scholarly journals A Novel Assessment Model Based on Molecular Subtypes of Hypoxia-Related LncRNAs for Prognosis of Bladder Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Xianwu Chen ◽  
Yan Zhang ◽  
Feifan Wang ◽  
Xuejian Zhou ◽  
Qinghe Fu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Tumor Therapy ◽  
Assessment Model ◽  
Immune Checkpoints ◽  
Cox Regression Analysis ◽  
Model Based

Hypoxia is a common feature in various tumors that regulates aggressiveness. Previous studies have demonstrated that some dysregulated long non-coding RNAs (lncRNAs) are correlated with tumor progression, including bladder cancer (BCa). However, the prognostic effect of hypoxia-related lncRNAs (HRLs) and their clinical relevance, as well as their regulatory effect on the tumor immune microenvironment, are largely unknown in BCa. A co-expression analysis between hypoxia genes and lncRNA expression, which was downloaded from the TCGA database, was performed to identify HRLs. Univariate Cox regression analysis was performed to select the most desirable lncRNAs for molecular subtype, and further LASSO analysis was performed to develop a prognostic model. This molecular subtype based on four HRLs (AC104653, AL136084, AL139393, and LINC00892) showed good performance in the tumor microenvironment and tumor mutation burden. The prognostic risk model suggested better performance in predicting BCa patients’ prognosis and obtained a close correlation with clinicopathologic features. Furthermore, four of five first-line clinical chemotherapies showed different sensitivities to this model, and nine immune checkpoints showed different expression in the molecular subtypes or the risk model. In conclusion, this study indicates that this molecular subtype and risk model based on HRLs may be useful in improving the prognostic prediction of BCa patients with different clinical situations and may help to find a useful target for tumor therapy.

scholarly journals The Prognostic Value of the m6A Score in Multiple Myeloma Based on Machine Learning

2021 ◽  
Vol 1 (3) ◽  
pp. 77-87
Author(s):  
Gong Xiao ◽  
Qiongjing Yuan ◽  
Wei Wang
Keyword(s):  
Multiple Myeloma ◽  
Cancer Progression ◽  
Risk Score ◽  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Microarray Dataset ◽  
Immune Checkpoints ◽  
Bioinformatics Analyses ◽  
Cox Regression Analysis

Background: Multiple myeloma (MM) is one of the most common cancers of the blood system. N6-methyladenosine (m6A) plays an important role in cancer progression. We aimed to investigate the prognostic relevance of the m6A score in multiple myeloma through a series of bioinformatics analyses. Methods: The microarray dataset GSE4581 and GSE57317 used in this study were downloaded from the Gene Expression Omnibus (GEO) database. The m6A score was calculated using the GSVA package. The Random forests, univariate Cox regression analysis and Lasso analyses were performed for the differentially expressed genes (DEGs). Kaplan–Meier analysis and an ROC curve were used to diagnose the effectiveness of the model. Results: The GSVA R software package was used to predict the function. A total of 21 m6A genes were obtained, and 286 DEGs were identified between high and low m6A score groups. The risk model was constructed and composed of PRX, LBR, RB1, FBXL19-AS1, ARSK, MFAP3L, SLC44A3, UNC119 and SHCBP1. Functional analysis of risk score showed that with the increase in the risk score, Activated CD4 T cells, Memory B cells and Type 2 T helper cells were highly infiltrated. Conclusions: Immune checkpoints such as HMGB1, TGFB1, CXCL9 and HAVCR2 were significantly positively correlated with the risk score. We believe that the m6A score has a certain prognostic value in multiple myeloma.

Prognostic Nomogram Incorporating Immunohistochemical Results for the Overall Survival of Patients with Bladder Cancer.

