Open Access

Identification of a 13‑gene‑based classifier as a potential biomarker to predict the effects of fluorouracil‑based chemotherapy in colorectal cancer

  • Authors:
    • Zuhuan Gan
    • Qiyuan Zou
    • Yan Lin
    • Zihai Xu
    • Zhong Huang
    • Zhichao Chen
    • Yufeng Lv
  • View Affiliations

  • Published online on: March 19, 2019     https://doi.org/10.3892/ol.2019.10159
  • Pages: 5057-5063
  • Copyright: © Gan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The aim of the current study was to develop a predictor classifier for response to fluorouracil‑based chemotherapy in patients with advanced colorectal cancer (CRC) using microarray gene expression profiles of primary CRC tissues. Using two expression profiles downloaded from the Gene Expression Omnibus database, differentially expressed genes (DEGs) between responders and non‑responders to fluorouracil‑based chemotherapy were identified. A total of 791 DEGs, including 303 that were upregulated and 488 that were downregulated in responders, were identified. Functional enrichment analysis revealed that the DEGs were primarily involved in ‘cell mitosis’, ‘DNA replication’ and ‘cell cycle’ signaling pathways. Following feature selection using two methods, a random forest classifier for response to fluorouracil‑based chemotherapy with 13 DEGs was constructed. The accuracy of the 13‑gene classifier was 0.930 in the training set and 0.810 in the validation set. The receiver operating characteristic curve analysis revealed that the area under the curve was 1.000 in the training set and 0.873 in the validation set (P=0.227). The 13‑gene‑based classifier described in the current study may be used as a potential biomarker to predict the effects of fluorouracil‑based chemotherapy in patients with CRC.

Introduction

Colorectal cancer (CRC) is the third most commonly diagnosed cancer in males and the second in females, and it is one of the most common causes of cancer mortality (1). Localized CRCs are amenable to curative surgical resection, however, ~25% of patients present with metastatic disease and ~50% of patients will develop metastases (2). Fluorouracil-based chemotherapy remains the primary treatment for metastatic CRC (3). 5-fluorouracil (5-FU) alone has an objective response rate of ~20% (4). The addition of irinotecan or oxaliplatin to 5-FU increases the objective response rate to ~50% (5). The effects of 5-FU/leucovorin combined with irinotecan (FOLFIRI) or oxaliplatin (FOLFOX) in the first-line treatment of metastatic CRC are comparable (6). In the last decade, the addition of targeted therapies based on these chemotherapy regimens has improved the therapeutic approach and significantly increased progression-free survival and overall survival times (79). Fluorouracil-based chemotherapy remains the primary treatment for metastatic CRC. However, ~50% of patients are resistant to fluorouracil-based chemotherapy. In addition, the side effects of systemic chemotherapy, including neurotoxicity, myelotoxicity and gastrointestinal toxicity, may have a major impact on the quality of life of the patients and may lead to life-threatening complications (3). Therefore, identifying effective strategies that predict response to chemotherapy are required. Using these strategies, patients that are predicted to not respond to chemotherapy may receive other potentially effective treatments as early as possible and avoid unnecessary side effects. Gene expression profiling is used to predict the clinical outcome of patients with CRC (1012). Previous studies have revealed that gene expression profiling may be used to predict cancer response to chemotherapy, including breast cancer and CRC (1315).

The aim of the present study was to develop a predictor classifier for response to fluorouracil-based chemotherapy in patients with advanced CRC using microarray gene expression profiles of primary CRC tissues.

