ObjectiveTo improve the therapeutic effect of Buyang Huanwutang（BYHW） on diabetic kidney disease （DKD） and explore new methods for developing new Chinese medicine decoctions，we utilized 5-hydroxymethylcytosine （5hmC）-Seal sequencing technology and network pharmacology to modify BYHW. MethodsWe selected 14 diabetes mellitus （DM） patients and 15 DKD patients hospitalized in the Department of Endocrinology of Peking University Third Hospital in 2021. Circulating free DNA （cfDNA） in the patients’ plasma was sequenced. After data processing and screening， we performed temporal clustering analysis to select a DKD 5hmC gene set， which was then cross-validated with a DKD database gene set to obtain the DKD gene set. We retrieved target genes of the seven herbal components of BYHW from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） and the Encyclopedia of Traditional Chinese Medicine （ETCM）， and performed cross-analysis with the DKD gene set to identify common genes shared by the disease and the Chinese medicines. A protein-protein interaction （PPI） network was constructed for the common genes to screen out the key genes. Chinese medicines targeting these key genes were searched against ETCM to identify removable Chinese medicines. Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis was performed on non-common DKD genes， and key genes in DKD-related pathways were selected based on machine learning. The GSE30529 dataset was used to verify the expression trends of 5hmC-modified genes and the feasibility of target genes as drug targets. TCMBank was used to search for target genes and obtain compounds targeting these genes and the corresponding Chinese medicines to construct a "key target-compound-Chinese medicine" network. Molecular docking was employed to verify the binding affinity of compounds with key targets. TCMSP and ETCM were used to search and count the candidate Chinese medicines targeting DKD-related genes， and a new decoction was formed by adding the selected Chinese medicines. A mouse model of DKD was established to examine the efficacy of the new decoction based on the mouse body mass， random blood glucose， urinary microalbumin （mALB）， serum creatinine （Scr）， and blood urea nitrogen （BUN） and by hematoxylin-eosin staining， periodic acid-Schiff staining， Masson staining， immunofluorescence assay， and Real-time PCR. ResultsThe cross-analysis results showed that the DKD gene set included 507 genes， of which 30 were target genes of BYHW. The PPI analysis indicated that the top 15% target genes regarding the degree were interleukin-6 （IL-6）， Toll-like receptor 4 （TLR4）， lactotransferrin （LTF）， lipoprotein lipase （LPL）， and sterol regulatory element-binding transcription factor 1 （SREBF1）. Persicae Semen and Pheretima in BYHW were unrelated to key genes and removed. Machine learning identified 10 potential target genes， among which TBC1 domain family member 5 （TBC1D5）， RAD51 paralog B （RAD51B）， and proteasome 20S subunit alpha 6 （PSMA6） had expression trends consistent with the GSE30529 dataset and could serve as drug targets. The "key target-compound-Chinese medicine" network and molecular docking results indicated that the compounds with good binding affinity to target proteins were arginine， glycine， myristicin， serine， and tyrosine， corresponding to 121 Chinese medicines. The top 10 Chinese medicines targeting DKD-related genes were Lycii Fructus， Ginseng Radix et Rhizoma， Dioscoreae Rhizoma， Rehmanniae Radix Praeparata， Isatidis Radix， Glehniae Radix， Ophiopogonis Radix， Allii Sativi Bulbus， Isatidis Folium， and Bolbostemmatis Rhizoma. Based on traditional Chinese medicine theory， the new decoction was obtained after removal of Persicae Semen and Pheretima and addition of Rehmanniae Radix Praeparata and Dioscoreae Rhizoma. Animal experiment results indicated that the modified BYHW improved the kidney function and inhibited renal fibrosis in DKD mice， with better effects than the original decoction. ConclusionThe BYHW modified based on 5hmC-Seal sequencing demonstrates better performance in inhibiting fibrosis and ameliorating DKD than the original decoction. This elucidates the biomedical theory behind the epigenetic modification of traditional Chinese medicine prescriptions， potentially offering new perspectives for the exploration of these prescriptions

