Transcriptional signatures for coupled predictions of stage II and III colorectal cancer metastasis and fluorouracil-based adjuvant chemotherapy benefit.

The current study suggests that the identification of predictive signatures of fluorouracil (5-FU) response for stage II and III colorectal cancer (CRC) could be confounded by chemotherapy-irrelevant low relapse risk. Using the samples of patients with stage II and III CRC who were treated with curative surgery only, we identified a signature with which to predict chemotherapy-irrelevant relapse risk for patients after curative surgery. By applying this signature to the samples of patients with stage II and III CRC who were treated with 5-FU-based adjuvant chemotherapy (ACT) after surgery, we predicted the relapse risk if treated with surgery only. From high-risk samples, we further identified another signature with which to predict therapeutic benefit from 5-FU-based ACT. On the basis of the relative expression orderings of gene pairs, a postsurgery relapse risk signature that consisted of 44 gene pairs was developed and verified in 3 independent data sets. A 5-FU therapeutic benefit signature that consisted of 4 gene pairs was then developed to predict the response of 5-FU-based ACT for those patients with high relapse risk after curative surgery. The signature was verified in 4 independent datasets. For patients with stage II and III CRC, the coupled signatures can first identify patients with high relapse risk after curative surgery, then predict therapeutic benefit from 5-FU-based ACT.-Song, K., Guo, Y., Wang, X., Cai, H., Zheng, W., Li, N., Song, X., Ao, L., Guo, Z., Zhao, W. Transcriptional signatures for coupled predictions of stage II and III colorectal cancer metastasis and fluorouracil-based adjuvant chemotherapy benefit.

Evaluating hepatocellular carcinoma cell lines for tumour samples using within-sample relative expression orderings of genes.

BACKGROUND & AIMS: Concerns are raised about the representativeness of cell lines for tumours due to the culture environment and misidentification. Liver is a major metastatic destination of many cancers, which might further confuse the origin of hepatocellular carcinoma cell lines. Therefore, it is of crucial importance to understand how well they can represent hepatocellular carcinoma. METHODS: The HCC-specific gene pairs with highly stable relative expression orderings in more than 99% of hepatocellular carcinoma but with reversed relative expression orderings in at least 99% of one of the six types of cancer, colorectal carcinoma, breast carcinoma, non-small-cell lung cancer, gastric carcinoma, pancreatic carcinoma and ovarian carcinoma, were identified. RESULTS: With the simple majority rule, the HCC-specific relative expression orderings from comparisons with colorectal carcinoma and breast carcinoma could exactly discriminate primary hepatocellular carcinoma samples from both primary colorectal carcinoma and breast carcinoma samples. Especially, they correctly classified more than 90% of liver metastatic samples from colorectal carcinoma and breast carcinoma to their original tumours. Finally, using these HCC-specific relative expression orderings from comparisons with six cancer types, we identified eight of 24 hepatocellular carcinoma cell lines in the Cancer Cell Line Encyclopedia (Huh-7, Huh-1, HepG2, Hep3B, JHH-5, JHH-7, C3A and Alexander cells) that are highly representative of hepatocellular carcinoma. Evaluated with a REOs-based prognostic signature for hepatocellular carcinoma, all these eight cell lines showed the same metastatic properties of the high-risk metastatic hepatocellular carcinoma tissues. CONCLUSIONS: Caution should be taken for using hepatocellular carcinoma cell lines. Our results should be helpful to select proper hepatocellular carcinoma cell lines for biological experiments.

[Study of the RNA secondary structure prediction].

This paper proposes algorithm in predicting the RNA secondary structure that combines several sequence comparisons, searches the eigenvalue for subsequence division with dynamic programing, utilizing the minimum free energy method. Moreover, the paper assesses the results derived from this new algorithm based on base-pairs distance, climbing distance and morphology distance. The paper also compares the assessment result and the prediction results of different prediction tools, and analyzes the advantages of the new method and its improvement direction.

