Structure of the Nav1.4-ß1 Complex from Electric Eel.

Voltage-gated sodium (Nav) channels initiate and propagate action potentials. Here, we present the cryo-EM structure of EeNav1.4, the Nav channel from electric eel, in complex with the ß1 subunit at 4.0 Å resolution. The immunoglobulin domain of ß1 docks onto the extracellular L5I and L6IV loops of EeNav1.4 via extensive polar interactions, and the single transmembrane helix interacts with the third voltage-sensing domain (VSDIII). The VSDs exhibit "up" conformations, while the intracellular gate of the pore domain is kept open by a digitonin-like molecule. Structural comparison with closed NavPaS shows that the outward transfer of gating charges is coupled to the iris-like pore domain dilation through intricate force transmissions involving multiple channel segments. The IFM fast inactivation motif on the III-IV linker is plugged into the corner enclosed by the outer S4-S5 and inner S6 segments in repeats III and IV, suggesting a potential allosteric blocking mechanism for fast inactivation.

Negative regulation of AMPK signaling by high glucose via E3 ubiquitin ligase MG53.

As a master regulator of metabolism, AMP-activated protein kinase (AMPK) is activated upon energy and glucose shortage but suppressed upon overnutrition. Exaggerated negative regulation of AMPK signaling by nutrient overload plays a crucial role in metabolic diseases. However, the mechanism underlying the negative regulation is poorly understood. Here, we demonstrate that high glucose represses AMPK signaling via MG53 (also called TRIM72) E3-ubiquitin-ligase-mediated AMPKα degradation and deactivation. Specifically, high-glucose-stimulated reactive oxygen species (ROS) signals AKT to phosphorylate AMPKα at S485/491, which facilitates the recruitment of MG53 and the subsequent ubiquitination and degradation of AMPKα. In addition, high glucose deactivates AMPK by ROS-dependent suppression of phosphorylation of AMPKα at T172. These findings not only delineate the mechanism underlying the impairment of AMPK signaling in overnutrition-related diseases but also highlight the significance of keeping the yin-yang balance of AMPK signaling in the maintenance of metabolic homeostasis.

Simulating institutional heterogeneity in sustainability science.

Sustainability outcomes are influenced by the laws and configurations of natural and engineered systems as well as activities in socio-economic systems. An important subset of human activity is the creation and implementation of institutions, formal and informal rules shaping a wide range of human behavior. Understanding these rules and codifying them in computational models can provide important missing insights into why systems function the way they do (static) as well as the pace and structure of transitions required to improve sustainability (dynamic). Here, we conduct a comparative synthesis of three modeling approaches- integrated assessment modeling, engineering-economic optimization, and agent-based modeling-with underexplored potential to represent institutions. We first perform modeling experiments on climate mitigation systems that represent specific aspects of heterogeneous institutions, including formal policies and institutional coordination, and informal attitudes and norms. We find measurable but uneven aggregate impacts, while more politically meaningful distributional impacts are large across various actors. Our results show that omitting institutions can influence the costs of climate mitigation and miss opportunities to leverage institutional forces to speed up emissions reduction. These experiments allow us to explore the capacity of each modeling approach to represent insitutions and to lay out a vision for the next frontier of endogenizing institutional change in sustainability science models. To bridge the gap between modeling, theories, and empirical evidence on social institutions, this research agenda calls for joint efforts between sustainability modelers who wish to explore and incorporate institutional detail, and social scientists studying the socio-political and economic foundations for sustainability transitions.

Palladium metallene confined on MXene with increased hydroxyl binding strength for highly efficient ethanol electrooxidation.

Rational design and synthesis of high-performance electrocatalysts for ethanol oxidation reaction (EOR) is crucial to large-scale commercialization of direct ethanol fuel cells, but it is still an incredible challenge. Herein, a unique Pd metallene/Ti3C2Tx MXene (Pdene/Ti3C2Tx)-supported electrocatalyst is constructed via an in-situ growth approach for high-efficiency EOR. The resulting Pdene/Ti3C2Tx catalyst achieves an ultrahigh mass activity of 7.47 A mgPd-1 under alkaline condition, as well as high tolerance to CO poisoning. In situ attenuated total reflection-infrared spectroscopy studies combined with density functional theory calculations reveal that the excellent EOR activity of Pdene/Ti3C2Tx catalyst is attributed to the unique and stable interfaces which reduce the reaction energy barrier of *CH3CO intermediate oxidation and facilitate oxidative removal of CO poisonous species by increasing the Pd-OH binding strength.

