Prognosis-associated Genes Research Articles

A Key mRNA-miRNA-lncRNA Competing Endogenous RNA Triple Sub-network Linked to Diagnosis and Prognosis of Hepatocellular Carcinoma.

Growing evidence has illustrated critical roles of competing endogenous RNA (ceRNA) regulatory network in human cancers including hepatocellular carcinoma. In this study, we aimed to find promising diagnostic and prognostic biomarkers for patients with hepatocellular carcinoma. Three novel unfavorable prognosis-associated genes (CELSR3, GPSM2, and CHEK1) was first identified. We also demonstrated that these genes were significantly upregulated in hepatocellular carcinoma cell lines and tissues. Next, 154 potential miRNAs of CELSR3, GPSM2, and CHEK1 were predicted. CHEK1-hsa-mir-195-5p/hsa-mir-497-5p and GPSM2-hsa-mir-122-5p axes were defined as two key pathways in carcinogenesis of hepatocellular carcinoma by combination of in silico analysis and experimental validation. Subsequently, lncRNAs binding to hsa-mir-195-5p, hsa-mir-497-5p, and hsa-mir-122-5p were predicted via starBase and miRNet databases. After performing expression analysis and survival analysis for these predicted lncRNAs, we showed that nine lncRNAs (SNHG1, SNHG12, LINC00511, HCG18, FGD5-AS1, CERS6-AS1, NUTM2A-AS1, SNHG16, and ASB16-AS1) were markedly increased in hepatocellular carcinoma and their upregulation indicated poor prognosis. Moreover, a similar mRNA-miRNA-lncRNA analysis for six “known” genes (CLEC3B, DNASE1L3, PTTG1, KIF2C, XPO5, and UBE2S) was performed. Subsequently, a comprehensive mRNA-miRNA-lncRNA triple ceRNA network linked to prognosis of patients with hepatocellular carcinoma was established. Moreover, all RNAs in this network exhibited significantly diagnostic values for patients with hepatocellular carcinoma. In summary, the current study constructed a mRNA-miRNA-lncRNA ceRNA network associated with diagnosis and prognosis of hepatocellular carcinoma.

Integrated Analysis of the Functions and Prognostic Values of RNA Binding Proteins in Lung Squamous Cell Carcinoma.

Lung cancer is the leading cause of cancer-related deaths worldwide. Dysregulation of RNA binding proteins (RBPs) has been found in a variety of cancers and is related to oncogenesis and progression. However, the functions of RBPs in lung squamous cell carcinoma (LUSC) remain unclear. In this study, we obtained gene expression data and corresponding clinical information for LUSC from The Cancer Genome Atlas (TCGA) database, identified aberrantly expressed RBPs between tumors and normal tissue, and conducted a series of bioinformatics analyses to explore the expression and prognostic value of these RBPs. A total of 300 aberrantly expressed RBPs were obtained, comprising 59 downregulated and 241 upregulated RBPs. Functional enrichment analysis indicated that the differentially expressed RBPs were mainly associated with mRNA metabolic processes, RNA processing, RNA modification, regulation of translation, the TGF-beta signaling pathway, and the Toll-like receptor signaling pathway. Nine RBP genes (A1CF, EIF2B5, LSM1, LSM7, MBNL2, RSRC1, TRMU, TTF2, and ZCCHC5) were identified as prognosis-associated hub genes by univariate, least absolute shrinkage and selection operator (LASSO), Kaplan–Meier survival, and multivariate Cox regression analyses, and were used to construct the prognostic model. Further analysis demonstrated that high risk scores for patients were significantly related to poor overall survival according to the model. The area under the time-dependent receiver operator characteristic curve of the prognostic model was 0.712 at 3 years and 0.696 at 5 years. We also developed a nomogram based on nine RBP genes, with internal validation in the TCGA cohort, which showed a favorable predictive efficacy for prognosis in LUSC. Our results provide novel insights into the pathogenesis of LUSC. The nine-RBP gene signature showed predictive value for LUSC prognosis, with potential applications in clinical decision-making and individualized treatment.

Genomics and Prognosis Analysis of Epithelial-Mesenchymal Transition in Glioma.

Background: Epithelial-mesenchymal transition (EMT) is regulated by induction factors, transcription factor families and an array of signaling pathways genes, and has been implicated in the invasion and progression of gliomas.Methods: We obtained the Clinicopathological data sets from Chinese Glioma Genome Atlas (CGGA). The “limma” package was used to analyze the expression of EMT-related genes in gliomas with different pathological characteristics. We used the “ConsensusClusterPlus” package to divide gliomas into two groups to study their correlation with glioma malignancy. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to select seven prognosis-associated genes to build the risk signature, and the coefficients obtained from the LASSO algorithm were used to calculate the risk score which we applied to determine the prognostic value of the risk signature. Univariate and multivariate Cox regression analyses were used to determine whether the risk signature is an independent prognostic indicator.Results: We analyzed the differentially expressed 22 common epithelial-mesenchymal transition-associated genes in 508 gliomas graded by different clinicopathological features. Two glioma subgroups (EM1/2) were identified by consistent clustering of the proteins, of which the EM1 subgroup had a better prognosis than the EM2 subgroup, and the EM2 group was associated with cancer migration and proliferation. Significant enrichment analysis revealed that EMT-related transcriptional regulators and signaling pathways genes were highly related to glioma malignancies. Seven EMT-related genes were used to derive risk scores, which served as independent prognostic markers and prediction factors for the clinicopathological features of glioma. And we found the overall survival (OS) was significantly different between the low- and high-risk groups, the ROC curve indicated that the risk score can predict survival rates for glioma patients.Conclusion: EMT-related induction factors, transcriptional regulators and signaling pathways genes are important players in the malignant progression of glioma and may help in decision making regarding the choice of prognosis assessment and provide us clues to understand EMT epigenetic modification in glioma.

