Identification of IGF2BP3 as an Adverse Prognostic Biomarker of Gliomas

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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Development and Validation of an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma

Frontiers in Oncology ◽

10.3389/fonc.2021.726745 ◽

2021 ◽

Vol 11 ◽

Author(s):

Kebing Huang ◽

Xiaoyu Yue ◽

Yinfei Zheng ◽

Zhengwei Zhang ◽

Meng Cheng ◽

...

Keyword(s):

High Risk ◽

Prognostic Model ◽

Cox Regression ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Migration And Invasion ◽

Aberrant Expression ◽

Cox Regression Analysis ◽

Genome Atlas

Glioma is well known as the most aggressive and prevalent primary malignant tumor in the central nervous system. Molecular subtypes and prognosis biomarkers remain a promising research area of gliomas. Notably, the aberrant expression of mesenchymal (MES) subtype related long non-coding RNAs (lncRNAs) is significantly associated with the prognosis of glioma patients. In this study, MES-related genes were obtained from The Cancer Genome Atlas (TCGA) and the Ivy Glioblastoma Atlas Project (Ivy GAP) data sets of glioma, and MES-related lncRNAs were acquired by performing co-expression analysis of these genes. Next, Cox regression analysis was used to establish a prognostic model, that integrated ten MES-related lncRNAs. Glioma patients in TCGA were divided into high-risk and low-risk groups based on the median risk score; compared with the low-risk groups, patients in the high-risk group had shorter survival times. Additionally, we measured the specificity and sensitivity of our model with the ROC curve. Univariate and multivariate Cox analyses showed that the prognostic model was an independent prognostic factor for glioma. To verify the predictive power of these candidate lncRNAs, the corresponding RNA-seq data were downloaded from the Chinese Glioma Genome Atlas (CGGA), and similar results were obtained. Next, we performed the immune cell infiltration profile of patients between two risk groups, and gene set enrichment analysis (GSEA) was performed to detect functional annotation. Finally, the protective factors DGCR10 and HAR1B, and risk factor SNHG18 were selected for functional verification. Knockdown of DGCR10 and HAR1B promoted, whereas knockdown of SNHG18 inhibited the migration and invasion of gliomas. Collectively, we successfully constructed a prognostic model based on a ten MES-related lncRNAs signature, which provides a novel target for predicting the prognosis for glioma patients.

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Construction of a novel immune gene-based model for prognosis prediction of colorectal cancer

10.21203/rs.3.rs-279544/v1 ◽

2021 ◽

Author(s):

Bin Xie ◽

jie lin

Keyword(s):

Gene Expression ◽

Cox Regression ◽

Expression Profiles ◽

Colon Adenocarcinoma ◽

Gene Set Enrichment Analysis ◽

Immune Gene ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Risk Of Death ◽

Immune Related Genes

Abstract Background Colon adenocarcinoma (COAD) is the third leading cause of cancer-related death. Although surgical treatment and chemotherapy of COAD have made significant progress, its immunotherapy also has great potential, nowadays. Methods Gene expression profiles and clinical data of COAD patients were obtained from The Cancer Genome Atlas_Colon Adenocarcinoma (TCGA_COAD) and Gene Expression Omnibus (GEO) databases, which were further detected for immune-related genes. Immune-related genes were downloaded from Immunology Database and Analysis Portal (ImmPort). LASSO Cox regression analysis was utilized to analyze the immune-related prognostic signature. The prognostic value of the signature was validated by ROC curve. To further detected the potential pathway about immune-related genes in COAD patients, Gene Set Enrichment Analysis (GESA) was used to identify the most significant pathways. Results Finally, a total of 436 immune-related mRNA were identified. Eleven prognosis-related genes were selected to establish an immune-related prognostic signature, which divided patients into high and low risk groups. Several biological processes, such as immune response was enriched. Moreover, our prognosis model has better performance in predicting the 1-, 3-, 5- and 8-years overall survival (OS) for patients from the TCGA and GEO cohort. Also, the complicated signature obtained by combining immune-related signatures with clinical factors provides a more accurate OS predicting compared with individual molecular signatures. Conclusion We have established a prognostic signature consisting of 11 immune-related genes, which may be potential biomarkers for identifying COAD with a high risk of death. Then, the possibility including immunotherapy in personalized COAD treatment was evaluated.

