Prognostic Efficacy of the RTN1 Gene in Patients with Diffuse Large B-Cell Lymphoma

doi:10.21203/rs.2.11441/v2

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).

Cancer Biology

Oncology

DLBCL

RTN1

Prognosis

Survival

Reticulon family gene 1 (RTN1) (formerly termed “neuroendocrine-specific protein” [NSP]) is a reticulon-encoding gene that is associated with the endoplasmic reticulum. Reticulons play critical roles in membrane trafficking or neuroendocrine secretion in neuroendocrine cells. RTN1 encodes 3 variants—namely NSP-A, -B, and –C—which are attached to the endoplasmic reticulum by means of 2 putative transmembrane domains in the homologous C-terminal region ^1-4.

Previous investigations have introduced RTN1 as a potential diagnostic/therapeutic marker of neurological diseases and cancers ^{2, 5-7}. RTN1 was proposed as a potential marker for carcinomas with neuroendocrine characteristics ². It has been shown that RTN1 reduces the anti-apoptotic activity of a protein encoded by BCL2-like 1 (BCL2L1) (ie, B-cell lymphoma-extra large [Bcl-xL]). Indeed, RTN1 can change the subcellular localization of the Bcl-xL protein from the mitochondria to the endoplasmic reticulum, which disrupts its anti-apoptotic action ⁵.

Because of the major shortages of previous prognostic gene signatures developed based on gene expression profiling ^8-13, we sought to find the gene(s) that can reproducibly predict the clinical outcome in patients with diffuse large B-cell lymphoma (DLBCL). Some of the shortcomings of the previous signatures hindering their clinical utility include the infeasibility to reproduce a prognostic signature in external datasets, negligible overlaps between the developed signatures, and large numbers of genes in the developed prognostic genes (180 genes, 90 genes, and 27 genes in signatures developed by Lenz et al [2008], Alizadeh et al [2000], and Wright et al [2003], respectively). In our efforts to find the gene(s) reproducibly associated with survival via bioinformatics and computational approaches, we obtained a surprising result: The RTN1 gene was robustly and reliably associated with a favorable outcome in 1636 patients with DLBCL (including 7 gene expression data sets). Furthermore, the RTN1 gene remained as one of the most powerful independent prognostic factors in comparison with the international prognostic index (IPI) components.

RTN1 as the most robust and reproducible prognostic gene in all the data sets

First, a univariate Cox proportional hazards analysis was run so as to find genes significantly associated with overall survival in all the datasets. The analysis revealed that 3 genes—namely APOC1, RTN1, and PLAU—fulfilled the criteria and were significantly associated with the clinical outcome at an FDR <10% and a P value <0.001. When the FDR cutoff value was set at 5%, only RTN1 met our rigorous pipeline criteria and was significantly associated with survival at a P value <0.001 in the 1636 patients encompassing all the 7 data sets (Table 2). Meanwhile, this gene showed a consistent positive association with survival in all the data sets (hazard ratio range [HR]: 0.41 to 0.79) (Table 2). Overall survival was significantly different between the low-risk and high-risk groups reconstructed based on the median of the RTN1 expression values (> median value vs. < median value) at a P value < 0.0001. The rates of overall survival at 5 years in the high-risk and low-risk groups in the different datasets were as follows: GSE31312 (51% vs. 72%), GSE10846 (48% vs. 67%), GSE32916/69051 (43% vs. 60%), GSE4475 (18% vs. 58%), GSE34171 (60% vs. 87%), and GSE11318 (36% vs. 60%) (Figure 1). Moreover, RTN1 was differentially expressed between the 2 classes (i.e., long survival vs. short survival) in all the datasets in the SAM analysis (P<0.001).

Additionally, the multivariate Cox proportional hazards analysis indicated that RTN1 remained independent of routine IPI components in both GSE10846 (HR: 0.78 [0.67 to 0.90]) and GSE31312 (HR: 0.77 [0.66 to 0.88]) (Ps<0.0001). Nonetheless, among the IPI parameters, only age remained an independent predictor in both datasets. Some other the IPI factors were only significant in 1 dataset (Table 3).

