A robust two-gene signature for glioblastoma survival prediction

Glioblastoma multiforme (GBM) is a highly malignant brain tumor. We explored the prognostic gene signature in 443 GBM samples by systematic bioinformatics analysis, using GSE16011 with microarray expression and corresponding clinical data from Gene Expression Omnibus as the training set. Meanwhile, patients from The Chinese Glioma Genome Atlas database (CGGA) were used as the test set and The Cancer Genome Atlas database (TCGA) as the validation set. Through Cox regression analysis, Kaplan-Meier analysis, t-distributed Stochastic Neighbor Embedding algorithm, clustering, and receiver operating characteristic analysis, a two-gene signature (GRIA2 and RYR3) associated with survival was selected in the GSE16011 dataset. The GRIA2-RYR3 signature divided patients into two risk groups with significantly different survival in the GSE16011 dataset (median: 0.72, 95% confidence interval [CI]: 0.64-0.98, vs median: 0.98, 95% CI: 0.65-1.61 years, logrank test P < .001), the CGGA dataset (median: 0.84, 95% CI: 0.70-1.18, vs median: 1.21, 95% CI: 0.95-2.94 years, logrank test P = .0017), and the TCGA dataset (median: 1.03, 95% CI: 0.86-1.24, vs median: 1.23, 95% CI: 1.04-1.85 years, logrank test P = .0064), validating the predictive value of the signature. And the survival predictive potency of the signature was independent from clinicopathological prognostic features in multivariable Cox analysis. We found that after transfection of U87 cells with small interfering RNA, GRIA2 and RYR3 influenced the biological behaviors of proliferation, migration, and invasion of glioblastoma cells. In conclusion, the two-gene signature was a robust prognostic model to predict GBM survival.     

Identification of an autophagy-related gene signature predicting overall survival for hepatocellular carcinoma

The poor prognosis of hepatocellular carcinoma (HCC) calls for the development of accurate prognostic models. The growing number of studies indicating a correlation between autophagy activity and HCC indicates there is a commitment to finding solutions for the prognosis of HCC from the perspective of autophagy. We used a cohort in The Cancer Genome Atlas (TCGA) to evaluate the expression of autophagy-related genes in 371 HCC samples using univariate Cox and lasso Cox regression analysis, and the prognostic features were identified. A prognostic model was established by combining the expression of selected genes with the multivariate Cox regression coefficient of each gene. Eight autophagy-related genes were selected as prognostic features of HCC. We established the HCC prognostic risk model in TCGA dataset using these identified prognostic genes. The model’s stability was confirmed in two independent verification sets ({"type":"entrez-geo","attrs":{"text":"GSE14520","term_id":"14520"}}GSE14520 and {"type":"entrez-geo","attrs":{"text":"GSE36376","term_id":"36376"}}GSE36376). The model had a good predictive power for the overall survival (OS) of HCC (hazard ratio = 2.32, 95% confidence interval = 1.76–3.05, P<0.001). Moreover, the risk score computed by the model did not depend on other clinical parameters. Finally, the applicability of the model was demonstrated through a nomogram (C-index = 0.701). In the present study, we established an autophagy-related risk model having a high prediction accuracy for OS in HCC. Our findings will contribute to the definition of prognosis and establishment of personalized therapy for HCC patients.     

A novel autophagy-related lncRNA prognostic risk model for breast cancer

Long non-coding RNAs (lncRNAs) are well known as crucial regulators to breast cancer development and are implicated in controlling autophagy. LncRNAs are also emerging as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy-related lncRNAs with prognostic value in breast cancer. In this study, we identified autophagy-related lncRNAs in breast cancer by constructing a co-expression network of autophagy-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA). We evaluated the prognostic value of these autophagy-related lncRNAs by univariate and multivariate Cox proportional hazards analyses and eventually obtained a prognostic risk model consisting of 11 autophagy-related lncRNAs (U62317.4, LINC01016, LINC02166, C6orf99, LINC00992, BAIAP2-DT, AC245297.3, AC090912.1, Z68871.1, LINC00578 and LINC01871). The risk model was further validated as a novel independent prognostic factor for breast cancer patients based on the calculated risk score by Kaplan-Meier analysis, univariate and multivariate Cox regression analyses and time-dependent receiver operating characteristic (ROC) curve analysis. Moreover, based on the risk model, the low-risk and high-risk groups displayed different autophagy and oncogenic statues by principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation. Taken together, these findings suggested that the risk model of the 11 autophagy-related lncRNAs has significant prognostic value for breast cancer and might be autophagy-related therapeutic targets in clinical practice.     

Gene regulation network analysis reveals core genes associated with survival in glioblastoma multiforme

Glioblastoma multiforme (GBM) is a very serious mortality of central nervous system cancer. The microarray data from GSE2223 , GSE4058 , GSE4290 , GSE13276 , GSE68848 and GSE70231 (389 GBM tumour and 67 normal tissues) and the RNA‐seq data from TCGA‐GBM dataset (169 GBM and five normal samples) were chosen to find differentially expressed genes (DEGs). RRA (Robust rank aggregation) method was used to integrate seven datasets and calculate 133 DEGs (82 up‐regulated and 51 down‐regulated genes). Subsequently, through the PPI (protein‐protein interaction) network and MCODE/ cytoHubba methods, we finally filtered out ten hub genes, including FOXM1, CDK4, TOP2A, RRM2, MYBL2, MCM2, CDC20, CCNB2, MYC and EZH2, from the whole network. Functional enrichment analyses of DEGs were conducted to show that these hub genes were enriched in various cancer‐related functions and pathways significantly. We also selected CCNB2, CDC20 and MYBL2 as core biomarkers, and further validated them in CGGA, HPA and CCLE database, suggesting that these three core hub genes may be involved in the origin of GBM. All these potential biomarkers for GBM might be helpful for illustrating the important role of molecular mechanisms of tumorigenesis in the diagnosis, prognosis and targeted therapy of GBM cancer.     

A phase I/II trial of hydroxychloroquine in conjunction with radiation therapy and concurrent and adjuvant temozolomide in patients with newly diagnosed glioblastoma multiforme

Preclinical studies indicate autophagy inhibition with hydroxychloroquine (HCQ) can augment the efficacy of DNA-damaging therapy. The primary objective of this trial was to determine the maximum tolerated dose (MTD) and efficacy of HCQ in combination with radiation therapy (RT) and temozolomide (TMZ) for newly diagnosed glioblastoma (GB). A 3 + 3 phase I trial design followed by a noncomparative phase II study was conducted in GB patients after initial resection. Patients received HCQ (200 to 800 mg oral daily) with RT and concurrent and adjuvant TMZ. Quantitative electron microscopy and immunoblotting were used to assess changes in autophagic vacuoles (AVs) in peripheral blood mononuclear cells (PBMC). Population pharmacokinetic (PK) modeling enabled PK-pharmacodynamic correlations. Sixteen phase I subjects were evaluable for dose-limiting toxicities. At 800 mg HCQ/d, 3/3 subjects experienced Grade 3 and 4 neutropenia and thrombocytopenia, 1 with sepsis. HCQ 600 mg/d was found to be the MTD in this combination. The phase II cohort (n = 76) had a median survival of 15.6 mos with survival rates at 12, 18, and 24 mo of 70%, 36%, and 25%. PK analysis indicated dose-proportional exposure for HCQ. Significant therapy-associated increases in AV and LC3-II were observed in PBMC and correlated with higher HCQ exposure. These data establish that autophagy inhibition is achievable with HCQ, but dose-limiting toxicity prevented escalation to higher doses of HCQ. At HCQ 600 mg/d, autophagy inhibition was not consistently achieved in patients treated with this regimen, and no significant improvement in overall survival was observed. Therefore, a definitive test of the role of autophagy inhibition in the adjuvant setting for glioma patients awaits the development of lower-toxicity compounds that can achieve more consistent inhibition of autophagy than HCQ.     

