Non-genomic and Immune Evolution of Melanoma Acquiring MAPKi Resistance.

Clinically acquired resistance to MAPK inhibitor (MAPKi) therapies for melanoma cannot be fully explained by genomic mechanisms and may be accompanied by co-evolution of intra-tumoral immunity. We sought to discover non-genomic mechanisms of acquired resistance and dynamic immune compositions by a comparative, transcriptomic-methylomic analysis of patient-matched melanoma tumors biopsied before therapy and during disease progression. Transcriptomic alterations across resistant tumors were highly recurrent, in contrast to mutations, and were frequently correlated with differential methylation of tumor cell-intrinsic CpG sites. We identified in the tumor cell compartment supra-physiologic c-MET up-expression, infra-physiologic LEF1 down-expression and YAP1 signature enrichment as drivers of acquired resistance. Importantly, high intra-tumoral cytolytic T cell inflammation prior to MAPKi therapy preceded CD8 T cell deficiency/exhaustion and loss of antigen presentation in half of disease-progressive melanomas, suggesting cross-resistance to salvage anti-PD-1/PD-L1 immunotherapy. Thus, melanoma acquires MAPKi resistance with highly dynamic and recurrent non-genomic alterations and co-evolving intra-tumoral immunity.

Real-time detection of 20 amino acids and discrimination of pathologically relevant peptides with functionalized nanopore.

Precise identification and quantification of amino acids is crucial for many biological applications. Here we report a copper(II)-functionalized Mycobacterium smegmatis porin A (MspA) nanopore with the N91H substitution, which enables direct identification of all 20 proteinogenic amino acids when combined with a machine-learning algorithm. The validation accuracy reaches 99.1%, with 30.9% signal recovery. The feasibility of ultrasensitive quantification of amino acids was also demonstrated at the nanomolar range. Furthermore, the capability of this system for real-time analyses of two representative post-translational modifications (PTMs), one unnatural amino acid and ten synthetic peptides using exopeptidases, including clinically relevant peptides associated with Alzheimer's disease and cancer neoantigens, was demonstrated. Notably, our strategy successfully distinguishes peptides with only one amino acid difference from the hydrolysate and provides the possibility to infer the peptide sequence.

Enhancing genome-wide populus trait prediction through deep convolutional neural networks.

As a promising model, genome-based plant breeding has greatly promoted the improvement of agronomic traits. Traditional methods typically adopt linear regression models with clear assumptions, neither obtaining the linkage between phenotype and genotype nor providing good ideas for modification. Nonlinear models are well characterized in capturing complex nonadditive effects, filling this gap under traditional methods. Taking populus as the research object, this paper constructs a deep learning method, DCNGP, which can effectively predict the traits including 65 phenotypes. The method was trained on three datasets, and compared with other four classic models-Bayesian ridge regression (BRR), Elastic Net, support vector regression, and dualCNN. The results show that DCNGP has five typical advantages in performance: strong prediction ability on multiple experimental datasets; the incorporation of batch normalization layers and Early-Stopping technology enhancing the generalization capabilities and prediction stability on test data; learning potent features from the data and thus circumventing the tedious steps of manual production; the introduction of a Gaussian Noise layer enhancing predictive capabilities in the case of inherent uncertainties or perturbations; fewer hyperparameters aiding to reduce tuning time across datasets and improve auto-search efficiency. In this way, DCNGP shows powerful predictive ability from genotype to phenotype, which provide an important theoretical reference for building more robust populus breeding programs.

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine.

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.

Revealing Allosteric Mechanism of Amino Acid Binding Proteins from Open to Closed State.

Amino acid binding proteins (AABPs) undergo significant conformational closure in the periplasmic space of Gram-negative bacteria, tightly binding specific amino acid substrates and then initiating transmembrane transport of nutrients. Nevertheless, the possible closure mechanisms after substrate binding, especially long-range signaling, remain unknown. Taking three typical AABPs-glutamine binding protein (GlnBP), histidine binding protein (HisJ) and lysine/arginine/ornithine binding protein (LAOBP) in Escherichia coli (E. coli)-as research subjects, a series of theoretical studies including sequence alignment, Gaussian network model (GNM), anisotropic network model (ANM), conventional molecular dynamics (cMD) and neural relational inference molecular dynamics (NRI-MD) simulations were carried out. Sequence alignment showed that GlnBP, HisJ and LAOBP have high structural similarity. According to the results of the GNM and ANM, AABPs' Index Finger and Thumb domains exhibit closed motion tendencies that contribute to substrate capture and stable binding. Based on cMD trajectories, the Index Finger domain, especially the I-Loop region, exhibits high molecular flexibility, with residues 11 and 117 both being potentially key residues for receptor-ligand recognition and initiation of receptor allostery. Finally, the signaling pathway of AABPs' conformational closure was revealed by NRI-MD training and trajectory reconstruction. This work not only provides a complete picture of AABPs' recognition mechanism and possible conformational closure, but also aids subsequent structure-based design of small-molecule oncology drugs.

