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

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

BioMed Research International ◽

10.1155/2020/7272960 ◽

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

10.21203/rs.3.rs-568705/v1 ◽

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.

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Immune-Related Genes to Construct a Novel Prognostic Model of Breast Cancer: A Chemosensitivity-Based Study

Frontiers in Immunology ◽

10.3389/fimmu.2021.734745 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhi-Min Deng ◽

Wei Hu ◽

Fang-Fang Dai ◽

Meng-Qin Yuan ◽

Min Hu ◽

...

Keyword(s):

Breast Cancer ◽

Prognostic Model ◽

Risk Model ◽

Cox Regression ◽

Univariate Analysis ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Related Gene ◽

Predictive Capacity ◽

Gene Pairs

Chemotherapy combined with surgery is effective for patients with breast cancer (BC). However, chemoresistance restricts the effectiveness of BC treatment. Immune microenvironmental changes are of pivotal importance for chemotherapy responses. Thus, we sought to construct and validate an immune prognostic model based on chemosensitivity status in BC. Here, immune-related and chemosensitivity-related genes were obtained from GSE25055. Then, univariate analysis was employed to identify prognostic-related gene pairs from the intersection of the two parts of the genes, and modified least absolute shrinkage and selection operator (LASSO) analysis was performed to build a prognostic model. Furthermore, we investigated the efficiency of this model from various perspectives, and further validation was performed using the Cancer Genome Atlas (TCGA) cohorts. We identified seven immune and chemosensitivity-related gene pairs and incorporated them into the Cox regression model. After multilevel validation, the risk model was found to be closely related to the survival rate, various clinical characteristics, tumor mutation burden (TMB) score, immune checkpoints, and response to chemotherapeutic drugs. In addition, the model was verified to exhibit predictive capacity as an independent factor over other candidate clinical features. Notably, the constructed nomogram was more accurate than any single factor. Altogether, the risk score model and the nomogram have potential predictive value and may have important practical implications.

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Identification of hepatocellular carcinoma risk using a novel prognostic model based on apoptosis-related genes

10.21203/rs.3.rs-141169/v1 ◽

2021 ◽

Author(s):

Renjie Liu ◽

Guifu Wang ◽

Chi Zhang ◽

Dousheng Bai

Keyword(s):

Hepatocellular Carcinoma ◽

Molecular Mechanisms ◽

Risk Model ◽

Cox Regression ◽

Malignant Tumors ◽

Enrichment Analysis ◽

Risk Groups ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Risk Scores

Abstract Background Dysregulation of the balance between proliferation and apoptosis is the basis in human hepatocarcinogenesis. There is a possible association of apoptosis dysregulation with poor prognosis in many malignant tumors, such as hepatocellular carcinoma (HCC). However, the prognostic effect of Apoptosis-related genes (ARGs) on HCC is still unclear. Methods A total of 161 ARGs expression levels were analyzed based on The Cancer Genome Atlas (TCGA) database(https://cancergenome.nih.gov/) to screen for differentially expressed ARGs. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to determine the underlying molecular mechanisms of screened ARGs in HCC. ARGs prognostic values were identified using Cox regression to subsequently establish a prognostic risk model and scoring in patients. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were plotted to determine the prognostic value in the model. Results Compared to normal specimens, 43 highly upregulated and 8 downregulated ARGs respectively and their normal counterparts in HCC specimens were screened. KEGG analysis demonstrated pathways correlated with these 51 genes which included MAPK, P53, TNF, PI3K-Akt signaling pathways. With Cox regression, 5 prognostic correlated with ARGs (PPP2R5B, SQSTM1, TOP2A, BMF, and LGALS3) were obtained to develop the prognosis model. According to the median of risk scores, patients were categorized into high-risk and low-risk groups. Patients in low-risk groups had a significantly higher two-year or five-year survival probability (p < 0.0001). The risk model had better potency than other clinical characteristics, with the area under the ROC curve (AUC = 0.741). Prognosis of HCC patients was established from a plotted nomogram. Conclusion This present study established a novel prognostic risk model for predicting HCC according to the expression of ARGs. The present advancement can potentially contribute to prediction prognosis and individualized treatment of HCC patients.

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Development of A Prognostic Index and Screening of Prognosis Related Genes Based on an Immunogenomic Landscape Analysis of Bladder Cancer

10.21203/rs.3.rs-135671/v1 ◽

2021 ◽

Author(s):

GenYi Qu ◽

Guang Yang ◽

Yong Xu ◽

Maolin Xiang ◽

Cheng Tang

Keyword(s):

Bladder Cancer ◽

Cox Regression ◽

Malignant Tumors ◽

Expression Profiles ◽

Prognostic Index ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Cox Analysis

Abstract Background: Bladder cancer (BLCA) is one of the most common urinary tract malignant tumors. It is associated with poor outcomes, and its etiology and pathogenesis are not fully understood. There is great hope for immunotherapy in treating many malignant tumors; therefore, it is worthwhile to explore the use of immunotherapy for BLCA.Methods: Gene expression profiles and clinical information were obtained from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were downloaded from the Immunology Database and Analysis Portal. Differentially-expressed and survival-associated IRGs in patients with BLCA were identified using computational algorithms and Cox regression analysis. We also performed functional enrichment analysis. Based on IRGs, we employed multivariate Cox analysis to develop a new prognostic index.Results: We identified 261 IRGs that were differentially expressed between BLCA tissue and adjacent tissue, 30 of which were significantly associated with the overall survival (all P<0.01). According to multivariate Cox analysis, nine survival-related IRGs (MMP9, PDGFRA, AHNAK, OAS1, OLR1, RAC3, IGF1, PGF, and SH3BP2) were high-risk genes. We developed a prognostic index based on these IRGs and found it accurately predicted BLCA outcomes associated with the TNM stage. Intriguingly, the IRG-based prognostic index reflected infiltration of macrophages.Conclusions: An independent IRG-based prognostic index provides a practical approach for assessing patients' immune status and prognosis with BLCA. This index independently predicted outcomes of BLCA.

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