Expression of RTN1 in the molecular subtypes of DLBCL

The expression of RTN1 was compared between the different molecular subtypes of DLBCL (i.e., ABC-like, GCB-like, and type 3) using the one-way ANOVA test. The examination indicated that the expression of RTN1 was significantly higher in the subtype with the better overall survival (i.e., GCB-like) than in the subtype with the inferior survival (i.e., ABC-like) (Ps<0.05) in both GSE10846 and GSE31312. We also checked whether overall survival was significantly different between the groups based on RTN1 in the different molecular subtypes of DLBCL. Our analysis revealed that overall survival was significantly different between the 2 risk groups in the GCB-like subtype (Ps<0.05), whereas there was no significant association between the 2 risk groups in the other subtypes (i.e., ABC-like and type 3) (Figure 2).

Correlation between RTN1 and BCL2L1 expressions

Tagamei et al (2000) indicated that RTN1 only changes the subcellular localization of Bcl-xL from mitochondria to the endoplasmic reticulum and does not alter the expression level of the corresponding gene (i.e., BCL2L1). Our analysis revealed no significant and consistent correlations between the RTN1 probe-sets (n=2) and the BCL2L1 probe-sets (n=4), where some inconsistent (a mix of positive and negative results) and poor correlation coefficients (r<0.59) were obtained in the different analyses (Figure 3 and Supplementary Table 1). Nevertheless, an elevation in RTN1 expression did not suppress BCL2L1 expression. There was a good and significant correlation between the 2 RTN1 probe-sets (r=0.82, P<0.01) (Figure 4 and Supplementary Table 1).

RTN1 at the genome level

Possible CNAs of the RTN1 gene (14q23.1: Start 59,595,976/End 59,870,966) were checked through an analysis on the SNP data of 570 patients with DLBCL. The upstream and downstream regions that might partly include the RTN1 region were also explored in order to detect possible aberrations. The analysis indicated no significant chromosomal aberrations in the region of the RTN1 gene (14q23.1: Start 59,595,976/End 59,870,966). Nonetheless rare chromosomal gains (CN=3, amplification of 59,526,724-60,901,663 segment) and losses (CN=1, loss of 59,580,209-60,388,423 segment) were detected in 2 samples of GSE58718 and GSE34171, correspondingly, where these abnormalities were not recurrent (frequency <1%). No chromosomal instability was found in the region of interest in GSE57277.

Several studies have proposed various prognostic signatures comprising different numbers of genes via gene expression analysis ^8-14. Indeed, there is a minimum of overlap between these signatures and in many situations, there is no common gene in the suggested signatures. In addition, we found no genes of the previous signatures that were reproducibly associated with the clinical outcome in our data sets. Another disadvantage of these signatures is their large number of genes. Indubitably, the use of such large signatures is impractical in routine clinical practice ^15-16.

In the current study, we assessed the prognostic efficacy of RTN1 in several large cohorts of patients with DLBCL. This gene was consistently associated with a favorable outcome in all the datasets, comprised of 1636 patients with DLBCL. The association between the RTN1 gene expression and a favorable clinical outcome in all the datasets was significant at an FDR <5%, which means that the probability of a false positive was extremely low. Furthermore, it remained as a one of the most powerful independent prognostic factors in comparison with the IPI components. Although RTN1 was not the most powerful gene associated with overall survival, it was the only gene that reproducibly predicted a favorable clinical outcome. This gene was previously reported as a member of the stromal-1 signature in a 108-gene model developed by Lenz et al (2008). Additionally, the upregulation of RTN1 in CXCR4- DLBCL versus CXCR4+ DLBCL was indicated, where CXCR4- and CXCR4+ subtypes were associated with better and poorer overall survival, respectively ¹⁷.

