Background Evidence from multiple studies suggests metabolic abnormalities play an important role in lung cancer. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. The present study aimed to explore differences in the global metabolic response between male and female patients in LUAD and to identify the metabolic genes associated with lung cancer susceptibility. Methods Transcriptome and clinical LUAD data were acquired from The Cancer Genome Atlas (TCGA) database. Information on metabolic genes and metabolic subsystems were collected from the Recon3D human metabolic model. Two validation datasets (GSE68465 and GSE72094) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis, gene set enrichment analysis and protein-protein interaction networks were used to identified key metabolic pathways and genes. Functional experiments were used to verify the effects of genes on proliferation, migration, and invasion in lung cancer cells in vitro. Results Samples of tumors and adjacent non-tumor tissue from both male and female patients exhibited distinct global patterns of gene expression. In addition, we found large differences in methionine and cysteine metabolism, pyruvate metabolism, cholesterol metabolism, nicotinamide adenine dinucleotide (NAD) metabolism, and nuclear transport between male and female LUAD patients. We identified 34 metabolic genes associated with lung cancer

Complement factor B (CFB) serves a pivotal role in the alternative signaling pathway of the complement system and exerts a key role in the labelling of target particles, resulting from effective clearance of the target. The present study aimed to investigate the association between low expression levels of CFB and the clinical features and survival status of patients with lung adenocarcinoma (LUAD). Patient data were based on RNA-sequencing and clinical data from The Cancer Genome Atlas database. All patients were divided into two groups based on the median expression of CFB. Kaplan-Meier curve and univariate Cox regression analyses were used to investigate the association between CFB and survival status. Gene set enrichment analysis was used to examine the effects of CFB expression on signaling pathway impairment. Furthermore, reverse transcription-quantitative PCR (RT-qPCR) and western blotting were used to verify the relative expression levels of CFB in LUAD tissues. The data revealed that residual tumor classification, Karnofsky performance score and cancer stage were associated with overall survival, and that Karnofsky performance score and stage were associated with disease-free survival. The results demonstrated that high expression levels of CFB were associated with increased patient overall and disease-free survival according to both continuous and categorical models. The results of multivariate analysis identified that high expression levels of CFB were associated with increased overall and disease-free survival according to both the continuous model [hazard ratio (HR), 0.48; 95% confidence interval (95% CI), 0.25-0.93; P=0.029 for overall survival; HR, 0.29; 95% CI, 0.15-0.59; P=0.001 for disease-free survival] and the categorical model (HR, 0.46; 95% CI, 0.22-0.93; P=0.031 for overall survival; HR, 0.25; 95% CI, 0.12-0.55; P=0.001 for disease-free survival) after adjusting for corresponding covariates (residual tumour classification, Karnofsky performance score and stage). Furthermore, the results of both RT-qPCR and western blotting indicated that the relative mRNA and protein expression levels of CFB in lung tumor tissues were downregulated compared with those in adjacent non-tumor tissues. Collectively, the present results suggested that CFB expression was an independent predictor of overall and disease-free survival in patients with LUAD.

ObjectiveTo investigate trends in clinical monitoring indices in HIV/AIDS patients receiving antiretroviral therapy (ART) at baseline and after treatment in Yunnan Province, China and to provide the basis for guiding clinical treatment to obtain superior clinical outcomes.MethodsA total of 96 HIV/AIDS patients who had started and persisted in highly active ART treatment from September 2009 to September 2019 were selected. Of these, 54 had a CD4 cell count < 200 cells/μl while 42 had a CD4 cell count ≥ 200 cells/μl. Routine blood tests, liver and renal function, and lipid levels were measured before and 3, 6, 9, and 12 months after treatment. Lymphocyte subset counts and viral load were measured once per year, and recorded for analysis and evaluation. Three machine learning models (support vector machine [SVM], random forest [RF], and multi-layer perceptron [MLP]) were constructed that used the clinical indicators above as parameters. Baseline and follow-up results of routine blood and organ function tests were used to analyze and predict CD4+ T cell data after treatment during long-term follow-up. Predictions of the three models were preliminarily evaluated.ResultsThere were no statistical differences in gender, age, or HIV transmission route in either patient group. Married individuals were substantially more likely to have <200 CD4+ cells/μl. There was a strong positive correlation between ALT and AST (r = 0.587) and a positive correlation between CD4 cell count and platelet count (r = 0.347). Platelet count was negatively correlated with ALT (r = -0.229), AST (r = -0.251), and positively correlated with WBCs (r = 0.280). Compared with the CD4 cell count < 200 cells/μl group, all three machine learning models exhibited a better predictive capability than for patients with a CD4 cell count ≥ 200 cells/μl. Of all indicators, the three models best predicted the CD4/CD8 ratio, with results that were highly consistent. In patients with a CD4 cell count < 200 cells/μl, the SVM model had the best performance for predicting the CD4/CD8 ratio, while the CD4/CD8 ratio was best predicted by the RF model in patients with a CD4 cell count ≥ 200 cells/μl.ConclusionBy the incorporation of clinical indicators in SVM, RF, and MLP machine learning models, the immune function and recuperation of HIV/AIDS patients can be predicted and evaluated, thereby better guiding clinical treatment.

