scholarly journals Identification of a Ubiquitin Related Genes Signature for Predicting Prognosis of Prostate Cancer

2022 ◽  
Vol 12 ◽  
Author(s):  
Guoda Song ◽  
Yucong Zhang ◽  
Hao Li ◽  
Zhuo Liu ◽  
Wen Song ◽  
...  

Background: Ubiquitin and ubiquitin-like (UB/UBL) conjugations are one of the most important post-translational modifications and involve in the occurrence of cancers. However, the biological function and clinical significance of ubiquitin related genes (URGs) in prostate cancer (PCa) are still unclear.Methods: The transcriptome data and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA), which was served as training cohort. The GSE21034 dataset was used to validate. The two datasets were removed batch effects and normalized using the “sva” R package. Univariate Cox, LASSO Cox, and multivariate Cox regression were performed to identify a URGs prognostic signature. Then Kaplan-Meier curve and receiver operating characteristic (ROC) curve analyses were used to evaluate the performance of the URGs signature. Thereafter, a nomogram was constructed and evaluated.Results: A six-URGs signature was established to predict biochemical recurrence (BCR) of PCa, which included ARIH2, FBXO6, GNB4, HECW2, LZTR1 and RNF185. Kaplan-Meier curve and ROC curve analyses revealed good performance of the prognostic signature in both training cohort and validation cohort. Univariate and multivariate Cox analyses showed the signature was an independent prognostic factor for BCR of PCa in training cohort. Then a nomogram based on the URGs signature and clinicopathological factors was established and showed an accurate prediction for prognosis in PCa.Conclusion: Our study established a URGs prognostic signature and constructed a nomogram to predict the BCR of PCa. This study could help with individualized treatment and identify PCa patients with high BCR risks.

2021 ◽  
Author(s):  
Liu-qing Zhou ◽  
Jie-yu Zhou ◽  
Yao Hu

Abstract Background: N6-methyladenosine (m6A) modifications play an essential role in tumorigenesis. m6A modifications are known to modulate RNAs, including mRNAs and lncRNAs. However, the prognostic role of m6A-related lncRNAs in head and neck squamous cell carcinoma (HNSCC) is poorly understood.Methods: Based on LASSO Cox regression, enrichment analysis, univariate and multivariate Cox regression analysis, a risk prognostic model, and consensus clustering analysis, we analyzed the 12 m6A-related lncRNAs in HNSCC samples data using the data from The Cancer Genome Atlas (TCGA) database.Results: We found twelve m6A-related lncRNAs in the training cohort and validated in all cohorts by Kaplan-Meier and Cox regression analyses, and revealing their independent prognostic value in HNSCC. Moreover, ROC analysis was conducted, confirming the strong predictive ability of this signature for HNSCC prognosis. GSEA and detailed immune infiltration analyses revealed specific pathways associated with m6A-related lncRNAs.Conclusions: In this study, a novel risk model including twelve genes (SAP30L-AS1, AC022098.1, LINC01475, AC090587.2, AC008115.3, AC015911.3, AL122035.2, AC010226.1, AL513190.1, ZNF32-AS1, AL035587.1 and AL031716.1) was built. It could accurately predict HNSCC prognosis and provide potential prediction outcome and new therapeutic target for HNSCC patients.


Author(s):  
Zhuolun Sun ◽  
Yunhua Mao ◽  
Xu Zhang ◽  
Shuo Lu ◽  
Hua Wang ◽  
...  

