A computational bioinformatics analysis of gene expression identifies candidate agents for prostate cancer

Transcriptomic analysis of castration, chemo-resistant and metastatic prostate cancer elucidates complex genetic crosstalk leading to disease progression

Prostate adenocarcinoma, with its rising numbers and high fatality rate, is a daunting healthcare challenge to clinicians and researchers alike. The mainstay of our meta-analysis was to decipher differentially expressed genes (DEGs), their corresponding transcription factors (TFs), miRNAs (microRNA) and interacting pathways underlying the progression of prostate cancer (PCa). We have chosen multiple datasets from primary, castration-resistant, chemo-resistant and metastatic prostate cancer stages for investigation. From our tissue-specific and disease-specific co-expression networks, fifteen hub genes such as ACTB, ACTN1, CDH1, CDKN1A, DDX21, ELF3, FLNA, FLNC, IKZF1, ILK, KRT13, KRT18, KRT19, SVIL and TRIM29 were identified and validated by molecular complex detection analysis as well as survival analysis. In our attempt to highlight hub gene-associated mutations and drug interactions, FLNC was found to be most commonly mutated and CDKN1A gene was found to have highest druggability. Moreover, from DAVID and gene set enrichment analysis, the focal adhesion and oestrogen signalling pathways were found enriched which indicates the involvement of hub genes in tumour invasiveness and metastasis. Finally by Enrichr tool and miRNet, we identified transcriptional factors SNAI2, TP63, CEBPB and KLF11 and microRNAs, namely hsa-mir-1-3p, hsa-mir-145-5p, hsa-mir-124-3p and hsa-mir-218-5p significantly controlling the hub gene expressions. In a nutshell, our report will help to gain a deeper insight into complex molecular intricacies and thereby unveil the probable biomarkers and therapeutic targets involved with PCa progression.

Expression profiling revealed keratins and interleukins as potential biomarkers in squamous cell carcinoma of horn in Indian bullocks (Bos indicus)

Horn cancer is most prevalent in Bos indicus and poorly defined genetic landscape makes disease diagnosis and treatment difficult. In this study, RNA-Seq and data analysis using CLC Genomics Workbench was employed to identify biomarkers associated with horn cancer. As a result, a total of 149 genes were found significant differentially expressed in horn cancer samples compared to horn normal samples. The study revealed 'keratins' and 'interleukins' as apex groups of significant differentially expressed genes (DEGs). Functional analysis showed that the upregulated keratins support metastasis of tumor via cell proliferation, migration, and affecting cell stability, while downregulated interleukins along with other associated chemokine receptors deprive the immune response to tumor posing clear path for metastasis of horn cancer. Combi-action of both the group facilitates the tumor microenvironment to reproduce tumorigenesis. Analysis of pathways enriched in DEGs and exemplified protein-protein interaction network indicated actual role of DEGs in horn cancer at a fine level. Important effect of deregulated expression of keratin and interleukin genes in horn cancer enrolling their candidacy as potential biomarkers for horn cancer prognosis. This study appraises the possibility to mitigate horn cancer at fine resolution to extract attainable identification of prognostic molecular portraits.

Repurposing Drugs by In Silico Methods to Target BCR Kinase Domain in Chronic Myeloid Leukemia

BACKGROUND

Targeted therapy in the form of highly selective tyrosine kinase inhibitors (TKIs) has transformed the treatment of chronic myeloid leukemia (CML). However, mutations in the kinase domain contribute to drug resistance against TKIs which compromises the treatment response. Our aim is to explore regions outside the BCR-ABL oncoprotein to identify potential therapeutic targets to curb drug resistance by targeting growth factor receptor-bound protein-2 (Grb-2) which binds to BCR-ABL at the phosphorylated tyrosine (Y177) thereby activating the Ras and PI3K/AKT signaling pathway.

METHODS

We have used in silico methods to repurpose drugs for identifying their potential to inhibit the binding of Grb-2 with Y177 by occupying the active binding site of the BCR domain.

RESULTS

Differentially expressed genes from GEO dataset were found to be associated with hematopoietic cell lineage, NK cell-mediated cytotoxicity, NF-κB and chemokine signaling, cytokine-cytokine receptor interaction, histidine metabolism and transcriptional misregulation in cancer. The fold recognition method of SPARKS-X tool was used to model the BCR domain (Z-score = 8.21). Connectivity Map generated a drug list based on the gene expression profile, which were docked with BCR. Schrodinger XP glide docking identified Diphosphopyridine nucleotide, Hesperidin, Butirosin, Ovoflavin, and Nor-dihydroguaiaretic acid to show strong interaction in close proximity to the active binding pocket containing Y177 of the target protein and was further validated using iGEMDOCK and Parallelized Open Babel and AutoDock suite Pipeline (POAP).

CONCLUSION

Our study not only extends our current knowledge about repurposing drugs for newer indications but also provides a route towards combinatorial therapy with standard drugs used for CML treatment. However, the efficacy of these repurposed drugs needs to be further investigated using in vitro and in vivo studies.<br />.

Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning

BACKGROUND

Genome-scale metabolic models (GEMs) offer insights into cancer metabolism and have been used to identify potential biomarkers and drug targets. Drug repositioning is a time- and cost-effective method of drug discovery that can be applied together with GEMs for effective cancer treatment.

METHODS

In this study, we reconstruct a prostate cancer (PRAD)-specific GEM for exploring prostate cancer metabolism and also repurposing new therapeutic agents that can be used in development of effective cancer treatment. We integrate global gene expression profiling of cell lines with >1000 different drugs through the use of prostate cancer GEM and predict possible drug-gene interactions.

