Development of a Ferroptosis-Related lncRNA Signature to Predict the Prognosis and Immune Landscape of Bladder Cancer

The tight relationship between ferroptotic cell death and immune response demonstrated by recent studies enlightened us to detect the underlying roles of ferroptosis-related long noncoding RNAs (frlncRNAs) in the tumor microenvironment of bladder cancer (BCa). We collected 121 ferroptosis regulators from previous studies. Based on their expression values, 408 cases with BCa were clustered. The patients in different clusters showed diverse immune infiltration, immunotherapy response, and chemotherapy effectiveness, revalidating the tight correlation with ferroptosis and tumor immunity. Through differential, coexpression, Kaplan-Meier, Lasso, and Cox analysis, we developed a 22-lncRNA-pair signature to predict the prognosis of BCa based on gene-pair strategy, where there is no need for definite expression values. The areas under the curves are all over 0.8. The risk model also helped to predict immune infiltration, immunotherapeutic outcomes, and chemotherapy sensitivity. Totally, the prognostic assessment model indicated a promising predictive value, also providing clues for the interaction between ferroptosis and BCa immunity.

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Prognostic Significance of The Neutrophil-To-Lymphocyte Ratio in Patients With Non-Muscle Invasive Bladder Cancer Treated With Intravesical Bacillus Calmette–Guérin and The Relationship With The CUETO Scoring Model

10.21203/rs.3.rs-116331/v1 ◽

2020 ◽

Author(s):

Jin Woo Kim ◽

Jae-Wook Chung ◽

Eun Hye Lee ◽

So Young Chun ◽

Dong Jin Park ◽

...

Keyword(s):

Bladder Cancer ◽

Tumor Recurrence ◽

Risk Model ◽

Bacillus Calmette Guerin ◽

Muscle Invasive Bladder Cancer ◽

Free Survival ◽

Scoring Model ◽

Lymphocyte Ratio ◽

Kaplan Meier ◽

Muscle Invasive

Abstract Background: The neutrophil-to-lymphocyte ratio (NLR) is a marker of the systemic inflammatory response, which is associated with tumor recurrence and progression. In this study, we evaluated the predictability of a modified Club Urológico Español de Tratamiento Oncológico (CUETO) scoring model and preoperative NLR in patients with non-muscle invasive bladder cancer (NMIBC). Methods: From August 2005 to May 2016, a total of 281 patients received intravesical bacillus Calmette–Guérin therapy after transurethral resection of a bladder tumor. The pathologic stage of all patients was Ta or T1. Multivariate Cox regression analysis and Kaplan–Meier curves were used to evaluate the prognosis predictability of the CUETO risk model and NLR. Of 281 patients, 84 (29.9%) experienced recurrence and 14 (5.0%) developed progression. The mean follow-up period was 46 months. The cut-off value for NLRs was 2.29. Results: Of the study patients, 108 (38.4%) displayed a high NLR (> 2.29). In Kaplan–Meier curve analysis, a high NLR was associated with lower recurrence-free survival (RFS) (P < 0.001) and progression-free survival (PFS) (P = 0.002). CUETO scores were associated with RFS (P < 0.001), but not with PFS (P = 0.423). A combination of NLRs and the CUETO risk model correlated with RFS (P < 0.001) and PFS (P = 0.002). In multivariate analysis, female gender, concomitant carcinoma in situ (CIS), tumor number >3, recurrent tumors, and a high NLR were independent factors predicting recurrence (all P < 0.05). Concomitant CIS, recurrent tumors, and a high NLR were independent factors for predicting progression (all P < 0.05). Conclusion: In patients with NMIBC, an NLR >2.29 was identified as a significant factor for predicting tumor recurrence and progression. Inclusion of preoperative NLR enhanced the accuracy of the CUETO model to predict disease progression. We therefore recommend that patients with a high NLR receive more aggressive management.