Materials and methods

Data processing

The raw microarray data (CEL files) of three datasets [GSE52735 (16), GSE62080 (15) and GSE69657 (17)] and corresponding clinical data were downloaded from the Gene Expression Omnibus database (www.ncbi.nlm.nih.gov/geo). The microarray data of the 3 datasets were based on the GPL570 Affymetrix Human Genome U133 Plus 2.0 Array platform (Affymetrix; Thermo Fisher Scientific Inc., Waltham, MA, USA). The GSE52735 set contained 37 advanced CRC samples treated with a fluoropyrimidine-based chemotherapy regimen (specific chemotherapy regimens were not available). A total of 23 of the samples were classified as responders and 14 samples were classified as non-responders to the chemotherapy regimen according to Response Evaluation Criteria in Solid Tumors (RECIST) (18). The GSE62080 dataset contained 21 advanced CRC samples treated with the FOLFIRI regimen. A total of 9 samples were classified as responders and 12 samples were classified as non-responders according to the World Health Organization (WHO) criteria (19). The GSE69657 dataset contained 30 advanced CRC samples treated with the FOLFOX4 regimen. However, the raw microarray data was available for only 16 samples. A total of 7 of these samples were classified as responders and 9 samples were classified as non-responders according to RECIST. Two different evaluation criteria used in these three studies due to long time intervals between the studies, Previous studies have revealed that the RECIST criteria are comparable with the WHO criteria in evaluating the response of solid tumors (2023). Preprocessing and normalization of the raw data were analyzed using the ‘affy’ (version 3.8) package (24) in R (www.r-project.org; version 3.5), using robust multi-array average for background correction and quantiles for normalization. Kernel and nearest neighbor averaging methods were used to impute the missing values using the ‘impute’ package (bioconductor.org/packages/impute; version 3.8) in R. The ComBat function in the ‘sva’ (version 3.8) package (25) was applied to remove batch effects. If one gene matched multiple probes, the average value of the probes was calculated as the expression of the corresponding gene. To build a robust predictive classifier, the GSE52735 and GSE62080 datasets were used as the training set (n=58), while the GSE69657 dataset was used as the validation set (n=16).

Screening of differentially expressed genes (DEGs) and enrichment analysis

Following preprocessing of the raw expression data, the DEGs between responders and non-responders in the training set were screened using the unpaired t-test in the ‘limma’ (version 3.8) package (26) in R. A DEG was defined as |log2 fold change (FC)|≥0.263 and P<0.05. The Gene Ontology (GO; http://geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) pathway enrichment analyses of DEGs were performed using the ‘clusterProfiler’ (version 3.8) package (27) in R with a cut-off of q<0.01.

Principal component analysis (PCA) prior to and following feature selection using the least absolute shrinkage and selection operator (LASSO) method

The expression values of DEGs in each sample were extracted. The LASSO logistic regression model analysis was performed using the ‘glmnet’ package (CRAN.R-project.org/package=glmnet; version 2.0-16) in R. The LASSO method is used to select optimal features in high-dimensional microarray data with a powerful predictive value and a low correlation between each other to prevent over-fitting (28). In the training set, the LASSO logistic regression model was used to select the optimal predictive markers. PCA using the expression profiles of the DEGs was performed prior to feature selection using the LASSO method. PCA was subsequently performed using the expression profiles of the optimal DEGs identified using by the LASSO method. Samples were plotted in two-dimensional plots across the first two principal components.

Feature selection using Boruta and random forest classifier construction

A lower-dimensional model may reduce costs and is more likely to be used by clinicians (29). Following DEGs selection by the LASSO method, a feature selection was performed using the ‘Boruta’ package (www.jstatsoft.org/article/view/v036i11; version 6.0.0) in R. Boruta is a random forest-based feature selection method, which provides an unbiased and stable selection of important and non-important attributes from an information system. A variable importance (VIMP) measure may be calculated and visualized based on Boruta. In the current study, DEGs selected by Boruta were used to develop a gene-based classifier for response to fluorouracil-based chemotherapy in advanced CRCs. The random forest classifier was developed using the ‘randomForest’ package (CRAN.R-project.org/package=randomForest; version 4.6-14) in R. The validation set (GSE69657) was used to confirm the robustness and transferability of the classifier. The performance of the classifier was assessed by accuracy, sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and receiver operating characteristic (ROC) curves in the training and validation sets. The ROC curves were drawn and compared using the ‘pROC’ (version 1.13.0) package (30) in R.

Results

DEGs in responders and non-responders and enrichment analysis

The training set included 32 responders and 26 non-responders. According to the cut-off criteria (|log2FC|≥0.263 and P<0.05), 791 genes were identified as differentially expressed between responders and non-responders. A total of 303 genes were upregulated and 488 genes were downregulated in responders. Functional enrichment analysis revealed that the biological process of DEGs were primarily involved in ‘cell mitosis’, ‘DNA replication’ and ‘cell cycle’ signaling pathways. The results of enrichment analysis are presented in Fig. 1.

