scholarly journals Comprehensive Analysis of Cell Cycle-Related Genes in Patients With Prostate Cancer

2022 ◽  
Vol 11 ◽  
Author(s):  
Zehua Liu ◽  
Rongfang Pan ◽  
Wenxian Li ◽  
Yanjiang Li
Keyword(s):  
Prostate Cancer ◽  
Cell Cycle ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Cancer Center ◽  
Clinical Prognosis ◽  
Normal Tissues

This study aimed to identify critical cell cycle-related genes (CCRGs) in prostate cancer (PRAD) and to evaluate the clinical prognostic value of the gene panel selected. Gene set variation analysis (GSVA) of dysregulated genes between PRAD and normal tissues demonstrated that the cell cycle-related pathways played vital roles in PRAD. Patients were classified into four clusters, which were associated with recurrence-free survival (RFS). Moreover, 200 prognostic-related genes were selected using the Kaplan–Meier (KM) survival analysis and univariable Cox regression. The prognostic CCRG risk score was constructed using random forest survival and multivariate regression Cox methods, and their efficiency was validated in Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70770. We identified nine survival-related genes: CCNL2, CDCA5, KAT2A, CHTF18, SPC24, EME2, CDK5RAP3, CDC20, and PTTG1. Based on the median risk score, the patients were divided into two groups. Then the functional enrichment analyses, mutational profiles, immune components, estimated half-maximal inhibitory concentration (IC50), and candidate drugs were screened of these two groups. In addition, the characteristics of nine hub CCRGs were explored in Oncomine, cBioPortal, and the Human Protein Atlas (HPA) datasets. Finally, the expression profiles of these hub CCRGs were validated in RWPE-1 and three PRAD cell lines (PC-3, C4-2, and DU-145). In conclusion, our study systematically explored the role of CCRGs in PRAD and constructed a risk model that can predict the clinical prognosis and immunotherapeutic benefits.

scholarly journals Application of an Autophagy-Related Gene Prognostic Risk Model Based on TCGA Database in Cervical Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Huadi Shi ◽  
Fulan Zhong ◽  
Xiaoqiong Yi ◽  
Zhenyi Shi ◽  
Feiyan Ou ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Survival Prediction ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Model Based

Background: Autophagy plays an important role in the development of cancer. However, the prognostic value of autophagy-related genes (ARGs) in cervical cancer (CC) is unclear. The purpose of this study is to construct a survival model for predicting the prognosis of CC patients based on ARG signature.Methods: ARGs were obtained from the Human Autophagy Database and Molecular Signatures Database. The expression profiles of ARGs and clinical data were downloaded from the TCGA database. Differential expression analysis of CC tissues and normal tissues was performed using R software to screen out ARGs with an aberrant expression. Univariate Cox, Lasso, and multivariate Cox regression analyses were used to construct a prognostic model which was validated by using the test set and the entire set. We also performed an independent prognostic analysis of risk score and some clinicopathological factors of CC. Finally, a clinical practical nomogram was established to predict individual survival probability.Results: Compared with normal tissues, there were 63 ARGs with an aberrant expression in CC tissues. A risk model based on 3 ARGs was finally obtained by Lasso and Cox regression analysis. Patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both train set and validation set. The ROC curve validated its good performance in survival prediction, suggesting that this model has a certain extent sensitivity and specificity. Multivariate Cox analysis showed that the risk score was an independent prognostic factor. Finally, we mapped a nomogram to predict 1-, 3-, and 5-year survival for CC patients. The calibration curves indicated that the model was reliable.Conclusion: A risk prediction model based on CHMP4C, FOXO1, and RRAGB was successfully constructed, which could effectively predict the prognosis of CC patients. This model can provide a reference for CC patients to make precise treatment strategy.

scholarly journals Integrated analysis of autophagy-related genes(ATGs) reveals the prognostic value of oral squamous cell carcinoma

2020 ◽  
Author(s):  
Guangzhao Huang ◽  
Zhi-yun Li ◽  
Yu Rao ◽  
Xiao-zhi Lv
Keyword(s):  
Survival Analysis ◽  
Signaling Pathway ◽  
Risk Score ◽  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Score Model

