scholarly journals Abnormal TACC3 Expression is an Independent Prognostic Biomarker in Lung Carcinoma

2020 ◽  
Author(s):  
Dongwei He ◽  
Xiaoyan Fan ◽  
Yulong Zhang ◽  
You Li
Keyword(s):  
Risk Factor ◽  
Lung Carcinoma ◽  
Independent Risk Factor ◽  
Cox Regression ◽  
Coiled Coil ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Roc Curve Analysis

Abstract Background: Abnormal expression of transforming acidic coiled-coil protein 3 (TACC3) correlates with tumorigenesis of many human malignancies. However, the expression pattern of TACC3 and its clinical significance have not been well characterized in lung carcinoma (LUAD) so far. Objective: To investigate the association of TACC3 expression level with the clinicopathological characteristics and prognosis of LUAD patients.Methods: In the study, based on Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and The Cancer Genome Atlas (TCGA) databases, the expression of TACC3 was determined in LUAD patients. Further, the expression of TACC3 was established using qRT-PCR in LUAD patients. Results: Our results showed that TACC3 was significantly overexpressed in LUAD tumors compared with non-tumors in the above public databases (all p<0.01). A receiver operating characteristic (ROC) curve analysis suggested that TACC3 may have diagnostic value in LUAD patients (normal vs tumor: AUC = 0.940). Kaplan-Meier analysis further demonstrated that high TACC3 expression in tumors was significantly associated with worse overall survival (OS) in LUAD patients (all p<0.01). In addition, Univariate and multivariate Cox regression analyses showed that TACC3 was an independent risk factor for OS among LUAD patients (HR = 1.02, 95% CI: 1.01-1.04, p = 0.00823; HR=1.43, 95% CI: 1.17-1.70, p <0.001). Finally, using gene set enrichment analysis (GSEA 3.0), we found that a series of potential pathways related to TACC3 were highly enriched with the high TACC3 expression phenotype (p = 0.024, p = 0.003, respectively). Conclusions: The present study provides evidence that TACC3 expression is upregulated in tumors and may be an independent risk factor for prognosis in LUAD patients.

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.

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

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.

scholarly journals Bioinformatics Analysis of Autophagy-related LncRNAs in Esophageal Carcinoma

2020 ◽  
Author(s):  
junbai fan ◽  
Dan Wu ◽  
Yi Ding
Keyword(s):  
High Risk ◽  
Esophageal Carcinoma ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Risk Patients ◽  
Risk Patients

Abstract Background: Esophageal carcinoma (ESCA) is a malignant tumor with high invasiveness and mortality. Autophagy has multiple roles in the development of cancer; however, there are limited data on autophagy genes associated with long non-coding RNAs (lncRNAs) in ESCA. The purpose of this study was to screen potential diagnostic and prognostic molecules, and to identify gene co-expression networks associated with autophagy in ESCA. Methods: We downloaded transcriptome expression profiles from The Cancer Genome Atlas and autophagy-related gene data from the Human Autophagy Database, and analyzed the co-expression of mRNAs and lncRNAs. In addition, the diagnostic and prognostic value of autophagy-related lncRNAs was analyzed by multivariate Cox regression. Furthermore, Kyoto Encyclopedia of Genes and Genomes analysis was carried out for high-risk patients, and enriched pathways were analyzed by gene set enrichment analysis. Results: The results showed that genes of high-risk patients were enriched in protein export and spliceosome. Based on Cox stepwise regression and survival analysis, we identified seven autophagy-related lncRNAs with prognostic and diagnostic value, with the potential to be used as a combination to predict the prognosis of patients with ESCA. Finally, a co-expression network related to autophagy was constructed. Conclusion: These results suggest that autophagy-related lncRNAs and the spliceosome play important parts in the pathogenesis of ESCA. Our findings provide new insight into the molecular mechanism of ESCA and suggest a new method for improving its treatment.

scholarly journals Bioinformatics Analysis of Autophagy-related lncRNAs in Esophageal Carcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Dan Wu ◽  
Yi Ding ◽  
JunBai Fan
Keyword(s):  
High Risk ◽  
Esophageal Carcinoma ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Risk Patients ◽  
Risk Patients

Background: Esophageal carcinoma (ESCA) is a malignant tumor with high invasiveness and mortality. Autophagy has multiple roles in the development of cancer; however, there are limited data on autophagy genes associated with long non-coding RNAs (lncRNAs) in ESCA. The purpose of this study was to screen potential diagnostic and prognostic molecules and to identify gene co-expression networks associated with autophagy in ESCA. Methods: We downloaded transcriptome expression profiles from The Cancer Genome Atlas and autophagy-related gene data from the Human Autophagy Database and analyzed the co-expression of mRNAs and lncRNAs. In addition, the diagnostic and prognostic value of autophagy-related lncRNAs was analyzed by multivariate Cox regression. Furthermore, Kyoto Encyclopedia of Genes and Genomes analysis was carried out for high-risk patients, and enriched pathways were analyzed by gene set enrichment analysis. Results: The results showed that genes of high-risk patients were enriched in protein export and spliceosome. Based on Cox stepwise regression and survival analysis, we identified seven autophagy-related lncRNAs with prognostic and diagnostic value, with the potential to be used as a combination to predict the prognosis of patients with ESCA. Finally, a co-expression network related to autophagy was constructed. Conclusion: These results suggest that autophagy-related lncRNAs and the spliceosome play important parts in the pathogenesis of ESCA. Our findings provide new insight into the molecular mechanism of ESCA and suggest a new method for improving its treatment.