AIDE: annotation-assisted isoform discovery with high precision.

Genome-wide accurate identification and quantification of full-length mRNA isoforms is crucial for investigating transcriptional and posttranscriptional regulatory mechanisms of biological phenomena. Despite continuing efforts in developing effective computational tools to identify or assemble full-length mRNA isoforms from second-generation RNA-seq data, it remains a challenge to accurately identify mRNA isoforms from short sequence reads owing to the substantial information loss in RNA-seq experiments. Here, we introduce a novel statistical method, annotation-assisted isoform discovery (AIDE), the first approach that directly controls false isoform discoveries by implementing the testing-based model selection principle. Solving the isoform discovery problem in a stepwise and conservative manner, AIDE prioritizes the annotated isoforms and precisely identifies novel isoforms whose addition significantly improves the explanation of observed RNA-seq reads. We evaluate the performance of AIDE based on multiple simulated and real RNA-seq data sets followed by PCR-Sanger sequencing validation. Our results show that AIDE effectively leverages the annotation information to compensate the information loss owing to short read lengths. AIDE achieves the highest precision in isoform discovery and the lowest error rates in isoform abundance estimation, compared with three state-of-the-art methods Cufflinks, SLIDE, and StringTie. As a robust bioinformatics tool for transcriptome analysis, AIDE enables researchers to discover novel transcripts with high confidence.

HIP1R targets PD-L1 to lysosomal degradation to alter T cell-mediated cytotoxicity.

Expression of programmed cell death 1 (PD-1) ligand 1 (PD-L1) protects tumor cells from T cell-mediated immune surveillance, and immune checkpoint blockade (ICB) therapies targeting PD-1 and PD-L1 have exhibited significant clinical benefits. However, the relatively low response rate and observed ICB resistance highlight the need to understand the molecular regulation of PD-L1. Here we show that HIP1R targets PD-L1 to lysosomal degradation to alter T cell-mediated cytotoxicity. HIP1R physically interacts with PD-L1 and delivers PD-L1 to the lysosome through a lysosomal targeting signal. Depletion of HIP1R in tumor cells caused PD-L1 accumulation and suppressed T cell-mediated cytotoxicity. A rationally designed peptide (PD-LYSO) incorporating the lysosome-sorting signal and the PD-L1-binding sequence of HIP1R successfully depleted PD-L1 expression in tumor cells. Our results identify the molecular machineries governing the lysosomal degradation of PD-L1 and exemplify the development of a chimeric peptide for targeted degradation of PD-L1 as a crucial anticancer target.

Therapy-induced tumour secretomes promote resistance and tumour progression.

Drug resistance invariably limits the clinical efficacy of targeted therapy with kinase inhibitors against cancer. Here we show that targeted therapy with BRAF, ALK or EGFR kinase inhibitors induces a complex network of secreted signals in drug-stressed human and mouse melanoma and human lung adenocarcinoma cells. This therapy-induced secretome stimulates the outgrowth, dissemination and metastasis of drug-resistant cancer cell clones and supports the survival of drug-sensitive cancer cells, contributing to incomplete tumour regression. The tumour-promoting secretome of melanoma cells treated with the kinase inhibitor vemurafenib is driven by downregulation of the transcription factor FRA1. In situ transcriptome analysis of drug-resistant melanoma cells responding to the regressing tumour microenvironment revealed hyperactivation of several signalling pathways, most prominently the AKT pathway. Dual inhibition of RAF and the PI(3)K/AKT/mTOR intracellular signalling pathways blunted the outgrowth of the drug-resistant cell population in BRAF mutant human melanoma, suggesting this combination therapy as a strategy against tumour relapse. Thus, therapeutic inhibition of oncogenic drivers induces vast secretome changes in drug-sensitive cancer cells, paradoxically establishing a tumour microenvironment that supports the expansion of drug-resistant clones, but is susceptible to combination therapy.