Immune implication of an autophagy-related prognostic signature in uveal melanoma

Uveal Melanoma (UM) is a rare cancer deriving from melanocytes within the uvea. It has a high rate of metastasis, especially to the liver, and a poor prognosis thereafter. Autophagy, an intracellular programmed digestive process, has been associated with the development and progression of cancers, with controversial pro- and anti-tumour roles. Although previous studies have been conducted on autophagy-related genes (ARGs) in various cancer types, its role in UM requires a deeper understanding for improved diagnosis and development of novel therapeutics. In the present study, Zheng et al. used univariate Cox regression followed by least absolute shrinkage and selection operator (Lasso) regression to identify a robust 9-ARG signature prognostic of survival in a total of 230 patients with UM. The authors used the Cancer Genome Atlas (TCGA) UM cohort as a training cohort (n=80) to identify the signature and validated it in another four independent cohorts of 150 UM patients from the Gene Expression Omnibus (GEO) repository ({"type":"entrez-geo","attrs":{"text":"GSE22138","term_id":"22138"}}GSE22138, {"type":"entrez-geo","attrs":{"text":"GSE27831","term_id":"27831"}}GSE27831, {"type":"entrez-geo","attrs":{"text":"GSE44295","term_id":"44295"}}GSE44295 and {"type":"entrez-geo","attrs":{"text":"GSE84976","term_id":"84976"}}GSE84976). This 9-ARG signature was also significantly associated with the enrichment of cancer hallmarks, including angiogenesis, IL6-KJAK-STAT3 signalling, reactive oxygen species pathway and oxidative phosphorylation. More importantly, this signature is associated with immune-related functional pathways and immune cell infiltration. Thus, this 9-ARG signature predicts prognosis and provides deeper insights into the immune mechanisms in UM, with potential implications for future immunotherapy.     

A phase I/II trial of hydroxychloroquine in conjunction with radiation therapy and concurrent and adjuvant temozolomide in patients with newly diagnosed glioblastoma multiforme

Preclinical studies indicate autophagy inhibition with hydroxychloroquine (HCQ) can augment the efficacy of DNA-damaging therapy. The primary objective of this trial was to determine the maximum tolerated dose (MTD) and efficacy of HCQ in combination with radiation therapy (RT) and temozolomide (TMZ) for newly diagnosed glioblastoma (GB). A 3 + 3 phase I trial design followed by a noncomparative phase II study was conducted in GB patients after initial resection. Patients received HCQ (200 to 800 mg oral daily) with RT and concurrent and adjuvant TMZ. Quantitative electron microscopy and immunoblotting were used to assess changes in autophagic vacuoles (AVs) in peripheral blood mononuclear cells (PBMC). Population pharmacokinetic (PK) modeling enabled PK-pharmacodynamic correlations. Sixteen phase I subjects were evaluable for dose-limiting toxicities. At 800 mg HCQ/d, 3/3 subjects experienced Grade 3 and 4 neutropenia and thrombocytopenia, 1 with sepsis. HCQ 600 mg/d was found to be the MTD in this combination. The phase II cohort (n = 76) had a median survival of 15.6 mos with survival rates at 12, 18, and 24 mo of 70%, 36%, and 25%. PK analysis indicated dose-proportional exposure for HCQ. Significant therapy-associated increases in AV and LC3-II were observed in PBMC and correlated with higher HCQ exposure. These data establish that autophagy inhibition is achievable with HCQ, but dose-limiting toxicity prevented escalation to higher doses of HCQ. At HCQ 600 mg/d, autophagy inhibition was not consistently achieved in patients treated with this regimen, and no significant improvement in overall survival was observed. Therefore, a definitive test of the role of autophagy inhibition in the adjuvant setting for glioma patients awaits the development of lower-toxicity compounds that can achieve more consistent inhibition of autophagy than HCQ.     

