Development of a novel prognostic signature for predicting the overall survival of bladder cancer patients

Abstract Background: Bladder cancer is one of the most common malignancies. So far, no effective biomarker for bladder cancer prognosis has been identified. Aberrant DNA methylation is frequently observed in the bladder cancer and holds considerable promise as a biomarker for predicting the overall survival (OS) of patients. Materials and methods: We downloaded the DNA methylation and transcriptome data for bladder cancer from The Cancer Genome Atlas (TCGA), a public database, screened hypo-methylated and up-regulated genes, similarly, hyper-methylation with low expression genes, then retrieved the relevant methylation sites. Cox regression analysis was used to identify a nine-methylation site signature of a training group. Predictive ability was validated in a test group by receiver operating characteristic (ROC) analysis. Results: We identified nine bladder cancer-specific methylation sites as potential prognostic biomarkers and established a risk score system based on the methylation site signature to evaluate the OS. The performance of the signature was accurate, with area under curve was 0.73 in the training group and 0.71 in the test group. Taking clinical features into consideration, we constructed a nomogram consisting of the nine-methylation site signature and patients’ clinical variables, and found that the signature was an independent risk factor. Conclusions: Overall, the significant nine methylation sites could be novel prediction biomarkers, which could aid in treatment and also predict the overall survival likelihoods of bladder cancer patients.

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Identification of an 11-Autophagy-Related-Gene Signature as Promising Prognostic Biomarker for Bladder Cancer Patients

Biology ◽

10.3390/biology10050375 ◽

2021 ◽

Vol 10 (5) ◽

pp. 375

Author(s):

Chaoting Zhou ◽

Alex Heng Li ◽

Shan Liu ◽

Hong Sun

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Related Gene ◽

Training Cohort ◽

Cox Regression Analysis ◽

Therapeutic Decisions

Background: Survival rates for highly invasive bladder cancer (BC) patients have been very low, with a 5-year survival rate of 6%. Accurate prediction of tumor progression and survival is important for diagnosis and therapeutic decisions for BC patients. Our study aims to develop an autophagy-related-gene (ARG) signature that helps to predict the survival of BC patients. Methods: RNA-seq data of 403 BC patients were retrieved from The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) database. Univariate Cox regression analysis was performed to identify overall survival (OS)-related ARGs. The Lasso Cox regression model was applied to establish an ARG signature in the TCGA training cohort (N = 203). The performance of the 11-gene ARG signature was further evaluated in a training cohort and an independent validation cohort (N = 200) using Kaplan-Meier OS curve analysis, receiver operating characteristic (ROC) analysis, as well as univariate and multivariate Cox regression analysis. Results: Our study identified an 11-gene ARG signature that is significantly associated with OS, including APOL1, ATG4B, BAG1, CASP3, DRAM1, ITGA3, KLHL24, P4HB, PRKCD, ULK2, and WDR45. The ARGs-derived high-risk bladder cancer patients exhibited significantly poor OS in both training and validation cohorts. The prognostic model showed good predictive efficacy, with the area under the ROC curve (AUCs) for 1-year, 3-year, and 5-year overall survival of 0.702 (0.695), 0.744 (0.640), and 0.794 (0.658) in the training and validation cohorts, respectively. A prognostic nomogram, which included the ARGs-derived risk factor, age and stage for eventual clinical translation, was established. Conclusion: We identified a novel ARG signature for risk-stratification and robust prediction of overall survival for BC patients.

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

Bioscience Reports ◽

10.1042/bsr20212253 ◽

2022 ◽

Author(s):

Thongher Lia ◽

Yanxiang Shao ◽

Parbatraj Regmi ◽

Xiang Li

Keyword(s):

Bladder Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis

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

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Analysis of relapse-associated alternative mRNA splicing and construction of a prognostic signature predicting relapse in I–III colon cancer

10.21203/rs.3.rs-17501/v1 ◽

2020 ◽

Author(s):

Zhiyuan Zhang ◽

Qingyang Feng ◽

Peng Zheng ◽

Yang Lv ◽

Yihao Mao ◽

...

