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

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

10.21203/rs.3.rs-517245/v1 ◽

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.

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

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02419-8 ◽

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.

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Immune Characteristics-Related Typing of Colorectal Cancer and the Establishment of 14-Gene Prognostic Model

10.21203/rs.3.rs-1093264/v1 ◽

2021 ◽

Author(s):

Jianxing Ma ◽

Chen Wang

Keyword(s):

Colorectal Cancer ◽

Risk Model ◽

Cox Regression ◽

Nonnegative Matrix ◽

Test Group ◽

Progression Free Survival ◽

Risk Groups ◽

The Cancer Genome Atlas ◽

Lasso Regression ◽

Immune Related Genes

Abstract This study is to establish NMF (nonnegative matrix factorization) typing related to the tumor microenvironment (TME) of colorectal cancer (CRC) and to construct a gene model related to prognosis to be able to more accurately estimate the prognosis of CRC patients. NMF algorithm was used to classify samples merged clinical data of differentially expressed genes (DEGs) of TCGA that are related to the TME shared in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and survival differences between subtype groups were compared. By using createData Partition command, TCGA database samples were randomly divided into train group and test group. Then the univariate Cox analysis, Lasso regression and multivariate Cox regression models were used to obtain risk model formula, which is used to score the samples in the train group, test group and GEO database, and to divide the samples of each group into high-risk and low-risk groups, according to the median score of the train group. After that, the model was validated. Patients with CRC were divided into 2, 3, 5 subtypes respectively. The comparison of patients with overall survival (OS) and progression-free survival (PFS) showed that the method of typing with the rank set to 5 was the most statistically significant (p=0.007, p<0.001, respectively). Moreover, the model constructed containing 14 immune-related genes (PPARGC1A, CXCL11, PCOLCE2, GABRD, TRAF5, FOXD1, NXPH4, ALPK3, KCNJ11, NPR1, F2RL2, CD36, CCNF, DUSP14) can be used as an independent prognostic factor, which is superior to some previous models in terms of patient prognosis. The 5-type typing of CRC patients and the 14 immune-related genes model constructed by us can accurately estimate the prognosis of patients with CRC.

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Six-long Non-coding RNA Signature to Predict the Prognosis of Lung Adenocarcinoma

10.21203/rs.3.rs-56767/v1 ◽

2020 ◽

Author(s):

Yang Wang ◽

Chengping Hu

Keyword(s):

Lung Adenocarcinoma ◽

Cox Regression ◽

Survival Rates ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Protein Coding ◽

Cox Regression Analysis ◽

Protein Coding Genes

Abstract Background: Long non-coding RNAs (lncRNAs) have been reported to play essential roles in tumorigenesis and cancers prognosis, and they can be a potential cancer prognostic markers. However, in lung adenocarcinoma(LUAD), how lncRNA signatures predict the survival of patients is poorly understood. Our study aims to explore lncRNA signatures and prognostic function in LUAD.Methods: The expression and prognosis data of lncRNAs in LUAD patients was collected from the Cancer Genome Atlas (TCGA) data. All analyses were performed using the R package (version 3.6.2). Metascape, STRING and Cytoscape were used for enrichment analysis and function prediction of the lncRNA co-expressed protein-coding genes.Results: We have collected lncRNA expression data in 466 LUAD tumors, and a six-lncRNA signature(RP11-79H23.3, RP11-309M7.1, CTD-2357A8.3, RP11-108P20.4, U47924.29, LHFPL3-AS2) has been shown to be significantly related to LUAD patients’ overall survival. According to the lncRNA signatures, the high-risk and low-risk groups were divided in LUAD patients with different survival rates. Further multivariable cox regression analysis showed that the prognostic value of this signature was independent of clinical factors. The potential functional roles and hub co-expressed protein-coding genes in the six prognostic lncRNAs are shown in the functional enrichment analysis.Conclusions: These results showed that these six lncRNAs could be independent predicted prognostic biomarkers in LUAD patients.

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Comprehensive Analysis and Prognosis Prediction of N6-methyladenosine-related Lncrnas in Immune Microenvironment Infiltration of Gastric Cancer

10.21203/rs.3.rs-1021150/v1 ◽

2021 ◽

Author(s):

Jianfeng Huang ◽

Wenzheng Chen ◽

Changyu Chen ◽

Tao Xiao ◽

Zhigang Jie

Keyword(s):

Gastric Cancer ◽

Risk Model ◽

Cox Regression ◽

Cytotoxic T Lymphocyte ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Rna Modification ◽

