Dataset Information

A robust gene signature for the prediction of early relapse in stage I-III colon cancer.

ABSTRACT: Colon cancer patients experiencing early relapse consistently exhibited poor survival. The aim of our study was to develop an mRNA signature that can help to detect early relapse cases in stage I-III colon cancer. Public microarray datasets of stage I-III colon cancer samples were extracted from the Gene Expression Omnibus database. Propensity score matching analysis was performed between patients in the early relapse group and the long-term survival group from GSE39582 discovery series (N = 386), and patients were 1 : 1 matched. Global mRNA expression changes were then analyzed between the paired groups to identify the differentially expressed genes. Lasso Cox regression modeling analysis was conducted for the selection of prognostic mRNA. Fifteen mRNA were finally identified to build an early relapse classifier. With specific risk score formula, patients were classified into a high-risk group and a low-risk group. Relapse-free survival was significantly different between the two groups in every series, including discovery [hazard ratio (HR): 2.547, 95% confidence interval (CI): 1.708-3.797, P < 0.001)], internal validation (HR: 5.146, 95% CI: 1.968-13.457, P < 0.001), and external validation (HR: 1.977, 95% CI: 1.295-3.021, P < 0.001) sets of patients. Time-dependent receiver-operating characteristic at 1 year suggested more prognostic accuracy of the classifier [area under curve (AUC = 0.703)] than the American Joint Commission on Cancer tumor-node-metastasis staging system (AUC = 0.659) in all 951 patients. In conclusion, we developed a robust mRNA signature that can effectively classify colon cancer patients into groups with low and high risks of early relapse. This mRNA signature may help select high-risk colon cancer patients who require more aggressive therapeutic intervention.

SUBMITTER: Dai W

PROVIDER: S-EPMC5891048 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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A robust gene signature for the prediction of early relapse in stage I-III colon cancer.

Dai Weixing W Li Yaqi Y Mo Shaobo S Feng Yang Y Zhang Long L Xu Ye Y Li Qingguo Q Cai Guoxiang G

Molecular oncology 20180216 4

Colon cancer patients experiencing early relapse consistently exhibited poor survival. The aim of our study was to develop an mRNA signature that can help to detect early relapse cases in stage I-III colon cancer. Public microarray datasets of stage I-III colon cancer samples were extracted from the Gene Expression Omnibus database. Propensity score matching analysis was performed between patients in the early relapse group and the long-term survival group from GSE39582 discovery series (N = 386 ...[more]

PMID: 29377588

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Project description:Purpose: The benefit of postoperative adjuvant chemotherapy in patients with Dukes´ B colorectal cancer is still uncertain and its routine use is not recommended. The five years survival rate is approximately 75% and identification of the patients at high risk of recurrence would represent an important strategy for the use of adjuvant chemotherapy. In this study we identify new prognostic markers for tumor relapse in Dukes´ B colon cancer patients. Patients and Methods: We retrospectively analyzed gene expression profiles in frozen tumor specimens from 16 patients with Dukes´ B colorectal cancer by using high density oligonucleotide microarrays. Data were normalized and subsequently analyzed with two different statistical procedures. The intensity value associated to each spot is the result of subtracting a gaussian function of the noise from the foreground values {Kooperberg, 2002}. After this background subtraction, base 2 logarithms of all data were calculated and genes with more than two missing values were excluded from the analysis. The remaining missing values were replaced by using the KNN imputation method {Troyanskaya, 2001}. The great number of genes present in the microarray allowed us to consider that the overall fluorescence intensity must be the same for all slides, as the vast majority of genes will not change their expression between the two conditions tested. So we used the quantile normalization method {Bolstad, 2003}, which equalizes not only the average intensity but also the range of intensity values between slides. We decided to use two different statistical procedures in the search of significant differences in gene expression between the relapsed and non-relapsed patients. Only the genes selected by both statistical procedures were considered as differentially expressed between relapsed and non-relapsed patients. The first test was a permutation t-test for comparison of two means {Dudoit, 2003} and the second one a variation of the Fisher test based on the work presented by Iizuka et al. {Iizuka, 2003} in which they searched for the optimal number of genes that could differentiate between two groups of samples. Briefly, we used the same algorithms for the calculation of the Fisher criterion, but instead of looking for the optimal subset of genes able to differentiate between groups, what requires a lot of computing capacity, we tried different numbers of candidate genes and selected a number which yielded very good classification results when evaluated by means of Fast ICA {Lee, 2003} and Hierarchical Clustering {Everitt, 1974}. The procedure consisted of selecting the 30 genes with the highest Fisher criterion value in 6 rounds of iteration, each round leaving one sample of the relapsed group and two of the non-relapsed group out of the calculations (a variant of the “leave one out” method). We selected the genes present in at least 3 of the iterations. The intersection of the groups of genes selected by the two statistical procedures was selected as a prognostic signature for relapse in Dukes´stage B colon cancer. Results: Our results show a group of 48 genes differentially expressed between the relapsed and non-relapsed groups with an associated probability below 0,001 in the t test. Another statistical procedure based on the Fisher criterion resulted in 11 genes able to separate both groups. In order to minimize false positives we only considered a good gene signature the 8 genes selected by both statistical procedures. These genes are ribosomal protein S5, chromodomain helicase DNA binding protein 2, lysosomal ATPase V0 subunit A isoform 1, zinc finger protein 148, brain protein I3 (BRI3), hypothetical protein MGC23401 and one unknown clone. In order to verify the obtained results, the differential expression of the first two genes was confirmed by real time PCR. Keywords: repeat sample