Various roles of RTN1 in the biology of cancers have been previously investigated. As was previously described in the introduction, RTN1 induces its antitumor activity through interaction with Bcl-xL on the endoplasmic reticulum and reduces its anti-apoptotic activity ⁵. A previous investigation revealed that a member of the RTN family (ie, RTN-1C) sensitizes neuroepithelioma cells to fenretinide-induced apoptosis through interaction with glucosylceramide synthase ¹⁸. Moreover, RTN1-encoded proteins have been proposed as a category criterion for human lung cancer ¹⁹. Previous research has also demonstrated that NSP-reticulon expression is restricted to lung carcinoma cells with a neuroendocrine phenotype ^{6-7, 19}. Another RTN1 paralog (i.e., RTN3) has a similar antitumor activity through the enrichment of TRAIL-mediated apoptosis via the downregulation of c-FLIP and the upregulation of death receptor 5 ²⁰.

In light of the results of the present study, RTN1 can be considered a potential prognostic gene that can robustly predict survival in patients with DLBCL. Further studies can explore the prognostic efficacy of RTN1 in depth. In this way, the prognostic efficacy of this gene can be well again compared with the regular prognostic parameters. This gene can be experimentally validated in large cohorts of the patients with DLBCL. Because of some major limitations, we could not collect enough homogenous DLBCLs with the confirmed survival time for subsequent experimental procedure. Indeed, survival time as most important clinical metadata wasn’t recorded for many patients.

Datasets

The Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) database was searched to find the gene expression profiling datasets of patients with DLBCL. Only datasets containing clinical metadata (especially overall survival) (7 datasets) were retained, and the rest were excluded. Additionally, every effort was made to select expression datasets from all types of microarray chips such as Affymetrix and Illumina, if possible. The datasets were downloaded in SOFT file format and were subsequently transformed logarithmically using tools provided in geWorkbench 2.5.1 package ²¹, if necessary. More details on the clinical characteristics of the studied datasets are provided in Table 1. The datasets included GSE10846 (n= 420), GSE31312 (n=470), GSE32918 (n=172), GSE69051 (n=157), GSE4475 (n=123), GSE11318 (n=203), and GSE34171 (n=91). Since GSE32918 and GSE69051 have originated from a similar research study ²² and have some common samples, they were merged as a single data set and termed “GSE32918/69051”. The number of samples for these datasets was determined after corrections were made based on the common samples (172 samples for GSE32918 and 157 samples for GSE69051). In addition, the genetic aberrations of the desired gene(s) at the genome level were evaluated by employing 3 single nucleotide polymorphism (SNP) array data sets—namely GSE58718 (n=242), GSE57277 (n=148), and GSE34171 (n=180). GSE34171 contains both gene expression and SNP data (Table 1).

Identification of the common gene(s) associated with survival in the gene expression data sets

The association between gene expression and overall survival was examined using the univariate Cox proportional hazards analysis as previously described^23-24. In this analysis, the association between a group of covariates (genes) and the response variable (overall survival) was evaluated. The univariate Cox analysis was performed using BRB-Array tools, developed by Richard Simon and the BRB-ArrayTools Development Team. In this analysis, the findings were strengthened by employing rigorous pipeline criteria and retaining only genes with a P value <0.001 and a false discovery rate (FDR) <5%. Subsequently, the common gene(s) significantly associated with overall survival between all the data sets was/were extracted. For this purpose, only common gene(s) with consistent associations were selected, while genes with inconsistent associations (negatively associated with overall survival in a dataset and positively associated with overall survival in another) were excluded. Moreover, the patients were categorized into 2 risk groups (high-risk vs. low-risk) based on the median of the selected common gene expression values (> median value vs. < median value), and overall survival was compared between the groups using the Kaplan–Meier analysis and log-rank test at a P value <0.01. The Kaplan–Meier analysis and the log-rank test were performed in SPSS 16.0 package (Chicago, USA). Since RTN1 was the only gene that fulfilled the criteria and was selected as the final gene, the subsequent analyses were exclusively performed on this gene.

As a confirmatory step, we checked whether RTN1 was differentially expressed between the 2 predefined survival classes using the significance analysis of microarray (SAM) analysis. In this analysis, 2 classes (long survival [≥5 y] vs. short survival [<5 y]) were created and, thereafter, the genes that were differentially expressed were detected. The SAM analysis was performed using the method added in BRB-Array tools. In this analysis, the FDR and the number of permutations were set at 5% and 1000, respectively.

