Survival marker genes of colorectal cancer derived from consistent transcriptomic profiling

Jorge Martinez-Romero, Santiago Bueno-Fortes, Manuel Martín-Merino, Ana Ramirez de Molina, Javier De Las Rivas, Jorge Martinez-Romero, Santiago Bueno-Fortes, Manuel Martín-Merino, Ana Ramirez de Molina, Javier De Las Rivas

Abstract

Background: Identification of biomarkers associated with the prognosis of different cancer subtypes is critical to achieve better therapeutic assistance. In colorectal cancer (CRC) the discovery of stable and consistent survival markers remains a challenge due to the high heterogeneity of this class of tumors. In this work, we identified a new set of gene markers for CRC associated to prognosis and risk using a large unified cohort of patients with transcriptomic profiles and survival information.

Results: We built an integrated dataset with 1273 human colorectal samples, which provides a homogeneous robust framework to analyse genome-wide expression and survival data. Using this dataset we identified two sets of genes that are candidate prognostic markers for CRC in stages III and IV, showing either up-regulation correlated with poor prognosis or up-regulation correlated with good prognosis. The top 10 up-regulated genes found as survival markers of poor prognosis (i.e. low survival) were: DCBLD2, PTPN14, LAMP5, TM4SF1, NPR3, LEMD1, LCA5, CSGALNACT2, SLC2A3 and GADD45B. The stability and robustness of the gene survival markers was assessed by cross-validation, and the best-ranked genes were also validated with two external independent cohorts: one of microarrays with 482 samples; another of RNA-seq with 269 samples. Up-regulation of the top genes was also proved in a comparison with normal colorectal tissue samples. Finally, the set of top 100 genes that showed overexpression correlated with low survival was used to build a CRC risk predictor applying a multivariate Cox proportional hazards regression analysis. This risk predictor yielded an optimal separation of the individual patients of the cohort according to their survival, with a p-value of 8.25e-14 and Hazard Ratio 2.14 (95% CI: 1.75-2.61).

Conclusions: The results presented in this work provide a solid rationale for the prognostic utility of a new set of genes in CRC, demonstrating their potential to predict colorectal tumor progression and evolution towards poor survival stages. Our study does not provide a fixed gene signature for prognosis and risk prediction, but instead proposes a robust set of genes ranked according to their predictive power that can be selected for additional tests with other CRC clinical cohorts.

Keywords: Bioinformatics; Cancer; Colon; Colorectal cancer; Gene Expression; Gene marker; Kaplan-Meier analysis; Survival; Transcriptomics.

Conflict of interest statement

Ethics approval and consent to participate

Ethics approval and consent to participate is “not applicable”, because this work does not include samples from new patients or donors. All the information and data of human samples used in this work come from data sets already public in open repositories and corresponded to Anonymized Patient Level Data (APLD). Moreover, the Ethical Committees of our Research Centers (CiC-IBMCC and IMDEA-Food) supervised the adequate use of the data corresponding to human samples.

Consent for publication

Not applicable.

Competing interests

Not applicable.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Symmetric heatmaps representing the similarity between the overall gene expression signal of the samples compared with each other. Each heatmap is composed of 210 samples (30 × 7, 30 samples random selected from each batch, i.e. from each one of the 7 GSE datasets). The samples of each batch are identified by a color in the top bar below the top dendrograms (following the colors legend). Each heatmap represents a different preprocessing and normalization method performed to merge the datasets in one batch. The methods applied were: a RMA; b RMA plus ComBat; c fRMA; d fRMA plus ComBat; e fRMA plus scaling of the data using mean-centered expression values
Fig. 2
Fig. 2
Plots presenting the distribution of the 1273 samples from 7 datasets (GSEs) obtained by Principal Component Analysis (PCA) of the global gene expression profile of each sample; that converts the signal of each sample using an orthogonal transformation in linearly uncorrelated variables called principal components or dimensions. Each plot presents the values of the two main dimensions (dim 1 versus dim 2) and corresponds to the PCA results obtained using the expression data calculated with different preprocessing and normalization methods. The methods applied were: a RMA; b RMA plus ComBat; c fRMA; d fRMA plus ComBat; e fRMA plus scaling of the data using mean-centered expression values. The samples of each batch are identified by color dots following the colors legend
Fig. 3
Fig. 3
Kaplan-Meier plots of the survival analysis of the set of 1273 samples from colorectal cancer (CRC) patients. The patients are separated in two groups (high in red and low in green) according to the expression profiles of 4 genes: a DCBLD2, b PTPN14, c EPHB2, d DUS1L. These genes provided the best split between patients of high and low risk based in their expression levels. In the case of genes DCBLD2 and PTPN14 (labelled in red) the over-expression is correlated with poor survival; and in the case of genes EPHB2 and DUS1L (labelled in green) the over-expression is correlated with good survival. In all cases the adjusted p.values of the analyses are very significant (as indicated inside each plot), indicating that the two populations represented by the two curves have a very clear difference in their overall survival
Fig. 4
Fig. 4
Risk prediction done for the cohort of 1273 patients of CRC based in the multivariate analysis using the top 100 genes that showed up-regulation correlated with poor prognosis (i.e. overexpressed in low survival cases). a Plot presenting the patients according to their risk score, from Low (blue) to High (red) risk. A recursive algorithm using 10-fold cross-validation finds the value of risk score (marked with a vertical black line) that allows the best splitting of the cohort in two groups. b Kaplan-Meier plot showing the separation of these two groups: a high-risk group including 425 individuals (in red) and a low-risk group including 848 individuals (in blue). The analysis has been done using a multivariate Cox proportional-hazards regression. As shown, the division is very significant (p-value = 8.25e-14) and allows an optimal separation of individuals according to their survival. The analysis of the beta factors assigned by the regression to each of the top 100 genes (i.e. to each variable within the multivariate vector) allows the identification of the genes that are the most influential factors in this risk analysis and therefore it helps in the selection of the best “gene survival markers”