Strategy used to synthesize high activity and low Pd catalyst for Suzuki coupling reaction: an experimental and theoretical investigation.

Unveiling the reaction mechanism is significant for developing high-performance catalysts. In this paper, a series of precisely controlled PdxM147-x (M = Cu, Pt, Au, Rh, Ru) dendrimer encapsulated nanoparticles (DENs) has been successfully synthesized. The mechanisms of PdxM147-x as catalysts for Suzuki cross-coupling reactions were investigated by combining experimental and theoretical methods. The experimental results indicate that Pd74Cu73 DEN shows similar activity to Pd147 DEN and excellent substrate adaptability under mild reaction conditions. Moreover, the Cu component can play an important role in tuning the catalytic activity of PdxCu147-x DEN. Density functional theory (DFT) calculations illustrate that the similar activities of the Pd147 and Pd74Cu73 DENs originate from the comparable energy barriers of the rate-determining steps. The partial density of states (PDOS) and electron density differences demonstrate that Cu decreases the intensities of the valence orbitals of the top and edge Pd atoms and weakens orbital interactions between the intermediates and Pd74Cu73 DEN, leading to low desorption energies of the products. This work can provide a promising strategy to reduce the cost of Pd catalysts in Suzuki cross-coupling reactions.

Mechanisms of Resistance to Checkpoint Blockade Therapy.

Immune checkpoint blockades (ICBs), as a major breakthrough in cancer immunotherapy, target CTLA-4 and the PD-1/PD-L1 axis and reinvigorate anti-tumor activities by disrupting co-inhibitory T-cell signaling. With unprecedented performance in clinical trials, ICBs have been approved by FDA for the treatment of malignancies such as melanoma, non-small-cell lung cancer, colorectal cancer, and hepatocellular carcinoma. However, while ICBs are revolutionizing therapeutic algorithms for cancers, the frequently observed innate, adaptive or acquired drug resistance remains an inevitable obstacle to a durable antitumor activity, thus leading to non-response or tumor relapse. Researches have shown that resistance could occur at each stage of the tumor's immune responses. From the current understanding, the molecular mechanisms for the resistance of ICB can be categorized into the following aspects: 1. Tumor-derived mechanism, 2. T cell-based mechanism, and 3. Tumor microenvironment-determined resistance. In order to overcome resistance, potential therapeutic strategies include enhancing antigen procession and presentation, reinforcing the activity and infiltration of T cells, and destroying immunosuppression microenvironment. In future, determining the driving factors behind ICB resistance by tools of precision medicine may maximize clinical benefits from ICBs. Moreover, efforts in individualized dosing, intermittent administration and/or combinatory regimens have opened new directions for overcoming ICB resistance.

A novel pan-cancer biomarker plasma heat shock protein 90alpha and its diagnosis determinants in clinic.

A sensitive and specific diagnosis biomarker, in principle scalable to most cancer types, is needed to reduce the prevalent cancer mortality. Meanwhile, the investigation of diagnosis determinants of a biomarker will facilitate the interpretation of its screening results in clinic. Here we design a large-scale (1558 enrollments), multicenter (multiple hospitals), and cross-validation (two datasets) clinic study to validate plasma Hsp90α quantified by ELISA as a pan-cancer biomarker. ROC curve shows the optimum diagnostic cutoff is 69.19 ng/mL in discriminating various cancer patients from all controls (AUC 0.895, sensitivity 81.33% and specificity 81.65% in test cohort; AUC 0.893, sensitivity 81.72% and specificity 81.03% in validation cohort). Similar results are noted in detecting early-stage cancer patients. Plasma Hsp90α maintains also broad-spectrum for cancer subtypes, especially with 91.78% sensitivity and 91.96% specificity in patients with AFP-limited liver cancer. In addition, we demonstrate levels of plasma Hsp90α are determined by ADAM10 expression, which will affect Hsp90α content in exosomes. Furthermore, Western blotting and PRM-based quantitative proteomics identify that partial false ELISA-negative patients secret high levels of plasma Hsp90α. Mechanism analysis reveal that TGFß-PKCγ gene signature defines a distinct pool of hyperphosphorylated Hsp90α at Theronine residue. In clinic, a mechanistically relevant population of false ELISA-negative patients express also higher levels of PKCγ. In sum, plasma Hsp90α is a novel pan-cancer diagnosis biomarker, and cancer diagnosis with plasma Hsp90α is particularly effective in those patients with high expression of ADAM10, but may be insufficient to detect the patients with low ADAM10 and those with hyperphosphorylated Hsp90α.