Abstract Background: Increasing evidence demonstrated that autophagy paly a crucial role in initiation and progression of OSCC. The aim of this study was to explore the prognostic value of autophagy-related genes(ATGs) in patients with OSCC. RNA-seq and clinical data were downloaded from TCGA database following extrating ATGs expression profiles. Then, differentially expressed analysis was performed in R software EdgeR package, and the potential biological function of differentially expressed ATGs were explored by GO and KEGG enrichment analysis. Furthermore, a risk score model based on ATGs was constructed to predict the overall survival. Moreover, univariate, multivariate cox regression and survival analysis were used to select autophagy related biomarkers which were identified by RT-qPCR in OSCC cell lines, OSCC tissues and matched normal mucosal tissues. Results: Total of 232 ATGs were extrated and 37 genes were differentially expressed in OSCC. GO and KEGG analysis indicated that these differentially expressed genes were mainly located in autophagosome membrane, and associated with apoptosis, platinum drug resistance, ErbB signaling pathway and TNF signaling pathway. Furthermore, a risk score model including 9 variables was constructed and subsequently identified with univariate, multivariate cox regression, survival analysis and Receiver Operating Characteristic curve(ROC). Moreover, ATG12 and BID were identified as potential autophagy related biomakers. Conclusion: This study successfully constructed a risk model to predict the prognosis of patients with OSCC, and the risk score may be as a independent prognostic biomarker in OSCC. ATG12 and BID were identified as potential biomarkers in tumor diagnosis and treatment of OSCC.

scholarly journals Development of Prognostic Indicator Based on Autophagy-Related lncRNA Analysis in Colon Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Weige Zhou ◽  
Shijing Zhang ◽  
Hui-biao Li ◽  
Zheyou Cai ◽  
Shuting Tang ◽  
...  
Keyword(s):  
Risk Score ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Lasso Regression

There were no systematic researches about autophagy-related long noncoding RNA (lncRNA) signatures to predict the survival of patients with colon adenocarcinoma. It was necessary to set up corresponding autophagy-related lncRNA signatures. The expression profiles of lncRNAs which contained 480 colon adenocarcinoma samples were obtained from The Cancer Genome Atlas (TCGA) database. The coexpression network of lncRNAs and autophagy-related genes was utilized to select autophagy-related lncRNAs. The lncRNAs were further screened using univariate Cox regression. In addition, Lasso regression and multivariate Cox regression were used to develop an autophagy-related lncRNA signature. A risk score based on the signature was established, and Cox regression was used to test whether it was an independent prognostic factor. The functional enrichment of autophagy-related lncRNAs was visualized using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Ten prognostic autophagy-related lncRNAs (AC027307.2, AC068580.3, AL138756.1, CD27-AS1, EIF3J-DT, LINC01011, LINC01063, LINC02381, AC073896.3, and SNHG16) were identified to be significantly different, which made up an autophagy-related lncRNA signature. The signature divided patients with colon adenocarcinoma into the low-risk group and the high-risk group. A risk score based on the signature was a significantly independent factor for the patients with colon adenocarcinoma (HR=1.088, 95%CI=1.057−1.120; P<0.001). Additionally, the ten lncRNAs were significantly enriched in autophagy process, metabolism, and tumor classical pathways. In conclusion, the ten autophagy-related lncRNAs and their signature might be molecular biomarkers and therapeutic targets for the patients with colon adenocarcinoma.

Association of the clinical cell-cycle risk score with metastasis after radiation therapy and identification of men with prostate cancer who can forgo combined androgen deprivation therapy.

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 195-195
Author(s):  
Jonathan David Tward ◽  
Constantine Mantz ◽  
Neal D. Shore ◽  
Paul Nguyen ◽  
Isla Garraway ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cell Cycle ◽  
Radiation Therapy ◽  
Androgen Deprivation Therapy ◽  
Risk Score ◽  
Cancer Metastasis ◽  
Cox Regression ◽  
Risk Group ◽  
Androgen Deprivation ◽  
Comprehensive Cancer Center

