TransAC4C-a novel interpretable architecture for multi-species identification of N4-acetylcytidine sites in RNA with single-base resolution.

N4-acetylcytidine (ac4C) is a modification found in ribonucleic acid (RNA) related to diseases. Expensive and labor-intensive methods hindered the exploration of ac4C mechanisms and the development of specific anti-ac4C drugs. Therefore, an advanced prediction model for ac4C in RNA is urgently needed. Despite the construction of various prediction models, several limitations exist: (1) insufficient resolution at base level for ac4C sites; (2) lack of information on species other than Homo sapiens; (3) lack of information on RNA other than mRNA; and (4) lack of interpretation for each prediction. In light of these limitations, we have reconstructed the previous benchmark dataset and introduced a new dataset including balanced RNA sequences from multiple species and RNA types, while also providing base-level resolution for ac4C sites. Additionally, we have proposed a novel transformer-based architecture and pipeline for predicting ac4C sites, allowing for highly accurate predictions, visually interpretable results and no restrictions on the length of input RNA sequences. Statistically, our work has improved the accuracy of predicting specific ac4C sites in multiple species from less than 40% to around 85%, achieving a high AUC > 0.9. These results significantly surpass the performance of all existing models.

IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations.

Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.

Strong Local Polarization Fluctuations Enabled High Electrostatic Energy Storage in Pb-Free Relaxors.

Electrostatic energy-storage ceramic capacitors are essential components of modern electrified power systems. However, improving their energy-storage density while maintaining high efficiency to facilitate cutting-edge miniaturized and integrated applications remains an ongoing challenge. Herein, we report a record-high energy-storage density of 20.3 J cm-3 together with a high efficiency of 89.3% achieved by constructing a relaxor ferroelectric state with strongly enhanced local polarization fluctuations. This is realized by incorporating highly polarizable, heterovalent, and large-sized Zn and Nb ions into a Bi0.5Na0.5TiO3-BaTiO3 ferroelectric matrix with very strong tetragonal distortion. Element-specific local structure analysis revealed that the foreign ions strengthen the magnitude of the unit-cell polarization vectors while simultaneously reducing their orientation anisotropy and forming strong fluctuations in both magnitude and orientation within 1-3 nm polar clusters. This leads to a particularly high polarization variation (ΔP) of 72 µC cm-2, low hysteresis, and a high effective polarization coefficient at a high breakdown strength of 80 kV mm-1. This work has surpassed the current energy density limit of 20 J cm-3 in bulk Pb-free ceramics and has demonstrated that controlling the local structure via the chemical composition design can open up new possibilities for exploring relaxors with high energy-storage performance.

Chemical Framework to Design Linear-like Relaxors toward Capacitive Energy Storage.

ABO3-type perovskite relaxor ferroelectrics (RFEs) have emerged as the preferred option for dielectric capacitive energy storage. However, the compositional design of RFEs with high energy density and efficiency poses significant challenges owing to the vast compositional space and the absence of general rules. Here, we present an atomic-level chemical framework that captures inherent characteristics in terms of radius and ferroelectric activity of ions. By categorizing A/B-site ions as host framework, rattling, ferroelectrically active, and blocking ions and assembling these four types of ions with specific criteria, linear-like relaxors with weak locally correlated and highly extendable unit-cell polarization vectors can be constructed. As example, we demonstrate two new compositions of Bi0.5K0.5TiO3-based and BaTiO3-based relaxors, showing extremely high recoverable energy densities of 17.3 and 12.1 J cm-3, respectively, both with a high efficiency of about 90%. Further, the role of different types of ions in forming heterogeneous polar structures is identified through element-specific local structure analysis using neutron total scattering combined with reverse Monte Carlo modeling. Our work not only opens up new avenues toward rational compositional design of high energy storage performance lead-free RFEs but also sheds light on atomic-level manipulation of functional properties in compositionally complex ferroelectrics.

Amorphous Oxyhalide Matters for Achieving Lithium Superionic Conduction.