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KIAA0101 in Malignant Pleural Mesothelioma: a Potential Diagnostic and Prognostic Marker

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210707105634 ◽

2021 ◽

Vol 24 ◽

Author(s):

Ping Lin ◽

Yuean Zhao ◽

Xiaoqian Li ◽

Zongan Liang

Keyword(s):

Gene Expression ◽

Regression Analysis ◽

Malignant Pleural Mesothelioma ◽

Cox Regression ◽

Expression Profiles ◽

Gene Set Enrichment Analysis ◽

High Expression ◽

Pleural Mesothelioma ◽

Hub Genes ◽

Cox Regression Analysis

Background: Currently, there are no reliable diagnostic and prognostic markers for malignant pleural mesothelioma (MPM). The objective of this study was to identify hub genes that could be helpful for diagnosis and prognosis in MPM by using bioinformatics analysis. Materials and Methods: The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA), LASSO regression analysis, Cox regression analysis, and Gene Set Enrichment Analysis (GSEA) were performed to identify hub genes and their functions. Results: A total of 430 up-regulated and 867 downregulated genes in MPM were identified based on the GSE51024 dataset. According to the WGCNA analysis, differentially expressed genes were classified into 8 modules. Among them, the pink module was most closely associated with MPM. According to genes with GS > 0.8 and MM > 0.8, six genes were selected as candidate hub genes (NUSAP1, TOP2A, PLOD2, BUB1B, UHRF1, KIAA0101) in the pink module. In the LASSO model, three genes (NUSAP1, PLOD2, and KIAA0101) were identified with non-zero regression coefficients and were considered hub genes among the 6 candidates. The hub gene-based LASSO model can accurately distinguish MPM from controls (AUC = 0.98). Moreover, the high expression level of KIAA0101, PLOD2, and NUSAP1 were all associated with poor prognosis compared to the low level in Kaplan–Meier survival analyses. After further multivariate Cox analysis, only KIAA0101 (HR = 1.55, 95% CI = 1.05-2.29) was identified as an independent prognostic factor among these hub genes. Finally, GSEA revealed that high expression of KIAA0101 was closely associated with 10 signaling pathways. Conclusion: Our study identified several hub genes relevant to MPM, including NUSAP1, PLOD2, and KIAA0101. Among these genes, KIAA0101 appears to be a useful diagnostic and prognostic biomarker for MPM, which may provide new clues for MPM diagnosis and therapy.

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

10.1101/2020.05.15.098038 ◽

2020 ◽

Author(s):

Liang Zhao ◽

Jiayue Zhang ◽

Zhiyuan Liu ◽

Yu Wang ◽

Shurui Xuan ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Clinical Decision Making ◽

Cox Regression ◽

Expression Profiles ◽

Gene Signature ◽

Molecular Characteristics ◽

Low Grade ◽

Diffuse Glioma ◽

Cox Regression Analysis

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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Expression of LOX Suggests Poor Prognosis in Gastric Cancer

Frontiers in Medicine ◽

10.3389/fmed.2021.718986 ◽

2021 ◽

Vol 8 ◽

Author(s):

Jinfeng Zhu ◽

Chen Luo ◽

Jiefeng Zhao ◽

Xiaojian Zhu ◽

Kang Lin ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Logistic Regression ◽

