scholarly journals ELK3: A New Molecular Marker for the Diagnosis and Prognosis of Glioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhendong Liu ◽  
Zhishuai Ren ◽  
Cheng Zhang ◽  
Rongjun Qian ◽  
Hongbo Wang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Clinical Sample ◽  
Quantitative Polymerase Chain Reaction ◽  
Malignant Gliomas ◽  
Enrichment Analysis ◽  
Pathological Process ◽  
Gene Set Enrichment Analysis ◽  
Cell Counting ◽  
Diagnosis And Prognosis ◽  
And Migration

ETS transcription factor ELK3 (ELK3), a novel oncogene, affects pathological processes and progression of many cancers in human tissues. However, it remains unclear whether ELK3, as a key gene, affects the pathological process of gliomas and the prognosis of patients with gliomas. This study aimed to comprehensively and systematically reveal the correlation between ELK3 and the malignant progression of gliomas by analyzing clinical sample information stored in multiple databases. We revealed the putative mechanism of ELK3 involvement in malignant gliomas progression and identified a new and efficient biomarker for glioma diagnosis and targeted therapy. Based on the sample data from multiple databases and real-time quantitative polymerase chain reaction (RT-qPCR), the abnormally high expression of ELK3 in gliomas was confirmed. Kaplan-Meier and Cox regression analyses demonstrated that a high ELK3 expression was markedly associated with low patient survival and served as an independent biomarker of gliomas. Wilcox and Kruskal-Wallis tests revealed that expression of ELK3 was positively correlated with several clinical characteristics of patients with gliomas, such as age, WHO classification, and recurrence. Moreover, Cell Counting Kit‐8 (CCK-8), immunofluorescence, and wound healing assays confirmed that ELK3 overexpression markedly promoted the proliferation and migration of glioma cells. Finally, gene set enrichment analysis (GSEA) and western blotting confirmed that overexpression of ELK3 regulated the JAK–STAT signaling pathway and upregulate the expression of signal transducer and activator of transcription 3 (STAT3) and phosphorylated STAT3 (P-STAT3) to promote the malignant transition of gliomas. Therefore, ELK3 may serve as an efficient biomarker for the diagnosis and prognosis of gliomas and it can also be used as a therapeutic target to improve the poor prognosis of patients with gliomas.

scholarly journals The increasing expression of GPX7 related to the malignant clinical features leading to poor prognosis of glioma patients

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jiawei Yao ◽  
Xin Chen ◽  
Zhendong Liu ◽  
Ruotian Zhang ◽  
Cheng Zhang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Multidimensional Data ◽  
Normal Brain ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis ◽  
Kaplan Meier ◽  
Potential Biomarker ◽  
Therapy Target

Abstract Background Glioma is the most common malignant brain tumor in adults. The standard treatment scheme of glioma is surgical resection combined alternative radio- and chemotherapy. However, the outcome of glioma patients was unsatisfied. Here, we aimed to explore the molecular and biological function characteristics of GPX7 in glioma. Methods The multidimensional data of glioma samples were downloaded from Chinese Glioma Genome Atlas (CGGA). RT-qPCR method was used to identify the expression status of GPX7. Kaplan–Meier curves and Cox regression analysis were used to explore the prognostic value of GPX7. Gene Set Enrichment Analysis (GSEA) was applied to investigate the GPX7-related functions in glioma. Results The results indicated that the expression of GPX7 in glioma was higher compared to that in normal brain tissue. Univariate and multivariate Cox regression analyses confirmed that the expression value of GPX7 was an independent prognostic factor in glioma. The GSEA analysis showed that GPX7 was significantly enriched in the cell cycle pathway, ECM pathway, focal adhesion pathway, and toll-like receptor pathway. Conclusions The GPX7 was recommended as an independent risk factor for patients diagnosed with glioma for the first time and GPX7 could be potentially used as the therapy target in future. Furthermore, we attempted to explore a potential biomarker for improving the diagnosis and prognosis of patients with glioma.

scholarly journals DNAJC10 correlates with tumor immune characteristics and predicts the prognosis of glioma patients

