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

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

10.21203/rs.3.rs-35127/v2 ◽

2020 ◽

Author(s):

Jiaxing Lin ◽

Jieping Yang ◽

Xiao Xu ◽

Yutao Wang ◽

Meng Yu ◽

...

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

High Risk ◽

B Cell ◽

Cancer Patients ◽

Risk Score ◽

Prognostic Model ◽

Cell Memory ◽

Cox Regression Analysis ◽

B Cell Memory

Abstract Background: Bladder cancer is the tenth most common cancer globally, 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 the least absolute shrinkage and selection operator regression to construct a prognostic Cox model. Kaplan-Meier analysis, receiver operating characteristic curve, and univariate / 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 11-genes model could stratify the risk of patients in all five cohorts, and the prognosis was worse in the group with a high-risk score. The area under the curve values of the five cohorts in the first year are all greater than 0.65. Furthermore, this model's predictive ability is stronger than that of age, gender, grade, and T stage. Through the weighted co-expression network analysis, the gene module related to the model was found, and the key genes in this module were mainly enriched in the tumor microenvironment. 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 proposed eleven-genes model is a promising biomarker for estimating overall survival in bladder cancer. This model can be used to stratify the risk of bladder cancer patients, which is beneficial to the realization of individualized treatment.

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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/v3 ◽

2020 ◽

Author(s):

Jiaxing Lin ◽

Jieping Yang ◽

Xiao Xu ◽

Yutao Wang ◽

Meng Yu ◽

...

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

High Risk ◽

B Cell ◽

Cancer Patients ◽

Risk Score ◽

Prognostic Model ◽

Cell Memory ◽

Cox Regression Analysis ◽

B Cell Memory

Abstract Background: Bladder cancer is the tenth most common cancer globally, 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 the least absolute shrinkage and selection operator regression to construct a prognostic Cox model. Kaplan-Meier analysis, receiver operating characteristic curve, and univariate / 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 11-genes model could stratify the risk of patients in all five cohorts, and the prognosis was worse in the group with a high-risk score. The area under the curve values of the five cohorts in the first year are all greater than 0.65. Furthermore, this model's predictive ability is stronger than that of age, gender, grade, and T stage. Through the weighted co-expression network analysis, the gene module related to the model was found, and the key genes in this module were mainly enriched in the tumor microenvironment. 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 proposed eleven-genes model is a promising biomarker for estimating overall survival in bladder cancer. This model can be used to stratify the risk of bladder cancer patients, which is beneficial to the realization of individualized treatment.

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

Bioscience Reports ◽

10.1042/bsr20194432 ◽

2020 ◽

Vol 40 (6) ◽

Author(s):

Huamei Tang ◽

Lijuan Kan ◽

Tong Ou ◽

Dayang Chen ◽

Xiaowen Dou ◽

...

Keyword(s):

Dna Methylation ◽

Overall Survival ◽

Bladder Cancer ◽

Cancer Patients ◽

Cox Regression ◽

Test Group ◽

Training Group ◽

The Cancer Genome Atlas ◽

Methylation Site ◽

Cox Regression Analysis

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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Construction and Validation of Prognostic Markers of Liver Cancer Based on Autophagy Genes

Anti-Cancer Agents in Medicinal Chemistry ◽

10.2174/1871520621666210329100052 ◽

2021 ◽

Vol 21 ◽

Author(s):

Dawei Zhou ◽

Junchen Wan ◽

Jiang Luo ◽

Yuhao Tao

Keyword(s):

Regression Analysis ◽

High Risk ◽

Liver Cancer ◽

Cancer Patients ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Primary Liver Cancer ◽

Risk Groups ◽

Cox Regression Analysis

Background: Liver cancer is one of the most common diseases in the world. At present, the mechanism of autophagy genes in liver cancer is not very clear. Therefore, it is meaningful to study the role and prognostic value of autophagy genes in liver cancer. Objective: The purpose of this study is to conduct a bioinformatics analysis of autophagy genes related to primary liver cancer to establish a prognostic model of primary liver cancer based on autophagy genes. Results: Through difference analysis, 31 differential autophagy genes were screened out and then analyzed by GO and KEGG analysis. At the same time, we built a PPI network. To optimize the evaluation of the prognosis of liver cancer patients, we integrated multiple autophagy genes to establish a prognostic model. By using univariate cox regression analysis, 15 autophagy genes related to prognosis were screened out. Then we included these 15 genes into the Least Absolute Shrinkage and Selection Operator (LASSO), and performed multi-factor cox regression analysis on the 9 selected genes to construct a prognostic model. The risk score of each patient was calculated based on 4 genes(BIRC5, HSP8, SQSTM1, and TMEM74) which participated in the establishing of the model, then the patients were divided into high-risk groups and low-risk groups. In the multivariate cox regression analysis, the risk score was the independent prognostic factors (HR=1.872, 95%CI=1.544-2.196, P<0.001). Survival analysis showed that the survival time of the low-risk group was significantly longer than that of the high-risk group. Combining clinical characteristics and autophagy genes, we constructed a nomogram for predicting prognosis. The external dataset GSE14520 proved that the nomogram has a good prediction for individual patients with primary liver cancer. Conclusion: This study provided potential autophagy-related markers for liver cancer patients to predict their prognosis and revealed part of the molecular mechanism of liver cancer autophagy. At the same time, the certain gene pathways and protein pathways related to autophagy may provide some inspiration for the development of anticancer drugs.

