of interleukin (IL)-6, IL-8, soluble tumor necrosis factor receptor 1 (sTNF-R1), and IL-12 (p40), both at baseline and post-treatment, were significantly associated with worse OS. Conversely, low IL-6 and high TWEAK levels at baseline were associated with irAEs. Among identified factors, interferon (IFN) γ and IL-12 (p40) were repeatedly identified; high baseline levels of these factors were risk factors for worse OS and PFS, as well as progressive disease. Notably, using correlation and principal component analysis, factors significantly associated with clinical outcomes were broadly classified into three groups exhibiting similar expression patterns.Discussion: Measuring plasma levels of soluble immune mediators, such as IL-6, IL-8, sTNF-R1, IFNγ, and IL-12 (p40), could be recommended for predicting prognosis and irAEs in ICI-treated patients with aUC.

The financial technology (FinTech) has promoted the wide application of FinTech with the help of artificial intelligence, blockchain, cloud computing, data science and other new technologies. Trust evaluation has become a forward-looking issue for the rapid development of FinTech. The existing trust evaluation methods of FinTech do not consider the impact of the timeliness, reliability and non-invasive factors of trust on trust evaluation, which results in low accuracy of trust evaluation and incapability of effectively identifying malicious behaviors of users. Firstly, this paper introduces the blockchain technology to construct a four-layer architecture structure and multiple trust evaluation indicators based on blockchain service data. Secondly, the paper proposes a blockchain based FinTech trust evaluation mechanism (BFTEM), which utilizes blockchain to record relevant data and multiple trust parameters during the transmission process of each block, and verify the trust degree issued by the trust holder through the comprehensive trust value of the user. Finally, the block generation time, throughput, delayed response time and comprehensive trust value are experimentally studied through simulation experiments. Simulation experiments show that BFTEM mechanism can better improve the security and reliability of FinTech data, and has advantages in improving the accuracy of trust evaluation and expanding potential applications.

Background Immune checkpoint inhibitor (ICI) therapy has substantially improved the overall survival (OS) in patients with non-small-cell lung cancer (NSCLC); however, its response rate is still modest. In this study, we developed a machine learning-based platform, namely the Cytokine-based ICI Response Index (CIRI), to predict the ICI response of patients with NSCLC based on the peripheral blood cytokine profiles.Methods We enrolled 123 and 99 patients with NSCLC who received anti-PD-1/PD-L1 monotherapy or combined chemotherapy in the training and validation cohorts, respectively. The plasma concentrations of 93 cytokines were examined in the peripheral blood obtained from patients at baseline (pre) and 6 weeks after treatment (early during treatment: edt). Ensemble learning random survival forest classifiers were developed to select feature cytokines and predict the OS of patients undergoing ICI therapy.Results Fourteen and 19 cytokines at baseline and on treatment, respectively, were selected to generate CIRI models (namely preCIRI14 and edtCIRI19), both of which successfully identified patients with worse OS in two completely independent cohorts. At the population level, the prediction accuracies of preCIRI14 and edtCIRI19, as indicated by the concordance indices (C-indices), were 0.700 and 0.751 in the validation cohort, respectively. At the individual level, patients with higher CIRI scores demonstrated worse OS [hazard ratio (HR): 0.274 and 0.163, and p

Abstract Nuclear magnetic resonance (NMR)-based relaxometry is widely used in various fields of research because of its advantages such as simple sample preparation, easy handling, and relatively low cost compared with metabolomics approaches. However, there have been no reports on the application of the T 2 relaxation curves in metabolomics studies involving the evaluation of metabolic mixtures, such as geographical origin determination and feature extraction by pattern recognition and data mining. In this study, we describe a data mining method for relaxometric data (i.e., relaxometric learning). This method is based on a machine learning algorithm supported by the analytical framework optimized for the relaxation curve analyses. In the analytical framework, we incorporated a variable optimization approach and bootstrap resampling-based matrixing to enhance the classification performance and balance the sample size between groups, respectively. The relaxometric learning enabled the extraction of features related to the physical properties of fish muscle and the determination of the geographical origin of the fish by improving the classification performance. Our results suggest that relaxometric learning is a powerful and versatile alternative to conventional metabolomics approaches for evaluating fleshiness of chemical mixtures in food and for other biological and chemical research requiring a nondestructive, cost-effective, and time-saving method.

