scholarly journals A novel Signature Constructed using Ferroptosis-Related lncRNA Pairs May Predict the Prognosis of Bladder Cancer Patients

2021 ◽  
Author(s):  
Hao Wu ◽  
Li Zuo ◽  
Zi-Yi Zhang ◽  
Ze Zhang ◽  
Sheng-Lin Gao ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Malignant Tumors ◽  
Tumor Development ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Tumor Immune Microenvironment ◽  
Cancer Transcriptome ◽  
Cancer Genome Atlas ◽  
Stratified Analysis ◽  
Promote Tumor Development

Abstract Background:Bladder cancer is one of the most common malignant tumors of the urinary system, and its incidence has been increasing in recent years. Ferroptosis is a recently discovered type of cell death, and some studies have suggested that it is closely associated with tumors. It can promote tumor apoptosis and also promote tumor development. Moreover, it has been reported that a correlation exists between long non-coding RNAs (lncRNAs) pairs and tumors. Herein, we developed an lncRNA pair signature associated with ferroptosis to predict the prognosis of bladder cancer. Methods: We combined the bladder cancer transcriptome data from the Cancer Genome Atlas (TCGA) database to identify ferroptosis-related lncRNA (FRlncRNA) pairs. Using univariate and multivariate Cox analyses and LASSO regression analysis, we identified a FRlncRNA pair signature. We subsequently assessed the predictive prognostic value of this signature and validated the results. Results: The signature included 18 lncRNA pairs and was highly accurate for clinical prediction in patients with bladder cancer. Univariate and multivariate Cox analyses and stratified analysis indicated that the model was an independent prognostic factor. Additionally, we detected a positive correlation between this signature and the tumor immune microenvironment. Conclusion: The FRlncRNA pair signature has good prognostic and clinical predictive value in patients with bladder cancer.

scholarly journals Identification of a Novel Tumor Microenvironment Prognostic Signature for Advanced-Stage Serous Ovarian Cancer

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3343
Author(s):  
Mingjun Zheng ◽  
Junyu Long ◽  
Anca Chelariu-Raicu ◽  
Heather Mullikin ◽  
Theresa Vilsmaier ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Malignant Tumors ◽  
Biological Response ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Serous Ovarian Cancer ◽  
Lasso Regression ◽  
Cancer Genome Atlas

(1) Background: The tumor microenvironment is involved in the growth and proliferation of malignant tumors and in the process of resistance towards systemic and targeted therapies. A correlation between the gene expression profile of the tumor microenvironment and the prognosis of ovarian cancer patients is already known. (2) Methods: Based on data from The Cancer Genome Atlas (379 RNA sequencing samples), we constructed a prognostic 11-gene signature (SNRPA1, CCL19, CXCL11, CDC5L, APCDD1, LPAR2, PI3, PLEKHF1, CCDC80, CPXM1 and CTAG2) for Fédération Internationale de Gynécologie et d’Obstétrique stage III and IV serous ovarian cancer through lasso regression. (3) Results: The established risk score was able to predict the 1-, 3- and 5-year prognoses more accurately than previously known models. (4) Conclusions: We were able to confirm the predictive power of this model when we applied it to cervical and urothelial cancer, supporting its pan-cancer usability. We found that immune checkpoint genes correlate negatively with a higher risk score. Based on this information, we used our risk score to predict the biological response of cancer samples to an anti-programmed death ligand 1 immunotherapy, which could be useful for future clinical studies on immunotherapy in ovarian cancer.

scholarly journals CXCL11 Correlates with Anti-tumor Immune Microenvironment and Improved Prognosis in Colon Cancer

2020 ◽  
Author(s):  
Yingying Cao ◽  
Youwei Zhang ◽  
Nanlin Jiao ◽  
Tiantian Sun ◽  
Yanru Ma ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Tumor Development ◽  
Specific Effect ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment ◽  
Antibody Treatment ◽  
Tumor Immune Microenvironment ◽  
Cancer Genome Atlas ◽  
Immunological Regulation