References

    1. Linnekamp JF, Wang X, Medema JP, Vermeulen L. Colorectal cancer heterogeneity and targeted therapy: a case for molecular disease subtypes. Cancer Res. 2015;75:245–249. doi: 10.1158/0008-5472.CAN-14-2240.
    1. Dienstmann R, Vermeulen L, Guinney J, Kopetz S, Tejpar S, Tabernero J. Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer. Nat Rev Cancer. 2017;17:79–92. doi: 10.1038/nrc.2016.126.
    1. Liu R, Zhang W, Liu ZQ, Zhou HH. Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis. BMC Genomics. 2017;18:361. doi: 10.1186/s12864-017-3761-z.
    1. Guinney J, Dienstmann R, Wang X, de Reyniès A, Schlicker A, Soneson C, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21:1350–1356. doi: 10.1038/nm.3967.
    1. Vargas T, Moreno-Rubio J, Herranz J, Cejas P, Molina S, González-Vallinas M, et al. ColoLipidGene: signature of lipid metabolism-related genes to predict prognosis in stage-II colon cancer patients. Oncotarget. 2015;6:7348–7363. doi: 10.18632/oncotarget.3130.
    1. Sveen A, Ågesen TH, Nesbakken A, Meling GI, TO R, Liestøl K, et al. ColoGuidePro: a prognostic 7-gene expression signature for stage III colorectal cancer patients. Clin Cancer Res. 2012;18:6001–6010. doi: 10.1158/1078-0432.CCR-11-3302.
    1. Kopetz S, Tabernero J, Rosenberg R, Jiang ZQ, Moreno V, Bachleitner-Hofmann T, et al. Genomic classifier ColoPrint predicts recurrence in stage II colorectal cancer patients more accurately than clinical factors. Oncologist. 2015;20:127–133. doi: 10.1634/theoncologist.2014-0325.
    1. The American Cancer Society medical and editorial content team. Colorectal Cancer Stages. . Accessed 06 Oct 2017.
    1. Tauriello DVF, Batlle E. Targeting the microenvironment in advanced colorectal Cancer. Trends Cancer. 2016;2:495–504. doi: 10.1016/j.trecan.2016.08.001.
    1. Risueño A, Fontanillo C, Dinger ME, De Las Rivas J. GATExplorer: genomic and transcriptomic explorer; mapping expression probes to gene loci, transcripts, exons and ncRNAs. BMC Bioinformatics. 2010;11:221. doi: 10.1186/1471-2105-11-221.
    1. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe-level data. Biostatistics. 2003;4:249–264. doi: 10.1093/biostatistics/4.2.249.
    1. Stein CK, Qu P, Epstein J, Buros A, Rosenthal A, Crowley J, et al. Removing batch effects from purified plasma cell gene expression microarrays with modified ComBat. BMC Bioinformatics. 2015;16:63. doi: 10.1186/s12859-015-0478-3.
    1. McCall MN, Bolstad BM, Irizarry RA. Frozen robust multiarray analysis (fRMA) Biostatistics. 2010;11:242–253. doi: 10.1093/biostatistics/kxp059.
    1. Cancer Genome Atlas Network Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487:330–337. doi: 10.1038/nature11252.
    1. Aguirre-Gamboa R, Gomez-Rueda H, Martínez-Ledesma E, Martínez-Torteya A, Chacolla-Huaringa R, Rodriguez-Barrientos A, et al. SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysis. PLoS One. 2013;8:e74250. doi: 10.1371/journal.pone.0074250.
    1. Gui J, Li H. Penalized cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics. 2005;21:3001–3008. doi: 10.1093/bioinformatics/bti422.
    1. Sameer AS. Colorectal cancer: molecular mutations and polymorphisms. Front Oncol. 2013;3:114. doi: 10.3389/fonc.2013.00114.
    1. Fessler E, Medema JP. Colorectal Cancer subtypes: developmental origin and microenvironmental regulation. Trends Cancer. 2016;2(9):505–518. doi: 10.1016/j.trecan.2016.07.008.
    1. Bijlsma MF, Sadanandam A, Tan P, Vermeulen L. Molecular subtypes in cancers of the gastrointestinal tract. Nat Rev Gastroenterol Hepatol. 2017;14(6):333–342. doi: 10.1038/nrgastro.2017.33.