Background: Epilepsy is a debilitating brain disease with complex inheritance and frequent treatment resistance. However, the role of STX1B single nucleotide polymorphisms (SNPs) in epilepsy treatment remains unknown.Objective: This study aimed to explore the genetic association of STX1B SNPs with treatment response in patients with epilepsy in a Han Chinese population.Methods: We first examined the associations between STX1B SNPs and epilepsy in 1000 Han Chinese and the associations between STX1B SNPs and drug-resistant epilepsy in 450 subjects. Expression quantitative trait loci analysis was then conducted using 16 drug-resistant epileptic brain tissue samples and results from the BrainCloud database (http://eqtl.brainseq.org).Results: The allelic frequencies of rs140820592 were different between the epilepsy and control groups (p = 0.002) after Bonferroni correction. The rs140820592 was associated with significantly lower epilepsy risk among 1,000 subjects in the dominant model after adjusting for gender and age and Bonferroni correction (OR = 0.542, 95%CI = 0.358–0.819, p = 0.004). The rs140820592 also conferred significantly lower risk of drug-resistant epilepsy among 450 subjects using the same dominant model after adjusting for gender and age and Bonferroni correction (OR = 0.260, 95%CI = 0.103–0.653, p = 0.004). Expression quantitative trait loci analysis revealed that rs140820592 was associated with STX1B expression level in drug-resistant epileptic brain tissues (p = 0.012), and this result was further verified in the BrainCloud database (http://eqtl.brainseq.org) (p = 2.3214 × 10–5).Conclusion: The STX1B rs140820592 may influence the risks of epilepsy and drug-resistant epilepsy by regulating STX1B expression in brain tissues.

Background Prior to being spread throughout broader China, multiple human immunodeficiency virus (HIV)-1 genotypes were originally discovered in the Yunnan Province. As the HIV-1 epidemic continues its spread in Yunnan, knowledge of the influence of gender, age, and ethnicity to instances of HIV reservoirs will benefit monitoring the spread of HIV. Methods The degree to which T cells are depleted during an HIV infection depends on the levels of immune activation. T-cell subsets were assessed in newly-diagnosed HIV/AIDS patients in Yunnan, and the influence of age, gender, and ethnicity were investigated. Patients that were newly diagnosed with the HIV-infection between the years 2015 and 2018 at the First Affiliated Hospital of Kunming Medical College were selected for this study (N = 408). The lymphocyte levels and T cell subsets were retrospectively measured in whole blood samples by FACS analysis. Results The median CD4 count was 224 ± 191 cells/μl. Significantly higher mean frequencies and absolute numbers were observed in CD3 + , CD3 + CD4 + , CD3 + CD8 + , CD45 + , and CD3 + CD4 + /CD45 + in females compared to males. Han patients showed a higher total number of CD3+T cells and the ratio of CD3 + /CD45 + cells compared to any other ethnic minority (P < 0.001). The numbers of CD3+ T-cells, CD3+CD8+ T cells, and CD45+ T cells were highest in the age group ≥ 60. Significant differences were observed in the counts of CD3+, CD3+CD8+, and CD45 + cells and the ratio of CD3 + /CD45 + and CD3 + CD4 + /CD45 + cells between the ≤ 29 and 30–59 age groups. Conclusion This study has revealed that low levels of CD4 + T cells can be observed in newly-diagnosed HIV/AIDS patients in the Yunnan province. It has also been demonstrated that gender, age, and ethnicity have a significant association with the ratio of T-cell subsets that may contribute to virus progression and disease prognosis in individuals belonging to certain subsets of the population. This study has highlighted the importance of HIV/AIDS screening in at-risk populations to ensure timely and adequate clinical management in Yunnan.