Recent insights into breast milk microRNA: their role as functional regulators.

Breast milk (BM) is a primary biofluid that plays a crucial role in infant development and the regulation of the immune system. As a class of rich biomolecules in BM, microRNAs (miRNAs) are regarded as active factors contributing to infant growth and development. Surprisingly, these molecules exhibit resilience in harsh conditions, providing an opportunity for infants to absorb them. In addition, many studies have shown that miRNAs in breast milk, when absorbed into the gastrointestinal system, can act as a class of functional regulators to effectively regulate gene expression. Understanding the absorption pattern of BM miRNA may facilitate the creation of formula with a more optimal miRNA balance and pave the way for novel drug delivery techniques. In this review, we initially present evidence of BM miRNA absorption. Subsequently, we compile studies that integrate both in vivo and in vitro findings to illustrate the bioavailability and biodistribution of BM miRNAs post-absorption. In addition, we evaluate the strengths and weaknesses of previous studies and discuss potential variables contributing to discrepancies in their outcomes. This literature review indicates that miRNAs can be absorbed and act as regulatory agents.

Mechanism of Sophorae Flavescentis Radix (Kushen) in treating NSCLC: Insights from miRNA-mRNA network analysis.

ETHNOPHARMACOLOGICAL RELEVANCE: Sophorae Flavescentis Radix (Kushen) is the primary herb component of Compound Kushen Injection (CKI), an approved clinical treatment for tumors. Despite CKI's widespread use, the underlying mechanisms of Kushen regarding microRNA-target and pathway remain unclear in non-small cell lung cancer (NSCLC). AIM OF THE STUDY: This study aimed to elucidate the crucial miRNAs-targets and pathways responsible for the Kushen's impact on NSCLC. MATERIALS AND METHODS: CCK8, colony formation, and apoptosis assays were performed to assess the effects of Kushen on NSCLC cells. Subsequently, we treated the A549 cell line with varying concentrations of Kushen to obtain mRNA and miRNA expression profiles. A DE (differentially expressed) miRNAs-DEGs network was then constructed to identify the critical miRNA-mRNA interaction influenced by Kushen. Furthermore, we performed clinical significance and prognosis analyses of hub genes to narrow down key genes and their corresponding miRNAs. Finally, the effects of Kushen on critical miRNA-mRNA interaction and related pathway were verified by in vitro and in vivo experiments. RESULTS: In this study, we initially demonstrated that Kushen significantly inhibited cell proliferation, suppressed colony formation, and induced apoptosis in the A549 cells, PC9 cells, and the A549 zebrafish xenograft model. Through expression profile analysis, a DE miRs-DEGs network was constructed with 16 DE miRs and 68 DEGs. Through the network analysis and expression validation, we found Kushen could significantly down-regulate miR-183-5p expression and up-regulate EGR1 expression. Additionally, Kushen affected the PTEN/Akt pathway, increasing PTEN expression and decreasing pAkt expression. Finally, matrine, the essential active compound of Kushen, also inhibited cell growth, induced apoptosis, and regulated miR-183-5p/EGR1 and PTEN/AKT pathway. CONCLUSIONS: Altogether, these findings supported the critical role of miR-183-5p/EGR1 and the PTEN/AKT pathway in the beneficial effects of Kushen on NSCLC, highlighting the therapeutic potential of Kushen in NSCLC treatment.

Effects of Natural Products on Enzymes Involved in Ferroptosis: Regulation and Implications.

Ferroptosis is a form of regulated cell death that is characterized by the accumulation of iron-dependent lipid peroxides. The regulation of ferroptosis involves both non-enzymatic reactions and enzymatic mechanisms. Natural products have demonstrated potential effects on various enzymes, including GPX4, HO-1, NQO1, NOX4, GCLC, and GCLM, which are mainly involved in glutathione metabolic pathway or oxidative stress regulation, and ACSL3 and ACSL4, which mainly participate in lipid metabolism, thereby influencing the regulation of ferroptosis. In this review, we have provided a comprehensive overview of the existing literature pertaining to the effects of natural products on enzymes involved in ferroptosis and discussed their potential implications for the prevention and treatment of ferroptosis-related diseases. We also highlight the potential challenge that the majority of research has concentrated on investigating the impact of natural products on the expression of enzymes involving ferroptosis while limited attention is given to the regulation of enzyme activity. This observation underscores the considerable potential and scope for exploring the influence of natural products on enzyme activity.

