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Upregulation of SLC2A3 Gene and Prognosis in Colorectal Carcinoma

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Upregulation of SLC2A3 Gene and Prognosis in Colorectal Carcinoma Kim et al. BMC Cancer (2019) 19:302 https://doi.org/10.1186/s12885-019-5475-x RESEARCH ARTICLE Open Access Upregulation of SLC2A3 gene and prognosis in colorectal carcinoma: analysis of TCGA data Eunyoung Kim1†, Sohee Jung2†, Won Seo Park3, Joon-Hyop Lee4, Rumi Shin5, Seung Chul Heo5, Eun Kyung Choe6, Jae Hyun Lee7, Kwangsoo Kim2* and Young Jun Chai5* Abstract Background: Upregulation of SLC2A genes that encode glucose transporter (GLUT) protein is associated with poor prognosis in many cancers. In colorectal cancer, studies reporting the association between overexpression of GLUT and poor clinical outcomes were flawed by small sample sizes or subjective interpretation of immunohistochemical staining. Here, we analyzed mRNA expressions in all 14 SLC2A genes and evaluated the association with prognosis in colorectal cancer using data from the Cancer Genome Atlas (TCGA) database. Methods: In the present study, we analyzed the expression of SLC2A genes in colorectal cancer and their association with prognosis using data obtained from the TCGA for the discovery sample, and a dataset from the Gene Expression Omnibus for the validation sample. Results: SLC2A3 was significantly associated with overall survival (OS) and disease-free survival (DFS) in both the discovery sample (345 patients) and validation sample (501 patients). High SLC2A3 expression resulted in shorter OS and DFS. In multivariate analyses, high SLC2A3 levels predicted unfavorable OS (adjusted HR 1.95, 95% CI 1.22–3.11; P = 0.005) and were associated with poor DFS (adjusted HR 1.85, 95% CI 1.10–3.12; P = 0.02). Similar results were found in the discovery set. Conclusion: Upregulation of the SLC2A3 genes is associated with decreased OS and DFS in colorectal cancer patients. Therefore, assessment of SLC2A3 gene expression may useful for predicting prognosis in these patients. Keywords: Colorectal cancer, Solute carrier 2A (SLC2A), Glucose transporter (GLUT), The cancer genome atlas (TCGA), Prognosis Background prognostication, and anticancer drug development target- Colorectal cancer (CRC) is the third most common can- ing CRC [4]. Despite research efforts, genetic biomarkers cer and the fourth-leading cause of cancer death in the currently have limited value as diagnostic or prognostic world [1, 2]. Most CRCs originate from non-cancerous markers [5]. lesions by one or a combination of three different mech- Among the biomarkers, the solute carrier 2A (SLC2A) anisms: chromosomal instability, CpG island methylator gene family that encodes glucose transporter (GLUT) phenotype, and microsatellite instability [3]. Biomarkers of proteins has been widely investigated. GLUT proteins fa- these cytogenetic alterations are of interest for diagnosis, cilitate glucose influx into cancer cells which is neces- sary for cancer cell proliferation. Upregulation of SLC2A genes is associated with poor prognosis in many cancers, * Correspondence: [email protected]; [email protected] †Eunyoung Kim and Sohee Jung have contributed equally as co-first authors including hepatocellular carcinoma, non–small cell lung 2Division of Clinical Bioinformatics, Biomedical Research Institute, Seoul cancer, and thyroid carcinoma [6–8]. National University Hospital, Seoul, Republic of Korea An association between overexpression of the subtypes 5Department of Surgery, Seoul Metropolitan Government - Seoul National University Boramae Medical Center, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul of GLUT proteins and poor clinical outcomes has been 156-70, Republic of Korea reported in CRC [9]. However, these studies were flawed Full list of author information is available at the end of the article © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Kim et al. BMC Cancer (2019) 19:302 Page 2 of 10 by small sample sizes or subjective interpretation of im- Table 1 Patient demographics munohistochemical (IHC) staining. In this study, we an- Discovery Set Validation Set alyzed the mRNA expression of all 14 SLC2A genes (TCGA, N = 345) (GSE39582, N = 501) (corresponding to 14 GLUT proteins) and evaluated the Age, yr associations with prognosis in CRC using data from the Median 67 68.1 Cancer Genome Atlas (TCGA) database. Interquartile range (55–75) (59–76) Sex Methods Data acquisition Female 157 (46%) 229 (46%) The TCGA CRC data was downloaded from cBioPortal Male 188 (54%) 272 (54%) for Cancer Genomics (http://www.cbioportal.org/). The AJCC TNM Stage dataset contains survival data with clinical