MHCLMDA: multihypergraph contrastive learning for miRNA-disease association prediction.

The correct prediction of disease-associated miRNAs plays an essential role in disease prevention and treatment. Current computational methods to predict disease-associated miRNAs construct different miRNA views and disease views based on various miRNA properties and disease properties and then integrate the multiviews to predict the relationship between miRNAs and diseases. However, most existing methods ignore the information interaction among the views and the consistency of miRNA features (disease features) across multiple views. This study proposes a computational method based on multiple hypergraph contrastive learning (MHCLMDA) to predict miRNA-disease associations. MHCLMDA first constructs multiple miRNA hypergraphs and disease hypergraphs based on various miRNA similarities and disease similarities and performs hypergraph convolution on each hypergraph to capture higher order interactions between nodes, followed by hypergraph contrastive learning to learn the consistent miRNA feature representation and disease feature representation under different views. Then, a variational auto-encoder is employed to extract the miRNA and disease features in known miRNA-disease association relationships. Finally, MHCLMDA fuses the miRNA and disease features from different views to predict miRNA-disease associations. The parameters of the model are optimized in an end-to-end way. We applied MHCLMDA to the prediction of human miRNA-disease association. The experimental results show that our method performs better than several other state-of-the-art methods in terms of the area under the receiver operating characteristic curve and the area under the precision-recall curve.

JLONMFSC: Clustering scRNA-seq data based on joint learning of non-negative matrix factorization and subspace clustering.

The development of single cell RNA sequencing (scRNA-seq) has provided new perspectives to study biological problems at the single cell level. One of the key issues in scRNA-seq data analysis is to divide cells into several clusters for discovering the heterogeneity and diversity of cells. However, the existing scRNA-seq data are high-dimensional, sparse, and noisy, which challenges the existing single-cell clustering methods. In this study, we propose a joint learning framework (JLONMFSC) for clustering scRNA-seq data. In our method, the dimension of the original data is reduced to minimize the effect of noise. In addition, the graph regularized matrix factorization is used to learn the local features. Further, the Low-Rank Representation (LRR) subspace clustering is utilized to learn the global features. Finally, the joint learning of local features and global features is performed to obtain the results of clustering. We compare the proposed algorithm with eight state-of-the-art algorithms for clustering performance on six datasets, and the experimental results demonstrate that the JLONMFSC achieves better performance in all datasets. The code is avalable at https://github.com/lanbiolab/JLONMFSC.

Improving anti-cancer drug response prediction using multi-task learning on graph convolutional networks.

Predicting the therapeutic effect of anti-cancer drugs on tumors based on the characteristics of tumors and patients is one of the important contents of precision oncology. Existing computational methods regard the drug response prediction problem as a classification or regression task. However, few of them consider leveraging the relationship between the two tasks. In this work, we propose a Multi-task Interaction Graph Convolutional Network (MTIGCN) for anti-cancer drug response prediction. MTIGCN first utilizes an graph convolutional network-based model to produce embeddings for both cell lines and drugs. After that, the model employs multi-task learning to predict anti-cancer drug response, which involves training the model on three different tasks simultaneously: the main task of the drug sensitive or resistant classification task and the two auxiliary tasks of regression prediction and similarity network reconstruction. By sharing parameters and optimizing the losses of different tasks simultaneously, MTIGCN enhances the feature representation and reduces overfitting. The results of the experiments on two in vitro datasets demonstrated that MTIGCN outperformed seven state-of-the-art baseline methods. Moreover, the well-trained model on the in vitro dataset GDSC exhibited good performance when applied to predict drug responses in in vivo datasets PDX and TCGA. The case study confirmed the model's ability to discover unknown drug responses in cell lines.

Coexistence of Ferromagnetism and Superconductivity at KTaO3 Heterointerfaces.

