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1. Efficient and generalizable prediction of molecular alterations in multiple cancer cohorts using H&E whole slide images

Author

Ingale, Kshitij, Hong, Sun Hae, Hu, Qiyuan, Zhang, Renyu, Osinski, Bo, Khoshdeli, Mina, Och, Josh, Nagpal, Kunal, Stumpe, Martin C., and Joshi, Rohan P.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Molecular testing of tumor samples for targetable biomarkers is restricted by a lack of standardization, turnaround-time, cost, and tissue availability across cancer types. Additionally, targetable alterations of low prevalence may not be tested in routine workflows. Algorithms that predict DNA alterations from routinely generated hematoxylin and eosin (H&E)-stained images could prioritize samples for confirmatory molecular testing. Costs and the necessity of a large number of samples containing mutations limit approaches that train individual algorithms for each alteration. In this work, models were trained for simultaneous prediction of multiple DNA alterations from H&E images using a multi-task approach. Compared to biomarker-specific models, this approach performed better on average, with pronounced gains for rare mutations. The models reasonably generalized to independent temporal-holdout, externally-stained, and multi-site TCGA test sets. Additionally, whole slide image embeddings derived using multi-task models demonstrated strong performance in downstream tasks that were not a part of training. Overall, this is a promising approach to develop clinically useful algorithms that provide multiple actionable predictions from a single slide.

Published
2024

2. Enhancing Instance-Level Image Classification with Set-Level Labels

Author

Zhang, Renyu, Khan, Aly A., Chen, Yuxin, and Grossman, Robert L.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Instance-level image classification tasks have traditionally relied on single-instance labels to train models, e.g., few-shot learning and transfer learning. However, set-level coarse-grained labels that capture relationships among instances can provide richer information in real-world scenarios. In this paper, we present a novel approach to enhance instance-level image classification by leveraging set-level labels. We provide a theoretical analysis of the proposed method, including recognition conditions for fast excess risk rate, shedding light on the theoretical foundations of our approach. We conducted experiments on two distinct categories of datasets: natural image datasets and histopathology image datasets. Our experimental results demonstrate the effectiveness of our approach, showcasing improved classification performance compared to traditional single-instance label-based methods. Notably, our algorithm achieves 13% improvement in classification accuracy compared to the strongest baseline on the histopathology image classification benchmarks. Importantly, our experimental findings align with the theoretical analysis, reinforcing the robustness and reliability of our proposed method. This work bridges the gap between instance-level and set-level image classification, offering a promising avenue for advancing the capabilities of image classification models with set-level coarse-grained labels.

Published
2023

3. An Adam-enhanced Particle Swarm Optimizer for Latent Factor Analysis

Author

Chen, Jia, Zhang, Renyu, and Liu, Yuanyi

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

Digging out the latent information from large-scale incomplete matrices is a key issue with challenges. The Latent Factor Analysis (LFA) model has been investigated in depth to an alyze the latent information. Recently, Swarm Intelligence-related LFA models have been proposed and adopted widely to improve the optimization process of LFA with high efficiency, i.e., the Particle Swarm Optimization (PSO)-LFA model. However, the hyper-parameters of the PSO-LFA model have to tune manually, which is inconvenient for widely adoption and limits the learning rate as a fixed value. To address this issue, we propose an Adam-enhanced Hierarchical PSO-LFA model, which refines the latent factors with a sequential Adam-adjusting hyper-parameters PSO algorithm. First, we design the Adam incremental vector for a particle and construct the Adam-enhanced evolution process for particles. Second, we refine all the latent factors of the target matrix sequentially with our proposed Adam-enhanced PSO's process. The experimental results on four real datasets demonstrate that our proposed model achieves higher prediction accuracy with its peers.