Prostate cancer (PCa) represents one of the most prevalent types of cancers and is a large health burden for men. The pathogenic mechanisms of PCa still need further investigation. The aim of this study was to construct an effective signature to predict the prognosis of PCa patients and identify the biofunctions of signature-related genes. First, we screened differentially expressed genes (DEGs) between PCa and normal control tissues in The Cancer Genome Atlas (TCGA) and GSE46602 datasets, and we performed weighted gene co-expression network analysis (WGCNA) to determine gene modules correlated with tumors. In total, 124 differentially co-expressed genes were retained. Additionally, five genes (ARHGEF38, NETO2, PRSS21, GOLM1, and SAPCD2) were identified to develop the prognostic signature based on TCGA dataset. The five-gene risk score was verified as an independent prognostic indicator through multivariate Cox regression analyses. The expression of the five genes involved in the signature was detected in the Gene Expression Omnibus (GEO), Gene Expression Profiling Interactive Analysis (GEPIA), and Oncomine databases. In addition, we utilized DiseaseMeth 2.0 and MEXPRESS for further analysis and found that abnormal methylation patterns may be a potential mechanism for these five DEGs in PCa. Finally, we observed that these genes, except PRSS21, were highly expressed in tumor samples and PCa cells. Functional experiments revealed that silencing ARHGEF38, NETO2, GOLM1, and SAPCD2 suppressed the proliferation, migration, and invasiveness of PCa cells. In summary, this prognostic signature had significant clinical significance for treatment planning and prognostic evaluation of patients with PCa. Thus, ARHGEF38, NETO2, GOLM1, and SAPCD2 may serve as oncogenes in PCa.


2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Liu-qing Zhou ◽  
Jin-xiong Shen ◽  
Jie-yu Zhou ◽  
Yao Hu ◽  
Hong-jun Xiao

AbstractN6-methyladenosine (m6A) modifications play an essential role in tumorigenesis. These modifications modulate RNAs, including mRNAs and lncRNAs. However, the prognostic role of m6A-related lncRNAs in head and neck squamous cell carcinoma (HNSCC) is poorly understood. Based on LASSO Cox regression, enrichment analysis, univariate and multivariate Cox regression analysis, a prognostic risk model, and consensus clustering analysis, we analyzed 12 m6A-related lncRNAs in HNSCC sample data from The Cancer Genome Atlas (TCGA) database. We found 12 m6A-related lncRNAs in the training cohort and validated them in all cohorts by Kaplan–Meier and Cox regression analyses, revealing their independent prognostic value in HNSCC. Moreover, ROC analysis was conducted, confirming the strong predictive ability of this signature for HNSCC survival. GSEA and detailed immune infiltration analyses revealed specific pathways associated with m6A-related lncRNAs. In this study, a novel risk model including twelve genes (SAP30L-AS1, AC022098.1, LINC01475, AC090587.2, AC008115.3, AC015911.3, AL122035.2, AC010226.1, AL513190.1, ZNF32-AS1, AL035587.1 and AL031716.1) was built. It could accurately predict HNSCC outcomes and could provide new therapeutic targets for HNSCC patients.


2020 ◽  
Author(s):  
Jun Hu ◽  
Fang Wang ◽  
Logen Liu ◽  
Wenfeng Ning

Abstract BACKGROUND: Mounting evidence has shown that long noncoding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) which participate in the initiation and progression of cancers. In the ceRNA network, lncRNAs, microRNAs (miRNAs) and mRNAs, communicate with and co-regulate each other. Rarely there is a systematic lncRNA-mediated ceRNA network and potential specific ceRNA pairs or triples of esophageal cancer (EC). In this study, we investigate the lncRNA-mediated ceRNA network in EC and screen the potential prognostic lncRNA biomarkers.METHODS: We obtained mRNA, miRNA, and lncRNA expression data and relevant clinical features on patients with EC from The Cancer Genome Atlas (TCGA), and used the edgR package to identify differentially expressed mRNAs, lncRNAs and miRNAs between EC samples and normal samples. The EC ceRNA network was constructed based on miRNA target prediction through the databases of miRcode, miRDB, miRTarBase and TargetScan. And then Pearson’s correlation analysis was adopted to identify co-expression mRNA-lncRNA pairs. Finally, the robust likelihood-based survival analysis and Cox regression models were used to identify prognosis-related lncRNAs, which was evaluated by Kaplan-Meier and receiver operating characteristic (ROC) curve analysis.RESULTS: A total of 3,200 mRNAs, 131 miRNAs and 1,338 lncRNAs were identified as significantly differentially expressed in EC, of which, 30 mRNAs, 15 lncRNAs, and 8 miRNAs were incorporated in the ceRNA network. According to the ceRNA network node degrees, lncRNA MAGI2-AS3, hsa-mir-93 and TGFBR2 were the key genes. Also, the ceRNA network revealed some important ceRNA pairs and triples, such as SNX29P2-TGFBR2 and MAGI2-AS-hsa-mir-143-COL1A1. Finally, we developed a six-lncRNA signature (ZNF341-AS1, AC130324.2, AC027271.1, AL591212.1, AL732314.4 and LOC105372352), with improved diagnostic potential for EC with the area under the ROC curve of 0.93.CONCLUSIONS: our present work sheds new light on the tumorigenesis roles of lncRNA-mediated ceRNA network in EC and identifies a six‐lncRNA model that could be used as candidate prognostic signature.


BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rui Shen ◽  
Bo Liu ◽  
Xuesen Li ◽  
Tengbo Yu ◽  
Kuishuai Xu ◽  
...  

Abstract Background Sarcomas is a group of heterogeneous malignant tumors originated from mesenchymal tissue and different types of sarcomas have disparate outcomes. The present study aims to identify the prognostic value of immune-related genes (IRGs) in sarcoma and establish a prognostic signature based on IRGs. Methods We collected the expression profile and clinical information of 255 soft tissue sarcoma samples from The Cancer Genome Atlas (TCGA) database and 2498 IRGs from the ImmPort database. The LASSO algorithm and Cox regression analysis were used to identify the best candidate genes and construct a signature. The prognostic ability of the signature was evaluated by ROC curves and Kaplan-Meier survival curves and validated in an independent cohort. Besides, a nomogram based on the IRGs and independent prognostic clinical variables was developed. Results A total of 19 IRGs were incorporated into the signature. In the training cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.938, 0.937 and 0.935, respectively. The Kaplan-Meier survival curve indicated that high-risk patients were significantly worse prognosis (P < 0.001). In the validation cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.730, 0.717 and 0.647, respectively. The Kaplan-Meier survival curve also showed significant distinct survival outcome between two risk groups. Furthermore, a nomogram based on the signature and four prognostic variables showed great accuracy in whole sarcoma patients and subgroup analyses. More importantly, the results of the TF regulatory network and immune infiltration analysis revealed the potential molecular mechanism of IRGs. Conclusions In general, we identified and validated an IRG-based signature, which can be used as an independent prognostic signature in evaluating the prognosis of sarcoma patients and provide potential novel immunotherapy targets.


PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11697
Author(s):  
Feng Jiang ◽  
Min Liang ◽  
Xiaolu Huang ◽  
Wenjing Shi ◽  
Yumin Wang

Background PIMREG is upregulated in multiple cancer types. However, the potential role of PIMREG in lung adenocarcinoma (LUAD) remains unclear. The present study aimed to explore its clinical significance in LUAD. Methods Using the Cancer Genome Atlas (TCGA) databases, we obtained 513 samples of LUAD and 59 normal samples from the Cancer Genome Atlas (TCGA) databases to analyze the relationship between PIMREG and LUAD. We used t and Chi-square tests to evaluate the level of expression of PIMREG and its clinical implication in LUAD. The prognostic value of PIMREG in LUAD was identified through the Kaplan–Meier method, Cox regression analysis, and nomogram. Gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were performed to screen biological pathways and analyze the correlation of the immune infiltrating level with the expression of PIMREG in LUAD. Results PIMREG was highly expressed in patients with LUAD. Specifically, the level of PIMREG gradually increased from pathological stage I to IV. Further, we validated the higher expression of PIMREG expressed in LUAD cell lines. Moreover, PIMREG had a high diagnostic value, with an -AUC of 0.955. Kaplan–Meier survival and Cox regression analyses revealed that the high expression of PIMREG was independently associated with poor clinical outcomes. In our prognostic nomogram, the expression of PIMREG implied a significant prognostic value. Gene set enrichment analysis (GSEA) identified that the high expression PIMREG phenotype was involved in the mitotic cell cycle, mRNA splicing, DNA repair, Rho GTPase signaling, TP53 transcriptional regulation, and translation pathways. Next, we also explored the correlation of PIMREG and tumor-immune interactions and found a negative correlation between PIMREG and the immune infiltrating level of T cells, macrophages, B cells, dendritic cells (DCs) , and CD8+ T cells in LUAD. Conclusions High levels of PIMREG correlated with poor prognosis and immune infiltrates in LUAD.


