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

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.

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Identification of Epithelial–Mesenchymal Transition-Related Prognostic lncRNAs Biomarkers Associated With Melanoma Microenvironment

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.679133 ◽

2021 ◽

Vol 9 ◽

Author(s):

Bo Xiao ◽

Liyan Liu ◽

Zhuoyuan Chen ◽

Aoyu Li ◽

Pingxiao Wang ◽

...

Keyword(s):

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Enrichment Analysis ◽

Roc Curves ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Mesenchymal Transition ◽

Cox Regression Analysis ◽

Kaplan Meier

Melanoma is the most common cancer of the skin, associated with a worse prognosis and distant metastasis. Epithelial–mesenchymal transition (EMT) is a reversible cellular biological process that plays significant roles in diverse tumor functions, and it is modulated by specific genes and transcription factors. The relevance of EMT-related lncRNAs in melanoma has not been determined. Therefore, RNA expression data and clinical features were collected from the TCGA database (N = 447). Melanoma samples were randomly assigned into the training (315) and testing sets (132). An EMT-related lncRNA signature was constructed via comprehensive analyses of lncRNA expression level and corresponding clinical data. The Kaplan-Meier analysis showed significant differences in overall survival in patients with melanoma in the low and high-risk groups in two sets. Receiver operating characteristic (ROC) curves were used to measure the performance of the model. Cox regression analysis indicated that the risk score was an independent prognostic factor in two sets. Besides, a nomogram was constructed based on the independent variables. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the potential biological functions in the two risk groups. Furthermore, the melanoma microenvironment was evaluated using ESTIMATE and CIBERSORT algorithms in the risk groups. This study indicates that EMT-related lncRNAs can function as potential independent prognostic biomarkers for melanoma survival.

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Overexpression of HOXA10 is associated with unfavorable prognosis of acute myeloid leukemia

10.21203/rs.3.rs-16941/v1 ◽

2020 ◽

Author(s):

Chao Guo ◽

Qian-qian Ju ◽

Chun-xia Zhang ◽

Ming Gong ◽

Zhen-ling Li ◽

...

Keyword(s):

Cox Regression ◽

Cox Model ◽

Prognostic Significance ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

High Group ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

The Impact

Abstract Background HOXA family genes were crucial transcription factors involving cell proliferation and apoptosis. While few studies have focused on HOXA10 in AML. We aimed to investigate the prognostic significance of HOXA10. Methods We downloaded datasets from GEO and BeatAML database, to compare HOXA expression level between AML patients and controls. Kaplan-Meier curves were used to estimate the impact of HOXA10 expression on AML survival. The differentially expressed genes, miRNAs, lncRNAs and methylated regions between HOXA10-high and -low groups were obtained using R (version 3.6.0). Accordingly, the gene set enrichment analysis (GSEA) was accomplished using MSigDB database. Moreover, the regulatory TFs/microRNAs/lncRNAs of HOXA10 were identified. A LASSO-Cox model fitted OS to clinical and HOXA10-associated genetic variables by glmnet package. Results HOXA10 was overexpressed in AML patients than that in controls. The HOXA10-high group is significantly associated with shorter OS and DFS. A total of 1219 DEGs, 131 DEmiRs, 282 DElncRs were identified to be associated with HOXA10. GSEA revealed that 12 suppressed and 3 activated pathways in HOXA10-high group. Furthermore, the integrated regulatory network targeting HOXA10 was established. The LASSO-Cox model fitted OS to AML-survival risk scores, which included age, race, molecular risk, expression of IKZF2/LINC00649/LINC00839/FENDRR and has-miR-424-5p. The time dependent ROC indicated a satisfying AUC (1-year AUC 0.839, 3-year AUC 0.871 and 5-year AUC 0.813). Conclusions Our study identified HOXA10 overexpression as an adverse prognostic factor for AML. The LASSO-COX regression analysis revealed novel prediction model of OS with superior diagnostic utility.

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High CTHRC1 expression may be closely associated with angiogenesis and indicates poor prognosis in lung adenocarcinoma patients

Cancer Cell International ◽

10.1186/s12935-019-1041-5 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 1

Author(s):

Yangshan Chen ◽

Yu Sun ◽

Yongmei Cui ◽

Yiyan Lei ◽

Neng Jiang ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Predictive Model ◽