Published
2023

4. A Dynamic-Neighbor Particle Swarm Optimizer for Accurate Latent Factor Analysis

Author

Chen, Jia, Chun, Yixian, Liu, Yuanyi, Zhang, Renyu, and Hu, Yang

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

High-Dimensional and Incomplete matrices, which usually contain a large amount of valuable latent information, can be well represented by a Latent Factor Analysis model. The performance of an LFA model heavily rely on its optimization process. Thereby, some prior studies employ the Particle Swarm Optimization to enhance an LFA model's optimization process. However, the particles within the swarm follow the static evolution paths and only share the global best information, which limits the particles' searching area to cause sub-optimum issue. To address this issue, this paper proposes a Dynamic-neighbor-cooperated Hierarchical PSO-enhanced LFA model with two-fold main ideas. First is the neighbor-cooperated strategy, which enhances the randomly chosen neighbor's velocity for particles' evolution. Second is the dynamic hyper-parameter tunning. Extensive experiments on two benchmark datasets are conducted to evaluate the proposed DHPL model. The results substantiate that DHPL achieves a higher accuracy without hyper-parameters tunning than the existing PSO-incorporated LFA models in representing an HDI matrix.

Published
2023

5. Extraction, Purification, Characterization and in Vitro Hypoglycemic Activity of Polysaccharides from Fruiting Body of Phlebopus portentosus

Author

PAN Zhangchao, ZHANG Renyu, LI Huanchang, ZENG Niankai, WANG Yong

Subjects
phlebopus portentosus, polysaccharides, separation and purification, structural characteristics, hypoglycemic activity, Food processing and manufacture, TP368-456
Abstract

In order to study the structural properties and biological activities of the polysaccharides of Phlebopus portentosus, water-soluble polysaccharides from the fruiting body of P. portentosus were extracted by hot water extraction followed by ethanol precipitation, deproteinized using Sevag reagent, decolorized using macroporous adsorption resin, and purified to homogeneity by successive chromatography on DEAE-52 cellulose and Sephacryl S-400HR columns. The molecular mass of the purified polysaccharides was determined by high performance gel permeation chromatography (HPGPC), and the monosaccharide composition by high performance liquid chromatography (HPLC) following pre-column derivatization with 1-phenyl-3-methyl-5-pyrazolone (PMP). Ultraviolet (UV) absorption spectroscopy was used to examine whether the polysaccharide samples contained proteins or nucleic acids, and the types of functional groups, structure and morphology were characterized by infrared spectroscopy, nuclear magnetic resonance (NMR) spectroscopy and scanning electron microscopy (SEM), respectively. The polysaccharide with high α-glucosidase inhibitory activity was selected to evaluate its inhibition kinetics on α-glucosidase. Furthermore, its in vitro hypoglycemic mechanism and inhibitory type were clarified. The results showed that among four macroporous resins (HP 2MGL, HP 20, SP 850 and SP 825) tested, SP 825 macroporous resin had the best decolorization efficiency. The fraction PPP-0 had a weight average molecular weight (mw) of 31 059 Da. Moreover, it was a heteropolysaccharide mainly composed of galactose, glucose, fucose and mannose that were linked by α- and β-glycosidic bonds. In vitro hypoglycemic experiments showed that the crude polysaccharide and the three purified polysaccharide fractions all exhibited inhibitory activity on α-glucosidase, among which PPP-2 had the highest inhibitory rate, followed by PPP-0. The inhibition was competitive and reversible. This study could provide a theoretical basis for the development of functional foods with hypoglycemic activity and hypoglycemic agents from P. portentosus polysaccharides.

Published
2024
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6. Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing

Author

Zhang, Renyu, Khan, Aly A., Grossman, Robert L., and Chen, Yuxin

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Active learning has demonstrated data efficiency in many fields. Existing active learning algorithms, especially in the context of batch-mode deep Bayesian active models, rely heavily on the quality of uncertainty estimations of the model, and are often challenging to scale to large batches. In this paper, we propose Batch-BALanCe, a scalable batch-mode active learning algorithm, which combines insights from decision-theoretic active learning, combinatorial information measure, and diversity sampling. At its core, Batch-BALanCe relies on a novel decision-theoretic acquisition function that facilitates differentiation among different equivalence classes. Intuitively, each equivalence class consists of hypotheses (e.g., posterior samples of deep neural networks) with similar predictions, and Batch-BALanCe adaptively adjusts the size of the equivalence classes as learning progresses. To scale up the computation of queries to large batches, we further propose an efficient batch-mode acquisition procedure, which aims to maximize a novel information measure defined through the acquisition function. We show that our algorithm can effectively handle realistic multi-class classification tasks, and achieves compelling performance on several benchmark datasets for active learning under both low- and large-batch regimes. Reference code is released at https://github.com/zhangrenyuuchicago/BALanCe.