The global motion affecting electron transfer in Plasmodium falciparum type II NADH dehydrogenases: a novel non-competitive mechanism for quinoline ketone derivative inhibitors.

With the emergence of drug-resistant Plasmodium falciparum, the treatment of malaria has become a significant challenge; therefore, the development of antimalarial drugs acting on new targets is extremely urgent. In Plasmodium falciparum, type II nicotinamide adenine dinucleotide (NADH) dehydrogenase (NDH-2) is responsible for catalyzing the transfer of two electrons from NADH to flavin adenine dinucleotide (FAD), which in turn transfers the electrons to coenzyme Q (CoQ). As an entry enzyme for oxidative phosphorylation, NDH-2 has become one of the popular targets for the development of new antimalarial drugs. In this study, reliable motion trajectories of the NDH-2 complex with its co-factors (NADH and FAD) and inhibitor, RYL-552, were obtained by comparative molecular dynamics simulations. The influence of cofactor binding on the global motion of NDH-2 was explored through conformational clustering, principal component analysis and free energy landscape. The molecular interactions of NDH-2 before and after its binding with the inhibitor RYL-552 were analyzed, and the key residues and important hydrogen bonds were also determined. The results show that the association of RYL-552 results in the weakening of intramolecular hydrogen bonds and large allosterism of NDH-2. There was a significant positive correlation between the angular change of the key pocket residues in the NADH-FAD-pockets that represents the global functional motion and the change in distance between NADH-C4 and FAD-N5 that represents the electron transfer efficiency. Finally, the possible non-competitive inhibitory mechanism of RYL-552 was proposed. Specifically, the association of inhibitors with NDH-2 significantly affects the global motion mode of NDH-2, leading to widening of the distance between NADH and FAD through cooperative motion induction; this reduces the electron transfer efficiency of the mitochondrial respiratory chain. The simulation results provide useful theoretical guidance for subsequent antimalarial drug design based on the NDH-2 structure and the respiratory chain electron transfer mechanism.

PD-L1 Nanobody Competitively Inhibits the Formation of the PD-1/PD-L1 Complex: Comparative Molecular Dynamics Simulations.

The anti-PD-L1 monoclonal antibody (mAb) targeting PD-1/PD-L1 immune checkpoint has achieved outstanding results in clinical application and has become one of the most popular anti-cancer drugs. The mechanism of molecular recognition and inhibition of PD-L1 mAbs is not yet clear, which hinders the subsequent antibody design and modification. In this work, the trajectories of PD-1/PD-L1 and nanobody/PD-L1 complexes were obtained via comparative molecular dynamics simulations. Then, a series of physicochemical parameters including hydrogen bond, dihedral angle distribution, pKa value and binding free energy, and so forth, were all comparatively analyzed to investigate the recognition difference between PD-L1 and PD-1 and nanobody. Both LR113 (the amino acid residues in PD-L1 are represented by the lower left sign of L) and LR125 residues of PD-L1 undergo significant conformational change after association with mAbs, which dominates a strong electrostatic interaction. Solvation effect analysis revealed that solvent-water enhanced molecular recognition between PD-L1 and nanobody. By combining the analyses of the time-dependent root mean squared fluctuation (RMSF), free energy landscape, clustering and energy decomposition, the potential inhibition mechanism was proposed that the nanobody competitively and specifically bound to the ß-sheet groups of PD-L1, reduced the PD-L1's flexibility and finally blocked the formation of PD-1/PD-L1 complex. Based on the simulation results, site-directed mutagenesis of ND99 (the amino acid residues in Nano are displayed by the lower left sign of N) and NQ116 in the nanobody may be beneficial for improving antibody activity. This work offers some structural guidance for the design and modification of anticancer mAbs based on the structure of the PD-1/PD-L1 complex.

RAF inhibitor resistance is mediated by dimerization of aberrantly spliced BRAF(V600E).

