BACKGROUND:The pathogenesis of rheumatoid arthritis has not yet been fully clarified,and recent studies have shown that mitophagy is associated with rheumatoid arthritis,but the key mechanisms need to be explored in depth.OBJECTIVE:To identify and validate the core interaction genes of mitophagy in rheumatoid arthritis using multiple machine learning algorithms and to analyze its immunoregulatory process.METHODS:The rheumatoid arthritis transcriptome expression dataset GSE15573 was retrieved from the GEO database as an independent training set,with the GSE97779 and GSE55235 collections used as independent validation sets.The differentially expressed genes of rheumatoid arthritis were screened using the training set,and"WGCNA"analysis was also performed.Then we downloaded the mitophagy-related genes from the"MitoCarta3.0"database,and intersected them with the differentially expressed genes of rheumatoid arthritis and the genes in the"WGCNA"analysis module to obtain the rheumatoid arthritis-mitophagy-related genes,and then analyzed the related genes for functional enrichment to clarify the cellular pathways.Feature genes were initially identified using the"Random Forest"and"Lasso"algorithms.The overlapping genes from these two methods were further refined using the"GMM"algorithm to identify the core interaction genes between rheumatoid arthritis and mitophagy.A predictive model was then developed and validated using an external dataset.Finally,"CIBERSORT"was employed to analyze the proportions and interactions of immune cell subsets during immune infiltration,while"ssGSEA"was used to examine the associations between the rheumatoid arthritis-mitophagy core interaction genes and immune cell subsets."ssGSEA"was also utilized to analyze the"GO"and"KEGG"biological pathways of the core interaction genes.RESULTS AND CONCLUSION:(1)Totally 807 differentially expressed genes in rheumatoid arthritis were obtained by differential analysis,1 208 genes were selected from two feature modules by"WGCNA"analysis,1136 genes were sorted out from the MitoCarta 3.0 database,and 53 HUB genes were obtained from the intersection of the three genes as rheumatoid arthritis-mitophagy related genes.(2)The results of functional enrichment analysis of related genes showed that the cellular processes were mainly related to mitophagy,peroxisome metabolism,cellular senescence,and necroptosis.(3)The three machine learning algorithms identified four rheumatoid arthritis-mitophagy core interaction genes(DNAJA3,C12orf65,AKR7A2,and PDHB).The area under the curve of nomoscore was 0.989,and the area under the curve values of rheumatoid arthritis-mitophagy core interaction genes verified by the receiver operating characteristic curve of external patient samples were all greater than 0.7.(5)Immunoregulatory analysis showed that the mitophagy process in rheumatoid arthritis was closely associated with memory B cells,M0 macrophages,activated memory CD4 T cells,and resting memory CD4 T cells.(6)The biological pathway analysis revealed that the core interaction genes were strongly associated with 821"GO"pathways(|cor|＞0.8,P＜0.001)and 48"KEGG"pathways(|cor|＞0.8,P＜0.001).The key biological processes identified were related to mitochondrial DNA metabolic process,mitochondrial DNA repair,mitochondrial DNA replication,mitochondrial genome maintenance,positive regulation of mitochondrial depolarization,and positive regulation of mitochondrial outer membrane permeabilization involved in apoptotic signaling pathway.To conclude,DNAJA3,C12orf65,AKR7A2,and PDHB are the core interaction genes of the mitophagy process in rheumatoid arthritis,which play key roles in disease progression by participating in specific immune processes and have precise and predictive effects on the diagnosis of rheumatoid arthritis.