Published
2021

11. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Author

Wang, Sheng, Sun, Siqi, Li, Zhen, Zhang, Renyu, and Xu, Jinbo

Subjects
Quantitative Biology - Biomolecules, Computer Science - Learning, Quantitative Biology - Quantitative Methods, Statistics - Machine Learning
Abstract

Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks. This deep neural network allows us to model very complex sequence-contact relationship as well as long-range inter-contact correlation. Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. Tested on three datasets of 579 proteins, the average top L long-range prediction accuracy obtained our method, the representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints can yield correct folds (i.e., TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively. Further, our contact-assisted models have much better quality than template-based models. Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast, when the training proteins of our method are used as templates, homology modeling can only do so for 10 of them. One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein prediction. Finally, in recent blind CAMEO benchmark our method successfully folded 5 test proteins with a novel fold.

Published
2016
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13. Large HBV Surface Protein-Induced Unfolded Protein Response Dynamically Regulates p27 Degradation in Hepatocellular Carcinoma Progression

Author

Guo, Yixiao, primary, Shao, Jie, additional, Zhang, Renyu, additional, Han, Mingwei, additional, Kong, Lingmin, additional, Liu, Zekun, additional, Li, Hao, additional, Wei, Ding, additional, Lu, Meng, additional, Zhang, Shuai, additional, Zhang, Cong, additional, Wei, Haolin, additional, Chen, Zhinan, additional, and Bian, Huijie, additional

Published
2023
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16. Effects of Metabolites, Sex, Sire, and Muscle Type on Chilled Lamb Meat Colour.

Author

Zhang, Renyu, Wu, Guojie, Staincliffe, Maryann, McEwan, John C., and Farouk, Mustafa M.

Subjects
LAMB (Meat), ERECTOR spinae muscles, NUTRITION, MEAT industry, DISPLAY of merchandise, METABOLITES, MYOGLOBIN
Abstract

Meat is an important source of high-value protein providing sustainable nutrition for human health. The discolouration of meat results in significant waste, which threatens the sustainability of meat production in terms of availability, affordability, and utilisation. Advancing the knowledge of factors and underlying mechanisms for meat discolouration supports the sustainability transformation of meat production practices. Previous studies found that colour stability may be associated with signature changes in certain metabolites, including NADH, glutamate, methionine, and testosterone. This study aimed to confirm the effect of these metabolites and sex, sire, and muscle type on lamb meat colour. NADH and glutamate improved colour stability as evidenced by the increased metmyoglobin reductase activity, while methionine and testosterone had detrimental effects. Overall, lamb meat was discoloured with retail display for up to 10 days at 4 °C. The semitendinosus muscle had higher L*, b*, and hue angle and lower a* (p < 0.05) than other muscles, especially in ewes. Lamb meat from rams had a higher L* and hue angle and lower a* than the ewes (p < 0.05), especially in the colour-labile group, suggesting an interaction between sex and sire. The outcomes of this study will help make the production of meat more sustainable by assisting the meat industry in improving the selection of animals for meat production and processing practices to reduce meat waste due to discolouration. [ABSTRACT FROM AUTHOR]

Published
2023
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17. Trade-in Remanufacturing, Customer Purchasing Behavior, and Government Policy

Author

Zhang, Fuqiang and Zhang, Renyu

Subjects
Apple Inc. -- Product enhancement, Purchasing, Remanufacturing -- Product enhancement -- Prices and rates, Computer industry -- Product enhancement -- International economic relations, Microcomputer industry, Computer industry, Company pricing policy, Product enhancement, Business
Abstract

Abstract. Trade-in remanufacturing is a commonly adopted business practice under which firms collect used products for remanufacturing by allowing repeat customers to trade in used products for upgraded ones at [...]

Published
2018
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18. The Impact of Social Nudges on User-Generated Content for Social Network Platforms.

