&lt;p&gt;Identification and validation of an individualized autophagy-clinical prognostic index in bladder cancer patients&lt;/p&gt;

Wang, Shi‐Shuo; Chen, Gang; Li, Sheng‐Hua; Pang, Jin‐Shu; Cai, Kai-Teng; Yan, Haibiao; Huang, Zhi‐Guang; He, Rong‐Quan

doi:10.2147/ott.s197676

Cited by 44 publications

(45 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TCGA is an open-access database that uses a genome-wide approach to reveal the genetic characteristics of cancers. Many studies on cancers such as BLCA have screened diagnostic and prognostic biomarkers using TCGA [18][19][20]. Patient subgroups with different treatment response and prognosis can also be identified using TCGA.…”

Section: Discussionmentioning

confidence: 99%

Profiles of Immune Infiltration in Bladder Cancer and its Clinical Significance: an Integrative Genomic Analysis

Zhu

Liu

et al. 2020

Int. J. Med. Sci.

View full text Add to dashboard Cite

Tumor-infiltrating immune cells are closely related to the prognosis of bladder cancer. Analysis of tumor infiltrating immune cells is usually based on immunohistochemical analysis. Since many immune cell marker proteins are not specific for different immune cells, which may induce misleading or incomplete. CIBERSORT is an algorithm to estimate specific cell types in a mixed cell population using gene expression data. In this study, the CIBERSORT algorithm was used to identify the immune cell infiltration signatures. The gene expression profiles, mutation data, and clinical data were collected from The Cancer Genome Atlas (TCGA) database. Unsupervised consensus clustering was used to acquire the immune cell infiltration subtypes of bladder cancer based on the fractions of 22 immune cell types. Four immune cell clusters with different immune infiltrate and mutation characteristics were identified. In addition, this stratification has a prognostic relevance, with cluster 2 having the best outcome, cluster 1 the worst. These clusters showed distinct mRNA expression patterns. The characteristic genes in subtype cluster 1 were mainly involved in cell division, those in subtype cluster 2 were mainly related in antigen processing and presentation, those in subtype cluster 3 were mainly involved in epidermal cell differentiation, and those in subtype cluster 4 were mainly related in the humoral immune response. These differences may affect the development of the bladder cancer, the sensitivity to treatment as well as the prognosis. Through further validation, this study may contribute to the development of personalized therapy and precision medical treatments.

show abstract

Section: Discussionmentioning

confidence: 99%

Profiles of Immune Infiltration in Bladder Cancer and its Clinical Significance: an Integrative Genomic Analysis

Zhu

Liu

et al. 2020

Int. J. Med. Sci.

View full text Add to dashboard Cite

show abstract

“…Therefore, it is necessary to explore alternative methods for diagnosis and accurate postoperative tumour staging. Page 2 of 12 Wang et al J Transl Med (2020) 18:149 Autophagy is considered a vital catabolic process within eukaryotic cells, allowing lysosomes to degrade damaged, senescent, or nonfunctional proteins and organelles [4,5]. Early studies have reported that autophagy is involved in many pathophysiological processes such as immune responses, inflammation, neurodegenerative diseases, tumourigenesis and cancer progression [6,7].…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a survival model for lung adenocarcinoma based on autophagy-associated genes

Wang¹,

Yao²,

Xiao³

et al. 2020

J Transl Med

View full text Add to dashboard Cite

Background: Given that abnormal autophagy is involved in the pathogenesis of cancers, we sought to explore the potential value of autophagy-associated genes in lung adenocarcinoma (LUAD). Methods:RNA sequencing and clinical data on tumour and normal samples were acquired from The Cancer Genome Atlas (TCGA) database and randomly assigned to training and testing groups. Differentially expressed autophagy-associated genes (AAGs) were screened. Within the training group, Cox regression and Lasso regression analyses were conducted to screen five prognostic AAGs, which were used to develop a model. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were plotted to determine the performance of the model in both groups. Immunohistochemistry was used to demonstrate the differential expression of AAGs in tumour and normal tissues at the protein level. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were utilized to further elucidate the roles of AAGs in LUAD. Results:The data from the TCGA database included 497 tumour and 54 normal samples, within which 30 differentially expressed AAGs were screened. Using Cox regression and Lasso regression analyses for the training group, 5 prognostic AAGs were identified and the prognostic model was constructed. Patients with low risk had better overall survival (OS) in the training group (3-year OS, 73.0% vs 48.0%; 5-year OS, 45.0% vs 33.8%; P = 1.305E−04) and in the testing group (3-year OS, 66.8% vs 41.2%; 5-year OS, 31.7% vs 25.8%; P = 1.027E−03). The areas under the ROC curves (AUC) were significant for both the training and testing groups (3-year AUC, 0.810 vs 0.894; 5-year AUC, 0.792 vs 0.749). Conclusions:We developed a survival model for LUAD and validated the performance of the model, which may provide superior outcomes for the patients.

show abstract

“…Currently, autophagy is considered as a "double-edged sword", since it not only inhibits tumor progression by depriving cells of nutrients and energy, but also promotes tumor progression by conferring cells with nutrients and energy [19,20]. in predicting overall survival of bladder cancer [22]. However, the prognostic signi cance of autophagy genes in endometrial cancer has not been reported.…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of an autophagy-related gene expression signature for endometrial cancer patients

Wang

Liu

et al. 2020

Preprint

View full text Add to dashboard Cite

Background : Autophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC.Methods : First, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariat, LASSO, and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, gene set enrichment and somatic mutation analyses were also used for these prognostic autophagy-related genes.Results : A total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into highrisk and low-risk groups (P<0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group.Gene set enrichment analysis revealed high risk score was associated with tumor initiation and progression associated pathways.Conclusions : Based on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics. * Hui Wang, Xiaoling Ma, and Jinhui Liu contributed equally to this work Background As an evolutionarily ancient and highly conserved biological behavior, autophagy plays a

show abstract

<p>Identification and validation of an individualized autophagy-clinical prognostic index in bladder cancer patients</p>

Cited by 44 publications

References 42 publications

Profiles of Immune Infiltration in Bladder Cancer and its Clinical Significance: an Integrative Genomic Analysis

Profiles of Immune Infiltration in Bladder Cancer and its Clinical Significance: an Integrative Genomic Analysis

Development and validation of a survival model for lung adenocarcinoma based on autophagy-associated genes

Prognostic value of an autophagy-related gene expression signature for endometrial cancer patients

Contact Info

Product

Resources

About