BACKGROUND:Mitochondrial autophagy is closely related to the occurrence and development of steroid-induced osteonecrosis of the femoral head(SONFH),but specific biomarkers and regulatory mechanisms remain unclear. OBJECTIVE:To identify the key biomarkers of mitochondrial autophagy in steroid-induced osteonecrosis of the femoral head using machine learning algorithms and to conduct an immune infiltration analysis. METHODS:The SONFH datasets GSE123568 and GSE74089 were downloaded from the GEO database,serving as the training and validation sets,respectively.Differentially expressed genes between SONFH and control groups were selected,and weighted gene co-expression network analysis was performed.Mitochondrial autophagy-related genes were obtained from MitoCarta3.0 and intersected with differentially expressed genes and module genes.Two machine learning algorithms were utilized to identify key genes of SONFH mitochondrial autophagy,and validated using an external validation set.CIBERSORT and immune infiltration analysis were employed to assess the proportion of immune cells,and ssGSEA was used to analyze the correlation between mitochondrial autophagy genes and immune cells. RESULTS AND CONCLUSION:Differential analysis identified a total of 1 163 differentially expressed genes,including 663 upregulated genes and 500 downregulated genes.Weighted gene co-expression network analysis identified 4 key modules,comprising 1 412 module genes.Intersection with mitochondrial autophagy genes yielded 39 intersecting genes as disease-related mitochondrial autophagy genes.Gene ontology enrichment analysis showed that the biological processes were mainly related to heme metabolism,mitochondrial transport,nucleotide bisphosphate metabolism and thioester metabolism,and the cellular components were mainly related to mitochondrial matrix,mitochondrial outer membrane,organelle outer membrane and mitochondrial inner membrane,and the molecular functions were mainly related to fatty acid ligase activity,iron-sulfur cluster binding,and cofactor A ligase activity.Kyoto Encyclopedia of Genes and Genomes enrichment analysis mapped out a total of six pathways,which were mainly related to fatty acid degradation,mitochondrial autophagy,butyric acid metabolism,fatty acid biosynthesis and cofactor biosynthesis.Through LASSO regression and RFE-SVM algorithm analysis,four intersecting genes(ALDH5A1,FBXL4,MCL1,and STOM)were identified.The receiver operating characteristic curves of the four core genes and the diagnostic column chart validation set were all greater than 0.9.The occurrence and development of SONFH were related to immune cells such as dendritic cells,bone marrow-derived suppressor cells,regulatory T cells,and central memory CD8 T cells.To conclude,the four key mitochondrial autophagy genes ALDH5A1,FBXL4,MCL1,and STOM play a crucial role in the progression of SONFH through osteoclast differentiation and immune mechanisms.Additionally,all four genes have good disease prediction efficacy and can serve as biomarkers for the diagnosis and treatment of SONFH.

Objective:To explore the value and related mechanism of preoperative serum sialic acid (SA) on evaluating microvascular invasion (MVI) in patients with intrahepatic cholangiocarcinoma (ICC).Methods:A total of 91 patients who underwent surgical resection and were pathologically diagnosed with ICC from December 2020 to September 2024 at the Oriental Hepatobiliary Surgery Hospital affiliated to the Naval Medical University were included in this retrospective analysis. The patients were divided into non-MVI (41 cases) and MVI groups (50 cases). The general data and laboratory examination indexes were collected and analyzed. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for predicting MVI. The predictive value of serum indicators for MVI was evaluated by receiver operating characteristic curves. The correlation between MVI and SA was analyzed by point-biserial correlation. ICC cells stably overexpressing β-galactoside α2, 6-sialyltransferase 1 (ST6GAL1) were generated through lentiviral transfection. ST6GAL1 protein expression and mRNA expression were detected by Western blot and quantitative real-time polymerase chain reaction, respectively. Sambucus nigra (SNA) lectin fluorescence staining was used to detect α2, 6-sialylation levels on cells. Cell migration ability was assessed by wound healing and Transwell assays, and cell proliferation was evaluated by colony formation assays.Results:Compared with the non-MVI group, patients in the MVI group exhibited significantly higher levels of fibrinogen, aspartate aminotransferase, gamma-glutamyl transferase, alkaline phosphatase, SA and 5′-nucleotidase (5′-NT) (all P<0.05). Multivariate logistic regression analysis revealed that SA ( OR=1.01,95% CI 1.01-1.02, P=0.023) was the only independent predictor for MVI. The area under curve of SA in predicting MVI was 0.757 (95% CI 0.640-0.870), sensitivity 67.65%, specificity 77.78%. SA was positively correlated with MVI ( r=0.443, P<0.001). ICC cells overexpressing ST6GAL1 were featured with increased mean fluorescence intensity of SNA lectin, and increased level of α2, 6-sialylation on the cell surface (both P<0.05). The number of colonies formed by hypersialylated ICC cells was also increased ( P<0.05), and both the migration rate and the number of migrating cells were significantly higher ( P<0.05). Conclusions:Serum SA is an independent predictor for MVI in ICC patients. Hypersialylation in ICC cells is associated with higher malignancy.