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Identification of a Pyroptosis-Related lncRNA Risk Model for Predicting Prognosis and Immune Response in Colon Adenocarcinoma

10.21203/rs.3.rs-1234682/v1 ◽

2022 ◽

Author(s):

Yuying Tan ◽

Liqing Lu ◽

Xujun Liang ◽

Yongheng Chen

Keyword(s):

Regression Analysis ◽

Risk Model ◽

Cox Regression ◽

Malignant Tumors ◽

Colon Adenocarcinoma ◽

Sequencing Data ◽

Cox Regression Analysis ◽

Immune Infiltration ◽

Cerna Network ◽

Kaplan Meier

Abstract Background: Colon adenocarcinoma (COAD) is one of the most common malignant tumors and diagnosed at an advanced stage with poor prognosis in the world. Pyroptosis is involved in the initiation and progression of tumors. This research focused on constructing a pyroptosis-related ceRNA network to generate a reliable risk model for risk prediction and immune infiltration analysis of COAD.Methods: Transcriptome data, miRNA-sequencing data and clinical information were downloaded from the TCGA database. Firstly, differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) were identified to construct a pyroptosis-related ceRNA network. Secondly, a pyroptosis-related lncRNA risk model was developed applying univariate Cox regression analysis and least absolute shrinkage and selection operator method (LASSO) regression analysis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were utilized to functionally annotate RNAs contained in the ceRNA network. In addition, Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, univariate and multivariate Cox regression, and nomogram were applied to validate this risk model. Finally, the relationship of this risk model with immune cells and immune checkpoint blockade (ICB) related genes were analyzed.Results: Totally 5373 DEmRNAs, 1159 DElncRNAs and 355 DEmiRNAs were identified. A pyroptosis-related ceRNA regulatory network containing 132 lncRNAs, 7miRNAs and 5 mRNAs was constructed and a ceRNA-based pyroptosis-related risk model including 11 lncRNAs was built. Tumor tissues were classified into high- and low- risk groups according to the median risk score. Kaplan-Meier analysis showed that the high-risk group had a shorter survival time; ROC analysis, independent prognostic analysis and nomogram further indicated the risk model was a significant independent prognostic factor which had excellent ability to predict patients’ risk. Moreover, immune infiltration analysis indicated that the risk model was related to immune infiltration cells (i.e., B cells naïve, T cells follicular helper, Macrophages M1, etc.) and ICB-related genes (i.e., PD-1, CTLA4, HAVCR2, etc).Conclusions: This pyroptosis-related lncRNA risk model possessed good prognostic value and the ability to predict the outcome of ICB immunotherapy in COAD.

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Risk and prognosis of secondary bladder cancer after postoperative radiotherapy for gynecological cancer

Bosnian Journal of Basic Medical Sciences ◽

10.17305/bjbms.2021.6338 ◽

2021 ◽

Author(s):

Li Wen ◽

Guansheng Zhong ◽

Yingjiao Zhang ◽

Miaochun Zhong

Keyword(s):

Bladder Cancer ◽

Cumulative Incidence ◽

Risk Model ◽

Postoperative Radiotherapy ◽

Gynecological Cancer ◽

Clinicopathological Characteristics ◽

Kaplan Meier ◽

Increased Risk ◽

The Us ◽

Chi Squared

The aim of this study was to investigate the impacts of radiation therapy (RT) on the occurrence risk of secondary bladder cancer (SBC) and on the patients’ survival outcome after being diagnosed with gynecological cancer (EC). The data was obtained from the SEER database between 1973 and 2015. Chi-squared test was used to compare the clinicopathological characteristics among the different groups. Fine and Gray’s competing risk model was used to assess the cumulative incidence and occurrence risk of SBC in GC survivors. Kaplan-Meier method was utilized for survival analysis. A total of 123,476 GC patients were included, among which 31,847 (25.8%) patients received RT while 91629 (74.2%) patients did not. The cumulative incidence of SBC was 1.59% or 0.73% among patients who had received prior GC specific RT or not, respectively. All EBRT (standardized incidence ratio (SIR) =2.49, 95% CI [2.17-2.86]), brachytherapy (SIR =1.96, 95% CI [1.60-2.38]), and combinational RT modality groups (SIR =2.73, 95% CI [2.24-3.28]) had dramatically higher SBC incidence as compared to the US general population. Receiving EBRT (HR = 2.83, 95% CI [2.34–3.43]), brachytherapy (HR = 2.17, 95% CI [1.67–2.82]), and combinational RT modality (HR = 2.97, 95% CI [2.34–3.77]) were independent risk factors for SBC development. Survival detriment was observed in SBC patients who received RT after GC diagnosis, as compared to those who did not receive RT. In conclusion, patients who underwent RT after GC had an increased risk of developing bladder as a secondary primary cancer. A long-term surveillance for SBC occurrence is necessary for GC patients who have received prior RT.

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

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.718991 ◽

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.

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Immune and Stromal Signature-Based Prognostic Gene of Bladder Cancer

10.21203/rs.3.rs-51838/v1 ◽

2020 ◽

Author(s):

Xiaonan Zheng ◽

Liansha Tang ◽

Xinyang Liao ◽

Kun Jin ◽

Xianghong Zhou ◽

...

