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Despite advances in diagnostic and therapeutic methods, the prognosis of patients with hepatocellular carcinoma (HCC) remains poor due to the delay in diagnosis. Herein, we aimed to discover a highly sensitive and specific biomarker for HCC based on genomic big data analysis and create an HCC-targeted imaging probe using carbon nanodots (CNDs) as contrast agents. In genomic analysis, we selected glucose transporter 2 (GLUT2) as a potential imaging target for HCC. We confirmed the target suitability by immunohisto-chemistry tests of 339 patient samples, where 81.1% of the patients exhibited underexpression of GLUT2

Cancer cell heterogeneity is a serious problem in the control of tumor progression because it can cause chemoresistance and metastasis. Heterogeneity can be generated by various mechanisms, including genetic evolution of cancer cells, cancer stem cells (CSCs), and niche heterogeneity. Because the genetic heterogeneity of CSCs has been poorly characterized, the genetic mutation status of CSCs was examined using Exome-Seq and RNA-Seq data of liver cancer. Here we show that different surface markers for liver cancer stem cells (LCSCs) showed a unique propensity for genetic mutations. Cluster of differentiation 133 (CD133)-positive cells showed frequent mutations in the

Background The aim of this study was to develop a scoring system to stratify the risk of papillary thyroid cancer (PTC) and to select the proper management. Methods We performed a systematic search of MEDLINE and Embase. Data regarding patients' prognoses were obtained from the included studies. Odds ratios (ORs) with statistical significance were extracted from the publications. To generate a risk scoring system (RSS), ORs were summed (RSS1), and summed after natural-logarithmic transformation (RSS2). RSS1 and RSS2 were compared to the eighth edition of the American Joint Committee on Cancer (AJCC) staging system and the 2015 American Thyroid Association (ATA) guidelines for thyroid nodules and differentiated thyroid carcinoma. Results Five meta-analyses were eligible for inclusion in the study. Eight variables (sex, tumour size, extrathyroidal extension, BRAF mutation, TERT mutation, histologic subtype, lymph node metastasis, and distant metastasis) were included. RSS1 was the best of the analysed models. Conclusion We developed and validated a new RSS derived from previous meta-analyses for patients with PTC. This RSS seems to be superior to previously published systems.

Accurate prediction of malignancies is important in choosing therapeutic strategies. Although there are many genetic and cytogenetic prognostic factors for acute myeloid leukemia (AML), prognosis is difficult to predict because of the heterogeneity of AML. Prognostic factors, including messenger RNA (mRNA) expression, have been determined for other malignancies, but not for AML. A total of 402 patients from The Cancer Genome Atlas, GSE12417 (GPL96, 97), and GSE12417 (GPL570) were included in this study. In Kaplan-Meier curve analyses, high expression of family with sequence similarity 213 member A (FAM213A), which activates antioxidant proteins, was associated with worse prognosis of AML. Similar to the results of the survival curve, C-indices and area under the curve values were high. Current prognostic factors of AML, unlike those of other cancers, do not take mRNA expression into consideration. Thus, the development of mRNA-based prognostic factors would be beneficial for accurate prediction of the survival of AML patients. Additionally, in vivo validation using zebrafish revealed that fam213a is important for myelopoiesis at the developmental stage and is a negative regulator of the p53 tumor suppressor gene. The findings implicate fam213a as a novel prognostic factor for AML patients.

Renal cell carcinoma (RCC) is the most typical type of kidney cancer in adults. Hypercalcemia is a well known paraneoplastic syndrome associated with RCC and recent studies have reported that hypercalcemia is closely related to the poor prognosis of RCC patients. Clear cell RCC (ccRCC) is the most common and aggressive subtype of RCC. Although the histological classification of RCC is important for determination of appropriate treatment strategies, effective biomarkers for predicting prognosis of ccRCC have not yet been identified. Since calcium levels affect the prognosis of RCC patients, we evaluated whether the calcium-sensing genes on the plasma membrane, including those encoding calcium channels, CaSR, GPRC6a, and DYSF, could be used as biomarkers to predict the prognosis of ccRCC patients.Information from 537 patients from The Cancer Genome Atlas (TCGA; n = 446) and International Cancer Genome Consortium (ICGC; n = 91) was used in this study. Among these genes, DYSF was the only gene whose expression correlated with overall survival of both TGCA and ICGC patients.Although DYSF gene expression was higher in ccRCC tissue than in normal kidney tissue, Kaplan-Meier curves showed that the survival rate of ccRCC patients with high DYSF expression was significantly higher than that of patients with low DYSF expression (TCGA, P 0.0001; ICGC, P = 0.0011). We also validated the potential of DYSF as a prognostic biomarker for ccRCC by conducting a time-dependent area under the curve (AUC) analysis and 5-years receiver operating characteristic curve analysis. Finally, multivariate regression analysis revealed that the expression of DYSF is independent of other prognostic parameters (TCGA, P = 0.017; ICGC, P = 0.006).These results suggested that DYSF may play a suppressive role in the progression of ccRCC and could act as a promising prognostic biomarker for predicting the survival of ccRCC patients.

