OBJECTIVES: To identify potential molecular targets of quercetin in the treatment of clear cell renal carcinoma (ccRCC). METHODS: The therapeutic targets of quercetin were screened from multiple databases by network pharmacology analysis, and the targets significantly correlated with ccRCC were screened from 4907 plasma proteins using a Mendelian randomization method. The drug-disease network model was constructed to screen the potential key targets. The functions of these targets were evaluated via bioinformatics analysis, and the screened targets were verified in cultured ccRCC cells. RESULTS: Network pharmacology analysis combined with Mendelian randomization identified TP53 (OR=3.325, 95% CI: 1.805-6.124, P=0.0001), ARF4 (OR=0.173, 95% CI: 0.065-0.456, P=0.0003), and DPP4 (OR=0.463, 95% CI: 0.302-0.711, P=0.0004) as the core targets in quercetin treatment of ccRCC. Bioinformatics analysis showed that TP53 was highly expressed in ccRCC, and patients with high TP53 expressions had worse survival outcomes. Molecular docking studies showed that the binding energy between quercetin and TP53 was -5.83 kcal/mol. In cultured 786-O cells, CCK-8 assay and wound healing assay showed that treatment with quercetin significantly inhibited cell proliferation and migration. Quercetin treatment also strongly suppressed the expression of TP53 at both the mRNA and protein levels in 786-O cells as shown by RT-qPCR and Western blotting. CONCLUSIONS TP53 may be the key target of quercetin in the treatment of ccRCC, which sheds light on potential molecular mechanism that mediate the therapeutic effect of quercetin.
Humans ; Quercetin/pharmacology* ; Carcinoma, Renal Cell/genetics* ; Cell Proliferation/drug effects* ; Kidney Neoplasms/genetics* ; Cell Movement/drug effects* ; Tumor Suppressor Protein p53/metabolism* ; Cell Line, Tumor ; Computational Biology

OBJECTIVES: To explore the role of oxidative stress and immune infiltration in rheumatoid arthritis (RA). METHODS: RA datasets GSE55235 (10 RA vs 10 normal samples) and GSE55457 (13 RA vs 10 normal samples) from the GEO database were merged as the test set to identify the differentially expressed genes (DEGs) in RA using R. The DEGs were intersected with oxidative stress-related genes to obtain oxidative stress-associated DEGs. KEGG and GO enrichment analyses of the DEGs were performed, and the RA-related pathways and biological processes were analyzed using GSEA. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape, and the top 10 key genes were obtained using the Degree algorithm. The validation dataset GSE1919 from GEO database was used for ROC analysis of the key genes to obtain the core genes, and their correlations with infiltrating immune cells were analyzed using CIBERSORT. The results were verified by RT-qPCR for detecting expression levels of the core genes in RA and normal joint samples. RESULTS: We identified 89 oxidative stress-associated DEGs. Enrichment analysis suggested that these DEGs were involved in the biological processes including oxidative stress, chemical stress response, reactive oxygen species response, and lipopolysaccharide response. ROC analysis showed that the 5 core genes (STAT1, MMP9, MYC, CCL5, and JUN) all had AUC values >0.7, indicating their high diagnostic sensitivity and specificity for RA. These genes were closely correlated with immune cells, particularly T cells. RT-qPCR confirmed significant differential expressions of the core genes between RA and normal samples. CONCLUSIONS Oxidative stress and diverse immune responses are features of RA, and the immune responses contribute to activation of oxidative stress. The identified core genes can potential serve as new diagnostic markers for RA.
Arthritis, Rheumatoid/genetics* ; Oxidative Stress/genetics* ; Humans ; Computational Biology ; Protein Interaction Maps ; Gene Expression Profiling ; Gene Regulatory Networks