2022 ◽  
Author(s):  
Feng Liu ◽  
Zewei Tu ◽  
Junzhe Liu ◽  
Xiaoyan Long ◽  
Bing Xiao ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Activation ◽  
Cox Regression ◽  
Quantitative Polymerase Chain Reaction ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Clinical Samples ◽  
Prognostic Role ◽  
Cox Regression Analysis

Background: The role of DNAJC10 in cancers have been reported but its function in glioma is not clear. We reveal the prognostic role and underlying functions of DNAJC10 in glioma in this study. Methods: Reverse Transcription and Quantitative Polymerase Chain Reaction (RT-qPCR) was used to quantify the relative DNAJC10 mRNA expression of clinical samples. Protein expressions of clinical samples were tested by Western blot. The overall survival (OS) of glioma patients with different DNAJC10 expression was compared by Kaplan-Meier method (two-sided log-rank test). Single-sample gene set enrichment analysis (ssGSEA) was used to estimate the immune cell infiltrations and immune-related function levels. The independent prognostic role of DNAJC10 was determined by univariate and multivariate Cox regression analysis. The DNAJC10-based nomogram model was established using multivariate Cox regression by R package “rms”. Results: Higher DNAJC10 is observed in gliomas and it’s upregulated in higher grade, IDH-wild, 1p/19q non-codeletion, MGMT unmethylated gliomas. Gliomas with higher DNAJC10 expression present poorer prognosis compared with low-DNAJC10 gliomas. The predictive accuracy of 1/3/5-OS of DNAJC10 is found stable and robust using time-dependent ROC model. Enrichment analysis recognized that T-cell activation and T-cell receptor signaling were enriched in higher DNAJC10 gliomas. Immune/stromal cell infiltrations, tumor mutation burden (TMB), copy Number Alteration (CNA) burden, and immune check-point genes were also positively correlated with DNAJC10 expression in gliomas. DNAJ10-based nomogram model was established and showed strong prognosis-predictive ability. Conclusion: Higher DNAJC10 expression correlates with poor prognosis of glioma and it was a potential prognostic biomarker for glioma.

scholarly journals Construction and validation of an RNA-binding protein-associated prognostic model for colorectal cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11219
Author(s):  
Yandong Miao ◽  
Hongling Zhang ◽  
Bin Su ◽  
Jiangtao Wang ◽  
Wuxia Quan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis

Colorectal cancer (CRC) is one of the most prevalent and fatal malignancies, and novel biomarkers for the diagnosis and prognosis of CRC must be identified. RNA-binding proteins (RBPs) are essential modulators of transcription and translation. They are frequently dysregulated in various cancers and are related to tumorigenesis and development. The mechanisms by which RBPs regulate CRC progression are poorly understood and no clinical prognostic model using RBPs has been reported in CRC. We sought to identify the hub prognosis-related RBPs and to construct a prognostic model for clinical use. mRNA sequencing and clinical data for CRC were obtained from The Cancer Genome Atlas database (TCGA). Gene expression profiles were analyzed to identify differentially expressed RBPs using R and Perl software. Hub RBPs were filtered out using univariate Cox and multivariate Cox regression analysis. We used functional enrichment analysis, including Gene Ontology and Gene Set Enrichment Analysis, to perform the function and mechanisms of the identified RBPs. The nomogram predicted overall survival (OS). Calibration curves were used to evaluate the consistency between the predicted and actual survival rate, the consistency index (c-index) was calculated, and the prognostic effect of the model was evaluated. Finally, we identified 178 differently expressed RBPs, including 121 up-regulated and 57 down-regulated proteins. Our prognostic model was based on nine RBPs (PNLDC1, RRS1, HEXIM1, PPARGC1A, PPARGC1B, BRCA1, CELF4, AEN and NOVA1). Survival analysis showed that patients in the high-risk subgroup had a worse OS than those in the low-risk subgroup. The area under the curve value of the receiver operating characteristic curve of the prognostic model is 0.712 in the TCGA cohort and 0.638 in the GEO cohort. These results show that the model has a moderate diagnostic ability. The c-index of the nomogram is 0.77 in the TCGA cohort and 0.73 in the GEO cohort. We showed that the risk score is an independent prognostic biomarker and that some RBPs may be potential biomarkers for the diagnosis and prognosis of CRC.

