Effect of Evn-50 on cell growth and apoptosis in tamoxifen-resistance human breast cancer cell line MCF-7/TAM-R / 浙江大学学报·医学版

<p><b>OBJECTIVE</b>To investigate the effect of Evn-50 extracted from Vitex negundo on human breast cancer cell line MCF-7 and MCF-7/TAM-R cells in vitro.</p><p><b>METHODS</b>MCF-7 and tamoxifen-resistant MCF-7/TAM-R cells were treated with Evn-50,tamoxifen or combination of Evn-50 and tamoxifen. Cell proliferation inhibition rates were determined by MTT assay. The apoptosis rate and the change of cell cycle were detected by PI staining flow cytometry. Protein expression of phospho-MAPK 44/42 (Thr202/Tyr204),MAPK P44/42, phospho-AKT (Ser473) and AKT were detected with Western blotting.</p><p><b>RESULTS</b>The viability of MCF-7 cells was decreased in combination group [(28.65 ±11.43)%] and Evn-50 group [(53.02 ±15.14)%] compared with TAM group (P<0.01). The cell viability of MCF-7/TAM-R in combination group [(42.11 ±14.30)%] was significantly lower than that in TAM group [(92.18 ±13.16)%] (P<0.01). The cell apoptosis rate was dependent on the time of treatment in all groups,the effects on apoptosis and G2/M phase cells were most prominent at 72 h (P<0.01). Western blotting revealed that protein levels of phosphorylated AKT and p-MAPK44/42 decreased,while the expression of total AKT and MAPK44/42 was stable. In MCF-7/TAM-R cells,the expression of phosphorylation of AKT and MAPK44/42 protein was not changed in Evn-50 or TAM alone group,but significantly inhibited in the combination group at 72 h.</p><p><b>CONCLUSION</b>Evn-50 can inhibit cell growth and induce apoptosis in MCF-7 and MCF-7/TAM-R cells,it can reverse tamoxifen-resistance of MCF-7/TAM-R cells.The mechanisms may be related to the down-regulation of phosphorylated ERK1/2 in MAPK signal pathway and phosphorylated AKT in AKT signal pathway.</p>

A case-control study on risk factors of female breast cancer in Zhejiang province / 浙江大学学报·医学版

<p><b>OBJECTIVE</b>To investigate the risk factors on female breast cancer in Zhejiang province.</p><p><b>METHODS</b>A case-control study was conducted in 200 cases of female breast cancer with histopathological diagnosis and 200 matched controls from Zhejiang province.</p><p><b>RESULTS</b>Univariate conditional logistic regression showed that family history of malignant tumor and breast cancer, housing decoration in last 10 years, mammary hyperplasia, adverse life events, bra with steel rings, sleeping with bra, high fat and pickle intake, poor sleep were positively related to breast cancer; whereas environmental friendly decoration materials, long decoration time interval, workplace condition, more lactation and parity, high fruits intake, sufficient sleep were negatively related to breast cancer. Multivariate conditional logistic regression analysis showed that the risk factors included family history of other tumors [odds ratio (OR)= 1.571,95% confidence interval(CI):1.029-2.396],mammary hyperplasia (OR=3.066,95%CI:1.834-5.126), job-related life events (OR=4.575,95%CI:1.690-12.390),the death of a loved one (OR=2.555,95%CI:1.475-4.424), wearing bra at night (OR=1.902,95%CI:1.177-3.072),high fat intake (OR=2.709,95%CI:1.546-4.749) and salted food (OR=2.460,95%CI:1.300-4.653). Factors as environmental friendly decoration materials (OR=0.517,95%CI:0.339-0.789),workplaces condition (OR=0.430,95%CI:0.243-0.762),more lactation (OR=0.109,95%CI:0.013-0.896),enough sleep (OR=0.424,95%CI:0.205-0.880) were protective factors.</p><p><b>CONCLUSION</b>Hereditary,psychological factors,lifestyle,environment and diet related factors are significantly associated with risk of breast cancer.</p>

Classification and diagnostic prediction of colorectal cancer using protein profiling of serum and bioinformatics / 浙江大学学报·医学版

<p><b>OBJECTIVE</b>To develop a bioinformatic tool and to use it to identify proteomic patterns in serum, distinguishing colorectal cancer from colorectal adenoma and healthy individuals.</p><p><b>METHODS</b>182 serum samples including 55 colorectal cancer patients, 35 colorectal adenoma and 92 healthy individuals were subjected to analysis by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry.</p><p><b>RESULT</b>The diagnostic pattern combined of 4 candidate biomarkers (M/Z 5911, 8922, 8 944, and 8817) could separate colorectal patients from healthy control with a specificity of 93.3%, sensitivity of 90.9%, and Youden index value of 0.84242. The diagnostic pattern combined of 7 candidate biomarkers (M/Z 17247, 18420 ,5911, 9294, 4654, 21694, and 21742) could separate colorectal cancer patients from colorectal adenoma patients with a specificity of 83.2%, sensitivity of 89.3%, and Youden index value of 0.72484.</p><p><b>CONCLUSION</b>Combination of SELDI with bioinformatics tool can identify some new biomarkers from the sera of colorectal cancer patients, which has a high sensitivity and specificity to distinguish colorectal cancer patients from healthy control.</p>