OBJECTIVES: To explore the potential of pyrroline-5-carboxylate reductase 1 (PYCR1) as a pan-cancer biomarker and investigate its expression, function, and clinical significance in bladder cancer (BLCA). METHODS: Bioinformatics analysis was conducted to evaluate the associations of PYCR1 with prognosis, immune microenvironment remodeling, tumor mutation burden (TMB), and microsatellite instability (MSI) in cancer patients. Using the TCGA-BLCA dataset, univariate and multivariate regression analyses were performed to assess the potential of PYCR1 as an independent prognostic risk factor for BLCA, and a clinical decision model was constructed. The IMvigor210 cohort was utilized to evaluate the potential of PYCR1 for independently predicting the efficacy of immunotherapy. The pRRophetic was employed to screen candidate chemotherapeutic agents for treating BLCA with high PYCR1 expression. The CMap-XSum algorithm and molecular docking techniques were used to explore and validate small molecule inhibitors of PYCR1. RESULTS: A high expression of PYCR1 was significantly associated with poor prognosis, immune cell infiltration, TMB and MSI in various tumors (r>0.3). PYCR1 was overexpressed in BLCA, and high PYCR1 expression was closely related to poor prognosis in BLCA patients (HR: 1.14, 95% CI: 1.02-1.68, P=0.006). The IC₅₀ of the anti-cancer drugs cetuximab, 5-fluorouracil, and doxorubicin increased significantly in BLCA cell lines with high PYCR1 expressions (P<0.0001). CONCLUSIONS High PYCR1 expression is an independent risk factor for poor prognosis in BLCA patients and can serve as a significant indicator for clinical decision-making as well as a marker for predicting sensitivity to chemotherapeutic agents and the efficacy of immunotherapy.
Humans ; Urinary Bladder Neoplasms/genetics* ; Immunotherapy ; Prognosis ; Pyrroline Carboxylate Reductases/metabolism* ; Biomarkers, Tumor/genetics* ; delta-1-Pyrroline-5-Carboxylate Reductase ; Microsatellite Instability ; Tumor Microenvironment ; Mutation ; Computational Biology ; Molecular Docking Simulation

OBJECTIVES: To establish a prognostic model for primary liver cancer (PLC) using bioinformatics methods. METHODS: Based on the data from 404 patients in the Cancer Genome Atlas (TCGA) database, we constructed a prognostic model integrating the differentially expressed genes, anoikis, and immune-related genes (DAIs) using univariate Cox regression and the LASSO-Cox approach. The predictive ability of the model was evaluated using Kaplan-Meier method and receiver-operating characteristic curves, and a nomogram was developed to facilitate its clinical applications. Gene set enrichment analysis (GSEA) was performed to explore the associated pathways and relationship between the DAIs and the tumor immune microenvironment, and the half-maximal inhibitory concentration (IC₅₀) of liver cancer drugs was calculated using the "pRRophetic" R package. We also detected the expression of SEMA7A in paired tumor and adjacent tissues from liver cancer patients. RESULTS: We constructed and validated a prognostic model based on 7 DAIs (NR4A3, SEMA7A, IL11, AR, BIRC5, EGF, and SPP1), and obtained consistent results in both the TCGA training cohort and GEO validation cohort (GSE14520), where the patients in the low-risk group were characterized by more favorable clinical outcomes and immune status. By integrating this prognostic signature with clinical information, a composite nomogram was generated. Somatic mutation analysis showed that TTN, TP53, and CTNNB1 mutations accounted for the largest proportion of total mutations, and the patients in the low-risk-low-TMB group had higher survival rate. Drug sensitivity analysis revealed differences in sensitivity to chemotherapeutic agents between high- and low-risk groups and between TP53 mutations and non-mutations. In clinical tissue specimens, SEMA7A expression was significantly higher in liver cancer tissues than in the adjacent tissues. CONCLUSIONS We established a new prognostic model based on DAIs for predicting clinical outcomes and therapeutic response of patients with primary liver cancer.
Humans ; Liver Neoplasms/diagnosis* ; Prognosis ; Anoikis ; Nomograms ; Computational Biology ; Tumor Microenvironment ; Semaphorins/metabolism*