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Survival Outcomes ◽

Immune Infiltration ◽

Protein Protein Interaction ◽

The Core ◽

Prognostic Gene ◽

Kaplan Meier ◽

Core Module ◽

Geo Dataset

Abstract Background: Immune and stromal cells in the tumor microenvironment have exhibit critical relevance with tumorigenesis and progression. Therefore, our analysis was conducted to explore the potential prognostic factors and the therapeutic targets of bladder cancer (BC) that affiliate to immune and stromal infiltration signature.Methods: Immune and stromal score were calculated for BC patients from TCGA database using ESTIMATE algorithm to predict the level of infiltration. Kaplan-Meier curves were utilized to assess the correlation of immune/stromal infiltration with survival outcomes. Differential expression genes (DEGs) were identified. Enrichment analyses, Kaplan-Meier curves, protein-protein interaction (PPI) network construction were performed to describe and screen the core module genes, whose prognostic value was further validated by an independent GEO dataset. Transcriptional factors (TFs) and ncRNA. correlated with the core module were identified using RAID 2.0 and TRRUST 2.0 database, and drugs potentially regulative for BC were accordingly screened using DrugBank.Results: 393 and 93 BC patients were enrolled from TCGA and GEO respectively. Higher stromal infiltration indicated worse overall survival (P = 0.015), and higher immune infiltration indicated an improvement on overall survival (P = 0.042). 562 DEGs were identified and 123 of them associated with survival outcomes. PPI has identified the core module genes, in which EFNB2 was the only core prognostic gene that was validated by both TCGA (P = 0.042) and GEO dataset (P = 0.036). Four TFs and Five ncRNAs (e.g. HIF1A) were associated with the regulation of the core module genes, and six drugs were screened as potential candidates to regulate BC.Conclusion: Higher immune infiltration in bladder tumor was correlated with improved survival and higher stromal infiltration corelated with worse survival. Furthermore, a higher expression of EFNB2 was tied with poorer prognosis of BC, which was validated by two independent datasets.

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The “effect” of T classification on colorectal liver metastasis.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e15049 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e15049-e15049

Author(s):

Lunpo Wu ◽

Hongjuan Zheng ◽

Jianfei Fu ◽

Jinlin Du ◽

Shu Zheng ◽

...

Keyword(s):

Colorectal Cancer ◽

Lymph Node ◽

Predictive Accuracy ◽

Assessment Model ◽

Discriminative Ability ◽

Cox Models ◽

Tissue Samples ◽

Kaplan Meier ◽

Cox Analysis ◽

T Classification

e15049 Background: T classification is considered as a detail and credible category of the depth of tumor invasion. Generally, with the increasing T category, the risk of metastases should be continuously rising. However, there is a group of metastatic patients with early T classification, who were supposed to have a low metastatic probability. Our study aims to present the T classification on metastatic liver colorectal cancer (CRLM) in both clinical and biological aspects, and explore preoperative predictions to develop a convenient individual assessment model for clinicians to speculate whether these patients, whose prognosis is extremely poor. Methods: Tissue samples of primary colorectal cancer were obtained at our center. Patients with CRLM during 2010 to 2014 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier and Cox models were used to analyze the survival differences. We identified preoperative prognostic factors based on the Cox analysis and constructed a nomogram model. The predictive accuracy and discriminative ability were measured by concordance index (C-index) and calibration curve. Results: The mRNA array from our hospital showed that there is an obvious difference between the T1/2N0M1 subgroup and the T3/4N0M1 subgroup. Patients with early T classification (T1) were more often have tumors located in rectum, with well differentiation, with no lymph node metastasis and a higher CEA level. Further survival analysis indicated that early T classification (T1) was an independent prognostic factor with poorer survival. When the lymph node (N) status was taken into consideration, patients with T1 N+ had an obvious better survival than T1 N0 patients. A clinical nomogram was constructed based on preoperative factors. The calibration curves for probability of 1-, 2-, and 5-year overall survival showed a good agreement between nomogram prediction and actual observation. Conclusions: The prognosis of T1M1 is extremely poorer than T3/4M1. The prognosis of T1N+ is better than T1N0. It is time to pay more attention to the high-risk monitoring and screening of T1 in early colon cancer.