The coexistence of superconductivity and ferromagnetism is a long-standing issue in superconductivity due to the antagonistic nature of these two ordered states. Experimentally identifying and characterizing novel heterointerface superconductors that coexist with magnetism presents significant challenges. Here, we report the observation of two-dimensional long-range ferromagnetic order in a KTaO3 heterointerface superconductor, showing the coexistence of superconductivity and ferromagnetism. Remarkably, our direct current superconducting quantum interference device measurements reveal an in-plane magnetization hysteresis loop persisting above room temperature. Moreover, first-principles calculations and X-ray magnetic circular dichroism measurements provide decisive insights into the origin of the observed robust ferromagnetism, attributing it to oxygen vacancies that localize electrons in nearby Ta 5d states. Our findings suggest KTaO3 heterointerfaces as time-reversal symmetry breaking superconductors, injecting fresh momentum into the exploration of the intricate interplay between superconductivity and magnetism enhanced by the strong spin-orbit coupling inherent to the heavy Ta in 5d orbitals.

Whole-genome resequencing of major populations revealed domestication-related genes in yaks.

BACKGROUND: The yak is a symbol of the Qinghai-Tibet Plateau and provides important basic resources for human life on the plateau. Domestic yaks have been subjected to strong artificial selection and environmental pressures over the long-term. Understanding the molecular mechanisms of phenotypic differences in yak populations can reveal key functional genes involved in the domestication process and improve genetic breeding. MATERIAL AND METHOD: Here, we re-sequenced 80 yaks (Maiwa, Yushu, and Huanhu populations) to identify single-nucleotide polymorphisms (SNPs) as genetic variants. After filtering and quality control, remaining SNPs were kept to identify the genome-wide regions of selective sweeps associated with domestic traits. The four methods (π, XPEHH, iHS, and XP-nSL) were used to detect the population genetic separation. RESULTS: By comparing the differences in the population stratification, linkage disequilibrium decay rate, and characteristic selective sweep signals, we identified 203 putative selective regions of domestic traits, 45 of which were mapped to 27 known genes. They were clustered into 4 major GO biological process terms. All known genes were associated with seven major domestication traits, such as dwarfism (ANKRD28), milk (HECW1, HECW2, and OSBPL2), meat (SPATA5 and GRHL2), fertility (BTBD11 and ARFIP1), adaptation (NCKAP5, ANTXR1, LAMA5, OSBPL2, AOC2, and RYR2), growth (GRHL2, GRID2, SMARCAL1, and EPHB2), and the immune system (INPP5D and ADCYAP1R1). CONCLUSIONS: We provided there is an obvious genetic different among domestic progress in these three yak populations. Our findings improve the understanding of the major genetic switches and domestic processes among yak populations.

Transcriptomics and metabolomics of blood, urine and ovarian follicular fluid of yak at induced estrus stage.

To gain a deeper understanding of the metabolic differences within and outside the body, as well as changes in transcription levels following estrus in yaks, we conducted transcriptome and metabolome analyses on female yaks in both estrus and non-estrus states. The metabolome analysis identified 114, 13, and 91 distinct metabolites in urine, blood, and follicular fluid, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis highlighted an enrichment of pathways related to amino acid and lipid metabolism across all three body fluids. Our transcriptome analysis revealed 122 differentially expressed genes within microRNA (miRNA) and 640 within long non-coding RNA (lncRNA). Functional enrichment analysis of lncRNA and miRNA indicated their involvement in cell signaling, disease resistance, and immunity pathways. We constructed a regulatory network composed of 10 lncRNAs, 4 miRNAs, and 30 mRNAs, based on the targeted regulation relationships of the differentially expressed genes. In conclusion, the accumulation of metabolites such as amino acids, steroids, and organic acids, along with the expression changes of key genes like miR-129 during yak estrus, provide initial insights into the estrus mechanism in yaks.

Dense Multibutterfly Spots-Enhanced Miniaturized Optical Fiber Photoacoustic Gas Sensor.

A miniaturized optical fiber photoacoustic gas sensor enhanced by dense multibutterfly spots is reported for the first time. The principle of space light transmission of neglecting paraxial approximation is theoretically analyzed for designing a dense multibutterfly spots-based miniature multipass cell. In a multipass photoacoustic tube with a diameter of 16 mm, the light beam is reflected about a hundred times. The light spots on the mirror surfaces at both ends of the photoacoustic tube form a dense multibutterfly distribution. The volume of the micro multipass gas chamber is only 5.3 mL. An optical fiber cantilever based on F-P interference is utilized as a photoacoustic pressure detector. Compared with that of the single-pass structure, the gas detection ability of the photoacoustic system with dense multibutterfly spots is improved by about 50 times. The proposed miniaturized sensor realizes a detection limit of 3.4 ppb for C2H2 gas with an averaging time of 100 s. The recognized coefficients of minimum detectable absorption (αmin) and normalized noise equivalent absorption are 1.9 × 10-8 cm-1 and 8.4 × 10-10 W cm-1 Hz-1/2, respectively.

