Genomic-Wide Analysis with Microarrays in Human Oncology

Rh type C-glycoprotein functions as a novel tumor suppressor gene by inhibiting tumorigenicity and metastasis in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC), a major histologic subtype of head and neck cancer, presents great mortality and morbidity worldwide. The aim of this study is to discover new potential biomarkers closely correlated with HNSCC progression. In this study, weighted gene co-expression network analysis was applied to construct a co-expression network, and the brown module was identified as the most correlated with HNSCC progression. Hub gene identification combined with survival analyses determined RHCG as a candidate biomarker for cancer progression and prognosis prediction. Further experimental results proved that RHCG was aberrantly downregulated in HNSCC tissues and cell lines. Moreover, decreased RHCG expression was shown to be associated with advanced stage and dismal prognosis in HNSCC patients. Functional assays revealed that RHCG could inhibit cell viability, clonogenicity, cell migration in vitro and suppress tumor formation in vivo. Further bioinformatics study demonstrated that DNA promoter hypermethylation of RHCG could lead to its downregulation and serve as potential prognostic maker in HNSCC. Our study reveals that RHCG acts as a tumor suppressor gene that plays a crucial role in inhibiting tumorigenicity and metastasis in HNSCC, which will shed light on the potential diagnostic and therapeutic strategies for HNSCC.

Pharmacogenetics: Role of Single Nucleotide Polymorphisms

Genome sequencing methods have basically similar algorithms, although they show a few differences between the platforms. The human genome contains approximately three billion base pairs, and this amount is huge and therefore impossible to sequence at one step. However, this amount is not a problem for developed technology. Researchers break DNA into small random pieces and then sequence and reassemble. Library preparation, sequencing, bioinformatic approaches and reporting. High-quality library preparation is critical and the most important part of the next-generation sequencing workflow. Successful sequencing directly requires high-quality libraries. Sequencing is second step and all high-throughput sequencing approaches are generally based on conventional Sanger sequencing. After preparation of library and sequencing, later steps are completely computer-based (in silico) approaches called as bioinformatics.

Construction of diagnosis system and gene regulatory networks based on microarray analysis

A microarray analysis generally contains expression data of thousands of genes, but most of them are irrelevant to the disease of interest, making analyzing the genes concerning specific diseases complicated. Therefore, filtering out a few essential genes as well as their regulatory networks is critical, and a disease can be easily diagnosed just depending on the expression profiles of a few critical genes. In this study, a target gene screening (TGS) system, which is a microarray-based information system that integrates F-statistics, pattern recognition matching, a two-layer K-means classifier, a Parameter Detection Genetic Algorithm (PDGA), a genetic-based gene selector (GBG selector) and the association rule, was developed to screen out a small subset of genes that can discriminate malignant stages of cancers. During the first stage, F-statistic, pattern recognition matching, and a two-layer K-means classifier were applied in the system to filter out the 20 critical genes most relevant to ovarian cancer from 9600 genes, and the PDGA was used to decide the fittest values of the parameters for these critical genes. Among the 20 critical genes, 15 are associated with cancer progression. In the second stage, we further employed a GBG selector and the association rule to screen out seven target gene sets, each with only four to six genes, and each of which can precisely identify the malignancy stage of ovarian cancer based on their expression profiles. We further deduced the gene regulatory networks of the 20 critical genes by applying the Pearson correlation coefficient to evaluate the correlationship between the expression of each gene at the same stages and at different stages. Correlationships between gene pairs were calculated, and then, three regulatory networks were deduced. Their correlationships were further confirmed by the Ingenuity pathway analysis. The prognostic significances of the genes identified via regulatory networks were examined using online tools, and most represented biomarker candidates. In summary, our proposed system provides a new strategy to identify critical genes or biomarkers, as well as their regulatory networks, from microarray data.

Innovative array-based assay for omics pattern in melanoma

Cutaneous melanoma remains a major health issue and still an important challenge for research. Thus, omics complex evaluation can provide a more specific molecular classification for this heterogeneous disease. Complex omics analysis based on genomic and proteomic microarrays can identify disease markers that prognosticate disease evolution or can monitor therapies efficacy. Among the technologies that gained momentum in the last years, array-based comparative genomic hybridization offered the possibility to analyze chromosomal numerical aberrations within cutaneous melanomas providing important support for molecular classification of melanoma tumors. This technology can identify new chromosomal alterations and discover new deregulated melanoma genes that can be further used as therapy targets. Integrating genetic profiling with clinical and pathological parameters would lead to seminal improvements in diagnosis, prognosis, and therapy.