A 13-gene risk score system and a nomogram survival model for predicting the prognosis of clear cell renal cell carcinoma

BackgroundRenal cell carcinoma (RCC) is the second common malignant tumor in the urinary system, and 85% of RCC cases are clear cell RCC (ccRCC). This study is designed to build a risk score system for ccRCC. MethodsThe gene methylation and expression data of ccRCC samples were downloaded from The Cancer Genome Atlas database (training set) and ArrayExpress database (validation set). The differentially methylated genes (DMGs) and differentially expressed genes (DEGs) were identified by limma package, and their intersecting genes with negative Pearson correlation coefficients were remained using cor.test function. Prognosis-associated genes were identified by survival package, and the optimal DMGs were obtained using penalized package. After risk score system was built, nomogram survival model was constructed using rms package. Additionally, pathways were enriched for the DEGs between high- and low-risk groups using Gene Set Enrichment Analysis. ResultsThere were 3,638 DMGs and 2,702 DEGs between tumor and normal samples. Among the 312 intersecting genes, 43 prognosis-associated genes were identified. A total of 13 optimal DMGs (BTBD19, ADAM8, BGLAP, TNFRSF13C, JPH4, BEST1, GNRH2, UBE2QL1, CHODL, GDF9, UPB1, KCNH3; and ADAMTSL4) were obtained for building the risk score system. After pathological M, pathological T, platelet qualitative, and RS status were revealed to be independent prognostic factors, a nomogram survival model was constructed. For the 920 DEGs between the high- and low-risk samples, 6 significant pathways were enriched. ConclusionThe 13-gene risk score system and the nomogram survival model might be used for prognostic prediction of ccRCC patients.

Longitudinal Analysis of Gene Expression Changes During Cervical Carcinogenesis Reveals Potential Therapeutic Targets.

Despite advances in the treatment of cervical cancer (CC), the prognosis of patients with CC remains to be improved. This study aimed to explore candidate gene targets for CC. CC datasets were downloaded from the Gene Expression Omnibus database. Genes with similar expression trends in varying steps of CC development were clustered using Short Time-series Expression Miner (STEM) software. Gene functions were then analyzed using the Gene Ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Protein interactions among genes of interest were predicted, followed by drug-target genes and prognosis-associated genes. The expressions of the predicted genes were determined using real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting. Red and green profiles with upward and downward gene expressions, respectively, were screened using STEM software. Genes with increased expression were significantly enriched in DNA replication, cell-cycle-related biological processes, and the p53 signaling pathway. Based on the predicted results of the Drug-Gene Interaction database, 17 drug-gene interaction pairs, including 3 red profile genes (TOP2A, RRM2, and POLA1) and 16 drugs, were obtained. The Cancer Genome Atlas data analysis showed that high POLA1 expression was significantly correlated with prolonged survival, indicating that POLA1 is protective against CC. RT-qPCR and Western blotting showed that the expressions of TOP2A, RRM2, and POLA1 gradually increased in the multistep process of CC. TOP2A, RRM2, and POLA1 may be targets for the treatment of CC. However, many studies are needed to validate our findings.

Identification of potential key genes in esophageal adenocarcinoma using bioinformatics.

Esophageal adenocarcinoma (EAC) is the predominant pathological subtype of esophageal cancer in Europe and the USA. The present bioinformatics study analyzed a high-throughput sequencing dataset, GSE94869, to determine differentially expressed genes (DEGs) in order to identify key genes, biological processes and pathways associated with EAC. Functional enrichment analysis was performed using the Database for Annotation Visualization and Integrated Discovery. The co-expression network of the DEGs was established using Weighted Gene Co-Expression Network Analysis and visualized using Cytoscape. A Kaplan-Meier analysis based on The Cancer Genome Atlas (TCGA) database was used to identify prognosis-associated genes. Univariate and multivariate Cox proportional hazard models were used to identify genes with a prognostic value regarding relapse-free survival (RFS), while validation of the differential expression of prognosis-associated genes was performed using a box plot based on data from TCGA and another microarray dataset, GSE26886. A total of 130 DEGs, comprising 82 upregulated and 48 downregulated genes, were identified. The upregulated DEGs were significantly associated with extracellular matrix organization, disassembly, and the phosphoinositide-3 kinase/AKT, Rap1 and Ras signaling pathways, while the downregulated genes were associated with the Wnt signalling pathway. Subsequently, two co-expression modules were established and 20 hub genes were identified. The blue module was associated with the Rap1 signaling pathway, while the turquoise module was associated with the Ras and Rap1 signaling pathways. Among them, methyltransferase like 7B (METTL7B) was associated with RFS. Furthermore, the overexpression of METTL7B in EAC was successfully validated using data from TCGA and GSE26886. The present study identified key genes and provides potential biomarkers for the diagnosis and treatment of EAC.

Analysis of clinical significance and prospective molecular mechanism of main elements of the JAK/STAT pathway in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one the most common malignancies and has poor prognosis in patients. The aim of the present study is to explore the clinical significance of the main genes involved in the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway in HCC. GSE14520, a training cohort containing 212 hepatitis B virus-infected HCC patients from the Gene Expression Omnibus database, and data from The Cancer Genome Atlas as a validation cohort containing 370 HCC patients, were used to analyze the diagnostic and prognostic significance for HCC. Joint-effect analyses were performed to determine diagnostic and prognostic significance. Nomograms and risk score models were constructed to predict HCC prognosis using the two cohorts. Additionally, molecular mechanism analysis was performed for the two cohorts. Prognosis-associated genes in the two cohorts were further validated for differential expression using reverse transcription-quantitative polymerase chain reaction of 21 pairs of hepatitis B virus-infected HCC samples. JAK2, TYK2, STAT3, STAT4 and STAT5B had diagnostic significance in the two cohorts (all area under curves >0.5; P≤0.05). In addition, JAK2, STAT5A, STAT6 exhibited prognostic significance in both cohorts (all adjusted P≤0.05). Furthermore, joint-effect analysis had advantages over using one gene alone. Molecular mechanism analyses confirmed that STAT6 was enriched in pathways and terms associated with the cell cycle, cell division and lipid metabolism. Nomograms and risk score models had advantages for HCC prognosis prediction. When validated in 21 pairs of HCC and non-tumor tissue, STAT6 was differentially expressed, whereas JAK2 was not differentially expressed. In conclusion, JAK2, STAT5A and STAT6 may be potential prognostic biomarkers for HCC. JAK2, TYK2, STAT3, STAT4 and STAT5B may be potential diagnostic biomarkers for HCC. STAT6 has a role in HCC that may be mediated via effects on the cell cycle, cell division and lipid metabolism.

Identification of critical genes associated with human osteosarcoma metastasis based on integrated gene expression profiling.

Osteosarcoma is the most common type of malignant bone cancer, which often affects teenagers and young adults. The present study aimed to screen for critical genes and microRNAs (miRNAs/miRs) involved in osteosarcoma. A total of four microarray datasets (accession numbers GSE32981, GSE21257, GSE14827 and GSE14359) were downloaded from the Gene Expression Omnibus database. Following data preprocessing, module analysis was performed to identify the stable modules using the weighted gene co-expression network analysis (WGCNA) package. The differentially expressed genes (DEGs) between metastatic samples and non-metastatic samples were screened, followed by gene co-expression network construction, and Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Subsequently, prognosis-associated genes were screened and a miRNA-target gene regulatory network was constructed. Finally, the data for critical genes were validated. WGCNA analysis identified six modules; blue and yellow modules were significantly positively associated with osteosarcoma metastasis. A total of 1,613 DEGs were screened between primary tissue samples and metastatic samples. Following comparison of the genes in the two (blue and yellow) modules, a total of 166 DEGs were identified (metastatic samples vs. non-metastatic samples). Functional enrichment analysis demonstrated that these DEGs were mainly involved in ‘defense response’, ‘p53 signaling pathway’ and ‘lysosome’. By utilizing the clinical information in GSE21257, 10 critical genes associated with osteosarcoma prognosis were obtained, including CTP synthase 2 (CTPS2), tumor protein p53 inducible protein 3 (TP53I3) and solute carrier family 1 member 1 (SLC1A1). In addition, hsa-miR-422a and hsa-miR-194 were highlighted in the miRNA-target gene network. Finally, matrix metallopeptidase 3 (MMP3) and vascular endothelial growth factor B (VEGFB) were predicted as critical genes in osteosarcoma metastasis. CTPS2, TP53I3 and SLC1A1 may serve major roles in osteosarcoma development, and hsa-miR-422a, hsa-miR-194, MMP3 and VEGFB may be associated with osteosarcoma metastasis.

Using Prognosis-Related Gene Expression Signature and Connectivity Map for Personalized Drug Repositioning in Multiple Myeloma.

BackgroundMultiple myeloma (MM) is the second most common hematologic cancer with poor prognosis. Novel therapeutic strategies are needed to decrease the high mortality rate. The aim of this study was to identify prospective agents for MM.Material/MethodsA microarray dataset was mined, which contains the transcriptome profiles of 588 MM patients. Univariate Cox analysis was performed to analyze the relationships between genes and clinical outcome. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were determined. Protective and risky genes were uploaded to Connectivity Map (CMAP) database to identify the potentially unknown effects of existing drugs. An example was selected to be docked on the known molecules.ResultsA total of 1445 genes significantly correlated with the event free survival (EFS) of MM patients were identified and included 676 protective and 769 risky indicators. KEGG pathway analysis revealed that these prognosis-associated genes were enriched in the “cell cycle,” “DNA replication,” and “P53 signaling pathway”. The top t3 most significant potential molecules were vorinostat, trifluoperazine, and thioridazine. CDK1 (cyclin-dependent kinase-1) ranked as the core in the class of prognosis-related genes in MM based on protein-protein interaction (PPI) network analysis. With Sybyl-X 2.0, the majority of the top 10 molecules aforementioned displayed high binding forces with CDK1. Among these molecules, trichostatin A had the greatest ability in combining with CDK1.ConclusionsGenes that mainly accumulate in the cell cycle pathway play an essential role in the prognosis of MM, and these prognosis-related genes also have great value in drug development.

