CanCellVar: A database for single-cell variants map in human cancer.

Numerous variants, including both single-nucleotide variants (SNVs) in DNA and A>G RNA edits in mRNA as essential drivers of cellular proliferation and tumorigenesis, are commonly associated with cancer progression and growth. Thus, mining and summarizing single-cell variants will provide a refined and higher-resolution view of cancer and further contribute to precision medicine. Here, we established a database, CanCellVar, which aims to provide and visualize the comprehensive atlas of single-cell variants in tumor microenvironment. The current CanCellVar identified ∼3 million variants (∼1.4 million SNVs and ∼1.4 million A>G RNA edits) involved in 2,754,531 cells of 5 major cell types across 37 cancer types. CanCellVar provides the basic annotation information as well as cellular and molecular function properties of variants. In addition, the clinical relevance of variants can be obtained including tumor grade, treatment, metastasis, and others. Several flexible tools were also developed to aid retrieval and to analyze cell-cell interactions, gene expression, cell-development trajectories, regulation, and molecular structure affected by variants. Collectively, CanCellVar will serve as a valuable resource for investigating the functions and characteristics of single-cell variations and their roles in human tumor evolution and treatment.

Using machine learning methods to study the tumour microenvironment and its biomarkers in osteosarcoma metastasis.

Background: The long-term prognosis for patients with osteosarcoma (OS) metastasis remains unfavourable, highlighting the urgent need for research that explores potential biomarkers using innovative methodologies. Methods: This study explored potential biomarkers for OS metastasis by analysing data from the Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO) databases. The synthetic minority oversampling technique (SMOTE) was employed to tackle class imbalances, while genes were selected using four feature selection algorithms (Monte Carlo feature selection [MCFS], Borota, minimum-redundancy maximum-relevance [mRMR], and light gradient-boosting machine [LightGBM]) based on the gene expression matrix. Four machine learning (ML) algorithms (support vector machine [SVM], extreme gradient boosting [XGBoost], random forest [RF], and k-nearest neighbours [kNN]) were utilized to determine the optimal number of genes for building the model. Interpretable machine learning (IML) was applied to construct prediction networks, revealing potential relationships among the selected genes. Additionally, enrichment analysis, survival analysis, and immune infiltration were performed on the featured genes. Results: In DS1, DS2, and DS3, the IML algorithm identified 53, 45, and 46 features, respectively. Using the merged gene set, we obtained a total of 79 interpretable prediction rules for OS metastasis. We subsequently conducted an in-depth investigation on 39 crucial molecules associated with predicting OS metastasis, elucidating their roles within the tumour microenvironment. Importantly, we found that certain genes act as both predictors and differentially expressed genes. Finally, our study unveiled statistically significant differences in survival between the high and low expression groups of TRIP4, S100A9, SELL and SLC11A1, and there was a certain correlation between these genes and 22 various immune cells. Conclusions: The biomarkers discovered in this study hold significant implications for personalized therapies, potentially enhancing the clinical prognosis of patients with OS.

The value of speckle-tracking stratified strain combined with myocardial work measurement in evaluating left ventricular function in patients with heart failure with preserved ejection fraction.