BACKGROUND:The pathogenesis of rheumatoid arthritis has not yet been fully clarified,and recent studies have shown that mitophagy is associated with rheumatoid arthritis,but the key mechanisms need to be explored in depth.OBJECTIVE:To identify and validate the core interaction genes of mitophagy in rheumatoid arthritis using multiple machine learning algorithms and to analyze its immunoregulatory process.METHODS:The rheumatoid arthritis transcriptome expression dataset GSE15573 was retrieved from the GEO database as an independent training set,with the GSE97779 and GSE55235 collections used as independent validation sets.The differentially expressed genes of rheumatoid arthritis were screened using the training set,and"WGCNA"analysis was also performed.Then we downloaded the mitophagy-related genes from the"MitoCarta3.0"database,and intersected them with the differentially expressed genes of rheumatoid arthritis and the genes in the"WGCNA"analysis module to obtain the rheumatoid arthritis-mitophagy-related genes,and then analyzed the related genes for functional enrichment to clarify the cellular pathways.Feature genes were initially identified using the"Random Forest"and"Lasso"algorithms.The overlapping genes from these two methods were further refined using the"GMM"algorithm to identify the core interaction genes between rheumatoid arthritis and mitophagy.A predictive model was then developed and validated using an external dataset.Finally,"CIBERSORT"was employed to analyze the proportions and interactions of immune cell subsets during immune infiltration,while"ssGSEA"was used to examine the associations between the rheumatoid arthritis-mitophagy core interaction genes and immune cell subsets."ssGSEA"was also utilized to analyze the"GO"and"KEGG"biological pathways of the core interaction genes.RESULTS AND CONCLUSION:(1)Totally 807 differentially expressed genes in rheumatoid arthritis were obtained by differential analysis,1 208 genes were selected from two feature modules by"WGCNA"analysis,1136 genes were sorted out from the MitoCarta 3.0 database,and 53 HUB genes were obtained from the intersection of the three genes as rheumatoid arthritis-mitophagy related genes.(2)The results of functional enrichment analysis of related genes showed that the cellular processes were mainly related to mitophagy,peroxisome metabolism,cellular senescence,and necroptosis.(3)The three machine learning algorithms identified four rheumatoid arthritis-mitophagy core interaction genes(DNAJA3,C12orf65,AKR7A2,and PDHB).The area under the curve of nomoscore was 0.989,and the area under the curve values of rheumatoid arthritis-mitophagy core interaction genes verified by the receiver operating characteristic curve of external patient samples were all greater than 0.7.(5)Immunoregulatory analysis showed that the mitophagy process in rheumatoid arthritis was closely associated with memory B cells,M0 macrophages,activated memory CD4 T cells,and resting memory CD4 T cells.(6)The biological pathway analysis revealed that the core interaction genes were strongly associated with 821"GO"pathways(|cor|＞0.8,P＜0.001)and 48"KEGG"pathways(|cor|＞0.8,P＜0.001).The key biological processes identified were related to mitochondrial DNA metabolic process,mitochondrial DNA repair,mitochondrial DNA replication,mitochondrial genome maintenance,positive regulation of mitochondrial depolarization,and positive regulation of mitochondrial outer membrane permeabilization involved in apoptotic signaling pathway.To conclude,DNAJA3,C12orf65,AKR7A2,and PDHB are the core interaction genes of the mitophagy process in rheumatoid arthritis,which play key roles in disease progression by participating in specific immune processes and have precise and predictive effects on the diagnosis of rheumatoid arthritis.