2021 ◽  
Author(s):  
Yen-ting Lin ◽  
Can-Xuan Li ◽  
Jie Chen

Abstract Background: Ferroptosis is a novel defined type of programmed cell death (PCD) with widespread functions involved in physical conditions or multiple diseases including malignancies. However, the relationship between ccRCC and ferroptosis-related regulators remains poorly known. Herein, we investigate the prognostic values and potential mechanisms of ferroptosis-related genes (FRGs) in ccRCC.Methods: Ferroptosis-related genes were obtained from FerrDb database, GeneCards database and previously published literatures. The gene expression profile of ferroptosis-related regulators and corresponding clinicopathological information were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed ferroptosis-related genes (DE-FRGs) were screened between ccRCC specimens and noncancerous specimens. Among these genes, prognostic DE-FRGs were identified using univariate COX analysis and LASSO regression analysis. Further multivariate COX regression was employed to identify prognosis-related hub DE-FRGs and establish a prognostic model. Results: We identified seven hub genes (HMGCR, MT1G, BID, EIF4A1, FOXM1, TFAP2C and CHAC1) from the DE-FRGs using univariate Cox regression analysis, LASSO and multivariate Cox regression analysis, and used them to establish a novel clinical predictive model in the TCGA train cohort (n = 374). Subsequently, we assessed the prognostic value of the model. Survival analysis showed that high-risk patients had a reduced overall survival (OS), the time-dependent receiver operating characteristic (ROC) curve analysis confirmed the signature's diagnostic performance. Additionally, multivariate Cox regression analysis suggested that the risk score was an independent prognostic factor. Additionally, we verified the prognostic performance of the risk model in the testing cohort (n=156), and the entire group (n=530) using Kaplan-Meier curve and ROC curve analyses. Functional analysis indicated that several carcinogenic pathways were enriched, and tumor-infiltrating immune cell abundances, and the expression levels of immunosuppressive molecules were different between two risk groups. Finally, external databases (ONCMINE, GEPIA, HPA, Kaplan-Meier plotter and cbioportal) were used to confirm the expression patterns, prognostic value, and genetic mutations of 7 hub FRGs in ccRCC.Conclusions: Collectively, we successfully constructed a novel ferroptosis-related risk signature that was significantly associated with the prognosis of ccRCC.


2020 ◽  
Author(s):  
Rui Shen ◽  
Xiangying Meng ◽  
Jianyi Li ◽  
Tengbo Yu ◽  
Kuishuai Xu

Abstract Background Sarcomas is a group of heterogeneous malignant tumors originated from mesenchymal tissue and different types of sarcomas have disparate outcomes. The present study aims to identify the prognostic value of immune-related genes (IRGs) in sarcoma and establish a prognostic signature based on IRGs. Methods We collected the expression profile and clinical information of 255 soft tissue sarcoma samples from The Cancer Genome Atlas (TCGA) database and 2498 IRGs from the ImmPort database. The LASSO algorithm and Cox regression analysis were used to identify the best candidate genes and construct a signature. The prognostic ability of the signature was evaluated by ROC curves and Kaplan-Meier survival curves and validated in an independent cohort. Besides, a nomogram based on the IRGs and independent prognostic clinical variables was developed. Results A total of 19 IRGs were incorporated into the signature. In the training cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.938, 0.937 and 0.935, respectively. The Kaplan-Meier survival curve indicated that high-risk patients were significantly worse prognosis(P < 0.001). In the validation cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.730, 0.717 and 0.647, respectively. The Kaplan-Meier survival curve also showed significant distinct survival outcome between two risk groups. Furthermore, a nomogram based on the signature and four prognostic variables showed great accuracy in whole sarcoma patients and subgroup analyses. More importantly, the results of the TF regulatory network and immune infiltration analysis revealed the potential molecular mechanism of IRGs. Conclusions In general, we identified and validated an IRG-based signature, which can be used as an independent prognostic signature in evaluating the prognosis of sarcoma patients and provide potential novel immunotherapy targets.


