Identifying mutated driver pathways in cancer by integrating multi-omics data

Since the driver pathway in cancer plays a crucial role in the formation and progression of cancer, it is very imperative to identify driver pathways, which will offer important information for precision medicine or personalized medicine. In this paper, an improved maximum weight submatrix problem model is proposed by integrating such three kinds of omics data as somatic mutations, copy number variations, and gene expressions. The model tries to adjust coverage and mutual exclusivity with the average weight of genes in a pathway, and simultaneously considers the correlation among genes, so that the pathway having high coverage but moderate mutual exclusivity can be identified. By introducing a kind of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm PGA-MWS is presented to solve the model. Experimental comparisons among algorithms GA, MOGA, iMCMC and PGA-MWS were performed on biological and simulated data sets. The experimental results show that, compared with the other three algorithms, the PGA-MWS one based on the improved model can identify the gene sets with high coverage but moderate mutual exclusivity and scales well. Many of the identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in literatures. The experimental results indicate that the proposed approach may become a useful complementary tool for detecting cancer pathways.     

Synthesis and characterization of new galactosylated-based N2O-donors tridentate ligands

The synthesis and characterization of three novel N₂O-donor ligands containing the group 4-[1-β-d-2,3,4,6-tetra-O-acetyl-galactosyl)]benzaldehyde are presented. The insertion of this group was designed to increase the absorption of the prodrug in tumor cells, and is part of an ongoing work in our group with tridentate ligands to develop potential cobalt(III) prodrugs. The synthetic route described here allowed the isolation of pure ligands with yields ranged 81–89%. Finally, compounds were characterized by IR, NMR and HRMS (ESI⁺).     

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.     

Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer

In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease–gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git.     

Induction of senescence in cancer cells by 5′-Aza-2′-deoxycytidine: Bioinformatics and experimental insights to its targets

5′-Aza-2′-deoxycytidine (5-Aza-dC) is a demethylating drug that causes genome-wide hypomethylation resulting in the expression of several tumor suppressor genes causing growth arrest of cancer cells. Cancer is well established as a multifactorial disease and requires multi-module therapeutics. Search for new drugs and their approval by FDA takes a long time. Keeping this in view, research on new functions of FDA-approved anticancer drugs is desired to expand the list of multi-module functioning drugs for cancer therapy. In this study, we conducted an analysis for new functions of 5-Aza-dC by applying bio-chemo-informatics approach. The potential of 5-Aza-dC bioactivity was analyzed by PASS online and Molinspiration. Target proteins were predicted by SuperPred. The protein networks and biological processes were analyzed by Biological Networks using Gene Ontology tool, BINGO, based on BIOGRID database. Interactions between 5-Aza-dC and targeted proteins were examined by Autodoc Vina integrated into pyrx software. Induction of p53 by 5-Aza-dC was tested in vitro using cancer cells. Bioinformatics analyses predicted that 5-Aza-dC functions as a p53 inducer, radiosensitizer, and inhibitor of some enzymes. It was predicted to target proteins including MDM2, POLA1, POLB, and CXCR4 that are involved in the induction of DNA damage response and p53-HDM2-p21 signaling. In this study, we provide experimental evidence showing HDM2 is one of the targets of 5-AZA-dC leading to activation of p53 pathway and growth arrest of cells. Furthermore, we found that the combinatorial treatment of 5-AZA-dC with three other drugs caused drug resistance. We discuss that 5-Aza-dC-induced senescence is a multi-module drug that controls cell proliferation phenotype not only by proteins but also by noncoding miRNAs. Further studies are warranted to dissect these mechanisms and establish 5-Aza-dC as an effective multi-module anticancer reagent.     

Discovering DNA methylation patterns for long non-coding RNAs associated with cancer subtypes

Despite growing evidence demonstrates that the long non-coding ribonucleic acids (lncRNAs) are critical modulators for cancers, the knowledge about the DNA methylation patterns of lncRNAs is quite limited. We develop a systematic analysis pipeline to discover DNA methylation patterns for lncRNAs across multiple cancer subtypes from probe, gene and network levels. By using The Cancer Genome Atlas (TCGA) breast cancer methylation data, the pipeline discovers various DNA methylation patterns for lncRNAs across four major subtypes such as luminal A, luminal B, her2-enriched as well as basal-like. On the probe and gene level, we find that both differentially methylated probes and lncRNAs are subtype specific, while the lncRNAs are not as specific as probes. On the network level, the pipeline constructs differential co-methylation lncRNA network for each subtype. Then, it identifies both subtype specific and common lncRNA modules by simultaneously analyzing multiple networks. We show that the lncRNAs in subtype specific and common modules differ greatly in terms of topological structure, sequence conservation as well as expression. Furthermore, the subtype specific lncRNA modules serve as biomarkers to improve significantly the accuracy of breast cancer subtypes prediction. Finally, the common lncRNA modules associate with survival time of patients, which is critical for cancer therapy.     