PCA and feature selection using LASSO

For the first feature selection, LASSO logistic regression was performed using the expression data of DEGs in the training set. The group-wise classifications in 10-fold cross-validations were computed as default. A total of 31 DEGs were identified as optimal genes (Fig. 2A) with non-zero regression coefficients (Table I). Fig. 2B presents the results of PCA prior to feature selection using LASSO and Fig. 2C presents the results of PCA following feature selection using LASSO. As demonstrated in Fig. 2C, responders and non-responders are easily distinguished using the 31 DEGs selected by LASSO.

Table I.

Overview of the 31 optimal genes.

Table I.

Overview of the 31 optimal genes.

GeneLog2 fold change (Responder/non-responder)P-valueCoefficients provided by least absolute shrinkage and selection operatorVariable importance provided by Boruta
Matrix metallopeptidase 12−1.0690.002−0.154Tentative
C-X-C motif chemokine ligand 11−1.0160.015−0.184Rejected
Forkhead box P20.9570.0030.032Tentative
Small muscle protein X-linked0.7660.0030.575Confirmed
Pleckstrin homology like domain family A member 1−0.6250.000−0.584Confirmed
Prostaglandin reductase 2−0.6020.000−0.792Confirmed
Chitinase 10.5690.0020.976Confirmed
S100 calcium binding protein A2−0.5410.039−0.091Rejected
Histone cluster 1 H2B family member c0.5390.0010.927Confirmed
RP1-74M1.3−0.5150.005−0.023Tentative
Formin homology 2 domain containing 30.4690.0130.855Confirmed
RNA binding motif protein 3−0.4510.001−0.555Tentative
Tubulin polymerization promoting protein family member 30.4120.0110.442Rejected
Cadherin related family member 20.4110.0470.913Tentative
OTUD6B antisense RNA 1 (head to head)0.3870.0120.651Confirmed
Teashirt zinc finger homeobox 10.3840.0040.347Tentative
Cholinergic receptor nicotinic β1 subunit−0.3650.000−3.574Confirmed
Stromal antigen 3-like 4 (pseudogene)−0.3640.005−0.554Rejected
RPA interacting protein−0.3430.000−0.825Confirmed
Leucine rich repeat neuronal 1−0.3340.017−0.331Rejected
Heparan-α-glucosaminide N-acetyltransferase0.3340.0061.374Rejected
MINDY lysine 48 deubiquitinase 3−0.3200.001−0.253Tentative
THAP domain containing 5−0.3080.016−0.432Rejected
DNA ligase 40.2980.0021.692Confirmed
Zinc finger protein 2−0.2910.004−1.885Tentative
ASAP1 intronic transcript 20.2890.0040.045Confirmed
Small integral membrane protein 30−0.2870.001−0.973Confirmed
c-Maf inducing protein0.2820.0010.208Confirmed
ADAMTS like 20.2780.0051.088Tentative
Nucleoporin 133−0.2730.011−1.718Tentative
DEAD-box helicase 28−0.2670.003−0.063Tentative
Features selection using Boruta and construct of the random forest classifier
The Boruta function was used to further select features among the 31 DEGs. A total of 13 genes were confirmed as important, 7 genes were rejected and 11 tentative genes remained (Table I)

Fig. 3 presents the variables' importance. These 13 important DEGs included small muscle protein X-linked, pleckstrin homology like domain family A member 1 (PHLDA1), prostaglandin reductase 2 (PTGR2), chitinase 1 (CHIT1), histone cluster 1 H2B family member c, formin homology 2 domain containing 3, OTUD6B antisense RNA 1 (head to head), cholinergic receptor nicotinic β1 subunit (CHRNB1), RPA interacting protein, DNA ligase 4 (LIG4), ASAP1 intronic transcript 2, small integral membrane protein 30 and c-Maf inducing protein. A random forest classifier was constructed using these 13 important DEGs.

Performance of the gene-based classifier

The accuracy of the 13-gene classifier was 0.930 in the training set and 0.810 in the validation set. Based on accuracy, Se, Sp, PPV, NPV and area under curve (AUC) values, the sample recognition efficiency of the classifier was high (Table II). ROC curve analysis revealed that the AUC was 1.000 in the training set and 0.873 in the validation set (P=0.227; Fig. 4).

