scholarly journals A Robust Hypoxia Risk Score Predicts the Clinical Outcomes and Tumor Microenvironment Immune Characters in Bladder Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhi Liu ◽  
Qiao Tang ◽  
Tiezheng Qi ◽  
Belaydi Othmane ◽  
Zhe Yang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Cancer Immunotherapy ◽  
Clinical Outcomes ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
Unmet Need ◽  
The Cancer Genome Atlas

BackgroundBladder cancer (BLCA) is one of the most common urinary malignancies with poor prognosis. There is an unmet need to develop novel robust tools to predict prognosis and treatment efficacy for BLCA.MethodsThe hypoxia-related genes were collected from the Molecular Signatures Database. The TCGA-BLCA cohort was downloaded from the Cancer Genome Atlas and then was randomly divided into training and internal validation sets. Two external validation cohorts were gathered from Gene Expression Omnibus. Also, another independent validation cohort (Xiangya cohort) was collected from our hospital. The Cox regression model with the LASSO algorithm was applied to develop the hypoxia risk score. Then, we correlated the hypoxia risk score with the clinical outcomes, the tumor microenvironment (TME) immune characteristics, and the efficacy prediction for several treatments, which included cancer immunotherapy, chemotherapy, radiotherapy, and targeted therapies.ResultsHypoxia risk score was an independent prognostic factor. A high-risk score indicated an inflamed TME based on the evidence that hypoxia risk score positively correlated with the activities of several cancer immunity cycles and the infiltration levels of many tumor-infiltrating immune cells, such as CD8 + T cells, Dendritic cells, and NK cells. Consistently, the hypoxia risk score was positively related to the expression of several immune checkpoints, such as PD-L1, PD-1, CTLA-4, and LAG-3, as well as the T cell inflamed score. Furthermore, the hypoxia risk score positively correlated with the enrichment scores of most immunotherapy-positive gene signatures. Therefore, patients with higher risk score may be more sensitive to cancer immunotherapy. Meanwhile, the hypoxia risk score was positively related to the sensitivities of several chemotherapeutic drugs, including Cisplatin, Docetaxel, Paclitaxel, Bleomycin, Camptothecin, and Vinblastine. Similarly, the enrichment scores for radiotherapy-predicted pathways and EGFR ligands were higher in the high-risk score group. Conversely, the enrichment scores of several immunosuppressive oncogenic pathways were significantly higher in the low-risk score group, such as the WNT-β-catenin network, PPARG network, and FGFR3 network.ConclusionsWe developed and validated a new hypoxia risk score, which could predict the clinical outcomes and the TME immune characteristics of BLCA. In general, the hypoxia risk score may aid in the precision medicine for BLCA.

scholarly journals Prognostic characterization of the pyroptosis-related subtypes and tumor microenvironment infiltration in glioma

2021 ◽  
Author(s):  
Jiarong He ◽  
Jiaoying Jia ◽  
Jiawei Lei ◽  
Jingyu Yu ◽  
Wen Zhou ◽  
...  
Keyword(s):  
High Risk ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Survival Data ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Future Research ◽  
Genome Atlas

Abstract Background Pyroptosis could regulate tumor cell trafficking, invasion, and metastasis, as well as tumor microenvironment (TME). However, prognostic characteristics of pyroptosis-related genes (PRGs) and their impact on the progression of glioma remain insufficient. Methods The genetic, transcriptional, and survival data of glioma patients used for bioinformatics analysis were obtained from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. Results We first screened two different molecular subtypes and found that PRG variations were associated with the characteristics of TME cell infiltration, clinicopathological characteristics, and prognosis of patients with glioma. After Cox regression of differentially expressed genes, a risk-score for predicting overall survival (OS) and progression-free survival (PFS) was calculated and its predictive accuracy in glioma patients was then validated. The high-risk group of PRGs signature showed a significantly poor OS compared to the low-risk group (training cohort, p <0.001, validation cohort, p <0.001). A high risk-score implies more immune cell infiltration and a better immunotherapy response to immune checkpoint blockers. Furthermore, the risk-score was significantly associated with the chemotherapeutic drug sensitivity and cancer stem cell (CSC) index. Subsequently, we established a highly accurate nomogram to facilitate the applicability in the preliminary clinical application of the risk-score. Conclusion Our findings may lay the foundation for future research targeting pyroptosis in glioma and pave the way for evaluating prognosis and developing more effective immunotherapy strategies.

