Dataset Information

Benchmarking pathway analysis methods offer novel prognostic cancer biomarkers and therapeutics: application in bladder cancer

ABSTRACT: The pathogenesis of cancer is typically driven by alterations in multiple cellular pathways that are challenging to identify and target. However, it is unclear how suitable the existing pathway analysis tools are for unbiased discovery and ranking of dysregulated and/or cancer-specific pathways. Here, we created a new platform called Benchmark to evaluate the potential of pathway analysis tools for discovery under experimental conditions. Unexpectedly, we found that despite wide-spread success in confirming hypothesized dysregulated pathways, common pathway analysis tools are less than ideal for unbiased discovery. Nevertheless, our pathway ensemble tool (PET) that combines the rank statistics from the exisiting methods significantly enhanced discovery. We applied PET to transcriptomics data from 12 independent tumor types to identify prognostic pathways. We showed that the genes from prognostic pathways are excellent biomarkers and can define cancer molecular subtypes. Moreover, drug prediction to normalize genes from prognostic pathways identified effective known and novel drugs. Finally, in vitro and/or a xenograft models of bladder cancer treated with the top predicted drug significantly restricted tumor cells. We anticipate that our unbiased approach for pathway discovery will have tangible impacts on cancer management.

ORGANISM(S): Homo sapiens

PROVIDER: GSE222567 | GEO | 2024/06/03

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Purpose: Presence of pelvic lymph node metastases is the main prognostic factor in early stage cervical cancer patients, primarily treated with surgery. Aim of this study was to identify cellular tumor pathways associated with pelvic lymph node metastasis in early stage cervical cancer. Experimental Design: Gene expression profiles (Affymetrix U133 plus 2.0) of 20 patients with negative (N0) and 19 with positive lymph nodes (N+), were compared with gene sets that represent all 285 presently available pathway signatures. Validation immunostaining of tumors of 274 consecutive early stage cervical cancer patients was performed for representatives of the identified pathways. Results: Analysis of 285 pathways resulted in identification of five pathways (TGF-β, NFAT, ALK, BAD, and PAR1) that were dysregulated in the N0, and two pathways (β-catenin and Glycosphingolipid Biosynthesis Neo Lactoseries) in the N+ group. Class comparison analysis revealed that five of 149 genes that were most significantly differentially expressed between N0 and N+ tumors (P<0.001) were involved in β-catenin signaling (TCF4, CTNNAL1, CTNND1/p120, DKK3 and WNT5a). Immunohistochemical validation of two well-known cellular tumor pathways (TGF-β and β-catenin) confirmed that the TGF-β pathway (positivity of Smad4) was related to N0 (OR:0.20, 95%CI:0.06-0.66) and the β-catenin pathway (p120 positivity) to N+ (OR:1.79, 95%CI:1.05-3.05). Conclusions: Our study provides new, validated insights in the molecular mechanism of lymph node metastasis in cervical cancer. Pathway analysis of the microarray expression profile suggested that the TGF-β and p120-associated non-canonical β-catenin pathways are important in pelvic lymph node metastasis in early stage cervical cancer. For the microarray experiment, we selected fresh frozen primary cervical cancer tissue, containing at least 80% tumor cells, of patients with histologically confirmed N0 (n=20) and of patients with N+ (n=19). The N0 and N+ groups were matched for age, FIGO stage and histology (all squamous cell carcinoma).

Project description:An integrated systems approach was used with bioinformatics tools to understand the the role of aldehyde dehydrogenase 1A1 (ALDH1A1) in human colon cancer cells. We combined transcriptomics, proteomics and untargeted metabolomics to gain insight into the mechanisms affected by the suppression of the ALDH1A1 gene in COLO320 cells. RNA-seq and proteomics analyses revealed 1747 transcripts and 336 proteins that were differentially expressed in cells in which the ALDH1A1 had been suppressed by shRNA transfection. Transcriptomics pathway analysis showed significant signaling pathways, such as Wnt/β-catenin (p=3.47E-6), molecular mechanisms of cancer (7.41E-5) and others relevant to lipid metabolism, such as cholesterol biosynthesis I, II and III (p=1.82E-4). Proteomics pathway analysis identified oxidative phosphorylation to be the most highly significant pathway (p=5.01E-31). Pathway analysis of the genes common to the transcriptomics and proteomics analyses revealed the asparagine biosynthesis (p=3.16E-3) and cholesterol biosynthesis the most statistically significant (p=3.36E-3). Univariate analysis of the untargeted metabolomics dataset revealed 859 ions statistically significant. Network analysis showed alterations in pathways linked to energy and lipid metabolism, such carnitine shuttle (p=5.3E-4) and fatty acid oxidation (p=2.9E-2). A systems biology approach was used to integrate all three datasets and a total of 61 pathways were generated. A direct association between the suppression of ALDH1A1 and the Vitamin A (retinol) metabolism pathway and down-regulation of retinol and the UGT2B17, UGT2A3 and PRDX6 genes was shown. In conclusion, the present results confirm the role of ALDH1A1 in retinol metabolism and shows for the first time the influence of this gene on lipid metabolism pathways that may be crucial for cholesterol synthesis in human colon cancer cells. In addition, they demonstrate how an integrated systems approach using unbiased bioinformatics tools can be used to understand the interplay of cellular pathways.

Dataset Information

Benchmarking pathway analysis methods offer novel prognostic cancer biomarkers and therapeutics: application in bladder cancer

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure