Human cancer databases (review)

Abstract

Cancer is one of the four major non‑communicable diseases (NCD), responsible for ~14.6% of all human deaths. Currently, there are >100 different known types of cancer and >500 genes involved in cancer. Ongoing research efforts have been focused on cancer etiology and therapy. As a result, there is an exponential growth of cancer‑associated data from diverse resources, such as scientific publications, genome‑wide association studies, gene expression experiments, gene‑gene or protein‑protein interaction data, enzymatic assays, epigenomics, immunomics and cytogenetics, stored in relevant repositories. These data are complex and heterogeneous, ranging from unprocessed, unstructured data in the form of raw sequences and polymorphisms to well‑annotated, structured data. Consequently, the storage, mining, retrieval and analysis of these data in an efficient and meaningful manner pose a major challenge to biomedical investigators. In the current review, we present the central, publicly accessible databases that contain data pertinent to cancer, the resources available for delivering and analyzing information from these databases, as well as databases dedicated to specific types of cancer. Examples for this wealth of cancer‑related information and bioinformatic tools have also been provided.

Figure 1

Example of searching for and downloading data from The Cancer Genome Atlas (TCGA). (A) Attributes are selected and (B) the results are presented on a different page. The user can select samples by clicking on them (shadowed). The data can be downloaded by clicking on the ‘Build Archive’ button.

Figure 2

Example of querying COSMIC database. (A) Cancer attributes are selected. (B) A list of the top 20 genes involved in the particular type of cancer. (C) A list of the mutations along with links and pertinent information and (D) distribution of the mutations present in the KRAS gene.

Figure 2

Example of querying COSMIC database. (A) Cancer attributes are selected. (B) A list of the top 20 genes involved in the particular type of cancer. (C) A list of the mutations along with links and pertinent information and (D) distribution of the mutations present in the KRAS gene.

Figure 3

Example of querying Integrated Tumor Transcriptome Array and Clinical data Analysis (ITTACA) database. (A) A study was selected and (B) a new set of patient groups from clinical parameters was added. In a new page, (C) the values ‘alive’ and ‘dead of disease’ under the clinical parameter ‘Patient status’ were chosen. (D) Two different groups corresponding to the two different patient statuses were evident. The patient groups were subsequently analyzed using a survival curve. (E) A Kaplan-Meier curve based on overall survival was generated.

Figure 4

Screenshots showing (A) the results of selecting Breast Cancer (BC) as query. (B) A table is provided where the differentially expressed proteins (DEPs) in BC are shown, including pertinent information and links. By selecting two experiments, (C) the results are returned in a tabulated form where the DEPs in the two experiments are shown. There are links to Universal Protein Resource (UniProt) and back to the experiments.

Figure 5

Example of querying IntOGen for the genes lost in a type of bladder cancer. Dataset, Filters and Attributes were selected. By clicking on the ‘Results’ button, the results are returned and can be viewed and exported in several formats.

Figure 6

Results of a gene-centered search of Cervical Cancer gene DataBase (CCDB) using BCL2 as query. Information is provided regarding gene ID, gene description, synonyms, chromosomal location and the molecules with which the gene interacts. There are also links to mRNA/CCDS/Protein sequence entries, to homologous genes from various species and gene ontology information.