CaMeRe: A Novel Tool for Inference of Cancer Metabolic Reprogramming

Abstract

Metabolic reprogramming is prevalent in cancer, largely due to its altered chemical environments such as the distinct intracellular concentrations of O₂, H₂O₂ and H⁺, compared to those in normal tissue cells. The reprogrammed metabolisms are believed to play essential roles in cancer formation and progression. However, it is highly challenging to elucidate how individual normal metabolisms are altered in a cancer-promoting environment; hence for many metabolisms, our knowledge about how they are changed is limited. We present a novel method, CaMeRe (CAncer MEtabolic REprogramming), for identifying metabolic pathways in cancer tissues. Based on the specified starting and ending compounds, along with gene expression data of given cancer tissue samples, CaMeRe identifies metabolic pathways connecting the two compounds via collection of compatible enzymes, which are most consistent with the provided gene-expression data. In addition, cancer-specific knowledge, such as the expression level of bottleneck enzymes in the pathways, is incorporated into the search process, to enable accurate inference of cancer-specific metabolic pathways. We have applied this tool to predict the altered sugar-energy metabolism in cancer, referred to as the Warburg effect, and found the prediction result is highly accurate by checking the appearance and ranking of those key pathways in the results of CaMeRe. Computational evaluation indicates that the tool is fast and capable of handling large metabolic network inference in cancer tissues. Hence, we believe that CaMeRe offers a powerful tool to cancer researchers for their discovery of reprogrammed metabolisms in cancer. The URL of CaMeRe is http://csbl.bmb.uga.edu/CaMeRe/.

Keywords: cancer; glycosylation; metabolic reprogramming; path-searching; web server.

Figure 1

The interface of CaMeRe to search for metabolic pathways in cancer tissues.

Figure 2

Visualization of a metabolic pathway. The red node is the starting compound and the yellow one is the ending compound. A user can check the details of all edges and nodes by clicking on each of them to obtain detailed information about specific enzymes or compounds.

Figure 3

(A) Workflow of pathway searching function of CaMeRe. (B) Workflow of analyzing the uploaded cancer samples.

Figure 4

The result of searching from D-glyceraldehyde 3-phosphate to 1,3-bisphospho-D-glycerate exhibits in the first line whose bottleneck largely surpasses the second line's, which means the expression level of the enzyme involved in the reaction route 1 is much higher than that in the reaction route 2 and suggests the conspicuousness of reaction route 1.

Figure 5

Reaction catalyzed by GFAT1.

Figure 6

The result of searching from beta-D-fructofuranose 6-phosphate to L-glutamate exhibits in BLCA.

Figure 7

The result of searching from beta-D-fructofuranose 6-phosphate to L-glutamate exhibits in normal tissue.

Figure 8

The result of searching from D-gluconate 6-phosphate to D-ribulose 5-phosphate in BLCA.

Figure 9

The result of searching from D-gluconate 6-phosphate to D-ribulose 5-phosphate in Breast invasive carcinoma (BRCA).

Figure 10

The result of searching from D-gluconate 6-phosphate to D-ribulose 5-phosphate in Thyroid carcinoma (THCA).