2020 ◽  
Author(s):  
Tianwei Wang ◽  
Yunyan Wang
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Bladder Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Validation Cohort ◽  
Multivariable Analysis ◽  
Clinical Information ◽  
Internal Validation ◽  
Cox Regression Analysis

Abstract Objectives: In this study, we want to combine GATA3, VEGF, EGFR and Ki67 with clinical information to develop and validate a prognostic nomogram for bladder cancer.Methods: A total of 188 patients with clinical information and immunohistochemistry were enrolled in this study, from 1996 to 2018. Univariable and multivariable cox regression analysis was applied to identify risk factors for nomogram of overall survival (OS). The calibration of the nomogram was performed and the Area Under Curve (AUC) was calculated to assess the performance of the nomogram. Internal validation was performed with the validation cohort., the calibration curve and the AUC were calculated simultaneously.Results: Univariable and multivariable analysis showed that age (HR: 2.229; 95% CI: 1.162-4.274; P=0.016), histology (HR: 0.320; 95% CI: 0.136-0.751; P=0.009), GATA3 (HR: 0.348; 95% CI: 0.171-0.709; P=0.004), VEGF (HR: 2.295; 95% CI: 1.225-4.301; P=0.010) and grade (HR: 4.938; 95% CI: 1.339-18.207; P=0.016) remained as independent risk factors for OS. The age, histology, grade, GATA3 and VEGF were included to build the nomogram. The accuracy of the risk model was further verified with the C-index. The C-index were 0.65 (95% CI, 0.58-0.72) and 0.58 (95% CI, 0.46-0.70) in the training and validation cohort respectively. Conclusions: A combination of clinical variables with immunohistochemical results based nomogram would predict the overall survival of patients with bladder cancer.

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 Gene Expression Analysis Reveals Novel Gene Prognostic Signatures Involved in Medulloblastoma Subgroups

2021 ◽  
Author(s):  
Fangcheng Li ◽  
Hong-Yao Yuan ◽  
Cheng Chen ◽  
Jun-Ping Pan ◽  
Xin-Ke Xu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Gene Expression Analysis ◽  
Cox Regression ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Specific Treatment ◽  
Single Factor ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Pathway Enrichment

Abstract PurposeMedulloblastoma is a malignant childhood tumor with four molecular subtypes: WNT, SHH, G3, and G4. The prognosis of these four molecular subtypes is different. WNT has the best prognosis, followed by SHH, and G3 and G4 subtypes have the worst prognosis. This study aimed to identify various molecular subtypes of medulloblastoma can independently predict the prognosis of patients and provide specific treatment way for them.MethodsBased on the data in the GSE37418 dataset, the WGCNA method was used to find the genes most related to these molecular subtypes, and then the top ten hub genes in each subtype were found through the cytohubba plug-in of cytoscape. GO pathway enrichment of four interested modules was used, and then GSE85217 was used for clinical trials in single-factor Cox regression analysis .ResultsThe information was subjected to single-factor Cox regression analysis, and twelve hub genes with the most significant prognostic effects on medulloblastoma were found, and then these genes were subjected to multi-factor Cox regression analysis on each molecular subtype, and finally GNG3 was determined .The combination of CALCB and GCGR can predict the development of SHH well (p=0.0011, AUC=0.734), SOCS3 and HOXC10 can better predict the development of G4 (p=0.044, AUC=0.618), and the combination of ADCY8 and LHX3 can predict G3 Development (p=0.034, AUC=0.675).ConclusionThis report showed a possible evidence that OS-related features of various molecular subtypes of medulloblastoma can independently predict the prognosis of patients with each subtype of medulloblastoma, and provide new therapeutic targets for them.

scholarly journals Prognostic Risk Model of Immune-Related Genes in Colorectal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Yucheng Qian ◽  
Jingsun Wei ◽  
Wei Lu ◽  
Fangfang Sun ◽  
Maxwell Hwang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Assessment Model ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Tumor Immune Microenvironment ◽  
Risk Scoring ◽  
Immune Related Genes