ObjectiveTo improve the therapeutic effect of Buyang Huanwutang（BYHW） on diabetic kidney disease （DKD） and explore new methods for developing new Chinese medicine decoctions，we utilized 5-hydroxymethylcytosine （5hmC）-Seal sequencing technology and network pharmacology to modify BYHW. MethodsWe selected 14 diabetes mellitus （DM） patients and 15 DKD patients hospitalized in the Department of Endocrinology of Peking University Third Hospital in 2021. Circulating free DNA （cfDNA） in the patients’ plasma was sequenced. After data processing and screening， we performed temporal clustering analysis to select a DKD 5hmC gene set， which was then cross-validated with a DKD database gene set to obtain the DKD gene set. We retrieved target genes of the seven herbal components of BYHW from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） and the Encyclopedia of Traditional Chinese Medicine （ETCM）， and performed cross-analysis with the DKD gene set to identify common genes shared by the disease and the Chinese medicines. A protein-protein interaction （PPI） network was constructed for the common genes to screen out the key genes. Chinese medicines targeting these key genes were searched against ETCM to identify removable Chinese medicines. Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis was performed on non-common DKD genes， and key genes in DKD-related pathways were selected based on machine learning. The GSE30529 dataset was used to verify the expression trends of 5hmC-modified genes and the feasibility of target genes as drug targets. TCMBank was used to search for target genes and obtain compounds targeting these genes and the corresponding Chinese medicines to construct a "key target-compound-Chinese medicine" network. Molecular docking was employed to verify the binding affinity of compounds with key targets. TCMSP and ETCM were used to search and count the candidate Chinese medicines targeting DKD-related genes， and a new decoction was formed by adding the selected Chinese medicines. A mouse model of DKD was established to examine the efficacy of the new decoction based on the mouse body mass， random blood glucose， urinary microalbumin （mALB）， serum creatinine （Scr）， and blood urea nitrogen （BUN） and by hematoxylin-eosin staining， periodic acid-Schiff staining， Masson staining， immunofluorescence assay， and Real-time PCR. ResultsThe cross-analysis results showed that the DKD gene set included 507 genes， of which 30 were target genes of BYHW. The PPI analysis indicated that the top 15% target genes regarding the degree were interleukin-6 （IL-6）， Toll-like receptor 4 （TLR4）， lactotransferrin （LTF）， lipoprotein lipase （LPL）， and sterol regulatory element-binding transcription factor 1 （SREBF1）. Persicae Semen and Pheretima in BYHW were unrelated to key genes and removed. Machine learning identified 10 potential target genes， among which TBC1 domain family member 5 （TBC1D5）， RAD51 paralog B （RAD51B）， and proteasome 20S subunit alpha 6 （PSMA6） had expression trends consistent with the GSE30529 dataset and could serve as drug targets. The "key target-compound-Chinese medicine" network and molecular docking results indicated that the compounds with good binding affinity to target proteins were arginine， glycine， myristicin， serine， and tyrosine， corresponding to 121 Chinese medicines. The top 10 Chinese medicines targeting DKD-related genes were Lycii Fructus， Ginseng Radix et Rhizoma， Dioscoreae Rhizoma， Rehmanniae Radix Praeparata， Isatidis Radix， Glehniae Radix， Ophiopogonis Radix， Allii Sativi Bulbus， Isatidis Folium， and Bolbostemmatis Rhizoma. Based on traditional Chinese medicine theory， the new decoction was obtained after removal of Persicae Semen and Pheretima and addition of Rehmanniae Radix Praeparata and Dioscoreae Rhizoma. Animal experiment results indicated that the modified BYHW improved the kidney function and inhibited renal fibrosis in DKD mice， with better effects than the original decoction. ConclusionThe BYHW modified based on 5hmC-Seal sequencing demonstrates better performance in inhibiting fibrosis and ameliorating DKD than the original decoction. This elucidates the biomedical theory behind the epigenetic modification of traditional Chinese medicine prescriptions， potentially offering new perspectives for the exploration of these prescriptions

Objective:The transcriptome data was utilized to screen cuproptosis-related genes(CRGs)in oral squamous cell car-cinoma(OSCC),and the characteristic genes were identified for constructing a prognostic model for predicting patients'survival time.Methods:OSCC transcriptome gene expression and clinical data were obtained from TCGA and GEO.Through Lasso regres-sion analysis and Cox regression analysis,relevant prognostic genes were screened and prognostic models were constructed.Ac-cording to the median value of risk scores,patients were divided into high and low risk groups,and their survival rates were com-pared.Finally,the predictive performance of the model was verified.Results:In this study,9 characteristic genes with prognostic value(ENO2,P4HA1,SLC2A3,AQP1,PLS1,NXPH4,CTSG,TRAC,THBS1)were screened out and a 9-gene prognostic model was constructed.The survival rate of high-risk group based on prognostic model was significantly lower than that of low-risk group.The area under curve(AUC)of receiver operating characteristic curve(ROC)was 0.701,0.729 and 0.702 at 1 year,3 years and 5 years,respectively,which verifies that the risk model has good predictive performance.The nomogram predicted that the 1-year,3-year,and 5-year survival probabilities of OSCC patients are 89.6％,72.4％,and 63.9％respectively,and the cal-ibration curve confirmed the accuracy of the nomogram prediction.Conclusion:The 9-gene prognostic model based on CRGs screening could predict the prognosis of OSCC patients,which is helpful for clinical personalized treatment of OSCC patients and prediction of their survival rate.

Objective:The transcriptome data was utilized to screen cuproptosis-related genes(CRGs)in oral squamous cell car-cinoma(OSCC),and the characteristic genes were identified for constructing a prognostic model for predicting patients'survival time.Methods:OSCC transcriptome gene expression and clinical data were obtained from TCGA and GEO.Through Lasso regres-sion analysis and Cox regression analysis,relevant prognostic genes were screened and prognostic models were constructed.Ac-cording to the median value of risk scores,patients were divided into high and low risk groups,and their survival rates were com-pared.Finally,the predictive performance of the model was verified.Results:In this study,9 characteristic genes with prognostic value(ENO2,P4HA1,SLC2A3,AQP1,PLS1,NXPH4,CTSG,TRAC,THBS1)were screened out and a 9-gene prognostic model was constructed.The survival rate of high-risk group based on prognostic model was significantly lower than that of low-risk group.The area under curve(AUC)of receiver operating characteristic curve(ROC)was 0.701,0.729 and 0.702 at 1 year,3 years and 5 years,respectively,which verifies that the risk model has good predictive performance.The nomogram predicted that the 1-year,3-year,and 5-year survival probabilities of OSCC patients are 89.6％,72.4％,and 63.9％respectively,and the cal-ibration curve confirmed the accuracy of the nomogram prediction.Conclusion:The 9-gene prognostic model based on CRGs screening could predict the prognosis of OSCC patients,which is helpful for clinical personalized treatment of OSCC patients and prediction of their survival rate.