Consensus Clustering ◽

Immune Microenvironment ◽

Cox Regression Analysis

Abstract BackgroundN6-methyladenosine (m6A) RNA modification plays an important role in regulating tumor microenvironment (TME) infiltration. However, the relationship between the expression pattern of m6A-related long non-coding RNAs (lncRNAs) and the immune microenvironment of gastric cancer (GC) is unclear. MethodsIn this study, 23 m6A-related lncRNAs were identified by Pearson’s correlation analysis and univariate Cox regression analysis. According to the expression of these lncRNAs, we identified two distinct molecular clusters by consensus clustering and compared the differences of the TME and enriched pathways between the two clusters. We further constructed a prognostic risk signature and verified it using The Cancer Genome Atlas training and testing cohorts. ResultsThe results showed that cluster 1 was associated with tumor-related and immune activation-related pathways. In addition, cluster 1 was also associated with higher ImmuneScore, StromalScore, and ESTIMATEScore. The results of the stratified survival analysis and independent prognosis analysis indicated that the risk signature is an independent prognostic indicator for patients with GC. In addition, it can effectively predict survival status in patients with different clinical characteristics. Furthermore, our risk model showed that low risk scores were significantly correlated with high expression of programmed death-1 (PD-1) and cytotoxic T-lymphocyte associated protein 4 (CTLA4), as well as sensitivity to chemotherapeutic drugs (e.g., paclitaxel and oxaliplatin). ConclusionsThis evidence contributes to our understanding of the regulation of TME infiltration by m6A-related lncRNAs and my lead to more effective immunotherapy and chemotherapy for patients with GC.

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m6A RNA Methylation Regulators Impact Prognosis and Tumor Microenvironment in Renal Papillary Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.598017 ◽

2021 ◽

Vol 11 ◽

Author(s):

Lianze Chen ◽

Baohui Hu ◽

Xinyue Song ◽

Lin Wang ◽

Mingyi Ju ◽

...

Keyword(s):

Tumor Microenvironment ◽

Cell Carcinoma ◽

Risk Model ◽

Cox Regression ◽

Expression Patterns ◽

The Cancer Genome Atlas ◽

Rna Modification ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Rna Methylation

Accumulating evidence has proven that N6-methyladenosine (m6A) RNA methylation plays an essential role in tumorigenesis. However, the significance of m6A RNA methylation modulators in the malignant progression of papillary renal cell carcinoma (PRCC) and their impact on prognosis has not been fully analyzed. The present research set out to explore the roles of 17 m6A RNA methylation regulators in tumor microenvironment (TME) of PRCC and identify the prognostic values of m6A RNA methylation regulators in patients afflicted by PRCC. We investigated the different expression patterns of the m6A RNA methylation regulators between PRCC tumor samples and normal tissues, and systematically explored the association of the expression patterns of these genes with TME cell-infiltrating characteristics. Additionally, we used LASSO regression to construct a risk signature based upon the m6A RNA methylation modulators. Two-gene prognostic risk model including IGF2BP3 and HNRNPC was constructed and could predict overall survival (OS) of PRCC patients from the Cancer Genome Atlas (TCGA) dataset. The prognostic signature-based risk score was identified as an independent prognostic indicator in Cox regression analysis. Moreover, we predicted the three most significant small molecule drugs that potentially inhibit PRCC. Taken together, our study revealed that m6A RNA methylation regulators might play a significant role in the initiation and progression of PRCC. The results might provide novel insight into exploration of m6A RNA modification in PRCC and provide essential guidance for therapeutic strategies.

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A prognostic model for hepatocellular carcinoma based on apoptosis-related genes

10.21203/rs.3.rs-141169/v2 ◽

2021 ◽

Author(s):

Renjie Liu ◽

Guifu Wang ◽

Chi Zhang ◽

Dousheng Bai

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Model ◽

Molecular Mechanisms ◽

Risk Model ◽

Cox Regression ◽

Risk Groups ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Risk Scores

Abstract Background: Dysregulation of the balance between proliferation and apoptosis is the basis for human hepatocarcinogenesis. In many malignant tumors, such as hepatocellular carcinoma (HCC), there is a correlation between apoptotic dysregulation and poor prognosis. However, the prognostic values of apoptosis-related genes (ARGs) in HCC have not been elucidated. Methods: To screen for differentially expressed ARGs, the expression levels of 161 ARGs from The Cancer Genome Atlas (TCGA) database(https://cancergenome.nih.gov/) were analyzed. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to evaluate the underlying molecular mechanisms of differentially expressed ARGs in HCC. The prognostic values of ARGs were established using Cox regression, and subsequently, a prognostic risk model for scoring patients was developed. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were plotted to determine the prognostic value of the model. Results: Compared to normal tissues, 43 highly up-regulated and 8 down-regulated ARGs in HCC tissues were screened. GO analysis results revealed that these 51 genes are indeed related to the apoptosis function. KEGG analysis revealed that these 51 genes were correlated with MAPK, P53, TNF, and PI3K-AKT signaling pathways, while Cox regression revealed that 5 ARGs (PPP2R5B, SQSTM1, TOP2A, BMF, and LGALS3) were associated with prognosis and were, therefore, obtained to develop the prognostic model. Based on the median risk scores, patients were categorized into high-risk and low-risk groups. Patients in the low-risk groups exhibited significantly elevated two-year or five-year survival probabilities (p < 0.0001). The risk model had a better clinical potency than the other clinical characteristics, with the area under the ROC curve (AUC = 0.741). The prognosis of HCC patients was established from a plotted nomogram. Conclusion: Based on the differential expression of ARGs, we established a novel risk model for predicting HCC prognosis. This model can also be used to inform the individualized treatment of HCC patients.