Table II.

Performance of the 13-gene classifier.

Table II.

Performance of the 13-gene classifier.

CohortSensitivitySpecificityPositive predictive valueNegative predictive valueAccuracyArea under the curve
Training set0.9700.9600.9100.9600.9301.000
Validation set0.8600.8800.7500.8800.8100.873

Discussion

Personalized treatment may improve the treatment outcome of patients with tumors (31). In CRC, the gene expression levels of vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR) provide the basis for selecting EGFR and VEGF inhibitor combinations (3236). Monoclonal antibodies against VEGF and EGFR have been approved for treatment of metastatic CRC in combination with 5-FU-based regimens (3). The identification of subsets of patients that respond to specific chemotherapy regimens remains a challenge (3). A previous study demonstrated that tumors with microsatellite instability (MSI) respond well to 5-FU-based therapies; however, further studies are required to substantiate these results (37). Another previously published study suggested that MSI status does not affect the outcome of the treatment (38). Therefore, effective tools for predicting the outcome of chemotherapy are currently lacking. The present study identified 13 genes from 791 DEGs using two feature selection algorithms and developed a 13-gene predictor classifier for response to fluorouracil-based chemotherapy in CRC. The predictor classifier demonstrated high accuracy in the training and validation sets. The training set included two datasets from different centers, and the validation set was from an additional independent center. ROC curve analysis revealed that the AUC was 1.000 in the training set and 0.873 in the validation set, and their difference was not significant (P=0.227). These results suggested that the classifier was robust. The study established a foundation for further research into personalized treatment of CRC.

Previous studies have attempted to identify a single biomarker to predict response to fluorouracil-based chemotherapy in CRC (17,3942). However, there is currently no single biomarker that is routinely applied in clinical practice. CRC is a heterogeneous disease, which is compounded by changes in the molecular profile of the tumor as it progresses (3). An in vitro study demonstrated that the measurement of multiple, rather than single marker genes, may provide a more accurate assessment of drug response in colon carcinoma (43). Previous studies have been designed to identify a pattern of gene expression capable of predicting response to fluorouracil-based chemotherapy in CRC (15,16). One study identified a set of 14 genes for predicting response to the FOLFIRI regimen based on 21 samples (15), and an expression profile of 7 genes was identified in another study (16). Compared with the two aforementioned studies, the current study performed a comprehensive analysis of more samples (n=58) from two centers and validated the predictor classifier in an independent dataset (n=16). Furthermore, to the best of the authors' knowledge, the current study is the first to construct a random forest classifier to predict response to chemotherapy in CRC. Considering the limited ability of Cox regression analysis to process high-dimensional data (44), it was not performed in the current study. A random forest algorithm was used to construct the classifier, which was subsequently validated with an independent dataset. The results obtained in the current study suggest that the robust classifier developed warrants further investigation.

Functional enrichment analysis revealed that certain DEGs identified in the present study are involved in DNA replication and cell cycle pathways; however, none of the 13 genes were involved in these two signaling pathways. A previous study suggested that PHLDA1 may be associated with CRC progression (45). A previous study demonstrated that PTGR2-knockdown gastric cancer cells rendered them more sensitive to cisplatin and 5-FU compared with the PTGR2-overexpressing cells (46). In addition, two variants of CHIT1, rs61745299 and rs35920428, may increase expression of the gene and have been associated with CRC (47). CHRNB1 may be a biomarker for the detection of relapsed and early relapsed CRC (48). In addition, LIG4 may mediate Wnt signaling-induced radioresistance in CRC (49). With the exception of the aforementioned studies, the association between the 13 genes identified in the current study and CRC or chemotherapy has not been investigated. Therefore, it is not clear whether these genes are causal or merely markers for response to fluorouracil-based chemotherapy in CRC.

Although the current study provides novel insights into the treatment of CRC, it has some limitations. The present study was based on a relatively small sample size; however, it is worth noting that the sample size in our study is relatively large compared with previous studies (15,16). Future studies are required to verify and improve the 13-gene signature in a larger independent cohort of patients.

In conclusion, the current study identified a 13-gene predictor classifier for the response to fluorouracil-based chemotherapy in patients with advanced CRC.