PurposeWe focused on immune-related genes (IRGs) derived from transcriptomic studies, which had the potential to stratify patients’ prognosis and to establish a risk assessment model in colorectal cancer.SummaryThis article examined our understanding of the molecular pathways associated with intratumoral immune response, which represented a critical step for the implementation of stratification strategies toward the development of personalized immunotherapy of colorectal cancer. More and more evidence shows that IRGs play an important role in tumors. We have used data analysis to screen and identify immune-related molecular biomarkers of colon cancer. We selected 18 immune-related prognostic genes and established models to assess prognostic risks of patients, which can provide recommendations for clinical treatment and follow-up. Colorectal cancer (CRC) is a leading cause of cancer-related death in human. Several studies have investigated whether IRGs and tumor immune microenvironment (TIME) could be indicators of CRC prognoses. This study aimed to develop an improved prognostic signature for CRC based on IRGs to predict overall survival (OS) and provide new therapeutic targets for CRC treatment. Based on the screened IRGs, the Cox regression model was used to build a prediction model based on 18-IRG signature. Cox regression analysis revealed that the 18-IRG signature was an independent prognostic factor for OS in CRC patients. Then, we used the TIMER online database to explore the relationship between the risk scoring model and the infiltration of immune cells, and the results showed that the risk model can reflect the state of TIME to a certain extent. In short, an 18-IRG prognostic signature for predicting CRC patients’ survival was firmly established.

scholarly journals Role of a Pyroptosis-Related lncRNA Signature in Risk Stratification and Immunotherapy of Ovarian Cancer

2022 ◽  
Vol 8 ◽  
Author(s):  
Zeyu Zhang ◽  
Zhijie Xu ◽  
Yuanliang Yan
Keyword(s):  
Ovarian Cancer ◽  
Risk Stratification ◽  
Survival Data ◽  
Risk Model ◽  
Cox Regression ◽  
Roc Curves ◽  
Immune Checkpoints ◽  
Random Allocation ◽  
Cox Regression Analysis

Background: Pyroptosis is a newly recognized form of cell death. Emerging evidence has suggested the crucial role of long non-coding RNAs (lncRNAs) in the tumorigenesis and progression of ovarian cancer (OC). However, there is still poor understanding of pyroptosis-related lncRNAs in OC.Methods: The TCGA database was accessed for gene expression and clinical data of 377 patients with OC. Two cohorts for training and validation were established by random allocation. Correlation analysis and Cox regression analysis were performed to identify pyroptosis-related lncRNAs and construct a risk model.Results: Six pyroptosis-related lncRNAs were included in the final signature with unfavorable survival data. Subsequent ROC curves showed promising predictive value of patient prognosis. Further multivariate regression analyses confirmed the signature as an independent risk factor in the training (HR: 2.242, 95% CI: 1.598–3.145) and validation (HR: 1.884, 95% CI: 1.204–2.95) cohorts. A signature-based nomogram was also established with a C-index of.684 (95% CI: 0.662–0.705). Involvement of the identified signature in multiple immune-related pathways was revealed by functional analysis. Moreover, the signature was also associated with higher expression of three immune checkpoints (PD-1, B7-H3, and VSIR), suggesting the potential of the signature as an indicator for OC immunotherapies.Conclusion: This study suggests that the identified pyroptosis-related lncRNA signature and signature-based nomogram may serve as methods for risk stratification of OC. The signature is also associated with the tumor immune microenvironment, potentially providing an indicator for patient selection of immunotherapy in OC.

scholarly journals A Novel Risk Model Based on Lipid Metabolism-Associated Genes Predicts Prognosis and Indicates Immune Microenvironment in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhimin Ye ◽  
Shengmei Zou ◽  
Zhiyuan Niu ◽  
Zhijie Xu ◽  
Yongbin Hu
Keyword(s):  
Breast Cancer ◽  
Lipid Metabolism ◽  
Risk Model ◽  
Cox Regression ◽  
Validation Cohort ◽  
Consensus Clustering ◽  
Immune Microenvironment ◽  
Training Cohort ◽  
Cox Regression Analysis ◽  
Model Based