A Risk Score Model Associated with Tumor Microenvironment Predicts Disease-Free Survival After Radical Prostatectomy

2020 ◽  
Author(s):  
Hao Zhao ◽  
Xuening Zhang ◽  
Zhan Shi ◽  
Songhe Shi
Keyword(s):  
Tumor Microenvironment ◽  
Gleason Score ◽  
Risk Score ◽  
Regression Models ◽  
Cox Regression ◽  
External Validation ◽  
Disease Free Survival ◽  
Data Set ◽  
Free Survival ◽  
Disease Free

Abstract Background Tumor microenvironment (TME) and immune checkpoint inhibitors has been shown to promote active immune responses through different mechanisms. We aimed to identify the important prognostic genes and prognostic characteristics related to TME in prostate cancer (PCa).Methods The gene transcriptome profiles and clinical information of PCa patients were obtained from the TCGA database, and the immune, stromal and estimate scores were calculated by the ESTIMATE algorithm. We evaluated the prognostic value of risk score (RS) model based on univariate Cox and LASSO Cox regression models analysis, and established a nomogram to predict disease-free survival (DFS) in PCa patients. The GSE70768 data set was used for external validation. Finally, 22 subsets of tumor-infiltrating immune cells (Tiics) were analyzed using the Cibersort algorithm.Results In this study, the patients with higher immune, stromal, and estimate scores were associated with poorer DFS, higher Gleason score, and higher AJCC T stage. Based on the immune and stromal scores, the Venny diagram screened out 515 cross DEGs. The univariate COX and Lasso Cox regression models were used to select 18 DEGs from 515 DEGs, and constructed a RS model. The DFS of the high-RS group was significantly lower than that of the low-RS group (P<0.001). The AUC of 1-year, 3-year and 5-year DFS rates in RS model were 0.778, 0.754 and 0.750, respectively. In addition, the RS model constructed from 18 genes was found to be more sensitive than Gleason score (1, 3, 5 year AUC= 0.704, 0.677 and 0.682). The nomograms of DFS were established based on RS and Gleason scores. The AUC of the nomograms in the first, third, and fifth years were 0.802, 0.808, and 0.796, respectively. These results have been further validated in GEO. In addition, the proportion of Tregs was higher in high-RS patients (P<0.05), and the expression of five immune checkpoints (CTLA-4, PD-1, LAG-3, TIM-3 and TIGIT) was higher in high-RS patients (P<0.05).Conclusion We identified 18 TME-related genes from the TCGA database, which were significantly related to DFS in PCa patients.

scholarly journals Identification of a 5-Gene Metabolic Signature for Predicting Prognosis Based on an Integrated Analysis of Tumor Microenvironment in Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaolin Yu ◽  
Xiaomei Zhang ◽  
Yanxia Zhang
Keyword(s):  
Tumor Microenvironment ◽  
Confidence Interval ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
External Validation ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Lasso Regression ◽  
The Status

Lung adenocarcinoma (LUAD) is a common subtype of lung cancer with a depressing survival rate. The reprogramming of tumor metabolism was identified as a new hallmark of cancer in tumor microenvironment (TME), and we made a comprehensive exploration to reveal the prognostic role of the metabolic-related genes. Transcriptome profiling data of LUAD were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Based on the extracted metabolic-related genes, a novel 5-gene metabolic prognostic signature (including GNPNAT1, LPGAT1, TYMS, LDHA, and PTGES) was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. This signature confirmed its robustness and accuracy by external validation in multiple databases. It could be an independent risk factor for LUAD, and the nomograms possessed moderately accurate performance with the C-index of 0.755 (95% confidence interval: 0.706–0.804) and 0.691 (95% confidence interval: 0.636–0.746) in training set and testing set. This signature could reveal the metabolic features according to the results of gene set enrichment analysis (GSEA) and meanwhile monitor the status of TME through ESTIMATE scores and the infiltration levels of immune cells. In conclusion, this gene signature is a cost-effective tool which could indicate the status of TME to provide more clues in the exploration of new diagnostic and therapeutic strategy.