195 Background: This study evaluated the ability of the combined clinical cell-cycle risk score (CCR) to prognosticate the risk of prostate cancer metastasis in men receiving dose-escalated radiation therapy (RT) with or without androgen deprivation therapy (ADT). Methods: The CCR score is a validated model that combines the cell cycle progression score (CCP) with the UCSF Cancer of the Prostate Risk Assessment score (CAPRA). The CCR score and a CCR-based multimodality threshold score (2.112) were evaluated in a retrospective, multi-institutional cohort of men with National Comprehensive Cancer Center (NCCN) intermediate- or high-risk localized disease (N = 741) who received single (RT) or multimodality therapy (ADT with RT). Effects of prognostic variables were analyzed using Kaplan-Meier and Cox regression methods. Results: Median follow-up was 5.9 years. CCR predicted metastasis [hazard ratio (HR) 2.21, 95% Confidence Interval (CI) 1.70-2.87, p < 0.001]. The CCR score was a better prognosticator of metastasis (C-index 0.78) than either NCCN-risk group (C-index 0.70), CAPRA score (C-index 0.71), or CCP score (C-index 0.69) alone. In bivariate analyses, the CCR score remained highly prognostic for metastasis when comparing any ADT vs none (HR 2.19, 95% CI 1.62 to 2.97, p < 0.001), ADT duration as a continuous variable (HR 2.05, 95% CI 1.54-2.72, p < 0.001), or ADT use given as less than or at the recommended duration for each NCCN risk group (HR 2.22, 95% CI 1.71-2.88, p < 0.001). Men with CCR scores either below or above the threshold (2.112) had a 10-year risk of metastasis of 4.2% and 25.3%, respectively. For men below the threshold receiving RT alone versus RT+ADT, the 10-year risk of metastasis was 4.2% and 3.9%, respectively. Conclusions: CCR is a highly precise and accurate predictor of metastasis in men undergoing dose-escalated RT, with or without ADT. CCR adds clinically actionable information relative to guideline recommended therapies that are based on NCCN risk groups or CAPRA alone. Men with scores below the multimodality threshold may not significantly reduce their 10-year risk of metastasis with the addition of ADT.

scholarly journals The Prognostic Value of Risk Score Based on Immunogenenomic Landscape Analysis in Glioma

2020 ◽  
Author(s):  
Haitao Luo ◽  
Kai Huang ◽  
Chuming Tao ◽  
Mioaojing Wu ◽  
Minhua Ye ◽  
...  
Keyword(s):  
Risk Score ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Low Grade ◽  
Clinical Prognosis ◽  
Cox Analysis ◽  
Genome Atlas

Abstract Background: Glioma is a lethal intracranial tumor, and inflammation plays an important role in the initiation and development of glioma. Hence, there is an urgent need to conduct a bioinformatics analysis of immune-related genes (IRGs) for glioma. The present study aims to explore the association of the risk score with clinical outcomes and predict the prognosis with glioma. Methods: In The Cancer Genome Atlas (TCGA) database, 462 low grade glioma (LGG) samples and 166 glioblastoma (GBM) samples were reviewed, and IRGs correlated with the prognosis were selected by performing a survival analysis and establishing a Cox regression model. The potential molecular mechanism of these IRGs were also explored with assistance of computational biology. The risk score based on seven survival-associated IRGs was determined with the help of the multivariable Cox analysis, the patients were divided into two subgroups according to their risk score. Results: It was found that these differentially expressed IRGs were involved with the cytokine-cytokine receptor through functional enrichment analysis. The risk score based on the seven IRGs (SSTR5、CXCL10、CCL13、SAA1、CCL21、CCL27 and HTR1A) performed well in predicting patient’s the overall survival (OS), and correlated with age, 1p/19q codeletion status, IDH status, and WHO grades, both in the training (TCGA) datasets and the validation ((Chinese Glioma Genome Atlas) CGGA) datasets. The risk score also could reflect infiltration through several types of immune cells. Conclusions: This present study screened some IRGs associated with the patient’s clinical characteristic and prognosis, connect to the immune repertoire, demonstrated the importance of the risk score as a promising biomarker for estimating the clinical prognosis of glioma.

scholarly journals Identification of a Glycolysis-Related LncRNA Signature to Predict Survival in Diffuse Glioma Patients

2021 ◽  
Vol 10 ◽  
Author(s):  
Yangyang Wang ◽  
Wenjianlong Zhou ◽  
Shunchang Ma ◽  
Xiudong Guan ◽  
Dainan Zhang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Diffuse Glioma ◽  
Roc Curve Analysis