Comprehensive Analysis of 343 Genes Using Targeted Sequencing Panel By Next-Generation Sequencer in 77 Pediatric AML Patients with Normal and Complex Karyotypes: Jccg Study, JPLSG AML-05

IntroductionAcute myeloid leukemia (AML) is a molecularly and clinically heterogeneous disease caused by various genetic alterations. Some prognosis-associated chromosomal aberrations and gene mutations such as t(8;21), inv(16), monosomy 7, and FLT3-ITD have been adopted for risk stratification. Although treatment outcomes have improved via stratification therapy, relapse and mortality are still observed in 40% and 30% patients, respectively. Patients with an intermediate risk with no favorable or recurrent factors are considered to exhibit varied biology and outcomes. Further studies are warranted to evaluate the accuracy of prognosis in these patients.MethodsAmong 369 patients with de novo AML participated in the Japanese AML-05 study conducted by the Japanese Pediatric Leukemia/Lymphoma Study Group during 2006-2010, 77 patients including 59 with normal karyotype-AML (NK-AML) and 18 with complex karyotype-AML (CK-AML) were enrolled. Targeted sequencing was performed using a 343-gene custom panel and next-generation sequencer. Reportedly, these 343 genes are associated with hematopoietic malignancy or solid tumor pathogenesis. Correlations among gene mutations, other cytogenetic alterations, and clinical characteristics were investigated.ResultsIn all, 187 mutations in 61 genes (average: 2.42 mutations/patient) were detected, and 72 patients (93%) had at least one genetic mutation.Among patients with NK-AML, 51 (86%) had one of the following driver mutations: FLT3-ITD, KMT2A-PTD, CEBPA, or NPM. Interestingly, internal tandem duplication (ITD) of BCOR (BCOR-ITD) was detected along with several novel ITDs in patients with unclear AML pathogenesis.Among patients with CK-AML, the following mutations regarding myelodysplastic syndrome (MDS) pathogenesis were detected: TP53: 3 (17%), JAK2: 2 (11%), ASXL1: 2 (11%), U2AF1: 1 (7%), SF3A1: 1 (7%), RUNX1: 2 (11%), and BCOR/BCORL1: 3 (17%). Consequently, 15 of 18 (83%) patients with CK-AML had some genetic mutations related to MDS. Eight types of transcription factor mutations and five of epigenetic factor mutations were detected in 10 patients. Eight of these (80%) relapsed or died. Three RUNX1 rearrangements (RUNX1-CBFA2T2, RUNX1-CBFA2T3, and RUNX1-FNBP1) and other fusions (PICALM-MLL10 and MYB-GATA1) were detected in patients with CK-AML. Interestingly, these patients had a low transcription factor or epigenetic factor mutation number; all of them survived without relapse.DiscussionWe detected several novel ITDs other than FLT3-ITD in patients with NK-AML. KIT-ITD was reported in adult and pediatric patients with AML; BCOR-ITD was linked to the pathogenesis of pediatric clear-cell sarcoma of the kidney. However, the clinical significance of ITDs, other than FLT3-ITD, has not been revealed in AML, for which further studies are being planned.This study identified the characteristic genetic background (i.e., MDS) in patients with CK-AML. Most patients with CK-AML (17/18; 94%) were diagnosed as AML with MDS related changes, according to World Health Organization classification. Fifteen of those (88%) had genetic mutations related to MDS pathogenesis. Furthermore, several RUNX1 rearrangements were detected in patients with CK-AML. Reportedly, RUNX1-CBFA2T2 and RUNX1-CBFA2T3 are recurrent fusions in adult AML. Particularly, RUNX1-CBFA2T3 has a gene expression profile similar to that of RUNX1-RUNX1T1, which may explain the favorable outcome in patients with such rearrangements.Despite the small sample size of this study, the findings indicate two major subgroups of pediatric CK-AML: 1) CK-AML with MDS-related genetic mutations (i.e., transcription factor and epigenetic factor mutations) linked to poor outcomes and 2) CK-AML with few of these mutations and with fusion genes (e.g., RUNX1) linked to favorable outcomes. DisclosuresNo relevant conflicts of interest to declare.

Pathway- and clinical-factor-based risk model predicts the prognosis of patients with gastric cancer.

