Molecular and clinical characterization of 5mC regulators in glioma: results of a multicenter study

DNA methylation has been widely reported to associate with the progression of glioma. DNA methylation at the 5 position of cytosine (5-methylcytosine, 5mC), which is regulated by 5mC regulators ("writers", "erasers" and "readers"), is the most critical modification pattern. However, a systematic study on the role of these regulators in glioma is still lacking. In this study, we collected gene expression profiles and corresponding clinical information of gliomas from three independent public datasets. Gene expression of 21 5mC regulators was analyzed and linked to clinicopathological features. A novel molecular classification of glioma was developed using consensus non-negative matrix factorization (CNMF) algorithm, and the tight association with molecular characteristics as well as tumor immune microenvironment was clarified. Sixteen prognostic factors were identified using univariate Cox regression analysis, and a 5mC regulator-based gene signature was further constructed via the least absolute shrinkage and selection operator (LASSO) cox analysis. This risk model was proved as an efficient predictor of overall survival for diffuse glioma, glioblastoma (GBM), and low-grade glioma (LGG) patients in three glioma cohorts. The significant correlation between risk score and intratumoral infiltrated immune cells, as well as immunosuppressive pathways, was found, which explained the difference in clinical outcomes between high and low-risk groups. Finally, a nomogram incorporating the gene signature and other clinicopathological risk factors was established, which might direct clinical decision making. In summary, our work highlights the potential clinical application value of 5mC regulators in prognostic stratification of glioma and their potentialities for developing novel treatment strategies.

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Gene expression profile correlates with molecular and clinical features in patients with myelofibrosis

Blood Advances ◽

10.1182/bloodadvances.2020003614 ◽

2021 ◽

Vol 5 (5) ◽

pp. 1452-1462

Author(s):

Sebastiano Rontauroli ◽

Sara Castellano ◽

Paola Guglielmelli ◽

Roberta Zini ◽

Elisa Bianchi ◽

...

Keyword(s):

Gene Expression ◽

High Risk ◽

Clinical Features ◽

Clinical Decision Making ◽

Cox Regression ◽

Risk Group ◽

Myeloproliferative Neoplasms ◽

Expression Profiles ◽

Prognostic Information ◽

Cox Regression Analysis

Abstract Myelofibrosis (MF) belongs to the family of classic Philadelphia-negative myeloproliferative neoplasms (MPNs). It can be primary myelofibrosis (PMF) or secondary myelofibrosis (SMF) evolving from polycythemia vera (PV) or essential thrombocythemia (ET). Despite the differences, PMF and SMF patients are currently managed in the same way, and prediction of survival is based on the same clinical and genetic features. In the last few years, interest has grown concerning the ability of gene expression profiles (GEPs) to provide valuable prognostic information. Here, we studied the GEPs of granulocytes from 114 patients with MF, using a microarray platform to identify correlations with patient characteristics and outcomes. Cox regression analysis led to the identification of 201 survival-related transcripts characterizing patients who are at high risk for death. High-risk patients identified by this gene signature displayed an inferior overall survival and leukemia-free survival, together with clinical and molecular detrimental features included in contemporary prognostic models, such as the presence of high molecular risk mutations. The high-risk group was enriched in post-PV and post-ET MF and JAK2V617F homozygous patients, whereas pre-PMF was more frequent in the low-risk group. These results demonstrate that GEPs in MF patients correlate with their molecular and clinical features, particularly their survival, and represent the proof of concept that GEPs might provide complementary prognostic information to be applied in clinical decision making.

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Identification of IGF2BP3 as an Adverse Prognostic Biomarker of Gliomas

Frontiers in Genetics ◽

10.3389/fgene.2021.743738 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chao Sun ◽

Xin Zheng ◽

Yingxin Sun ◽

Ju Yu ◽

Minfeng Sheng ◽

...