WGRLR: A Weighted Group Regularized Logistic Regression for Cancer Diagnosis and Gene Selection.

Sparse regressions applied to cancer diagnosis suffer from noise reduction, gene grouping, and group significance evaluation. This paper presented the weighted group regularized logistic regression (WGRLR) for dealing with the above problems. Clean data was separated from noisy gene expression profile data, based on which gene grouping and model building were performed. An interpretable gene group significance evaluation criterion was proposed based on symmetrical uncertainty and module eigengene. A group-wise individual gene significance evaluation criterion was also presented. The performances of the proposed method were compared with WGGL, ASGL-CMI, SGL, GL, Elastic Net, and lasso on acute leukemia and brain cancer data. Experimental results demonstrate that the proposed method is superior to the other six methods in cancer diagnosis accuracy and gene selection.

Logistic regression with adaptive sparse group lasso penalty and its application in acute leukemia diagnosis.

Cancer diagnosis based on gene expression profile data has attracted extensive attention in computational biology and medicine. It suffers from three challenges in practical applications: noise, gene grouping, and adaptive gene selection. This paper aims to solve the above problems by developing the logistic regression with adaptive sparse group lasso penalty (LR-ASGL). A noise information processing method for cancer gene expression profile data is first presented via robust principal component analysis. Genes are then divided into groups by performing weighted gene co-expression network analysis on the clean matrix. By approximating the relative value of the noise size, gene reliability criterion and robust evaluation criterion are proposed. Finally, LR-ASGL is presented for simultaneous cancer diagnosis and adaptive gene selection. The performance of the proposed method is compared with the other four methods in three simulation settings: Gaussian noise, uniformly distributed noise, and mixed noise. The acute leukemia data are adopted as an experimental example to demonstrate the advantages of LR-ASGL in prediction and gene selection.

Identification of Tamoxifen-Resistant Breast Cancer Cell Lines and Drug Response Signature.

Breast cancer cell lines are frequently used to elucidate the molecular mechanisms of the disease. However, a large proportion of cell lines are affected by problems such as mislabeling and cross-contamination. Therefore, it is of great clinical significance to select optimal breast cancer cell lines models. Using tamoxifen survival-related genes from breast cancer tissues as the gold standard, we selected the optimal cell line model to represent the characteristics of clinical tissue samples. Moreover, using relative expression orderings of gene pairs, we developed a gene pair signature that could predict tamoxifen therapy outcomes. Based on 235 consistently identified survival-related genes from datasets GSE17705 and GSE6532, we found that only the differentially expressed genes (DEGs) from the cell line dataset GSE26459 were significantly reproducible in tissue samples (binomial test, p = 2.13E-07). Finally, using the consistent DEGs from cell line dataset GSE26459 and tissue samples, we used the transcriptional qualitative feature to develop a two-gene pair (TOP2A, SLC7A5; NMU, PDSS1) for predicting clinical tamoxifen resistance in the training data (logrank p = 1.98E-07); this signature was verified using an independent dataset (logrank p = 0.009909). Our results indicate that the cell line model from dataset GSE26459 provides a good representation of the characteristics of clinical tissue samples; thus, it will be a good choice for the selection of drug-resistant and drug-sensitive breast cancer cell lines in the future. Moreover, our signature could predict tamoxifen treatment outcomes in breast cancer patients.

Adaptive multinomial regression with overlapping groups for multi-class classification of lung cancer.

