Construction and validation of an eight-gene signature with great prognostic value in bladder cancer

et al. 2020

Cancer Cell Int

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. 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 11-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.

Section: Introductionmentioning

confidence: 99%

A robust 11-genes prognostic model can predict overall survival in bladder cancer patients based on five cohorts

Lin

et al. 2020

Cancer Cell Int

“…DNA-related process and eNOS activity were associated with the cell lines with transitional cell carcinoma lymphatic metastasis. Most studies used the patient’s expression profile to conduct the WGCNA analysis and identified the module related to the stage [ 55 , 56 , 57 , 58 ]. Di et al [ 55 ] identified the THY1, AEBP1, CDH11, COL1A1, COL1A2, COL11A1, MMP2, PXDN, BGN, COL5A1, and COL8A1 as prognostic genes, which had different expression levels in different stages.…”

Section: Discussionmentioning

confidence: 99%

“…Chen et al [ 56 ] identified TPST1 and P3H4, which is associated with prognostic according to the differential expression profiles. Besides, Yan et al [ 57 ] also identified the prognostic genes (ASPM, C4orf46, CCNB1, DIAPH3, MLF1, TFR2, NSUN6, and OIP5) with the patient’s transcriptional profiles. On the other hand, Ding et al [ 58 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of GSN and LAMC2 as Key Prognostic Genes of Bladder Cancer by Integrated Bioinformatics Analysis

Wang

Cai

et al. 2020

Cancers

Bladder cancer is a common malignancy with mechanisms of pathogenesis and progression. This study aimed to identify the prognostic hub genes, which are the central modulators to regulate the progression and proliferation in the specific subtype of bladder cancer. The identification of the candidate hub gene was performed by weighted gene co-expression network analysis to construct a free-scale gene co-expression network. The gene expression profile of GSE97768 from the Gene Expression Omnibus database was used. The association between prognosis and hub gene was evaluated by The Cancer Genome Atlas database. Four gene-expression modules were significantly related to bladder cancer disease: the red module (human adenocarcinoma lymph node metastasis), the darkturquioise module (grade 2 carcinoma), the lightgreen module (grade 3 carcinoma), and the royalblue module (transitional cell carcinoma lymphatic metastasis). Based on betweenness centrality and survival analysis, we identified laminin subunit gamma-2 (LAMC2) in the grade 2 carcinoma, gelsolin (GSN) in the grade 3 carcinoma, and homeodomain-interacting protein kinase 2 (HIPK2) in the transitional cell carcinoma lymphatic metastasis. Subsequently, the protein levels of LAMC2 and GSN were respectively down-regulated and up-regulated in tumor tissue with the Human Protein Atlas (HPA) database. Our results suggested that LAMC2 and GSN are the central modulators to transfer information in the specific subtype of the disease.

“…Good models provide effective guidance for doctors and patients to make optimal treatment decisions. However, in the study of the bladder cancer model, many models can only be veri ed in two or three cohorts [8,9] and do not have clinical extensibility.…”

Section: Introductionmentioning

confidence: 99%

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

Lin

et al. 2020

Preprint

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.