Gastric cancer (GC) has a high incidence and mortality rate. If discovered late, GC tends to have a poor prognosis. Improvements in the prognostic accuracy of GC through combined analysis of multiple relevant genes and clinical factors may solve this problem. In the present study, GSE62254 (including 300 GC tissues), obtained from the Gene Expression Omnibus database, was used as a training set, and the mRNA-sequencing data of GC (including 384 GC tissues) downloaded from the Cancer Genome Atlas database served as a validation set. Based on the t-test and Wilcoxon test, the significantly differentially expressed genes (DEGs) were obtained by screening the intersecting DEGs. The prognosis-associated genes and clinical factors were identified using Cox regression analysis in the R survival package. The optimal prognosis-associated pathways were examined using the Cox-proportional hazards (Cox-PH) model in the R penalized package. Finally, risk prediction models were constructed and validated using the Cox-PH model and the Kaplan-Meier method, respectively. There were a total of 382 significant DEGs, including 268 upregulated genes and 114 downregulated genes. A total of 50 prognosis-associated genes were identified, 16 optimal prognosis-associated pathways (including mitochondrial pathway and the tyrosine-protein kinase JAK-signal transducer and activator of transcription signaling pathway, which involve caspase 7, phosphoinositide-3-kinase regulatory subunit 3, peroxisome proliferator-activated receptor γ and collagen triple helix repeat containing 1) and four prognosis-associated clinical factors [including Pathologic_N, Pathologic_stage, mutL homolog 1 (MLH1) mutation and recurrence]. The pathway- and clinical-factor-based risk prediction model exhibited marked prognostic accuracy. The clinical-factor-based risk prediction model with improved P-values for prognosis prediction may be superior to the pathway-based risk prediction model in predicting the prognosis of GC patients.

Upregulated VEGFA and DLL4 act as potential prognostic genes for clear cell renal cell carcinoma.

PurposeAs a typical hypervascular tumor, clear cell renal cell carcinoma (ccRCC) is the most common type of RCC. This study was aimed to explore the prognostic genes for ccRCC, focusing on the roles of vascular endothelial growth factor A (VEGFA) and Delta-like ligand 4 (DLL4) in the disease.Materials and methodsThe mRNA-sequencing data of kidney renal clear cell carcinoma (KIRC) were obtained from The Cancer Genome Atlas (TCGA) database, including 469 tumor samples and 68 adjacent normal samples. Using limma package, differentially expressed genes (DEGs) were analyzed by differential expression and subgroup analyses and confirmed using validation dataset GSE53757. Followed by enrichment analysis, protein–protein interaction (PPI) network analysis and protein subcellular localization were performed using multifaceted analysis tool for human transcriptome tool, and Cytoscape software and InnateDB database, respectively. Moreover, survival analysis was conducted to identify key prognosis-associated genes. In addition, VEGFA and DLL4 levels were detected using real-time quantitative PCR (qRT-PCR).ResultsA total of 1,984 DEGs were screened in the KIRC tumor samples. VEGFA was located in extracellular space and could interact with placental growth factor (PGF) and angiopoietin 2 (ANGPT2) in the PPI network. Subgroup analysis suggested that VEGFA was significantly upregulated in stages I, II, and III ccRCC tumor samples. Survival analysis showed that TIMP1 was among the top four prognosis-associated genes. qRT-PCR analysis confirmed that the expression levels of DLL4 and VEGFA were significantly upregulated in tumor samples.ConclusionVEGFA and DLL4 might be prognostic genes for ccRCC. Besides, PGF, ANGPT2, and TIMP1 might also be related to the prognosis of ccRCC patients.

Identification of a gene signature associated with radiotherapy and prognosis in gliomas

Glioma is one of the most common primary brain tumors with poor prognosis. Although radiotherapy is an important treatment method for gliomas, the efficacy is still limited by the high occurrence of radioresistance and the underlying molecular mechanism is unclear. Here, we performed a data mining work based on four glioma expression datasets. These datasets were classified into training set and validation set. Radiotherapy-induced differential expressed genes and prognosis-associated genes were screened using different classifiers. The Kaplan-Meier curves along with the two-sided Log Rank (Mantel-Cox) test were used to evaluate overall survival. We found the gene expression profiles of gliomas between those patients received radiotherapy and those patients without received radiotherapy were quite different. A 20-gene signature was identified, which was associated with radiotherapy.Furthermore, a novel 5-gene signature (HOXC10, LOC101928747, CYB561D2, RPL36A and RPS4XP2) as an independent predictor of glioma patients’ prognosis was further derived from the 20-gene signature. These findings provided a new insight into the molecular mechanism of radioresistance in gliomas. The 5-gene signature might represent therapeutic target for gliomas.

A five-gene based risk score with high prognostic value in colorectal cancer.