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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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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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A novel RNA binding protein-associated prognostic model to predict overall survival in hepatocellular carcinoma patients

10.21203/rs.3.rs-37845/v1 ◽

2020 ◽

Author(s):

Ye Liu ◽

Zhixiang Qin ◽

Hai Yang ◽

Yang Gu ◽

Kun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

High Risk ◽

Prognostic Model ◽

Rna Binding ◽

Cox Regression ◽

Risk Group ◽

Differentially Expressed ◽

Data Set ◽

Cox Regression Analysis

Abstract Background Hepatocellular carcinoma (HCC) represents one of the deadliest malignancies worldwide. Despite significant advances in diagnosis and treatment, the mortality rate from HCC persists at a substantial level. This research strives to establish a prognostic model based on the RNA binding proteins (RBPs) that can predict HCC patients’ OS. Methods There was an RNA-seq data set derived from the Cancer Genome Atlas (TCGA) databank which was included in our research as well as a Microarray data set (GSE14520). The differentially expressed RBPs between HCC and normal tissues were investigated in TCGA dataset. Subsequently, the TCGA data set was randomly split into a training and a testing cohort. The prognostic model of the training cohort was developed by applying univariate Cox regression and lasso Cox regression analyses and multivariate Cox regression analysis. In order to evaluate the prognostic value of the model, a comprehensive survival assessment was conducted. Results A total of 133 differentially expressed RBPs were identified. Five RBPs (RPL10L, EZH2, PPARGC1A, ZNF239, IFIT1) were used to construct the model. The model accurately predicted the prognosis of liver cancer patients in both the TCGA cohort and the GSE14520 validation cohort. HCC patients could be assigned into a high-risk group and a low-risk group by this model, and the overall survival of these two groups was significantly different. Furthermore, the risk scores obtained by our model were highly correlated with immune cell infiltration. . Conclusions Five RBPs-related prognostic models were constructed and validated to predict OS reliably in HCC individuals. It helps to identify patients at high risk of mortality with the risk prediction score, which optimizes personalized therapeutic decision-making.

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Lymphadenectomy in Early-Stage Intermediate-/High-Risk Endometrioid Endometrial Cancer: Clinical Characteristics and Outcomes in an Australian Cohort

International Journal of Gynecological Cancer ◽

10.1097/igc.0000000000001039 ◽

2017 ◽

Vol 27 (7) ◽

pp. 1379-1386 ◽

Cited By ~ 1

Author(s):

Rhonda Farrell ◽

Suzanne C. Dixon ◽

Jonathan Carter ◽

Penny M. Webb

Keyword(s):

Overall Survival ◽

Endometrial Cancer ◽

High Risk ◽

Adjuvant Treatment ◽

Cox Regression ◽

Early Stage ◽

Cox Regression Analysis ◽

Cancer Study ◽

Stage Ib ◽

Australian Cohort

ObjectiveThe role of lymphadenectomy (LND) in early-stage endometrial cancer (EC) remains controversial. Previous studies have included low-risk patients and nonendometrioid histologies for which LND may not be beneficial, whereas long-term morbidity after LND is unclear. In a large Australian cohort of women with clinical early-stage intermediate-/high-risk endometrioid EC, we analyzed the association of LND with clinicopathological characteristics, adjuvant treatment, survival, patterns of disease recurrence, and morbidity.Materials and MethodsFrom a larger prospective study (Australian National Endometrial Cancer Study), we analyzed data from 328 women with stage IA grade 3 (n = 63), stage IB grade 1 to 3 (n = 160), stage II grade 1 to 3 (n = 71), and stage IIIC1/2 grade 1 to 3 (n = 31/3) endometrioid EC. Overall survival (OS) was estimated using Kaplan-Meier methods. The association of LND with OS was assessed using Cox regression analysis adjusted for age, stage, grade, and adjuvant treatment. The association with risk of recurrent disease was analyzed using logistic regression adjusted for age, stage, and grade. Morbidity data were analyzed using χ2 tests.ResultsMedian follow-up was 45.8 months. Overall survival at 3 years was 93%. Lymphadenectomy was performed in 217 women (66%), 16% of this group having positive nodes. Median node count was 12. There were no significant differences in OS between LND and no LND groups, or by number of nodes removed. After excluding stage IB grade 1/2 tumors, there was no association between LND and OS among a “high-risk” group of 190 women with a positive node rate of 24%. However, a similar cohort (n = 71) of serous EC in the Australian National Endometrial Cancer Study had improved survival after LND. Women who underwent LND had significantly higher rates of critical events (5% vs 0%, P = 0.02) and lymphoedema (23% vs 4%, P < 0.0001).ConclusionsIn this cohort with early-stage intermediate-/high-risk endometrioid EC, LND did not improve survival but was associated with significantly increased morbidity.

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Development of prognosis model for colon cancer based on autophagy-related genes

World Journal of Surgical Oncology ◽

10.1186/s12957-020-02061-w ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Xu Wang ◽

Yuanmin Xu ◽

Ting Li ◽

Bo Chen ◽

Wenqi Yang

Keyword(s):

Colon Cancer ◽

Regression Analysis ◽

High Risk ◽

Cancer Patients ◽

Risk Score ◽

Cox Regression ◽

Patient Survival ◽

Expression Profiles ◽

Cox Regression Analysis ◽

Risk Patients

Abstract Background Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied. Methods Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazard models were used to investigate the association between ARG expression profiles and patient prognosis. Results Twenty ARGs were significantly associated with the overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3-, and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians. Conclusion The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

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Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211041425 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110414

Author(s):

Xiaoyong Li ◽

Jiaqong Lin ◽

Yuguo pan ◽

Peng Cui ◽

Jintang Xia

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Related Gene ◽

Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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