Acknowledgements

Not applicable.

Funding

This research was supported by the Youth Science Foundation of Guangxi Medical University (grant no. GXMUYSF 201716) and the Basic Ability Enhancement Program for Young and Middle-aged Teachers of Guangxi (grant no. 2017KY0120).

Availability of data and materials

All data generated or analyzed during the present study are included in this published article.

Authors' contributions

YFL designed the study and revised the manuscript. ZHG and QYZ analyzed the data and wrote the manuscript. YL, ZHX, ZH, and ZCC assisted with analyzing the data and writing the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J and Jemal A: Global cancer statistics, 2012. CA Cancer J Clin. 65:87–108. 2015. View Article : Google Scholar : PubMed/NCBI

2 

Van Cutsem E, Cervantes A, Nordlinger B and Arnold D; ESMO Guidelines Working Group, : Metastatic colorectal cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. 25 Suppl 3:iii1–iii9. 2014. View Article : Google Scholar : PubMed/NCBI

3 

Brenner H, Kloor M and Pox CP: Colorectal cancer. Lancet. 383:1490–1502. 2014. View Article : Google Scholar : PubMed/NCBI

4 

de Gramont A, Bosset JF, Milan C, Rougier P, Bouché O, Etienne PL, Morvan F, Louvet C, Guillot T, François E and Bedenne L: Randomized trial comparing monthly low-dose leucovorin and fluorouracil bolus with bimonthly high-dose leucovorin and fluorouracil bolus plus continuous infusion for advanced colorectal cancer: A French intergroup study. J Clin Oncol. 15:808–815. 1997. View Article : Google Scholar : PubMed/NCBI

5 

Goldberg RM, Sargent DJ, Morton RF, Fuchs CS, Ramanathan RK, Williamson SK, Findlay BP, Pitot HC and Alberts SR: A randomized controlled trial of fluorouracil plus leucovorin, irinotecan, and oxaliplatin combinations in patients with previously untreated metastatic colorectal cancer. J Clin Oncol. 22:23–30. 2004. View Article : Google Scholar : PubMed/NCBI

6 

Tournigand C, André T, Achille E, Lledo G, Flesh M, Mery-Mignard D, Quinaux E, Couteau C, Buyse M, Ganem G, et al: FOLFIRI followed by FOLFOX6 or the reverse sequence in advanced colorectal cancer: A randomized GERCOR study. J Clin Oncol. 22:229–237. 2004. View Article : Google Scholar : PubMed/NCBI

7 

Yamazaki K, Nagase M, Tamagawa H, Ueda S, Tamura T, Murata K, Eguchi Nakajima T, Baba E, Tsuda M, Moriwaki T, et al: Randomized phase III study of bevacizumab plus FOLFIRI and bevacizumab plus mFOLFOX6 as first-line treatment for patients with metastatic colorectal cancer (WJOG4407G). Ann Oncol. 27:1539–1546. 2016. View Article : Google Scholar : PubMed/NCBI

8 

Petrelli F, Borgonovo K, Cabiddu M, Ghilardi M, Lonati V, Maspero F, Sauta MG, Beretta GD and Barni S: FOLFIRI-bevacizumab as first-line chemotherapy in 3500 patients with advanced colorectal cancer: A pooled analysis of 29 published trials. Clin Colorectal Cancer. 12:145–151. 2013. View Article : Google Scholar : PubMed/NCBI

9 

Saltz LB, Clarke S, Díaz-Rubio E, Scheithauer W, Figer A, Wong R, Koski S, Lichinitser M, Yang TS, Rivera F, et al: Bevacizumab in combination with oxaliplatin-based chemotherapy as first-line therapy in metastatic colorectal cancer: A randomized phase III study. J Clin Oncol. 26:2013–2019. 2008. View Article : Google Scholar : PubMed/NCBI

10 

Wang Y, Jatkoe T, Zhang Y, Mutch MG, Talantov D, Jiang J, McLeod HL and Atkins D: Gene expression profiles and molecular markers to predict recurrence of Dukes' B colon cancer. J Clin Oncol. 22:1564–1571. 2004. View Article : Google Scholar : PubMed/NCBI

