Application of Cholesterol Production Gene Signature to Prognosis Prediction in Young Breast Cancer Patients
A cholesterol and breast cancer technology, applied in the field of biomedical applications, can solve the problems of young breast cancer patients raised by no scholars
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specific Embodiment 1
[0044] Get breast cancer transcription group data (HTSEQ-FPKM) and clinical data from the TCGA and GEO database. Patients with breast cancer in the age of ≤ 45 years old, using 183 cases of breast cancer specimens and 30 normal specimens construction training sets, compared to the transcriptional expression of cholesterol generation genes by R language, using LIMMA and Pheatmap packets, statistical analysis, Differentially expressed genes (P figure 1 As shown, 97 up-regulated genes and 124 down-regulatory genes were prelimed.
specific Embodiment 2
[0046] By using single variable Cox regression analysis and LASSO regression analysis, it was determined that a gene associated with a prognosis of young breast cancer patients was set, and P figure 2 As shown, 5 pre-rear correlation genes are finally screens, respectively, of GRAMD1C, NFKBIA, INHBA, CD24, and ACSS2. The risk assessment model of the five breast cancer prognosis related genes was used. The model is based on the following formula: -1.169 × Gramd1c-0.992 × NFKBIa + 0.432 × inhba + 0.261 × CD24-0.839 × Acss2; where Gramd1c, NFKBIA, INHBA, CD24 and ACSS2 are mRNA levels of the corresponding gene.
[0047] After removing 4 cases of follow-up information, 179 cases of breast cancer patients were divided into high-risk group (n = 89) and low-risk groups (n = 90), such as the median value of the risk score. figure 2 As shown, Kaplan-Meier survival analysis showed that the prognosis of the low-risk group was significantly better than the high-risk group (P <0.001), and the ...
specific Embodiment 3
[0049] Combined with the risk assessment model of Example 2, single factors and multi-factors analysis were performed on age, clinical staging, surgical mode (labo-breast surgery and breast allocation), T staging, N staging and risk score. Such as image 3 As shown, the risk assessment model is an independent risk factor in the prognosis of young breast cancer patients (P <0.05).
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