Objective To identify immune cell-related biomarkers in lung adenocarcinoma (LUAD) using weighted gene co-expression network analysis (WGCNA). Methods Based on data from The Cancer Genome Atlas (TCGA) database, a gene co-expression network was constructed for the TCGA-LUAD dataset using the "WGCNA" R package, and genes were clustered into different modules. Concurrently, the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm was applied to the tumor samples in the TCGA-LUAD dataset. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to evaluate the biological functions of genes within the most significantly correlated module. Candidate hub genes from the key module were intersected with a protein-protein interaction (PPI) network to identify the final hub genes. The prognostic performance of these hub genes and their correlation with immune cell infiltration were validated using Kaplan-Meier curves and the Tumor IMmune Estimation Resource (TIMER) algorithm. Finally, a multivariate Cox regression analysis was conducted on the identified hub genes to construct a prognostic risk model. Results In the co-expression network, the brown module was found to be highly correlated with the ImmuneScore, StromalScore, and ESTIMATE Score. Five immune-related hub genes were identified: CD53, PLEK, SPI1, IL10RA, and C3AR1. Enrichment analysis of the brown module revealed that its genes were primarily enriched in GO terms such as "regulation of innate immune response" and KEGG pathways like the "NF-kappa B signaling pathway". Furthermore, the expression levels of these five hub genes were significantly and positively correlated with the infiltration abundance of various immune cells. The immune relevance of the model was validated by the Immunophenoscore (IPS) and the Tumor Immune Dysfunction and Exclusion (TIDE) score. Moreover, the established RiskScore demonstrated significant potential in predicting the response to immunotherapy. Conclusion These five immune-related key genes may serve as novel and effective potential therapeutic targets for LUAD immunotherapy, facilitating the development of personalized diagnosis and treatment strategies for patients with LUAD.

Objective:To investigate the long-term survival rate and the influencing factors for prognosis in patients aged 80 years and over who underwent hemodialysis.Methods:Clinical data of 82 patients with end stage renal disease (ESRD) who started dialysis in≥80 years of age and underwent hemodialysis for more than three months in our hospital from May 2008 to May 2017 were retrospectively analyzed.Kaplan-Meier method was used to analyze the survival rate of patients.Log-rank test was used to compare survival rate of hemodialysis patients aged≥80 years versus aged<80 years.Cox regression model was performed to analyze the influencing factors for prognosis in elderly patients undergoing hemodialysis.Results:The median survival time of 82 hemodialysis patients aged≥80 years was 34 months, and their 6-month, 12-month, 24-month and 36-month survival rates were 90.0%, 73.6%, 60.8% and 49.4%, respectively.The top three death causes were cardiovascular events, septic shock, and malignant tumors.The median survival time of the hemodialysis patients aged<80 years was 52 months, and their 6-month, 12-month, 24-month and 36-month survival rates were 94.7%, 85.5%, 72.4% and 65.8%, respectively.Log-rank test demonstrated that the survival rate was lower in patients aged≥80 years than in patients aged<80 years( χ2=5.284, P=0.022). Multivariate Cox regression analysis indicated that diabetes mellitus was an independent risk factor for the survival in hemodialysis patients aged≥80 years( HR=3.470, 95% CI: 1.364~8.827, P=0.009), while targeted levels of body mass index(BMI)( HR=0.806, 95% CI: 0.706~0.921, P=0.001)and serum albumin level( HR=0.861, 95% CI: 0.789~0.938, P=0.001)were the independent protective factors for the survival in hemodialysis patients aged≥80 years. Conclusions:The survival rate is lower in hemodialysis patients aged≥80 years than in those aged<80 years.The top death causes is cardiovascular events, septic shock, and malignant tumors.Diabetes mellitus, low levels of BMI and serum albumin are the main risk factors for the survival in advanced elderly patients undergoing hemodialysis.

OBJECTIVES: To explore the genetic variation sites of caveolin （CAV） and their correlation with sudden unexplained death （SUD）. METHODS: The blood samples were collected from SUD group （71 cases）, coronary artery disease （CAD） group （62 cases） and control group （60 cases）, respectively. The genome DNA were extracted and sequencing was performed directly by amplifying gene coding region and exon-intron splicing region of CAV1 and CAV3 using PCR. The type of heritable variation of CVA was confirmed and statistical analysis was performed. RESULTS: A total of 4 variation sites that maybe significative were identified in SUD group, and two were newfound which were CAV1： c.45C>T （T15T） and CAV1：c.512G>A （R171H）, and two were SNP loci which were CAV1：c.246C>T （rs35242077） and CAV3：c.99C>T （rs1008642） and had significant difference （P<0.05） in allele and genotype frequencies between SUD and control groups. Forementioned variation sites were not found in CAD group. CONCLUSIONS The variants of CAV1 and CAV3 may be correlated with a part of SUD group.
Caveolins/genetics* ; Coronary Artery Disease ; Death, Sudden/etiology* ; Exons ; Genotype ; Humans ; Male ; Polymerase Chain Reaction ; Polymorphism, Single Nucleotide