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An Immune-Related lncRNA Model for Predicting Prognosis, Immune Landscape and Chemotherapeutic Response in Bladder Cancer

10.21203/rs.3.rs-953343/v1 ◽

2021 ◽

Author(s):

Jian Hou ◽

Songwu Liang ◽

Zhimin Xie ◽

Genyi Qu ◽

Yong Xu ◽

...

Keyword(s):

Bladder Cancer ◽

Immune Cell ◽

Risk Model ◽

Pearson Correlation ◽

Predictive Performance ◽

Information Criterion ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Lasso Regression ◽

Chemotherapeutic Response

Abstract Objective: Long noncoding RNAs (lncRNAs) participate in cancer immunity. Herein, we characterized the clinical significance of immune-related lncRNA model and its associations with immune infiltrations and chemosensitivity in bladder cancer.Methods: Transcriptome data of bladder cancer specimens were employed from The Cancer Genome Atlas. Dysregulated immune-related lncRNAs were screened via Pearson correlation and differential expression analyses, followed by recognition of lncRNA pairs. Then, a LASSO regression model was constructed. Receiver operator characteristic curves of one-, three- and five-year survival were plotted. Akaike information criterion (AIC) value of one-year survival was determined as the cutoff of high- and low-risk subgroups. The differences in survival, clinical features, immune cell infiltrations and chemosensitivity were compared between subgroups.Results: Totally, 90 immune-related lncRNA pairs were selected, 15 of which were put into the prognostic model. The area under the curves of one-, three- and five-year survival were 0.806, 0.825 and 0.828, confirming the favorable predictive performance of this model. According to the AIC value, we clustered subjects into high- and low-risk subgroups. High-risk score indicated unfavorable outcomes. This risk model was in relation to survival status, age, stage and TNM. In comparison to conventional clinicopathological characteristics, the risk model displayed higher predictive efficacy and was an independent predictor. Also, it could well characterize immune cell infiltration landscape and predict immune checkpoint expression and sensitivity to cisplatin and methotrexate.Conclusion: This model conducted by paring immune-related lncRNAs regardless of expressions exhibited a favorable efficacy in predicting prognosis, immune landscape and chemotherapeutic response in bladder cancer.

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An RNA-sequencing-based transcriptome for a significantly prognostic novel driver signature identification in bladder urothelial carcinoma

PeerJ ◽

10.7717/peerj.9422 ◽

2020 ◽

Vol 8 ◽

pp. e9422

Author(s):

Danqi Liu ◽

Boting Zhou ◽

Rangru Liu

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Regression Analysis ◽

Urothelial Carcinoma ◽

Cox Regression ◽

Protein Coding ◽

Cox Regression Analysis ◽

Protein Coding Genes ◽

Bladder Urothelial Carcinoma ◽

Rna Signature

Bladder cancer (BC) is the ninth most common malignancy worldwide. Bladder urothelial carcinoma (BLCA) constitutes more than 90% of bladder cancer (BC). The five-year survival rate is 5–70%, and patients with BLCA have a poor clinical outcome. The identification of novel clinical molecular markers in BLCA is still urgent to allow for predicting clinical outcomes. This study aimed to identify a novel signature integrating the three-dimension transcriptome of protein coding genes, long non-coding RNAs, microRNAs that is related to the overall survival of patients with BLCA, contributing to earlier prediction and effective treatment selection, as well as to the verification of the established model in the subtypes identified. Gene expression profiling and the clinical information of 400 patients diagnosed with BLCA were retrieved from The Cancer Genome Atlas (TCGA) database. A univariate Cox regression analysis, robust likelihood-based survival modelling analysis and random forests for survival regression and classification algorithms were used to identify the critical biomarkers. A multivariate Cox regression analysis was utilized to construct a risk score formula with a maximum area under the curve (AUC = 0.7669 in the training set). The significant signature could classify patients into high-risk and low-risk groups with significant differences in overall survival time. Similar results were confirmed in the test set (AUC = 0.645) and in the entire set (AUC = 0.710). The multivariate Cox regression analysis indicated that the five-RNA signature was an independent predictive factor for patients with BLCA. Non-negative matrix factorization and a similarity network fusion algorithm were applied for identifying three molecular subtypes. The signature could separate patients in every subtype into high- and low- groups with a distinct difference. Gene set variation analysis of protein-coding genes associated with the five prognostic RNAs demonstrated that the co-expressed protein-coding genes were involved in the pathways and biological process of tumourigenesis. The five-RNA signature could serve as to some degree a reliable independent signature for predicting outcome in patients with BLCA.

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

BMC Cancer ◽

10.1186/s12885-021-08447-7 ◽

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.

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