The recently surged halide-based solid electrolytes (SEs) are great candidates for high-performance all-solid-state batteries (ASSBs), due to their decent ionic conductivity, wide electrochemical stability window, and good compatibility with high-voltage oxide cathodes. In contrast to the crystalline phases in halide SEs, amorphous components are rarely understood but play an important role in Li-ion conduction. Here, we reveal that the presence of amorphous component is common in halide-based SEs that are prepared via mechanochemical method. The fast Li-ion migration is found to be associated with the local chemistry of the amorphous proportion. Taking Zr-based halide SEs as an example, the amorphization process can be regulated by incorporating O, resulting in the formation of corner-sharing Zr-O/Cl polyhedrons. This structural configuration has been confirmed through X-ray absorption spectroscopy, pair distribution function analyses, and Reverse Monte Carlo modeling. The unique structure significantly reduces the energy barriers for Li-ion transport. As a result, an enhanced ionic conductivity of (1.35 ± 0.07) × 10-3 S cm-1 at 25 °C can be achieved for amorphous Li3ZrCl4O1.5. In addition to the improved ionic conductivity, amorphization of Zr-based halide SEs via incorporation of O leads to good mechanical deformability and promising electrochemical performance. These findings provide deep insights into the rational design of desirable halide SEs for high-performance ASSBs.

Role of dietary nutrients and metabolism in colorectal cancer.

Colorectal cancer (CRC) is one of the most common malignancies and the leading causes of cancer related deaths worldwide. The development of CRC is driven by a combination of genetic and environmental factors. There is growing evidence that changes in dietary nutrition may modulate the CRC risk, and protective effects on the risk of developing CRC have been advocated for specific nutrients such as glucose, amino acids, lipid, vitamins, micronutrients and prebiotics. Metabolic crosstalk between tumor cells, tumor microenvironment components and intestinal flora further promote proliferation, invasion and metastasis of CRC cells and leads to treatment resistance. This review summarizes the research progress on CRC prevention, pathogenesis, and treatment by dietary supplementation or deficiency of glucose, amino acids, lipids, vitamins, micronutri-ents, and prebiotics, respectively. The roles played by different nutrients and dietary crosstalk in the tumor microenvironment and metabolism are discussed, and nutritional modulation is inspired to be beneficial in the prevention and treatment of CRC.

Expandable Li Percolation Network: The Effects of Site Distortion in Cation-Disordered Rock-Salt Cathode Material.

Cation-disordered rock-salt (DRX) materials receive intensive attention as a new class of cathode candidates for high-capacity lithium-ion batteries (LIBs). Unlike traditional layered cathode materials, DRX materials have a three-dimensional (3D) percolation network for Li+ transportation. The disordered structure poses a grand challenge to a thorough understanding of the percolation network due to its multiscale complexity. In this work, we introduce the large supercell modeling for DRX material Li1.16Ti0.37Ni0.37Nb0.10O2 (LTNNO) via the reverse Monte Carlo (RMC) method combined with neutron total scattering. Through a quantitative statistical analysis of the material's local atomic environment, we experimentally verified the existence of short-range ordering (SRO) and uncovered an element-dependent behavior of transition metal (TM) site distortion. A displacement from the original octahedral site for Ti4+ cations is pervasive throughout the DRX lattice. Density functional theory (DFT) calculations revealed that site distortions quantified by the centroid offsets could alter the migration barrier for Li+ diffusion through the tetrahedral channels, which can expand the previously proposed theoretical percolating network of Li. The estimated accessible Li content is highly consistent with the observed charging capacity. The newly developed characterization method here uncovers the expandable nature of the Li percolation network in DRX materials, which may provide valuable guidelines for the design of superior DRX materials.

Local Chemical Clustering Enabled Ultrahigh Capacitive Energy Storage in Pb-Free Relaxors.