Project description:Background and objectives: Multi-gene signature can be used as prognostic indicator in many types of cancer, but the association with early-relapse in patients with stage I-III clear cell and papillary renal cell cancer (RCC) is unknown. We aim to establish a mRNAs signature for improving prediction of early-relapse in patients with stage I-III clear cell and papillary RCC. Methods: The data of 610 patients with stage I-III RCC from The Cancer Genome Atlas (TCGA) and 270 patients from Fudan University Shanghai Cancer Center (FUSCC) were extracted. Propensity score matching analysis, linear models for microarray data VOOM method, least absolute shrinkage and selection operation Cox regression modeling analysis was conducted in turn for selecting multi-mRNA signature. Survival differences were assessed by Kaplan-Meier estimate and compared using log-rank test. Multivariable Cox regression and time-dependent receiver operating characteristic curves were used to evaluate the association of mRNAs signature with relapse-free survival (RFS). Results: Seventeen mRNAs were identified to constitute the early-relapse signature. Among patients with stage I-III RCC, those with high-risk score calculated from 17 mRNAs signature showed shorter RFS than those with low-risk score, both in TCGA discovery and internal validation sets, and in FUSCC discovery and internal validation sets (all p < 0.05). In multivariable Cox regression analysis, the 17 mRNAs signature remained an independent prognostic factor both in TCGA discovery (HR 2.43, 95%CI 1.98-2.96) and internal validation sets (HR 1.66, 95%CI 1.19-2.30), and FUSCC discovery (HR 1.28, 95%CI 1.13-1.43) and internal validation sets (HR 1.65, 95%CI 1.11-2.48). Additionally, the 17 mRNAs signature achieved a higher accuracy for RFS estimation beyond clinical indicator. Conclusion: The 17 mRNAs signature could classify stage I-III RCC patients into low- or high-risk of early-relapse, and will help to guide interventions to optimize survival outcomes.

Project description:Background: The current staging system is imprecise for prognostic prediction of early-stage non-small cell lung cancer (NSCLC). This study aimed to develop a robust prognostic signature for early-stage NSCLC, allowing classification of patients with a high risk of poor outcome and specific treatment decision. Method: In the present study, a comprehensive genome-wide profiling analysis was conducted using a retrospective pool of early-stage NSCLC patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE31210, GSE37745, and GSE50081 and The Cancer Genome Atlas (TCGA). Cox proportional hazards models were implemented to determine the association between gene expression levels and overall patient survival in each dataset. The common genes among all datasets were selected as candidate prognostic genes. A risk score model was developed and validated using four independent datasets and the entire cohort. The Kaplan-Meier with log-rank test was used to assess survival difference. Results: A univariate Cox proportional hazards regression analysis for each dataset showed that a total of 2280 genes in GSE31210, 762 genes in GSE37745, 871 genes in GSE50081, and 666 genes in TCGA were identified as candidate protective genes, while overall 2131 genes in GSE31210, 913 in GSE37745, 1107 in GSE50081, and 997 in TCGA were identified as candidate risky genes. There were 8 common genes associated with overall survival, including 7 mRNA and 1 lncRNA. By using the Step-wise multivariate Cox analysis, an 8-gene prognostic signature (CDCP1, HMMR, TPX2, CIRBP, HLF, KBTBD7, SEC24B-AS1, and SH2B1) for early-stage NSCLC was developed. Patients in the high-risk group had shorter overall survival than those in the low-risk group. Multivariate regression and stratified analysis suggested that the prognostic power of the 8-gene signature was independent of other clinical factors. Furthermore, the 8-gene signature achieved AUC values of 0.726, 0.701, 0.725 and 0.650 in GSE31210, GSE37745, GSE50081 and TCGA, respectively. Moreover, the combination of the 8-gene signature and the stage resulted to a better patient classification for survival prediction and treatment decision. Conclusion: This study developed a robust gene signature with great value for prognostic prediction in early-stage NSCLC, which may contribute to patient classification and personalized treatment decisions.

Dataset Information

A robust gene signature for the prediction of early relapse in stage I-III colon cancer.

Publications

A robust gene signature for the prediction of early relapse in stage I-III colon cancer.

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