Abstract Background: CXCL11 has been considered to be responsible for tumor development, but the specific effect of CXCL11 in colon cancer was still obscure. Therefore, the prognostic value and immunological regulation effect of CXCL11 in colon cancer were evaluated in this study.Methods: Three independent datasets were used for mRNA-related analysis: one dataset from the Cancer Genome Atlas (TCGA, n=451) and two single-cell RNA sequencing (scRNA-seq) datasets from Gene Expression Omnibus (GEO): GSE146771 and GSE132465. In addition, the patient cohort (the Yijishan Hospital cohort, YJSHC, n=108) was utilized for cell infiltration-related analysis, accordingly. Both CXCL11 mRNA expression and CXCL11+ (CXCL11-producing) cells were assessed in colon cancer, whose effect on prognosis and immunological regulation was also studied. Results: High CXCL11 expression were associated with better prognosis in colon cancer, which was still significant even if clinicopathological factors were adjusted. Furthermore, CXCL11 positively correlated with anti-tumor cells infiltration, such as CD8+ T cells and natural killer cells. Meanwhile, CXCL11 correlated positively with several genes associated with DC, NK and T recruitment，and a gene set of cytotoxic genes. Notably, CXCL11 correlated positively with several immune checkpoint related genes including of PD-L1. Conclusions: CXCL11 contributed to anti-tumor immune microenvironment and could improve prognosis in patients with colon cancer. Especially, it’s a potential approach that inducible expression of CXCL11 by genetic and pharmacological interventions is able to improve prognosis and response to anti-PD-1 (programmed cell death protein-1) antibody treatment in colon cancer. However, it requires to be verified by further prospective investigations.

scholarly journals Prognostic Value of Immune-Related lncRNA Pairs In Patients with Bladder Cancer

2021 ◽  
Author(s):  
zhenzhen Gao ◽  
Dongjuan Wu ◽  
Wenwen Zheng ◽  
taohong Zhu ◽  
Ting Sun ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Regression Model ◽  
Risk Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Univariate Analysis ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Efficacy Evaluation ◽  
Specific Expression

Abstract Background: The characteristics of immune-related long non-coding ribonucleic acids (ir-lncRNAs), regardless of their specific expression level, have important implications for the prognosis of patients with bladder cancer. Methods: Based on The Cancer Genome Atlas (TCGA) database, we downloaded original transcript data, obtained the ir-lncRNAs using a coexpression method, and identified the differentially expressed pairs of ir-lncRNAs (DE-ir-lncRNAs) using univariate analysis. The lncRNA pairs were verified using a Lasso regression test. Thereafter, receiver operating characteristic curves (ROC) were generated; the area under the curve was calculated; the Akaike information criterion (AIC) of the 5-y ROC was determined; the optimal cutoff value of the high- and low-risk populations of patients with bladder cancer was confirmed, and the optimal risk model was established. The clinical value of the model was verified using survival analysis, clinicopathological characteristics, presence of tumor-infiltrating immune cells, and chemotherapy efficacy evaluation. Results: In total, 49 pairs of DE-ir-lncRNAs were identified, and 21 pairs were included in the Cox regression model. In this study, ir-lncRNA pairs were obtained, and a risk regression model was established on the premise of not involving the specific expression value of transcripts. Conclusions: The method and model used in this study have important clinical predictive value for bladder cancer and other malignant tumors.

scholarly journals Prognostic value of immune-related lncRNA pairs in patients with bladder cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Zhenzhen Gao ◽  
Dongjuan Wu ◽  
Wenwen Zheng ◽  
Taohong Zhu ◽  
Ting Sun ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Regression Model ◽  
Risk Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Univariate Analysis ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Efficacy Evaluation ◽  
Specific Expression

Abstract Background The characteristics of immune-related long non-coding ribonucleic acids (ir-lncRNAs), regardless of their specific levels, have important implications for the prognosis of patients with bladder cancer. Methods Based on The Cancer Genome Atlas database, original transcript data were analyzed. The ir-lncRNAs were obtained using a coexpression method, and their differentially expressed pairs (DE-ir-lncRNAs) were identified by univariate analysis. The lncRNA pairs were verified using a Lasso regression test. Thereafter, receiver operating characteristic curves were generated, and an optimal risk model was established. The clinical value of the model was verified through the analysis of patient survival rates, clinicopathological characteristics, presence of tumor-infiltrating immune cells, and chemotherapy efficacy evaluation. Results In total, 49 pairs of DE-ir-lncRNAs were identified, of which 21 were included in the Cox regression model. A risk regression model was established on the premise of not involving the specific expression value of the transcripts. Conclusions The method and model used in this study have important clinical predictive value for bladder cancer and other malignant tumors.

scholarly journals 90-Gene Expression Profiling for Tissue Origin Diagnosis of Cancer of Unknown Primary

2021 ◽  
Vol 11 ◽  
Author(s):  
Yi Zhang ◽  
Lei Xia ◽  
Dawei Ma ◽  
Jing Wu ◽  
Xinyu Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Cancer Of Unknown Primary ◽  
The Cancer Genome Atlas ◽  
Unknown Primary ◽  
Gene Expression Assay ◽  
Primary Location ◽  
Cancer Genome Atlas ◽  
Expression Characteristics

Cancer of unknown primary (CUP), in which metastatic diseases exist without an identifiable primary location, accounts for about 3–5% of all cancer diagnoses. Successful diagnosis and treatment of such patients are difficult. This study aimed to assess the expression characteristics of 90 genes as a method of identifying the primary site from CUP samples. We validated a 90-gene expression assay and explored its potential diagnostic utility in 44 patients at Jiangsu Cancer Hospital. For each specimen, the expression of 90 tumor-specific genes in malignant tumors was analyzed, and similarity scores were obtained. The types of malignant tumors predicted were compared with the reference diagnosis to calculate the accuracy. In addition, we verified the consistency of the expression profiles of the 90 genes in CUP secondary malignancies and metastatic malignancies in The Cancer Genome Atlas. We also reported a detailed description of the next-generation coding sequences for CUP patients. For each clinical medical specimen collected, the type of malignant tumor predicted and analyzed by the 90-gene expression assay was compared with its reference diagnosis, and the overall accuracy was 95.4%. In addition, the 90-gene expression profile generally accurately classified CUP into the cluster of its primary tumor. Sequencing of the exome transcriptome containing 556 high-frequency gene mutation oncogenes was not significantly related to the 90 genes analysis. Our results demonstrate that the expression characteristics of these 90 genes can be used as a powerful tool to accurately identify the primary sites of CUP. In the future, the inclusion of the 90-gene expression assay in pathological diagnosis will help oncologists use precise treatments, thereby improving the care and outcomes of CUP patients.

scholarly journals Tumor Expression Profile Analysis Developed and Validated a Prognostic Model Based on Immune-Related Genes in Bladder Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Bingqi Dong ◽  
Jiaming Liang ◽  
Ding Li ◽  
Wenping Song ◽  
Shiming Zhao ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Regression Analysis ◽  
Malignant Tumors ◽  
Immune Therapy ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Set Enrichment Analysis ◽  
Lasso Regression ◽  
Potential Biomarker ◽  
Immune Related Genes

Background: Bladder cancer (BLCA) ranks 10th in incidence among malignant tumors and 6th in incidence among malignant tumors in males. With the application of immune therapy, the overall survival (OS) rate of BLCA patients has greatly improved, but the 5-year survival rate of BLCA patients is still low. Furthermore, not every BLCA patient benefits from immunotherapy, and there are a limited number of biomarkers for predicting the immunotherapy response. Therefore, novel biomarkers for predicting the immunotherapy response and prognosis of BLCA are urgently needed.Methods: The RNA sequencing (RNA-seq) data, clinical data and gene annotation files for The Cancer Genome Atlas (TCGA) BLCA cohort were extracted from the University of California, Santa Cruz (UCSC) Xena Browser. The BLCA datasets GSE31684 and GSE32894 from the Gene Expression Omnibus (GEO) database were extracted for external validation. Immune-related genes were extracted from InnateDB. Significant differentially expressed genes (DEGs) were identified using the R package “limma,” and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the DEGs were performed using R package “clusterProfiler.” Least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the signature model. The infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. The performance of the model was evaluated with receiver operating characteristic (ROC) curves and calibration curves.Results: In total, 1,040 immune-related DEGs were identified, and eight signature genes were selected to construct a model using LASSO regression analysis. The risk score of BLCA patients based on the signature model was negatively correlated with OS and the immunotherapy response. The ROC curve for OS revealed that the model had good accuracy. The calibration curve showed good agreement between the predictions and actual observations.Conclusions: Herein, we constructed an immune-related eight-gene signature that could be a potential biomarker to predict the immunotherapy response and prognosis of BLCA patients.

scholarly journals Deciphering N6-Methyladenosine-Related Genes Signature to Predict Survival in Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jie Zhu ◽  
Min Wang ◽  
Daixing Hu
Keyword(s):  
Lung Adenocarcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Lasso Regression ◽  
Gene Expressions ◽  
Tumor Tissues ◽  
Cancer Genome Atlas ◽  
Selection Operator ◽  
Cox Analysis

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death. Among these, lung adenocarcinoma (LUAD) accounts for most cases. Due to the improvement of precision medicine based on molecular characterization, the treatment of LUAD underwent significant changes. With these changes, the prognosis of LUAD becomes diverse. N6-methyladenosine (m6A) is the most predominant modification in mRNAs, which has been a research hotspot in the field of oncology. Nevertheless, little has been studied to reveal the correlations between the m6A-related genes and prognosis in LUAD. Thus, we conducted a comprehensive analysis of m6A-related gene expressions in LUAD patients based on The Cancer Genome Atlas (TCGA) database by revealing their relationship with prognosis. Different expressions of the m6A-related genes in tumor tissues and non-tumor tissues were confirmed. Furthermore, their relationship with prognosis was studied via Consensus Clustering Analysis, Principal Components Analysis (PCA), and Least Absolute Shrinkage and Selection Operator (LASSO) Regression. Based on the above analyses, a m6A-based signature to predict the overall survival (OS) in LUAD was successfully established. Among the 479 cases, we found that most of the m6A-related genes were differentially expressed between tumor and non-tumor tissues. Six genes, HNRNPC, METTL3, YTHDC2, KIAA1429, ALKBH5, and YTHDF1 were screened to build a risk scoring signature, which is strongly related to the clinical features pathological stages (p<0.05), M stages (p<0.05), T stages (p < 0.05), gender (p=0.04), and survival outcome (p=0.02). Multivariate Cox analysis indicated that risk value could be used as an independent prognostic factor, revealing that the m6A-related genes signature has great predictive value. Its efficacy was also validated by data from the Gene Expression Omnibus (GEO) database.

scholarly journals Exploring the Pathological Mechanism of Bladder Cancer Based on Tumor Mutational Burden Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yao Ma ◽  
Xiao-Fei Feng ◽  
Wan-Xia Yang ◽  
Chong-Ge You
Keyword(s):  
Bladder Cancer ◽  
High Throughput Sequencing ◽  
The Cancer Genome Atlas ◽  
Bladder Neoplasm ◽  
Urinary Bladder Neoplasm ◽  
Related Data ◽  
Mutation Burden ◽  
Tumor Mutational Burden ◽  
Cancer Genome Atlas ◽  
Tumor Genome

Although immunotherapy has progressed in the treatment of bladder cancer, some patients still have poor prognosis. New therapeutic targets are eager to be discovered to improve the outcomes of bladder cancer. With the development of high-throughput sequencing and tumor profiling, potential tumor biomarkers were identified. Through the interpretation of related data from the Cancer Genome Atlas database (TCGA), some key genes have been discovered to drive the development and prognosis of urinary bladder neoplasm. On account of the success of immunotherapy in many cancer types, we established the relationship between tumor mutation burden and immune microenvironment of bladder cancer and found the changes of several immune cells in this disease. Based on the understanding of the bladder tumor genome and immune environment, this study is supposed to provide new therapies for the treatment of bladder neoplasm.

scholarly journals A Three Protein-Coding Gene Prognostic Model Predicts Overall Survival in Bladder Cancer Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiang-hui Ning ◽  
Yuan-yuan Qi ◽  
Fang-xin Wang ◽  
Song-chao Li ◽  
Zhan-kui Jia ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Zinc Finger Protein ◽  
Risk Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Lasso Regression ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Differentially Methylated Genes

Bladder cancer (BLCA) is the most common urinary tract tumor and is the 11th most malignant cancer worldwide. With the development of in-depth multisystem sequencing, an increasing number of prognostic molecular markers have been identified. In this study, we focused on the role of protein-coding gene methylation in the prognosis of BLCA. We downloaded BLCA clinical and methylation data from The Cancer Genome Atlas (TCGA) database and used this information to identify differentially methylated genes and construct a survival model using lasso regression. We assessed 365 cases, with complete information regarding survival status, survival time longer than 30 days, age, gender, and tumor characteristics (grade, stage, T, M, N), in our study. We identified 353 differentially methylated genes, including 50 hypomethylated genes and 303 hypermethylated genes. After annotation, a total of 227 genes were differentially expressed. Of these, 165 were protein-coding genes. Three genes (zinc finger protein 382 (ZNF382), galanin receptor 1 (GALR1), and structural maintenance of chromosomes flexible hinge domain containing 1 (SMCHD1)) were selected for the final risk model. Patients with higher-risk scores represent poorer survival than patients with lower-risk scores in the training set ( HR = 2.37 , 95% CI 1.43-3.94, p = 0.001 ), in the testing group ( HR = 1.85 , 95% CI 1.16-2.94, p = 0.01 ), and in the total cohort ( HR = 2.06 , 95% CI 1.46-2.90, p < 0.001 ). Further univariate and multivariate analyses using the Cox regression method were conducted in these three groups, respectively. All the results indicated that risk score was an independent risk factor for BLCA. Our study screened the different methylation protein-coding genes in the BLCA tissues and constructed a robust risk model for predicting the outcome of BLCA patients. Moreover, these three genes may function in the mechanism of development and progression of BLCA, which should be fully clarified in the future.

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