Keyword(s):

Colon Cancer ◽

Alternative Splicing ◽

Protein Interactions ◽

Cox Regression ◽

Molecular Subtypes ◽

Test Group ◽

Training Group ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Alternative Mrna Splicing

Abstract Background : The literature depicting the effects of alternative splicing (AS) events on relapse of colon cancer is little and there is no signature based on the alternative splicing. Methods : The bioinformatic analysis was performed based on data of The Cancer Genome Atlas (TCGA) to identify the relapse-associated ASs, the potential interactions were further analyzed and a robust signature was built after univariate Cox regression, LASSO Cox regression, and multivariate Cox regression analysis to predict the relapse in I–III colon cancer. Molecular subtypes was identified based on the signature. Results : We identified 1912 ASs of 1384 mRNA, based on the relapse-associated ASs, we constructed the network of protein-protein interactions (PPI) and ASs-splicing factors (SF) interactions. 1294 of proteins with 7396 interactions were included in the PPI network. 14 SFs combined with 78 relapse-associated ASs were included in the AS-SF network. We finally built a robust signature to predict the relapse of I–III colon cancer with a considerable AUC value in both the training group and the test group (0.857,0.839). Based on the ASs involved in the signature, samples were classified into 4 molecular subgroups distinguishing the relapse rate in diverse groups. Conclusion : Our study provides a profile of relapse-associated ASs in I–III colon cancer and build a robust signature to predict the relapse of I–III colon cancer patients and further classify the patients into 4 molecular subtypes.

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The timing from perioperative chemotherapy and disease recurrence could have clinical impact on survival in bladder cancer patients treated with salvage chemotherapy.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.7_suppl.444 ◽

2019 ◽

Vol 37 (7_suppl) ◽

pp. 444-444

Author(s):

Eiji Kikuchi ◽

Nozomi Hayakawa ◽

Koichirou Ogihara ◽

Minami Omura ◽

Ryuichi Mizuno ◽

...

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Cancer Patients ◽

Radical Cystectomy ◽

Cox Regression ◽

Prognostic Indicator ◽

Disease Recurrence ◽

Salvage Chemotherapy ◽

Perioperative Chemotherapy ◽

Cox Regression Analysis

444 Background: Our aim was to clarify whether the duration between perioperative chemotherapy and disease recurrence could affect therapeutic efficacy of salvage chemotherapy in bladder cancer patients treated with radical cystectomy. Methods: We retrospectively identified 201 patients treated with radical cystectomy and perioperative chemotherapy of neoadjuvant chemotherapy (NAC) and/or adjuvant chemotherapy (AC) for bladder cancer at our 7 institutions between 2003 and 2015. Of them 56 patients received salvage chemotherapy for disease recurrence and were included in the present analysis. We classified these patients according to the time from perioperative chemotherapy received to disease recurrence ( < 12 months, 12-24 months, and 24 < months) and compared their clinical characteristics and survival outcomes. Results: Overall, 33, 14, and 9 patients developed disease recurrence in < 12 months, 12-24 months, and < 24 months, respectively after perioperative chemotherapy. Patients in the 12-24 months group had a higher smoking rate compared to those in the other two groups, and were higher rate of female in comparison to the < 24 months group. Twenty-four (42.8%) patients received NAC alone, 23 (41.1%) received AC alone, and 9 (16.1%) received both NAC and AC. Twenty-two (66.7%), 9 (64%), and 4 (44.4%) patients received NAC in the < 12 months group, the 12-24 months group, and the < 24 months group, respectively. Furthermore, 19 (57.6%), 7 (50%), and 6 (66.7%) patients received AC in the < 12 months group, the 12-24 months group, and the < 24 months group, respectively. The 5 year overall survival in the < 12 months group was 26.6%, which was significantly lower than those in the 12-24 months group (51.1%, p < 0.001) and in the 24 months group (46.9%, p = 0.014). Multivariate Cox regression analysis revealed that disease recurrence after perioperative chemotherapy within 12 months was the only independent prognostic indicator for overall death (p = 0.032). Conclusions: Bladder cancer patients with disease recurrence within 12 months from their perioperative chemotherapy have a worse overall survival after salvage chemotherapy.