Regression Analysis ◽

Poor Prognosis ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

High Expression ◽

Expression Level ◽

Cox Regression Analysis

Background: Lysyl oxidase (LOX) is a key enzyme for the cross-linking of collagen and elastin in the extracellular matrix. This study evaluated the prognostic role of LOX in gastric cancer (GC) by analyzing the data of The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) dataset.Methods: The Wilcoxon rank-sum test was used to calculate the expression difference of LOX gene in gastric cancer and normal tissues. Western blot and immunohistochemical staining were used to evaluate the expression level of LOX protein in gastric cancer. Kaplan-Meier analysis was used to calculate the survival difference between the high expression group and the low expression group in gastric cancer. The relationship between statistical clinicopathological characteristics and LOX gene expression was analyzed by Wilcoxon or Kruskal-Wallis test and logistic regression. Univariate and multivariate Cox regression analysis was used to find independent risk factors affecting the prognosis of GC patients. Gene set enrichment analysis (GSEA) was used to screen the possible mechanisms of LOX and GC. The CIBERSORT calculation method was used to evaluate the distribution of tumor-infiltrating immune cell (TIC) abundance.Results: LOX is highly expressed in gastric cancer tissues and is significantly related to poor overall survival. Wilcoxon or Kruskal-Wallis test and Logistic regression analysis showed, LOX overexpression is significantly correlated with T-stage progression in gastric cancer. Multivariate Cox regression analysis on TCGA and GEO data found that LOX (all p < 0.05) is an independent factor for poor GC prognosis. GSEA showed that high LOX expression is related to ECM receptor interaction, cancer, Hedgehog, TGF-beta, JAK-STAT, MAPK, Wnt, and mTOR signaling pathways. The expression level of LOX affects the immune activity of the tumor microenvironment in gastric cancer.Conclusion: High expression of LOX is a potential molecular indicator for poor prognosis of gastric cancer.

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Clinical Value of lncRNA LUCAT1 Expression in Liver Cancer and its Potential Pathways

Journal of Gastrointestinal and Liver Diseases ◽

10.15403/jgld-356 ◽

2019 ◽

Vol 28 (4) ◽

pp. 439-447 ◽

Cited By ~ 7

Author(s):

Yan Jiao ◽

Yanqing Li ◽

Bai Ji ◽

Hongqiao Cai ◽

Yahui Liu

Keyword(s):

Liver Cancer ◽

Roc Curve ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Rank Test ◽

P Value ◽

Rna Seq ◽

Cox Regression Analysis

Background and Aims: Emerging studies indicate that long noncoding RNAs (lncRNAs) play a role as prognostic markers in many cancers, including liver cancer. Here, we focused on the lncRNA lung cancer-associated transcript 1 (LUCAT1) for liver cancer prognosis. Methods: RNA-seq and phenotype data were downloaded from the Cancer Genome Atlas (TCGA). Chisquare tests were used to evaluate the correlations between LUCAT1 expression and clinical features. Survival analysis and Cox regression analysis were used to compare different LUCAT1 expression groups (optimal cutoff value determined by ROC). The log-rank test was used to calculate the p-value of the Kaplan-Meier curves. A ROC curve was used to evaluate the diagnostic value. Gene Set Enrichment Analysis (GSEA) was performed, and competing endogenous RNA (ceRNA) networks were constructed to explore the potential mechanism. Results: Data mining of the TCGA -Liver Hepatocellular Carcinoma (LIHC) RNA-seq data of 371 patients showed the overexpression of LUCAT1 in cancerous tissue. High LUCAT1 expression was associated with age (p=0.007), histologic grade (p=0.009), T classification (p=0.022), and survival status (p=0.002). High LUCAT1 patients had a poorer overall survival and relapse-free survival than low LUCAT1 patients. Multivariate analysis identified LUCAT1 as an independent risk factor for poor survival. The ROC curve indicated modest diagnostic performance. GSEA revealed the related signaling pathways, and the ceRNA network uncovered the underlying mechanism. Conclusion: High LUCAT1 expression is an independent prognostic factor for liver cancer.