Objective:To explore the value and related mechanism of preoperative serum sialic acid (SA) on evaluating microvascular invasion (MVI) in patients with intrahepatic cholangiocarcinoma (ICC).Methods:A total of 91 patients who underwent surgical resection and were pathologically diagnosed with ICC from December 2020 to September 2024 at the Oriental Hepatobiliary Surgery Hospital affiliated to the Naval Medical University were included in this retrospective analysis. The patients were divided into non-MVI (41 cases) and MVI groups (50 cases). The general data and laboratory examination indexes were collected and analyzed. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for predicting MVI. The predictive value of serum indicators for MVI was evaluated by receiver operating characteristic curves. The correlation between MVI and SA was analyzed by point-biserial correlation. ICC cells stably overexpressing β-galactoside α2, 6-sialyltransferase 1 (ST6GAL1) were generated through lentiviral transfection. ST6GAL1 protein expression and mRNA expression were detected by Western blot and quantitative real-time polymerase chain reaction, respectively. Sambucus nigra (SNA) lectin fluorescence staining was used to detect α2, 6-sialylation levels on cells. Cell migration ability was assessed by wound healing and Transwell assays, and cell proliferation was evaluated by colony formation assays.Results:Compared with the non-MVI group, patients in the MVI group exhibited significantly higher levels of fibrinogen, aspartate aminotransferase, gamma-glutamyl transferase, alkaline phosphatase, SA and 5′-nucleotidase (5′-NT) (all P<0.05). Multivariate logistic regression analysis revealed that SA ( OR=1.01,95% CI 1.01-1.02, P=0.023) was the only independent predictor for MVI. The area under curve of SA in predicting MVI was 0.757 (95% CI 0.640-0.870), sensitivity 67.65%, specificity 77.78%. SA was positively correlated with MVI ( r=0.443, P<0.001). ICC cells overexpressing ST6GAL1 were featured with increased mean fluorescence intensity of SNA lectin, and increased level of α2, 6-sialylation on the cell surface (both P<0.05). The number of colonies formed by hypersialylated ICC cells was also increased ( P<0.05), and both the migration rate and the number of migrating cells were significantly higher ( P<0.05). Conclusions:Serum SA is an independent predictor for MVI in ICC patients. Hypersialylation in ICC cells is associated with higher malignancy.

Objective To observe the expression of GLIS3 in triple negative breast cancer(TNBC)and analyze its relationship with the prognosis of TNBC patients.Methods Bioinformatic analysis was applied to analyze the expression level of GLIS3 in Gene Expression Omnibus(GEO).Kaplan-Meier survival curve was plotted to evaluate the impact of GLIS3 expression on the survival rate of patients based on DNA chip data.A total of 125 patients pathologically diagnosed as TNBC in the Fourth Hospital of Hebei Medical University from January to December 2014 were enrolled by cluster random sampling.Among them,53 patients had complete tissue specimens,medical records and follow-up data.Immunohistochemical assay was used to detect the expression of GLIS3 in TNBC and adjacent tissues to tumors,while the relationships between GLIS3 protein expression in breast cancer tissues and clinicopathological parameters such as age,menstrual status,tumor size,clinical stage,histological grade,pathological type,axillary lymph node metastasis,vascular tumor thrombus,and expression of TP53 and Ki-67 were analyzed.Kaplan-Meier survival curve was plotted to analyze the effect of GLIS3 on the overall and disease-free survival of TNBC patients.Cox regression model was established to identify the risk factors impacting the prognosis of the patients.Results Analysis of GEO data showed that the expression of GLIS3 in TNBC was significantly higher than that in paracancer tissues(P＜0.05).The expression of GLIS3 was notably higher in the TNBC tissue than the adjacent tissue to tumor(P＜0.05).A marked augmentation of GLIS3 expression was observed in both the advanced and larger-sized tumors(P＜0.05).Univariate analysis of Cox regression model revealed that lymph node metastasis,TNM stage and GLIS3 expression were all related to disease-free survival of TNBC patients(P＜0.05).Univariate and multivariate analyses displayed that TNM stage was related to the overall survival of TNBC patients(P＜0.05).The patients with high expression of GLIS3 had significant shorter disease-free survival time than those with low expression(P＜0.05),but had no statistical difference in overall survival(P＞0.05).Conclusion GLIS3 protein is highly expressed in TNBC tissues,and tumor size and TNM stage are correlated with its high expression.The high expression of GLIS3 suggests that the patients have poor prognosis and low disease-free survival rate.

