Joint analysis of multiple high-dimensional data types using sparse matrix approximations of rank-1 with applications to ovarian and liver cancer

Capecitabine inhibits epithelial‑to‑mesenchymal transition and proliferation of colorectal cancer cells by mediating the RANK/RANKL pathway

Colorectal cancer (CRC) is the third most prevalent malignancy globally. Capecitabine is an important form of chemotherapy for colorectal cancer. The present study aims to investigate the underlying mechanism of action of the drug in CRC cells. In the present study, 50 pairs of CRC and adjacent normal tissues were collected, and CRC cell lines (SW480, SW620, HT29, LOVO and HCT116) and NCM460 colonic epithelial cells were also purchased and used. Western blotting was used to measure the expression levels of proteins involved in the receptor activator of nuclear factor-κB (RANK)/receptor activator of nuclear factor-κB ligand (RANKL) pathway and epithelial-to-mesenchymal transition (EMT), including RANK, RANKL, osteoprotegerin (OPG), E-cadherin, vimentin and N-cadherin. Proliferation and migration were measured using MTT, Cell Counting Kit-8, EdU, Transwell and wound healing assays, respectively. In the present study, it was found that the RANK/RANKL pathway was activated in cancer tissues and cells. Additionally, it was observed that capecitabine treatment reduced the protein expression of RANK, RANKL and OPG in HT29 cells, suggesting that capecitabine has a repressive effect on the RANK/RANKL pathway. Furthermore, functional experiments revealed that the proliferative ability and the EMT process observed in HT29 cells were inhibited after they were treated with capecitabine or transfected with si-RANK. Rescue assays were then performed, which revealed that the promotion of RANK via transfection of cells with 50 nM pcDNA3.1-RANK reversed the inhibitory effects of capecitabine on HT29 cell proliferation and EMT. These findings suggest that the regulatory role of capecitabine is at least partially mediated through the RANK/RANKL pathway in colorectal cancer. The present study demonstrated that capecitabine-induced repression of CRC is exerted by inhibiting the RANK/RANKL pathway, where this new mechanism potentially provides a novel therapeutic target.

Combining Algorithms to Find Signatures That Predict Risk in Early-Stage Stomach Cancer

This study applied two mathematical algorithms, lattice up-stream targeting (LUST) and D-basis, to the identification of prognostic signatures from cancer gene expression data. The LUST algorithm looks for metagenes, which are sets of genes that are either overexpressed or underexpressed in the same patients. Whereas LUST runs unsupervised by clinical data, the D-basis algorithm uses implications and association rules to relate gene expression to clinical outcomes. The D-basis selects a small subset of the metagene (a signature) to predict survival. The two algorithms, LUST and D-basis, were combined and applied to mRNA expression and clinical data from The Cancer Genome Atlas (TCGA) for 203 stage 1 and 2 stomach cancer patients. Two small (four-gene) signatures effectively predict survival in early-stage stomach cancer patients. These signatures could be used as a guide for treatment. The first signature (DU4) consists of genes that are underexpressed on the long-survival/low-risk group: FLRT2, KCNB1, MYOC, and TNXB. The second signature consists of genes that are overexpressed on the short-survival/high-risk group: ASB5, SFRP1, SMYD1, and TACR2. Another nine-gene signature (REC9) predicts recurrence: BNC2, CCDC8, DPYSL3, MOXD1, MXRA8, PRELP, SCARF2, TAGLN, and ZNF423. Each patient is assigned a score that is a linear combination of the expression levels for the genes in the signature. Scores below a selected threshold predict low-risk/long survival, whereas high scores indicate a high risk of short survival. The metagenes associate with TCGA cluster C1. Both our signatures and cluster C1 identify tumors that are genomically silent, and have a low mutation load or mutation count. Furthermore, our signatures identify tumors that are predominantly in the WHO classification of poorly cohesive and the Lauren class of diffuse samples, which have a poor prognosis.

Prognostic gene biomarker identification in liver cancer by data mining

BACKGROUND

Liver cancer is a common cancer that enormously threatens the health of people worldwide. With the continuous advances of high-throughput gene sequencing technology and computer data mining technology, researchers can understand liver cancer based on the current accumulation of gene expression data and clinical information.

