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Archive > Volume 31 Issue 8 > 2024,31(8):803-814. DOI:10.3872/j.issn.1007-385X.2024.08.008 Prev Next
Clinical Research
Construction of a prognostic model with angiogenesis-related immune genes in ovarian cancer and analysis of tumor microenvironment
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Abstract:
Objective: To explore angiogenesis-related immune genes (ARIGs) in ovarian cancer using the bioinformatics method, investigate their relationship with ovarian cancer prognosis, and elucidate potential differences in tumor microenvironment and immunotherapy response among patients with different prognosis, so as to provide new therapeutic targets for ovarian cancer patients. Methods: Transcriptome and survival data for ovarian cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. The differentially expressed genes were analyzed using R software, and the correlation between ARIGs and immune-related genes was identified by the Pearson correlation coefficient, leading to the selection of differentially expressed ARIGs. A prognostic model was constructed by LASSO regression analysis, and the clinical features and risk scores were evaluated through COX analysis. Patients were divided into a high-risk group and a low-risk group. Single sample gene set enrichment analysis (ssGSEA) and tumor immune dysfunction and exclusion (TIDE) were used to analyze the correlation between the prognostic risk model and immune invasion and immunotherapy response. Finally, 85 pairs of tumor tissues and fallopian tube tissues of ovarian cancer patients who were surgically treated in the Fourth Hospital of Hebei Medical University from May 2015 to May 2016 were collected. The expression of five differentially expressed ARIGs in ovarian cancer tissues was verified by qPCR and WB, and their relationship with clinicopathological features of ovarian cancer patients was analyzed. The biological function of these ARIGs in ovarian cancer cells was preliminarily explored. Results: A total of 142 differentially expressed ARIGs were screened by bioinformatics analysis. Lasso and Cox regression analyses identified five genes (PTGER3, SCTR, IGHG1, HSPA8, IGF2) as prognostic genes, and a prognostic risk model was constructed. Patients in the high-risk group had a worse prognosis. Moreover, significant differences were observed in immune cell infiltration and immunotherapy response between patients with different risk scores. Finally, qPCR and WB verified that these 5 genes were highly expressed in ovarian cancer tissues, with HSPA8 being the most highly expressed. High HSPA8 expression was positively correlated with advanced FIGO stage, poor histological grade, lymph node metastasis and peritoneal metastasis in ovarian cancer patients (all P<0.001). Cell function experiments confirmed that HSPA8 could promote the proliferation, migration, and invasion of ovarian cancer cells(P<0.01). Conclusion: The five differentially expressed ARIGs can effectively predict the prognosis of ovarian cancer patients and are related to immune cell infiltration and immunotherapy efficacy. Preliminary evidence suggests that these genes play a pro-carcinogenic role in ovary cancer.
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