Multi-class classification has attracted much attention in cancer diagnosis and treatment and many machine learning methods have emerged for addressing this issue recently. However, class imbalance and gene selection problems occur in classifying lung cancer data. In this paper, an adaptive multinomial regression with a sparse overlapping group lasso penalty is proposed to perform classification and grouped gene selection for lung cancer gene expression data. An overlapped grouping strategy with biological interpretability is proposed, which highlights the importance of gene groups from the minority classes. By using the conditional mutual information, the gene significance within each group is evaluated and the data-driven weights are constructed. Based on the grouping strategy and constructed weights, a regularized adaptive multinomial regression is presented and the solving algorithm is developed, which can not only select the important gene groups for each class in performing multi-class classification, but also adaptively select important genes within each group. The experiment results show that the proposed method significantly outperforms the other 6 methods on classification accuracy, and the selected genes are disease-causing genes for lung cancer.

Grouped gene selection and multi-classification of acute leukemia via new regularized multinomial regression.

Diagnosing acute leukemia is the necessary prerequisite to treating it. Multi-classification on the gene expression data of acute leukemia is help for diagnosing it which contains B-cell acute lymphoblastic leukemia (BALL), T-cell acute lymphoblastic leukemia (TALL) and acute myeloid leukemia (AML). However, selecting cancer-causing genes is a challenging problem in performing multi-classification. In this paper, weighted gene co-expression networks are employed to divide the genes into groups. Based on the dividing groups, a new regularized multinomial regression with overlapping group lasso penalty (MROGL) has been presented to simultaneously perform multi-classification and select gene groups. By implementing this method on three-class acute leukemia data, the grouped genes which work synergistically are identified, and the overlapped genes shared by different groups are also highlighted. Moreover, MROGL outperforms other five methods on multi-classification accuracy.

Shared liver-like transcriptional characteristics in liver metastases and corresponding primary colorectal tumors.

Background & Aims: Primary tumors of colorectal carcinoma (CRC) with liver metastasis might gain some liver-specific characteristics to adapt the liver micro-environment. This study aims to reveal potential liver-like transcriptional characteristics associated with the liver metastasis in primary colorectal carcinoma. Methods: Among the genes up-regulated in normal liver tissues versus normal colorectal tissues, we identified "liver-specific" genes whose expression levels ranked among the bottom 10% ("unexpressed") of all measured genes in both normal colorectal tissues and primary colorectal tumors without metastasis. These liver-specific genes were investigated for their expressions in both the primary tumors and the corresponding liver metastases of seven primary CRC patients with liver metastasis using microdissected samples. Results: Among the 3958 genes detected to be up-regulated in normal liver tissues versus normal colorectal tissues, we identified 12 liver-specific genes and found two of them, ANGPTL3 and CFHR5, were unexpressed in microdissected primary colorectal tumors without metastasis but expressed in both microdissected liver metastases and corresponding primary colorectal tumors (Fisher's exact test, P < 0.05). Genes co-expressed with ANGPTL3 and CFHR5 were significantly enriched in metabolism pathways characterizing liver tissues, including "starch and sucrose metabolism" and "drug metabolism-cytochrome P450". Conclusions: For primary CRC with liver metastasis, both the liver metastases and corresponding primary colorectal tumors may express some liver-specific genes which may help the tumor cells adapt the liver micro-environment.

Epigallocatechin-3-gallate(EGCG) : mechanisms and the combined applications.

EGCG is the most important pharmacological component in tea. Researches have confirmed its effects, including anti-tumor, anti-inflammation, anti-aging, anti-obesity, anti-diabetic, cardiovascular disease prevention and protection, immunoregulation and neuroprotection. Paradoxically, the clinical application of EGCG is very rare. One of the most important reasons is its poor stability and low bioavailability. Excepting for altering the dosage form or synthesizing the analogues to overcome the loss during absorption, an increasing number of studies indicate that EGCG can exert certain auxiliary effect and enhance chemosensitivity in combined medication. The pharmacological action, the pharmacology network including mutation of signaling receptor and modulation of intracellular signaling pathway, and the combination treatment strategy of EGCG are clarified and sorted out, both the possible targets and combinatorial applications based on the characteristics of EGCG are systematically summarized.