Author

Zeng, Zhiyu, Dai, Hengchen, Zhang, Dennis J., Zhang, Heng, Zhang, Renyu, Xu, Zhiwei, and Shen, Zuo-Jun Max

Subjects
NUDGE theory, USER-generated content, SOCIAL networks, SOCIAL impact, TEACHER development
Abstract

Content-sharing social network platforms rely heavily on user-generated content to attract users and advertisers, but they have limited authority over content provision. We develop an intervention that leverages social interactions between users to stimulate content production. We study social nudges, whereby users connected with a content provider on a platform encourage that provider to supply more content. We conducted a randomized field experiment (N = 993 , 676) on a video-sharing social network platform where treatment providers could receive messages from other users encouraging them to produce more, but control providers could not. We find that social nudges not only immediately boosted video supply by 13.21% without changing video quality but also, increased the number of nudges providers sent to others by 15.57%. Such production-boosting and diffusion effects, although declining over time, lasted beyond the day of receiving nudges and were amplified when nudge senders and recipients had stronger ties. We replicate these results in a second experiment. To estimate the overall production boost over the entire network and guide platforms to utilize social nudges, we combine the experimental data with a social network model that captures the diffusion and over-time effects of social nudges. We showcase the importance of considering the network effects when estimating the impact of social nudges and optimizing platform operations regarding social nudges. Our research highlights the value of leveraging co-user influence for platforms and provides guidance for future research to incorporate the diffusion of an intervention into the estimation of its impacts within a social network. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Funding: H. Dai thanks the University of California, Los Angeles (UCLA) [Hellman Fellowship and Faculty Development Award] for funding support. R. Zhang is grateful for financial support from the Hong Kong Research Grants Council [Grant 16505418]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4622. [ABSTRACT FROM AUTHOR]

Published
2023
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20. Active Packaging for the Extended Shelf-Life of Meat: Perspectives from Consumption Habits, Market Requirements and Packaging Practices in China and New Zealand

Author

Li, Xin, primary, Zhang, Renyu, additional, Hassan, Mohammad Mahbubul, additional, Cheng, Zhe, additional, Mills, John, additional, Hou, Chengli, additional, Realini, Carolina E., additional, Chen, Li, additional, Day, Li, additional, Zheng, Xiaochun, additional, Zhang, Dequan, additional, and Hicks, Talia M., additional

Published
2022
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28. Additional file 1 of Population genomics of Zea species identifies selection signatures during maize domestication and adaptation

Author

Xu, Gen, Zhang, Xuan, Chen, Wenkang, Zhang, Renyu, Li, Zhi, Wen, Weiwei, Warburton, Marilyn L., Li, Jiansheng, Li, Huihui, and Yang, Xiaohong

Abstract

Additional file 1: Figure S1. Geographical distribution of all teosinte accessions. Figure S2. Genetic relationships of maize and teosinte assessed by PCA. Figure S3. Evaluation of the ascertainment bias caused by Syngenta SNPs. Figure S4. Haplotype richness in maize and teosinte groups estimated via window-based methods. Figure S5. Co-localization of putative selective sweeps with public GWAS hits for flowering time. Figure S6. Genetic relationships of maize and teosinte assessed by PCA using 36,839 common SNPs between this study and Hufford et al.���s study. Table S1. Population divergence among maize and teosinte subgroups estimated by pairwise FST values between different groups. Table S2. List of known domestication, improvement and adaptation genes in maize. Table S3. Comparisons of selective sweeps identified in this study and previous studies, and the factors affecting the identification of selective sweeps.

Published
2022
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32. Identifying QTL and candidate genes for prolificacy in maize

Author

Wang, Min, Zhang, Renyu, Zhao, Yangzi, Yao, Jieyuan, Li, Weiya, Yang, Zhixing, Sun, Fei, and Yang, Xiaohong