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Identification of prognostic genes in uveal melanoma microenvironment

PLoS ONE ◽

10.1371/journal.pone.0242263 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0242263

Author(s):

Huan Luo ◽

Chao Ma

Keyword(s):

Tumor Microenvironment ◽

Uveal Melanoma ◽

Cox Regression ◽

Validation Cohort ◽

Chromosome 3 ◽

Training Cohort ◽

Immune Infiltration ◽

Kaplan Meier ◽

The Status ◽

Cox Analysis

Background Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. Many previous studies have demonstrated that the infiltrating of immune and stromal cells in the tumor microenvironment contributes significantly to prognosis. Methods Dataset TCGA-UVM, download from TCGA portal, was taken as the training cohort, and GSE22138, obtained from GEO database, was set as the validation cohort. ESTIMATE algorithm was applied to find intersection differentially expressed genes (DEGs) among tumor microenvironment. Kaplan-Meier analysis and univariate Cox regression model were performed on intersection DEGs to initial screen for potential prognostic genes. Then these genes entered into the validation cohort for validation using the same methods as that in the training cohort. Moreover, we conducted correlation analyses between the genes obtained in the validation cohort and the status of chromosome 3, chromosome 8q, and tumor metastasis to get prognosis genes. At last, the immune infiltration analysis was performed between the prognostic genes and 6 main kinds of tumor-infiltrating immune cells (TICs) for understanding the role of the genes in the tumor microenvironment. Results 959 intersection DEGs were found in the UM microenvironment. Kaplan-Meier and Cox analysis was then performed in the training and validation cohorts on these DEGs, and 52 genes were identified with potential prognostic value. After comparing the 52 genes to chromosome 3, chromosome 8q, and metastasis, we obtained 21 genes as the prognostic genes. The immune infiltration analysis showed that Neutrophil had the potential prognostic ability, and almost every prognostic gene we had identified was correlated with abundances of Neutrophil and CD8+ T Cell. Conclusions Identifying 21 prognosis genes (SERPINB9, EDNRB, RAPGEF3, HFE, RNF43, ZNF415, IL12RB2, MTUS1, NEDD9, ZNF667, AZGP1, WARS, GEM, RAB31, CALHM2, CA12, MYEOV, CELF2, SLCO5A1, ISM1, and PAPSS2) could accurately identify patients' prognosis and had close interactions with Neutrophil in the tumor environment, which may provide UM patients with personalized prognosis prediction and new treatment insights.

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Prediction of Poor Prognosis of HCC by Early Warning Model for Co-Expression of miRNA and mRNA Based on Bioinformatics Analysis

Technology in Cancer Research & Treatment ◽

10.1177/1533033820959353 ◽

2020 ◽

Vol 19 ◽

pp. 153303382095935

Author(s):

Zi-jian Su ◽

Chun-cheng Lin ◽

Jian-hui Pan ◽

Jian-hua Zhang ◽

Tao Han ◽

...

Keyword(s):

High Risk ◽

Poor Prognosis ◽

Target Genes ◽

Risk Model ◽

Prognostic Significance ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Venn Diagram ◽

Kaplan Meier ◽

Cox Analysis

Objective: Hepatocellular Carcinoma (HCC) has the highest mortality rate worldwide with the intractability of its extremely complicated pathogenesis and unclear mechanism. The limited survival highlights the need for the further detection of prognosis for HCC. MicroRNAs (miRNAs) and messenger RNAs (mRNAs) have been identified as regulatory factors and target genes in human cancers, while some studies also found post-transcriptional modification plays a crucial role in the occurrence and development of HCC. The present study aimed to elucidate the prognostic significance of miRNA and mRNA models in HCC. Methods: Data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. The miRNA and mRNA expressions were tested by the Wilcoxon and used funrich software to predict mRNA that might be related to miRNA. Then we determined the intersection with overlapped mRNA and miRNA Venn diagram, and screened out hub gene by using Degree algorithm in Cytoscape software. The COX models, with TCGA data as the training set and ICGC data as the test set, were constructed. All patients were divided into high-risk and low-risk groups. Data on overall survival of different groups were collected and analyzed by Kaplan-Meier method, and independent risk factors affecting prognosis were assessed by Cox analysis. Results: The miRNA and mRNA polygenic risk model showed a good true positive rate. Kaplan-Meier curve and Cox analysis suggested that the high-risk group was associated with poor prognosis, and the risk score could be used as an independent risk factor for HCC. Conclusion: Tumor risk models constructed in this study could effectively predict the prognosis of patients, which is expected to provide a reference for the prognostic stratification and treatment strategy development of HCC.