scholarly journals Overexpressed XRCC2 as an independent risk factor for poor prognosis in glioma patients

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Zhendong Liu ◽  
Wang Zhang ◽  
Xingbo Cheng ◽  
Hongbo Wang ◽  
Lu Bian ◽  
...  
Keyword(s):  
Risk Factor ◽  
Expression Pattern ◽  
Poor Prognosis ◽  
Independent Risk Factor ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Cellular Mechanisms ◽  
Molecular Features ◽  
Meta Analyses

Abstract Background XRCC2, a homologous recombination-related gene, has been reported to be associated with a variety of cancers. However, its role in glioma has not been reported. This study aimed to find out the role of XRCC2 in glioma and reveal in which glioma-specific biological processes is XRCC2 involved based on thousands of glioma samples, thereby, providing a new perspective in the treatment and prognostic evaluation of glioma. Methods The expression characteristics of XRCC2 in thousands of glioma samples from CGGA and TCGA databases were comprehensively analyzed. Wilcox or Kruskal test was used to analyze the expression pattern of XRCC2 in gliomas with different clinical and molecular features. The effect of XRCC2 on the prognosis of glioma patients was explored by Kaplan–Meier and Cox regression. Gene set enrichment analysis (GSEA) revealed the possible cellular mechanisms involved in XRCC2 in glioma. Connectivity map (CMap) was used to screen small molecule drugs targeting XRCC2 and the expression levels of XRCC2 were verified in glioma cells and tissues by RT-qPCR and immunohistochemical staining. Results We found the overexpression of XRCC2 in glioma. Moreover, the overexpressed XRCC2 was associated with a variety of clinical features related to prognosis. Cox and meta-analyses showed that XRCC2 is an independent risk factor for the poor prognosis of glioma. Furthermore, the results of GSEA indicated that overexpressed XRCC2 could promote malignant progression through involved signaling pathways, such as in the cell cycle. Finally, doxazosin, quinostatin, canavanine, and chrysin were identified to exert anti-glioma effects by targeting XRCC2. Conclusions This study analyzed the expression pattern of XRCC2 in gliomas and its relationship with prognosis using multiple datasets. This is the first study to show that XRCC2, a novel oncogene, is significantly overexpressed in glioma and can lead to poor prognosis in glioma patients. XRCC2 could serve as a new biomarker for glioma diagnosis, treatment, and prognosis evaluation, thus bringing new insight into the management of glioma.

A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

2021 ◽  
Author(s):  
Yanjia Hu ◽  
Jing Zhang ◽  
Jing Chen
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Lower Grade ◽  
Prognostic Signature ◽  
Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

scholarly journals Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

2021 ◽  
Vol 27 ◽  
Author(s):  
Aoshuang Qi ◽  
Mingyi Ju ◽  
Yinfeng Liu ◽  
Jia Bi ◽  
Qian Wei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Antigen Processing ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Gene Set ◽  
Antigen Processing And Presentation

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC.Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis.Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC.Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

scholarly journals Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

2020 ◽  
Vol 40 (8) ◽  
Author(s):  
Sihan Chen ◽  
Guodong Cao ◽  
Wei Wu ◽  
Yida Lu ◽  
Xiaobo He ◽  
...  
Keyword(s):  
Cox Regression ◽  
Mechanistic Model ◽  
Single Gene ◽  
Colon Adenocarcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Gene Markers ◽  
Cox Regression Analysis

Abstract Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

scholarly journals Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11273
Author(s):  
Lei Yang ◽  
Weilong Yin ◽  
Xuechen Liu ◽  
Fangcun Li ◽  
Li Ma ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Overall Survival ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Hub Genes ◽  
Cox Regression Analysis

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

scholarly journals A Prognostic Autophagy-Related Long Non-coding RNA (ARlncRNA) Signature in Acute Myeloid Leukemia (AML)