A Robust Drug-Target Interaction Prediction Framework with Capsule Network and Transfer Learning.

Drug-target interactions (DTIs) are considered a crucial component of drug design and drug discovery. To date, many computational methods were developed for drug-target interactions, but they are insufficiently informative for accurately predicting DTIs due to the lack of experimentally verified negative datasets, inaccurate molecular feature representation, and ineffective DTI classifiers. Therefore, we address the limitations of randomly selecting negative DTI data from unknown drug-target pairs by establishing two experimentally validated datasets and propose a capsule network-based framework called CapBM-DTI to capture hierarchical relationships of drugs and targets, which adopts pre-trained bidirectional encoder representations from transformers (BERT) for contextual sequence feature extraction from target proteins through transfer learning and the message-passing neural network (MPNN) for the 2-D graph feature extraction of compounds to accurately and robustly identify drug-target interactions. We compared the performance of CapBM-DTI with state-of-the-art methods using four experimentally validated DTI datasets of different sizes, including human (Homo sapiens) and worm (Caenorhabditis elegans) species datasets, as well as three subsets (new compounds, new proteins, and new pairs). Our results demonstrate that the proposed model achieved robust performance and powerful generalization ability in all experiments. The case study on treating COVID-19 demonstrates the applicability of the model in virtual screening.

Panax notoginseng alleviates oxidative stress through miRNA regulations based on systems biology approach.

BACKGROUND: Herbal medicine Sanqi (SQ), the dried root or stem of Panax notoginseng (PNS), has been reported to have anti-diabetic and anti-obesity effects and is usually administered as a decoction for Chinese medicine. Alternative to utilizing PNS pure compound for treatment, we are motivated to propose an unconventional scheme to investigate the functions of PNS mixture. However, studies providing a detailed overview of the transcriptomics-based signaling network in response to PNS are seldom available. METHODS: To explore the reasoning of PNS in treating metabolic disorders such as insulin resistance, we implemented a systems biology-based approach with RNA sequencing (RNA-seq) and miRNA sequencing data to elucidate key pathways, genes and miRNAs involved. RESULTS: Functional enrichment analysis revealed PNS up-regulating oxidative stress-related pathways and down-regulating insulin and fatty acid metabolism. Superoxide dismutase 1 (SOD1), peroxiredoxin 1 (PRDX1), heme oxygenase-1 (Hmox1) and glutamate cysteine ligase (GCLc) mRNA and protein levels, as well as related miRNA levels, were measured in PNS treated rat pancreatic ß cells (INS-1). PNS treatment up-regulated Hmox1, SOD1 and GCLc expression while down-regulating miR-24-3p and miR-139-5p to suppress oxidative stress. Furthermore, we verified the novel interactions between miR-139-5p and miR-24-3p with GCLc and SOD1. CONCLUSION: This work has demonstrated the mechanism of how PNS regulates cellular molecules in metabolic disorders. Therefore, combining omics data with a systems biology strategy could be a practical means to explore the potential function and molecular mechanisms of Chinese herbal medicine in the treatment of metabolic disorders.

Quantitative model for genome-wide cyclic AMP receptor protein binding site identification and characteristic analysis.

Cyclic AMP receptor proteins (CRPs) are important transcription regulators in many species. The prediction of CRP-binding sites was mainly based on position-weighted matrixes (PWMs). Traditional prediction methods only considered known binding motifs, and their ability to discover inflexible binding patterns was limited. Thus, a novel CRP-binding site prediction model called CRPBSFinder was developed in this research, which combined the hidden Markov model, knowledge-based PWMs and structure-based binding affinity matrixes. We trained this model using validated CRP-binding data from Escherichia coli and evaluated it with computational and experimental methods. The result shows that the model not only can provide higher prediction performance than a classic method but also quantitatively indicates the binding affinity of transcription factor binding sites by prediction scores. The prediction result included not only the most knowns regulated genes but also 1089 novel CRP-regulated genes. The major regulatory roles of CRPs were divided into four classes: carbohydrate metabolism, organic acid metabolism, nitrogen compound metabolism and cellular transport. Several novel functions were also discovered, including heterocycle metabolic and response to stimulus. Based on the functional similarity of homologous CRPs, we applied the model to 35 other species. The prediction tool and the prediction results are online and are available at: https://awi.cuhk.edu.cn/∼CRPBSFinder.

Cinnamomi ramulus inhibits cancer cells growth by inducing G2/M arrest.