Glycolysis refers to one of the critical phenotypes of tumor cells, regulating tumor cell phenotypes and generating sufficient energy for glioma cells. A range of noticeable genes [such as isocitrate dehydrogenase (IDH), phosphatase, and tensin homolog (PTEN), or Ras] overall impact cell proliferation, invasion, cell cycle, and metastasis through glycolysis. Moreover, long non-coding RNAs (LncRNAs) are increasingly critical to disease progression. Accordingly, this study aimed to identify whether glycolysis-related LncRNAs have potential prognostic value for glioma patients. First, co-expression network between glycolysis-related protein-coding RNAs and LncRNAs was established according to Pearson correlation (Filter: |r| &gt; 0.5 &amp; P &lt; 0.001). Furthermore, based on univariate Cox regression, the Least Absolute Shrinkage and Selection Operator (LASSO) analysis and multivariate Cox regression, a predictive model were built; vital glycolysis-related LncRNAs were identified; the risk score of every single patient was calculated. Moreover, receiver operating characteristic (ROC) curve analysis, gene set enrichment analysis (GSEA), GO and KEGG enrichment analysis were performed to assess the effect of risk score among glioma patients. 685 cases (including RNA sequences and clinical information) from two different cohorts of the Chinese Glioma Genome Atlas (CGGA) database were acquired. Based on the mentioned methods, the risk score calculation formula was yielded as follows: Risk score = (0.19 × EXPFOXD2-AS1) + (−0.27 × EXPAC062021.1) + (−0.16 × EXPAF131216.5) + (−0.05 × EXPLINC00844) + (0.11 × EXPCRNDE) + (0.35 × EXPLINC00665). The risk score was independently related to prognosis, and every single mentioned LncRNAs was significantly related to the overall survival of patients. Moreover, functional enrichment analysis indicated that the biologic process of the high-risk score was mainly involved in the cell cycle and DNA replication signaling pathway. This study confirmed that glycolysis-related LncRNAs significantly impact poor prognosis and short overall survival and may act as therapeutic targets in the future.

scholarly journals Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis

2020 ◽  
Vol 7 ◽  
Author(s):  
Mingde Cao ◽  
Junhui Zhang ◽  
Hualiang Xu ◽  
Zhujian Lin ◽  
Hong Chang ◽  
...  
Keyword(s):  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
External Validation ◽  
Treatment Strategies ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Immune Related Genes

Osteosarcoma (OS) is a malignant disease that develops rapidly and is associated with poor prognosis. Immunotherapy may provide new insights into clinical treatment strategies for OS. The purpose of this study was to identify immune-related genes that could predict OS prognosis. The gene expression profiles and clinical data of 84 OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. According to non-negative matrix factorization, two molecular subtypes of immune-related genes, C1 and C2, were acquired, and 597 differentially expressed genes between C1 and C2 were identified. Univariate Cox analysis was performed to get 14 genes associated with survival, and 4 genes (GJA5, APBB1IP, NPC2, and FKBP11) obtained through least absolute shrinkage and selection operator (LASSO)-Cox regression were used to construct a 4-gene signature as a prognostic risk model. The results showed that high FKBP11 expression was correlated with high risk (a risk factor), and that high GJA5, APBB1IP, or NPC2 expression was associated with low risk (protective factors). The testing cohort and entire TARGET cohort were used for internal verification, and the independent GSE21257 cohort was used for external validation. The study suggested that the model we constructed was reliable and performed well in predicting OS risk. The functional enrichment of the signature was studied through gene set enrichment analysis, and it was found that the risk score was related to the immune pathway. In summary, our comprehensive study found that the 4-gene signature could be used to predict OS prognosis, and new biomarkers of great significance for understanding the therapeutic targets of OS were identified.

scholarly journals Establishment of a Novel Risk Score System of Immune Genes Associated With Prognosis in Esophageal Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhenghua Fei ◽  
Rongrong Xie ◽  
Zhi Chen ◽  
Junhui Xie ◽  
Yuyang Gu ◽  
...  
Keyword(s):  
Risk Score ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Survival Outcomes ◽  
Immune Gene ◽  
Clinical Prognosis ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Normal Tissues ◽  
Immune Related Genes