The beta-2 adrenergic receptor (ADRB2) regulates the proliferation, apoptosis, angiogenesis, migration, and metastasis of cancer cells. However, its function in the progression of clear cell renal cell carcinoma (ccRCC) is unknown. Here, we report that ADRB2 can be a novel prognostic factor for patients with ccRCC. The differential expression of ADRB2 in low-stage (stages I and II), high-stage (stages III and IV), low-grade (grades I and II), and high-grade (grades III and IV) ccRCC was identified in cohorts of patients from The Cancer Genome Atlas and the International Cancer Genome Consortium. We evaluated ADRB2 expression as a prognostic factor using the Kaplan-Meier survival curve, multivariate analysis, time-dependent area under the curve (AUC) of Uno’s C-index, and AUC of the receiver operating characteristics (ROC) at five years. Kaplan-Meier analysis revealed that reduced ADRB2 expression is associated with poor prognosis in ccRCC patients. Analysis of C-indices and AUC-ROC further confirmed this result. Moreover, multivariate analysis confirmed the prognostic significance of ADRB2 expression. Collectively, these findings suggest that ADRB2 is a potential prognostic factor for ccRCC.

Background Advances in genomics have greatly improved the survival rate in cancer patients. However, due to genetic heterogeneity, pancreatic ductal adenocarcinoma (PDAC) is still difficult to diagnose early, and its survival rate is extremely low. Therefore, we identified biomarkers that predict the prognosis of PDAC patients using independent cohort data. Materials and methods To develop a novel prognostic biomarker, we used the gene expression and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Kaplan-Meier survival curve using median values of genes as cutoff showed that EIF4G1 was the only statistically significant gene in the 3 cohorts. We analyzed the prognostic significance of EIF4G1 using the time-dependent area under the curve (AUC) of Uno's C-index, the AUC value of the receiver operating characteristics (ROC) at 3 years, and multivariate Cox analysis. We also compared EIF4G1 levels between tumors and matched non-tumor tissues. Results EIF4G1 is the only prognostic gene in patients with PDAC, which was selected by Kaplan-Meier survival analysis. The survival curve showed that high expression of EIF4G1 was associated with poor prognosis of PDAC with a good discriminative ability in 3 independent cohorts. The risk stratifying ability of EIF4G1 was demonstrated by analyzing C-indices and AUC values. Multivariate Cox regression confirmed its prognostic significance. EIF4G1 expression was significantly higher in PDAC tissues than in the matched normal tissues. Conclusion EIF4G1 could be used as a novel prognostic marker for PDAC and to determine suitable treatment options.

Although pathological observations provide approximate prognoses, it is difficult to achieve prognosis in patients with existing prognostic factors. Therefore, it is very important to find appropriate biomarkers to achieve accurate cancer prognosis. Renal cell carcinoma (RCC) has several subtypes, the discrimination of which is crucial for proper treatment. Here, we present a novel biomarker, VNN3, which is used to prognose clear cell renal cell carcinoma (ccRCC), the most common and aggressive subtype of kidney cancer. Patient information analyzed in our study was extracted from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts. VNN3 expression was considerably higher in stages III and IV than in stages I and II. Moreover, Kaplan–Meier curves associated high VNN3 expression with poor prognoses (TCGA, p

As the importance of personalized therapeutics in aggressive papillary thyroid cancer (PTC) increases, accurate risk stratification is required. To develop a novel prognostic scoring system for patients with PTC (n = 455), we used mRNA expression and clinical data from The Cancer Genome Atlas. We performed variable selection using Network‐Regularized high‐dimensional Cox‐regression with gene network from pathway databases. The risk score was calculated using a linear combination of regression coefficients and mRNA expressions. The risk score and clinical variables were assessed by several survival analyses. The risk score showed high discriminatory power for the prediction of event‐free survival as well as the presence of metastasis. In multivariate analysis, the risk score and presence of metastasis were significant risk factors among the clinical variables that were examined together. In the current study, we developed a risk scoring system that will help to identify suitable therapeutic options for PTC.