Prognostic efficacy of the RTN1 gene in a multivariate model

The prognostic efficacy of RTN1 was also evaluated in a multivariate Cox proportional-hazards regression analysis, where the RTN1 gene expression and all the individual components of the international prognostic index (IPI) (ie, age, stage, lactate dehydrogenase level, Eastern Cooperative Oncology Group [ECOG] performance status, and number of extranodal sites) ²⁵ were entered as covariate variables. Additionally, the molecular subtype (ie, ABC-like, GCB-like, and type 3) and sex were incorporated as another 2 variables into the model. The multivariate analysis was performed solely on the datasets with the clinical IPI data (i.e., GSE10846 and GSE31312). This analysis was carried out using Survival package (http://cran.r-project.org/package=survival) and SPSS 16.0 package (Chicago, USA).

Prognostic efficacy of the RTN1 gene in molecular subtypes of DLBCL

We also checked the prognostic worth of RTN1 in molecular subtypes of DLBCL (i.e., ABC-like, GCB-like, and type 3) using similar strategy described above. In brief, in each subtypes two risk groups constituted based on median of the RTN1 expression values (> median value vs. < median value) and then overall survival was compared between the groups using the Kaplan–Meier analysis and log-rank test at a P value <0.01.

Correlation between RTN1 and BCL2L1 expressions

One of the main targets of RTN1 is BCL2L1 via the inhibition of its anti-apoptotic activity ⁵. Moreover, BCL2L1 is a member of BCL-2 protein families deregulated in lymphoma tumors, especially DLBCL ^26-28. Accordingly, the associations between 2 probe-sets of RTN1 (i.e., 203485_at and 210222_s_at) and 4 probe-sets of BCL2L1 (i.e., 206665_s_at, 212312_at, 215037_s_at, and 231228_at) were evaluated using correlation analysis. The correlations were graded based on the classification proposed by Papasouliotis et al (2006) ²⁹(i.e., r=0.93 to 0.100 as excellent, r=0.80 to 0.92 as good, r=0.59 to 0.79 as fair, and r<0.59 as poor correlations). The correlation analysis was performed using SPSS 16.0 package (Chicago, USA) in all the datasets, and a P value <0.05 was considered significant.

Evaluation of RTN1 at the genome level in the DLBCL samples

For the assessment of the chromosomal aberrations of the RTN1 gene, 3 datasets—namely GSE58718 (n=242), GSE57277 (n=148), and GSE34171 (n=180)—were used to extract copy number variations (CNVs) from the SNP data. GSE58718 was generated based on Illumina HumanCNV370-Duov1 DNA Analysis BeadChip, while GSE57277 and GSE34171 were generated using Affymetrix Mapping 250K SNP Arrays. In brief, PennCNV package ³⁰ was used to call and analyze the CNV data. For the Illumina datasets, signal intensity data in the form of log R ratios (LRRs) and B allele frequencies (BAFs) were directly generated from the downloaded raw file. For the Affymetrix datasets, LRRS and BAFs were calculated by processing raw intensity (.CEL) files in Affymetrix Power Tools (https://www.affymetrix.com/support/developer/powertools/changelog/index.html), followed by PennCNV-Affy package. Finally, these LRRS and BAFs were used to generate CNV calls. CNVs with lengths <1 kb, confidence scores <10, or containing <5 SNPs were discarded. A CNV was considered to be a recurring acquired copy number alteration (rCNA) if it occurred in more than 2.5% of the patients and was not reported in the Database of Genomic Variants, build 36 (hg18) (DGV, http://projects.tcag.ca/variation/) ³¹. The location of RTN1 was explored on chromosome 14 (14q23.1: Start 59,595,976/End 59,870,966) for chromosomal aberrations.

Acknowledgements

We wish to thank Mr. Pedram Amouzadeh who assisted in the proof-reading of the manuscript.

Authors’ Contributions

MZA, SMN, and AA participated in the study design and analysis of the data. MZA and SMN wrote the manuscript. All authors read and approved the final manuscript.

Competing interest

The authors declare that there is no conflict of interest.