Background: Heart failure with preserved ejection fraction (HFpEF) is a highly prevalent progressive disease accompanied by poor quality of life, high utilization of medical resources, morbidity, and mortality. However, the role of left ventricular (LV) systolic dysfunction has yet to be well elaborated despite the preservation of the LV ejection fraction. This study aimed to explore the diagnostic value of speckle-tracking stratified strain combined with myocardial work (MW) measurement in evaluating LV systolic dysfunction in patients with HFpEF. Methods: A total of 125 study consecutive individuals, 64 HFpEF patients, and 61 controls were prospectively enrolled in the Fourth Affiliated Hospital of Harbin Medical University. In addition to the conventional echocardiographic parameters, LV stratified strain and MW parameters were statistically compared between the HFpEF and control groups. The global longitudinal strain (GLS) of the subendocardium, myocardium, and subepicardium (GLSendo, GLSmyo, and GLSepi); the transmural gradient (ΔGLS); the global myocardial work index (GWI), global myocardial work efficiency (GWE), global myocardial constructive work (GCW), and the global myocardial wasted work (GWW) were included. Area under the receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of these univariate and multivariable logistic models in detecting impaired LV systolic function in HFpEF. Ten-fold cross-validation was used to evaluate the generalizability of the predictive model. Results: Stratified strains values showed a gradient decline from GLSendo to GLSepi in both control and HFpEF patients. Compared with the control group, HFpEF patients had a significantly reduced GLSepi, GLSmyo, GLSendo, ΔGLS, GWI, GWE, and GCW and a significantly increased GWW (all P<0.001). In the derivation set, the optimal logistic model (combined stratified strain and MW variables) demonstrated the highest performance in predicting LV systolic function impairment in HFpEF patients. The best-performing model with a mean area under the curve (AUC) of 0.966 [95% confidence interval (CI): 0.88 to 1] accessed by 10-fold cross-validation. In the validation set, the AUC of the optimal logistic model was 0.933 (95% CI: 0.85 to 1), the sensitivity was 87%, and the specificity was 93%. Conclusions: Both speck-tracking stratified strain and MW measurement may sensitively detect impairment of LV myocardial function at an early stage for patients with HFpEF. Combining the two techniques may improve the quality of HFpEF diagnosis and may provide a reference value for the early diagnosis of HFpEF in the future.

Herpes Simplex Keratitis as a Complication of Pterygium Surgery.

BACKGROUND Infectious keratitis after pterygium surgery is a rare but potentially devastating complication. The present study presents 5 cases of herpes simplex keratitis (HSK) after pterygium surgery. CASE REPORT This study was conducted in our clinic in a 5-year period from February 2017 to September 2021. The 5 patients were men, aged between 42 and 73 years, with no prior history of herpes simplex virus (HSV) infections. Symptoms appeared near 1 month (median 30 days, range 10 to 70 days) after primary pterygium surgery. Diagnosis was based on clinical symptoms and laboratory test results, such as tear HSV-sIgA, corneal tissue polymerase chain reaction, and next-generation sequencing of metagenomics. The epithelial (1/5) and stromal (4/5) subtypes of HSK were identified. The patients received topical ganciclovir gel, immunosuppressive eyedrops, and oral acyclovir tablets, along with additional surgical interventions if necessary. Three were healed with conservative therapy, 1 eye required amniotic membrane transplantation due to corneal melt, and 1 was perforated and followed by corneal grafting. Finally, a literature review of previous publications on HSK after ocular surgeries was conducted. CONCLUSIONS HSK is a rare but serious complication that can arise after uneventful pterygium surgery. It is worthy of attention that both epithelial and stromal forms can occur. Timely diagnosis and treatment are crucial to prevent unfavorable outcomes. Consequently, routine corneal fluorescein staining, tear sIgA examination, and corneal scraping for polymerase chain reaction or next-generation sequencing of metagenomics should be performed in any suspected cases.

A machine learning model identifies M3-like subtype in AML based on PML/RARα targets.

The typical genomic feature of acute myeloid leukemia (AML) M3 subtype is the fusion event of PML/RARα, and ATRA/ATO-based combination therapy is current standard treatment regimen for M3 subtype. Here, a machine-learning model based on expressions of PML/RARα targets was developed to identify M3 patients by analyzing 1228 AML patients. Our model exhibited high accuracy. To enable more non-M3 AML patients to potentially benefit from ATRA/ATO therapy, M3-like patients were further identified. We found that M3-like patients had strong GMP features, including the expression patterns of M3 subtype marker genes, the proportion of myeloid progenitor cells, and deconvolution of AML constituent cell populations. M3-like patients exhibited distinct genomic features, low immune activity and better clinical survival. The initiative identification of patients similar to M3 subtype may help to identify more patients that would benefit from ATO/ATRA treatment and deepen our understanding of the molecular mechanism of AML pathogenesis.

Causal effects of gut microbiota in the development of lung cancer and its histological subtypes: A Mendelian randomization study.

