| Objective: Chromatin regulatory factors(CRs),as the upstream regulatory factors of epigenetics,play an important role in normal physiology and disease by altering epigenetic modification.Their abnormal functions can induce the signaling of breast cancer cell processes,and are regarded as promising emerging molecules for breast cancer treatment.In this paper,bioinformatics methods were used to analyze the expression and main functions of CRs-related genes in breast cancer,and key genes were screened to construct prognostic models,so as to provide guidance for effectively evaluating the prognosis of breast cancer patients.Methods: The genes with significant differences were screened out by in the gene expression synthesis(GEO)database.GO functional enrichment and KEGG pathway enrichment analysis were performed for differential genes.Univariate Cox regression analysis was used to screen the best prognostic genes among differential genes.The CRs-related gene prognostic model was established in the GSE42568 dataset by LASSO regression method,and survival analysis and time-dependent ROC analysis were performed to evaluate the predictive ability of the model,and the model was further verified in the GSE45255 dataset.Thirty pairs of clinical breast cancer and paracancer tissue samples were selected,and the expression of the genes used in the prognostic model was verified clinically by RT-q PCR.T test was used to analyze the relationship between the prognostic model and clinical parameters such as age,estrogen receptor,progesterone receptor,human epidermal growth factor-2 receptor,tumor grade,tumor T stage,and lymph node status.Univariate and multivariate Cox analyses were used to evaluate the prognostic ability of the model.The 3-and 5-year survival rates of breast cancer patients were evaluated.Results: 1.104 breast cancer specimens and 17 normal breast tissue specimens were included in the GSE42568 dataset,and 707 common CRs-related genes were obtained through intersection for differential gene analysis.A total of 168 CRs-related differential genes were screened,including 136 up-regulated genes and 32 down-regulated genes.2.GO analysis showed that differential genes were mainly enriched in functions related to histone modification.KEGG analysis showed that differential genes were mainly involved in lysine degradation,Notch signaling pathway,cell cycle,viral life-cycle-hiv-1,glucagon signaling pathway,thyroid hormone signaling pathway,transcription dysregulation in cancer,and viral carcinogen-related pathways.3.A total of 12 optimal prognostic genes were screened by univariate Cox regression analysis,and a prognostic model of breast cancer patients based on four CRs-related genes(MORF4L1,NCOA4,TTK and JMJD4)was established by LASSO regression method.Patients were divided into high and low risk groups according to the critical value.Survival analysis showed poor prognosis in the high-risk group.4.The results of RT-q PCR showed that the expressions of MORF4L1(p=0.02)and NCOA4(p=2.3e-03)decreased in breast cancer tissues,while the expressions of TTK(p=1.1e-04)and JMJD4(p=2.6e-04)increased,which was consistent with the results of difference analysis.5.Clinical correlation analysis showed age ≥ 60(p<0.0001),G1-2(p=0.047),lymph node metastasis(p<0.00057),T 2-3(p<0.0001)patients in the high-risk group had a worse prognosis than the low-risk group,and the difference was statistically significant.6.Univariate and multivariate Cox analysis showed that risk score and lymph node status were independent prognostic factors for BC,and a graph was further constructed to evaluate3-year and 5-year survival rates of breast cancer patients.Conclusions: In this study,four CRs-related genes(MORF4L1,NCOA4,TTK and JMJD4)were screened out by bioinformatics method,which can be used as biological markers for breast cancer diagnosis.Moreover,four gene prognostic models were constructed,which can effectively predict the prognosis of breast cancer patients. |
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