This study aims to predict expression of estrogen receptor (ER) in breast cancer by radiomics. Firstly, breast cancer images are segmented automatically by phase-based active contour (PBAC) method. Secondly, high-throughput features of ultrasound images are extracted and quantized. A total of 404 high-throughput features are divided into three categories, such as morphology, texture and wavelet. Then, the features are selected by R language and genetic algorithm combining minimum-redundancy-maximum-relevance (mRMR) criterion. Finally, support vector machine (SVM) and AdaBoost are used as classifiers, achieving the goal of predicting ER by breast ultrasound image. One hundred and four cases of breast cancer patients were conducted in the experiment and optimal indicator was obtained using AdaBoost. The prediction accuracy of molecular marker ER could achieve 75.96% and the highest area under the receiver operating characteristic curve (AUC) was 79.39%. According to the results of experiment, the feasibility of predicting expression of ER in breast cancer using radiomics was verified.
Objective To detect expression of miR-106a-5p in gastric cancer cells and gastric cancer tissue, and to analyze relationship between it’s expression with clinicopathologic characteristics, and in addition, to analyze its target genes and enriched pathway with bioinformatics method. Methods The expressions of miR-106a-5p in the different differentiation of gastric cancer cells AGS (well differentiation), MKN-28 (middle differentiation), HGC-27 (undifferentiation), MGC-803 (low differentiation), BGC-823 (low differentiation), MKN-45 (middle differentiation) and SGC-7901 (middle differentiation), the normal gastric mucosal epithelial cells GES-1, and the gastric cancer tissue and the corresponding adjacent tissue were detected by the real-time fluorescent quantitative PCR. Furthermore, the target genes of miR-106a-5p were predicted by using more than three softwares affiliated to mirWALK web database and the signal pathways of target genes were enriched by DAVID 6.7 software. Results The expressions of miR-106a-5p in the different differentiation degree of the gastric cancer cells (AGS, SGC-7901, MKN-45, MGC-803, BGC-823, and HGC-27) were up-regulated except the MKN-28 cell line as compared with the normal gastric mucosa cell line GES-1 (P<0.010 orP<0.001), and the expression of miR-106a-5p in the gastric cancer tissue was also up-regulated as compared with the corresponding adjacent tissue, the expression of miR-106a-5p in the gastric cancer tissue was associated with the lymph node metastasis or the invasion depth. The results of the bioinformatics analysis showed that the target genes of miR-106a-5p were enriched in the multiple signaling pathways associated with the cancer. Conclusion miR-106a-5p is a molecular marker of high expression in gastric cancer and a potential cancer gene associated with lymph node metastasis and invasion depth.
This study aims to predict expression of Ki67 molecular marker in pancreatic cystic neoplasm using radiomics. We firstly manually segmented tumor area in multi-detector computed tomography (MDCT) images. Then 409 high-throughput features were automatically extracted and the least absolute shrinkage selection operator (LASSO) regression model was used for feature selection. After 200 bootstrapping repetitions of LASSO, 20 most frequently selected features made up the optimal feature set. Then 200 bootstrapping repetitions of support vector machine (SVM) classifier with 10-fold cross-validation were used to avoid overfitting and accurately predict the Ki67 expression. The highest prediction accuracy could achieve 85.29% and the highest area under the receiver operating characteristic curve (AUC) was 91.54% with a sensitivity (SENS) of 81.88% and a specificity (SPEC) of 86.75%. According to the results of experiment, the feasibility of predicting expression of Ki67 in pancreatic cystic neoplasm based on radiomics was verified.
ObjectiveTo observe the expressions of miR-143-3p in gastric cancer cells and gastric carcinoma tissues with its clinical significance, and to analyze the target genes with enriched pathway by using bioinformatics methods.MethodsThe expressions of miR-143-3p in different differentiation gastric cancer cells and normal gastric mucosa cell line, and the expressions in gastric cancer tissues and adjacent tissues were detected by real-time fluorescent quantitative PCR. In addition, OncomiR and YM500 databases were used to analyze the expression of miR-143-3p in gastric cancer tissues compared with adjacent tissues. Furthermore, the targets of miR-143-3p were predicted by using the software of miRecords website database, and at least three software-supported target genes were chosen to analyze the enriched the signal pathways in which the target gene was involved with DAVID 6.7 software.ResultsThe expressions of miR-143-3p in the different differentiation degree of gastric cancer cells compared with normal gastric mucosa cell line were downregulated (P<0.001), and the expression of miR-143-3p in gastric cancer tissues compared with adjacent tissues was also downregulated (downregulated in 36 cases, upregulated in 18 cases, and no alteration in 4 cases). The expression of miR-143-3p in gastric cancer tissues was associated with lymph node metastasis and invasion depth (P<0.05). Bioinformatics analysis results showed that the target genes of miR-143-3p were enriched in 38 signaling pathways associated with cancer.ConclusionMiR-143-3p is a down-regulated molecular marker in gastric cancer and a potentially clinically related tumor suppressor gene, which may be involved in the cancerous phenotype in carcinogenesis and development of gastric cancer.