Objective Thyroid cancer is the most common malignant endocrine tumour, and its incidence has continuously increased worldwide over the past three decades. We focused on the association of multifocal papillary thyroid carcinoma (PTC) with messenger RNA (mRNA) expression to characterize how molecular and histopathologic features relate to multifocality. Design A retrospective cohort study. Patients The primary and processed data were downloaded from The Cancer Genome Atlas. A total of 490 patients were included in this study. Methods The statistical significance of differences in sex, age, histology, LN metastasis and recurrence were analysed using chi-squared test. To identify differentially expressed genes between BRAF (+) multifocal and unifocal PTCs and between BRAF (-) multifocal and unifocal PTCs, we used the Significance Analysis of Microarray. Over-representation analysis is conducted using CPDB. Results A total of 237 patients had BRAF (+) PTCs, whereas 253 had BRAF (-) PTCs. There were 110 patients with multifocal PTCs and 127 with unifocal PTCs in the BRAF (+) group and 116 patients with multifocal PTCs and 137 with unifocal PTCs in the BRAF (-) group. In BRAF (+) group, multifocal PTCs had increased expression of 158 mRNAs as compared to that in unifocal PTCs. Ten mRNAs were involved in Wnt-related pathways, and seven mRNAs were included in pluripotency-related pathways. Conclusion Multifocal PTCs have higher expression of mRNAs in Wnt- and pluripotency-related pathways when BRAF mutation is present. This might be the mechanism that accounts for the difference between multifocal and unifocal PTCs.

With the recent emphasis on the importance of personalized genomic medicine, studies have performed prognostic stratification using gene signatures in cancers. However, these studies have not considered gene networks with clinical data. Therefore, this study aimed to develop a novel prognostic score using grouped variable selection for patients with osteosarcoma. We assessed messenger RNA (mRNA) expression and clinical data from Gene Expression Omnibus to develop a novel prognostic scoring system for patients with osteosarcoma. Variable selection using Network-Regularized high-dimensional Cox-regression analysis with information regarding gene networks obtained from six large pathway databases was performed. We determined the risk score on the linear combination of regression coefficients and mRNA expression values. Log-rank test, UNO's c-index, and area under the curve (AUC) values were determined to evaluate the discriminatory power between the low- and high-risk groups. A recently reported next-generation Connectivity Map was used to identify future therapeutic targets for osteosarcoma. Our novel model had significantly high discriminatory power in predicting overall survival. An optimal c-index of 0.967 was obtained and time-dependent receiver operating characteristic analysis revealed an acceptable predictive value of AUC between 0.953 and 1.000. Knockdown of BACE2 or ING2 and linifanib treatment may improve the prognosis of patients with osteosarcoma. Herein, this novel prognostic scoring system would not only facilitate a more accurate prediction of patient prognosis, but also contribute to the selection of suitable therapeutic alternatives for osteosarcoma patients.

The generation and maintenance of consciousness are fundamental but difficult subjects in the fields of psychology, philosophy, neuroscience, and medicine. However, recent developments in neuro-imaging techniques coupled with network analysis have greatly advanced our understanding of consciousness. The present review focuses on large-scale functional brain networks based on neuro-imaging data to explain the awareness (contents) and wakefulness of consciousness. Despite limitations, neuroimaging data suggests brain maps for important psychological and cognitive processes such as attention, language, self-referential, emotion, motivation, social behavior, and wakefulness. We considered a review of these advancements would provide new insights into research on the neural correlates of consciousness.

