Predicting the survival time for diffuse large B-cell lymphoma using microarray data

Integrated analysis of genome‑wide gene expression and DNA methylation microarray of diffuse large B‑cell lymphoma with TET mutations

Diffuse large B-cell lymphoma (DLBCL), the most frequently occurring type of lymphoid malignancy, has been demonstrated to be associated with mutations of Ten-Eleven Translocation (TET). In order to explore the association between DLBCL and TET mutations, the present study analyzed the gene expression and methylation profiles in human DLBCL biopsy tissues with wildtype and mutated TET2. The microarray dataset GSE37365, containing two subseries: the genome-wide gene expression dataset GSE37362 and the DNA methylation microarray dataset GSE37363, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using the limma package of R. Furthermore, differentially methylated sites and differentially methylated regions were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed via GO stats and GSEABase packages respectively. Finally, the Pathview package was used to construct the network of enriched pathways. A total of 198 DEGs (106 up- and 92 downregulated) were obtained. A total of 602 shared differentially methylated genes (DMGs) were identified according to differentially methylated levels. A total of 12 overlapping genes were identified in DEGs and DMGs. It was observed that 9 of the 12 overlapped genes were downregulated and hypermethylated, with 24 GO terms and one KEGG pathway significantly enriched. The results of the present study demonstrated that the genes cryptochrome circadian clock 1, zinc finger protein (ZNF) interacting with K protein 1, ZNF134, ZNF256 and ZNF615, which were hypermethylated and downregulated in DLBCL patients with TET2 mutations, were the key genes in the association between DLBCL and TET mutations. These genes may act as potential biomarkers for the diagnosis of DLBCL in the future.

Predicting the Survival Time for Bladder Cancer Using an Additive Hazards Model in Microarray Data

BACKGROUND

One substantial part of microarray studies is to predict patients' survival based on their gene expression profile. Variable selection techniques are powerful tools to handle high dimensionality in analysis of microarray data. However, these techniques have not been investigated in competing risks setting. This study aimed to investigate the performance of four sparse variable selection methods in estimating the survival time.

METHODS

The data included 1381 gene expression measurements and clinical information from 301 patients with bladder cancer operated in the years 1987 to 2000 in hospitals in Denmark, Sweden, Spain, France, and England. Four methods of the least absolute shrinkage and selection operator, smoothly clipped absolute deviation, the smooth integration of counting and absolute deviation and elastic net were utilized for simultaneous variable selection and estimation under an additive hazards model. The criteria of area under ROC curve, Brier score and c-index were used to compare the methods.

RESULTS

The median follow-up time for all patients was 47 months. The elastic net approach was indicated to outperform other methods. The elastic net had the lowest integrated Brier score (0.137±0.07) and the greatest median of the over-time AUC and C-index (0.803±0.06 and 0.779±0.13, respectively). Five out of 19 selected genes by the elastic net were significant (P<0.05) under an additive hazards model. It was indicated that the expression of RTN4, SON, IGF1R and CDC20 decrease the survival time, while the expression of SMARCAD1 increase it.

CONCLUSION

The elastic net had higher capability than the other methods for the prediction of survival time in patients with bladder cancer in the presence of competing risks base on additive hazards model.

A Gene Selection Method for Survival Prediction in Diffuse Large B-Cell Lymphomas Patients using 1D Discrete Wavelet Transform

BACKGROUND

An important aspect of microarray studies includes the prediction of patient survival based on their gene expression profile. To deal with the high dimensionality of this data, use of a dimension reduction procedure along with the survival prediction model is necessary. This study aimed to present a new method based on wavelet transform for survival relevant gene selection.

METHODS

The data included 2042 gene expression measurements from 40 patients with Diffuse Large B-Cell Lymphomas (DLBCL). The pre-processing gene expression data is decomposed using third level of the 1D discrete wavelet transform. The detail coefficients at levels 1 and 2 are filtered out and expression data reconstructed using the approximation and detailed coefficients at the third level. All the genes are then scored based on the t score. Then genes with the highest scores are selected. By using forward selection method in Cox regression model, significant genes were identified.

RESULTS

The results showed wavelet-based gene selection method presents acceptable survival prediction. Using this method, six significant genes were selected. It was indicated the expression of GENE3359X and GENE3968X decreased the survival time, whereas the expression of GENE967X, GENE3980X, GENE3405X and GENE1813X increased the survival time.

CONCLUSION

Wavelet-based gene selection method is a potentially useful tool for the gene selection from microarray data in the context of survival analysis.