information, I 54 (16%) 31 (6%) somatic mutations, and mRNA expression counts. For II 130 (38%) 244 (49%) validation, we obtained independent microarray datasets III 111 (32%) 166 (33%) (GSE39582) from the Gene Expression Omnibus (GEO). mRNA counts of the validation set were measured by IV 50 (14%) 60 (12%) the Affymetrix Human Genome U133 Plus 2.0 Array in Microsatellite instability a log2 scale. Gene expression of the discovery set was MSI-L and MSS 291 (84%) measured by Illumina HiSeq platform and transformed MSI-H 54 (16%) into log2 scale. According to the publication guidelines, BRAF status the datasets may be used for publication without restric- V600E 50 (14%) 49 (10%) tion or limitation (https://cancergenome.nih.gov/publi- cations/publicationguidelines, https://www.ncbi.nlm.nih. Wild-type 295 (86%) 452 (90%) gov/geo/info/disclaimer.html). KRAS status Mutant 144 (42%) 198 (40%) Statistical analysis Wild-type 201 (58%) 303 (60%) The putative associations between conventional clinical OS event pathology parameters (age at diagnosis, sex, American Joint Event 78 (23%) 171 (34%) Committee on Cancer (AJCC) 7th edition TNM stage, microsatellite instability, and mutational status of KRAS Non-event 267 (77%) 330 (66%) (v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog) OS months and BRAF (v-Raf murine sarcoma viral oncogene homolog Median 31.4 56.5 B1 genes) and survival outcome were assessed by Range (0–147.9) (0–201) Chi-square tests. The Cox proportional-hazards model was DFS event used to identify genes associated with survival and to esti- Event 83 (24%) 150 (30%) mate mortality hazard ratios (HRs). The optimal cut-off points for SLC2A3 expression used to divide patients into Non-event 222 (64%) 346 (69%) low-risk and high-risk groups were determined using the Not available 40 (12%) 5 (1%) MaxStat package of R software (Maximally selected Rank DFS months Statistics). Maxstat computes the maximally selected Median 21 44 log-rank statistic to identifythecutpointwhichprovides Range (0–148) (0–201) the best separation (in which the standardized statistics take their maximum) into two groups. Kaplan-Meier analysis was performed to estimate the survival curves of the differ- ent subgroups and the log-rank test (Mantel–Cox) was The TCGA sample of 345 patients were the discovery set, used to compare the curve. Statistical analyses were per- and the GSE39582 sample of 501 patients were the valid- formed using R statistical software (version 3.4.1) [10]. All ation set. P-value were two sided. A P-value less than 0.05 was con- sidered statistically significant. Association between clinical parameters and survival outcome Results The associations between clinical variables and OS and Patient demographics DFS in the discovery set are summarized in Table 2. Our study sample comprised 846 patients. Patient character- Age > 65 was associated with worse OS compared to istics of the discovery and validation set are shown in Table 1. age ≤ 65 (P < 0.001). TNM stage III and IV was Kim et al. BMC Cancer (2019) 19:302 Page 3 of 10 Table 2 Association between the clinicopathological characteristics and survival outcome Overall survival Disease-free survival Non-event Event P valuea Non-event Event P valuea Age < = 65 141 23 < 0.001 115 43 1 > 65 126 55 107 40 Sex Female 124 33 0.606 103 34 0.472 Male 143 45 119 49 AJCC TNM stage I and II 157 27 < 0.001 137 32 < 0.001 III and IV 110 51 85 51 MSI MSI-L and MSS 224 67 0.802 184 72 0.520 MSI-H 43 11 38 11 BRAF status Wild-type 230 65 0.662 188 74 0.416 Mutant 37 13 34 9 KRAS status Wild-type 154 47 0.783 133 43 0.252 Mutant 113 31 89 40 aCalculated using the Chi-square test associated with OS (P < 0.001) and DFS (P < 0.001). Other clinicopathological factors (sex, MSI, BRAF,or KRAS status) were not associated with OS or DFS. Prognostic value of SLC2A3 Table 3 displays the associations between the mRNA ex- Table 3 Univariate cox regression analysis of SLC2 family genes pression values of SLC2 family genes and survival out- for OS and DFS comes. SLC2A3 was significantly associated with both Genea OS DFS P P OS ( = 0.013) and DFS ( = 0.014). There were associa- HR (95% CI) P valueb HR (95% CI) P valueb SLC2A6 SLC2A7 tions between the expression of and SLC2A1 1.13 (0.91–1.39) 0.271 1.28 (1.04–1.57) 0.018 with worse OS (P = 0.048 and 0.019); and SLC2A1 with SLC2A2 0.82 (0.61–1.09) 0.169 0.99 (0.8–1.24) 0.957 worse DFS (P = 0.018). SLC2A3 – – Patients were categorized into high and low SLC2A3 1.33 (1.06 1.66) 0.013 1.32 (1.06 1.64) 0.014 expression groups according to the cut-off value deter- SLC2A4 1.12 (0.9–1.39) 0.302 1.02 (0.82–1.27) 0.864 mined by Maxstat method.
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