Designing Pb-free relaxors with both a high capacitive energy density (Wrec) and high storage efficiency (η) remains a remarkable challenge for cutting-edge pulsed power technologies. Local compositional heterogeneity is crucial for achieving complex polar structure in solid solution relaxors, but its role in optimizing energy storage properties is often overlooked. Here, we report that an exceptionally high Wrec of 15.2 J cm-3 along with an ultrahigh η of 91% can be achieved through designing local chemical clustering in Bi0.5Na0.5TiO3-BaTiO3-based relaxors. A three-dimensional atomistic model derived from neutron/X-ray total scattering combined with reverse Monte Carlo method reveals the presence of subnanometer scale clustering of Bi, Na, and Ba, which host differentiated polar displacements, and confirming the prediction by density functional theory calculations. This leads to a polar state with small polar clusters and strong length and direction fluctuations in unit-cell polar vectors, thus manifesting improved high-field polarizability, steadily reduced hysteresis, and high breakdown strength macroscopically. The favorable polar structure features also result in a unique field-increased η, excellent stability, and superior discharge capacity. Our work demonstrates that the hidden local chemical order exerts a significant impact on the polarization characteristic of relaxors, and can be exploited for accessing superior energy storage performance.

Superior Capacitive Energy-Storage Performance in Pb-Free Relaxors with a Simple Chemical Composition.

Chemical design of lead-free relaxors with simultaneously high energy density (Wrec) and high efficiency (η) for capacitive energy-storage has been a big challenge for advanced electronic systems. The current situation indicates that realizing such superior energy-storage properties requires highly complex chemical components. Herein, we demonstrate that, via local structure design, an ultrahigh Wrec of 10.1 J/cm3, concurrent with a high η of 90%, as well as excellent thermal and frequency stabilities can be achieved in a relaxor with a very simple chemical composition. By introducing 6s2 lone pair stereochemical active Bi into the classical BaTiO3 ferroelectric to generate a mismatch between A- and B-site polar displacements, a relaxor state with strong local polar fluctuations can be formed. Through advanced atomic-resolution displacement mapping and 3D reconstructing the nanoscale structure from neutron/X-ray total scattering, it is revealed that the localized Bi enhances the polar length largely at several perovskite unit cells and disrupts the long-range coherent Ti polar displacements, resulting in a slush-like structure with extremely small size polar clusters and strong local polar fluctuations. This favorable relaxor state exhibits substantially enhanced polarization, and minimized hysteresis at a high breakdown strength. This work offers a feasible avenue to chemically design new relaxors with a simple composition for high-performance capacitive energy-storage.

Peripheral Th and NK cells are associated with response to lenvatinib plus PD-1 inhibitor in unresectable hepatocellular carcinoma.

The value of peripheral blood lymphocyte subpopulations in predicting responses to lenvatinib combination with programmed death-1 (PD-1) inhibitors in unresectable hepatocellular carcinoma (HCC) was investigated. Fifteen patients received objective responses (OR) and sixteen patients had non-objective responses (NOR) were analyzed. The counts of peripheral blood lymphocyte subpopulations from patients were measured before treatment, second (at week 3), and third doses (at week 6) of the PD-1 inhibitor administration, and correlated with responses. Helper T (Th) cells and natural killers (NK) cells were more abundant in the OR group and found to be important predictors of OR in a stepwise multivariate logistic regression analysis. These cutoff values of Th and NK cells could help to distinguish OR from NOR cases accurately and provide clinical benefits.

Conversion surgery for advanced hepatocellular carcinoma after combination treatment of lenvatinib and camrelizumab: a case report.