BackgroundBreast cancer (BRCA) is the most common tumor in women, and lipid metabolism involvement has been demonstrated in its tumorigenesis and development. However, the role of lipid metabolism-associated genes (LMAGs) in the immune microenvironment and prognosis of BRCA remains unclear.MethodsA total of 1076 patients with BRCA were extracted from The Cancer Genome Atlas database and randomly assigned to the training cohort (n = 760) or validation cohort (n = 316). Kaplan–Meier analysis was used to assess differences in survival. Consensus clustering was performed to categorize the patients with BRCA into subtypes. Using multivariate Cox regression analysis, an LMAG-based prognostic risk model was constructed from the training cohort and validated using the validation cohort. The immune microenvironment was evaluated using the ESTIMATE and tumor immune estimation resource algorithms, CIBERSORT, and single sample gene set enrichment analyses.ResultsConsensus clustering classified the patients with BRCA into two subgroups with significantly different overall survival rates and immune microenvironments. Better prognosis was associated with high immune infiltration. The prognostic risk model, based on four LMAGs (MED10, PLA2G2D, CYP4F11, and GPS2), successfully stratified the patients into high- and low-risk groups in both the training and validation sets. High risk scores predicted poor prognosis and indicated low immune status. Subgroup analysis suggested that the risk model was an independent predictor of prognosis in BRCA.ConclusionThis study demonstrated, for the first time, that LMAG expression plays a crucial role in BRCA. The LMAG-based risk model successfully predicted the prognosis and indicated the immune microenvironment of patients with BRCA. Our study may provide inspiration for further research on BRCA pathomechanisms.

scholarly journals Expression Profile Analysis Identifies a Novel Seven Immune-Related Gene Signature to Improve Prognosis Prediction of Glioblastoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Li Hu ◽  
Zhibin Han ◽  
Xingbo Cheng ◽  
Sida Wang ◽  
Yumeng Feng ◽  
...  
Keyword(s):  
Risk Model ◽  
Cox Regression ◽  
Profile Analysis ◽  
Expression Patterns ◽  
Gene Signature ◽  
Model Verification ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Cox Regression Analysis ◽  
Dismal Prognosis

Glioblastoma multiform (GBM) is a malignant central nervous system cancer with dismal prognosis despite conventional therapies. Scientists have great interest in using immunotherapy for treating GBM because it has shown remarkable potential in many solid tumors, including melanoma, non-small cell lung cancer, and renal cell carcinoma. The gene expression patterns, clinical data of GBM individuals from the Cancer Genome Atlas database (TCGA), and immune-related genes (IRGs) from ImmPort were used to identify differentially expressed IRGs through the Wilcoxon rank-sum test. The association between each IRG and overall survival (OS) of patients was investigated by the univariate Cox regression analysis. LASSO Cox regression assessment was conducted to explore the prognostic potential of the IRGs of GBM and construct a risk score formula. A Kaplan–Meier curve was created to estimate the prognostic role of IRGs. The efficiency of the model was examined according to the area under the receiver operating characteristic (ROC) curve. The TCGA internal dataset and two GEO external datasets were used for model verification. We evaluated IRG expression in GBM and generated a risk model to estimate the prognosis of GBM individuals with seven optimal prognostic expressed IRGs. A landscape of 22 types of tumor-infiltrating immune cells (TIICs) in glioblastoma was identified, and we investigated the link between the seven IRGs and the immune checkpoints. Furthermore, there was a correlation between the IRGs and the infiltration level in GBM. Our data suggested that the seven IRGs identified in this study are not only significant prognostic predictors in GBM patients but can also be utilized to investigate the developmental mechanisms of GBM and in the design of personalized treatments for them.

scholarly journals Predicting the Survival and Immune Landscape of Colorectal Cancer Patients Using an Immune-Related lncRNA Pair Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Chao Ma ◽  
Xin Zhang ◽  
Xudong Zhao ◽  
Nan Zhang ◽  
Sixin Zhou ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Kinase Inhibitor ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Immune Checkpoints ◽  
Cox Regression Analysis

