Biomedical subjects

Ye-xia Cheng

Publications and source records attributed to Ye-xia Cheng.

3 recordsLinked to original sources

PubMed2006

[Construction and utilization of the prognostic model of serous ovarian adenocarcinoma].

OBJECTIVE: To analyze the related factors with prognosis in patients with serous ovarian adenocarcinoma and to set up a prognostic model of serous ovarian adenocarcinoma. METHODS: The clinical, pathological and follow-up data of 104 cases with serous ovarian adenocarcinoma were retrospectively analyzed. Kaplan-meier univariate analysis was used to screen the prognostic factors; COX univariate and multivariate analyses were used to determine the risk coefficient of each factors and different layers in each factor. Pearson rank correlation was used to reject the influence of different factors with each other. And the prognostic model of serous ovarian adenocarcinoma was set up based on the result of the above study, which could be used to deduce the survival probability of patients with serous ovarian adenocarcinoma. RESULTS: International Federation of Gynecology and Obstetrics (FIGO) stage (P = 0.0029), histological grade (P = 0.0054), residual disease (P = 0.0000), metastasis of lymph nodes (P = 0.0000) and chemotherapy (P = 0.0000) were the related factors of prognosis in patients with serous ovarian adenocarcinoma, of which FIGO stage was the most important one, followed sequentially by histological grade, metastasis of lymph node, residual disease and chemotherapy (the independent risk coefficient of each factor was 1.3392, 0.9206, 0.7071, 0.6004, 0.4985 in sequence). We set up a prognosis model according to the prognostic index of each factors. The effect of chemotherapy and residual disease on prognosis could be quantified by this model, and the higher the score, the lower the survival probability of patients. CONCLUSIONS: FIGO stage, histological grade, residual disease, metastasis of lymph nodes and chemotherapy are important prognostic factors of serous ovarian adenocarcinoma. This model can be used to estimate the prognosis of patients with serous ovarian adenocarcinoma, and the effect of both chemotherapy and residual disease on the prognosis could be quantified by the model.

Adult↗

PubMed2005

[In vitro study of the antitumor immune responses induced by anti-idiotypic minibody vaccine of ovarian cancer].

OBJECTIVE: To evaluate whether anti-tumor immune response can be induced in vitro with 6B11 anti-idiotypic minibody. and to explore its probability as ovarian cancer vaccine. METHODS: Separated human peripheral blood mononuclear cells (PBMC) were stimulated and cultured by 6B11 minibody. The proliferations of PBMC and cytotoxin were observed by (3)HTdR and (51)Cr release test respectively. ELISA(Enzyme-Linked Immunosorbent Assay) test and Immune Flow Cytometry were used to analyze IFN-gamma in supernatant of the cultured cdlls and the change of T lymphocyte phenotype of PBMC with 6B11 minibody stimulated. RESULTS: 6B11 minibody could stimulate PBMC to proliferate, the best dose was 20 mg/L; it performed cytotoxin function to ovarian carcinoma cell line expressing OC166-9. IFN-gamma maintained at high level after stimulation. It stimulated proliferation of CD3(+) T cell and CD4(+) from PBMC after stimulation respectively. CD8(+) T cell proliferation was not clear. There was significant difference between stimulation and unstimulation in CD4(+)/CD8(+) ratio. CONCLUSION: 6B11 anti-idiotypic minibody can induce both humoral and cellular immunity against ovarian carcinoma in vitro. This paper has provided strong experimental evidence for clinical use of 6B11 minibody as anti-idiotype vaccines against ovarian carcinoma.

Antibodies, Anti-Idiotypic↗

PubMed2003

[Monitoring novel ovarian carcinoma associated genes using cDNA expression microarray].

OBJECTIVE: To explore the gene expression pattern of sample of human ovarian carcinoma. METHOD: The difference in gene expression between normal and neoplastic human ovarian tissues were investigated, we described the assembly and utilization of a 512 member cDNA microarray. RESULT: Thirty-seven genes expressed in ovarian cancer were screened out, 14 genes were up-regulated, 23 genes were down-regulated. CONCLUSION: cDNA microarray for analysis of gene expression pattern is an effective method to identify novel ovarian cancer associated genes.

Female↗