The discovery of a novel eight-mRNA-lncRNA signature predicting survival of hepatocellular carcinoma patients

Increasing evidence indicates that the expressions of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) undergo a frequent and aberrant change in carcinogenesis and cancer development. But some research was carried out on mRNA-lncRNA signatures for prediction of hepatocellular carcinoma (HCC) prognosis. We aimed to establish an mRNA-lncRNA signature to improve the ability to predict HCC patients’ survival. The subjects from the cancer genome atlas (TCGA) data set were randomly divided into two parts: training data set (n = 246) and testing data set (n = 124). Using computational methods, we selected eight gene signatures (five mRNAs and three lncRNAs) to generate the risk score model, which were significantly correlated with overall survival of patients with HCC in both training and testing data set. The signature had the ability to classify the patients in training data set into a high-risk group and low-risk group with significantly different overall survival (hazard ratio = 4.157, 95% confidence interval = 2.648-6.526, P < 0.001). The prognostic value was further validated in testing data set and the entire data set. Further analysis revealed that this signature was independent of tumor stage. In addition, Gene Set Enrichment Analysis suggested that high risk score group was associated with cell proliferation and division related pathways. Finally, we developed a well-performed nomogram integrating the prognostic signature and other clinical information to predict 3- and 5-year overall survival. In conclusion, the prognostic mRNAs and lncRNAs identified in our study indicate their potential role in HCC biogenesis. The risk score model based on the mRNA-lncRNA may be an efficient classification tool to evaluate the prognosis of patients’ with HCC.     

A prognostic eight-lncRNA expression signature in predicting recurrence of ER-positive breast cancer receiving endocrine therapy

Long noncoding RNAs (lncRNAs) have the main role in the tumorigenesis of breast cancer. In the present study, lncRNA expression profiling was collected to identify a lncRNA expression signature from the Gene Expression Omnibus database. An eight-lncRNA signature was established to predict the survival of patients with estrogen receptor (ER)-positive breast cancer receiving endocrine therapy. Patients were separated into a low-risk group and a high-risk group based on this signature. Patients in high-risk group have worse survival compared to those in low-risk group using Kaplan–Meier curve analysis with log-rank test. Receiver operating characteristic analysis suggested good diagnostic efficiency of the eight-lncRNA signature. When adjusting the clinical features, including age, grade, lymph node status, and tumor size, this signature was independently associated with the relapse-free survival. The prognostic value of the lncRNA prognostic model was then validated in validation sets. When validated in a cohort of patients treated with neoadjuvant chemotherapy and endocrine therapy, this signature demonstrated good performance as well. Besides, we have built a nomogram that integrated the conventional clinicopathological features and the eight-lncRNA-based signature. To sum up, our results indicated that the eight-lncRNA prognostic model was a reliable tool to group patients at high and low risk of disease relapse. This signature may have possible implication in prognostic evaluations of patients with ER-positive breast cancer receiving endocrine therapy.     

A risk signature-based on metastasis-associated genes to predict survival of patients with osteosarcoma

Osteosarcoma (OS) is the most common primary solid malignant bone tumor, and its metastasis is a prominent cause of high mortality in patients. In this study, a prognosis risk signature was constructed based on metastasis-associated genes. Four microarrays datasets with clinical information were downloaded from Gene Expression Omnibus, and 256 metastasis-associated genes were identified by limma package. Further, a protein-protein interaction network was constructed, and survival analysis was performed using data from the Therapeutically Applicable Research to Generate Effective Treatments data matrix, identifying 19 genes correlated with prognosis. Six genes were selected by the least absolute shrinkage and selection operator regression for multivariate cox analysis. Finally, a three-gene (MYC, CPE, and LY86) risk signature was constructed, and datasets GSE21257 and GSE16091 were used to validate the prediction efficiency of the signature. The survival times of low- and high-risk groups were significantly different in the training set and validation set. Additionally, gene set enrichment analysis revealed that the genes in the signature may affect the cell cycle, gap junctions, and interleukin-6 production. Therefore, the three-gene survival risk signature could potentially predict the prognosis of patients with OS. Further, proteins encoded by CPE and LY86 may provide novel insights into the prediction of OS prognosis and therapeutic targets.     