BACKGROUND: Hepatocellular carcinoma (HCC) is an aggressive malignancy with high morbidity and mortality. Conversion therapy can improve surgical resection rate and prolong survival time for patients with advanced HCC. We show that combination therapy with lenvatinib and camrelizumab is a novel approach to downstage unresectable HCC. CASE PRESENTATION: A 49-year-old man was diagnosed with massive HCC with hilar lymph node and lung metastases. Since radical resection was not feasible, lenvatinib and camrelizumab were administered as first-line therapy. After 10 cycles of camrelizumab and continuous oral administration of lenvatinib, the tumor exhibited striking shrinkage in volume indicating a partial radiological response, accompanied by a reduction in the alpha-fetoprotein levels, followed by salvage resection. Intriguingly, an improvement in predictive biomarkers, like lactate dehydrogenase (LDH) and neutrophil-to-lymphocyte ratio (NLR), was observed. Notably, the pathological examination found high levels of necrosis in the resected tumor, and flow cytometry analysis indicated a significant increase in the ratio of CD5+ and CD5- B lymphocytes in the peripheral blood. After the treatment, the overall survival period was over 24 months, and no recurrence was observed 17-month post-surgery. CONCLUSIONS: A combination of lenvatinib and camrelizumab may be a new conversion therapy for initially unresectable HCC to resectable HCC, thus contributing to improve the disease prognosis. In addition, the combination regimen could cause an activated immune response, and LDH, NLR, and CD5+ B-cell levels might be predictors for immunotherapy efficacy.

Ferroelectric Ordering in Nanosized PbTiO3.

The insight into the three-dimensional configuration of ferroelectric ordering in ferroelectric nanomaterials is motivated by the application of the development of functional nanodevices and the structural designing. However, the atomic deciphering of the spatial distribution of ordered structure remains challenging for the limitation of dimension and probing techniques. In this paper, a neutron pair distribution function (nPDF) was utilized to analyze the spontaneous polarization distribution of zero-dimensional PbTiO3 nanoparticles in three dimensions, via the application of reverse Monte Carlo (RMC) modeling. The comprehensive identification with transmission electron microscopy verified the linear characteristics of polarization along the c-axis in the main body, while electric polarization distribution on the surface was enhanced abnormally. In addition, the correlation of dipole vectors extending to three unit cells below the surface is retained. This work shows an application of the micro/macroscale information to effectively decode the polarization structure of nanoferroelectrics, providing new views of designing nanoferroelectric devices.

Hidden Local Symmetry Breaking in a Kagome-Lattice Magnetic Weyl Semimetal.

Exploring the relationship between intriguing physical properties and structural complexity is a central topic in studying modern functional materials. Co3Sn2S2, a newly discovered kagome-lattice magnetic Weyl semimetal, has triggered intense interest owing to the intimate coupling between topological semimetallic states and peculiar magnetic properties. However, the origins of the magnetic phase separation and spin glass state below TC in this ordered compound are two unresolved yet important puzzles in understanding its magnetism. Here, we report the discovery of local symmetry breaking surprisingly co-emerges with the onset of ferromagnetic order in Co3Sn2S2, by a combined use of neutron total scattering and half-polarized neutron diffraction. An anisotropic distortion of the cobalt kagome lattice at the atomic/nano level is also found, with distinct distortion directions among the two Co1 and four Co2 atoms. The mismatch of local and average symmetries occurs below TC, indicating that Co3Sn2S2 evolves to an intrinsically lattice disordered system when the ferromagnetic order is established. The local symmetry breaking with intrinsic lattice disorder provides new understanding of the puzzling magnetic properties. Our density functional theory (DFT) calculation indicates that the local symmetry breaking is expected to reorient local ferromagnetic moments, unveiling the existence of the ferromagnetic instability associated with the lattice instability. Furthermore, DFT calculation unveils that the local symmetry breaking could affect the Weyl property by breaking the mirror plane. Our findings highlight the fundamentally important role that the local symmetry breaking plays in advancing our understanding on the magnetic and topological properties in Co3Sn2S2, which may draw attention to explore the overlooked local symmetry breaking in Co3Sn2S2, its derivatives and more broadly in other topological Dirac/Weyl semimetals and kagome-lattice magnets.

Simultaneous determination of trace level riot control agents in environmental water by solid-phase microextraction and gas chromatography coupled with a flame ionization detector.