An individualized prognostic signature and multi­omics distinction for early stage hepatocellular carcinoma patients with surgical resection.

Previously reported prognostic signatures for predicting the prognoses of postsurgical hepatocellular carcinoma (HCC) patients are commonly based on predefined risk scores, which are hardly applicable to samples measured by different laboratories. To solve this problem, using gene expression profiles of 170 stage I/II HCC samples, we identified a prognostic signature consisting of 20 gene pairs whose within-sample relative expression orderings (REOs) could robustly predict the disease-free survival and overall survival of HCC patients. This REOs-based prognostic signature was validated in two independent datasets. Functional enrichment analysis showed that the patients with high-risk of recurrence were characterized by the activations of pathways related to cell proliferation and tumor microenvironment, whereas the low-risk patients were characterized by the activations of various metabolism pathways. We further investigated the distinct epigenomic and genomic characteristics of the two prognostic groups using The Cancer Genome Atlas samples with multi-omics data. Epigenetic analysis showed that the transcriptional differences between the two prognostic groups were significantly concordant with DNA methylation alternations. The signaling network analysis identified several key genes (e.g. TP53, MYC) with epigenomic or genomic alternations driving poor prognoses of HCC patients. These results help us understand the multi-omics mechanisms determining the outcomes of HCC patients.

Identification of Metabolites and Metabolic Pathways Related to Treatment with Bufei Yishen Formula in a Rat COPD Model Using HPLC Q-TOF/MS.

As a traditional Chinese medicine, Bufei Yishen Formula (BYF) is widely used in China as an effective treatment for chronic obstructive pulmonary disease (COPD). Because of the component complexity and multiple activities of Chinese herbs, the mechanism whereby BYF affects COPD is not yet fully understood. Herein, pulmonary function experiments and histomorphological assessments were used to evaluate the curative effect of BYF, which showed that BYF had an effect on COPD. Additionally, a high performance liquid chromatography quadrupole time-of-flight mass spectrometry (HPLC QTOF/MS) metabonomics method was used to analyze the mechanism of the actions of BYF on rats with COPD induced by a combination of bacteria and smoking. Partial least squares discriminate analysis (PLS-DA) was used to screen biomarkers related to BYF treatment. Candidate biomarkers were selected and pathways analysis of these metabolites showed that three types of metabolic pathways (unsaturated fatty acid metabolism-related pathways, phenylalanine metabolism-related pathways, and phospholipid metabolism-related pathways) were associated with BYF treatment. Importantly, arachidonic acid and related metabolic pathways might be useful targets for novel COPD therapies.

CCNB2 overexpression is a poor prognostic biomarker in Chinese NSCLC patients.

Cyclin B2 (CCNB2), a member of cyclin family proteins, serves a key role in progression of G2/M transition. The clinical value of CCNB2 in non-small cell lung cancer is still unknown. The aim of our study is to identify the role of CCNB2 in NSCLC patients. The status of CCNB2 in NSCLC tissues and normal lung tissues was observed in Gene Expression Omnibus (GEO: GSE19804). CCNB2 mRNA and protein expressions were detected in NSCLC and normal lung tissues by using Real-time PCR and immunohistochemistry. The association of CCNB protein expression with clinical characteristics of 186 NSCLC patients was analyzed by immunohistochemistry. Based on microarray data (GEO: GSE19804), we observed that CCNB2 was significantly overexpressed in NSCLC tissues compared with paired adjacent normal lung tissue. Furthermore, we verified mRNA and protein levels of CCNB2 expression were both increased in NSCLC tissues. We found high levels of CCNB2 protein were positively associated with the status of differentiated degree, tumor size, lymph node metastasis, distant metastasis, and clinical stage. Meanwhile, CCNB2 protein overexpression was an independent unfavorable prognostic factor for the overall survival of patients with NSCLC. In conclusion, overexpression of CCNB2 protein is associated with clinical progression and poor prognosis in NSCLC.