Keyword(s):

Gene Expression ◽

Roc Curve ◽

Cox Regression ◽

Expression Profiles ◽

Gene Set Enrichment Analysis ◽

Low Grade ◽

Aberrant Expression ◽

Cox Regression Analysis ◽

Underlying Mechanisms ◽

Genome Atlas

N6-methyladenosine (m6A) RNA modification can alter gene expression and function by regulating RNA splicing, stability, translocation, and translation. It is involved in various types of cancer. However, its role in gliomas is not well known. This study aimed to determine the prognostic value of the m6A RNA methylation regulator in gliomas and investigate the underlying mechanisms of the aberrant expression of m6A-related genes.mRNA expression profiles and clinical information of 448 glioma samples were obtained from The Cancer Genome Atlas and cBioportal. The expression of m6A-related genes in normal controls and low-grade glioma and glioblastoma was obtained from Gene Expression Profiling Interactive Analysis. Further, m6A-related gene expression and its relationship with prognosis were obtained through The Chinese Glioma Genome Atlas (CGGA). Multivariate Cox regression analyses were performed, and a nomogram was built with potential risk factors based on a multivariate Cox analysis to predict survival probability. Online tools such as Gene Set Enrichment Analysis, STRING, Cytoscape, and Molecular Complex Detection were applied for bioinformatics analysis and to investigate the underlying mechanisms of the aberrant expression of m6A-related genes. The multivariate Cox regression analysis found that higher expression levels of YTHDC2 and insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3, also called IMP3) were independent negative and positive prognostic factors for overall survival (OS), respectively. Data from the CGGA database showed that IGF2BP3 expression increased when the tumor grade increased. Receiver operating characteristic (ROC) curve was used to evaluate the predictive specificity and sensitivity. The area under the ROC curve indicated that the OS prediction was 0.92 (1-year) and 0.917 (3-years), indicating that m6A-related genes could predict patient survival. In addition, IGF2BP3 was closely related to the shorter survival period of patients. Copy number variation and DNA methylation, but not somatic mutations, might contribute to the abnormal upregulation of IGF2BP3 in gliomas. Significantly altered genes were identified, and the protein–protein interaction network was constructed. Based on the data presented, our study identified several m6A-related genes, especially IGF2BP3, that could be potential prognostic biomarkers of gliomas. The study unveiled the potential regulatory mechanism of IGF2BP3 in gliomas.

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Identification of a Novel Epithelial-to-Mesenchymal-related Gene Signature in Predicting Survival of Patients with Hepatocellular Carcinoma

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210303093629 ◽

2021 ◽

Vol 24 ◽

Author(s):

Simeng Xiao ◽

Junjie Hu ◽

Na Hu ◽

Lei Sheng ◽

Hui Rao ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Cancer Metastasis ◽

Cox Regression ◽

Expression Profiles ◽

Gene Signature ◽

Risk Scores ◽

Related Gene ◽

Cox Regression Analysis ◽

Mesenchymal Transformation

Background: Epithelial-mesenchymal transformation (EMT) promotes cancer metastasis including hepatocellular carcinoma. Therefore, EMT-related gene signature was explored. Objective: The present study was designed to develop an EMT-related gene signature for predicting the prognosis of patients with hepatocellular carcinoma. Methods: We conducted an integrated gene expression analysis based on tumor data of the patients with hepatocellular carcinoma from The Cancer Genome Atlas (TCGA), HCCDB18 and GSE14520 dataset. An EMT-related gene signature was constructed by least absolute shrinkage and selection operator (LASSO) and COX regression analysis of univariate and multivariate survival. Results: A 3-EMT gene signature was developed and validated based on gene expression profiles of hepatocellular carcinoma from three microarray platforms. Patients with a high risk score had a significantly worse overall survival (OS) than those with low risk scores. The EMT-related gene signature showed a high performance in accurately predicting prognosis and in examining the clinical characteristics and immune score analysis. Univariate and multivariate Cox regression analyses confirmed that the EMT-related gene signature was an independent prognostic factor for predicting survival in hepatocellular carcinoma patients. Compared with the existing models, our EMT-related gene signature reached higher area under curve (AUC). Conclusion: Our findings provide novel insight into understanding EMT and help identify hepatocellular carcinoma patients with poor prognosis.

