A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study.

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.

Prognostic significance of EIF4G1 in patients with pancreatic ductal adenocarcinoma.

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.

Development of a risk scoring system for patients with papillary thyroid cancer.

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.

Prognostic scoring system for osteosarcoma using network-regularized high-dimensional Cox-regression analysis and potential therapeutic targets.

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.

Prognostic role of TPD52 in acute myeloid leukemia: A retrospective multicohort analysis.

Generating accurate prognoses is extremely important for treating patients with cancer. Prognostic prediction based on messenger RNA (mRNA) expression has shown superior clinical value to other markers for some cancers but is not currently used for acute myeloid leukemia (AML). Lipid metabolism is associated with biological aspects of cancer progression, including massive proliferation, and abnormal signaling. Moreover, abnormalities in lipid metabolism have prognostic significance. Patients with AML display abnormalities in sphingolipid metabolism and fatty acid oxidation. TPD52 is a regulator of lipid metabolism and plays a role in the formation of lipid droplets and fatty acid storage. Although the prognostic significance of TPD52 expression has been reported for many types of cancer, it has not yet been assessed in patients with AML. Therefore, the aim of the current study was to assess the prognostic significance of TPD52 in AML using three independent AML cohorts: one from The Cancer Genome Atlas (TGCA; n = 142) and two from the National Center for Biotechnology Information: GSE12417 (GPL96-97; n = 162) and GSE12417 (GPL570; n = 78). TPD52 was found to be overexpressed in patients with AML (GSE84881; n = 23). The Kaplan-Meier curve revealed that TPD52 overexpression was associated with a poor prognosis for patients with AML with good discrimination ( P = 0.013, P = 0.005, and P = 0.032 for the TGCA, GSE12417, and GSE12417, respectively). Analysis of C-indices and area under the receiver operating characteristic curve values further supported this discriminative ability. Moreover, multivariate analysis confirmed the prognostic significance of TPD52 expression levels ( P = 0.0196). These results suggest that the TPD52 mRNA level is a potential biomarker for AML.

The AP2M1 gene expression is a promising biomarker for predicting survival of patients with hepatocellular carcinoma.

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.

TMEM18: A Novel Prognostic Marker in Acute Myeloid Leukemia.

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.

mRNA Expression of SLC5A5 and SLC2A Family Genes in Papillary Thyroid Cancer: An Analysis of The Cancer Genome Atlas.

PURPOSE: The present study investigated the dynamics and prognostic role of messenger RNA (mRNA) expression responsible for ¹8F-fluorodeoxyglucose (FDG) uptake in FDG positron emission tomography (PET) and radioactive iodine (¹³¹I) uptake in whole-body radioactive iodine scans (WBS) in papillary thyroid cancer (PTC) patients. MATERIALS AND METHODS: The primary and processed data were downloaded from the Genomic Data Commons Data Portal. Expression data for sodium/iodide symporter (solute carrier family 5 member 5, SLC5A5), hexokinase (HK1-3), glucose-6-phosphate dehydrogenase (G6PD), and glucose transporter (solute carrier family 2, SLC2A1-4) mRNA were collected. RESULTS: Expression of SLC5A5 mRNA were negatively correlated with SLC2A1 mRNA and positively correlated with SLC2A4 mRNA. In PTC with BRAF mutations, expressions of SLC2A1, SLC2A3, HK2, and HK3 mRNA were higher than those in PTC without BRAF mutations. Expression of SLC5A5, SLC2A4, HK1, and G6PD mRNA was lower in PTC without BRAF mutation. PTCs with higher expression of SLC5A5 mRNA had more favorable disease-free survival, but no association with overall survival. CONCLUSION: Expression of SLC5A5 mRNA was negatively correlated with SLC2A1 mRNA. This finding provides a molecular basis for the management of PTC with negative WBS using ¹8F-FDG PET scans. In addition, higher expression of SLC5A5 mRNA was associated with less PTC recurrence, but not with deaths.

Prognostic Value of MicroRNAs in Coronary Artery Diseases: A Meta-Analysis.

PURPOSE: Coronary artery diseases (CADs) are the leading causes of death in the world. Recent studies have reported that differentially expressed microRNAs (miRNAs) are associated with prognosis or major adverse cardiac events (MACEs) in CAD patients. In a previous meta-analysis, the authors made serious mistakes that we aimed to correct through an updated systematic review and meta-analysis of the prognostic value of altered miRNAs in patients with CADs. MATERIALS AND METHODS: We performed a systematic search of MEDLINE (from inception to May 2017) and EMBASE (from inception to May 2017) for English-language publications. Studies of CADs with results on miRNAs that reported survival data or MACEs were included. Data were extracted from each publication independently by two reviewers. RESULTS: After reviewing 515 articles, a total eight studies were included in this study. We measured pooled hazard ratios (HRs) and 95% confidence intervals (CIs) of miRNA 133a with a fixed-effect model (pooled HR, 2.35; 95% CI, 1.56-3.55). High expression of miRNA 133a, 208b, 126, 197, 223, and 122-5p were associated with high mortality. Additionally, high levels of miRNA 208b, 499-5p, 134, 328, and 34a were related with MACEs. CONCLUSION: The present study confirmed that miRNA 133a, which was associated with high mortality in CAD patients, holds prognostic value in CAD. More importantly, this study corrected issues raised against a prior meta-analysis and provides accurate information.

Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients.

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.

SLC2A2 (GLUT2) as a novel prognostic factor for hepatocellular carcinoma.

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 2-18fluoro-deoxy-D-glucose (18FDG)-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.

Outcome prediction with the revised American joint committee on cancer staging system and American thyroid association guidelines for thyroid cancer.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: AJCC/UICC 8th edition predicted patient outcome more accurately than the other staging systems.

Expression and prognostic significance of zinc fingers and homeoboxes family members in renal cell carcinoma.

Zinc fingers and homeoboxes (ZHX) is a transcription repressor family that contains three members; ZHX1, ZHX2, and ZHX3. Although ZHX family members have been associated with the progression of cancer, their values as prognostic factors in cancer patients have been poorly examined. Renal cell carcinoma (RCC) is a highly heterogeneous, aggressive cancer that responds variably to treatment. Thus, prognostic molecular markers are required to evaluate disease progression and to improve the survival. In clear cell RCC (ccRCC), ZHX1 and ZHX3 expression were found to be down-regulated but ZHX2 was up-regulated, and the expressions of ZHX1 and ZHX3 were significantly associated with pathological stage. Furthermore, Kaplan-Meier and multivariate regression analysis showed that reduction in the mRNA expression of ZHX1 was associated with poorer survival. Taken together, the present study shows loss of ZHX1 is correlated with ccRCC progression and suggests it is an independent prognostic marker in ccRCC.