Sepsis is a life-threatening organ dysfunction syndrome caused by a dysregulated host response to infection. Due to different infection sources, pathogens and basic conditions of patients, there is significant heterogeneity in clinical manifestations, response to treatment and prognosis of patients with sepsis. Accurate classification and individualized treatment of sepsis will help to further improve the prognosis of patients with sepsis. In recent years, the integration of artificial intelligence and bioinformatics has brought new opportunities for the research of sepsis classification. This review systematically introduces a variety of sepsis classification methods and their clinical application value. The clinical data in the electronic medical record, such as the dynamic changes of vital signs such as body temperature, can be used as the basis for sepsis classification. Different subtypes of body temperature trajectories have differences in physiological characteristics and prognosis, which contributes to predict the prognosis of patients and guide fluid management strategies. Biomarker classification can more comprehensively reflect the pathophysiological state of patients. Immune index classification is helpful to identify immunocompromised patients so as to carry out targeted immunotherapy. Transcriptome data and genotyping reveal the heterogeneity of sepsis at the molecular level and provide a new perspective for precision medicine. In addition, a detailed systematic review of sepsis-related organ function damage, such as acute respiratory distress syndrome (ARDS), acute kidney injury (AKI), and acute liver injury, has also been conducted, which is helpful to develop targeted organ protection and treatment strategies. These typing methods have shown good application prospects in clinical practice. However, there are still limitations in the current research, such as typing stability and biomarker selection, which need to be further explored. Future research should focus on the development of stable and efficient typing tools to achieve precise treatment of sepsis and improve the prognosis of patients.
Humans ; Sepsis/classification* ; Multiple Organ Failure/classification* ; Prognosis ; Artificial Intelligence ; Biomarkers ; Computational Biology ; Respiratory Distress Syndrome

OBJECTIVE: Circadian rhythm disruption (CRD) is a risk factor that correlates with poor prognosis across multiple tumor types, including hepatocellular carcinoma (HCC). However, its mechanism remains unclear. This study aimed to define HCC subtypes based on CRD and explore their individual heterogeneity. METHODS: To quantify CRD, the HCC CRD score (HCCcrds) was developed. Using machine learning algorithms, we identified CRD module genes and defined CRD-related HCC subtypes in The Cancer Genome Atlas liver HCC cohort (n = 369), and the robustness of this method was validated. Furthermore, we used bioinformatics tools to investigate the cellular heterogeneity across these CRD subtypes. RESULTS: We defined three distinct HCC subtypes that exhibit significant heterogeneity in prognosis. The CRD-related subtype with high HCCcrds was significantly correlated with worse prognosis, higher pathological grade, and advanced clinical stages, while the CRD-related subtype with low HCCcrds had better clinical outcomes. We also identified novel biomarkers for each subtype, such as nicotinamide n-methyltransferase and myristoylated alanine-rich protein kinase C substrate-like 1. CONCLUSION We classify the HCC patients into three distinct groups based on circadian rhythm and identify their specific biomarkers. Within these groups greater HCCcrds was associated with worse prognosis. This approach has the potential to improve prediction of an individual's prognosis, guide precision treatments, and assist clinical decision making for HCC patients. Please cite this article as: Li XJ, Chang L, Mi Y, Zhang G, Zhu SS, Zhang YX, et al. Integrated-omics analysis defines subtypes of hepatocellular carcinoma based on circadian rhythm. J Integr Med. 2025; 23(4): 445-456.
Humans ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; Circadian Rhythm/genetics* ; Prognosis ; Male ; Female ; Biomarkers, Tumor/genetics* ; Middle Aged ; Machine Learning ; Computational Biology

OBJECTIVES: This study aimed to explore the biological functions and clinical value of mothers against decapentaplegic homolog (SMAD) 7 in head and neck squamous cell carcinoma (HNSCC) through bioinformatics analysis and basic experiments. METHODS: The expression of SMAD7 in HNSCC in public databases was studied. Western blot was used to detect the expression of SMAD7 in HNSCC cell lines and normal epithelial cells. The SMAD7 highly expressed HNSCC cell line HSC-4 was silenced, and CCK-8, Transwell assays, and cell scratch experiments were conducted to study the effect of SMAD7 on the biological functions of HSC-4 cells. HNSCC expression profile data were obtained from UCSC xena, and genes related to SMAD7 were selected for gene ontology and Kyoto encyclopedia of genes and genomes gene enrichment analysis, construction of a co-expression gene interaction network, and screening of related cell signaling pathways. Western blot was used to detect the expression changes of proteins in the related cell signaling pathways in HNSCC cells with silenced SMAD7. cBioPortal was utilized to analyze the mutation rate of the SMAD7 gene, and the MethSurv database was used to analyze the methylation level of the SMAD7 gene and its correlation with prognosis. The receiver operating characteristic curve was used to assess the diagnostic value of SMAD7 for HNSCC. TIMER2.0 was used to analyze the correlation between SMAD7 expression and immune cell infiltration. RESULTS: SMAD7 was highly expressed in HNSCC tumor tissues and some cell lines. Silencing the expression of SMAD7 can significantly inhibit the proliferation, migration, and invasion of cancer cells. Silencing SMAD7 can induce the downregulation of vascular cell adhesion molecule 1 (VCAM-1). The bioinformatics analysis showed that the mutation rate of the SMAD7 gene and the methylation level were significantly correlated with the prognosis of patients with HNSCC. The expression of SMAD7 was related to the level of immune cell infiltration in HNSCC. CONCLUSIONS SMAD7 promotes the proliferation, migration, and invasion of HNSCC cells by regulating the expression of VCAM-1. It may be a potential tumor biomarker and therapeutic target for HNSCC.
Humans ; Smad7 Protein/metabolism* ; Prognosis ; Squamous Cell Carcinoma of Head and Neck ; Head and Neck Neoplasms/pathology* ; Cell Line, Tumor ; Cell Movement ; Cell Proliferation ; Signal Transduction ; Gene Expression Regulation, Neoplastic ; Gene Silencing ; Computational Biology