Systematic Optimization of Universal Real-Time Hypersensitive Fast Detection Method for HBsAg in Serum Based on SERS.

Hepatitis B virus (HBV) is a major cause of liver cirrhosis and hepatocellular carcinoma, with HBV surface antigen (HBsAg) being a crucial marker in the clinical detection of HBV. Due to the significant harm and ease of transmission associated with HBV, HBsAg testing has become an essential part of preoperative assessments, particularly for emergency surgeries where healthcare professionals face exposure risks. Therefore, a timely and accurate detection method for HBsAg is urgently needed. In this study, a surface-enhanced Raman scattering (SERS) sensor with a sandwich structure was developed for HBsAg detection. Leveraging the ultrasensitive and rapid detection capabilities of SERS, this sensor enables quick detection results, significantly reducing waiting times. By systematically optimizing critical factors in the detection process, such as the composition and concentration of the incubation solution as well as the modification conditions and amount of probe particles, the sensitivity of the SERS immune assay system was improved. Ultimately, the sensor achieved a sensitivity of 0.00576 IU/mL within 12 min, surpassing the clinical requirement of 0.05 IU/mL by an order of magnitude. In clinical serum assay validation, the issue of false positives was effectively addressed by adding a blocker. The final sensor demonstrated 100% specificity and sensitivity at the threshold of 0.05 IU/mL. Therefore, this study not only designed an ultrasensitive SERS sensor for detecting HBsAg in actual clinical serum samples but also provided theoretical support for similar systems, filling the knowledge gap in existing literature.

Colloidal Synthesis of Carbon Dot-ZnSe Nanoplatelet Van der Waals Heterostructures for Boosting Photocatalytic Generation of Methanol-Storable Hydrogen.

Methanol is not only a promising liquid hydrogen carrier but also an important feedstock chemical for chemical synthesis. Catalyst design is vital for enabling the reactions to occur under ambient conditions. This study reports a new class of van der Waals heterojunction photocatalyst, which is synthesized by hot-injection method, whereby carbon dots (CDs) are grown in situ on ZnSe nanoplatelets (NPLs), i.e., metal chalcogenide quantum wells. The resultant organic-inorganic hybrid nanoparticles, CD-NPLs, are able to perform methanol dehydrogenation through CH splitting. The heterostructure has enabled light-induced charge transfer from the CDs into the NPLs occurring on a sub-nanosecond timescale, with charges remaining separated across the CD-NPLs heterostructure for longer than 500 ns. This resulted in significantly heightened H2 production rate of 107 µmole·g-1·h-1 and enhanced photocurrent density up to 34 µA cm-2 at 1 V bias potential. EPR and NMR analyses confirmed the occurrence of α-CH splitting and CC coupling. The novel CD-based organic-inorganic semiconductor heterojunction is poised to enable the discovery of a host of new nano-hybrid photocatalysts with full tunability in the band structure, charge transfer, and divergent surface chemistry for guiding photoredox pathways and accelerating reaction rates.

Evaluating variations in the barriers to colorectal cancer screening associated with telehealth use in rural U.S. Pacific Northwest.

PURPOSE: The incidence and mortality rates of colorectal cancer (CRC) remain consistently high in rural populations. Telehealth can improve screening uptake by overcoming individual and environmental disadvantages in rural communities. The present study aimed to characterize varying barriers to CRC screening between rural individuals with and without experience in using telehealth. METHOD: The cross-sectional study surveyed 250 adults aged 45-75 residing in rural U.S. states of Alaska, Idaho, Oregon, and Washington from June to September 2022. The associations between CRC screening and four sets of individual and environmental factors specific to rural populations (i.e., demographic characteristics, accessibility, patient-provider factors, and psychological factors) were assessed among respondents with and without past telehealth adoption. RESULT: Respondents with past telehealth use were more likely to screen if they were married, had a better health status, had experienced discrimination in health care, and had perceived susceptibility, screening efficacy, and cancer fear, but less likely to screen when they worried about privacy or had feelings of embarrassment, pain, and discomfort. Among respondents without past telehealth use, the odds of CRC screening decreased with busy schedules, travel burden, discrimination in health care, and lower perceived needs. CONCLUSION: Rural individuals with and without previous telehealth experience face different barriers to CRC screening. The finding suggests the potential efficacy of telehealth in mitigating critical barriers to CRC screening associated with social, health care, and built environments of rural communities.