scholarly journals Angiogenesis-Related Gene Expression Signatures Predicting Prognosis in Gastric Cancer Patients

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3685
Author(s):  
Haoyu Ren ◽  
Jiang Zhu ◽  
Haochen Yu ◽  
Alexandr Bazhin ◽  
Christoph Westphalen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Clinical Stage ◽  
External Validation ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Related Gene ◽  
Angiogenesis Related Gene

Increasing evidence indicates that angiogenesis is crucial in the development and progression of gastric cancer (GC). This study aimed to develop a prognostic relevant angiogenesis-related gene (ARG) signature and a nomogram. The expression profile of the 36 ARGs and clinical information of 372 GC patients were extracted from The Cancer Genome Atlas (TCGA). Consensus clustering was applied to divide patients into clusters 1 and 2. Least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to identify the survival related ARGs and establish prognostic gene signatures, respectively. The Asian Cancer Research Group (ACRG) (n = 300) was used for external validation. Risk score of ARG signatures was calculated, and a prognostic nomogram was developed. Gene set enrichment analysis of the ARG model risk score was performed. Cluster 2 patients had more advanced clinical stage and shorter survival rates. ARG signatures carried prognostic relevance in both cohorts. Moreover, ARG-risk score was proved as an independent prognostic factor. The predictive value of the nomogram incorporating the risk score and clinicopathological features was superior to tumor, lymph node, metastasis (TNM) staging. The high-risk score group was associated with several cancer and metastasis-related pathways. The present study suggests that ARG-based nomogram could serve as effective prognostic biomarkers and allow a more precise risk stratification.

scholarly journals Development and validation of Pyroptosis‑related lncRNAs prediction model for bladder cancer

2022 ◽  
Author(s):  
Thongher Lia ◽  
Yanxiang Shao ◽  
Parbatraj Regmi ◽  
Xiang Li
Keyword(s):  
Bladder Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis

Bladder cancer is one of the highly heterogeneous disorders accompanied by a poor prognosis. This study aimed to construct a model based on pyroptosis‑related lncRNA to evaluate the potential prognostic application in bladder cancer. The mRNA expression profiles of bladder cancer patients and corresponding clinical data were downloaded from the public database from The Cancer Genome Atlas (TCGA). Pyroptosis‑related lncRNAs were identified by utilizing a co-expression network of Pyroptosis‑related genes and lncRNAs. The lncRNA was further screened by univariate Cox regression analysis. Finally, 8 pyroptosis-related lncRNA markers were established using Lasso regression and multivariate Cox regression analysis. Patients were separated into high and low-risk groups based on the performance value of the median risk score. Patients in the high-risk group had significantly poorer overall survival (OS) than those in the low-risk group (p &lt; 0.001), and In multivariate Cox regression analysis, the risk score was an independent predictive factor of OS ( HR&gt;1, P&lt;0.01). The area under the curve (AUC) of the 3- and 5-year OS in the receiver operating characteristic (ROC) curve were 0.742 and 0.739 respectively. In conclusion, these 8 pyroptosis-related lncRNA and their markers may be potential molecular markers and therapeutic targets for bladder cancer patients.

scholarly journals A signature of seven immune‐related genes predicts overall survival in male gastric cancer patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xin Xu ◽  
Yida Lu ◽  
Youliang Wu ◽  
Mingliang Wang ◽  
Xiaodong Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Immune Related Genes

Abstract Background Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC. Methods RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT. Results A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment. Conclusions Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

scholarly journals Identification of an autophagy-related gene marker for predicting the prognosis in lung adenocarcinoma patients