Abstract Functional diversity rather than species richness is critical for the understanding of ecological patterns and processes. This study aimed to develop novel integrated analytical strategies for the functional characterization of fish diversity based on the quantification, prediction and integration of the chemical and physical features in fish muscles. Machine learning models with an improved random forest algorithm applied on 1867 muscle nuclear magnetic resonance spectra belonging to 249 fish species successfully predicted the mobility patterns of fishes into four categories (migratory, territorial, rockfish, and demersal) with accuracies of 90.3–95.4%. Markov blanket-based feature selection method with an ecological–chemical–physical integrated network based on the Bayesian network inference algorithm highlighted the importance of nitrogen metabolism, which is critical for environmental adaptability of fishes in nutrient-rich environments, in the functional characterization of fish biodiversity. Our study provides valuable information and analytical strategies for fish home-range assessment on the basis of the chemical and physical characterization of fish muscle, which can serve as an ecological indicator for fish ecotyping and human impact monitoring.

Abstract Background Hepatocellular carcinoma (HCC) is one of the most deadly cancer worldwide. Multiple long noncoding RNAs (lncRNAs) are recently identified as crucial oncogenic factors or tumor suppressors. In this study, we explored the functon and mechanism of lncRNA double homeobox A pseudogene 8 (DUXAP8) in the progression of HCC. Methods Expression levels of DUXAP8 in HCC tissue samples were measured using qRT‐PCR. The association between pathological indexes and the expression of DUXAP8 was also analyzed. Human HCC cell lines SMMC‐7721 and QSG‐7701 were used in in vitro studies. CCK‐8 assay was used to assess the effect of DUXAP8 on HCC cell line proliferation. Scratch healing assay and Transwell assay were conducted to detect the effect of DUXAP8 on migration and invasion. Furthermore, dual‐luciferase reporter assay was used to confirm targeting relationship between miR‐422a and DUXAP8. Additionally, Western blot was used to detect the regulatory function of DUXAP8 on pyruvate dehydrogenase kinase 2 (PDK2). Results DUXAP8 expression HCC clinical samples was significantly increased and this was correlated with unfavorable pathological indexes. High expression of DUXAP8 was associated with shorter overall survival time of patients. Its overexpression remarkably facilitated the proliferation, metastasis, and epithelial‐mesenchymal transition of HCC cells. Accordingly, knockdown of it suppressed the malignant phenotypes of HCC cells. Overexpression of DUXAP8 significantly reduced the expression of miR‐422a by sponging it, but enhanced the expression of PDK2. Conclusions DUXAP8 was a sponge of tumor suppressor miR‐422a in HCC, enhanced the expression of PDK2 indirectly, and functioned as an oncogenic lncRNA.

It is essential to estimate the indoor pesticides/insecticides exposure risk since reports show that 80% of human exposure to pesticides occurs indoors. As one of the three major contamination sources, surface collected pesticides contributed significantly to this risk. Here, a highly sensitive liquid freestanding membrane (FSM) SERS method based on iodide modified silver nanoparticles (Ag NPs) was developed for quantitative detection of insecticide deltamethrin (DM) residues in solution phase samples and on surfaces with good accuracy and high sensitivity. The DM SERS spectrum from 500 to 2500 cm−1 resembled the normal Raman counterpart of solid DM. Similar bands at 563, 1000, 1165, 1207, 1735, and 2253 cm−1 were observed as in the literature. For the quantitative analysis, the strongest peak at 1000 cm−1 that was assigned to the stretching mode of the benzene ring and the deformation mode of C-C was selected. The peak intensity at 1000 cm−1 and the concentration of DM showed excellent linearity from 39 to 5000 ppb with a regression equation I = 649.428 + 1.327 C (correlation coefficient R2 = 0.991). The limit of detection (LOD) of the DM was found to be as low as 11 ppb. Statistical comparison between the proposed and the HPLC methods for the analysis of insecticide deltamethrin (DM) residues in solution phase samples showed no significant difference. DM residue analysis on the surface was mimicked by dropping DM pesticide on the glass surface. It is found that DM exhibited high residue levels up to one week after exposure. This proposed SERS method could find application in the household pesticide residues analysis.