BACKGROUND: Numerous studies have characterized the gut microbiome (GM) in lung cancer (LC). Yet, the causality between GM and LC and its subtypes remain uncharacterized. METHODS: Two-sample Mendelian randomization (MR) was designed to investigate the causal relationship between the GM and LC and its subtypes, using publicly available summary data of genome-wide association studies. The researchers ran two groups of MR analyses, including the genome-wide statistical significance threshold (5 × 10-8 ) and the locus-wide significance level (1 × 10-5 ). RESULTS: Using MR analysis, we ascertained 42 groups of GM that are intimately linked to LC and its subtypes at the locus-wide significance level. Of the 42 groups, 12 were in LC, nine in non-small cell lung cancer (NSCLC), six in small cell lung cancer (SCLC), two in lung adenocarcinomas, and 13 in lung squamous carcinomas. After false discovery rate correction, we still found a remarkable causal interaction between the Eubacterium ruminantium group and SCLC. Moreover, five groups of GM closely linked to LC and its subtypes were recognised at the genome-wide statistical significance threshold. This finding included one group each in LC, NSCLC and SCLC, two groups in lung adenocarcinoma and none in lung squamous carcinoma. None of the foregoing findings were heterogeneous or horizontal pleiotropy. Reverse MR revealed that genetic susceptibility to LC and its subtypes caused significant changes in three groups of GM. CONCLUSION: Our findings substantiate the causality between GM and LC and its subtypes. This study offers fresh insights into the function of GM in mediating the progression of LC.

Mechanism of Hirudin-Mediated Inhibition of Proliferation in Ovarian Cancer Cells.

To investigate the inhibitory effect of hirudin on the cell proliferation of human ovarian cancer A2780 cells by preventing thrombin and its underlying molecular mechanism. Cell Counting Kit-8 (CCK-8) method was used to detect the effect of different concentrations of hirudin and thrombin on the cell proliferation of A2780 cells. PAR-1 wild-type overexpression plasmid was constructed utilizing enzyme digestion identification, and it was transferred to A2780 cells. Sequencing and Western blot were used to detect the changes in PAR-1 protein expression. Western blot detection of PKCα protein phosphorylation in A2780 cells was performed. We also implemented quantitative PCR to detect the mRNA expression levels of epithelial-mesenchymal transition (EMT)-related genes, CDH2, Snail, and Vimentin, in A2780 cells. 1 µg/ml hirudin treatment maximally inhibited the promotion of A2780 cell proliferation by thrombin. Hirudin inhibited the binding of thrombin to the N-terminus of PAR-1, hindered PKCα protein phosphorylation in A2780 cells, and downregulated the mRNA expression levels of CDH2, Snail, and Vimentin. In conclusion, hirudin inhibits the cell proliferation of ovarian cancer A2780 cells, and the underlying mechanism may be through downregulating the transcription level of EMT genes, CDH2, Snail, and Vimentin. This study indicates that hirudin may have a therapeutic potential as an anti-cancer agent for ovarian cancer.

Open a new epoch of arsenic trioxide investigation: ATOdb.

Arsenic trioxide (ATO) is a great discovery in the treatment of acute promyelocytic leukemia (APL), which has been used in an increasing number of malignant diseases. Systematic integrative analysis will help to precisely understand the mechanism of ATO and find new combined drugs. Therefore, we developed a one-stop comprehensive database of ATO named ATOdb by manually compiling a wealth of experimentally supported ATO-related data from 3479 articles, and integrated analysis tools. The current version of ATOdb contains 8373 associations among 2300 ATO targets, 80 conditions and 262 combined drugs. Each entry in ATOdb contains detailed information on ATO targets, therapeutic/side effects, systems, cell names, cell types, regulations, detection methods, brief descriptions, references, etc. Furthermore, ATOdb also provides data visualization and analysis results such as the drug similarities, protein-protein interactions, and miRNA-mRNA relationships. An easy-to-use web interface was deployed in ATOdb for users to easily browse, search and download the data. In conclusion, ATOdb will serve as a valuable resource for in-depth study of the mechanism of ATO, discovery of new drug combination strategies, promotion of rational drug use and individualized treatments. ATOdb is freely available at http://bio-bigdata.hrbmu.edu.cn/ATOdb/index.jsp.