Introduction: Cinnamomi ramulus (CR) is one of the most widely used traditional Chinese medicine (TCM) with anti-cancer effects. Analyzing transcriptomic responses of different human cell lines to TCM treatment is a promising approach to understand the unbiased mechanism of TCM. Methods: This study treated ten cancer cell lines with different CR concentrations, followed by mRNA sequencing. Differential expression (DE) analysis and gene set enrichment analysis (GSEA) were utilized to analyze transcriptomic data. Finally, the in silico screening results were verified by in vitro experiments. Results: Both DE and GSEA analysis suggested the Cell cycle pathway was the most perturbated pathway by CR across these cell lines. By analyzing the clinical significance and prognosis of G2/M related genes (PLK1, CDK1, CCNB1, and CCNB2) in various cancer tissues, we found that they were up-regulated in most cancer types, and their down-regulation showed better overall survival rates in cancer patients. Finally, in vitro experiments validation on A549, Hep G2, and HeLa cells suggested that CR can inhibit cell growth by suppressing the PLK1/CDK1/ Cyclin B axis. Discussion: This is the first study to apply transcriptomic analysis to investigate the cancer cell growth inhibition of CR on various human cancer cell lines. The core effect of CR on ten cancer cell lines is to induce G2/M arrest by inhibiting the PLK1/CDK1/Cyclin B axis.

Extraction of microRNA-target interaction sentences from biomedical literature by deep learning approach.

MicroRNA (miRNA)-target interaction (MTI) plays a substantial role in various cell activities, molecular regulations and physiological processes. Published biomedical literature is the carrier of high-confidence MTI knowledge. However, digging out this knowledge in an efficient manner from large-scale published articles remains challenging. To address this issue, we were motivated to construct a deep learning-based model. We applied the pre-trained language models to biomedical text to obtain the representation, and subsequently fed them into a deep neural network with gate mechanism layers and a fully connected layer for the extraction of MTI information sentences. Performances of the proposed models were evaluated using two datasets constructed on the basis of text data obtained from miRTarBase. The validation and test results revealed that incorporating both PubMedBERT and SciBERT for sentence level encoding with the long short-term memory (LSTM)-based deep neural network can yield an outstanding performance, with both F1 and accuracy being higher than 80% on validation data and test data. Additionally, the proposed deep learning method outperformed the following machine learning methods: random forest, support vector machine, logistic regression and bidirectional LSTM. This work would greatly facilitate studies on MTI analysis and regulations. It is anticipated that this work can assist in large-scale screening of miRNAs, thereby revealing their functional roles in various diseases, which is important for the development of highly specific drugs with fewer side effects. Source code and corpus are publicly available at https://github.com/qi29.

MED1 Regulates BMP/TGF-ß in Endothelium: Implication for Pulmonary Hypertension.

BACKGROUND: Dysregulated BMP (bone morphogenetic protein) or TGF-ß (transforming growth factor beta) signaling pathways are imperative in idiopathic and familial pulmonary arterial hypertension (PAH) as well as experimental pulmonary hypertension (PH) in rodent models. MED1 (mediator complex subunit 1) is a key transcriptional co-activator and KLF4 (Krüppel-like factor 4) is a master transcription factor in endothelium. However, MED1 and KLF4 epigenetic and transcriptional regulations of the BMP/TGF-ß axes in pulmonary endothelium and their dysregulations leading to PAH remain elusive. We investigate the MED1/KLF4 co-regulation of the BMP/TGF-ß axes in endothelium by studying the epigenetic regulation of BMPR2 (BMP receptor type II), ETS-related gene (ERG), and TGFBR2 (TGF-ß receptor 2) and their involvement in the PH. METHODS: High-throughput screening involving data from RNA-seq, MED1 ChIP-seq, H3K27ac ChIP-seq, ATAC-seq, and high-throughput chromosome conformation capture together with in silico computations were used to explore the epigenetic and transcriptional regulation of BMPR2, ERG, and TGFBR2 by MED1 and KLF4. In vitro experiments with cultured pulmonary arterial endothelial cells (ECs) and bulk assays were used to validate results from these in silico analyses. Lung tissue from patients with idiopathic PAH, animals with experimental PH, and mice with endothelial ablation of MED1 (EC-MED1-/-) were used to study the PH-protective effect of MED1. RESULTS: Levels of MED1 were decreased in lung tissue or pulmonary arterial endothelial cells from idiopathic PAH patients and rodent PH models. Mechanistically, MED1 acted synergistically with KLF4 to transactivate BMPR2, ERG, and TGFBR2 via chromatin remodeling and enhancer-promoter interactions. EC-MED1-/- mice showed PH susceptibility. In contrast, MED1 overexpression mitigated the PH phenotype in rodents. CONCLUSIONS: A homeostatic regulation of BMPR2, ERG, and TGFBR2 in ECs by MED1 synergistic with KLF4 is essential for the normal function of the pulmonary endothelium. Dysregulation of MED1 and the resulting impairment of the BMP/TGF-ß signaling is implicated in the disease progression of PAH in humans and PH in rodent models.