11 

Eschrich S, Yang I, Bloom G, Kwong KY, Boulware D, Cantor A, Coppola D, Kruhøffer M, Aaltonen L, Orntoft TF, et al: Molecular staging for survival prediction of colorectal cancer patients. J Clin Oncol. 23:3526–3535. 2005. View Article : Google Scholar : PubMed/NCBI

12 

Notterman DA, Alon U, Sierk AJ and Levine AJ: Transcriptional gene expression profiles of colorectal adenoma, adenocarcinoma, and normal tissue examined by oligonucleotide arrays. Cancer Res. 61:3124–3130. 2001.PubMed/NCBI

13 

Wen WH, Bernstein L, Lescallett J, Beazer-Barclay Y, Sullivan-Halley J, White M and Press MF: Comparison of TP53 mutations identified by oligonucleotide microarray and conventional DNA sequence analysis. Cancer Res. 60:2716–2722. 2000.PubMed/NCBI

14 

Iwao-Koizumi K, Matoba R, Ueno N, Kim SJ, Ando A, Miyoshi Y, Maeda E, Noguchi S and Kato K: Prediction of docetaxel response in human breast cancer by gene expression profiling. J Clin Oncol. 23:422–431. 2005. View Article : Google Scholar : PubMed/NCBI

15 

Del Rio M, Molina F, Bascoul-Mollevi C, Copois V, Bibeau F, Chalbos P, Bareil C, Kramar A, Salvetat N, Fraslon C, et al: Gene expression signature in advanced colorectal cancer patients select drugs and response for the use of leucovorin, fluorouracil, and irinotecan. J Clin Oncol. 25:773–780. 2007. View Article : Google Scholar : PubMed/NCBI

16 

Estevez-Garcia P, Rivera F, Molina-Pinelo S, Benavent M, Gómez J, Limón ML, Pastor MD, Martinez-Perez J, Paz-Ares L, Carnero A and Garcia-Carbonero R: Gene expression profile predictive of response to chemotherapy in metastatic colorectal cancer. Oncotarget. 6:6151–6159. 2015. View Article : Google Scholar : PubMed/NCBI

17 

Li S, Lu X, Chi P and Pan J: Identification of HOXB8 and KLK11 expression levels as potential biomarkers to predict the effects of FOLFOX4 chemotherapy. Future Oncol. 9:727–736. 2013. View Article : Google Scholar : PubMed/NCBI

18 

Therasse P, Arbuck SG, Eisenhauer EA, Wanders J, Kaplan RS, Rubinstein L, Verweij J, Van Glabbeke M, van Oosterom AT, Christian MC and Gwyther SG: New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst. 92:205–216. 2000. View Article : Google Scholar : PubMed/NCBI

19 

Miller AB, Hoogstraten B, Staquet M and Winkler A: Reporting results of cancer treatment. Cancer. 47:207–214. 1981. View Article : Google Scholar : PubMed/NCBI

20 

Aras M, Erdil TY, Dane F, Gungor S, Ones T, Dede F, Inanir S and Turoglu HT: Comparison of WHO RECIST 1.1, EORTC, and PERCIST criteria in the evaluation of treatment response in malignant solid tumors. Nucl Med Commun. 37:9–15. 2016.PubMed/NCBI

21 

Khokher S, Qureshi MU and Chaudhry NA: Comparison of WHO and RECIST criteria for evaluation of clinical response to chemotherapy in patients with advanced breast cancer. Asian Pac J Cancer Prev. 13:3213–3218. 2012. View Article : Google Scholar : PubMed/NCBI

22 

Choi JH, Ahn MJ, Rhim HC, Kim JW, Lee GH, Lee YY and Kim IS: Comparison of WHO and RECIST criteria for response in metastatic colorectal carcinoma. Cancer Res Treat. 37:290–293. 2005. View Article : Google Scholar : PubMed/NCBI

23 

Park JO, Lee SI, Song SY, Kim K, Kim WS, Jung CW, Park YS, Im YH, Kang WK, Lee MH, et al: Measuring response in solid tumors: Comparison of RECIST and WHO response criteria. Jpn J Clin Oncol. 33:533–537. 2003. View Article : Google Scholar : PubMed/NCBI

24 

Gautier L, Cope L, Bolstad BM and Irizarry RA: affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20:307–315. 2004. View Article : Google Scholar : PubMed/NCBI