Colorectal cancer (CRC) is one of the most frequently occurring malignancies worldwide. The outcomes of patients with similar clinical symptoms or at similar pathological stages remain unpredictable. This inherent clinical diversity is most likely due to the genetic heterogeneity. The present study aimed to create a predicting tool to evaluate patient survival based on genetic profile. Firstly, three Gene Expression Omnibus (GEO) datasets (GSE9348, GSE44076 and GSE44861) were utilized to identify and validate differentially expressed genes (DEGs) in CRC. The GSE14333 dataset containing survival information was then introduced in order to screen and verify prognosis-associated genes. Of the 66 DEGs, the present study screened out 46 biomarkers closely associated to patient overall survival. By Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, it was demonstrated that these genes participated in multiple biological processes which were highly associated with cancer proliferation, drug-resistance and metastasis, thus further affecting patient survival. The five most important genes, MET proto-oncogene, receptor tyrosine kinase, carboxypeptidase M, serine hydroxymethyltransferase 2, guanylate cyclase activator 2B and sodium voltage-gated channel a subunit 9 were selected by a random survival forests algorithm, and were further made up to a linear risk score formula by multivariable cox regression. Finally, the present study tested and verified this risk score within three independent GEO datasets (GSE14333, GSE17536 and GSE29621), and observed that patients with a high risk score had a lower overall survival (P<0.05). Furthermore, this risk score was the most significant compared with other predicting factors including age and American Joint Committee on Cancer stage, in the model, and was able to predict patient survival independently and directly. The findings suggest that this survival associated DEGs-based risk score is a powerful and accurate prognostic tool and is promisingly implemented in a clinical setting.

Weighted gene co-expression network analysis of gene modules for the prognosis of esophageal cancer.

Esophageal cancer is a common malignant tumor, whose pathogenesis and prognosis factors are not fully understood. This study aimed to discover the gene clusters that have similar functions and can be used to predict the prognosis of esophageal cancer. The matched microarray and RNA sequencing data of 185 patients with esophageal cancer were downloaded from The Cancer Genome Atlas (TCGA), and gene co-expression networks were built without distinguishing between squamous carcinoma and adenocarcinoma. The result showed that 12 modules were associated with one or more survival data such as recurrence status, recurrence time, vital status or vital time. Furthermore, survival analysis showed that 5 out of the 12 modules were related to progression-free survival (PFS) or overall survival (OS). As the most important module, the midnight blue module with 82 genes was related to PFS, apart from the patient age, tumor grade, primary treatment success, and duration of smoking and tumor histological type. Gene ontology enrichment analysis revealed that "glycoprotein binding" was the top enriched function of midnight blue module genes. Additionally, the blue module was the exclusive gene clusters related to OS. Platelet activating factor receptor (PTAFR) and feline Gardner-Rasheed (FGR) were the top hub genes in both modeling datasets and the STRING protein interaction database. In conclusion, our study provides novel insights into the prognosis-associated genes and screens out candidate biomarkers for esophageal cancer.

Identification and validation of soluble carrier family expression signature for predicting poor outcome of renal cell carcinoma.

The soluble carrier (SLC) family plays an important role in cell metabolism. The purpose of the current study was to screen SLCs as potential prognostic factors in clear cell renal cell carcinoma (ccRCC). A total of 509 patients with ccRCC from The Cancer Genome Atlas (TCGA) cohort were enrolled in this study. The expression profile of SLCs was obtained from the TCGA RNAseq database. Metadata of the TCGA cohort, including age, sex, TNM stage, tumor grade, American Joint Committee on Cancer stage, laterality, and overall survival, were collected. Univariate and multivariate Cox proportional hazards regression models were used to analyze the relative factors. Prognosis-associated genes were further validated in a Fudan University Shanghai Cancer Center (FUSCC) cohort consisting of 178 patients. Among a total of 364 SLC transporters, 61 were independent predictors of ccRCC patient overall survival. Among the 61 SLC transporters, 26 were significantly downregulated and 23 were significantly upregulated in tumor tissues compared with non-malignant kidney tissues. Analyses of two open source, RNA expression data sets on sunitinib response revealed that SLC10A2 was downregulated in tyrosine kinase inhibitor-resistant samples. We validated SLC10A2 expression in the FUSCC cohort and showed that SLC10A2 expression was an independent prognostic predictor of overall survival of ccRCC (hazard ratio=0.432, 95% CI: 0.204-0.915). Our results identified a number of associations of SLC gene expression with prognosis of ccRCC patients, indicating that these genes may represent possible oncogenes that could serve as therapeutic targets of ccRCC.

The Clinical Features and Prognostic Impact of PRDM16 gene Expression in Adult Acute Myeloid Leukemia

