Dataset Information

An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer.

ABSTRACT: Screening programs for colorectal cancer (CRC) often rely on detection of blood in stools, which is unspecific and leads to a large number of colonoscopies of healthy subjects. Painstaking research has led to the identification of a large number of different types of biomarkers, few of which are in general clinical use. Here, we searched for highly accurate combinations of biomarkers by meta-analyses of genome- and proteome-wide data from CRC tumors. We focused on secreted proteins identified by the Human Protein Atlas and used our recently described algorithms to find optimal combinations of proteins. We identified nine proteins, three of which had been previously identified as potential biomarkers for CRC, namely CEACAM5, LCN2 and TRIM28. The remaining proteins were PLOD1, MAD1L1, P4HA1, GNS, C12orf10 and P3H1. We analyzed these proteins in plasma from 80 patients with newly diagnosed CRC and 80 healthy controls. A combination of four of these proteins, TRIM28, PLOD1, CEACAM5 and P4HA1, separated a training set consisting of 90% patients and 90% of the controls with high accuracy, which was verified in a test set consisting of the remaining 10%. Further studies are warranted to test our algorithms and proteins for early CRC diagnosis.

SUBMITTER: Gawel DR

PROVIDER: S-EPMC6821706 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer.

Gawel Danuta R DR Lee Eun Jung EJ Li Xinxiu X Lilja Sandra S Matussek Andreas A Schäfer Samuel S Olsen Renate Slind RS Stenmarker Margaretha M Zhang Huan H Benson Mikael M

Scientific reports 20191030 1

Screening programs for colorectal cancer (CRC) often rely on detection of blood in stools, which is unspecific and leads to a large number of colonoscopies of healthy subjects. Painstaking research has led to the identification of a large number of different types of biomarkers, few of which are in general clinical use. Here, we searched for highly accurate combinations of biomarkers by meta-analyses of genome- and proteome-wide data from CRC tumors. We focused on secreted proteins identified by ...[more]

PMID: 31666584

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Project description:BackgroundThe etiology of prostate cancer remained elusive, whether plasma protein levels are associated with prostate cancer is still unknown.MethodsWe have performed Mendelian randomization analyses to calculate the causal effects of plasma proteins on the risk of prostate cancer in the PRACTICAL consortium dataset using cis-protein quantitative trait loci (cis-pQTL) variants as instrumental variables for plasma proteins, and cis-expression quantitative trait locus (cis-eQTL) for the circulating gene expression. We also replicated the findings in the FinnGen consortium.ResultsGenetically proxied levels of 4 plasma proteins (CREB3L4, HDGF, SERPINA3, GNPNAT1) were identified as positively correlated with an increased risk of prostate cancer, while an increase in genetically proxied levels of 5 plasma proteins (TNFRSF6B, GSK3A, EIF4B, CLIC1, SMAD2) were significantly associated with a decreased risk of prostate cancer in the PRACTICAL consortium. Among the identified proteins, the causal effects of six proteins including CREB3L4, HDGF, SERPINA3, TNFRSF6B, EIF4B, and SMAD2 remained significant in the replication analyses in the FinnGen consortium and when combined with meta-analyses (SMAD2: OR 0.710, 95% CI 0.578-0.873, p-value = 0.001; CREB3L4: OR 1.260, 95% CI 1.164-1.364, p-value < 0.0001; HDGF: OR 1.072, 95% CI 1.021-1.125, p-value = 0.005; SERPINA3: OR 1.138, 95% CI 1.091-1.187, p-value < 0.0001; TNFRSF6B: OR 0.656, 95% CI 0.496-0.869, p-value = 0.003; EIF4B: OR 0.701, 95% CI 0.618-0.796, p-value < 0.0001). SMAD2 and CREB3L4 gene expressions proxied with cis-expression quantitative trait loci are also significantly associated with the risk of prostate cancer in both consortiums and when combined with meta-analyses (SMAD2: OR 0.787, 95% CI 0.719-0.861, p-value = 1.00 × 10-4; CREB3L4: OR 1.219, 95% CI 1.033-1.438, p-value = 0.019).ConclusionsOur consistent results highlighted the important roles of plasma SMAD2 and CREB3L4 in the risk of prostate cancer. Further investigations on these proteins may reveal their potential in the prevention and treatment of prostate cancer.

Project description:BackgroundProteomics-based technologies are emerging tools used for cancer biomarker discovery. Limited prospective studies have been conducted to evaluate the role of circulating proteins in colorectal cancer (CRC) development.MethodsA two-stage case-control proteomics study nested in the Shanghai Women's Health Study was conducted. A total of 1104 circulating proteins were measured in the discovery phase, consisting of 100 incident CRC cases and 100 individually matched controls. An additional 60 case-control pairs were selected for validation. Protein profiling at both stages was completed using the Olink platforms. Conditional logistic regression was used to evaluate the associations between circulating proteins and CRC risk. The elastic net method was employed to develop a protein score for CRC risk.ResultsIn the discovery set, 27 proteins showed a nominally significant association with CRC risk, among which 22 were positively and 5 were inversely associated. Six of the 27 protein markers were significantly associated with CRC risk in the validation set. In the analysis of pooled discovery and validation sets, odds ratios (ORs) per standard deviation (SD) increase in levels of these proteins were 1.54 (95% confidence interval (CI): 1.15-2.06) for CD79B; 1.71 (95% CI: 1.24-2.34) for DDR1; 2.04 (95% CI: 1.39-3.01) for EFNA4; 1.54 (95% CI: 1.16-2.02) for FLRT2; 2.09 (95% CI: 1.47-2.98) for LTA4H and 1.88 (95% CI: 1.35-2.62) for NCR1. Sensitivity analyses showed consistent associations for all proteins with the exclusion of cases diagnosed within the first two years after the cohort enrollment, except for CD79B. Furthermore, a five-protein score was developed based on the six proteins identified and showed significant associations with CRC risk in both discovery and validation sets (Discovery: OR1-SD = 2.46, 95% CI: 1.53-3.95; validation: OR1-SD = 4.16, 95% CI: 1.92-8.99).ConclusionsA panel of five protein markers was identified as potential biomarkers for CRC risk. Our findings provide novel insights into the etiology of CRC and may facilitate the risk assessment of the malignancy.

Dataset Information

An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer.

Publications

An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer.

Similar Datasets

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