25 

Leek JT, Johnson WE, Parker HS, Jaffe AE and Storey JD: The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 28:882–883. 2012. View Article : Google Scholar : PubMed/NCBI

26 

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43:e472015. View Article : Google Scholar : PubMed/NCBI

27 

Yu G, Wang LG, Han Y and He QY: clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS. 16:284–287. 2012. View Article : Google Scholar : PubMed/NCBI

28 

Wu TT, Chen YF, Hastie T, Sobel E and Lange K: Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics. 25:714–721. 2009. View Article : Google Scholar : PubMed/NCBI

29 

Cuyle PJ and Prenen H: Current and future biomarkers in the treatment of colorectal cancer. Acta Clin Belg. 72:103–115. 2017. View Article : Google Scholar : PubMed/NCBI

30 

Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC and Müller M: pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 12:772011. View Article : Google Scholar : PubMed/NCBI

31 

Stintzing S: Personalized treatment for colorectal carcinomas. Dtsch Med Wochenschr. 142:1652–1659. 2017.(In German). PubMed/NCBI

32 

Gustavsson B, Carlsson G, Machover D, Petrelli N, Roth A, Schmoll HJ, Tveit KM and Gibson F: A review of the evolution of systemic chemotherapy in the management of colorectal cancer. Clin Colorectal Cancer. 14:1–10. 2015. View Article : Google Scholar : PubMed/NCBI

33 

Iwamoto S, Takahashi T, Tamagawa H, Nakamura M, Munemoto Y, Kato T, Hata T, Denda T, Morita Y, Inukai M, et al: FOLFIRI plus bevacizumab as second-line therapy in patients with metastatic colorectal cancer after first-line bevacizumab plus oxaliplatin-based therapy: The randomized phase III EAGLE study. Ann Oncol. 26:1427–1433. 2015. View Article : Google Scholar : PubMed/NCBI

34 

Bazarbashi S, Aljubran A, Alzahrani A, Mohieldin A, Soudy H and Shoukri M: Phase I/II trial of capecitabine, oxaliplatin, and irinotecan in combination with bevacizumab in first line treatment of metastatic colorectal cancer. Cancer Med. 4:1505–1513. 2015. View Article : Google Scholar : PubMed/NCBI

35 

Heinemann V, von Weikersthal LF, Decker T, Kiani A, Vehling-Kaiser U, Al-Batran SE, Heintges T, Lerchenmüller C, Kahl C, Seipelt G, et al: FOLFIRI plus cetuximab versus FOLFIRI plus bevacizumab as first-line treatment for patients with metastatic colorectal cancer (FIRE-3): A randomised, open-label, phase 3 trial. Lancet Oncol. 15:1065–1075. 2014. View Article : Google Scholar : PubMed/NCBI

36 

Stremitzer S, Sebio A, Stintzing S and Lenz HJ: Panitumumab safety for treating colorectal cancer. Expert Opin Drug Saf. 13:843–851. 2014.PubMed/NCBI

37 

Saridaki Z, Souglakos J and Georgoulias V: Prognostic and predictive significance of MSI in stages II/III colon cancer. World J Gastroenterol. 20:6809–6814. 2014. View Article : Google Scholar : PubMed/NCBI

38 

Webber EM, Kauffman TL, O'Connor E and Goddard KA: Systematic review of the predictive effect of MSI status in colorectal cancer patients undergoing 5FU-based chemotherapy. BMC Cancer. 15:1562015. View Article : Google Scholar : PubMed/NCBI

39 

Nakanishi R, Kitao H, Fujinaka Y, Yamashita N, Iimori M, Tokunaga E, Yamashita N, Morita M, Kakeji Y and Maehara Y: FANCJ expression predicts the response to 5-fluorouracil-based chemotherapy in MLH1-proficient colorectal cancer. Ann Surg Oncol. 19:3627–3635. 2012. View Article : Google Scholar : PubMed/NCBI

40 

Simmer F, Venderbosch S, Dijkstra JR, Vink-Börger EM, Faber C, Mekenkamp LJ, Koopman M, De Haan AF, Punt CJ and Nagtegaal ID: MicroRNA-143 is a putative predictive factor for the response to fluoropyrimidine-based chemotherapy in patients with metastatic colorectal cancer. Oncotarget. 6:22996–23007. 2015. View Article : Google Scholar : PubMed/NCBI