Abstract

In maize, prolificacy, the number of ears per plant, is a trait of interest to maize breeders for breeding high grain-yielding cultivars or specialty corn, as well as being a model trait for decoding the molecular mechanism of maize evolution. Its genetic basis remains largely unknown. We identified a stable quantitative trait locus, qEN7, for ear number on chromosome 7 in both F2and F2:3populations derived from a single cross between the nonprolific inbred line Mo17 and the prolific inbred line LAN404 derived from the landrace PI217404. qEN7explained 10.7%–11.9% of phenotypic variation, and the LAN404 allele at this locus was associated with an increase of around one ear per plant. qEN7was confined by fine-mapping to a 0.56-Mb region containing eight annotated genes. Analysis of selection, gene expression patterns in various maize tissues, and sequence polymorphisms between the two parental lines suggested that Zm00001d020683, which encodes a putative INDETERMINATE DOMAIN (IDD) transcription factor, is the most likely candidate gene underlying qEN7. Zm00001d020683is expressed mainly in the vegetative meristem, immature ears, and internodes and has undergone selection during maize improvement. The identification of qEN7and the prediction of its candidate gene sheds some light on the evolution of maize ear number and provides a novel resource for breeding of multi-ear maize cultivars.

Published
2023
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34. Metabolic fingerprinting of in-bag dry- and wet-aged lamb with rapid evaporative ionisation mass spectroscopy

Author

Zhang, Renyu, Ross, Alastair B., Yoo, Michelle J.Y., and Farouk, Mustafa M.

Subjects
General Medicine, Food Science, Analytical Chemistry
Abstract

© This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. Deposited by shareyourpaper.org and openaccessbutton.org. We've taken reasonable steps to ensure this content doesn't violate copyright. However, if you think it does you can request a takedown by emailing help@openaccessbutton.org.

Published
2021

37. Comparative Statics Analysis of An Inventory Management Model with Dynamic Pricing, Market Environment Fluctuation, and Delayed Differentiation.

Author

Yang, Nan and Zhang, Renyu

Subjects
INVENTORY control, TIME-based pricing, COMPARATIVE method, COMPARATIVE studies, DYNAMIC models
Abstract

We consider a general joint pricing and inventory management model with delayed differentiation, in which a firm serves a market with multiple products made from a generic one. The firm holds inventory for the generic product which is produced using multiple resources. Moreover, the market size, the attractiveness of each product, the firm's productivity, and the procurement cost of each resource all evolve over the planning horizon driven by an exogenous Markov process. Comparative statics analysis is essential for studying this model, offering insights on the impact of market environment fluctuation upon the firm's optimal pricing and inventory policy. Hence, we propose a new approach that combines the advantages of implicit function theorem (IFT) and monotone comparative statics (MCS) approaches to perform comparative statics analysis in our joint pricing and inventory management model under market environment fluctuation. The new approach applies to our model where the standard IFT and MCS methods are not easily amenable. Using our new comparative statics approach, we characterize the optimal pricing and production policy of the firm, and offer insights on how the firm should adaptively respond to market environment fluctuations. [ABSTRACT FROM AUTHOR]

Published
2022
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40. Spoofing attack of drone

Author

Zhang, Renyu, Seow Chee Kiat, and School of Electrical and Electronic Engineering

Subjects
Engineering::Electrical and electronic engineering [DRNTU]
Abstract

Nowadays, the increasing application of drones makes our lives more and more convenient. Various tasks such as: surveillance, emergency rescue, aerial photography, etc. can be accomplished by drones now. In China, some express companies have begun to use drones to do the deliveries. There are two main ways to navigate a drone. The first way is to control drones through a remote controller. As a person is needed, sometimes, it is not a very profitable way. The other way is setting the destination for drones, and it will get to the destination automatically with the help of positioning services. This way can reduce the labor cost as the drones travel automatically. In preset, most positioning services is provided by global navigation satellite systems (GNSS). The global positioning system (GPS) is the most widely used GNSS. But it is known that civilian GPS signals are vulnerable to many kinds of attacks. In all of these attacks, the GPS spoofing is the most threatening one. The spoofing attack can mislead the victim into thinking it is in a position which is different from its real position. As the application demand of drones is increasing, the drones are likely to be victims of the spoofing attack. The 5th generation wireless systems, abbreviated 5G, is believed to be able to provide high quality and low delay positioning services with its features like ultra-dense networks, MIMO technology, device to device communications, etc. The observed time difference of arrival (OTDOA) positioning, which has been introduced to long-term evolution network, is a promising way to provide positioning services. In this work, the possibility of the spoofing attack on several drones which are keep in touch with each other and know each other relative positions is studied. The possible spoofed location with different number of spoofing signals is investigated. The feasibility study of spoofing attack on an OTDOA navigated drone and the simulation of it are included. Finally, some countermeasures against these attacks are proposed. Master of Science (Communications Engineering)