Background: MED30 is an evolutionarily new member of the mediator complex thatcan bridge transcription factors and preinitiation complexfor the transcription. Pancreatic cancer is highly chemo-resistant andis the fourth leading cause of cancer-related deaths. Rapid progression and poor prognosis of pancreatic cancer result in shortened survival rate following diagnosis.Methods: Expression of MED30 was investigated by the analysis of TCGA database and immunohistochemistry of patients tissues. Roles of MED30 were investigated by gain-of-function and loss-of-function studies.Results: Analysis of TCGA database and immunohistochemistry provedthe frequentamplification and overexpression of MED30 in the pancreatic cancer tissues compared with the normal pancreatic tissues at the chromosome, mRNA and protein levels. Notably its overexpression was association with poor prognosis of pancreatic cancer patients in the Kaplan-Meier curve analysis (P=0.00072) and multivariate analysis (P=0.027). Uno’s C-index values in the time-dependent Area Under the Curve (AUC) analysis and AUC valuein the receiver operating characteristics (ROC) curves showed its good performance as a prognostic marker. To reveal the functional roles of MED30 during the progression of pancreatic cancer, we overexpressed or knocked down MED30 using cDNA or siRNA, respectively. Gain-of-function and loss-of-function studies showed that MED30 can regulate proliferation, migration, and invasion of pancreatic cancer cells. Furthermore overexpression of MED30 promoted tumorigenicity in SCID mice significantly. Conclusions: MED30 is frequently amplified and overexpressed and its overexpression is associated with poor prognosis of pancreatic cancer patients. Moreover it has tumor-promoting roles in proliferation, migration, invasion and in vivo tumorigenicity of pancreatic cancer cells. Thus, MED30 could be a potent diagnostic and therapeutic target in pancreatic cancer.

Background/aim Because 50% of uveal melanoma metastasize within 10 years of diagnosis, there is urgent need for accurate prognostic factors. Materials and methods To identify genes that can act as prognostic factors in uveal melanoma, we performed survival analyses using three independent cohorts. Using log-rank test and univariate cox regression, genes which could stratify the prognosis in all cohorts simultaneously depending on their expression levels were selected as novel biomarkers. Hub genes were obtained by analyzing the interaction and relationship between the selected genes using String and Cytoscape. Additionally, prognostic power was calculated by using C-indices and AUC. Results A total of 37 oncogene-like and 14 tumor suppressor-like genes were selected. Protein-protein analysis revealed that NDUFB9, NDUFV2, CYC1 among oncogene-like genes, CTNNB1 among tumor suppressor-like genes were found to be hub genes and core biomarkers in uveal melanoma. Conclusion NDUFB9, NDUFV2, CYC1 and CTNNB1 genes may act as prognostic factors in uveal melanoma.

Background Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations. Objective Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA. Methods We used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral). Results Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data. Conclusions Using advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses.

Relapse of acute lymphoblastic leukemia (ALL) is dangerous and it worsens the prognosis of patients; however, prognostic markers or therapeutic targets for ALL remain unknown. In the present study, using databases such as TARGET, GSE60926 and GSE28460, we determined that KIF2C and its binding partner, KIF18B are overexpressed in patients with relapsed ALL compared to that in patients diagnosed with ALL for the first time. As 50% of the residues are exactly the same and the signature domain of KIF2C is highly conserved between human and zebrafish, we used zebrafish embryos as a model to investigate the function of kif2c in vivo. We determined that kif2c is necessary for lymphopoiesis in zebrafish embryos. Additionally, we observed that kif2c is not related to differentiation of HSCs; however, it is important for the maintenance of HSCs as it provides survival signals to HSCs. These results imply that the ALL relapse-related gene KIF2C is linked to the survival of HSCs. In conclusion, we suggest that KIF2C can serve as a novel therapeutic target for relapsed ALL.

Introduction: The zinc finger homeobox 4 (ZFHX4) protein is a crucial molecular regulator of tumor-initiating stem cell-like functions. Objective: This study aimed to determine the role of ZFHX4 in...

There is a growing need for the discovery of new prognostic factors for cases where the scoring and staging system of hepatocellular carcinoma (HCC) does not result in a clear definition. We analyzed whether AP-2 complex subunit mu (AP2M1) expression could be a new prognostic marker for HCC based on the roles of AP2M1 in influencing hepatocyte growth factor (HGF) promoter regulation and hepatitis C virus (HCV) assembly. Patient data were extracted from cohorts of the Gene Expression Omnibus (GSE10186), International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Differential expression value between matched cancer and normal liver was identified using ICGC cohort. Subsequently, we compared AP2M1 expression as a prognostic gene with other well-known prognostic genes for HCC, using the time-dependent area under the curve (AUC) of the Uno's C-index, the AUC value of the receiver operating characteristics at 5 years, Kaplan-Meier survival curve, and multivariate analysis. Particularly, TCGA and GSE10186 patients were divided into subgroups based on alcohol intake, hepatitis B, and C viral infections, and analyzed in the same methods. The AP2M1 expression values in patients with cancer were much higher than matched normal liver. The AP2M1 level showed excellent prognosis predictions in comparison with existing markers in the three independent cohorts (n = 647). In particular, it was more predictive of prognosis than other markers in alcohol intake and HCV infections. In conclusion, we were confident that AP2M1 provides sufficient value as a new prognostic marker for HCC especially patients with HCV infection and/or alcohol intake.

