Prediction of cancer outcome using DNA microarray technology: past, present and future

Identification of potential key genes and functional role of CENPF in osteosarcoma using bioinformatics and experimental analysis

Osteosarcoma, which arises from bone tissue, is considered to be one of the most common types of cancer in children and teenagers. As the etiology of osteosarcoma has not been fully elucidated, the overall prognosis for patients is generally poor. In recent years, the development of bioinformatical technology has allowed researchers to identify numerous molecular biological characteristics associated with the prognosis of osteosarcoma using online databases. In the present study, Gene Expression Omnibus (GEO) database was used and three microarray datasets were obtained. The GEO2R web tool was utilized and differentially expressed genes (DEGs) in osteosarcoma tissue were identified. Venn analysis was performed to determine the intersection of the DEG profiles. DEGs were analyzed by Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Protein-protein interactions (PPIs) between these DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes database, and the PPI network was then visualized using Cytoscape software. The top ten genes were identified based on measurement of degree, density of maximum neighborhood component, maximal clique centrality and mononuclear cell counts in the PPI network, and five overlapping genes [origin recognition complex subunit 6 (ORC6), IGF-binding protein 5 (IGFBP5), minichromosome maintenance 10 replication initiation factor (MCM10), MET proto-oncogene, receptor tyrosine kinase (MET) and centromere protein F (CENPF)] were identified. Additionally, three module networks were analyzed by Molecular Complex Detection (MCODE), and six key genes [ORC6, MCM10, DEP domain containing 1 (DEPDC1), CENPF, TIMELESS interacting protein (TIPIN) and shugoshin 1 (SGOL1)] were screened. Combined with the results from Cytoscape and MCODE, eight hub genes (ORC6, MCM10, DEPDC1, CENPF, TIPIN, SGOL1, MET and IGFBP5) were obtained. Furthermore, Kaplan-Meier plotter survival analysis was used to evaluate the prognostic value of these eight hub genes in patients with osteosarcoma. Oncomine and GEPIA databases were applied to further confirm the expression levels of hub genes in tissue. Finally, the functional roles of the core gene CENPF were investigated using Cell Counting Kit-8, wound healing and Transwell assays, which indicated that CENPF knockdown inhibited the proliferation, migration and invasion of osteosarcoma cells. These results provided potential prognostic markers, as well as a basis for further investigation of the mechanism underlying osteosarcoma.

Exploring the molecular subclasses and stage-specific genes of oral cancer: A bioinformatics analysis

The rate of people getting affected by Oral cancer in Sri Lanka is growing rapidly since the root cause of such cancer, betel quid chewing is tightly coupled with the tradition of the country. The five-year survival rate of the disease is also pretty low as it is typically detected at advanced stages. This urges a comprehensive study on the marker genes of oral cancer for the successful therapeutic revisions that would potentially identify cancer in its early stages. Further, the identification of molecular subclasses can assist in individualizing the treatment for this type of fatal disease. This study uses the bioinformatics analysis on the gene expression dataset of 56 oral cancer patients from Sri Lanka and the United Kingdom to identify the differentially expressed genes where these genes are later clustered and classified into molecular subclasses. Molecular subclasses are found by clustering the genes that stratify together and the stages were identified with the use of gene co-expression networks. Five molecular subclasses of oral cancer were identified and the genes associated with each tumour stage. Out of the genes that are clustered and classified, TAGLN2, CCND2 and CCL8 were well-known tumour suppressor genes and GPX3, GRN and ITGB4 genes are involved in several carcinomas. Putative marker genes of Oral Squamous Cell Carcinoma were identified which could facilitate the medical practitioner in the early detection of oral cancer and also in the improvement of treatment methods.

Ratiometric Array of Conjugated Polymers-Fluorescent Protein Provides a Robust Mammalian Cell Sensor

Supramolecular complexes of a family of positively charged conjugated polymers (CPs) and green fluorescent protein (GFP) create a fluorescence resonance energy transfer (FRET)-based ratiometric biosensor array. Selective multivalent interactions of the CPs with mammalian cell surfaces caused differential change in FRET signals, providing a fingerprint signature for each cell type. The resulting fluorescence signatures allowed the identification of 16 different cell types and discrimination between healthy, cancerous, and metastatic cells, with the same genetic background. While the CP-GFP sensor array completely differentiated between the cell types, only partial classification was achieved for the CPs alone, validating the effectiveness of the ratiometric sensor. The utility of the biosensor was further demonstrated in the detection of blinded unknown samples, where 121 of 128 samples were correctly identified. Notably, this selectivity-based sensor stratified diverse cell types in minutes, using only 2000 cells, without requiring specific biomarkers or cell labeling.