BackgroundFew studies have addressed the role of immune-related genes in the survival and prognosis of different esophageal cancer (EC) sub-types. We established two new prognostic model indexes by bioinformatics analysis to select patients with esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) who may benefit from immunotherapy.MethodsBased on TCGA and ImmPort data sets, we screened immune genes differentially expressed between tumor and normal tissues in ESCC and EAC and analyzed the relationship between these genes and patient survival outcomes. We established the risk score models of immune-related genes in ESCC and EAC by multivariate COX regression analysis.ResultsWe identified 12 and 11 immune-related differentially expressed genes associated with the clinical prognosis of ESCC and EAC respectively, based on which two prognostic risk score models of the two EC sub-types were constructed. It was found that the survival probability of patients with high scores was significantly lower than that of patients with low scores (p &lt; 0.001). BMP1, EGFR, S100A12, HLA-B, TNFSF18, IL1B, MAPT and OXTR were significantly related to sex, TNM stage or survival outcomes of ESCC or EAC patients (p &lt; 0.05). In addition, the risk score of ESCC was significantly correlated with the level of B cell infiltration in immune cells (p &lt; 0.05).ConclusionsThe prognosis-related immune gene model indexes described herein prove to be useful prognostic biomarkers of the two EC sub-types in that they may provide a reference direction for looking for the beneficiaries of immunotherapy for EC patients.

scholarly journals Identification of an Immunologic Signature of Lung Adenocarcinomas Based on Genome-Wide Immune Expression Profiles

2021 ◽  
Vol 7 ◽  
Author(s):  
Bo Ling ◽  
Guangbin Ye ◽  
Qiuhua Zhao ◽  
Yan Jiang ◽  
Lingling Liang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
External Validation ◽  
Immune Gene ◽  
Clinical Prognosis ◽  
Resistance Pathway ◽  
Lung Adenocarcinomas

Background: Lung cancer is one of the most common types of cancer, and it has a poor prognosis. It is urgent to identify prognostic biomarkers to guide therapy.Methods: The immune gene expression profiles for patients with lung adenocarcinomas (LUADs) were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The relationships between the expression of 45 immune checkpoint genes (ICGs) and prognosis were analyzed. Additionally, the correlations between the expression of 45 biomarkers and immunotherapy biomarkers, including tumor mutation burden (TMB), mismatch repair defects, neoantigens, and others, were identified. Ultimately, prognostic ICGs were combined to determine immune subgroups, and the prognostic differences between these subgroups were identified in LUAD.Results: A total of 11 and nine ICGs closely related to prognosis were obtained from the GEO and TCGA databases, respectively. CD200R1 expression had a significant negative correlation with TMB and neoantigens. CD200R1 showed a significant positive correlation with CD8A, CD68, and GZMB, indicating that it may cause the disordered expression of adaptive immune resistance pathway genes. Multivariable Cox regression was used to construct a signature composed of four prognostic ICGs (IDO1, CD274, CTLA4, and CD200R1): Risk Score = −0.002*IDO1+0.031*CD274−0.069*CTLA4−0.517*CD200R1. The median Risk Score was used to classify the samples for the high- and low-risk groups. We observed significant differences between groups in the training, testing, and external validation cohorts.Conclusion: Our research provides a method of integrating ICG expression profiles and clinical prognosis information to predict lung cancer prognosis, which will provide a unique reference for gene immunotherapy for LUAD.

scholarly journals Identification and Validation of a Prognostic Signature for Prostate Cancer Based on Ferroptosis-Related Genes

2021 ◽  
Vol 11 ◽  
Author(s):  
Huan Liu ◽  
Lei Gao ◽  
Tiancheng Xie ◽  
Jie Li ◽  
Ting-shuai Zhai ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Drug Targets ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Clinical Information ◽  
High Risk Group ◽  
Prognostic Signature

Ferroptosis, an iron-dependent form of selective cell death, is involved in the development of many cancers. However, ferroptosis related genes (FRGs) in prostate cancer (PCa) are not been well studied. In this study, we collected the mRNA expression profiles and clinical information of PCa patients from TCGA and MSKCC databases. The univariate, LASSO, and multivariate Cox regression analyses were performed to construct a prognostic signature. Seven FRGs, AKR1C3, ALOXE3, ATP5MC3, CARS1, MT1G, PTGS2, and TFRC, were included to establish a risk model, which was validated in the MSKCC dataset. The results showed that the high-risk group was apparently correlated with copy number alteration load, tumor burden mutation, immune cell infiltration, mRNAsi, immunotherapy, and bicalutamide response. Moreover, we found that TFRC overexpression induced the proliferation and invasion of PCa cell lines in vitro. These results demonstrate that this risk model can accurately predict prognosis, suggesting that FRGs are promising prognostic biomarkers and potential drug targets in PCa patients.

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