Air pollution and blood pressure in the elderly: evidence from a panel study in Nanjing, China.

Background: Air pollution is known to have notable negative effects on human health. Recently, the effect of air pollution on blood pressure among the elderly has attracted researchers' attention. However, the existing evidence is not consistent, given that positive, null, and negative outcomes are presented in the literature. In this study, we investigated the relationship between blood pressure (BP) and indices of air pollutants (PM2.5, PM10, and air quality index) in a specific elderly population through a panel study to address this knowledge gap. Methods: We obtained repeated BP measurements from January 2017 to May 2019 in a panel of 619 elderly with a total of 5106 records in Nanjing, China. Data on daily indices of ambient air pollutants, including fine particulate matter with an aerodynamic diameter of ≤ 2.5 µ m (PM2.5), ≤ 10 µ m (PM10), and air quality index (AQI) of the same period were obtained. We evaluated the association between BP and average concentrations of air pollutants in the past one-week, two-week, and four-week lags before measuring the BP. The non-linear panel regression models were used with fixed- and mixed-effects to control age, gender, and temperature. Results: In the non-linear panel fixed-effects model, the average concentration of PM2.5 is significantly associated with systolic BP (SBP) at all lags but is only significantly correlated with diastolic BP (DBP) at a one-week lag. An interquartile range (IQR) increase of one-week average moving PM2.5 (38.86 µg/m3) of our sample increases the SBP and DBP by 7.68% and 6.9%, respectively. PM10 shows the same pattern of effect on BP as PM2.5. AQI shows less significant associations with BP. In the non-linear mixed-effects model, the average concentrations of PM2.5 and PM10 are significantly associated with SBP at all lags but have no significant effect on DBP at one- and two-week lags. AQI is only significantly associated with DBP at a one-week lag. Conclusions: Exposures to ambient particulate matter (PM2.5 and PM10) were associated with increased BP among older people, indicating a potential link between air pollution and the high prevalence of hypertension. Air pollution is a well-recognized risk factor for future cardiovascular diseases and should be reduced to prevent hypertension among the elderly.

Mutation Analysis of Second Primary Tumors in Oral Cancer in Taiwanese Patients through Next-Generation Sequencing.

Head and neck cancer has poor overall survival. Patients with head and neck cancer more frequently develop second primary tumors than do patients with other cancers, leading to a poor prognosis. In this study, we used next-generation sequencing to analyze and compare mutations between first tumors and second tumors in oral cancer. We retrieved tumor tissues collected from 13 patients who were diagnosed twice as having cancer. We used driver gene and trunk mutations to distinguish between recurrent cancer and primary cancer in oral cancer. We observed unique driver gene mutations in three patients with an initial clinical diagnosis of recurrent cancer; hence, we believe that the corresponding patients had primary cancer. Four patients with an initial clinical diagnosis of primary cancer were found to actually have recurrent cancer according to our results. Genetic testing can be used to enhance the accuracy of clinical diagnosis.

Enzyme Activity of Natural Products on Cytochrome P450.

Drug-metabolizing enzymes, particularly the cytochrome P450 (CYP450) monooxygenases, play a pivotal role in pharmacokinetics. CYP450 enzymes can be affected by various xenobiotic substrates, which will eventually be responsible for most metabolism-based herb-herb or herb-drug interactions, usually involving competition with another drug for the same enzyme binding site. Compounds from herbal or natural products are involved in many scenarios in the context of such interactions. These interactions are decisive both in drug discovery regarding the synergistic effects, and drug application regarding unwanted side effects. Herein, this review was conducted as a comprehensive compilation of the effects of herbal ingredients on CYP450 enzymes. Nearly 500 publications reporting botanicals' effects on CYP450s were collected and analyzed. The countries focusing on this topic were summarized, the identified herbal ingredients affecting enzyme activity of CYP450s, as well as methods identifying the inhibitory/inducing effects were reviewed. Inhibitory effects of botanicals on CYP450 enzymes may contribute to synergistic effects, such as herbal formulae/prescriptions, or lead to therapeutic failure, or even increase concentrations of conventional medicines causing serious adverse events. Conducting this review may help in metabolism-based drug combination discovery, and in the evaluation of the safety profile of natural products used therapeutically.