Activated RAS promotes dimerization of members of the RAF kinase family. ATP-competitive RAF inhibitors activate ERK signalling by transactivating RAF dimers. In melanomas with mutant BRAF(V600E), levels of RAS activation are low and these drugs bind to BRAF(V600E) monomers and inhibit their activity. This tumour-specific inhibition of ERK signalling results in a broad therapeutic index and RAF inhibitors have remarkable clinical activity in patients with melanomas that harbour mutant BRAF(V600E). However, resistance invariably develops. Here, we identify a new resistance mechanism. We find that a subset of cells resistant to vemurafenib (PLX4032, RG7204) express a 61-kDa variant form of BRAF(V600E), p61BRAF(V600E), which lacks exons 4-8, a region that encompasses the RAS-binding domain. p61BRAF(V600E) shows enhanced dimerization in cells with low levels of RAS activation, as compared to full-length BRAF(V600E). In cells in which p61BRAF(V600E) is expressed endogenously or ectopically, ERK signalling is resistant to the RAF inhibitor. Moreover, a mutation that abolishes the dimerization of p61BRAF(V600E) restores its sensitivity to vemurafenib. Finally, we identified BRAF(V600E) splicing variants lacking the RAS-binding domain in the tumours of six of nineteen patients with acquired resistance to vemurafenib. These data support the model that inhibition of ERK signalling by RAF inhibitors is dependent on levels of RAS-GTP too low to support RAF dimerization and identify a novel mechanism of acquired resistance in patients: expression of splicing isoforms of BRAF(V600E) that dimerize in a RAS-independent manner.

Melanomas acquire resistance to B-RAF(V600E) inhibition by RTK or N-RAS upregulation.

Activating B-RAF(V600E) (also known as BRAF) kinase mutations occur in ∼7% of human malignancies and ∼60% of melanomas. Early clinical experience with a novel class I RAF-selective inhibitor, PLX4032, demonstrated an unprecedented 80% anti-tumour response rate among patients with B-RAF(V600E)-positive melanomas, but acquired drug resistance frequently develops after initial responses. Hypotheses for mechanisms of acquired resistance to B-RAF inhibition include secondary mutations in B-RAF(V600E), MAPK reactivation, and activation of alternative survival pathways. Here we show that acquired resistance to PLX4032 develops by mutually exclusive PDGFRß (also known as PDGFRB) upregulation or N-RAS (also known as NRAS) mutations but not through secondary mutations in B-RAF(V600E). We used PLX4032-resistant sub-lines artificially derived from B-RAF(V600E)-positive melanoma cell lines and validated key findings in PLX4032-resistant tumours and tumour-matched, short-term cultures from clinical trial patients. Induction of PDGFRß RNA, protein and tyrosine phosphorylation emerged as a dominant feature of acquired PLX4032 resistance in a subset of melanoma sub-lines, patient-derived biopsies and short-term cultures. PDGFRß-upregulated tumour cells have low activated RAS levels and, when treated with PLX4032, do not reactivate the MAPK pathway significantly. In another subset, high levels of activated N-RAS resulting from mutations lead to significant MAPK pathway reactivation upon PLX4032 treatment. Knockdown of PDGFRß or N-RAS reduced growth of the respective PLX4032-resistant subsets. Overexpression of PDGFRß or N-RAS(Q61K) conferred PLX4032 resistance to PLX4032-sensitive parental cell lines. Importantly, MAPK reactivation predicts MEK inhibitor sensitivity. Thus, melanomas escape B-RAF(V600E) targeting not through secondary B-RAF(V600E) mutations but via receptor tyrosine kinase (RTK)-mediated activation of alternative survival pathway(s) or activated RAS-mediated reactivation of the MAPK pathway, suggesting additional therapeutic strategies.

Suppression of leukemia development caused by PTEN loss.

Multiple genetic or molecular alterations are known to be associated with cancer stem cell formation and cancer development. Targeting such alterations, therefore, may lead to cancer prevention. By crossing our previously established phosphatase and tensin homolog (Pten)-null acute T-lymphoblastic leukemia (T-ALL) model onto the recombination-activating gene 1(-/-) background, we show that the lack of variable, diversity and joining [V(D)J] recombination completely abolishes the Tcrα/δ-c-myc translocation and T-ALL development, regardless of ß-catenin activation. We identify mammalian target of rapamycin (mTOR) as a regulator of ß-selection. Rapamycin, an mTOR-specific inhibitor, alters nutrient sensing and blocks T-cell differentiation from CD4(-)CD8(-) to CD4(+)CD8(+), the stage where the Tcrα/δ-c-myc translocation occurs. Long-term rapamycin treatment of preleukemic Pten-null mice prevents Tcrα/δ-c-myc translocation and leukemia stem cell (LSC) formation, and it halts T-ALL development. However, rapamycin alone fails to inhibit mTOR signaling in the c-Kit(mid)CD3(+)Lin(-) population enriched for LSCs and eliminate these cells. Our results support the idea that preventing LSC formation and selectively targeting LSCs are promising approaches for antileukemia therapies.