In this paper, a direct immersion solid-phase microextraction procedure for the simultaneous analyses of four primary riot control agents: 2-chloroacetophenone, o-chlorobenzylidene malonitrile, dibenz(b,f)-1,4-oxazepine, and oleoresin capsicum at µg/L concentration from environmental water was developed. Several parameters that influence the extraction effectiveness were investigated, including fiber type, extraction temperature, extraction time, starring rate, and salinity. Under the recommended conditions, the optimized method had reasonable linearity and accuracy. The average recovery of this method ranged from 84 to 108.1%. The limit of detection for all the analytes ranged from 0.2 to 3 µg/L and the limit of quantification ranged from 1 to 10 µg/L, respectively. A relative standard deviation from 3.0 to 4.3% can be achieved depending on the compounds. The procedure was applied to analyze all the four riot control agents simultaneously in several environmental samples.

Assessment of health benefit of PM2.5 reduction during COVID-19 lockdown in China and separating contributions from anthropogenic emissions and meteorology.

The national lockdown policies have drastically disrupted socioeconomic activities during the COVID-19 pandemic in China, which provides a unique opportunity to investigate the air quality response to such anthropogenic disruptions. And it is meaningful to evaluate the potential health impacts of air quality changes during the lockdown, especially for PM2.5 with adverse health effects. In this study, by using PM2.5 observations from 1388 monitoring stations nationwide in China, we examine the PM2.5 variations between the COVID-19 lockdown (February and March in 2020) and the same period in 2015-2019, and find that the national average of PM2.5 decreases by 18 µg/m3, and mean PM2.5 for most sites (about 75%) decrease by 30%-60%. The anthropogenic and meteorological contributions to these PM2.5 variations are also determined by using a stepwise multiple linear regression (MLR) model combined with the Kolmogorov-Zurbenko filter. Our results show that the change of anthropogenic emissions is a leading contributor to those widespread PM2.5 reductions, and meteorological conditions have the negative influence on PM2.5 reductions for some regions, such as Beijing-Tianjin-Hebei (BTH). Additionally, the avoided premature death due to PM2.5 reduction is estimated as a predicted number based on a log-linear concentration-response function. The total avoided premature death is 9952 in China, with dominant contribution (94%) from anthropogenic emission changes. For BTH, Yangtze River Delta, Pearl River Delta and Hubei regions, the reductions of PM2.5 are 24.1, 24.3, 13.5 and 29.5 µg/m3, with the avoided premature deaths of 1066, 1963, 454 and 583, respectively.

Probing the Local Site Disorder and Distortion in Pyrochlore High-Entropy Oxides.

High-entropy oxides (HEOs) have attracted great interest in diverse fields because of their inherent opportunities to tailor and combine materials functionalities. The control of local order/disorder in the class is by extension a grand challenge toward realizing their vast potential. Here we report the first examples of pyrochlore HEOs with five M-site cations, for Nd2M2O7, in which the local structure has been investigated by neutron diffraction and pair distribution function (PDF) analysis. The average structure of the pyrochlores is found to be orthorhombic Imma, in agreement with radius-ratio rules governing the structural archetype. The computed PDFs from density functional theory relaxed special quasirandom structure models are compared with real space PDFs in this work to evaluate M-site order/disorder. Reverse Monte Carlo combined with ab initio molecular dynamics and Metropolis Monte Carlo simulations demonstrates that Nd2(Ta0.2Sc0.2Sn0.2Hf0.2Zr0.2)2O7 is synthesized with its M-site local to nanoscale order highly randomized/disordered, while Nd2(Ti0.2Nb0.2Sn0.2Hf0.2Zr0.2)2O7+x exhibits a strong distortion of the TiO6 octahedron and small degree of Ti chemical short-range order (SRO) on the subnanometer scale. Calculations suggest that this may be intrinsic, energetically favored SRO rather than due to sample processing. These results offer an important demonstration that the engineered variation of participating ions in HEOs, even among those with very similar radii, provides richly diverse opportunities to control local order/disorder motifs-and therefore materials properties for future designs. This work also hints at the exquisite level of detail that may be needed in computational and experimental data analysis to guide structure-property tuning in the emerging HEO materials class.

Influence of Cation Size on the Local Atomic Structure and Electronic Properties of Ta Perovskite Oxynitrides.