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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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MicroRNA-99b predicts clinical outcome of osteosarcoma and suppresses tumor cell proliferation, migration and invasion

Diagnostic Pathology ◽

10.1186/s13000-019-0889-y ◽

2019 ◽

Vol 14 (1) ◽

Cited By ~ 1

Author(s):

Xin Shi ◽

Xingfa Guan

Keyword(s):

Cell Proliferation ◽

Cell Lines ◽

Prognostic Value ◽

Cox Regression ◽

Expression Profiles ◽

Migration And Invasion ◽

Aberrant Expression ◽

Cox Regression Analysis ◽

Normal Tissues

Abstract Background Osteosarcoma (OS) is a malignancy predominantly occurred in children and adolescents. Numerous microRNAs are involved in the pathogenesis of various cancers. This study aimed to investigate the expression profiles of miR-99b and its prognostic value in OS patients, and further analyze the biological function of miR-99b in the tumor progression by using OS cells. Methods Expression of miR-99b was measured using quantitative real-time PCR. Kaplan-Meier survival curves and Cox regression analysis were performed to evaluate the prognostic value of miR-99b. OS cell lines were used to investigate the effects of miR-99b on cell proliferation, migration and invasion. Results A significant decreased expression of miR-99b was observed in the OS tissues and cell lines respectively compared with the normal tissues and cells. Aberrant expression of miR-99b was associated with the patients’ metastasis and TNM stage, and could be used to predict the prognosis of OS. The expression of miR-99b was regulated in vitro by cell transfection, and we found that the overexpression of miR-99b led to suppressed cell proliferation, migration and invasion, whereas the knockdown of miR-99b resulted in the opposite results. Conclusions In one word, the aberrantly expressed miR-99b serves a prognostic biomarker for OS patients. OS cell proliferation, migration and invasion can be inhibited by the overexpression of miR-99b, suggesting that the methods to increase miR-99b expression may be novel therapeutic strategies in OS.

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Identification of the HMMR Gene as a Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma Based on Integrated Bioinformatics Analysis

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/5970085 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Honglan Guo ◽

Qinqiao Fan

Keyword(s):

Hepatocellular Carcinoma ◽

Roc Curve ◽

Cox Regression ◽

Enrichment Analysis ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Normal Tissues

Background. We aimed to investigate the expression of the hyaluronan-mediated motility receptor (HMMR) gene in hepatocellular carcinoma (HCC) and nonneoplastic tissues and to investigate the diagnostic and prognostic value of HMMR. Method. With the reuse of the publicly available The Cancer Genome Atlas (TCGA) data, 374 HCC patients and 50 nonneoplastic tissues were used to investigate the diagnostic and prognostic values of HMMR genes by receiver operating characteristic (ROC) curve analysis and survival analysis. All patients were divided into low- and high-expression groups based on the median value of HMMR expression level. Univariate and multivariate Cox regression analysis were used to identify prognostic factors. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of the HMMR genes involved in HCC. The diagnostic and prognostic values were further validated in an external cohort from the International Cancer Genome Consortium (ICGC). Results. HMMR mRNA expression was significantly elevated in HCC tissues compared with that in normal tissues from both TCGA and the ICGC cohorts (all P values <0.001). Increased HMMR expression was significantly associated with histologic grade, pathological stage, and survival status (all P values <0.05). The area under the ROC curve for HMMR expression in HCC and normal tissues was 0.969 (95% CI: 0.948–0.983) in the TCGA cohort and 0.956 (95% CI: 0.932–0.973) in the ICGC cohort. Patients with high HMMR expression had a poor prognosis than patients with low expression group in both cohorts (all P < 0.001 ). Univariate and multivariate analysis also showed that HMMR is an independent predictor factor associated with overall survival in both cohorts (all P values <0.001). GSEA showed that genes upregulated in the high-HMMR HCC subgroup were mainly significantly enriched in the cell cycle pathway, pathways in cancer, and P53 signaling pathway. Conclusion. HMMR is expressed at high levels in HCC. HMMR overexpression may be an unfavorable prognostic factor for HCC.

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