Over the past few decades, different genes have been used, with greater or lesser success, as biomarkers for prognostics. In the work presented here, by performing genome-wide sequential analyses across all genes and across all pathways, starting with TCGA and validating in two additional datasets, we saw how the single-gene approach fails to stratify patients robustly into prognostic groups. By applying the same strategy but with a different metric, that of pathway modifications, we identified one pathway that significantly and consistently stratified prognosis across the TCGA set and the two additional validation sets. In marked contrast, the expression levels of the genes composing the pathway did not provide valid prognosis stratification.
Methylation, copy-number variation and gene-expression have been established as molecular markers of tumor formation. Here, by looking into these genetic and epigenetic modifications in the PDGF pathway, we found this pathway to be significantly targeted by changes in copy number. Alterations in copy numbers may provide a causal explanation of why this pathway is a valid classifier. The expression level of a gene, in and of itself, fails to produce similar results; it is only the combined, synthetic, synergistic effects of subnetworks that identify phenotype affiliation. By isolating specific subnetworks, we were able to handle the NP-hard numeracy of network interactions. Further analysis revealed specific interactions at the core of the phenotypic clustering.
The lack in robustness of a single gene or even a set of genes emphasizes the importance of the pathway structure. While in a gene-set analysis every gene has the same weight of importance, in a pathway analysis a gene in calculated according to its location and contribution to the pathway.
Interestingly, expression levels of FOS are often higher in patients with a good prognosis than patients with poor prognosis. Studies on the oncogenic functions of FOS show it to be involved in the regulation of tumorogenesis, leading to down-regulation of tumor suppressor genes and eventually to invasive growth of cancer cells [48
]. In contrast, other studies have shown FOS to act as a tumor suppressor gene. The authors of a recent study on epithelial ovarian carcinoma showed that reduction in FOS expression was associated with significantly shorter overall survival rates. They explained that the tumor-suppressor activity of FOS could be a pro-apoptotic function, which might confer increased chemoresistance on tumors with low FOS protein levels [50
This JUN-FOS correlation was robustly present in Group1 throughout the three datasets, but there was no similar JUN-FOS correlation in Group2. This consistent correlation in the better survival group and the consistent lack of correlation in the second group lead us to propose that the prognosis-related correlation is highly significant and may indeed account for the differences in survival. A positive correlation indicates similar intracellular behavior: when JUN expression levels are high, FOS expression levels are high (and vice versa). That is, in well-controlled cases (better prognosis), when JUN behaves as an oncogene (high expression levels), FOS is highly expressed to suppress and oppose JUN activity. This behavior disappears in the poor prognosis cases, where this control mechanism fails and the gene correlation falls. Owing to their known close connection [51
] and their opposite functions in tumorigenesis, we assumed that the correlation in the better survival group and the lack of correlation in the poor prognosis group are not coincidental and are strongly connected to the prognostic outcome. In addition, the fact that neither FOS nor JUN alone stratified prognosis consistently across the three datasets supports the assumption that only their co-behavior in the PDGF pathway can potentially be a target for future therapeutics.
Our results demonstrate that pathway interactions are either associated with improved prognosis by "helping" the pathway counter the tumor, or with poor prognosis by "breaking down" the pathway's normal activity. Through better understanding of the pathway mechanisms and the interactions that undergo changes, we may find targets for new treatments. The fact that the pathway we identified did not correlate with age or tumor diameter and was found in all three datasets strengthens the hypothesis that this pathway is a core mechanism of the disease.
Recent study on the ovarian cancer dataset from the TCGA found a 193-gene signature that predict overall survival in the TCGA data and additional datasets [53
]. Interestingly, the pathway presented here outperforms the 193-gene signature in both the kaplan-meier p-value in the TCGA database (p-value of 0.02 compare to 0.007 in our results) and the number of genes in the prognosis classification (193 gene compare to 18 genes in the PDGF pathway). The work presented here, along with other studies, emphasizes the network unit as a biomarker [54
]. By making the transition from the gene as the unit of phenotypic affiliation to the molecular network as the unit of analysis, we obtained highly significant prognosis curves. Furthermore, this transition to the process instead of the single agent facilitates the discovery of a process-based classification.