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A Novel Gene Prognostic Signature Based on Differential DNA Methylation in Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.742578 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chunmei Zhu ◽

Shuyuan Zhang ◽

Di Liu ◽

Qingqing Wang ◽

Ningning Yang ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Dna Methylation ◽

High Risk ◽

Cox Regression ◽

Methylation Status ◽

Gene Signature ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

The Impact

Background: DNA methylation played essential roles in regulating gene expression. The impact of DNA methylation status on the occurrence and development of cancers has been well demonstrated. However, little is known about its prognostic role in breast cancer (BC).Materials: The Illumina Human Methylation450 array (450k array) data of BC was downloaded from the UCSC xena database. Transcriptomic data of BC was downloaded from the Cancer Genome Atlas (TCGA) database. Firstly, we used univariate and multivariate Cox regression analysis to screen out independent prognostic CpGs, and then we identified methylation-associated prognosis subgroups by consensus clustering. Next, a methylation prognostic model was developed using multivariate Cox analysis and was validated with the Illumina Human Methylation27 array (27k array) dataset of BC. We then screened out differentially expressed genes (DEGs) between methylation high-risk and low-risk groups and constructed a methylation-based gene prognostic signature. Further, we validated the gene signature with three subgroups of the TCGA-BRCA dataset and an external dataset GSE146558 from the Gene Expression Omnibus (GEO) database.Results: We established a methylation prognostic signature and a methylation-based gene prognostic signature, and there was a close positive correlation between them. The gene prognostic signature involved six genes: IRF2, KCNJ11, ZDHHC9, LRP11, PCMT1, and TMEM70. We verified their expression in mRNA and protein levels in BC. Both methylation and methylation-based gene prognostic signatures showed good prognostic stratification ability. The AUC values of 3-years, 5-years overall survival (OS) were 0.737, 0.744 in the methylation signature and 0.725, 0.715 in the gene signature, respectively. In the validation groups, high-risk patients were confirmed to have poorer OS. The AUC values of 3 years were 0.757, 0.735, 0.733 in the three subgroups of TCGA dataset and 0.635 in GSE146558 dataset.Conclusion: This study revealed the DNA methylation landscape and established promising methylation and methylation-based gene prognostic signatures that could serve as potential prognostic biomarkers and therapeutic targets.

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A novel ferroptosis related gene signature is associated with prognosis in patients with ovarian serous cystadenocarcinoma

Scientific Reports ◽

10.1038/s41598-021-90126-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zhixiang Yu ◽

Haiyan He ◽

Yanan Chen ◽

Qiuhe Ji ◽

Min Sun

Keyword(s):

Cox Regression ◽

Expression Profiles ◽

Critical Role ◽

Gene Signature ◽

Cancer Genome ◽

Training Data ◽

Hub Genes ◽

Validation Data ◽

Data Set ◽

Cox Regression Analysis

AbstractOvarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.

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Localized- and advanced-stage follicular lymphomas differ in their gene expression profiles

Blood ◽

10.1182/blood.2019000560 ◽

2020 ◽

Vol 135 (3) ◽

pp. 181-190 ◽

Cited By ~ 3

Author(s):

Annette M. Staiger ◽

Eva Hoster ◽

Vindi Jurinovic ◽

Stefan Winter ◽

Ellen Leich ◽

...

Keyword(s):