With the increasing proportion of human papilloma virus(HPV)infection in the pathogenic factors of oro-pharyngeal cancer,a series of changes have occurred in the surgical treatment.While the treatment mode has been im-proved,there are still many problems,including the inconsistency between diagnosis and treatment modes,the lack of popularization of reconstruction technology,the imperfect post-treatment rehabilitation system,and the lack of effective preventive measures.Especially in terms of treatment mode for early oropharyngeal cancer,there is no unified conclu-sion whether it is surgery alone or radiotherapy alone,and whether robotic minimally invasive surgery has better func-tional protection than radiotherapy.For advanced oropharyngeal cancer,there is greater controversy over the treatment mode.It is still unclear whether to adopt a non-surgical treatment mode of synchronous chemoradiotherapy or induction chemotherapy combined with synchronous chemoradiotherapy,or a treatment mode of surgery combined with postopera-tive chemoradiotherapy.In order to standardize the surgical treatment of oropharyngeal cancer in China and clarify the indications for surgical treatment of oropharyngeal cancer,this expert consensus,based on the characteristics and treat-ment status of oropharyngeal cancer in China and combined with the international latest theories and practices,forms consensus opinions in multiple aspects of preoperative evaluation,surgical indication determination,primary tumor re-section,neck lymph node dissection,postoperative defect repair,postoperative complication management prognosis and follow-up of oropharyngeal cancer patients.The key points include:① Before the treatment of oropharyngeal cancer,the expression of P16 protein should be detected to clarify HPV status;② Perform enhanced magnetic resonance imaging of the maxillofacial region before surgery to evaluate the invasion of oropharyngeal cancer and guide precise surgical resec-tion of oropharyngeal cancer.Evaluating mouth opening and airway status is crucial for surgical approach decisions and postoperative risk prediction;③ For oropharyngeal cancer patients who have to undergo major surgery and cannot eat for one to two months,it is recommended to undergo percutaneous endoscopic gastrostomy before surgery to effectively improve their nutritional intake during treatment;④ Early-stage oropharyngeal cancer patients may opt for either sur-gery alone or radiation therapy alone.For intermediate and advanced stages,HPV-related oropharyngeal cancer general-ly prioritizes radiation therapy,with concurrent chemotherapy considered based on tumor staging.Surgical treatment is recommended as the first choice for HPV unrelated oropharyngeal squamous cell carcinoma(including primary and re-current)and recurrent HPV related oropharyngeal squamous cell carcinoma after radiotherapy and chemotherapy;⑤ For primary exogenous T1-2 oropharyngeal cancer,direct surgery through the oral approach or da Vinci robotic sur-gery is preferred.For T3-4 patients with advanced oropharyngeal cancer,it is recommended to use temporary mandibu-lectomy approach and lateral pharyngotomy approach for surgery as appropriate;⑥ For cT1-2N0 oropharyngeal cancer patients with tumor invasion depth＞3 mm and cT3-4N0 HPV unrelated oropharyngeal cancer patients,selective neck dissection of levels ⅠB to Ⅳ is recommended.For cN+HPV unrelated oropharyngeal cancer patients,therapeutic neck dissection in regions Ⅰ-Ⅴ is advised;⑦ If PET-CT scan at 12 or more weeks after completion of radiation shows intense FDG uptake in any node,or imaging suggests continuous enlargement of lymph nodes,the patient should undergo neck dissection;⑧ For patients with suspected extracapsular invasion preoperatively,lymph node dissection should include removal of surrounding muscle and adipose connective tissue;⑨ The reconstruction of oropharyngeal cancer defects should follow the principle of reconstruction steps,with priority given to adjacent flaps,followed by distal pedicled flaps,and finally free flaps.The anterolateral thigh flap with abundant tissue can be used as the preferred flap for large-scale postoperative defects.

