Role of Bioinformatics in Detection of Cancer Biomarkers

Cancer biomarkers are described in Chap. 13. Bioinformatics is applied for the exploration of cancer-related biomarkers, such as predisposition markers, diagnostics markers, prognostics markers, and therapeutics markers. Because quite large amounts of data are produced by the whole genome SNP scanning, bioinformatics is necessary for the identification of SNPs associated with cancer predisposition. Individual cancer risks can be estimated accurately by detecting multiple SNPs affecting critical cancer-associated genes. Because expression profiles are determined by the signaling networks in cancer, network analyses promote the exploration of diagnostic biomarkers. Network analysis software using gene set enrichment program are developed by a variety of companies or groups; however, network analyses driven by human intelligence of experts is still powerful and more accurate. In the postgenomic era, bioinformatics is utilized for the identification of novel prognostic markers, recurrence prediction markers, and metastasis markers out of large amounts of genomics, transcriptomics, and proteomics data. Bioinformatics is utilized for the exploration of cancer-related biomarkers to select therapeutic optional choice among surgical operation, radiation therapy, and chemotherapy. Biomarkers for localized tumors with low metastatic potential support the selection of a surgical procedure, while biomarkers for infiltrating tumors with high metastatic potential support the select ion of radiation therapy and/or chemotherapy. Therapeutic biomarkers for squamous carcinoma of esophagus, lung, and cervical uterus have been characterized by using bioinformatics on transcriptomics data.