Prediction of immunotherapy response of bladder cancer with a pyroptosis-related signature indicating tumor immune microenvironment.

Background: Although prognostic models based on pyroptosis-related genes (PRGs) have been constructed in bladder cancer (BLCA), the comprehensive impact of these genes on tumor microenvironment (TME) and immunotherapeutic response has yet to be investigated. Methods: Based on expression profiles of 52 PRGs, we utilized the unsupervised clustering algorithm to identify PRGs subtypes and ssGSEA to quantify immune cells and hallmark pathways. Moreover, we screened feature genes of distinct PRGs subtypes and validated the associations with immune infiltrations in tissue using the multiplex immunofluorescence. Univariate, LASSO, and multivariate Cox regression analyses were employed to construct the scoring scheme. Results: Four PRGs clusters were identified, samples in cluster C1 were infiltrated with more immune cells than those in others, implying a favorable response to immunotherapy. While the cluster C2, which shows an extremely low level of most immune cells, do not respond to immunotherapy. CXCL9/CXCL10 and SPINK1/DHSR2 were identified as feature genes of cluster C1 and C2, and the specimen with high CXCL9/CXCL10 was characterized by more CD8 + T cells, macrophages and less Tregs. Based on differentially expressed genes (DEGs) among PRGs subtypes, a predictive model (termed as PRGs score) including five genes (CACNA1D, PTK2B, APOL6, CDK6, ANXA2) was built. Survival probability of patients with low-PRGs score was significantly higher than those with high-PRGs score. Moreover, patients with low-PRGs score were more likely to benefit from anti-PD1/PD-L1 regimens. Conclusion: PRGs are closely associated with TME and oncogenic pathways. PRGs score is a promising indicator for predicting clinical outcome and immunotherapy response.

Accumulation of TOX high mobility group box family member 3 promotes the oncogenesis and development of hepatocellular carcinoma through the MAPK signaling pathway.

Microvascular invasion (MVI) has been widely valued in the field of liver surgery because MVI positivity indicates poor prognosis in hepatocellular carcinoma (HCC) patients. However, the potential molecular mechanism underlying the poor prognosis of MVI-positive HCC patients is unclear. Therefore, this study focused on identifying the key genes leading to poor prognosis in patients with a high degree of malignancy of HCC by examining the molecular signaling pathways in MVI-positive HCC patients. Through RNA sequencing, TOX high mobility group box family member 3 (TOX3) was demonstrated to be significantly highly expressed in MVI-positive HCC tissues, which was associated with poor prognosis. The results of in vivo and in vitro showed that TOX3 can promote the oncogenesis and development of HCC by targeting key molecules of the MAPK and EMT signaling pathways. The IP-MS results indicated that proteasome degradation of TOX3 in HCC cells is potentially mediated by a tripartite motif containing 56 (TRIM56, an E3 ligase) in HCC cells. Inhibiting TRIM56 enhances TOX3 protein levels. Overall, our study identified TOX3 as a key gene in the MAPK and EMT signaling pathways in HCC, and its overexpression confers significant proliferation and invasiveness to tumor cells.

Development and validation of a point-of-care nursing mobile tool to guide the diagnosis of malnutrition in hospitalized adult patients: a multicenter, prospective cohort study.

Malnutrition is a prevalent and severe issue in hospitalized patients with chronic diseases. However, malnutrition screening is often overlooked or inaccurate due to lack of awareness and experience among health care providers. This study aimed to develop and validate a novel digital smartphone-based self-administered tool that uses facial features, especially the ocular area, as indicators of malnutrition in inpatient patients with chronic diseases. Facial photographs and malnutrition screening scales were collected from 619 patients in four different hospitals. A machine learning model based on back propagation neural network was trained, validated, and tested using these data. The model showed a significant correlation (p < 0.05) and a high accuracy (area under the curve 0.834-0.927) in different patient groups. The point-of-care mobile tool can be used to screen malnutrition with good accuracy and accessibility, showing its potential for screening malnutrition in patients with chronic diseases.