Partial anion substitution in transition metal oxides provides rich opportunities to control and tune physical and chemical properties, for example, combining the merits of oxides and nitrides. In addition, the possibility of resulting anion sublattice order provides a means to target polar and chiral structures based on a wide array of accessible structural archetypes by design. Here, we investigate the local structures of a family of perovskite tantalum oxynitrides-ATaO2N (A = Ba, Sr, and Ca)-using a combination of experimental and theoretical approaches including neutron total scattering, density functional theory (DFT), and ab initio molecular dynamics (AIMD) simulations. We present the first experimental study of chemical short-range order (CSRO) in CaTaO2N, confirming local cis N ordering of the anion sub-lattice. Our systematic exploration of a local structure across the A cation size series (from the larger Ba to the smaller Ca) reveals a perovskite motif increasingly distorted with respect to long-range average structures. DFT and AIMD simulations support the observed trends. Overall, structures with cis ordering of the nitrogen anions in each TaO4N2 octahedron are favored over those with trans ordering. With diminishing A cation size, local cis ordering and Ta off-centering play decreasing roles in overall lattice stability, overshadowed by the stabilizing effects of octahedral tilting. The influence of these factors on local dipole formation and frustrated dipole ordering are discussed.

Acute effects of air pollution on lupus nephritis in patients with systemic lupus erythematosus: A multicenter panel study in China.

Air pollution may trigger systemic lupus erythematosus (SLE). However, few studies have investigated the associations between air pollution and complications of SLE, such as lupus nephritis (LN). In this study, multicenter longitudinal data from 13 hospitals in China, including 8552 SLE patients with 24,762 visits, were used. Based on the generalized estimating equation (GEE) model, we assessed the associations of LN occurrence with short-term exposures to different air pollutants including particulate matter with an aerodynamic diameter < 2.5 µm (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3). We identified 2672 LN patients, and about half of them were from east China. Our results based on the entire data set showed that PM2.5 and NO2 were risk factors for LN within one month after exposure, with odds ratio of 1.16 (95% confidence interval (CI), 1.08-1.19) at lag 18 day and 1.19 (95% CI, 1.12-1.26) at lag 16 day relative to an interquartile range (IQR) increase in PM2.5 and NO2, respectively. This positive association between LN and NO2 was also observed for south, west, and east China. In addition, we found that the short term exposure to CO and O3 was not generally associated with LN. Finally, the negative associations of LN with SO2 were found for the entire region and east China. Our results implied that SLE patients may gain the health benefits of air quality improvement in China. Our work also provided evidence that short-term variations in air pollution may trigger LN, and further studies are needed to confirm these findings and the potential pathogenic mechanisms should be explored.

A novel prognostic signature based on four glycolysis-related genes predicts survival and clinical risk of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common cancer with limited cure and poor survival. In our study, a bioinformatic analysis was conducted to investigate the role of glycolysis in the pathogenesis and progression of HCC. METHODS: Single-sample gene set enrichment analysis (ssGESA) was used to calculate enrichment scores for each sample in TCGA-LIHC and GEO14520 according to the glycolysis gene set. Weighted gene co-expression network analysis identified a gene module closely related to glycolysis, and their function was investigated. Prognostic biomarkers were screened from these genes. Cox proportional hazard model and least absolute shrinkage and selection operator regression were used to construct the prognostic signature. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curve analyses evaluated the prediction performance of the prognostic signature in TCGA-LIHC and ICGC-LIRI-JP. Combination analysis data of clinical features and prognostic signature constructed a nomogram. Area under ROC curves and decision curve analysis were used to compare the nomogram and its components. RESULTS: The glycolysis pathway was upregulated in HCC and was unfavorable for survival. The determined gene module was mainly enriched in cell proliferation. A prognostic signature (CDCA8, RAB5IF, SAP30, and UCK2) was developed and validated. KM and ROC curves showed a considerable predictive effect. The risk score derived from the signature was an independent prognostic factor. The nomogram increased prediction efficiency by combining risk signature and TNM stage and performed better than component factors in net benefit. CONCLUSION: The gene signature may contribute to individual risk estimation, survival prognosis, and clinical management.