2021 ◽  
Vol 12 ◽  
Author(s):  
Chunxia Zhao ◽  
Yulu Wang ◽  
Famei Tu ◽  
Shuai Zhao ◽  
Xiaoying Ye ◽  
...  
Keyword(s):  
Cox Regression ◽  
Survival Curve ◽  
Pearson Correlation ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Kaplan Meier ◽  
Non Coding Rna ◽  
Novel Biomarkers ◽  
Meier Survival Curve

BackgroundSome studies have proven that autophagy and lncRNA play important roles in AML. Several autophagy related lncRNA signatures have been shown to affect the survival of patients in some other cancers. However, the role of autophagy related lncRNA in AML has not been explored yet. Hence, this study aims to find an autophagy related lncRNA signature that can affect survival for AML patients.MethodA Pearson correlation analysis, a Kaplan–Meier survival curve, a univariate cox regression, and a multivariate cox regression were performed to establish an autophagy related lncRNA signature. A univariate cox regression, a multivariate cox regression, a Kaplan–Meier survival curve, and a ROC curve were applied to confirm if the signature is an independent prognosis for AML patients. The relationship between the signature and the clinical features was explored by using a T test. Gene Set Enrichment Analysis (GSEA) was used to investigate the potential tumor related pathways.ResultsA four-autophagy related lncRNA (MIR133A1HG, AL359715.1, MIRLET7BHG, and AL356752.1) signature was established. The high risk score based on signature was related to the short survival time of AML patients. The signature was an independent factor for the prognosis for AML patients (HR = 1.684, 95% CI = 1.324–2.142, P < 0.001). The signature was correlated with age, leukocyte numbers, and FAB (M3 or non-M3). The P53, IL6/JAK/STAT3, TNF-α, INF-γ, and IL2/STAT5 pathways might contribute to the differences between the risk groups based on signature in AML.ConclusionThe four autophagy related lncRNAs and their signature might be novel biomarkers for predicting the survival of AML patients. Some biological pathways might be the potential mechanisms of the signature for the survival of AML patients.

scholarly journals ALG3 Contributes to the Malignant Biological Properties of Oral Squamous Cell Carcinoma Cells Through Regulating CDK-Cyclin Pathway

2020 ◽  
Author(s):  
Peihong Shao ◽  
Chengshi Wei ◽  
Yun Wang
Keyword(s):  
Western Blot ◽  
Colony Formation ◽  
Enrichment Analysis ◽  
Biological Properties ◽  
The Cancer Genome Atlas ◽  
Cell Counting ◽  
Invasion And Migration ◽  
Elevated Expression ◽  
Cancer Genome Atlas ◽  
And Migration

Abstract Background: In this study, we planned to investigate the function and potential mechanisms of Alpha-1,3-mannosyltransferase (ALG3) in oral squamous cell carcinoma (OSCC). Methods: Data from The Cancer Genome Atlas (TCGA) was used to analyze ALG3 expression and its effect on the prognosis of patients with OSCC. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was applied to explore the signaling pathways related to ALG3. In OSCC cells, ALG3 expression was measured by qPCR and western blot. Cell counting kit-8, colony formation, and transwell assays were implemented to detect the effects of ALG3 on the malignant biological properties OSCC cells. The expression of key proteins related to CDK-Cyclin pathway was detected by western blot. Results: The expression of ALG3 in OSCC samples was higher than that of the control samples, and the increase of ALG3 expression was related to unfavorable prognosis of OSCC patients. Additionally, the elevated expression of ALG3 was associated with pathological stage, lymph node metastasis and primary lesion in OSCC patients. ALG3 depletion blocked the growth, colony formation, invasion and migration of OSCC cells, while over-expression ALG3 reversed these phenomena. Moreover, exhaustion of ALG3 resulted in decreased expression of MCM7, CCNB2, CDK1 and PCNA, while these phenomena were inversed after ALG3 up-regulation. Conclusions: The enhancement of ALG3 expression promoted the aggressive biological behaviors of OSCC cells probably by promoting CDK-Cyclin pathway.

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