Objective:To study the differential gene expression and immune cell infiltration of gout patients,to find the key genes and immune cells of gout pathogenesis,and to explore the relationship between immune cells and gout.Methods:The gout chip GSE160170 was downloaded from the GEO database,and the differential gene expression analysis was carried out with the help of R language.Then,the STRING database was used to analyze the differential gene,and the Cytoscape software was used to screen the key genes,and then carry out enrichment analysis.At the same time,the infiltration of immune cells were analyzed.Results:The study found that IL-6,IL-1β,TNF,CCL3,CXCL8 and CXCL1 were key genes in the pathogenesis of gout,which were mainly exerted by IL-17,Toll-like receptor,NOD-like receptor,NF-κB and other signaling pathways.Processes such as cellular responses to lipo-polysaccharides,bacteria-derived molecules,and biological stimuli lead to disease;immune infiltration results indicate that memory B cells,activated NK cells,activated dendritic cells,activated mast cells and eosinophils were involved in the disease.It was signifi-cantly expressed in gout patients;the correlation analysis between immune cells showed that the expression of follicular helper T cells were positively correlated with the expression of activated mast cells,and the expression of unactivated NK cells and monocyte were negatively correlated.Conclusion:Key genes and differentially expressed immune cells are closely related to the pathogenesis of gout,providing new ideas for the study of the molecular mechanism of gout.

Objectives:To explore the association between the r'wave amplitude in lead V1 and impedance changes with left bundle branch pacing electrode implantation depth. Methods:A total of 78 patients with normal heart structure and underwent left bundle branch area pacing(LBBAP)in the Second Affiliated Hospital of Nanchang University from January 1,2019 to December 31,2021 were included in this retrospective analysis.Baseline data,intraoperative and imaging data,and 3,6,9 and 12 months of follow-up results were collected.Correlation and regression analysis were performed to define the feasibility using the r'wave in lead V1 during pacing and impedance changes to estimate the electrode depth. Results:r'waves at the end of the QRS complex in lead V1 during pacing were found in 70 cases(89.7%),and 8 cases(10.3%)showed rS,RS type QRS waves,or no r'wave at the end.Correlation analysis showed that r'wave amplitude was positively correlated with electrode depth(r=0.424,P<0.01),negatively correlated with impedance(r=-0.256,P=0.03).There was no significant statistical correlation between electrode implantation depth and impedance(r=-0.132,P=0.27).Regression analysis found that electrode depth was an important factor affecting r'wave amplitude(regression coefficient=0.056,P=0.000).Combined with the established regression model and impedance,it was found that the amplitude of the r'wave in lead V1 is at the range of 0.24-0.69 mV,and the impedance ranges from 648.30 to 828.90 Ω,and the electrode implantation depth is 6-11 mm,which is most suitable.The risk of perforation is low,and the left bundle branch can be successfully captured with a high probability.The pacing parameters are satisfactory,and the pacing QRS wave duration is narrow.During the intraoperative,postoperative 48 hours,and 12-month follow-up period,the patient did not experience complications such as electrode perforation,thromboembolism,cardiac tamponade,infection,or wire dislocation. Conclusions:Left bundle branch region pacing is a safe and feasible pacing method.During LBBAP,the amplitude of the r'wave in lead V1 at the range of 0.24-0.69 mV,and the impedance ranges from 648.30 to 828.90 Ω can be used to guide the pacing in the left bundle branch region and reduce the risk of electrode perforation.

Humans ; Receptors, Chimeric Antigen/therapeutic use* ; DNA-Binding Proteins/genetics* ; Precursor B-Cell Lymphoblastic Leukemia-Lymphoma ; Proto-Oncogene Proteins c-bcl-2/genetics* ; Cell- and Tissue-Based Therapy ; Oncogene Proteins, Fusion/genetics* ; Translocation, Genetic