2021 ◽  
Author(s):  
wenhao wang ◽  
Longjun Yang ◽  
Zhong Lu ◽  
Xiumei Sun ◽  
Jin Liu ◽  
...  
Keyword(s):  
High Risk ◽  
Signaling Pathway ◽  
Risk Score ◽  
Cox Regression ◽  
Treatment Strategies ◽  
Enrichment Analysis ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Gene Marker ◽  
Neuron Death

Abstract BackgroundLung adenocarcinoma (LUAD) is a tumor with high incidence rate and high mortality rate. Previous studies have found that autophagy plays a vital role in tumorigenesis and biological progression. The aim of our research is to screen and analyze autophagy-related genes (ATGs) using bioinformatics technology, then establish a gene expression model for predicting the prognosis of LUAD patients. MethodsDifferentially expressed ATGs in LUAD and normal tissues were screened by The Cancer Genome Atlas (TCGA) dataset. We applied Go and KEGG enrichment analysis of ATGs for identifying the relevant signaling pathways. Finally, the ATGs related with survival were assessed using univariate and multivariate COX regression analyses. This project was analyzed by R software. ResultsA total of 232 ATGs were obtained in LUAD. Subsequently, 30 ATGs were screened out as genes associated with prognosis. GO analysis revealed that the 30 differently expressed ATGs (DE-ATGs) were enriched in intrinsic apoptotic signaling pathway, macroautophagy, and neuron death. In addition, KEGG analysis revealed that the DE-ATGs were associated with autophagy (animal), ErbB signaling pathway and IL-17 signaling pathway. Then, the risk score model was established by the 8 ATGs (ATG4A, CCR2, MBTPS2, APOL1, ERO1A, SPHK1, ST13 and ITGA6), and LUAD patients were divided into high-risk and low-risk groups with the risk score. The risk score was significantly related to overall survival and prognosis by univariate and multivariate analysis (P < 0.001). The survival time of low-risk score patients was showed by the cumulative curve that was obviously longer than high-risk score patients (P< 0.001). Finally, the correlation of the autophagy-related risk characteristics and multiple clinical parameters was analyzed. ConclusionA prognostic signature and nomogram based on 8 ATGs was constructed. This research provides a new direction for the prognosis evaluation and guides the potential treatment strategies for LUAD.

scholarly journals Identification of an autophagy-related gene signature for survival prediction in patients with cervical cancer

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Hengyu Chen ◽  
Qingchun Deng ◽  
Wenwen Wang ◽  
Huishan Tao ◽  
Ying Gao
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Risk Score ◽  
Squamous Cell ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Cervical cancer is one of the most common female malignancy that occurs worldwide and is reported to cause over 300,000 deaths in 2018. Autophagy controls the survival and death of cancerous cells by regulating the degradation process of cytoplasm and cellular organelle. In the present study, the differentially expressed autophagy-related genes (ARGs) between healthy and cancerous cervical tissues (squamous cell neoplasms) were obtained using data from GTEx and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology (GO) as well as the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Next, we conducted univariate Cox regression assay and obtained 12 ARGs that were associated with the prognosis of cervical cancer patients. We carried out a multivariate Cox regression analysis and developed six ARG-related prognostic signature for the survival prediction of patients with squamous cell cervical cancer (Risk score = − 0.63*ATG3–0.42*BCL2 + 0.85*CD46–0.38*IFNG+ 0.23*NAMPT+ 0.82*TM9SF1). Following the calculation of risk score using the signature, the patients were divided into high and low-risk groups according to the median value. Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis (P < 0.001). The value for area under the curves corresponding to the receiver operating characteristic (ROC) was 0.740. As observed, the expression of IFNG was negatively associated with lymph node metastasis (P = 0.026), while a high-risk score was significantly associated with increased age (P = 0.008). To further validate the prognostic signature, we carried out a permutation test and confirmed the performance of the risk score. In conclusion, our study developed six ARG-related prognostic signature for patients with squamous cell cervical cancer, which might help in improving the prognostic predictions of such patients.

scholarly journals Identification of a tumor microenvironment-related seven-gene signature for predicting prognosis in bladder cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhi Wang ◽  
Lei Tu ◽  
Minfeng Chen ◽  
Shiyu Tong
Keyword(s):  
Bladder Cancer ◽  
Tumor Microenvironment ◽  
Predictive Model ◽  
Cell Lines ◽  
Cox Regression ◽  
Mrna Level ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Roc Curve Analysis ◽  
Qrt Pcr