Improving cancer driver gene identification using multi-task learning on graph convolutional network.

Cancer is thought to be caused by the accumulation of driver genetic mutations. Therefore, identifying cancer driver genes plays a crucial role in understanding the molecular mechanism of cancer and developing precision therapies and biomarkers. In this work, we propose a Multi-Task learning method, called MTGCN, based on the Graph Convolutional Network to identify cancer driver genes. First, we augment gene features by introducing their features on the protein-protein interaction (PPI) network. After that, the multi-task learning framework propagates and aggregates nodes and graph features from input to next layer to learn node embedding features, simultaneously optimizing the node prediction task and the link prediction task. Finally, we use a Bayesian task weight learner to balance the two tasks automatically. The outputs of MTGCN assign each gene a probability of being a cancer driver gene. Our method and the other four existing methods are applied to predict cancer drivers for pan-cancer and some single cancer types. The experimental results show that our model shows outstanding performance compared with the state-of-the-art methods in terms of the area under the Receiver Operating Characteristic (ROC) curves and the area under the precision-recall curves. The MTGCN is freely available via https://github.com/weiba/MTGCN.

A geometric deep learning framework for drug repositioning over heterogeneous information networks.

Drug repositioning (DR) is a promising strategy to discover new indicators of approved drugs with artificial intelligence techniques, thus improving traditional drug discovery and development. However, most of DR computational methods fall short of taking into account the non-Euclidean nature of biomedical network data. To overcome this problem, a deep learning framework, namely DDAGDL, is proposed to predict drug-drug associations (DDAs) by using geometric deep learning (GDL) over heterogeneous information network (HIN). Incorporating complex biological information into the topological structure of HIN, DDAGDL effectively learns the smoothed representations of drugs and diseases with an attention mechanism. Experiment results demonstrate the superior performance of DDAGDL on three real-world datasets under 10-fold cross-validation when compared with state-of-the-art DR methods in terms of several evaluation metrics. Our case studies and molecular docking experiments indicate that DDAGDL is a promising DR tool that gains new insights into exploiting the geometric prior knowledge for improved efficacy.

sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure.

MOTIVATION: Antimicrobial peptides (AMPs) are essential components of therapeutic peptides for innate immunity. Researchers have developed several computational methods to predict the potential AMPs from many candidate peptides. With the development of artificial intelligent techniques, the protein structures can be accurately predicted, which are useful for protein sequence and function analysis. Unfortunately, the predicted peptide structure information has not been applied to the field of AMP prediction so as to improve the predictive performance. RESULTS: In this study, we proposed a computational predictor called sAMPpred-GAT for AMP identification. To the best of our knowledge, sAMPpred-GAT is the first approach based on the predicted peptide structures for AMP prediction. The sAMPpred-GAT predictor constructs the graphs based on the predicted peptide structures, sequence information and evolutionary information. The Graph Attention Network (GAT) is then performed on the graphs to learn the discriminative features. Finally, the full connection networks are utilized as the output module to predict whether the peptides are AMP or not. Experimental results show that sAMPpred-GAT outperforms the other state-of-the-art methods in terms of AUC, and achieves better or highly comparable performance in terms of the other metrics on the eight independent test datasets, demonstrating that the predicted peptide structure information is important for AMP prediction. AVAILABILITY AND IMPLEMENTATION: A user-friendly webserver of sAMPpred-GAT can be accessed at http://bliulab.net/sAMPpred-GAT and the source code is available at https://github.com/HongWuL/sAMPpred-GAT/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network.

MOTIVATION: The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction. RESULTS: In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs an HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature representations of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets. AVAILABILITY AND IMPLEMENTATION: Python codes and dataset are available at https://github.com/stevejobws/iGRLDTI/.