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Identification of crucial genes of pyrimidine metabolism as biomarkers for gastric cancer prognosis

Cancer Cell International ◽

10.1186/s12935-021-02385-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zhengxin Wu ◽

Jinshui Tan ◽

Yifan Zhuang ◽

Mengya Zhong ◽

Yubo Xiong ◽

...

Keyword(s):

Gastric Cancer ◽

Regression Analysis ◽

Survival Analysis ◽

Risk Model ◽

Cox Regression ◽

Clinical Samples ◽

Pyrimidine Metabolism ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Cox Analysis

Abstract Background Metabolic reprogramming has been reported in various kinds of cancers and is related to clinical prognosis, but the prognostic role of pyrimidine metabolism in gastric cancer (GC) remains unclear. Methods Here, we employed DEG analysis to detect the differentially expressed genes (DEGs) in pyrimidine metabolic signaling pathway and used univariate Cox analysis, Lasso-penalizes Cox regression analysis, Kaplan–Meier survival analysis, univariate and multivariate Cox regression analysis to explore their prognostic roles in GC. The DEGs were experimentally validated in GC cells and clinical samples by quantitative real-time PCR. Results Through DEG analysis, we found NT5E, DPYS and UPP1 these three genes are highly expressed in GC. This conclusion has also been verified in GC cells and clinical samples. A prognostic risk model was established according to these three DEGs by Univariate Cox analysis and Lasso-penalizes Cox regression analysis. Kaplan–Meier survival analysis suggested that patient cohorts with high risk score undertook a lower overall survival rate than those with low risk score. Stratified survival analysis, Univariate and multivariate Cox regression analysis of this model confirmed that it is a reliable and independent clinical factor. Therefore, we made nomograms to visually depict the survival rate of GC patients according to some important clinical factors including our risk model. Conclusion In a word, our research found that pyrimidine metabolism is dysregulated in GC and established a prognostic model of GC based on genes differentially expressed in pyrimidine metabolism.

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CXCR3 from chemokine receptor family correlates with immune infiltration and predicts poor survival in osteosarcoma

Bioscience Reports ◽

10.1042/bsr20192134 ◽

2019 ◽

Vol 39 (11) ◽

Cited By ~ 3

Author(s):

Yin Tang ◽

Zhiqian Gu ◽

Youwei Fu ◽

Junjie Wang

Keyword(s):

Chemokine Receptors ◽

Plasma Cells ◽

Vascular Endothelial ◽

Free Survival ◽

Immune Infiltration ◽

Cxcr3 Expression ◽

Kaplan Meier ◽

Tnf Α ◽

Cox Analysis ◽

Endothelial Growth Factor

AbstractBackground: Chemokine receptors have a crucial role in regulating tumor mediating immunity and are also implicated in the prognosis of some cancers. Here, the association between CXC chemokine receptors (CXCR2–5) and prognosis in osteosarcoma was studied.Methods: Differences between CXCR2, CXCR3, CXCR4, and CXCR5 expression and overall survival (OS) and event-free survival (EFS) were compared using Kaplan–Meier analyses. The associations of CXCR3 expression with clinical features and the prognosis were also analyzed. The signaling pathways modulated by CXCR3 were investigated. The correlations between CXCR3 and immune infiltrates were investigated.Results: The expression of CXCR2, CXCR4, and CXCR5 was not associated with the prognosis, but CXCR3 low expression was correlated with worse OS and EFS of osteosarcoma, especially for female, patients aged less than 15.1 years, or patients without metastasis. Low CXCR3 expression was related to tumor site and histologic response (P<0.05), but not associated with other clinical characteristics. Multivariate Cox analysis revealed that CXCR3 remained independently associated with the prognosis, especially for OS (hazard ratio (HR) = 3.26, 95% CI = 1.15–9.24, P=0.026). The cell adhesion, apoptosis, metabolism, KRAS, P53, NOTCH, reactive oxygen species (ROS), PI3K/Akt/mTOR, vascular endothelial growth factor (VEGF), inflammation, and immune-related pathways such as IL-6/JAK/STAT3, TNF-α via NF-κB, Toll/NOD-like receptor, and complement were modulated by CXCR3. CXCR3 expression showed an especially positive correlation with immune infiltration of T cells CD8, macrophages M1, plasma cells, and NK cells activated.Conclusions: CXCR3 may be an independent risk factor for the prognosis and is most likely to benefit from immunotherapy in osteosarcoma.

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