Radiogenomic imaging-linking diagnostic imaging and molecular diagnostics

Radiogenomic imaging refers to the correlation between cancer imaging features and gene expression and is one of the most promising areas within science and medicine. High-throughput biological techniques have reshaped the perspective of biomedical research allowing for fast and efficient assessment of the entire molecular topography of a cell’s physiology providing new insights into human cancers. The use of non-invasive imaging tools for gene expression profiling of solid tumors could serve as a means for linking specific imaging features with specific gene expression patterns thereby allowing for more accurate diagnosis and prognosis and obviating the need for high-risk invasive biopsy procedures. This review focuses on the medical imaging part as one of the main drivers for the development of radiogenomic imaging.

Identification of ovarian cancer driver genes by using module network integration of multi-omics data

The increasing availability of multi-omics cancer datasets has created a new opportunity for data integration that promises a more comprehensive understanding of cancer. The challenge is to develop mathematical methods that allow the integration and extraction of knowledge from large datasets such as The Cancer Genome Atlas (TCGA). This has led to the development of a variety of omics profiles that are highly correlated with each other; however, it remains unknown which profile is the most meaningful and how to efficiently integrate different omics profiles. We developed AMARETTO, an algorithm to identify cancer drivers by integrating a variety of omics data from cancer and normal tissue. AMARETTO first models the effects of genomic/epigenomic data on disease-specific gene expression. AMARETTO's second step involves constructing a module network to connect the cancer drivers with their downstream targets. We observed that more gene expression variation can be explained when using disease-specific gene expression data. We applied AMARETTO to the ovarian cancer TCGA data and identified several cancer driver genes of interest, including novel genes in addition to known drivers of cancer. Finally, we showed that certain modules are predictive of good versus poor outcome, and the associated drivers were related to DNA repair pathways.

Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results

PURPOSE

To identify prognostic imaging biomarkers in non-small cell lung cancer (NSCLC) by means of a radiogenomics strategy that integrates gene expression and medical images in patients for whom survival outcomes are not available by leveraging survival data in public gene expression data sets.

MATERIALS AND METHODS

A radiogenomics strategy for associating image features with clusters of coexpressed genes (metagenes) was defined. First, a radiogenomics correlation map is created for a pairwise association between image features and metagenes. Next, predictive models of metagenes are built in terms of image features by using sparse linear regression. Similarly, predictive models of image features are built in terms of metagenes. Finally, the prognostic significance of the predicted image features are evaluated in a public gene expression data set with survival outcomes. This radiogenomics strategy was applied to a cohort of 26 patients with NSCLC for whom gene expression and 180 image features from computed tomography (CT) and positron emission tomography (PET)/CT were available.

RESULTS

There were 243 statistically significant pairwise correlations between image features and metagenes of NSCLC. Metagenes were predicted in terms of image features with an accuracy of 59%-83%. One hundred fourteen of 180 CT image features and the PET standardized uptake value were predicted in terms of metagenes with an accuracy of 65%-86%. When the predicted image features were mapped to a public gene expression data set with survival outcomes, tumor size, edge shape, and sharpness ranked highest for prognostic significance.

CONCLUSION

This radiogenomics strategy for identifying imaging biomarkers may enable a more rapid evaluation of novel imaging modalities, thereby accelerating their translation to personalized medicine.

Transcriptomics: mRNA and alternative splicing

Transcriptomics has emerged as a powerful approach for biomarker discovery. In the present review, the two main types of high throughput transcriptomic technologies - microarrays and next generation sequencing - that can be used to identify candidate biomarkers are briefly described. Microarrays, the mainstream technology of the last decade, have provided hundreds of valuable datasets in a wide variety of diseases including multiple sclerosis (MS), in which this approach has been used to disentangle different aspects of its complex pathogenesis. RNA-seq, the current next generation sequencing approach, is expected to provide similar power as microarrays but extending their capabilities to aspects up to now more difficult to analyse such as alternative splicing and discovery of novel transcripts.

Why most gene expression signatures of tumors have not been useful in the clinic

Omics technologies are expected to enhance our understanding of a variety of diseases and to open the door to patient-specific personalized medicine. Despite the extensive literature on the use of gene expression arrays to predict prognosis in cancer patients, poor progress has been made in the translation of gene expression signatures for use in the clinics. Breast cancer provides a ripe arena for an analysis of why such signatures have failed to fulfill their promise.