BackgroundAccumulating evidence has demonstrated that immune-related long non-coding ribonucleic acids (irlncRNAs) can be used as prognostic indicators of overall survival (OS) in patients with colorectal cancer (CRC). Our aim in this research, therefore, was to construct a risk model using irlncRNA pairs with no requirement for a specific expression level, in hope of reliably predicting the prognosis and immune landscape of CRC patients.MethodsClinical and transcriptome profiling data of CRC patients downloaded from the Cancer Genome Atlas (TCGA) database were analyzed to identify differentially expressed (DE) irlncRNAs. The irlncRNA pairs significantly correlated with the prognosis of patients were screened out by univariable Cox regression analysis and a prognostic model was constructed by Lasso and multivariate Cox regression analyses. A receiver operating characteristic (ROC) curve was then plotted, with the area under the curve calculated to confirm the reliability of the model. Based on the optimal cutoff value, CRC patients in the high- or low-risk groups were distinguished, laying the ground for evaluating the risk model from the following perspectives: survival, clinicopathological traits, tumor-infiltrating immune cells (TIICs), antitumor drug efficacy, kinase inhibitor efficacy, and molecules related to immune checkpoints.ResultsA prognostic model consisting of 15 irlncRNA pairs was constructed, which was found to have a high correlation with patient prognosis in a cohort from the TCGA (p < 0.001, HR = 1.089, 95% CI [1.067–1.112]). According to both univariate and multivariate Cox analyses, this model could be used as an independent prognostic indicator in the TCGA cohort (p < 0.001). Effective differentiation between high- and low-risk patients was also accomplished, on the basis of aggressive clinicopathological characteristics, sensitivity to antitumor drugs, and kinase inhibitors, the tumor immune infiltration status, and the expression levels of specific molecules related to immune checkpoints.ConclusionThe prognostic model established with irlncRNA pairs is a promising indicator for prognosis prediction in CRC patients.

scholarly journals Ferroptosis-Related Long Non-Coding RNA Signature Predicts the Prognosis of Bladder Cancer

2021 ◽  
Author(s):  
Jian Hou ◽  
Zhenquan Lu ◽  
Xiaobao Cheng ◽  
Runan Dong ◽  
Yi Jiang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Signaling Pathway ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Assessment Model ◽  
Differentially Expressed ◽  
Immune Checkpoints

Abstract Background: Ferroptosis is an iron-dependent programmed cell death modality that may have a tumor suppressor function. Therefore, regulating ferroptosis in tumor cells could serve as a novel therapeutic approach. This article focuses on ferroptosis-associated long non-coding RNAs (lncRNAs) and their potential application as a prognostic model for bladder cancer (BCa). Methods: We retrieved bladder cancer-related transcriptome information and clinical information from the TCGA database and ferroptosis-related gene sets from the FerrDb database. Minimum absolute shrinkage and selection operator regression and Cox regression models were used to identify and develop predictive models and validate the models' accuracy. Finally, we explore the inter-regulatory relationships between ferroptosis-related genes and immune cell infiltration, immune checkpoints, and m6A methylation genes. Results: Kaplan-Meier analyses revealed 11 differentially expressed lncRNAs associated with poor BCa prognosis. This signature (AUC = 0.720) could potentially be utilized to predict BCa prognosis. Our risk assessment model outperformed traditional clinicopathological features in predicting BCa prognosis. Additionally, GSEA revealed immune and tumor-related pathways in individuals in the low-risk group. TCGA showed that the p53 signaling pathway，ferroptosis，Kaposi sarcoma−associated herpesvirus infection，IL−17 signaling pathway，MicroRNAs in cancer，TNF signaling pathway，PI3K−Akt signaling pathway and HIF−1 signaling pathway were significantly different from those in the high-risk group. Immune checkpoints, such as PDCD-1 (PD-1), CTLA4, and LAG3, were also differentially expressed between the two risk groups. m6A methylation-related genes were likewise significantly differentially expressed between the two risk groups.Conclusion: A new ferroptosis-associated lncRNAs signature on the prognosis of BCa patients will provide new ideas for the treatment and management of BCa patients.

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