Background: Certain nuclear envelope proteins are associated with important cancer cell characteristics, including migration and proliferation. Abnormal expression of and genetic changes in nuclear envelope proteins have been reported in acute myeloid leukemia (AML) patients. Transmembrane protein 18 (TMEM18), a nuclear envelope protein, is involved in neural stem cell migration and tumorigenicity. Methods: To examine the prognostic significance of TMEM18 in AML patients, we analyzed an AML cohort from The Cancer Genome Atlas (TCGA, n = 142). Results: Kaplan-Meier survival analysis revealed that TMEM18 overexpression was associated with a better AML prognosis with good discrimination (p = 0.019). Interestingly, this ability to predict the prognosis was significant in male AML patients, but not in female ones. C-index and area-under-the-curve analyses further supported this discriminative ability and multivariate analysis confirmed its prognostic significance (p = 0.00347). Correlation analysis revealed that TMEM18 had a statistically significant positive correlation with nuclear envelop protein 133 (NUP133), NUP35, NUP54, NUP62, and NUP88. Conclusion: Because the current AML prognostic factors do not take mRNA expression into consideration unlike other cancers, the development of mRNA-based prognostic factors would be beneficial for accurate prediction of the survival of AML patients. Therefore, TMEM18 gene is a potential biomarker for AML.

Several staging systems have been developed to predict the risk of mortality in patients with differentiated thyroid cancer (DTC). However, none of them have been shown to be clearly superior to the other. We compared the patient outcome predictability of recently revised staging systems predictability of patient outcome using data from The Cancer Genome Atlas. To set a comparison among American Joint Committee on Cancer (AJCC)/Union for International Cancer Control (UICC) staging 7th, 8th editions, American Thyroid Association guidelines 2009 and 2015, concordance index (c-index), Akaike information criterion (AIC), Bayesian information criterion (BIC), and Brier score were applied to quantify the predictive ability of a survival model, to select the statistical model, and to measure the accuracy of probabilistic predictions. A total of 457 patients with papillary thyroid cancer having a mean age of 45.9 years were included in this study (120 males, 337 females). Among these patients, 43 (9.4%) experienced recurrence/progression during the follow-up (591.2 ± 833.5 months). Among the models used, the AJCC/UICC 8th edition, which showed the highest c-index and lowest AIC, BIC, and Brier score, was identified as the best among the models used. AJCC/UICC 8th edition predicted patient outcome more accurately than the other staging systems.

// Yun Hak Kim 1, 7 , Dae Cheon Jeong 2 , Kyoungjune Pak 3, 7 , Tae Sik Goh 1, 4, 7 , Chi-Seung Lee 5 , Myoung-Eun Han 1 , Ji-Young Kim 1 , Liu Liangwen 1 , Chi Dae Kim 6 , Jeon Yeob Jang 7, 8 , Wonjae Cha 7, 8 and Sae-Ock Oh 1 1 Department of Anatomy, School of medicine, Pusan National University, Yangsan, 50612, Republic of Korea 2 Department of Statistics, Korea University, Seoul 02841, Republic of Korea 3 Department of Nuclear Medicine, Pusan National University Hospital, Busan 49241, Republic of Korea 4 Department of Orthopaedic Surgery, Pusan National University Hospital, Busan 49241, Republic of Korea 5 Biomedical Research Institute, Pusan National University Hospital and School of Medicine, Pusan National University, Busan 49241, Republic of Korea 6 Department of Pharmacology, School of medicine, Pusan National University, Yangsan, 50612, Republic of Korea 7 BEER, Busan society of Evidence-based mEdicine and Research, Busan 49241, Republic of Korea 8 Department of Otorhinolaryngology-Head and Neck Surgery, Pusan National Hospital, Busan 49241, Republic of Korea Correspondence to: Sae-Ock Oh, email: hedgehog@pusan.ac.kr Keywords: prognosis, network-regularized high-dimensional Cox-regression (Net), breast cancer, gene network, gene signature Received: July 03, 2017 Accepted: August 04, 2017 Published: August 24, 2017 ABSTRACT Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients.