Published
2018

48. In-Bag Dry- vs. Wet-Aged Lamb: Quality, Consumer Acceptability, Oxidative Stability and In Vitro Digestibility.

Author

Zhang, Renyu, Yoo, Michelle J. Y., Realini, Carolina E., Staincliffe, Maryann, and Farouk, Mustafa M.

Subjects
LAMBS, MEAT quality, LAMB (Meat), AMINO acids, BAGS, FATTY acids
Abstract

The aim of this study was to produce in-bag dry-aged lamb and compare its meat quality, consumer acceptability, oxidative stability and in vitro digestibility to the wet-aged equivalents. Significantly higher pH, weight loss and reduced cook loss were observed in dry-aged lamb compared to the wet-aged (p < 0.0001). Dry-aged lamb had harder and chewier texture profiles and lower colour attributes (L*, a* and b*) than the wet-aged (p < 0.001). The dry-aged and wet-aged lamb were equally preferred (around 40% each) by the consumer panel, underpinning the niche nature of dry-aged meat. Significantly (p < 0.05) higher yeast and thiobarbituric acid reactive substances (TABRS) levels were observed in dry-aged lamb compared to the wet-aged. There was no difference in fatty acid profile, protein carbonyl content and pattern of proteolysis between ageing regimes (p > 0.05). Ageing regimes had no impact on overall digestibility; however, a greater gastric digestibility was observed in dry-aged lamb through the increased release of free amino acids (FAAs) compared to the wet-aged. Outcomes of this study demonstrated for the first time the possibility of producing dry-aged lamb legs of acceptable quality, oxidative stability and superior digestibility compared to the equivalent wet-aged lamb. [ABSTRACT FROM AUTHOR]

Published
2021
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50. UBE2S promotes glycolysis in hepatocellular carcinoma by enhancing E3 enzyme-independent polyubiquitination of VHL.

Author

Zhang R, Li C, Zhang S, Kong L, Liu Z, Guo Y, Sun Y, Zhang C, Yong Y, Lv J, Lu M, Liu M, Wu D, Zhang T, Yang H, Wei D, Chen Z, and Bian H

Abstract

Background/aims: Ubiquitination is widely involved in the progression of hepatocellular carcinoma (HCC) by regulating various cellular processes. However, systematic strategies for screening core ubiquitin-related genes, clarifying their functions and mechanisms, and ultimately developing potential therapeutics for patients with HCC are still lacking., Methods: Cox and LASSO regression analyses were performed to construct a ubiquitin-related gene prediction model for HCC. Loss- and gain-of-function studies, transcriptomic and metabolomics analysis were used to explore the function and mechanism of UBE2S on HCC cell glycolysis and growth., Results: Based on 1423 ubiquitin-related genes, a four-gene signature was successfully constructed to evaluate the prognosis of patients with HCC. UBE2S was identified in this signature with the potential to predict the survival of patients with HCC. E2F2 transcriptionally upregulated UBE2S expression by directly binding to its promoter. UBE2S positively regulated glycolysis in a HIF-1α-dependent manner, thus promoting the proliferation of HCC cells. Mechanistically, UBE2S enhanced K11-linkage polyubiquitination at lysine residues 171 and 196 of VHL independent of E3 ligase, thereby indirectly stabilizing HIF-1α protein levels by mediating the degradation of VHL by the proteasome. In particular, the combination of cephalomannine, a small molecule compound that inhibits the expression of UBE2S, and PX-478, an inhibitor of HIF-1α, significantly improved the anti-tumor efficacy., Conclusions: UBE2S is identified as a key biomarker in HCC among the thousands of ubiquitin-related genes and promotes glycolysis by E3 enzyme-independent ubiquitination, thus serving as a therapeutic target for the treatment of HCC.

Published
2024
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