利用主成分分析法分析乳腺癌术后患者的血清表面增强拉曼光谱

Using Principal Component Analysis to Research Surface Enhanced Raman Spectrum of Serum from Postoperative Breast Cancer Patients

为了探寻一种利用血清光谱检测诊断乳腺癌术后复发转移的方法,研究分析了10例乳腺癌术后复发转移患者与10例乳腺癌术后复查正常患者的血清表面增强拉曼光谱特征。首先从两组血清表面增强拉曼光谱特征峰的位移与峰强的变化上进行比较,发现其分子结构发生变化,脂类的有序性,糖质环境都发生了改变。然后基于Matlab,采用主成分分析法(Principal Component Analysis,PCA),对上述两组患者血清的表面增强拉曼光谱的13个指标进行分析,发现区分两组患者的效果可以达到95%左右。

In order to search a method which using spectrum of serum to diagnose postoperative breast cancer patients whether recurrence or metastasis, we compared the characteristics of SERS of serum between two groups of patients. One is that 10 serum samples from postoperative recurrent or metastatic breast cancer patients, the other is that 10 serum samples from postoperative the followed normal breast cancer patients. Firstly, intensity and shift of characteristics peak is analyzed, we found that the structure of molecule, the structure of lipid and the glucide saceharide environment are all changed. Through using the Matlab to complete the principal component analysis （PCA） , we analyzed the 13 elements of SERS of serum about the two groups patients. It reveals that the effectivity of distinguishing two groups patients is about 95 %.