METHODS

We downloaded the TCGA data of liver cancer on the cancer-related website (https://genome-cancer.ucsc.edu/proj/site/hgHeatmap/), comprising 438 patients and 20,530 genes. After removing some patients with missing survival data, we collected 397 patients' samples. Our data were collected from a public database without real patient participation. While matching the patient samples in the gene expression spectrum, we attained 330 samples with primary tumors and 50 samples with normal solid tissue.

RESULTS

After the 330 tumor tissue samples were randomized into two equal-numbered groups (one is a training set, and the other is a test set), we selected 26 gene biomarkers from the training set and validated them in the test set. Based on the selected 26 gene biomarkers, RBM14, ALG11, MAG, SETD3, HOXD10 and other 26 genes were considered independent risk factors for the prognosis of liver cancer, and genes such as GHR significantly affect human growth hormone for liver cancer. The findings discovered that low-risk patients survived remarkably better than the high-risk patients (P<0.001), and the area under the curve (AUC) of receiver operating characteristic curve (ROC) was greater than 0.5.

CONCLUSION

Our numerical results showed that these 26 gene biomarkers can be used to guide the effective prognostic therapy of patients with liver cancer.

Identification of prognostic gene biomarkers for metastatic skin cancer using data mining

Skin cancer is a common malignant tumor in China and throughout the world, and the rate of recurrence is considerably high, thus endangering the quality of life and health of patients, and increasing the economic burden and pressure to the families of those afflicted. Due to the limitations of traditional drug treatments, it is difficult to achieve the desired therapeutic effect of complete removal. However, targeted gene therapy may be a novel means of treating skin cancer, as the targeted nature of treatment may improve therapeutic outcomes. However, targeted gene therapy requires physicians to select the appropriate gene, which means suitable genetic biomarkers must be identified from complex genetic data. In the present study, the least absolute shrinkage and selection operator regression analysis method was used with 10-fold cross verification to reduce the dimensions of gene data in patients with skin cancer, and subsequently, 20 gene biomarkers were screened. A prognostic model was constructed using these 20 gene biomarkers, and the validity of the model was assessed using a training set and a verification set, which showed that the model performed well. Finally, gene function analysis of these 20 gene biomarkers was determined. Relevant studies were found to show that the genetic biomarkers identified in this paper may possess value for the follow-up clinical treatment of skin cancer.

Decision-Making based on Big Data Analytics for People Management in Healthcare Organizations

Big data analytics enables large-scale data sets integration, supporting people management decisions, and cost-effectiveness evaluation of healthcare organizations. The purpose of this article is to address the decision-making process based on big data analytics in Healthcare organizations, to identify main big data analytics able to support healthcare leaders' decisions and to present some strategies to enhance efficiency along the healthcare value chain. Our research was based on a systematic review. During the literature review, we will be presenting as well the different applications of big data in the healthcare context and a proposal for a predictive model for people management processes. Our research underlines the importance big data analytics can add to the efficiency of the decision-making process, through a predictive model and real-time analytics, assisting in the collection, management, and integration of data in healthcare organizations.

Investigating Maize Yield-Related Genes in Multiple Omics Interaction Network Data

Zea mays (maize) is the highest yielding food crop globally, feeding large numbers of people across the planet. It is thus especially important to explore the key genes that affect maize production with prior knowledge. Merging multiple datasets of different types can improve the accuracy of candidate genes prediction results, so we constructed interaction networks using gene, mRNA, protein, and expression profile datasets. A network propagation schedule was used considering combined scores obtained by integrating both network scores and significance scores for each candidate gene based on the guilt-by-association principle. An SVM model was used to optimize the weighted parameters to achieve more reliable results, according to the accuracy of label classification. We found that integrating multiple omics data with more data types improves the reliability of the results. We investigated the GO terms particularly associated with the top 100 candidate genes and the known genes, and analyzed the roles that these genes play in determining the phenotype of maize. We hope that the candidate genes identified here will provide a biological perspective and contribute to maize breeding research.