Identification of pathway-based prognostic gene signatures in patients with multiple myeloma

Prognostic efficacy of the RTN1 gene in patients with diffuse large B-cell lymphoma

Gene expression profiling has been vastly used to extract the genes that can predict the clinical outcome in patients with diverse cancers, including diffuse large B-cell lymphoma (DLBCL). With the aid of bioinformatics and computational analysis on gene expression data, various prognostic gene signatures for DLBCL have been recently developed. The major drawback of the previous signatures is their inability to correctly predict survival in external data sets. In other words, they are not reproducible in other datasets. Hence, in this study, we sought to determine the gene(s) that can reproducibly and robustly predict survival in patients with DLBCL. Gene expression data were extracted from 7 datasets containing 1636 patients (GSE10846 [n = 420], GSE31312 [n = 470], GSE11318 [n = 203], GSE32918 [n = 172], GSE4475 [n = 123], GSE69051 [n = 157], and GSE34171 [n = 91]). Genes significantly associated with overall survival were detected using the univariate Cox proportional hazards analysis with a P value < 0.001 and a false discovery rate (FDR) < 5%. Thereafter, significant genes common between all the datasets were extracted. Additionally, chromosomal aberrations in the corresponding region of the final common gene(s) were evaluated as copy number alterations using the single nucleotide polymorphism (SNP) data of 570 patients with DLBCL (GSE58718 [n = 242], GSE57277 [n = 148], and GSE34171 [n = 180]). Our results indicated that reticulon family gene 1 (RTN1) was the only gene that met our rigorous pipeline criteria and associated with a favorable clinical outcome in all the datasets (P < 0.001, FDR < 5%). In the multivariate Cox proportional hazards analysis, this gene remained independent of the routine international prognostic index components (i.e., age, stage, lactate dehydrogenase level, Eastern Cooperative Oncology Group [ECOG] performance status, and number of extranodal sites) (P < 0.0001). Furthermore, no significant chromosomal aberration was found in the RTN1 genomic region (14q23.1: Start 59,595,976/End 59,870,966).

Development of an RNA sequencing-based prognostic gene signature in multiple myeloma

Several prognostic gene signatures have been developed to predict the clinical outcome in patients with multiple myeloma (MM). The most salient disadvantage of the previous signatures is their non-reproducibility in external datasets. Given the disadvantages and the superiority of RNA sequencing over microarrays in transcriptome profiling to produce more reliable outputs, we sought to develop a reproducible RNA sequencing-based prognostic gene signature for MM. Genes significantly associated with survival were detected in The Cancer Genome Atlas (TCGA) MM RNA sequencing dataset (MMRF-CoMMpass) (n = 412) through a strict pipeline containing four rigid filters. The reproducibility of the selected genes was checked in an independent dataset (GSE24080), containing 559 newly diagnosed patients with MM. The RNA sequencing-based prognostic signature was reconstructed based on the final genes in the training dataset (MMRF-CoMMpass) and externally validated in five independent datasets (i.e. GSE2658, GSE13624, GSE9782, GSE6477 and GSE57317), containing 1461 MM cases. The RNA sequencing-based signature was reconstructed using finally five reproducible genes: CCT2, CKS1B, PRKDC, NONO and UBE2A. This signature was able to robustly discriminate between low- and high-risk patients in both training and validation datasets (Ps ≤ 0·001). Our signature was also independent of and more powerful than the routine MM prognostic factors (i.e. β2-microglobulin, albumin, age and sex) (Ps ≤ 0·01). Treatment regimens had no effect on RNA sequencing-based signature insofar as this signature succeeded in predicting the clinical outcome in various treatment groups (Ps ≤ 0·001).

Development of a Reproducible Prognostic Gene Signature to Predict the Clinical Outcome in Patients with Diffuse Large B-Cell Lymphoma

Alongside various clinical prognostic factors for diffuse large B-cell lymphoma (DLBCL) such as the international prognostic index (IPI) components (ie, age, tumor stage, performance status, serum lactate dehydrogenase concentration, and number of extranodal sites), prognostic gene signatures have recently shown promising efficacy. However, previously developed signatures for DLBCL suffer from many major inadequacies such as lack of reproducibility in external datasets, high number of members (genes) in a signature, and inconsistent association with the survival time in various datasets. Accordingly, we sought to find a reproducible prognostic gene signature with a minimal number of genes. Seven datasets—namely GSE10856 (420 samples), GSE31312 (470 samples), GSE69051 (157 samples), GSE32918 (172 samples), GSE4475 (123 samples), GSE11318 (203 samples), and GSE34171 (91 samples)—were employed. The datasets were randomly categorized into training (1219 samples comprising GSE10856, GSE31312, GSE69051, and GSE32918) and validation (417 samples consisting of GSE4475, GSE11318, and GSE34171) groups. Through the univariate Cox proportional hazards analysis, common genes associated with the overall survival time with a P value less than 0.001 and a false discovery rate less than 5% were identified in 1219 patients included in the 4 training datasets. Thereafter, the common genes were entered into a multivariate Cox proportional hazards analysis encompassing the common genes and the international prognostic index (IPI) factors as covariates, and then only common genes with a significant level of difference (P < 0.01 and z-score >2 or <−2) were selected to reconstruct the prognostic signature. After the analyses, a 7-gene prognostic signature was developed, which efficiently predicted the survival time in the training dataset (Ps < 0.0001). Subsequently, this signature was tested in 3 validation datasets. Our signature was able to strongly predict clinical outcomes in the validation datasets (Ps < 0.0001). In the multivariate Cox analysis, our outcome predictor was independent of the routine IPI components in both training datasets (Ps < 0.0001). Furthermore, our outcome predictor was the most powerful independent prognostic variable (Ps < 0.0001). We developed a potential reproducible prognostic gene signature which was able to robustly discriminate low-risk patients with DLBCL from high-risk ones.