A global database for modeling tumor-immune cell communication.

Communications between tumor cells and surrounding immune cells help shape the tumor immunity continuum. Recent breakthroughs in high-throughput technologies as well as computational algorithms had reported many important tumor-immune cell (TIC) communications, which were scattered in thousands of published studies and impeded systematical characterization of the TIC communications across cancer. Here, a comprehensive database, TICCom, was developed to model TIC communications, containing 739 experimentally-validated or manually-curated interactions collected from more than 3,000 literatures as well as 4,537,709 predicted interactions inferred via six computational algorithms by reanalyzing 32 scRNA-seq datasets and bulk RNA-seq data across 25 cancer types. The communications between tumor cells and 14 types of immune cells were characterized, and the involved ligand-receptor interactions were further integrated. 14190 human and 3650 mouse integrated ligand-receptor interactions with supplemented corresponding function information were also stored in the TICCom database. Our database would serve as a valuable resource for investigating TIC communications.

Intragenic ß-synuclein rearrangements in malignancy.

The synuclein family, consisting of α-, ß-, and γ-synuclein, is primarily expressed in neurons. Mutations of α- and ß-synuclein have been linked to Parkinson's disease and dementia with Lewy bodies, respectively. Recent studies have shown that synucleins are upregulated in various tumors, including breast, ovarian, meningioma, and melanoma, and high synuclein expression is associated with poor prognosis and drug resistance. We report a novel rearrangement of ß-synuclein in a pediatric T-cell acute lymphoblastic leukemia (T-ALL) case, where ß-synuclein (SNCB) is fused in-frame with ETS variant transcription factor 6 (ETV6), a gene frequently rearranged in acute leukemia including acute myeloid leukemia (AML), B-cell acute lymphoblastic leukemia (B-ALL), and T-ALL. An additional case of ß-synuclein rearrangement was identified in a squamous cell carcinoma of the lung through analysis of the public TCGA database. Both rearrangements involve the C-terminal of ß-synuclein. Since ß-synuclein shares extensive amino acid similarities with α-synuclein and α-synuclein binds to 14-3-3, an important regulator of apoptosis, the rearranged ß-synuclein may contribute to tumorigenesis by deregulating apoptosis. In addition, overexpression of synucleins has been shown to increase cell proliferation, suggesting that the rearranged ß-synuclein may also deregulate the cell cycle.

Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models.

Background: This study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC). Methods: In 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation. Results: A total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04). Conclusions: A comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.

Spatial transcriptome analysis of long non-coding RNAs reveals tissue specificity and functional roles in cancer.

Long non-coding RNAs (lncRNAs) play a significant role in maintaining tissue morphology and functions, and their precise regulatory effectiveness is closely related to expression patterns. However, the spatial expression patterns of lncRNAs in humans are poorly characterized. Here, we constructed five comprehensive transcriptomic atlases of human lncRNAs covering thousands of major tissue samples in normal and disease states. The lncRNA transcriptomes exhibited high consistency within the same tissues across resources, and even higher complexity in specialized tissues. Tissue-elevated (TE) lncRNAs were identified in each resource and robust TE lncRNAs were refined by integrative analysis. We detected 1 to 4684 robust TE lncRNAs across tissues; the highest number was in testis tissue, followed by brain tissue. Functional analyses of TE lncRNAs indicated important roles in corresponding tissue-related pathways. Moreover, we found that the expression features of robust TE lncRNAs made them be effective biomarkers to distinguish tissues; TE lncRNAs also tended to be associated with cancer, and exhibited differential expression or were correlated with patient survival. In summary, spatial classification of lncRNAs is the starting point for elucidating the function of lncRNAs in both maintenance of tissue morphology and progress of tissue-constricted diseases.

Dysregulation in Multiple Transcriptomic Endometrial Pathways Is Associated with Recurrent Implantation Failure and Recurrent Early Pregnancy Loss.

