scholarly journals Construction and Comprehensive Analyses of a METTL5-Associated Prognostic Signature With Immune Implication in Lung Adenocarcinomas

2021 ◽  
Vol 11 ◽  
Author(s):  
Sijin Sun ◽  
Kailun Fei ◽  
Guochao Zhang ◽  
Juhong Wang ◽  
Yannan Yang ◽  
...  
Keyword(s):  
Poor Prognosis ◽  
Cox Regression ◽  
Prognostic Score ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Normal Lung ◽  
Cox Regression Analysis ◽  
Tcga Dataset

For lung adenocarcinoma (LUAD), patients of different stages have strong heterogeneity, and their overall prognosis varies greatly. Thus, exploration of novel biomarkers to better clarify the characteristics of LUAD is urgent. Multi-omics information of LUAD patients were collected form TCGA. Three independent LUAD cohorts were obtained from gene expression omnibus (GEO). A multi-omics correlation analysis of METTL5 was performed in TCGA dataset. To build a METTL5-associated prognostic score (MAPS). Spathial and random forest methods were first applied for feature selection. Then, LASSO was implemented to develop the model in TCGA cohort. The prognostic value of MAPS was validated in three independent GEO datasets. Finally, functional annotation was conducted using gene set enrichment analysis (GSEA) and the abundances of infiltrated immune cells were estimated by ImmuCellAI algorithm. A total of 901 LUAD patients were included. The expression of METTL5 in LUAD was significantly higher than that in normal lung tissue. And high expression of METTL5 indicated poor prognosis in all different stages (P < 0.001, HR = 1.81). Five genes (RAC1, C11of24, METTL5, RCCD1, and SLC7A5) were used to construct MAPS and MAPS was significantly correlated with poor prognosis (P < 0.001, HR = 2.15). Furthermore, multivariate Cox regression analysis suggested MAPS as an independent prognostic factor. Functional enrichment revealed significant association between MAPS and several immune components and pathways. This study provides insights into the potential significance of METTL5 in LUAD and MAPS can serve as a promising biomarker for LUAD.

scholarly journals BACH1: A Potential Predictor of Survival in Early-Stage Lung Adenocarcinoma

2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Jin Zhou ◽  
Zheming Liu ◽  
Huibo Zhang ◽  
Tianyu Lei ◽  
Jiahui Liu ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Early Stage ◽  
External Validation ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Cox Regression Analysis

Purpose. Recent researches showed the vital role of BACH1 in promoting the metastasis of lung cancer. We aimed to explore the value of BACH1 in predicting the overall survival (OS) of early-stage (stages I-II) lung adenocarcinoma. Patients and Methods. Lung adenocarcinoma cases were screened from the Cancer Genome Atlas (TCGA) database. Functional enrichment analysis was performed to obtain the biological mechanisms of BACH1. Gene set enrichment analysis (GSEA) was performed to identify the difference of biological pathways between high- and low-BACH1 groups. Univariate and multivariate COX regression analysis had been used to screen prognostic factors, which were used to establish the BACH1 expression-based prognostic model in the TCGA dataset. The C-index and time-dependent AUC curve were used to evaluate predictive power of the model. External validation of prognostic value was performed in two independent datasets from Gene Expression Omnibus (GEO). Decision analysis curve was finally used to evaluate clinical usefulness of the BACH1-based model beyond pathologic stage alone. Results. BACH1 was an independent prognostic factor for lung adenocarcinoma. High-expression BACH1 cases had worse OS. BACH1-based prognostic model showed an ideal C-index and t -AUC and validated by two GEO datasets, independently. More importantly, the BACH1-based model indicated positive clinical applicability by DCA curves. Conclusion. Our research confirmed that BACH1 was an important predictor of prognosis in early-stage lung adenocarcinoma. The higher the expression of BACH1, the worse OS of the patients.

scholarly journals Low REST Expression Indicates a Biomarker of Poor Prognosis in Patients with Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chaoxiang Lv ◽  
Yuanguo Li ◽  
Qiqi Zhang ◽  
Yanyan Chen ◽  
Dandan Wei ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Cholesterol Homeostasis ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis

It was initially found that neural-restrictive silencer factor/repressor 1-silencing transcription factor (REST) is a transcriptional repressor of neuronal genes in nonneuronal cells. However, it is reported to be abundantly expressed in various types of aggressive cancer cells. In this study, we evaluated the expression patterns of REST in renal cell carcinoma and found that its expression is lower in tumor tissues compared to normal tissues. The chi-square test showed that the low REST expression was closely related to patients’ clinicopathologic parameters, including the pathologic stage and survival status. ROC curve showed that REST had excellent clinical diagnostic prospect. In addition, patients with low REST expression had poor over survival (OS) and relapse-free survival (RFS). Univariate and multivariate Cox regression analysis confirmed that the low REST expression was an independent predictor of poor prognosis in renal cell carcinoma. Gene set enrichment analysis identified P53 pathway, reactive oxygen species pathway, glycolysis, DNA repair, cholesterol homeostasis, and MYC targets V2 enriched with low REST expression phenotype. These results suggested that REST may be a novel biomarker for the diagnosis and prognosis of renal cell carcinoma in clinical applications.

