RESUMEN
Objective To explore the abnormal expression of ubiquitin D (UBD) and glypican-3 (GPC3) among patients of hepatocellular carcinoma (HCC) by using analysis tools of genomics and epigenetics, so as to study their prognostic effects. Methods The online tools called ULCAN( http://ualan.path.uab.edu) and Gene Expression Profiling Interative Analysis (GEPIA) were used to perform expression analysis in genomics and epigenetics of UBD and GPC3. Moreover, GEPIA was conducted to evaluate the survival effects on HCC patients. The GeneCards was used to find the localization of UBD and GPC3 in tumor tissue and normal tissue. The STRING was utilized to perform the construction of PPI network and gene annotation. The correlation between UBD and GPC3 in progress of HCC was revealed based on Pearson correlation coefficient. Results UBD and GPC3 were dramatically up-regulated in HCC tissues, with downregulation of methylation level. UBD was located in 6p22. 1 with primary expression in the nucleus, while GPC3 was located in Xq26. 2 with main expression in the plasma membrane, extracellular matrix, endoplasmic reticulum, lysosome and golgi apparatus. The enrichment analysis showed that, UBD was enriched in activities involving proteasome, such as post-translation protein modification, ubiquitination and deubiquitination. GPC3 was enriched in the biosynthetic and catabolic process of glycosaminoglycan, possessed relationship with proteoglycans in cancer, ECM-receptor interaction, cell adhesion molecules (CAMs). Both of UBD and GPC3 were shown to exhibit a positive linear correlation, which suggested that GPC3 and UBD mediated the pathological process of HCC in cooperation. The survival analysis showed that, GPC3 exhibited a critical effect on survival of HCC patients. Conclusion UBD and GPC3 represent up-regulation in tumor tissue, in which GPC3 possesses a greater impact on the prognosis of HCC. GPC3 could be potential to serve as a practical biomarker for early diagnosis and medical intervention.
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Objective To study the influences of mental disorders on female systemic lupus erythematosus(SLE)and analyze the factors. Methods We used symptom check list -90 (SCL-90) as a basis for judging mental disorders disease activity. Disease activity, social support and depreciation - discrimination were used as possible influencing factors. Social support and discomfort – discrimination were possible influencing factors. Multivariate unconditional logistic regression model was used to analyze the influencing factors of mental disorders. Results The total score of SCL-90 of patients with female SLE was significantly higher than that of norm models ［(136.39±48.66) vs (129.96±38.76)］ (P<0.05), in 289 SLE patients, the number of patients with mental disorders was 128 (44.3%). High monthly income(OR=0.770, 95% CI:0.604-0.981, P=0.034) was a protective factor for mental disorders. High disease activity (OR=1.792, 95% CI:1.023-3.138, P=0.042)and high discomfort–discrimination (OR=1.100, 95% CI:1.035-1.169, P=0.002)were risk factors for mental disorders. Conclusions Female SLE patients have a higher risk of mental disorders than the general population. And eliminating self-depreciation, reducing social discrimination, active employment, increasing monthly income, standardizing treatment and reducing disease activity may effectively alleviate mental disorders in SLE patients.