Biomanufacturing is an advanced manufacturing method that integrates biology, chemistry, and engineering. It utilizes renewable biomass and biological organisms as production media to scale up the production of target products through fermentation. Compared with petrochemical routes, biomanufacturing offers significant advantages in reducing CO₂ emissions, lowering energy consumption, and cutting costs. With the development of systems biology and synthetic biology and the accumulation of bioinformatics data, the integration of information technologies such as artificial intelligence, large models, and high-performance computing with biotechnology is propelling biomanufacturing into a data-driven era. This paper reviews the latest research progress on databases, knowledge bases, and large language models for biomanufacturing. It explores the development directions, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for scientific research in related areas.
Biotechnology/methods* ; Knowledge Bases ; Synthetic Biology ; Databases, Factual ; Artificial Intelligence ; Systems Biology ; Computational Biology ; Fermentation

Computational modeling is an invaluable tool for mechanism analysis, directed engineering, and rational design of biological parts, metabolic networks, and even cellular systems. It can provide new technological solutions to address biological challenges at different levels and has become a central focus of research in biomanufacturing. In the computational modeling of proteins, which are the key parts in biological systems, the traditional physics-based methods (computer software and mathematical model) have been widely used to study the physical and chemical processes in the functioning of proteins, and have thus been recognized as a powerful tool for understanding complex biological systems and guiding experimental designs. As the scale of computational modeling continues to expand, traditional modeling techniques face difficulties in balancing computational accuracy and speed. In recent years, the explosive growth of biological data has made it possible to construct high-performance artificial intelligence (AI) models, which brings new opportunities to the computational modeling of proteins, and the AI-enhanced physics-based computational modeling technologies have emerged. This combined strategy not only incorporates the chemical knowledge and established physical principles but also is powerful in data processing and pattern recognition, which greatly improves the computational efficiency and prediction accuracy, as well as possesses stronger interpretation ability, transferability, and robustness. The AI-enhanced physics-based computational modeling technologies have already shown great potential and value in biocatalysis, paving a new way for the future development of biomanufacturing.
Artificial Intelligence ; Proteins/chemistry* ; Computer Simulation ; Software ; Computational Biology/methods*

Predicting protein mutation effects is a key challenge in bioinformatics and protein engineering. Recent advancements in deep learning, particularly the development of protein language models (PLMs), have brought new opportunities to this field. This review summarizes the application of PLMs in predicting protein mutation effects, focusing on three main types of models: sequence-based models, structure-based models, and models that combine sequence and structural information. We analyze in detail the principles, advantages, and limitations of these models and discuss the application of unsupervised and supervised learning in model training. Furthermore, this paper discusses the main challenges currently faced, including the acquisition of high-quality datasets and the handling of data noise. Finally, we look ahead to future research directions, including the application prospects of emerging technologies such as multimodal fusion and few-shot learning. This review aims to provide researchers with a comprehensive perspective to further advance the prediction of protein mutation effects.
Mutation ; Proteins/chemistry* ; Computational Biology/methods* ; Deep Learning ; Protein Engineering