41 

Molina-Pinelo S, Carnero A, Rivera F, Estevez-Garcia P, Bozada JM, Limon ML, Benavent M, Gomez J, Pastor MD, Chaves M, et al: MiR-107 and miR-99a-3p predict chemotherapy response in patients with advanced colorectal cancer. BMC Cancer. 14:6562014. View Article : Google Scholar : PubMed/NCBI

42 

Candy PA, Phillips MR, Redfern AD, Colley SM, Davidson JA, Stuart LM, Wood BA, Zeps N and Leedman PJ: Notch-induced transcription factors are predictive of survival and 5-fluorouracil response in colorectal cancer patients. Br J Cancer. 109:1023–1030. 2013. View Article : Google Scholar : PubMed/NCBI

43 

Mariadason JM, Arango D, Shi Q, Wilson AJ, Corner GA, Nicholas C, Aranes MJ, Lesser M, Schwartz EL and Augenlicht LH: Gene expression profiling-based prediction of response of colon carcinoma cells to 5-fluorouracil and camptothecin. Cancer Res. 63:8791–8812. 2003.PubMed/NCBI

44 

Kursa MB and Rudnicki WR: Feature selection with the boruta package. J Stat Soft. 36:1–13. 2010. View Article : Google Scholar

45 

Zhang Z and Huang J: Intestinal stem cells-types and markers. Cell Biol Int. 37:406–414. 2013. View Article : Google Scholar : PubMed/NCBI

46 

Chang EY, Tsai SH, Shun CT, Hee SW, Chang YC, Tsai YC, Tsai JS, Chen HJ, Chou JW, Lin SY and Chuang LM: Prostaglandin reductase 2 modulates ROS-mediated cell death and tumor transformation of gastric cancer cells and is associated with higher mortality in gastric cancer patients. Am J Pathol. 181:1316–1326. 2012. View Article : Google Scholar : PubMed/NCBI

47 

Li FF, Yan P, Zhao ZX, Liu Z, Song DW, Zhao XW, Wang XS, Wang GY and Liu SL: Polymorphisms in the CHIT1 gene: Associations with colorectal cancer. Oncotarget. 7:39572–39581. 2016.PubMed/NCBI

48 

Chang YT, Yeh YS, Ma CJ, Huang CW, Tsai HL, Huang MY, Cheng TL and Wang JY: Optimization of a multigene biochip for detection of relapsed and early relapsed colorectal cancer. J Surg Res. 220:427–437. 2017. View Article : Google Scholar : PubMed/NCBI

49 

Jun S, Jung YS, Suh HN, Wang W, Kim MJ, Oh YS, Lien EM, Shen X, Matsumoto Y, McCrea PD, et al: LIG4 mediates Wnt signalling-induced radioresistance. Nat Commun. 7:109942016. View Article : Google Scholar : PubMed/NCBI

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June 2019
Volume 17 Issue 6

Print ISSN: 1792-1074
Online ISSN:1792-1082

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Copy and paste a formatted citation
APA
Gan, Z., Zou, Q., Lin, Y., Xu, Z., Huang, Z., Chen, Z., & Lv, Y. (2019). Identification of a 13‑gene‑based classifier as a potential biomarker to predict the effects of fluorouracil‑based chemotherapy in colorectal cancer. Oncology Letters, 17, 5057-5063. https://doi.org/10.3892/ol.2019.10159
MLA
Gan, Z., Zou, Q., Lin, Y., Xu, Z., Huang, Z., Chen, Z., Lv, Y."Identification of a 13‑gene‑based classifier as a potential biomarker to predict the effects of fluorouracil‑based chemotherapy in colorectal cancer". Oncology Letters 17.6 (2019): 5057-5063.
Chicago
Gan, Z., Zou, Q., Lin, Y., Xu, Z., Huang, Z., Chen, Z., Lv, Y."Identification of a 13‑gene‑based classifier as a potential biomarker to predict the effects of fluorouracil‑based chemotherapy in colorectal cancer". Oncology Letters 17, no. 6 (2019): 5057-5063. https://doi.org/10.3892/ol.2019.10159