BackgroundAcute myeloid leukemia (AML) is a complex disease caused by various genetic alterations. Some prognosis-associated cytogenetic aberrations or gene mutations such as FLT3-internal tandem duplication (ITD), t(8;21)(q22;q22)/RUNX1-RUNX1T1, and inv(16)(p13q22)/CBFB-MYH11 have been found and used to stratify the risk. Numerous gene mutations have been implicated in the pathogenesis of AML, including mutations of DNMT3A, IDH1/2, TET2 and EZH2 in addition to RAS, KIT, NPM1, CEBPA and FLT3in the recent development of massively parallel sequencing technologies. However, even after incorporating these molecular markers, the prognosis is unclear in a subset of AML patients.Recently, NUP98-NSD1 fusion gene was identified as a poor prognostic factor for AML. We have reported that all pediatric AML patients with NUP98-NSD1 fusion showed high expression of the PR domain containing 16 (PRDM16; also known as MEL1) gene, which is a zinc finger transcription factor located near the breakpoint at 1p36. PRDM16 is highly homologous to MDS1/EVI1, which is an alternatively spliced transcript of EVI1. Furthermore, PRDM16 is essential for hematopoietic stem cell maintenance and remarkable as a candidate gene to induce leukemogenesis. Recent reports revealed that high PRDM16 expression was a significant marker to predict poor prognosis in pediatric AML. However, the significance of PRDM16 expression is unclear in adult AML patients.MethodsA total of 151 adult AML patients (136 patients with de novo AML and 15 patients with relapsed AML) were analyzed. They were referred to our institution between 2004 and 2015 and our collaborating center between 1996 and 2013. The median length of follow-up for censored patients was 30.6 months. Quantitative RT-PCR analysis was performed using the 7900HT Fast Real Time PCR System with TaqMan Gene Expression Master Mix and TaqMan Gene Expression Assay. In addition to PRDM16, ABL1 was also evaluated as a control gene. We investigated the correlations between PRDM16 gene expression and other genetic alterations, such as FLT3-ITD, NPM1, and DNMT3A, and clarified the prognostic impact of PRDM16 expression in adult AML patients. Mutation analyses were performed by direct sequence analysis, Mutation Biased PCR, and the next-generation sequencer Ion PGM.ResultsPRDM16 overexpression was identified in 29% (44/151) of adult AML patients. High PRDM16 expression correlated with higher white blood cell counts in peripheral blood and higher blast ratio in bone marrow at diagnosis; higher coincidence of mutation in NPM1 (P = 0.003) and DNMT3A (P = 0.009); and lower coincidence of t(8;21) (P = 0.010), low-risk group (P = 0.008), and mutation in BCOR (P = 0.049). Conversely, there were no significant differences in age at diagnosis and sex distribution. Patients with high PRDM16 expression tended to be low frequency in M2 (P = 0.081) subtype, and the remaining subtype had no significant differences between high and low PRDM16 expression. Remarkably, PRDM16 overexpression patients were frequently observed in non-complete remission (55.8% vs. 26.3%, P = 0.001). Patients with high PRDM16 expression tended to have a cumulative incidence of FLT3-ITD (37% vs. 21%, P = 0.089) and MLL-PTD (15% vs. 5%, P = 0.121).We analyzed the prognosis of 139 patients who were traceable. The overall survival (OS) and median survival time (MST) of patients with high PRDM16 expression were significantly worse than those of patients with low expression (5-year OS, 17% vs. 32%; MST, 287 days vs. 673 days; P = 0.004). This trend was also significant among patients aged <65 years (5-year OS, 25% vs. 48%; MST, 361 days vs. 1565 days, P = 0.013). Moreover, high PRDM16 expression was a significant prognostic factor for FLT3-ITD negative patients aged < 65 years in the intermediate cytogenetic risk group (5-year OS, 29% vs. 58%; MST, 215 days vs. undefined; P = 0.032).ConclusionsWe investigated the correlations among PRDM16 expression, clinical features, and other genetic alterations to reveal clinical and prognostic significance. High PRDM16 expression was independently associated with non-CR and adverse outcomes in adult AML patients, as well as pediatric AML patients. Our finding indicated that the same pathogenesis may exist in both adult and pediatric AML patients with respect to PRDM16 expression, and measuring PRDM16 expression was a powerful tool to predict the prognosis of adult AML patients. DisclosuresInokuchi:Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria; Celgene: Honoraria; Pfizer: Honoraria.

Pathway-based gene signatures predicting clinical outcome of lung adenocarcinoma.

Lung adenocarcinoma is often diagnosed at an advanced stage with poor prognosis. Patients with different clinical outcomes may have similar clinico-pathological characteristics. The results of previous studies for biomarkers for lung adenocarcinoma have generally been inconsistent and limited in clinical application. In this study, we used inverse-variance weighting to combine the hazard ratios for the four datasets and performed pathway analysis to identify prognosis-associated gene signatures. A total of 2,418 genes were found to be significantly associated with overall survival. Of these, a 21-gene signature in the HMGB1/RAGE signalling pathway and a 31-gene signature in the clathrin-coated vesicle cycle pathway were significantly associated with prognosis of lung adenocarcinoma across all four datasets (all p-values < 0.05, log-rank test). We combined the scores for the three pathways to derive a combined pathway-based risk (CPBR) score. Three pathway-based signatures and CPBR score also had more predictive power than single genes. Finally, the CPBR score was validated in two independent cohorts (GSE14814 and GSE13213 in the GEO database) and had significant adjusted hazard ratios 2.72 (p-value < 0.0001) and 1.71 (p-value < 0.0001), respectively. These results could provide a more complete picture of the lung cancer pathogenesis.

