Microenvironmental genomic alterations reveal signaling networks for head and neck squamous cell carcinoma

Prognostic Correlation of an Autophagy-Related Gene Signature in Patients with Head and Neck Squamous Cell Carcinoma

Considerable evidence indicates that autophagy plays a vital role in the biological processes of various cancers. The aim of this study is to evaluate the prognostic value of autophagy-related genes in patients with head and neck squamous cell carcinoma (HNSCC). Transcriptome expression profiles and clinical data acquired from The Cancer Genome Atlas (TCGA) database were analyzed by Cox proportional hazards model and Kaplan–Meier survival analysis to screen autophagy-related prognostic genes that were significantly correlated with HNSCC patients' overall survival. Functional enrichment analyses were performed to explore biological functions of differentially expressed autophagy-related genes (ARGs) identified in HNSCC patients. Six ARGs (EGFR, HSPB8, PRKN, CDKN2A, FADD, and ITGA3) identified with significantly prognostic values for HNSCC were used to construct a risk signature that could stratify patients into the high-risk and low-risk groups. This signature demonstrated great value in predicting prognosis for HNSCC patients and was indicated as an independent prognostic factor in terms of clinicopathological characteristics (sex, age, clinical stage, histological grade, anatomic subdivision, alcohol history, smoking status, HPV status, and mutational status of the samples). The prognostic signature was also validated by data from the Gene Expression Omnibus (GEO) database and International Cancer Genome Consortium (ICGC). In conclusion, this study provides a novel autophagy-related gene signature for predicting prognosis of HNSCC patients and gives molecular insights of autophagy in HNSCC.

Integrating omics data and protein interaction networks to prioritize driver genes in cancer

Although numerous approaches have been proposed to discern driver from passenger, identification of driver genes remains a critical challenge in the cancer genomics field. Driver genes with low mutated frequency tend to be filtered in cancer research. In addition, the accumulation of different omics data necessitates the development of algorithmic frameworks for nominating putative driver genes. In this study, we presented a novel framework to identify driver genes through integrating multi-omics data such as somatic mutation, gene expression, and copy number alterations. We developed a computational approach to detect potential driver genes by virtue of their effect on their neighbors in network. Application to three datasets (head and neck squamous cell carcinoma (HNSC), thyroid carcinoma (THCA) and kidney renal clear cell carcinoma (KIRC)) from The Cancer Genome Atlas (TCGA), by comparing the Precision, Recall and F1 score, our method outperformed DriverNet and MUFFINN in all three datasets. In addition, our method was less affected by protein length compared with DriverNet. Lastly, our method not only identified the known cancer genes but also detected the potential rare drivers (PTPN6 in THCA, SRC, GRB2 and PTPN6 in KIRC, MAPK1 and SMAD2 in HNSC).

Single nucleotide polymorphisms in an Indian cohort and association of CNTN4, MMP2 and SNTB1 variants with oral cancer

Oral cancer is a high incidence cancer in India primarily due to the prevalent tobacco/areca nut chewing habits and hence a major health concern. India constitutes 26% of the global oral cancer burden. Besides the well-established risk factors, the genomic constitution of an individual plays a role in oral cancer. The aim of the current study was to analyse genomic variants represented as single nucleotide polymorphisms (SNPs), analyse their prevalence and investigate risk association of allelotypes/genotypes to oral cancers. Eleven SNPs in genes associated with biological functions were analysed in an Indian cohort (n = 1000) comprising 500 oral cancer patients and 500 long term tobacco habitués as controls, using Allelic discrimination Real-Time PCR assay with SYBR Green dye. Fisher's exact test and Odds Ratio were used for statistical analysis. Increased risk was observed for rs9849237 CC [P = 0.008; OR 1.412 (1.09-1.82)] and rs243865 CT [P = 0.004; OR 1.469 (1.13-1.90)] genotypes, whereas rs9849237 CT [P = 0.034; OR 0.755 (0.58-0.97)], rs243865 CC [P = 0.002; OR 0.669 (0.51-0.86)] and rs10090787 CC [P = 0.049; OR 0.774 (0.60-0.99)] genotypes indicated decreased risk to oral cancer. The other SNPs showed equidistribution in both groups. Our data indicated genotypes and alleles in specific SNPs rs9849237, rs243865 and rs10090787 with increased/decreased risk to oral cancer.

Network biology methods integrating biological data for translational science

The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.