Gene Expression ◽

Genetic Background ◽

Cox Regression ◽

Expression Profiles ◽

Low Grade ◽

Advanced Stage ◽

Free Survival ◽

Molecular Features ◽

Clinical Stages ◽

Follicular Lymphomas

Abstract The genetic background of follicular lymphomas (FLs) diagnosed in advanced clinical stages III/IV, and which are frequently characterized by t(14;18), has been substantially unraveled. Molecular features, as exemplified in the clinicogenetic risk model m7FLIPI, are important tools in risk stratification. In contrast, little information is available concerning localized-stage FL (clinical stages I/II), which accounts for ∼20% of newly diagnosed FL in which the detection rate of t(14;18) is only ∼50%. To investigate the genetic background of localized-stage FL, patient cohorts with advanced-stage FL or localized-stage FL, uniformly treated within phase 3 trials of the German Low-Grade Lymphoma Study Group, were comparatively analyzed. Targeted gene expression (GE) profiling of 184 genes using nCounter technology was performed in 110 localized-stage and 556 advanced-stage FL patients. By penalized Cox regression, a prognostic GE signature could not be identified in patients with advanced-stage FL, consistent with results from global tests and univariate regression. In contrast, it was possible to define robust GE signatures discriminating localized-stage and advanced-stage FL (area under the curve, 0.98) by penalized logistic regression. Of note, 3% of samples harboring an “advanced-stage signature” in the localized-stage cohort exhibited inferior failure-free survival (hazard ratio [HR], 7.1; P = .0003). Likewise, in the advanced-stage cohort, 7% of samples with a “localized-stage signature” had prolonged failure-free survival (HR, 2.3; P = .017) and overall survival (HR, 3.4; P = .072). These data support the concept of a biological difference between localized-stage and advanced-stage FL that might contribute to the superior outcome of localized FL.

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Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

Frontiers in Genetics ◽

10.3389/fgene.2020.595361 ◽

2020 ◽

Vol 11 ◽

Author(s):

Xin Qiu ◽

Qin-Han Hou ◽

Qiu-Yue Shi ◽

Hai-Xing Jiang ◽

Shan-Yu Qin

Keyword(s):

Oxidative Stress ◽

Gene Expression ◽

Pancreatic Cancer ◽

Cancer Progression ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Cox Regression Analysis

BackgroundIntratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.MethodsWe compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients’ gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.ResultsA total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.ConclusionOur study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.

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Identification of IFN-β Associated Genes Signature Predicting Overall Survival for Glioblastoma

10.21203/rs.3.rs-212352/v1 ◽

2021 ◽

Author(s):

Cheng Lijing ◽

Yuan Meiling ◽

Li Shu ◽

Chen Junjing ◽

Zhong Shupeng ◽

...

Keyword(s):

Overall Survival ◽

Clinical Decision Making ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Individual Treatment ◽

Normal Brain ◽

Data Set ◽

Cox Regression Analysis ◽

Prognosis Prediction

Abstract Background: Brain glioblastoma (GBM) is the most common primary malignant tumor of intracranial tumors. The prognosis of this disease is extremely poor. While the introduction of IFN-β regimen in the treatment of gliomas has significantly improved the outcome of patients, the underlying mechanism remains to be elucidated. Materials and methods: mRNA expression profiles and clinicopathological data were downloaded from TCGA-GBM and GSE83300 data set from the GEO. Univariate Cox regression analysis and lasso Cox regression model established a novel four‐gene IFN-β signature (including PRDX1, SEC61B, XRCC5, and BCL2L2) for GBM prognosis prediction. Further, GBM samples (n=50) and normal brain tissues (n=50) were then used for real-time polymerase chain reaction (PCR) experiments. Gene Set Enrichment Analyses (GSEA) was performed to further understand the underlying molecular mechanisms. Pearson correlation was applied to calculate the correlation between the lncRNAs and IFN-β associated genes. A lncRNA with a correlation coefficient |R2| > 0.3 and P < 0.05 was considered to be an IFN-β associated lncRNA.Results: Patients in the high‐risk group shown significantly poorer survival than patients in the low‐risk group. The signature was found to be an independent prognostic factor for GBM survival. Furthermore, GSEA revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four‐gene IFN-β signature for GBM prognosis prediction. The signature might contain potential biomarkers for metabolic therapy and treatment response prediction in GBM.Conclusions: Our study established a novel IFN-β associated genes signature to predict overall survival of GBM, which may help in clinical decision making for individual treatment.