CircNet 2.0: an updated database for exploring circular RNA regulatory networks in cancers.

Circular RNAs (circRNAs), which are single-stranded RNA molecules that have individually formed into a covalently closed continuous loop, act as sponges of microRNAs to regulate transcription and translation. CircRNAs are important molecules in the field of cancer diagnosis, as growing evidence suggests that they are closely related to pathological cancer features. Therefore, they have high potential for clinical use as novel cancer biomarkers. In this article, we present our updates to CircNet (version 2.0), into which circRNAs from circAtlas and MiOncoCirc, and novel circRNAs from The Cancer Genome Atlas database have been integrated. In total, 2732 samples from 37 types of cancers were integrated into CircNet 2.0 and analyzed using several of the most reliable circRNA detection algorithms. Furthermore, target miRNAs were predicted from the full-length circRNA sequence using three reliable tools (PITA, miRanda and TargetScan). Additionally, 384 897 experimentally verified miRNA-target interactions from miRTarBase were integrated into our database to facilitate the construction of high-quality circRNA-miRNA-gene regulatory networks. These improvements, along with the user-friendly interactive web interface for data presentation, search, and visualization, showcase the updated CircNet database as a powerful, experimentally validated resource, for providing strong data support in the biomedical fields. CircNet 2.0 is currently accessible at https://awi.cuhk.edu.cn/∼CircNet.

miRTarBase update 2022: an informative resource for experimentally validated miRNA-target interactions.

MicroRNAs (miRNAs) are noncoding RNAs with 18-26 nucleotides; they pair with target mRNAs to regulate gene expression and produce significant changes in various physiological and pathological processes. In recent years, the interaction between miRNAs and their target genes has become one of the mainstream directions for drug development. As a large-scale biological database that mainly provides miRNA-target interactions (MTIs) verified by biological experiments, miRTarBase has undergone five revisions and enhancements. The database has accumulated >2 200 449 verified MTIs from 13 389 manually curated articles and CLIP-seq data. An optimized scoring system is adopted to enhance this update's critical recognition of MTI-related articles and corresponding disease information. In addition, single-nucleotide polymorphisms and disease-related variants related to the binding efficiency of miRNA and target were characterized in miRNAs and gene 3' untranslated regions. miRNA expression profiles across extracellular vesicles, blood and different tissues, including exosomal miRNAs and tissue-specific miRNAs, were integrated to explore miRNA functions and biomarkers. For the user interface, we have classified attributes, including RNA expression, specific interaction, protein expression and biological function, for various validation experiments related to the role of miRNA. We also used seed sequence information to evaluate the binding sites of miRNA. In summary, these enhancements render miRTarBase as one of the most research-amicable MTI databases that contain comprehensive and experimentally verified annotations. The newly updated version of miRTarBase is now available at https://miRTarBase.cuhk.edu.cn/.

Role of endothelial cells in pulmonary fibrosis via SREBP2 activation.

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with limited treatment options. Despite endothelial cells (ECs) comprising 30% of the lung cellular composition, the role of EC dysfunction in pulmonary fibrosis (PF) remains unclear. We hypothesize that sterol regulatory element-binding protein 2 (SREBP2) plays a critical role in the pathogenesis of PF via EC phenotypic modifications. Transcriptome data demonstrate that SREBP2 overexpression in ECs led to the induction of the TGF, Wnt, and cytoskeleton remodeling gene ontology pathways and the increased expression of mesenchymal genes, such as snail family transcriptional repressor 1 (snai1), α-smooth muscle actin, vimentin, and neural cadherin. Furthermore, SREBP2 directly bound to the promoter regions and transactivated these mesenchymal genes. This transcriptomic change was associated with an epigenetic and phenotypic switch in ECs, leading to increased proliferation, stress fiber formation, and ECM deposition. Mice with endothelial-specific transgenic overexpression of SREBP2 (EC-SREBP2[N]-Tg mice) that were administered bleomycin to induce PF demonstrated exacerbated vascular remodeling and increased mesenchymal transition in the lung. SREBP2 was also found to be markedly increased in lung specimens from patients with IPF. These results suggest that SREBP2, induced by lung injury, can exacerbate PF in rodent models and in human patients with IPF.