2020 ◽  
Vol 43 (3) ◽  
pp. E49-59 ◽  
Author(s):  
Jiahong Chen ◽  
Maozhang Li ◽  
Shumin Fang ◽  
Xiaobo Zhou ◽  
Jinxian Liao ◽  
...  

Purpose: To investigate the clinical relevance and biological function of the kinesin super-family protein 4A (KIF4A) expression in prostate cancer (PCa). Methods: We examined 1) the relationship between the expression of KIF4A and clinico-pathological characteristics of PCa patients using a tissue microarray and the Cancer Genome Atlas database, 2) the prognostic value of KIF4A expression in patients using Kaplan-Meier plots and 3) the functions of KIF4A in LNCaP and DU145 cells, such as cell proliferation, cell cycle and cell apoptosis. Results: Compared with normal prostate, the mRNA and protein expressions of KIF4A were up-regulated in PCa. The up-regulation expression rates of KIF4A in PCa were significantly related to the Gleason score (P


2019 ◽  
Author(s):  
Jun Hu ◽  
Fang Wang ◽  
Logen Liu ◽  
Ning Wenfeng

Abstract BACKGROUND: Mounting evidence has shown that long noncoding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) which participate in the initiation and progression of cancers. In the ceRNA network, lncRNAs, microRNAs (miRNAs) and mRNAs, communicate with and co-regulate each other. Rarely there is a systematic lncRNA-mediated ceRNA network and potential specific ceRNA pairs or triples of esophageal cancer (EC). In this study, we investigate the lncRNA-mediated ceRNA network in EC and screen the potential prognostic lncRNA biomarkers. METHODS: We obtained mRNA, miRNA, and lncRNA expression data and relevant clinical features on patients with EC from The Cancer Genome Atlas (TCGA), and used the edgR package to identify differentially expressed mRNAs, lncRNAs and miRNAs between EC samples and normal samples. The EC ceRNA network was constructed based on miRNA target prediction through the databases of miRcode, miRDB, miRTarBase and TargetScan. And then Pearson’s correlation analysis was adopted to identify co-expression mRNA-lncRNA pairs. Finally, the robust likelihood-based survival analysis and Cox regression models were used to identify prognosis-related lncRNAs, which was evaluated by Kaplan-Meier and receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 3,200 mRNAs, 131 miRNAs and 1,338 lncRNAs were identified as significantly differentially expressed in EC, of which, 30 mRNAs, 15 lncRNAs, and 8 miRNAs were incorporated in the ceRNA network. According to the ceRNA network node degrees, lncRNA MAGI2-AS3, hsa-mir-93 and TGFBR2 were the key genes. Also, the ceRNA network revealed some important ceRNA pairs and triples, such as SNX29P2-TGFBR2 and MAGI2-AS-hsa-mir-143-COL1A1. Finally, we developed a six-lncRNA signature (ZNF341-AS1, AC130324.2, AC027271.1, AL591212.1, AL732314.4 and LOC105372352), with improved diagnostic potential for EC with the area under the ROC curve of 0.93. CONCLUSIONS: our present work sheds new light on the tumorigenesis roles of lncRNA-mediated ceRNA network in EC and identifies a six‐lncRNA model that could be used as candidate prognostic signature.


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