Funding

This study had no funding support.

Ethical Standards

Our study was performed using datasets deposited in GEO database. Hence, no ethical approval was required.

Availability of data and materials

The datasets in the manuscript were deposited in GEO database (http://www.ncbi.nlm.nih.gov/geo/) with the accession number GSE10846, GSE31312, GSE32918, GSE69051, GSE4475, GSE11318, GSE34171, GSE58718, GSE57277, and GSE34171. Other supporting data are included as supplementary files.

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Table 1. Clinical characteristics of the microarray datasets used in our study
Dataset	Number of patients	Chip manufacturer	Platform	Usage in our study
GSE10846	420	Affymetrix	GPL570	Survival analysis
GSE31312	470	Affymetrix	GPL570	Survival analysis
GSE32918	172	Illumina	GPL8432	Survival analysis
GSE69051	157	Illumina	GPL14951	Survival analysis
GSE4475	123	Affymetrix	GPL96	Survival analysis
GSE11318	203	Affymetrix	GPL570	Survival analysis
GSE34171	91	Affymetrix	GPL570	Survival analysis
GSE58718	242	Illumina	GPL6986	Chromosomal aberration
GSE57277	148	Affymetrix	GPL3720	Chromosomal aberration
GSE34171	180	Affymetrix	GPL6801	Chromosomal aberration

Table 2. Statistics of univariate Cox proportional hazard analysis of the RTN1 gene in the various datasets. Significant P values were bolded.
Dataset	HR¹	SE	95% CI²	P value
GSE10846	0.73	0.07	0.64-0.84	0.000
GSE31312	0.79	0.07	0.69-0.90	0.000
GSE32918/69051	0.78	0.07	0.68-0.90	0.000
GSE4475	0.41	0.27	0.24-0.69	0.000
GSE34171	0.53	0.17	0.38-0.73	0.000
GSE11318	0.69	0.10	0.57-0.83	0.000
1. Hazard ratio, 2. Hazard ratio 95% confidence interval

Table 3. Multivariate analysis of the RTN1 and common prognostic variables in DLBCL (the IPI components). The RTN1 gene was remained as a one of the most powerful independent prognostic factor. Significant P values were bolded.
*GSE10846*
Variable	HR¹	SE	95% CI²	P value
*RTN1*	0.78	0.08	0.67-0.90	0.000
Molecular subtype
GCB-like vs. type 3	0.98	0.30	0.55-1.76	0.96
ABC-like vs. type 3	1.36	0.29	0.78-2.39	0.28
Age (≥60 vs. <60 years)	1.88	0.18	1.32-2.68	0.000
Sex (male vs. female)	1.23	0.17	0.89-1.72	0.22
Stage (III/IV vs. I/II)	1.81	0.20	1.23-2.67	0.000
NES³ (≥2 vs. <2)	1.62	0.19	1.12-2.33	0.01
ECOG⁴ (≥2 vs. <2)	1.50	0.20	1.03-2.20	0.04
LDH⁵	1.58	0.20	1.07-2.35	0.02
GSE31312
*RTN1*	0.77	0.07	0.66-0.88	0.000
Molecular subtype
GCB-like vs. type 3	0.75	0.30	0.41-1.35	0.33
ABC-like vs. type 3	1.63	0.28	0.95-2.81	0.08
Age (≥60 vs. <60 years)	2.23	0.20	1.51-3.31	0.000
Sex (male vs. female)	1.04	0.18	0.73-1.50	0.82
Stage (III/IV vs. I/II)	1.30	0.20	0.88-1.92	0.20
NES (≥2 vs. <2)	1.16	0.34	0.59-2.27	0.67
ECOG (≥2 vs. <2)	2.23	0.20	1.52-3.27	0.000
LDH	1.12	0.03	1.06-1.18	0.000
1. Hazard ratio, 2. Hazard ratio 95% confidence interval, 3. No. of extranodal sites, 4. ECOG performance status, 5. Lactate dehydrogenase,

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Prognostic Efficacy of the RTN1 Gene in Patients with Diffuse Large B-Cell Lymphoma

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