// Yun Hak Kim 1, * , Dae Cheon Jeong 2, * , Kyoungjune Pak 3 , Myoung-Eun Han 1 , Ji-Young Kim 1 , Liu Liangwen 1 , Hyun Jin Kim 4 , Tae Woo Kim 5 , Tae Hwa Kim 6 , Dong Woo Hyun 7 and Sae-Ock Oh 1 1 Department of Anatomy, School of medicine, Pusan National University, Yangsan, Republic of Korea 2 Department of Statistics, Korea University, Seoul 02841, Republic of Korea 3 Department of Nuclear Medicine, Pusan National University Hospital, Busan, Republic of Korea 4 Eco-friendly new materials research center, Korea Research Institute of Chemical Technology (KRICT), Daejeon, Republic of Korea 5 Department of Orthopaedic Surgery, Pusan National University Hospital, Yangsan, Republic of Korea 6 Department of Internal Medicine, Pusan National University Hospital, Busan, Republic of Korea 7 Department of General Surgery, Pusan National University Hospital, Busan, Republic of Korea * These authors contributed equally to this work Correspondence to: Sae-Ock Oh, email: hedgehog@pusan.ac.kr Keywords: hepatocellular carcinoma (HCC), solute carrier 2A (SLC2A), glucose transporter (GLUT), the cancer genome atlas (TCGA), 2- 18 fluoro-deoxy-D-glucose ( 18 FDG) Received: June 10, 2017 Accepted: July 25, 2017 Published: August 14, 2017 ABSTRACT High rates of glucose transport via solute carrier (SLC2A, GLUT) family members are required to satisfy the high metabolic demands of cancer cells, and because of this characteristic of cancer cells 2- 18 fluoro-deoxy-D-glucose ( 18 FDG)-PET has become a powerful diagnostic tool. However, its sensitivity for hepatocellular carcinoma (HCC) is lower than for other malignancies, which suggests SLC2A family members are differentially expressed in HCC. In the present study, the expression patterns of SLC2A family members in tumor tissues and their associations with HCC progression were analyzed using data obtained from The Cancer Genome Atlas (TCGA). It was found that the expression of SLC2A2 (GLUT2) was higher in HCC than those of other members of the SLC2A family. The associations of the expression levels of SLC2A family members and previously known prognostic factors with clinical stages were examined using the T -test or the Mann-Whitney U test, and interestingly, SLC2A2 expression was found to be associated with an advanced clinical stage ( p = 0.0015). Furthermore, Kaplan-Meier analysis using the log-rank or the Gehan-Breslow-Wilcoxon test showed SLC2A2 expression was positively associated with overall survival ( p < 0.001, Gehan-Breslow-Wilcoxon test and p = 0.0145 by multivariate Cox regression). The prognostic significance of SLC2A2 was similar in both early and late stages. However, it was more significant in HCC patients without alcohol consumption history and hepatitis C infection. Taken together, SLC2A2 was associated with clinical stages and independently associated with overall survival in patients with HCC. We suggest that SLC2A2 be considered a new prognostic factor for HCC.

// Yun Hak Kim 1, 2, * , Tae Sik Goh 1, 3, * , Chi-Seung Lee 4 , Sae Ock Oh 2 , Jeung Il Kim 3 , Seung Hyeon Jeung 3 , Kyoungjune Pak 1, 5 1 BEER, Busan Society of Evidence-Based Medicine and Research, Busan, Republic of Korea 2 Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Gyeongnam, Republic of Korea 3 Department of Orthopaedic Surgery and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea 4 Biomedical Research Institute, Pusan National University Hospital and School of Medicine, Pusan National University, Busan, Republic of Korea 5 Department of Nuclear Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea * These authors have contributed equally in this work Correspondence to: Kyoungjune Pak, email: ilikechopin@me.com Keywords: microRNA, osteosarcoma, prognosis, meta-analysis Received: November 01, 2016 Accepted: December 01, 2016 Published: January 02, 2017 ABSTRACT BACKGROUND: Osteosarcoma is the most common primary bone malignancy. We meta-analyzed the prognostic value of altered miRNAs in patients with osteosarcoma. METHODS: Sources from MEDLINE (from inception to August 2016) and EMBASE (from inception to August 2016) were searched. Studies of osteosarcoma with results of miRNA and studies that reported survival data were included and two authors performed the data extraction independently. Any discrepancies were resolved by a consensus. The outcome was overall survival and event-free survival assessed using hazard ratios (HRs). RESULTS: After reviewing the full text of 65 articles, 25 studies including 2,278 patients were eligible in this study. The pooled HR for deaths was 1.40 (95% confidence interval [CI] 1.01-1.94, p =0.04) with random-effects model (χ 2 =113.08, p