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Epigenetic signature predicts overall survival clear cell renal cell carcinoma

Cancer Cell International ◽

10.1186/s12935-020-01640-x ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Yejinpeng Wang ◽

Liang Chen ◽

Lingao Ju ◽

Kaiyu Qian ◽

Xinghuan Wang ◽

...

Keyword(s):

Dna Methylation ◽

Renal Cell Carcinoma ◽

Overall Survival ◽

Cell Carcinoma ◽

Renal Cell ◽

Cox Regression ◽

Clear Cell ◽

The Cancer Genome Atlas ◽

Cell Renal Cell Carcinoma ◽

Cox Regression Analysis

Abstract Background Recently, increasing study have found that DNA methylation plays an important role in tumor, including clear cell renal cell carcinoma (ccRCC). Methods We used the DNA methylation dataset of The Cancer Genome Atlas (TCGA) database to construct a 31-CpG-based signature which could accurately predict the overall survival of ccRCC. Meanwhile, we constructed a nomogram to predict the prognosis of patients with ccRCC. Result Through LASSO Cox regression analysis, we obtained the 31-CpG-based epigenetic signature which were significantly related to the prognosis of ccRCC. According to the epigenetic signature, patients were divided into two groups with high and low risk, and the predictive value of the epigenetic signature was verified by other two sets. In the training set, hazard ratio (HR) = 13.0, 95% confidence interval (CI) 8.0–21.2, P < 0.0001; testing set: HR = 4.1, CI 2.2–7.7, P < 0.0001; entire set: HR = 7.2, CI 4.9–10.6, P < 0.0001, Moreover, combined with clinical indicators, the prediction of 5-year survival of ccRCC reached an AUC of 0.871. Conclusions Our study constructed a 31-CpG-based epigenetic signature that could accurately predicted overall survival of ccRCC and staging progression of ccRCC. At the same time, we constructed a nomogram, which may facilitate the prediction of prognosis for patients with ccRCC.

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A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD)

Cancer Cell International ◽

10.1186/s12935-021-01975-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Aisha Al-Dherasi ◽

Qi-Tian Huang ◽

Yuwei Liao ◽

Sultan Al-Mosaib ◽

Rulin Hua ◽

...

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Training Group ◽

The Cancer Genome Atlas ◽

Individual Treatment ◽

Significant Implication ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients. Methods Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature. Results A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis. Conclusion Our study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

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The Development of Three-DNA Methylation Signature as a Novel Prognostic Biomarker in Patients with Colorectal Cancer

BioMed Research International ◽

10.1155/2020/3497810 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Shu Gong ◽

Weijian Ye ◽

Tiankai Liu ◽

Shaofen Jian ◽

Wenhua Liu

Keyword(s):

Colorectal Cancer ◽

Dna Methylation ◽

Overall Survival ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Methylation Array ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Potential Biomarker ◽

And Gender

Aims. The prognosis of colorectal cancer (CRC) remains poor. This study aimed to develop and validate DNA methylation-based signature model to predict overall survival of CRC patients. Methods. The methylation array data of CRC patients were retrieved from The Cancer Genome Atlas (TCGA) database. These patients were divided into training and validation datasets. A risk score model was established based on Kaplan-Meier and multivariate Cox regression analysis of training cohort and tested in validation cohort. Results. Among total 14,626 DNA methylation candidate markers, we found that a three-DNA methylation signature (NR1H2, SCRIB, and UACA) was significantly associated with overall survival of CRC patients. Subgroup analysis indicated that this signature could predict overall survival of CRC patients regardless of age and gender. Conclusions. We established a prognostic model consisted of 3-DNA methylation sites, which could be used as potential biomarker to evaluate the prognosis of CRC patients.

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A robust eleven-genes prognostic model can predict overall survival in bladder cancer patients based on five cohorts

10.21203/rs.3.rs-35127/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jiaxing Lin ◽

Jieping Yang ◽

Xiao Xu ◽

Yutao Wang ◽

Meng Yu ◽

...