    1. Kocarnik JM, Shiovitz S, Phipps AI. Molecular phenotypes of colorectal cancer and potential clinical applications. Gastroenterol Rep. 2015;3(4):269–276.
    1. Aibar S, Fontanillo C, Droste C, Roson-Burgo B, Campos-Laborie FJ, Hernandez-Rivas JM, et al. Analyse multiple disease subtypes and build associated gene networks using genome-wide expression profiles. BMC Genomics. 2015;16(Suppl 5):S3. doi: 10.1186/1471-2164-16-S5-S3.
    1. Aibar S, Abaigar M, Campos-Laborie FJ, Sánchez-Santos JM, Hernandez-Rivas JM, De Las Rivas J. Identification of expression patterns in the progression of disease stages by integration of transcriptomic data. BMC Bioinformatics. 2016;17(Suppl 15):432. doi: 10.1186/s12859-016-1290-4.
    1. Moreno V, Sanz-Pamplona R. Altered pathways and colorectal cancer prognosis. BMC Med. 2015;13:76. doi: 10.1186/s12916-015-0307-6.
    1. Sanz-Pamplona R, Berenguer A, Cordero D, Riccadonna S, Solé X, Crous-Bou M, et al. Clinical value of prognosis gene expression signatures in colorectal cancer: a systematic review. PLoS One. 2012;7(11):e48877. doi: 10.1371/journal.pone.0048877.
    1. George B, Kopetz S. Predictive and prognostic markers in colorectal cancer. Curr Oncol Rep. 2011;13(3):206–215. doi: 10.1007/s11912-011-0162-3.
    1. Das V, Kalita J, Pal M. Predictive and prognostic biomarkers in colorectal cancer: a systematic review of recent advances and challenges. Biomed Pharmacother. 2017;87:8–19. doi: 10.1016/j.biopha.2016.12.064.
    1. Salazar R, Roepman P, Capella G, Moreno V, Simon I, Dreezen C, et al. Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer. J Clin Oncol. 2011;29(1):17–24. doi: 10.1200/JCO.2010.30.1077.
    1. Nguyen MN, Choi TG, Nguyen DT, Kim JH, Jo YH, Shahid M, et al. CRC-113 gene expression signature for predicting prognosis in patients with colorectal cancer. Oncotarget. 2015;6(31):31674–31692. doi: 10.18632/oncotarget.5183.
    1. Chen H, Sun X, Ge W, Qian Y, Bai R, Zheng S. A seven-gene signature predicts overall survival of patients with colorectal cancer. Oncotarget. 2016;8(56):95054–95065.
    1. Tian X, Zhu X, Yan T, Yu C, Shen C, Hu Y, et al. Recurrence-associated gene signature optimizes recurrence-free survival prediction of colorectal cancer. Mol Oncol. 2017;11(11):1544–1560. doi: 10.1002/1878-0261.12117.
    1. Xu G, Zhang M, Zhu H, Xu J. A 15-gene signature for prediction of colon cancer recurrence and prognosis based on SVM. Gene. 2017;604:33–40. doi: 10.1016/j.gene.2016.12.016.
    1. Li X, Jung JJ, Nie L, Razavian M, Zhang J, Samuel V, et al. The neuropilin-like protein ESDN regulates insulin signaling and sensitivity. Am J Physiol Heart Circ Physiol. 2016;310:H1184–H1193. doi: 10.1152/ajpheart.00782.2015.
    1. Masin M, Vazquez J, Rossi S, Groeneveld S, Samson N, Schwalie PC, et al. GLUT3 is induced during epithelial-mesenchymal transition and promotes tumor cell proliferation in non-small cell lung cancer. Cancer Metab. 2014;2:11. doi: 10.1186/2049-3002-2-11.
    1. Lee J, Sohn I, Do IG, Kim KM, Park SH, Park JO, et al. Nanostring-based multigene assay to predict recurrence for gastric cancer patients after surgery. PLoS One. 2014;9:e90133. doi: 10.1371/journal.pone.0090133.
    1. Wang L, Xiao X, Li D, Chi Y, Wei P, Wang Y, Ni S, Tan C, Zhou X, Du X. Abnormal expression of GADD45B in human colorectal carcinoma. J Transl Med. 2012;10:215. doi: 10.1186/1479-5876-10-215.
    1. Sztupinszki Z, Győrffy B. Colon cancer subtypes: concordance, effect on survival and selection of the most representative preclinical models. Sci Rep. 2016;6:37169. doi: 10.1038/srep37169.

Source: PubMed

Sponsors et collaborateurs

Les conditions médicales

Interventions en matière de drogue

3
S'abonner