Objective To investigate the association between the immune microenvironment and gene expression in endometrial cancer(EC)and discuss its predictive value for prognosis,and to identify critical immune-related genes through bioinformatics analysis and machine learning techniques and construct a prognostic model for providing new directions for personalized treatment of EC.Methods Based on data from the Cancer Genome Atlas(TCGA)program,tools such as DESeq2,edgeR,and limma were utilized to screen for differentially expressed genes.Immune-related genes were selected by integrating data from the Immunology Database and Analysis Portal(ImmPort).Machine learning algorithms including Lasso regression,univariate feature selection,Boruta and random forest were employed to refine the selection of feature genes.Univariate and multivariate Cox regression analyses were conducted to assess the prognostic value of the genes,followed by construction of a risk score model.Additionally,tumor immune infiltration was analyzed using CIBERSORT algorithm,and key gene expressions were validated through immunohistochemistry.Results The intersection of 3 difference analysis results and immune-related genes identified 62 differentially expressed immune genes,and 25 potential biomarkers which were selected by a variety of machine learning models were considered as prognosis related genes.Univariate and multivariate Cox regression analyses confirmed that INHBE,SLURP1 and TNFSF11 genes were significantly associated with survival in EC patients.The constructed risk score model effectively distinguished the survival rate of different prognostic groups,and was related to the degree of immune cell infiltration.Immunohistochemical analysis further verified the differences in the expression of these genes between tumor and normal tissues.Conclusion INHBE,SLURP1 and TNFSF11 are key prognostic biomarkers in EC immune microenvironment,and their expression levels are closely associated with immune cell infiltration and patient survival rate,providing theoretical basis for EC precision medicine.

Objective To investigate the association between the immune microenvironment and gene expression in endometrial cancer(EC)and discuss its predictive value for prognosis,and to identify critical immune-related genes through bioinformatics analysis and machine learning techniques and construct a prognostic model for providing new directions for personalized treatment of EC.Methods Based on data from the Cancer Genome Atlas(TCGA)program,tools such as DESeq2,edgeR,and limma were utilized to screen for differentially expressed genes.Immune-related genes were selected by integrating data from the Immunology Database and Analysis Portal(ImmPort).Machine learning algorithms including Lasso regression,univariate feature selection,Boruta and random forest were employed to refine the selection of feature genes.Univariate and multivariate Cox regression analyses were conducted to assess the prognostic value of the genes,followed by construction of a risk score model.Additionally,tumor immune infiltration was analyzed using CIBERSORT algorithm,and key gene expressions were validated through immunohistochemistry.Results The intersection of 3 difference analysis results and immune-related genes identified 62 differentially expressed immune genes,and 25 potential biomarkers which were selected by a variety of machine learning models were considered as prognosis related genes.Univariate and multivariate Cox regression analyses confirmed that INHBE,SLURP1 and TNFSF11 genes were significantly associated with survival in EC patients.The constructed risk score model effectively distinguished the survival rate of different prognostic groups,and was related to the degree of immune cell infiltration.Immunohistochemical analysis further verified the differences in the expression of these genes between tumor and normal tissues.Conclusion INHBE,SLURP1 and TNFSF11 are key prognostic biomarkers in EC immune microenvironment,and their expression levels are closely associated with immune cell infiltration and patient survival rate,providing theoretical basis for EC precision medicine.

Objective To investigate the efficacy of induction chemotherapy (ICT) followed by concurrent chemotherapy and helical tomotherapy (HT) in the patients with T4b squamous cell carcinoma of nasal cavity and paranasal sinus (SCCNP) for orbital organ preservation and high quality of life.Methods A total of 26 patients with the orbital involvement of T4b SCCNP between May 2008 and March 2013 were analyzed retrospectively.There were 17 males and 9 females;the average age was 54.7 years.The median follow-up time was 25 months (range 4-77 months).The patients received 1-2 cycles ICT with TP (docetaxel 70 mg/m2 on day 1 and cisplatin 40 mg/m2 on day 1-2,every 3 weeks) or TPF (docetaxel 70 mg/m2 on day 1 and cisplatin 70 mg/m2 on day 1-2,5-fu 700 mg/m2 on day 1-5,every 3 weeks),followed by concurrent HT (60-70 Gy) and chemotherapy with TP and/or epidermal growth factor receptor (EGFR) inhibitor.The Kaplan-Meier method was used to determine the 3-year overall survival rate and local control rate.Side-effects were evaluated with the established common terminology criteria for adverse events (CTCAE) version 4.0 criteria.Results All patients completed the planned chemotherapy and 96.2％ (25/26)patients completed the planned radiotherapy.The 3-year overall survival rate,the local control rate and real orbital preservation rate were 56.7％,79.5％ and 80.0％ respectively.The most common acute side effects higher than grade 2 were oral mucositis,radiodermatitis and dry eye syndrome.Conclusion The strategy including ICT followed by CCRT and/or EGFR inhibitor is an effective treatment for T4b SCCNP patients,with minimal toxicities,higher 3-year OS rate and orbital preservation rate,and also provides a new treatment option for T4b SCCNP patients.