Overlapping disease aetiologies associated with multiple altered biological processes have been identified that change the endometrial function leading to recurrent implantation failure (RIF) and recurrent early pregnancy loss (REPL). We aimed to provide a detailed insight into the nature of the biological malfunction and related pathways of differentially expressed genes in RIF and REPL. Endometrial biopsies were obtained from 9 women experiencing RIF, REPL and control groups. Affymetrix microarray analysis was performed to measure the gene expression level of the endometrial biopsies. Unsupervised clustering of endometrial samples shows scattered distribution of gene expression between the RIF, REPL and control groups. 2556 and 1174 genes (p value < 0.05, Fold change > 1.2) were significantly altered in the endometria of RIF and REPL patients' group, respectively compared to the control group. Downregulation in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the differentially expressed genes (DEGs) in RIF and REPL including ribosome and oxidative phosphorylation pathways. Gene Ontology (GO) analysis revealed ribosomes and mitochondria inner membrane as the most significantly downregulated cellular component (CC) affected in RIF and REPL. Determination of the dysregulated genes and related biological pathways in RIF and REPL will be key in understanding their molecular pathology and of major importance in addressing diagnosis, prognosis, and treatment issues

Surveying lncRNA-lncRNA cooperations reveals dominant effect on tumor immunity cross cancers.

Long non-coding RNAs (lncRNAs) can crosstalk with each other by post-transcriptionally co-regulating genes involved in the same or similar functions; however, the regulatory principles and biological insights in tumor-immune are still unclear. Here, we show a multiple-step model to identify lncRNA-lncRNA immune cooperation based on co-regulating functional modules by integrating multi-omics data across 20 cancer types. Moreover, lncRNA immune cooperative networks (LICNs) are constructed, which are likely to modulate tumor-immune microenvironment by regulating immune-related functions. We highlight conserved and rewired network hubs which can regulate interactions between immune cells and tumor cells by targeting ligands and activating or inhibitory receptors such as PDCD1, CTLA4 and CD86. Immune cooperative lncRNAs (IC-lncRNAs) playing central roles in many cancers also tend to target known anticancer drug targets. In addition, these IC-lncRNAs tend to be highly expressed in immune cell populations and are significantly correlated with immune cell infiltration. The similar immune mechanisms cross cancers are revealed by the LICNs. Finally, we identify two subtypes of skin cutaneous melanoma with different immune context and prognosis based on IC-lncRNAs. In summary, this study contributes to a comprehensive understanding of the cooperative behaviours of lncRNAs and accelerating discovery of lncRNA-based biomarkers in cancer.

Prognostic Nutritional Index Predicts Response and Prognosis in Cancer Patients Treated With Immune Checkpoint Inhibitors: A Systematic Review and Meta-Analysis.

Objective: To investigate the association between pretreatment prognostic nutritional index (PNI) and clinical survival outcomes for advanced-stage cancer patients treated with immune checkpoint inhibitors (ICIs). Methods: We conducted a comprehensive literature search to identify eligible studies concerning the relationship between pretreatment PNI and survival outcomes in advanced cancer patients treated with ICIs. Published data were extracted and pooled odds ratio (pOR) for objective response rate (ORR), disease control rate (DCR), and pooled hazard ratio (pHR) for overall survival (OS), progressive-free survival (PFS), along with 95% confidence intervals (95% CIs) were calculated. Results: Twelve studies with 1,359 participants were included in our study. A higher level of PNI indicated a greater ORR (pOR = 2.17, 95% CI = 1.52-3.10) and favorable DCR (pOR = 2.48, 95% CI = 1.87-3.29). Low PNI was associated with a shorter OS (pHR = 2.24, 95% CI = 1.57-3.20) and unfavorable PFS (pHR = 1.61, 95% CI = 1.37-1.88). Conclusion: Low PNI might be an effective biomarker of poor tumor response and adverse prognosis of advanced cancer patients with ICIs. Further studies are needed to verify the prognostic value of PNI in clinical practice.