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

High Risk ◽

B Cell ◽

Cancer Patients ◽

Prognostic Model ◽

Cox Regression ◽

Cell Memory ◽

Cox Regression Analysis ◽

B Cell Memory

Abstract Background: Bladder cancer is the tenth most common cancer in the world, but existing biomarkers and prognostic models are limited.Method: In this study, we used four bladder cancer cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases to perform univariate Cox regression analysis to identify common prognostic genes. We used selected genes to construct a prognostic model. Kaplan-Meier analysis, Receiver Operating Characteristic curve, and univariate and multivariate Cox analysis were used to evaluate the prognostic model for the four cohorts. Finally, a co-expression network, CIBERSORT, and ESTIMATE algorithm were used to explore the mechanism related to the model.Results: A total of 11 genes were identified from the four cohorts to construct the prognostic model, including eight risk genes (SERPINE2, PRR11, DSEL, DNM1, COMP, ELOVL4, RTKN, and MAPK12) and three protective genes (FABP6, C16orf74, and TNK1). The model and the 11 genes have excellent performance in predicting overall survival and have been confirmed in 5 cohorts. The model's predictive ability is stronger than other clinical features and has practical significance in clinical application.Through the analysis of the weighted co-expression network, the gene module related to the model was found, and the key genes in this module were mainly enriched in the items related to the tumor microenvironment. When comparing the level of immune cell infiltration in high-risk samples, B cell memory showed low infiltration in high-risk patients. Furthermore, in the case of low B cell memory infiltration and high-risk score, the prognosis of the patients was the worst.Conclusion: The model we developed has strong stability and good performance and can stratify the risk of bladder cancer patients, to achieve individualized treatment.

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Identification of prognostic and bone metastatic alternative splicing signatures in bladder cancer

10.21203/rs.3.rs-16236/v1 ◽

2020 ◽

Author(s):

Runzhi Huang ◽

Jiayao Zhang ◽

Tianjing Wang ◽

Jingyi Jia ◽

Dianwen Song ◽

...

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Alternative Splicing ◽

Bone Metastasis ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Cox Regression Analysis ◽

Predict Model ◽

Glycosphingolipid Biosynthesis

Abstract Background ： Bladder cancer, originating from the epithelium of the urinary bladder, was the second most common malignancy in the urinary system with a high metastasis rate and poor post-metastasis prognosis. Alternative splicing events (ASEs) were regarded as important markers of tumor progression and prognosis, however, their roles in bladder cancer bone metastasis haven’t been recognized. Methods ： In order to explore the mechanism of ASEs in bladder cancer bone metastasis, we downloaded the RNA sequencing data and ASEs data of 412 samples of primary BLCAs from The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases. The Cox regression analysis was used to identify overall survival-related ASEs (OS-SEs), then, based on the OS-SEs screened by Lasso regression, we constructed the predict model. Finally, univariate and multivariate independent prognostic analysis were performed to prove it as an independent prognostic factor. Results ： In this study, a predict model of OS in BLCA was constructed and the Area Under Curve of the model was 0.581. Its risk score was also proved to be an independent predictor with the good accuracy (P < 0.001). Among identified 390 SFs, Junction plakoglobin (JUP) was significantly correlated with overall survival and bone metastasis. In co-expression analysis, the co-expression pathway of ITGB4 was the glycosphingolipid biosynthesis ganglio series. Conclusions ： We speculated that JUP regulating the ITGB4 might play a key role in bone metastasis of bladder cancer through the glycosphingolipid biosynthesis ganglio series pathway (R = 0.220, P < 0.001), which was also related to prognosis.

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Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

BMC Gastroenterology ◽

10.1186/s12876-021-01734-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sheng Zheng ◽

Zizhen Zhang ◽

Ning Ding ◽

Jiawei Sun ◽

Yifeng Lin ◽

...

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

High Efficiency ◽

Cox Regression ◽

Risk Group ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Lasso Regression ◽

Sequencing Data ◽

Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

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