Abstract Data-driven approaches were applied to investigate the temporal and spatial changes of 1,022 individuals of wild yellowfin goby and its potential interaction with the estuarine environment in Japan. Nuclear magnetic resonance (NMR)-based metabolomics revealed that growth stage is a primary factor affecting muscle metabolism. Then, the metabolic, elemental and microbial profiles of the pooled samples generated according to either the same habitat or sampling season as well as the river water and sediment samples from their habitats were measured using NMR spectra, inductively coupled plasma optical emission spectrometry and next-generation 16 S rRNA gene sequencing. Hidden interactions in the integrated datasets such as the potential role of intestinal bacteria in the control of spawning migration, essential amino acids and fatty acids synthesis in wild yellowfin goby were further extracted using correlation clustering and market basket analysis-generated networks. Importantly, our systematic analysis of both the seasonal and latitudinal variations in metabolome, ionome and microbiome of wild yellowfin goby pointed out that the environmental factors such as the temperature play important roles in regulating the body homeostasis of wild fish.

The acute phase protein orosomucoid-1 (Orm1) is mainly expressed by hepatocytes (HPCs) under stress conditions. However, its specific function is not fully understood. Here, we report a role of Orm1 as an executer of HPC proliferation. Increases in serum levels of Orm1 were observed in patients after surgical resection for liver cancer and in mice undergone partial hepatectomy (PH). Transcriptome study showed that Orm1 became the most abundant in HPCs isolated from regenerating mouse liver tissues after PH. Both in vitro and in vivo siRNA-induced knockdown of Orm1 suppressed proliferation of mouse regenerating HPCs and human hepatic cells. Microarray analysis in regenerating mouse livers revealed that the signaling pathways controlling chromatin replication, especially the minichromosome maintenance protein complex genes were uniformly down-regulated following Orm1 knockdown. These data suggest that Orm1 is induced in response to hepatic injury and executes liver regeneration by activating cell cycle progression in HPCs.

The incidence and mortality of liver cancer, mostly hepatocellular carcinoma (HCC), have increased during the last two decades, partly due to persistent inflammation in the lipid-rich microenvironment associated with lifestyle diseases, such as obesity. Gangliosides are sialic acid-containing glycosphingolipids known to be important in the organization of the membrane and membrane protein-mediated signal transduction. Ganglioside synthesis is increased in several types of cancers and has been proposed as a promising target for cancer therapy. Here, we provide evidence that ganglioside synthesis was increased in the livers of an animal model recapitulating the features of activation and expansion of liver progenitor-like cells and liver cancer (stem) cells. Chemical inhibition of ganglioside synthesis functionally suppressed proliferation and sphere growth of liver cancer cells, but had no impact on apoptotic and necrotic cell death. Proteome-based mechanistic analysis revealed that inhibition of ganglioside synthesis downregulated the expression of AURKA, AURKB, TTK, and NDC80 involved in the regulation of kinetochore metaphase signaling, which is essential for chromosome segregation and mitotic progression and probably under the control of activation of TP53-dependent cell cycle arrest. These data suggest that targeting ganglioside synthesis holds promise for the development of novel preventive/therapeutic strategies for HCC treatment.

Conventional proton nuclear magnetic resonance (1H-NMR) has been widely used for identification and quantification of small molecular components in food. However, identification of major soluble macromolecular components from conventional 1H-NMR spectra is difficult. This is because the baseline appearance is masked by the dense and high-intensity signals from small molecular components present in the sample mixtures. In this study, we introduced an integrated analytical strategy based on the combination of additional measurement using a diffusion filter, covariation peak separation, and matrix decomposition in a small-scale training dataset. This strategy is aimed to extract signal profiles of soluble macromolecular components from conventional 1H-NMR spectral data in a large-scale dataset without the requirement of re-measurement. We applied this method to the conventional 1H-NMR spectra of water-soluble fish muscle extracts and investigated the distribution characteristics of fish diversity and muscle soluble macromolecular components, such as lipids and collagens. We identified a cluster of fish species with low content of lipids and high content of collagens in muscle, which showed great potential for the development of functional foods. Because this mechanical data processing method requires additional measurement of only a small-scale training dataset without special sample pretreatment, it should be immediately applicable to extract macromolecular signals from accumulated conventional 1H-NMR databases of other complex gelatinous mixtures in foods.