Abstract Background Accumulating evidences demonstrated tumor microenvironment (TME) of bladder cancer (BLCA) may play a pivotal role in modulating tumorigenesis, progression, and alteration of biological features. Currently we aimed to establish a prognostic model based on TME-related gene expression for guiding clinical management of BLCA. Methods We employed ESTIMATE algorithm to evaluate TME cell infiltration in BLCA. The RNA-Seq data from The Cancer Genome Atlas (TCGA) database was used to screen out differentially expressed genes (DEGs). Underlying relationship between co-expression modules and TME was investigated via Weighted gene co-expression network analysis (WGCNA). COX regression and the least absolute shrinkage and selection operator (LASSO) analysis were applied for screening prognostic hub gene and establishing a risk predictive model. BLCA specimens and adjacent tissues from patients were obtained from patients. Bladder cancer (T24, EJ-m3) and bladder uroepithelial cell line (SVHUC1) were used for genes validation. qRT-PCR was employed to validate genes mRNA level in tissues and cell lines. Results 365 BLCA samples and 19 adjacent normal samples were selected for identifying DEGs. 2141 DEGs were identified and used to construct co-expression network. Four modules (magenta, brown, yellow, purple) were regarded as TME regulatory modules through WGCNA and GO analysis. Furthermore, seven hub genes (ACAP1, ADAMTS9, TAP1, IFIT3, FBN1, FSTL1, COL6A2) were screened out to establish a risk predictive model via COX and LASSO regression. Survival analysis and ROC curve analysis indicated our predictive model had good performance on evaluating patients prognosis in different subgroup of BLCA. qRT-PCR result showed upregulation of ACAP1, IFIT3, TAP1 and downregulation of ADAMTS9, COL6A2, FSTL1,FBN1 in BLCA specimens and cell lines. Conclusions Our study firstly integrated multiple TME-related genes to set up a risk predictive model. This model could accurately predict BLCA progression and prognosis, which offers clinical implication for risk stratification, immunotherapy drug screen and therapeutic decision.

scholarly journals Construction and Validation of a Novel Eight-Gene Risk Score to Predict the Malignant Progression and Prognoses in Bladder Cancer Patients

2020 ◽  
Author(s):  
Ruiliang Wang ◽  
Zongtai Zheng ◽  
Shiyu Mao ◽  
Wentao Zhang ◽  
Ji Liu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Cox Model ◽  
Malignant Progression ◽  
The Cancer Genome Atlas ◽  
Invasive Bladder Cancer ◽  
Muscle Invasive Bladder Cancer ◽  
Normal Bladder ◽  
Muscle Invasive

Abstract The progression from non-muscle-invasive bladder cancer (NMIBC) to muscle-invasive bladder cancer (MIBC) largely predisposes a life-threatening risk. Owing to this, it is of paramount importance to find new relevant molecular models that will allow for effectively predict the malignant progression of BC. Based on the RNA-Sequence data of 49 BC patients in our center and weighted gene co-expression network analysis methods, a co-expression network was developed using these genes before selecting the three key modules. Univariate Cox regression was used to select the key module genes with prognostic value in The Cancer Genome Atlas Program (TCGA). Subsequently, we developed an eight-gene risk score using the Least absolute shrinkage and selection operator Cox model. Notably, the eight-gene risk score was observed to be closely related to the malignant clinical features and also showed a favorable predictive power of differentiation between NMIBC and MIBC in the training (TCGA) and the two validation sets (GSE3289 and GSE13507). Furthermore, we generated a nomogram for predicting the overall survival. Both the calibrations and decision curve analysis curves displayed predictive effectiveness of the nomogram. Lastly, the RT-qPCR results revealed that the majority of the eight genes were differentially expressed between BC cell lines and a normal bladder epithelial cell line. Hence, from our study, we established a model of eight-gene risk score, with the potential of predicting malignant progression and determine prognoses of BC patients.

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