scholarly journals Identification of AC003986.3 as an Independent Risk Factor for Glioma Patient Survival and Functional Analysis

2020 ◽  
Author(s):  
Qinglin Liu ◽  
Huijian Ge ◽  
Peng Jiang
Keyword(s):  
Risk Factor ◽  
Clinical Features ◽  
Independent Risk Factor ◽  
Cox Regression ◽  
Patient Survival ◽  
Cell Metabolism ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Pathological Grade ◽  
Gene Sets

Abstract Background: To validate the potential of AC003986.3 in predicting glioma patient survival and analyze its underlying mechanism.Methods: Gene expression and clinical features of the patients with gliomas were obtained from The Cancer Genome Atlatls. Correlation between AC003986.3 expression profile and patient clinical features and survival were analyzed. Multivariate Cox regression was employed to determine the risk factors for patient survival and construct the prediction model for survival. Validation of the multivariate Cox regression model was performed by comparing the survival curves between the model-predicted high and low death risk subgroups and calculating the accuracy of predicting 1, 2, 3, and 5 years survival by the model. Target genes were predicted with Ensemble Browser. Gene set enrichment analysis was performed to explore AC003986.3 related gene sets enrichment in Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways.Results: 655 samples with gene expression and complete clinical features were obtained from The Cancer Genome Atlas. Clinical features enrolled in this study were follow up time, survival status, race, gender, race and pathological grade. AC003986.3 expression was positively related to patient age and pathological grade. Patients with High AC003986.3 expression suffered a poorer survival than those with low expression. Multivariate Cox regression revealed that AC003986.3 expression was an independent risk factor for patient survival irrespective of age and pathological grade. Predicted by Ensemble Browser, TWIST1 was identified as the target of AC003986.3. Gene set enrichment analysis revealed that AC003986.3 related gene sets were mainly enriched in cell metabolism.Conclusions: AC003986.3 expression was closely related to age and pathological grade in glioma patients, and was an independent risk factor for patient survival irrespective of age and pathological grade. AC003986.3 was mainly involved in regulating tumor cell metabolism, and this effect is probably mediated by TWIST1.

scholarly journals Low UGP2 Expression Is Associated with Tumour Progression and Predicts Poor Prognosis in Hepatocellular Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Qiuyue Hu ◽  
Shen Shen ◽  
Jianhao Li ◽  
Liwen Liu ◽  
Xin Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Tumour Progression ◽  
Enrichment Analysis ◽  
Malignant Tumour ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
Uridine Diphosphate ◽  
Roc Curve Analysis

Hepatocellular carcinoma (HCC) is a malignant tumour associated with a high mortality rate and poor prognosis worldwide. Uridine diphosphate-glucose pyrophosphorylase 2 (UGP2), a key enzyme in glycogen biosynthesis, has been reported to be associated with the occurrence and development of various cancer types. However, its diagnostic value and prognostic value in HCC remain unclear. The present study observed that UGP2 expression was significantly downregulated at both the mRNA and protein levels in HCC tissues. Receiver operating characteristic (ROC) curve analysis revealed that UGP2 may be an indicator for the diagnosis of HCC. In addition, Kaplan-Meier and Cox regression multivariate analyses indicated that UGP2 is an independent prognostic factor of overall survival (OS) in patients with HCC. Furthermore, gene set enrichment analysis (GSEA) suggested that gene sets negatively correlated with the survival of HCC patients were enriched in the group with low UGP2 expression levels. More importantly, a significant correlation was identified between low UGP2 expression and fatty acid metabolism. In summary, the present study demonstrates that UGP2 may contribute to the progression of HCC, indicating a potential therapeutic target for HCC patients.

scholarly journals Bioinformatics analysis of autophagy-related lncRNAs in esophageal carcinoma

2020 ◽  
Author(s):  
Dan Wu ◽  
Yi Ding ◽  
junbai fan
Keyword(s):  
High Risk ◽  
Esophageal Carcinoma ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Risk Patients ◽  
Risk Patients