Preoperatively molecular staging with CM10 ProteinChip and SELDI-TOF-MS for colorectal cancer patients / 浙江大学学报（英文版）（B辑：生物医学和生物技术）

<p><b>OBJECTIVES</b>To detect the serum proteomic patterns by using SELDI-TOF-MS (surface enhanced laser desorption/ ionization-time of flight-mass spectrometry) technology and CM10 ProteinChip in colorectal cancer (CRC) patients, and to evaluate the significance of the proteomic patterns in the tumour staging of colorectal cancer.</p><p><b>METHODS</b>SELDI-TOF-MS and CM10 ProteinChip were used to detect the serum proteomic patterns of 76 patients with colorectal cancer, among them, 10 Stage I, 19 Stage II, 16 Stage III and 31 Stage IV samples. Different stage models were developed and validated by support vector machines, discriminant analysis and time-sequence analysis.</p><p><b>RESULTS</b>The Model I formed by 6 protein peaks (m/z: 2759.58, 2964.66, 2048.01, 4795.90, 4139.77 and 37761.60) could be used to distinguish local CRC patients (Stage I and Stage II) from regional CRC patients (Stage III) with an accuracy of 86.67% (39/45). The Model II formed by 3 protein peaks (m/z: 6885.30, 2058.32 and 8567.75) could be used to distinguish locoregional CRC patients (Stage I, Stage II and Stage III) from systematic CRC patients (Stage IV) with an accuracy of 75.00% (57/76). The Model III could distinguish Stage I from Stage II with an accuracy of 86.21% (25/29). The Model IV could distinguish Stage I from Stage III with accuracy of 84.62% (22/26). The Model V could distinguish Stage II from Stage III with accuracy of 85.71% (30/35). The Model VI could distinguish Stage II from Stage IV with accuracy of 80.00% (40/50). The Model VII could distinguish Stage III from Stage IV with accuracy of 78.72% (37/47). Different stage groups could be distinguished by the two-dimensional scattered spots figure obviously.</p><p><b>CONCLUSION</b>This method showed great success in preoperatively determining the colorectal cancer stage of patients.</p>

Preoperative molecular staging of colorectal cancers by CM10 ProteinChip and SELDI-TOF-MS analysis / 中华肿瘤杂志

<p><b>OBJECTIVE</b>To detect the serum proteomic patterns by using SELDI-TOF-MS and CM10 ProteinChip techniques in colorectal cancer (CRC) patients, and to evaluate the significance of the proteomic patterns in colorectal cancer staging.</p><p><b>METHODS</b>A total of 76 serum samples were obtained from CRC patients at different clinical stages, including Dukes A (n = 10), Dukes B (n = 19), Dukes C (n = 16) and Dukes D (n = 31). Different stage models were developed and validated by bioinformatics methods of support vector machines, discriminant analysis and time-sequence analysis.</p><p><b>RESULTS</b>The model I formed by six proteins of peaks at m/z 2759.6, 2964.7, 2048.0, 4795.9, 4139.8 and 37 761.6 could do the best as potential biomarkers to distinguish local CRC patients (Dukes A and Dukes B) from regional CRC patients (Dukes C ) with an accuracy of 86.7%. The model II formed by 3 proteins of peaks at m/z 6885.3, 2058.3 and 8567.8 could do the best to distinguish locoregional CRC patients (Dukes A, B and C) from systematic CRC patients (Dukes D) with an accuracy of 75.0%. The mode III could distinguish Dukes A from Dukes B with an accuracy of 86.2% (25/29). The model IV could distinguish Dukes A from Dukes C with an accuracy of 84.6% (22/26). The model V could distinguish Dukes B from Dukes C with an accuracy of 85.7% (30/35). The model VI could distinguish Dukes B from Dukes D with an accuracy of 80.0% (40/50). The model VII could distinguish Dukes C from Dukes D with an accuracy of 78.7% (37/47). Different stage groups could be distinguished by the two-dimensional scattered spots figure obviously.</p><p><b>CONCLUSION</b>Our findings indicate that this method can well be used in preoperative staging of colorectal cancers and the screened tumor markers may serve for guidance of integrating treatment of colorectal cancers.</p>

Application of serum protein pattern model in diagnosis of colorectal cancer / 中华肿瘤杂志

<p><b>OBJECTIVE</b>To explore the application of serum protein pattern models in diagnosis of colorectal cancer (CRC) by proteinchip technology.</p><p><b>METHODS</b>One hundred and forty-seven serum samples (55 CRC patients and 92 healthy individuals) randomly divided into training set (n = 87, 32 CRC patients and 55 healthy individuals) and test set (n = 60), were subjected for analysis by surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS). Four top-scored peaks in 5910, 8930, 4476 and 8817 were detected by proteinchip software version 3.0. and were trained by a multi-layer artificial neural network (ANN) with a back propagation algorithm. An artificial neural network classifier had developed for separating CRC from the healthy group. The classifier was then challenged with the test set (60 samples including 23 CRC patients and 37 healthy individuals) to determine the validity and accuracy of the classification system.</p><p><b>RESULTS</b>The artificial neural network classifier separated the CRC from the healthy samples, with sensitivity of 82.6% and specificity of 91.9%.</p><p><b>CONCLUSION</b>Combination of SELDI-TOF-MS with the artificial neural network yields significant higher sensitivity and specificity than CEA in the diagnosis of CRC, which should be further studied.</p>