scholarly journals Comprehensive Analysis of the Tumor Microenvironment and Ferroptosis-Related Genes Predict Prognosis with Ovarian Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiao-xue Li ◽  
Li Xiong ◽  
Yu Wen ◽  
Zi-jian Zhang
Keyword(s):  
Ovarian Cancer ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
Gene Set Enrichment Analysis ◽  
Drug Resistant ◽  
Cox Regression Analysis ◽  
Gene Set ◽  
Immune Infiltration ◽  
Tcga Dataset

The early diagnosis of ovarian cancer (OC) is critical to improve the prognosis and prevent recurrence of patients. Nevertheless, there is still a lack of factors which can accurately predict it. In this study, we focused on the interaction of immune infiltration and ferroptosis and selected the ESTIMATE algorithm and 15 ferroptosis-related genes (FRGs) to construct a novel E-FRG scoring model for predicting overall survival of OC patients. The gene expression and corresponding clinical characteristics were obtained from the TCGA dataset (n = 375), GSE18520 (n = 53), and GSE32062 (n = 260). A total of 15 FRGs derived from FerrDb with the immune score and stromal score were identified in the prognostic model by using least absolute shrinkage and selection operator (LASSO)–penalized COX regression analysis. The Kaplan–Meier survival analysis and time-dependent ROC curves performed a powerful prognostic ability of the E-FRG model via multi-validation. Gene Set Enrichment Analysis and Gene Set Variation Analysis elucidate multiple potential pathways between the high and low E-FRG score group. Finally, the proteins of different genes in the model were verified in drug-resistant and non–drug-resistant tumor tissues. The results of this research provide new prospects in the role of immune infiltration and ferroptosis as a helpful tool to predict the outcome of OC patients.

scholarly journals A Novel Ferroptosis-Related lncRNA Signature and an Immune Perspective of Cervical Cancer

2021 ◽  
Author(s):  
Pengxiang Li ◽  
Dongchun Qin ◽  
Xuefeng Lv ◽  
Lu Liu ◽  
Mengle Peng
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Immune Checkpoints ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background: Cervical cancer (CC) is the most common reproductive neoplasm in women, especially in developing countries. Ferroptosis, a novel type of cell death, and lncRNAs play critical roles in the prognosis of CC patients and antitumor immunity. Methods: A ferroptosis-related lncRNA signature (FRLS) was constructed by LASSO Cox regression analysis. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, multivariate analysis, and nomogram were used to evaluate and predict the FRLS. Based on the FRLS, immune-related genes, the tumor microenvironment (TME), immune checkpoints, and immunotherapy were investigated. Results: The FRLS was composed of ten lncRNAs and was markedly associated with the overall survival (OS) of CC patients. Gene set enrichment analysis (GSEA) demonstrated that the FRLS was largely associated with immune-related pathways. Weighted gene co-expression network analysis (WGCNA) was performed to analyze immune-related genes and to identify the optimal modules and genes. TLR4 was eventually identified, and its expression was verified in the Gene Expression Omnibus (GEO) database. Then, quantitative real-time PCR (qRT-PCR) was used to validate the results in CC and paracancerous tissues. Besides, our results showed that CD8+ T cells were significantly correlated between the low- and high-risk groups, and it could modulate ferroptosis during tumor immunotherapy. The expression of immune checkpoints was substantially different between the two groups. Additionally, tumor immune dysfunction and exclusion (TIDE) was applied to predict the sensitivity of immune checkpoint inhibitor (ICI) treatment. Conclusion: The FRLS established was significantly associated with prognosis; moreover, the FRLS is a prospective therapeutic target, and combined with immunotherapy, can be used in the treatment of CC patients.

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

2019 ◽  
Vol 19 (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.

scholarly journals Comprehensive Transcriptomic Analysis of Critical RNA Regulation Associated With Metabolism and Prognosis in Clear Cell Renal Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Si Liu ◽  
Honglan Zhou ◽  
Gang Wang ◽  
Xin Lian
Keyword(s):  
Gene Expression ◽  
Metabolic Pathways ◽  
Prognostic Model ◽  
Clear Cell ◽  
Prognostic Score ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
G1 Arrest ◽  
Cox Regression Analysis