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Systematic Characterization of Novel Immune Gene Signatures Predicts Prognostic Factors in Hepatocellular Carcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.686664 ◽

2021 ◽

Vol 9 ◽

Author(s):

Dafeng Xu ◽

Yu Wang ◽

Jincai Wu ◽

Yuliang Zhang ◽

Zhehao Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Cox Regression ◽

Expression Profiles ◽

Gene Signature ◽

Clinical Samples ◽

Immune Gene ◽

Cox Regression Analysis ◽

Immune Related Genes ◽

Cancer Tissues

Background: The prognosis of patients with hepatocellular carcinoma (HCC) is negatively affected by the lack of effective prognostic indicators. The change of tumor immune microenvironment promotes the development of HCC. This study explored new markers and predicted the prognosis of HCC patients by systematically analyzing immune characteristic genes.Methods: Immune-related genes were obtained, and the differentially expressed immune genes (DEIGs) between tumor and para-cancer samples were identified and analyzed using gene expression profiles from TCGA, HCCDB, and GEO databases. An immune prognosis model was also constructed to evaluate the predictive performance in different cohorts. The high and low groups were divided based on the risk score of the model, and different algorithms were used to evaluate the tumor immune infiltration cell (TIIC). The expression and prognosis of core genes in pan-cancer cohorts were analyzed, and gene enrichment analysis was performed using clusterProfiler. Finally, the expression of the hub genes of the model was validated by clinical samples.Results: Based on the analysis of 730 immune-related genes, we identified 64 common DEIGs. These genes were enriched in the tumor immunologic related signaling pathways. The first 15 genes were selected using RankAggreg analysis, and all the genes showed a consistent expression trend across multi-cohorts. Based on lasso cox regression analysis, a 5-gene signature risk model (ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1) was constructed. The signature has strong robustness and can stabilize different cohorts (TCGA-LIHC, HCCDB18, and GSE14520). Compared with other existing models, our model has better performance. CIBERSORT was used to assess the landscape maps of 22 types of immune cells in TCGA, GSE14520, and HCCDB18 cohorts, and found a consistent trend in the distribution of TIIC. In the high-risk score group, scores of Macrophages M1, Mast cell resting, and T cells CD8 were significantly lower than those of the low-risk score group. Different immune expression characteristics, lead to the different prognosis. Western blot demonstrated that ATG10, PRKCD, and SPP1 were highly expressed in cancer tissues, while IL18RAP and SLC11A1 expression in cancer tissues was lower. In addition, IL18RAP has a highly positive correlation with B cell, macrophage, Neutrophil, Dendritic cell, CD8 cell, and CD4 cell. The SPP1, PRKCD, and SLC11A1 genes have the strongest correlation with macrophages. The expression of ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1 genes varies among different immune subtypes and between different T stages.Conclusion: The 5-immu-gene signature constructed in this study could be utilized as a new prognostic marker for patients with HCC.

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A six-gene-based signature for breast cancer radiotherapy sensitivity prediction

10.21203/rs.3.rs-20019/v1 ◽

2020 ◽

Author(s):

Xing Chen ◽

Junjie Zheng ◽

Min ling Zhuo ◽

Ailong Zhang ◽

Zhenhui You

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Regression Analysis ◽

Cox Regression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Breast Cancer Radiotherapy

Abstract Background: Breast cancer (BRCA) represents the most common malignancy among women worldwide that with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Methods: we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity prediction. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA tumor samples compared with their paracancerous samples in the training set were identified by using edgeR Bioconductor package followed by dimensionality reduction through autoencoder method and univariate Cox regression analysis to screen genes among DEGs that with significant prognosis significance in patients that were previously treated with radiation. LASSO Cox regression method was applied to screen optimal genes for constructing radiotherapy sensitivity prediction signature. Results: 603 DEGs were obtained in BRCA tumor samples, and seven out of which were retained after univariate cox regression analysis. LASSO Cox regression analysis finally remained six genes based on which the radiotherapy sensitivity prediction model was constructed. The signature was proved to be robust in both training and independent testing sets and an independent marker for BRCA radiotherapy sensitivity prediction. Conclusions: this study should be helpful for BRCA patients’ therapeutics selection and clinical decision.

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