Objective: The Arkansas Prescription Monitoring Program (AR PMP) was implemented in 2013 to combat prescription drug abuse. All enrollees were invited to participate in a user survey available in February 2014, to identify makeup of users, utilization of the program, and changes made to health care practices after implementation of the program. Methods: Of the 3694 individual enrollees invited to participate, 1541 (41.7%) completed the survey. Data collected were analyzed to identify changes in health care practices by program frequency of use and user profession. Results: Medical doctors, advanced practice nurses, and pharmacists are the professions who use the program most frequently. Daily AR PMP users are considerably more likely than infrequent users to be prompted to access the program by the involvement of a controlled substance (CS) prescription or by office/facility policy requirements. Increased frequency of use of the AR PMP results in positive impacts on CS prescribing and dispensing practices. Conclusion: Compelling more users of the AR PMP to be prompted to access the program by the involvement of a CS prescription or by requirements per office/facility policy may increase frequency of use of the program and thereby changes in health care practices to combat prescription drug abuse.

Background/purposeAcyclic retinoid (ACR) is a promising chemopreventive agent for hepatocellular carcinoma (HCC) that selectively inhibits the growth of HCC cells (JHH7) but not normal hepatic cells (Hc). To better understand the molecular basis of the selective anti-cancer effect of ACR, we performed nuclear magnetic resonance (NMR)-based and capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS)-based metabolome analyses in JHH7 and Hc cells after treatment with ACR.Methodology/principal findingsNMR-based metabolomics revealed a distinct metabolomic profile of JHH7 cells at 18 h after ACR treatment but not at 4 h after ACR treatment. CE-TOFMS analysis identified 88 principal metabolites in JHH7 and Hc cells after 24 h of treatment with ethanol (EtOH) or ACR. The abundance of 71 of these metabolites was significantly different between EtOH-treated control JHH7 and Hc cells, and 49 of these metabolites were significantly down-regulated in the ACR-treated JHH7 cells compared to the EtOH-treated JHH7 cells. Of particular interest, the increase in adenosine-5'-triphosphate (ATP), the main cellular energy source, that was observed in the EtOH-treated control JHH7 cells was almost completely suppressed in the ACR-treated JHH7 cells; treatment with ACR restored ATP to the basal levels observed in both EtOH-control and ACR-treated Hc cells (0.72-fold compared to the EtOH control-treated JHH7 cells). Moreover, real-time PCR analyses revealed that ACR significantly increased the expression of pyruvate dehydrogenase kinases 4 (PDK4), a key regulator of ATP production, in JHH7 cells but not in Hc cells (3.06-fold and 1.20-fold compared to the EtOH control, respectively).Conclusions/significanceThe results of the present study suggest that ACR may suppress the enhanced energy metabolism of JHH7 cells but not Hc cells; this occurs at least in part via the cancer-selective enhancement of PDK4 expression. The cancer-selective metabolic pathways identified in this study will be important targets of the anti-cancer activity of ACR.

Resistance to docetaxel is a major problem to the success of docetaxel-based therapies for breast cancer. The present study was to identify the role of circABCB1 in altering the docetaxel resistance properties. Reverse transcription-quantitative PCR (qRT-PCR) was performed to quantify circABCB1 and miR-153-3p. The effects of circABCB1 on the viability, apoptosis and migration/invasion of docetaxel-resistant and -sensitive cells were investigated by cell function experiments, including Cell Counting Kit-8 and Transwell assays. Correlation between circABCB1 and the docetaxel-treated outcome was analyzed by multivariate Cox regression analysis, in addition to Kaplan-Meier analysis of time to treatment failure (TTF). The targeting relationship between circABCB1 and miR-153-3p was predicted and verified by dual-luciferase reporter assay and RNA immunoprecipitation. CircABCB1 was highly expressed in cancerous tissues, as well as the docetaxel-sensitive group and cells. The overexpression of circABCB1 contributed to cell viability, docetaxel-resistance and migration/invasion, but inhibited apoptosis. CircABCB1 can sponge miR-153-3p. CircABCB1 contributed to the docetaxel resistance of breast cancer, maybe via the miR-153-3p. [ABSTRACT FROM AUTHOR]