Photodynamic Therapy: Past,Present and Future

Though we crossed many milestones in the field of medicine and health care in eradicating some deadly diseases over the past decades, cancer remained a challenge taking the lives of millions of people and having adverse effects on the quality of life of survivors. Chemotherapy and radiotherapy, the two existing major treatment modalities, have severe side effects and patients undergoing these treatments experience unbearable pain. Consequently, clinicians and researchers are working for the alternate treatment regimens, which can provide complete cure with minimum or no side effects. To this end, the present review highlights the major advances and future promises of photodynamic therapy, an emerging and promising therapeutic modality for combating cancer. We delve on various important aspects of photodynamic therapy including principle, mechanism of action, brief history and development of photosensitizers from first generation to the existing third generation, delivery strategies, development or suppression of immunity, combination therapy and future prospects.     

An Electrochemical Nanosensor for Monitoring the Dynamics of Intracellular H2O2 Upon NADH Treatment

Reactive oxygen species (ROS)-based therapeutic strategies play an important role in cancer treatment. However, in situ, real-time and quantitative analysis of intracellular ROS in cancer treatment for drug screening is still a challenge. Herein we report one selective hydrogen peroxide (H₂O₂) electrochemical nanosensor, which is prepared by electrodeposition of Prussian blue (PB) and polyethylenedioxythiophene (PEDOT) onto carbon fiber nanoelectrode. With the nanosensor, we find that the level of intracellular H₂O₂ increases with NADH treatment and that increase is dose-dependent to the concentration of NADH. High-dose of NADH (above 10 mM) can induce cell death and intratumoral injection of NADH is validated for inhibiting tumor growth in mice. This study highlights the potential of electrochemical nanosensor for tracking and understanding the role of H₂O₂ in screening new anticancer drug.     

Immunogenic Cell Death Inducing Metal Complexes for Cancer Therapy

Cancer is one of the deadliest diseases worldwide. Recent statistics have shown that metastases and tumor relapse are the leading causes of cancer-associated deaths. While traditional treatments are able to efficiently remove the primary tumor, secondary tumors remain poorly accessible. Capitalizing on this there is an urgent need for novel treatment modalities. Among the most promising approaches, increasing research interest has been devoted to immunogenic cell death inducing agents that are able to trigger localized cell death of the cancer cells as well as induce an immune response inside the whole organism. Preliminary studies have shown that immunogenic cell death inducing compounds could be able to overcome metastatic and relapsing tumors. Herein, the application of metal complexes as immunogenic cell death inducing compounds is systematically reviewed.     

Selective In Situ Analysis of Mature microRNAs in Extracellular Vesicles Using a DNA Cage-Based Thermophoretic Assay

Mature microRNAs (miRNAs) in extracellular vesicles (EVs) are involved in different stages of cancer progression, yet it remains challenging to precisely detect mature miRNAs in EVs due to the presence of interfering RNAs (such as longer precursor miRNAs, pre-miRNAs) and the low abundance of tumor-associated miRNAs. By leveraging the size-selective ability of DNA cages and polyethylene glycol (PEG)-enhanced thermophoretic accumulation of EVs, we devised a DNA cage-based thermophoretic assay for highly sensitive, selective, and in situ detection of mature miRNAs in EVs with a low limit of detection (LoD) of 2.05 fM. Our assay can profile EV mature miRNAs directly in serum samples without the interference of pre-miRNAs and the need for ultracentrifugation. A clinical study showed that EV miR-21 or miR-155 had an overall accuracy of 90 % for discrimination between breast cancer patients and healthy donors, which outperformed conventional molecular probes detecting both mature miRNAs and pre-miRNAs. We envision that our assay can advance EV miRNA-based diagnosis of cancer.