FINDINGS

We identify the key reactions with altered fluxes based on the gene expression changes and predict the potential drug effect in prostate cancer treatment. We find that sulfamethoxypyridazine, azlocillin, hydroflumethiazide, and ifenprodil can be repurposed for the treatment of prostate cancer based on an in silico cell viability assay. Finally, we validate the effect of ifenprodil using an in vitro cell assay and show its inhibitory effect on a prostate cancer cell line.

INTERPRETATION

Our approach demonstate how GEMs can be used to predict therapeutic agents for cancer treatment based on drug repositioning. Besides, it paved a way and shed a light on the applicability of computational models to real-world biomedical or pharmaceutical problems.

Identification of key genes in prostate cancer gene expression profile by bioinformatics

The aim of this study was to identify key candidate genes in prostate cancer. The gene expression profiles of GSE32448, GSE45016, GSE46602 and GSE104749 were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between prostate cancer and normal samples were identified by R language. The gene ontology functional and pathway enrichment analyses of DEGs were performed by the Database for Annotation, Visualization and Integrated Discovery software followed by the construction of protein-protein interaction network. Hub gene identification was performed by the plug-in cytoHubba in Cytoscape software. The 217 DEGs were significantly enriched in biological processes including epithelial cell differentiation, response to estradiol and several pathways, mainly associated with protein digestion and absorption pathway in prostate cancer. Epithelial cell adhesion molecule, twist family basic helix-loop-helix transcription factor 1, CD38 molecule and vascular endothelial growth factor A were identified as hub genes. The expression levels of hub genes were consistent with data obtained in The Cancer Genome Atlas for prostate adenocarcinoma. These hub genes may be used as potential targets for prostate cancer diagnosis and treatment.

Cell division cycle 20 overexpression predicts poor prognosis for patients with lung adenocarcinoma

The cell division cycle 20, a key component of spindle assembly checkpoint, is an essential activator of the anaphase-promoting complex. Aberrant expression of cell division cycle 20 has been detected in various human cancers. However, its clinical significance has never been deeply investigated in non-small-cell lung cancer. By analyzing The Cancer Genome Atlas database and using some certain online databases, we validated overexpression of cell division cycle 20 in both messenger RNA and protein levels, explored its clinical significance, and evaluated the prognostic role of cell division cycle 20 in non-small-cell lung cancer. Cell division cycle 20 expression was significantly correlated with sex (p = 0.003), histological classification (p < 0.0001), and tumor size (p = 0.0116) in non-small-cell lung cancer patients. In lung adenocarcinoma patients, overexpression of cell division cycle 20 was significantly associated with bigger primary tumor size (p = 0.0023), higher MKI67 level (r = 0.7618, p < 0.0001), higher DNA ploidy level (p < 0.0001), and poor prognosis (hazard ratio = 2.39, confidence interval: 1.87-3.05, p < 0.0001). However, in lung squamous cell carcinoma patients, no significant association of cell division cycle 20 expression was observed with any clinical parameter or prognosis. Overexpression of cell division cycle 20 is associated with poor prognosis in lung adenocarcinoma patients, and its overexpression can also be used to identify high-risk groups. In conclusion, cell division cycle 20 might serve as a potential biomarker for lung adenocarcinoma patients.

Transcriptomic comparison of primary bovine horn core carcinoma culture and parental tissue at early stage

Aim:

Squamous cell carcinoma or SCC of horn in bovines (bovine horn core carcinoma) frequently observed in Bos indicus affecting almost 1% of cattle population. Freshly isolated primary epithelial cells may be closely related to the malignant epithelial cells of the tumor. Comparison of gene expression in between horn’s SCC tissue and its early passage primary culture using next generation sequencing was the aim of this study.

Materials and Methods:

Whole transcriptome sequencing of horn’s SCC tissue and its early passage cells using Ion Torrent PGM were done. Comparative expression and analysis of different genes and pathways related to cancer and biological processes associated with malignancy, proliferating capacity, differentiation, apoptosis, senescence, adhesion, cohesion, migration, invasion, angiogenesis, and metabolic pathways were identified.

Results:

Up-regulated genes in SCC of horn’s early passage cells were involved in transporter activity, catalytic activity, nucleic acid binding transcription factor activity, biogenesis, cellular processes, biological regulation and localization and the down-regulated genes mainly were involved in focal adhesion, extracellular matrix receptor interaction and spliceosome activity.

Conclusion:

The experiment revealed similar transcriptomic nature of horn’s SCC tissue and its early passage cells.

Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis

Motivation: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug therapies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corresponding genome-wide responses of multiple cell models.

Results: In this article, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on genome-wide gene expression across several cancer cell lines [Connectivity Map (CMap) database]. The task is formulated as searching for drug response components across multiple cancers to reveal shared effects of drugs and the chemical features that may be responsible. The components can be computed with an extension of a recent approach called Group Factor Analysis. We identify 11 components that link the structural descriptors of drugs with specific gene expression responses observed in the three cell lines and identify structural groups that may be responsible for the responses. Our method quantitatively outperforms the limited earlier methods on CMap and identifies both the previously reported associations and several interesting novel findings, by taking into account multiple cell lines and advanced 3D structural descriptors. The novel observations include: previously unknown similarities in the effects induced by 15-delta prostaglandin J2 and HSP90 inhibitors, which are linked to the 3D descriptors of the drugs; and the induction by simvastatin of leukemia-specific response, resembling the effects of corticosteroids.

Availability and implementation: Source Code implementing the method is available at: http://research.ics.aalto.fi/mi/software/GFAsparse

Contact:suleiman.khan@aalto.fi or samuel.kaski@aalto.fi

Supplementary Information:Supplementary data are available at Bioinformatics online.