Development and validation of a 10‐gene prognostic signature for acute myeloid leukaemia

Acute myeloid leukaemia (AML) is the most common type of adult acute leukaemia and has a poor prognosis. Thus, optimal risk stratification is of greatest importance for reasonable choice of treatment and prognostic evaluation. For our study, a total of 1707 samples of AML patients from three public databases were divided into meta‐training, meta‐testing and validation sets. The meta‐training set was used to build risk prediction model, and the other four data sets were employed for validation. By log‐rank test and univariate COX regression analysis as well as LASSO‐COX, AML patients were divided into high‐risk and low‐risk groups based on AML risk score (AMLRS) which was constituted by 10 survival‐related genes. In meta‐training, meta‐testing and validation sets, the patient in the low‐risk group all had a significantly longer OS (overall survival) than those in the high‐risk group (P < .001), and the area under ROC curve (AUC) by time‐dependent ROC was 0.5854‐0.7905 for 1 year, 0.6652‐0.8066 for 3 years and 0.6622‐0.8034 for 5 years. Multivariate COX regression analysis indicated that AMLRS was an independent prognostic factor in four data sets. Nomogram combining the AMLRS and two clinical parameters performed well in predicting 1‐year, 3‐year and 5‐year OS. Finally, we created a web‐based prognostic model to predict the prognosis of AML patients ( https://tcgi.shinyapps.io/amlrs_nomogram/ ).     

Expression and significance of SOX B1 genes in glioblastoma multiforme patients

The overall survival of glioblastoma multiforme (GBM) patients remains poor. To improve patient outcomes, effective diagnostic and prognostic biomarkers for GBM are needed. In this study, we first applied bioinformatic analyses to identify biomarkers for GBM, focusing on SOX (sex‐determining region on the Y chromosome (SRY)‐related high mobility group (HMG) box) B1 family members. The ONCOMINE, GEPIA, LinkedOmics and CCLE databases were used to assess mRNA expression levels of the SOX B1 family members in different cancers and normal tissue. Further bioinformatic analysis was performed using the ONCOMINE database in combination with the LinkedOmics data set to identify the prognostic value of SOX B1 family members for GBM. We found mRNA expression levels of all tested SOX B1 genes were significantly increased in GBM. In the LinkedOmics database, increased expression of SOX3 indicated a better overall survival. In GEPIA databases, increased expression of all SOX B1 family members suggested an improved overall survival, but none of them were statistically different. Then, Transwell assays and wound healing were employed to evaluate the motility and invasive captivity of U251 cells when silencing SOX2 and SOX3. We found exogenous inhibition of SOX2 appeared to reduce the migration and invasion of U251 cells in vitro. Collectively, our research suggested that SOX2 might serve as a cancer‐promoting gene to identify high‐risk GBM patients, and SOX3 had the potential to be a prognostic biomarker for GBM patients.     

A 4-microRNA signature for survival prognosis in pediatric and adolescent acute myeloid leukemia

Acute myeloid leukemia (AML) is a hematologic malignancy with significant molecular heterogeneity. MicroRNAs (miRNAs) play a critical role in AML diagnosis, pathogenesis, and prognosis of AML. Little has been done to identify a miRNA signature in pediatric and adolescent patients for predicting overall survival. This study aims to identify a panel of miRNA signature that could predict the prognosis of all younger AML patients with all subtypes of AML by analyzing data from The Cancer Genome Atlas (TCGA). A total of 229 patients under 23 years with miRNA data and corresponding clinical data from TCGA database were enrolled in this study. Through conducting multivariate analysis in the training test, it was identified that the high expression of hsa-miR-509 and hsa-miR-542 were independent poor prognostic factors, whereas that of hsa-miR-146a and hsa-miR-3667 had a trend to be favorable factors. A 4-miRNA signature was constructed by these miRNAs considering the weight of each. In testing group and all 229 patients’ cohort as well as 59 cytogenetically normal AML (CN-AML) patients’ cohort, higher risk score was associated with shorter overall survival (OS). All results were confidential by using powerful statistical analysis. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis were carried out to further develop leukemia-relevant mechanisms supporting the model. The results indicate that the 4-miRNA-based signature is a reliable prognostic biomarker for pediatric and adolescent AML patients.     