Tumor Angiogenesis ◽

Poor Prognosis ◽

Cox Regression ◽

Enrichment Analysis ◽

Progression Free Survival ◽

Gene Set Enrichment Analysis ◽

Cox Regression Analysis ◽

Potential Biomarker

Abstract Background This study aimed to investigate the prognostic value of the potential biomarker collagen triple helix repeat containing 1 (CTHRC1) in lung adenocarcinoma (LUAD) patients. Methods A total of 210 LUAD patients diagnosed between 2003 and 2016 in the Department of Pathology of the First Affiliated Hospital of Sun Yat-sen University were included in this study. The expression of CTHRC1 and vascular endothelial growth factor (VEGF), and microvessel density (MVD, determined by CD34 immunostaining) were evaluated by immunohistochemistry in LUAD tissues. The association between the expression of these proteins and clinicopathological features or clinical outcomes was analyzed. Results Here, we confirmed that CTHRC1 expression was associated with prognosis and can serve as a significant predictor for overall survival (OS) and progression-free survival (PFS) in LUAD. Additionally, we observed that CTHRC1 expression was positively associated with tumor angiogenesis markers, such as VEGF expression (P < 0.001) and MVD (P < 0.01). Then, we performed gene set enrichment analysis (GESA) and cell experiments to confirm that enhanced CTHRC1 expression can promote VEGF levels. Based on and cox regression analysis, a predictive model that included CTHRC1, VEGF and MVD was constructed and confirmed as a more accurate independent predictor for OS (P = 0.001) and PFS (P < 0.001) in LUAD than other parameters. Conclusions These results demonstrated that high CTHRC1 expression may be closely related to tumor angiogenesis and poor prognosis in LUAD. The predictive model based on the CTHRC1 level and tumor angiogenesis markers can be used to predict LUAD patient prognosis more accurately.

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Expression and prognosis of CDC45 in cervical cancer based on the GEO database

PeerJ ◽

10.7717/peerj.12114 ◽

2021 ◽

Vol 9 ◽

pp. e12114 ◽

Cited By ~ 1

Author(s):

Zikang He ◽

Xiaojin Wang ◽

Zhiming Yang ◽

Ying Jiang ◽

Luhui Li ◽

...

Keyword(s):

Cell Cycle ◽

Cervical Cancer ◽

Cox Regression ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Cox Regression Analysis ◽

Potential Biomarker ◽

Geo Database ◽

Differential Genes

Cervical cancer is one of the most common malignant tumors in women, and its morbidity and mortality are increasing year by year worldwide. Therefore, an urgent and challenging task is to identify potential biomarkers for cervical cancer. This study aims to identify the hub genes based on the GEO database and then validate their prognostic values in cervical cancer by multiple databases. By analysis, we obtained 83 co-expressed differential genes from the GEO database (GSE63514, GSE67522 and GSE39001). GO and KEGG enrichment analysis showed that these 83 co-expressed it mainly involved differential genes in DNA replication, cell division, cell cycle, etc.. The PPI network was constructed and top 10 genes with protein-protein interaction were selected. Then, we validated ten genes using some databases such as TCGA, GTEx and oncomine. Survival analysis demonstrated significant differences in CDC45, RFC4, TOP2A. Differential expression analysis showed that these genes were highly expressed in cervical cancer tissues. Furthermore, univariate and multivariate cox regression analysis indicated that CDC45 and clinical stage IV were independent prognostic factors for cervical cancer. In addition, the HPA database validated the protein expression level of CDC45 in cervical cancer. Further studies investigated the relationship between CDC45 and tumor-infiltrating immune cells via CIBERSORT. Finally, gene set enrichment analysis (GSEA) showed CDC45 related genes were mainly enriched in cell cycle, chromosome, catalytic activity acting on DNA, etc. These results suggested CDC45 may be a potential biomarker associated with the prognosis of cervical cancer.

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

PeerJ ◽

10.7717/peerj.11219 ◽

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.

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Overexpression of HOXA10 is associated with unfavorable prognosis of acute myeloid leukemia

10.21203/rs.3.rs-16941/v2 ◽

2020 ◽

Author(s):

Chao Guo ◽

Qian-qian Ju ◽

Chun-xia Zhang ◽

Ming Gong ◽

Zhen-ling Li ◽

...

Keyword(s):

Cox Regression ◽

Cox Model ◽

Prognostic Significance ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

High Group ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

The Impact

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Prognostic role of aberrant mTOR activation in patients with stage II and III colorectal cancer

Biomarkers in Medicine ◽

10.2217/bmm-2020-0141 ◽

2020 ◽

Vol 14 (12) ◽

pp. 1127-1137

Author(s):

Tong-Tong Zhang ◽

Yi-Qing Zhu ◽

Hong-Qing Cai ◽

Jun-Wen Zheng ◽

Jia-Jie Hao ◽

...

Keyword(s):

Colorectal Cancer ◽

Cox Regression ◽

Disease Free Survival ◽

Stage Ii ◽

Poor Prognostic Factor ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Potential Biomarker ◽

Risk Predictor

Aim: This study aimed to develop an effective risk predictor for patients with stage II and III colorectal cancer (CRC). Materials & methods: The prognostic value of p-mTOR (Ser2448) levels was analyzed using Kaplan–Meier survival analysis and Cox regression analysis. Results: The levels of p-mTOR were increased in CRC specimens and significantly correlated with poor prognosis in patients with stage II and III CRC. Notably, the p-mTOR level was an independent poor prognostic factor for disease-free survival and overall survival in stage II CRC. Conclusion: Aberrant mTOR activation was significantly associated with the risk of recurrence or death in patients with stage II and III CRC, thus this activated proteins that may serve as a potential biomarker for high-risk CRC.

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

Bioscience Reports ◽

10.1042/bsr20201427 ◽

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.

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

PeerJ ◽

10.7717/peerj.11273 ◽

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.

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

Frontiers in Genetics ◽

10.3389/fgene.2021.681867 ◽

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.

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