Prognostic Significance of Amino Acid Metabolism-Related Genes in Prostate Cancer Retrieved by Machine Learning

Abstract

Prostate cancer is among the leading cancers according to both incidence and mortality. Due to the high molecular, morphological and clinical heterogeneity, the course of prostate cancer ranges from slow growth that usually does not require immediate therapeutic intervention to aggressive and fatal disease that spreads quickly. However, currently available biomarkers cannot precisely predict the course of a disease, and novel strategies are needed to guide prostate cancer management. Amino acids serve numerous roles in cancers, among which are energy production, building block reservoirs, maintenance of redox homeostasis, epigenetic regulation, immune system modulation and resistance to therapy. In this article, by using The Cancer Genome Atlas (TCGA) data, we found that the expression of amino acid metabolism-related genes is highly aberrant in prostate cancer, which holds potential to be exploited in biomarker design or in treatment strategies. This change in expression is especially evident for catabolism genes and transporters from the solute carrier family. Furthermore, by using recursive partitioning, we confirmed that the Gleason score is strongly prognostic for progression-free survival. However, the expression of the genes SERINC3 (phosphatidylserine and sphingolipids generation) and CSAD (hypotaurine generation) can refine prognosis for high and low Gleason scores, respectively. Therefore, our results hold potential for novel prostate cancer progression biomarkers.

Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities

“…Most commonly, the abnormal expression of SLCs beyond glucose transporters has been associated with the increased transport of metabolites and building blocks necessary for cancer development, which has been used to identify SLCs as prognostic biomarkers in pan-cancer [121,122] and cancerspecific studies [123][124][125]. In this context, ML has also helped further discriminate between the genes with the biggest effect within the cancer signatures [126]. Furthermore, these transcriptomic analyses have been able to identify SLC co-expression patterns that effectively influence cancer development and that can be used as more precise biomarkers than unique SLC signatures [127,128].…”

Extracellular Matrix- and Integrin Adhesion Complexes-Related Genes in the Prognosis of Prostate Cancer Patients’ Progression-Free Survival

“…Survival trees obtained through the rpart method enable visual inspection and comparison of prognostic factors [42,43]. The basic principles of the rpart method are elaborated more closely in our previous publications [26,45]. Briefly, first we calculated the importance of individual variables.…”

“…By analyzing The Cancer Genome Atlas (TCGA) gene expression data, we found evidence that indicates high perturbations in matrisome and adhesome composition in prostate cancer, which we linked to the clinical information. In our previous article [26], we have shown that the Gleason score is the most informative prognostic variable in the analysis of progression-free survival (PFS) in the prostate cancer dataset. However, in this publication, we refined this result by adding the ECM and IAC genes' expression variables to the analysis.…”

HuR promotes castration-resistant prostate cancer progression by altering ERK5 activation via posttranscriptional regulation of BCAT1

“…In addition to the effects of classic hormones, evidence from genetic, bioinformatics and molecular biological studies suggests that abnormalities in amino acid metabolism play crucial roles at various stages of prostatic carcinogenesis and tumour progression [ 6 – 8 ]. Abnormal amino acid metabolism can promote the development of CRPC [ 9 ].…”