Construction and validation of a seven-gene signature for predicting overall survival in patients with kidney renal clear cell carcinoma via an integrated bioinformatics analysis

ABSTRACT

Kidney renal clear cell carcinoma (KIRC) remains a significant challenge worldwide because of its poor prognosis and high mortality rate, and accurate prognostic gene signatures are urgently required for individual therapy. This study aimed to construct and validate a seven-gene signature for predicting overall survival (OS) in patients with KIRC. The mRNA expression profile and clinical data of patients with KIRC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Prognosis-associated genes were identified, and a prognostic gene signature was constructed. Then, the prognostic efficiency of the gene signature was assessed. The results obtained using data from the TCGA were validated using those from the ICGC and other online databases. Gene set enrichment analyses (GSEA) were performed to explore potential molecular mechanisms. A seven-gene signature (PODXL, SLC16A12, ZIC2, ATP2B3, KRT75, C20orf141, and CHGA) was constructed, and it was found to be effective in classifying KIRC patients into high- and low-risk groups, with significantly different survival based on the TCGA and ICGC validation data set. Cox regression analysis revealed that the seven-gene signature had an independent prognostic value. Then, we established a nomogram, including the seven-gene signature, which had a significant clinical net benefit. Interestingly, the seven-gene signature had a good performance in distinguishing KIRC from normal tissues. GSEA revealed that several oncological signatures and GO terms were enriched. This study developed a novel seven-gene signature and nomogram for predicting the OS of patients with KIRC, which may be helpful for clinicians in establishing individualized treatments.     

Identification and validation of autophagy-related prognostic signature for head and neck squamous cell carcinoma

BackgroundMany studies have demonstrated that autophagy plays a significant role in regulating tumor growth and progression. However, the effect of autophagy-related genes (ARGs) on the prognosis have rarely been analyzed in head and neck squamous cell carcinoma (HNSCC).MethodsWe obtained differentially expressed ARGs from HNSCC mRNA data in The Cancer Genome Atlas (TCGA) database. And then we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore the autophagy-related biological functions. The overall survival (OS)-related and disease specific survival (DSS)-related ARGs were identified by univariate Cox regression analyses. With these genes, we established OS-related and DSS-related risk signature by LASSO regression method, respectively. We validated the reliability of the risk signature with receiver operating characteristic (ROC) analysis, Kaplan-Meier survival curves, clinical correlation analysis, and nomogram. Then we analyzed relationships between risk signature and immune cell infiltration.ResultsWe established the prognostic signatures based on 14 ARGs for OS and 12 ARGs for DSS. The ROC curves, survival analysis, and nomogram validated the predictive accuracy of the models. Clinic correlation analysis showed that the risk group was closely related to Stage, pathological T stage, pathological N stage and human papilloma virus (HPV) subtype. Cox regression demonstrated that the risk score was an independent predictor for the prognosis of HNSCC patients. Furthermore, patients in low-risk score group exhibited higher immunescore and distinct immune cell infiltration than high-risk score group. And we further analysis revealed that the copy number alterations (CNAs) of ARGs-based signature affected the abundance of tumor-infiltrating immune cells.ConclusionIn this study, we identified novel autophagy-related signature for the prediction of OS and DSS in patients with HNSCC. Meanwhile, our study provides a novel sight to understand the role of autophagy and elucidate the important role of autophagy in tumor immune microenvironment (TIME) of HNSCC.     

Identification of a thirteen-gene signature predicting overall survival for hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a malignant tumor of the digestive system characterized by mortality rate and poor prognosis. To indicate the prognosis of HCC patients, lots of genes have been screened as prognostic indicators. However, the predictive efficiency of single gene is not enough. Therefore, it is essential to identify a risk-score model based on gene signature to elevate predictive efficiency.Methods: Lasso regression analysis followed by univariate Cox regression was employed to establish a risk-score model for HCC prognosis prediction based on The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset {"type":"entrez-geo","attrs":{"text":"GSE14520","term_id":"14520"}}GSE14520. R package ‘clusterProfiler’ was used to conduct function and pathway enrichment analysis. The infiltration level of various immune and stromal cells in the tumor microenvironment (TME) were evaluated by single-sample GSEA (ssGSEA) of R package ‘GSVA’.Results: This prognostic model is an independent prognostic factor for predicting the prognosis of HCC patients and can be more effective by combining with clinical data through the construction of nomogram model. Further analysis showed patients in high-risk group possess more complex TME and immune cell composition.Conclusions: Taken together, our research suggests the thirteen-gene signature to possess potential prognostic value for HCC patients and provide new information for immunological research and treatment in HCC.