Abstract Background: Esophageal carcinoma (ESCA) is a malignant tumor with high invasiveness and mortality. Autophagy has multiple roles in the development of cancer; however, there are limited data on autophagy genes associated with long non-coding RNAs (lncRNAs) in ESCA. The purpose of this study was to screen potential diagnostic and prognostic molecules, and to identify gene co-expression networks associated with autophagy in ESCA. Methods: We downloaded transcriptome expression profiles from The Cancer Genome Atlas and autophagy-related gene data from the Human Autophagy Database, and analyzed the co-expression of mRNAs and lncRNAs. In addition, the diagnostic and prognostic value of autophagy-related lncRNAs was analyzed by multivariate Cox regression. Furthermore, Kyoto Encyclopedia of Genes and Genomes analysis was carried out for high-risk patients, and enriched pathways were analyzed by gene set enrichment analysis. Results: The results showed that genes of high-risk patients were enriched in protein export and spliceosome. Based on Cox stepwise regression and survival analysis, we identified seven autophagy-related lncRNAs with prognostic and diagnostic value, with the potential to be used as a combination to predict the prognosis of patients with ESCA. Finally, a co-expression network related to autophagy was constructed. Conclusion: These results suggest that autophagy-related lncRNAs and the spliceosome play important parts in the pathogenesis of ESCA. Our findings provide new insight into the molecular mechanism of ESCA and suggest a new method for improving its treatment.

scholarly journals Identification of AC003986.3 as an independent risk factor for glioma patient survival and functional analysis

2020 ◽  
Author(s):  
Qinglin Liu ◽  
Huijian Ge ◽  
Peng Liu ◽  
Youxiang Li ◽  
Peng Jiang
Keyword(s):  
Risk Factor ◽  
Clinical Features ◽  
Independent Risk Factor ◽  
Cox Regression ◽  
Patient Survival ◽  
Cell Metabolism ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Pathological Grade ◽  
Gene Sets

Abstract Background To validate the potential of AC003986.3 in predicting glioma patient survival and analyze its underlying function and mechanism. Methods Gene expression and clinical features of the patients were obtained from The Cancer Genome Atlatls. Correlation between AC003986.3 expression profile and patient clinical features and survival were analyzed. Multivariate Cox regression was employed to determine the risk factors for patient survival and construct the prediction model for survival. Validation of the multivariate Cox regression model was tested by comparing the survival curves between the model-predicted high and low death risk subgroups and calculating the accuracy of predicting 1, 2, 3, and 5 years survival by the model. Target genes were predicted with Ensemble Browser. Gene set enrichment analysis was performed to explore AC003986.3 related gene sets enrichment in Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. Results 655 samples with gene expression and complete clinical features were obtained from The Cancer Genome Atlas. Clinical features enrolled in this study were follow up time, survival status, race, gender, race and pathological grade. AC003986.3 expression was positively related to patient age and pathological grade. High AC003986.3 expression glioma patients suffered a poorer survival than those with low expression. Multivariate Cox regression revealed that AC003986.3 expression was an independent risk factor for patient survival irrespective of age and pathological grade. Predicted by Ensemble Browser, TWIST1 was the target of AC003986.3. Gene set enrichment analysis revealed that AC003986.3 related gene sets were mainly enriched in cell metabolism. Conclusions AC003986.3 expression is closely related to age and pathological grade in glioma patients, and is an independent risk factor for patient survival irrespective of age and pathological grade. AC003986.3 is mainly involved in regulating tumor cell metabolism, and this effect is probably mediated by TWIST1.

scholarly journals CKAP2L, as an Independent Risk Factor, Closely Related to the Prognosis of Glioma

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Li Zhu ◽  
Yanlei Zheng ◽  
Ronghua Hu ◽  
Chenchen Hu
Keyword(s):  
Cell Cycle ◽  
Risk Factor ◽  
Independent Risk Factor ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Biological Behavior ◽  
Therapeutic Effects ◽  
Cox Regression Analysis

Recent studies have found that cytoskeleton-associated protein 2 like (CKAP2L), an important oncogene, is involved in the biological behavior of many malignant tumors, but its function in the malignant course of glioma has not been confirmed. The main purpose of this study was to clarify the relationship between prognostic clinical characteristics of glioma patients and CKAP2L expression using data collected from the GEPIA, HPA, CGGA, TCGA, and GEO databases. CKAP2L expression was significantly increased in glioma. Further, Kaplan-Meier plots revealed that increased expression of CKAP2L was associated with shorter survival time of glioma patients in datasets retrieved from multiple databases. Cox regression analysis indicated that CKAP2L can serve as an independent risk factor but also has relatively reliable diagnostic value for the prognosis of glioma patients. The results of gene set enrichment analysis suggested that CKAP2L may play a regulatory role through the cell cycle, homologous recombination, and N-glycan biosynthesis cell signaling pathways. Several drugs with potential inhibitory effects on CKAP2L were identified in the CMap database that may have therapeutic effects on glioma. Finally, knockdown of CKAP2L inhibited the proliferation and invasion of cells by reducing the expression level of cell cycle-related proteins. This is the first study to demonstrate that high CKAP2L expression leads to poor prognosis in glioma patients, providing a novel target for diagnosis and treatment of glioma.

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) &lt; 1), and HLA-F was risky (HR &gt; 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.

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