Refractive Characteristics and Related Factors of Amblyopia after Lamellar Keratoscleroplasty in Children with Limbal Dermoids.

We examined the refractive characteristics and related factors of amblyopia in pediatric patients with limbal dermoids undergoing lamellar keratoscleroplasty. Forty-one children (mean age: 56.15 ± 22.47 months) were enrolled. Cycloplegic refraction, corneal topography, and anterior segment photography were performed. The corneal topographic and distribution characteristics of the refractive state were summarized, and the relationship between limbal dermoid invasion size and the refractive state was analyzed. The relationship between invasion size and amblyopia severity and the effect of clinical intervention at different times on amblyopia treatment were also analyzed. The spherical power distribution was −1.0−+10.75 D (average: +4.79 ± 3.09 D). The cylinder power was −1.25−−8.75 D (average: −4.19 ± 1.93 D). The axial range of astigmatism was 10−180° (average: 103.54 ± 58.16°). Equivalent spherical refraction was −3.88−+7.76 D (average: +2.70 ± 3.08 D). Twenty-five, fifteen, and one case had limboid dermoid invasion of the central circular zone (CCZ), paracentral annular zone (PCZ), and corneal limbus within 1 mm, respectively. Corneal topography of 39 patients showed flat, steep, and mean curvatures of 38.48 ± 2.12 D, 43.29 ± 1.97 D, and 40.70 ± 1.48 D, respectively. The mean astigmatism was 4.80 ± 2.93 D in the 3-mm optical region. Astigmatism was higher in CCZ than in PCZ invasion (p < 0.05). Postoperative visual acuity was positively correlated with patients' age and amblyopia treatment duration (r = 0.392, p = 0.048; r = 0.488, p = 0.011), and was negatively correlated with astigmatism (r = −0.646, p < 0.001). High hyperopia and astigmatism are the dominant refractive errors in patients with limbal dermoids undergoing lamellar keratoscleroplasty.

Extracellular Vesicles and Hepatocellular Carcinoma: Opportunities and Challenges.

The incidence of hepatocellular carcinoma (HCC) is increasing worldwide. Extracellular vesicles (EVs) contain sufficient bioactive substances and are carriers of intercellular information exchange, as well as delivery vehicles for nucleic acids, proteins and drugs. Although EVs show great potential for the treatment of HCC and their role in HCC progression has been extensively studied, there are still many challenges such as time-consuming extraction, difficult storage, easy contamination, and low drug loading rate. We focus on the biogenesis, morphological characteristics, isolation and extraction of EVs and their significance in the progression of HCC, tumor invasion, immune escape and cancer therapy for a review. EVs may be effective biomarkers for molecular diagnosis of HCC and new targets for tumor-targeted therapy.

Network Pharmacology-Based Analysis on Lonicera japonica for Chronic Osteomyelitis Treatment.

MATERIALS AND METHODS: Active compounds of LJP were examined established on the analysis platform, Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. DrugBank identified drug targets and annotated them on UniPort and GeneCards. Besides, the COM-related genes were identified on GeneCards. The network of the drug, main active compounds, targets, and diseases was built utilizing Cytoscape. STRING was utilized to build the protein-protein interaction network. Moreover, the KEGG and GO pathway enrichment analysis were applied to analyze biological function. RESULTS: 23 active compounds of LJP were screened, and 204 drug targets and 686 COM-related genes were identified. Forty-five intersection genes were overlapped from 204 drug targets and 686 COM-related genes. The drug-active compounds-target protein-diseases network was established based on 23 active compounds of LJP and 45 intersection genes. Moreover, the interaction of 45 intersection genes was explored by the PPI network, and the drug-active compounds-target protein-diseases network was formed grounded by 23 active compounds of LJP, 45 intersection genes, and PPI network. The KEGG and GO pathway enrichment analysis specified that 45 intersection genes primarily enriched in immune-related pathways and oxidative stress-related pathways. CONCLUSIONS: In the research done, the main active compounds of LJP and drug targets in the treatment of COM were identified. Our findings might provide the ingredient option of LJP and drug targets of LJP in COM treatment.