This study focuses on investigating the metabolism-related gene profile and prognosis of clear cell renal cell carcinoma (ccRCC) patients. The research data from the Gene Expression Omnibus database, including GSE40435, GSE53757, and GSE53000, were used to analyze the consistently differentially expressed RNAs (cDERs) by the MetaDE limma package. Gene expression profiling associated with metabolism was downloaded from the GSEA database. The cancer genome atlas (TCGA) dataset of ccRCC (the training set) and RNA sequencing data of E-MTAB-3267 from EBI ArrayExpress database (the validation set) were obtained to construct a prognostic model. A series of bioinformatics analysis, including functional enrichment analysis, Cox regression analysis, and constructing a prognostic score (PS) model, was performed. Further in vitro experiments including cell proliferation assay and flow cytometry were performed to validate our results. We constructed a metabolism-related prognostic model based on 27 DElncRNAs and 126 DEGs. Gene Set Enrichment Analysis revealed that 19 GO terms and 9 KEGG signaling pathways were significantly associated with lipid metabolic pathways. Furthermore, we generated a nomogram illustrating the association between the identified DERs and the tumor recurrence risk in ccRCC. The results from experimental validation showed that lncRNA SNHG20 was significantly upregulated in tumor tissues compared with adjacent tissues. Knockdown of SNHG20 suppressed the proliferation and induced cell cycle G0/G1 arrest, and apoptosis in ccRCC cells. Our study might contribute to a better understanding of metabolic pathways and to the further development of novel therapeutic approaches for ccRCC.

scholarly journals Development of Autophagy Signature-Based Prognostic Nomogram for Refined Glioma Survival Prognostication

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Yuxiang Fan ◽  
Xinyu Peng ◽  
Baoqin Li ◽  
Gang Zhao
Keyword(s):  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
World Health ◽  
Cox Regression Analysis ◽  
High Signature ◽  
Health Organization ◽  
Glioma Classification

The current glioma classification could be optimized to cover such a separate and individualized prognosis ranging from a few months to over ten years. Considering its highly conserved role and potential in therapies, autophagy might be a promising element to be incorporated as a refinement for improved survival prognostication. The expression and RNA-seq data of 881 glioma patients from the Gene Expression Omnibus and The Cancer Genome Atlas were included, mapped with autophagy-related genes. Weighted gene coexpression network analysis and Cox regression analysis were used for the autophagy signature establishment, which composed of MUL1, NPC1, and TRIM13. Validations were represented by Kaplan-Meier plots and receiver operating curves (ROC). Cluster analysis suggested the IDH1 mutant involved in the favorable prognosis of the signature clusters. The signature was also immune-related shown by the Gene Ontology analysis and the Gene Set Enrichment Analysis. The high signature risk group held a higher ESTIMATE score (p=2.6e−11) and stromal score (p=1.8e−10). CD276 significantly correlated with the signature (r=0.51, p<0.05). The final nomogram integrated with the autophagy signature, IDH1 mutation, and pathological grade was built with accuracy and discrimination (1-year survival AUC=0.812, 5-year survival AUC=0.822, and 10-year survival AUC=0.834). Its prognostic value and clinical utility were well-defined by the superiority in the comparisons with the current World Health Organization glioma classification in ROC (p<0.05) and decision curve analysis. The autophagy signature-based IDH1 mutation and grade nomogram refined glioma classification for a more individualized and clinically applicable survival estimation and inspired potential autophagy-related therapies.

scholarly journals A Five-Immune-Related lncRNA Signature Predicts Survival of Patients With Osteosarcoma

2021 ◽  
Author(s):  
Jie Huang ◽  
Hongyi Lai ◽  
Wentao Qin ◽  
Zhandong Bo ◽  
Zhen Tan ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Data Matrix ◽  
Lasso Regression ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background: Osteosarcoma (OS) is the most common primary solid malignant bone tumor, and its metastasis is a prominent cause of high mortality in patients.Methods: A risk signature was constructed based on re-annotating the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) data matrix, of the lncRNAs related to OS prognosis and immunity. From the OS transcription data, which is downloaded from the TARGET, the 1126 lncRNAs those harbour co-expressions with immunity genes were selected by Pearson correlation test and later divided into the training set (n=44) and validation set (n=41) with the caret package of R. With the training set we build the model related to Osteosarcoma prognosis by the univariate and multivariate Cox, and the Lasso regression analysis, and in combination with the clinical factors we conducted the multivariate Cox regression analysis to build the 1-year, 3-year and 5-year survival rate nomograms. Afterwards, we validated the ROC and the calibration curve of the subjects with the validation set and the whole dataset. Lastly, we performed functional enrichment analysis with the GSEA, GO and KEGG to figure out the biological functions of the prognosis genes.Results: The training set was performed in univariate and multivariate Cox regression analysis, identifying 25 lncRNAs correlated with prognosis. Eleven lncRNAs were selected by the least absolute shrinkage and selection operator (LASSO) regression for multivariate cox analysis and Kaplan-Meier (KM) survival analysis. Finally, lncRNAs (RP11-69E11.4, SNHG6, MIR210HG, RP11-750H9.5 and CTD-2341M24.1) risk signature was constructed, and the validation set and the whole dataset were used to evaluate the prediction stability and accuracy of the signature. The survival times of high- and low-risk groups were significantly different in the training set, validation set and the whole dataset. Further, function enrichment and gene set enrichment analysis revealed that the lncRNAs in the signature may affect the proliferation, migration, chemotaxis and combination of Osteosarcoma-related immune cells, and involve in every pathways of OS metabolism. Conclusion: The five lncRNAs survival risk signature could potentially predict the prognosis of OS patients, additionally, may provide novel insights for future clinical diagnosis and treatment of OS.

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.

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