Agricultural management practices influence the soil enzyme activity and bacterial community structure in tea plantations.

BACKGROUND: The soil quality and health of the tea plantations are dependent on agriculture management practices, and long-term chemical fertilizer use is implicated in soil decline. Hence, several sustainable practices are used to improve and maintain the soil quality. Here, in this study, changes in soil properties, enzymatic activity, and dysbiosis in bacterial community composition were compared using three agricultural management practices, namely conventional (CA), sustainable (SA), and transformational agriculture (TA) in the tea plantation during 2016 and 2017 period. Soil samples at two-months intervals were collected and analyzed. RESULTS: The results of the enzyme activities revealed that acid phosphatase, arylsulfatase, ß-glucosidase, and urease activities differed considerably among the soils representing the three management practices. Combining the redundancy and multiple regression analysis, the change in the arylsulfatase activity was explained by soil pH as a significant predictor in the SA soils. The soil bacterial community was predominated by the phyla Proteobacteria, Acidobacteria, Actinobacteria, Chloroflexi, and Bacteroidetes in the soil throughout the sampling period. Higher Alpha diversity scores indicated increased bacterial abundance and diversity in the SA soils. A significant relationship between bacterial richness indices (SOBS, Chao and ACE) and soil pH, K and, P was observed in the SA soils. The diversity indices namely Shannon and Simpson also showed variations, suggesting the shift in the diversity of less abundant and more common species. Furthermore, the agricultural management practices, soil pH fluctuation, and the extractable elements had a greater influence on bacterial structure than that of temporal change. CONCLUSIONS: Based on the cross-over analysis of the bacterial composition, enzymatic activity, and soil properties, the relationship between bacterial composition and biologically-driven ecological processes can be identified as indicators of sustainability for the tea plantation.

MethHC 2.0: information repository of DNA methylation and gene expression in human cancer.

DNA methylation is an important epigenetic regulator in gene expression and has several roles in cancer and disease progression. MethHC version 2.0 (MethHC 2.0) is an integrated and web-based resource focusing on the aberrant methylomes of human diseases, specifically cancer. This paper presents an updated implementation of MethHC 2.0 by incorporating additional DNA methylomes and transcriptomes from several public repositories, including 33 human cancers, over 50 118 microarray and RNA sequencing data from TCGA and GEO, and accumulating up to 3586 manually curated data from >7000 collected published literature with experimental evidence. MethHC 2.0 has also been equipped with enhanced data annotation functionality and a user-friendly web interface for data presentation, search, and visualization. Provided features include clinical-pathological data, mutation and copy number variation, multiplicity of information (gene regions, enhancer regions, and CGI regions), and circulating tumor DNA methylation profiles, available for research such as biomarker panel design, cancer comparison, diagnosis, prognosis, therapy study and identifying potential epigenetic biomarkers. MethHC 2.0 is now available at http://awi.cuhk.edu.cn/∼MethHC.

Hepatitis B virus pre-S2 deletion (nucleotide 1 to 54) in plasma predicts recurrence of hepatocellular carcinoma after curative surgical resection.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Despite curative surgical resection, high recurrence of HCC after surgery results in poor patient survival. To develop prognostic markers is therefore important for better prevention and therapy of recurrent HCC to improve patient outcomes. Deletion mutations over the pre-S1 and pre-S2 gene segments of hepatitis B virus (HBV) have been closely associated with recurrence of HCC after curative surgical resection. In this study, we applied a next-generation sequencing-based approach to further evaluate the association of pre-S deletion regions with HCC recurrence. We demonstrated that the pre-S2 deletion (nucleotide 1 to 54) was the most predominant deletion regions of pre-S gene in plasma of HBV-related HCC patients. Moreover, patients with the pre-S2 deletion (nucleotide 1 to 54) exhibited a significantly higher risk of HCC recurrence after curative surgical resection than those without. The pre-S2 deletion (nucleotide 1 to 54) in plasma represented a prognostic factor that independently predicted HCC recurrence with greater performance than other clinicopathological and viral factors. Our data suggest that detection of the pre-S2 deletion (nucleotide 1 to 54) in plasma may be a promising noninvasive strategy for identifying patients at high risk for HCC recurrence after curative surgical resection.