Upregulation of p300 in paclitaxel-resistant TNBC: implications for cell proliferation via the PCK1/AMPK axis.

OBJECTIVE: To explore the role of p300 in the context of paclitaxel (PTX) resistance in triple-negative breast cancer (TNBC) cells, focusing on its interaction with the phosphoenolpyruvate carboxykinase 1 (PCK1)/adenosine monophosphate-activated protein kinase (AMPK) pathway. METHODS: The expression of p300 and PCK1 at the messenger ribonucleic acid (mRNA) level was detected using a quantitative polymerase chain reaction. The GeneCards and GEPIA databases were used to investigate the relationship between p300 and PCK1. The MDA-MB-231/PTX cell line, known for its PTX resistance, was chosen to understand the specific role of p300 in such cells. The Lipofectamine™ 3000 reagent was used to transfer the p300 small interfering RNA and the overexpression of PCK1 plasmid into MDA-MB-231/PTX. The expression levels of p300, PCK1, 5'AMPK and phosphorylated AMPK (p-AMPK) were determined using the western blot test. RESULTS: In TNBC cancer tissue, the expression of p300 was increased compared with TNBC paracancerous tissue (P < 0.05). In the MDA-MB-231 cell line of TNBC, the expression of p300 was lower than in the PTX-resistant TNBC cells (MDA-MB-231/PTX) (P < 0.05). The PCK1 expression was decreased in the TNBC cancer tissue compared with TNBC paracancerous tissue, and the PCK1 expression was reduced in MDA-MB-231/PTX than in MDA-MB-231 (P < 0.05) indicating that PCK1 was involved in the resistance function. Additionally, p-AMPK was decreased in MDA-MB-231/PTX compared with MDA-MB-231 (P < 0.05). The adenosine triphosphate (ATP) level was also detected and was significantly lower in MDA-MB-231/PTX than in MDA-MB-231 (P < 0.05). Additionally, cell proliferation increased significantly in MDA-MB-231/PTX at 48 and 72 h (P < 0.05) suggesting that MDA-MB-231/PTX cells obtained the resistance function which was associated with AMPK and ATP level. When p300 was inhibited, p-AMPK and ATP levels elevated in MDA-MB-231/PTX (P < 0.05). When PCK1 was suppressed, the ATP consumption rate decreased, and cell proliferation increased (P < 0.05). However, there were no changes in p300. CONCLUSIONS: In MDA-MB-231/PTX, p300 can inhibit p-AMPK and ATP levels by inhibiting PCK1 expression. Our findings suggest that targeting p300 could modulate the PCK1/AMPK axis, offering a potential therapeutic avenue for overcoming PTX resistance in TNBC.

Exploration of a hypoxia-immune-related microenvironment gene signature and prediction model for hepatitis C-induced early-stage fibrosis.

BACKGROUND: Liver fibrosis contributes to significant morbidity and mortality in Western nations, primarily attributed to chronic hepatitis C virus (HCV) infection. Hypoxia and immune status have been reported to be significantly correlated with the progression of liver fibrosis. The current research aimed to investigate the gene signature related to the hypoxia-immune-related microenvironment and identify potential targets for liver fibrosis. METHOD: Sequencing data obtained from GEO were employed to assess the hypoxia and immune status of the discovery set utilizing UMAP and ESTIMATE methods. The prognostic genes were screened utilizing the LASSO model. The infiltration level of 22 types of immune cells was quantified utilizing CIBERSORT, and a prognosis-predictive model was established based on the selected genes. The model was also verified using qRT-PCR with surgical resection samples and liver failure samples RNA-sequencing data. RESULTS: Elevated hypoxia and immune status were linked to an unfavorable prognosis in HCV-induced early-stage liver fibrosis. Increased plasma and resting NK cell infiltration were identified as a risk factor for liver fibrosis progression. Additionally, CYP1A2, CBS, GSTZ1, FOXA1, WDR72 and UHMK1 were determined as hypoxia-immune-related protective genes. The combined model effectively predicted patient prognosis. Furthermore, the preliminary validation of clinical samples supported most of the conclusions drawn from this study. CONCLUSION: The prognosis-predictive model developed using six hypoxia-immune-related genes effectively predicts the prognosis and progression of liver fibrosis. The current study opens new avenues for the future prediction and treatment of liver fibrosis.