The Prognostic Impact of High MEL1 Gene Expression in Pediatric Acute Myeloid Leukemia

BackgroundAcute myeloid leukemia (AML) is a complex disease caused by mutations, epigenetic modifications, and deregulated expression of genes, leading to increased proliferation and decreased differentiation of hematopoietic progenitor cells. Although, many prognosis-associated gene alterations have been identified in adult AML, genetic alterations responsible for an adverse outcome in most patients with pediatric AML have remained obscure.In our previous study, we have identified NUP98-NSD1 fusion gene in 6 of 124 pediatric AML patients. As a result of gene expression profile using microarray, all these 6 patients have showed high expression of HOXA9, HOXA10, HOXB3, HOXB5, HOXB6, and a zinc finger transcription factor gene MDS1-EVI1-like-1 (MEL1), and clustered a distinct poor prognostic subgroup [4-year-overall survival (OS); 33%]. Furthermore, this subgroup has also formed the bigger poor prognostic subgroup involving the 18 patients without NUP98-NSD1 fusion gene. We defined this subgroup as NUP98-NSD1 signature consisting of 24 of 124 patients (19%), 4-year-OS; 37.5%. MEL1 gene expression represented the specific characteristics of this subgroup.To verify the significance of MEL1 gene expression and the relationship with other poor prognostic markers such as FLT3-ITD, MLL-PTD, and NPM1, we investigated MEL1 expression by real-time PCR in 369 pediatric AML cases other than acute promyelocytic leukemia (APL) and Down syndrome-associated AML, because these subtypes formed genetically different unique entities and treated by other protocols. Interestingly, MEL1 is highly homologous to MDS1/EVI1 gene, which is an alternatively spliced transcript of the EVI1 genes. As EVI1 high expression has been reported as a poor prognostic marker in pediatric AML patients, especially in patients with MLL rearrangement, we also measured and examined the EVI1 gene expression.MethodsBetween 2006 and 2010, 485 de novo pediatric AML patients participated in the Japanese AML-05 study conducted by the Japanese Pediatric Leukemia/Lymphoma Study Group (JPLSG). Among them, 369 samples were available in this study. Quantitative RT-PCR analysis was performed in these patients using the 7900HT Fast Real Time PCR System with TaqMan Gene Expression Master Mix and TaqMan Gene Expression Assay. In addition to EVI1 and MEL1, ABL1 was also evaluated as a control gene. We investigated the correlations between these gene expressions and other genetic alterations, and clarified the prognostic impact of MEL1 gene.ResultsA total of 84 of 369 patients (22.8%) showed high expression of MEL1 gene. The number of patients with MEL1 high expressions were gradually enriched in order of the intermediate risk (IR; 34 of 142 patients, or 23.9%), high risk (HR; 21 of 48 patients, or 43.8%), and non-complete remission (non-CR; 20 of 39 patients, or 51.3%), but were almost all absent in patients with a low risk (LR) cytogenetic profile (4 of 120 patients, or 3.3%) consisting of t(8;21) and inv(16). The absolute values of MEL1 gene expression have also gradually increased in order of LR < IR < HR < non-CR. The overall survival among patients with MEL1 high expressions was significantly lower than that among patients without such gene aberrant expression (50% vs. 82%, P<0.001). Remarkably, MEL1 gene expression is extremely useful to stratify the AML patients with FLT3-ITD (4-year OS; high 30% vs. low 75%, P<0.001) and normal karyotype (4-year OS; high 48% vs. low 84%, P<0.001), whom we could not exactly stratify into the appropriate risk so far. On the other hand, a total of 58 of 369 patients (15.7%) have showed high expression of EVI1 gene. The patients with high expression of EVI1 gene have been mainly found in the IR group, especially in patients with MLL rearrangement. Furthermore, MEL1 high group and EVI1high group have the tendency of mutual exclusive in this study.ConclusionsMEL1 high expression was highly recurrent in de novo pediatric AML patients with high/intermediate-risk cytogenetic profiles and was independently associated with a poor outcome. Combined with MEL1expression and genetic alterations enable to clarify the genetic background of pediatric AML profiles. The data in this study showed a way in which integrated mutational profiling of a clinical trial cohort can advance our understanding of the biologic characteristics of AML, improve current prognostic models, and inform prospective therapeutic decisions. DisclosuresNo relevant conflicts of interest to declare.

Downregulation of HNF-1B in Renal Cell Carcinoma Is Associated With Tumor Progression and Poor Prognosis

The aim of this study was to identify new prognostic factors in metastatic renal cell carcinoma (RCC) based on the analysis of precisely defined metastatic tissue. Expression profiling was done on 26 snap-frozen samples of clear-cell RCC metastases with complete follow-up (up to 116 months) using laser microdissection and oligonucleotide microarrays (Affymetrix). A prognosis-associated gene signature was determined using the semi-supervised principal components analysis method. Validation was performed with quantitative RT-PCR on samples of normal renal tissue (n = 6), RCC primary tumor (n = 57), and RCC metastases (n = 59). Immunohistochemistry (IHC) was done to localize HNF-1B. Analysis of expression data revealed a three-gene signature consisting of HNF-1B, KIAA1919, and SYDE1, which discriminated well between 2 prognosis groups (P < .05), independently of the TNMG classification. Expression of HNF-1B was analyzed in detail. HNF-1B mRNA expression correlated with malignant transformation and progression (normal renal tissue > primary tumor > metastasis; P < .0001). There was a significant correlation between high HNF-1B mRNA expression in primary tumor and better prognosis (P < .05). IHC showed a specific nuclear HNF-1B staining confined to the tumor cells of the primary tumors and of the metastases. The level of HNF-1B mRNA expression significantly decreases with tumor progression, and patients with high HNF-1B mRNA levels have a significantly better prognosis. HNF-1B might be a useful prognostic factor for metastatic RCC and also a potential therapeutic target in the future.