Dataset Information

A novel lysosome-related gene signature coupled with gleason score for prognosis prediction in prostate cancer.

ABSTRACT: Background: Prostate cancer (PCa) is highly heterogeneous, which makes it difficult to precisely distinguish the clinical stages and histological grades of tumor lesions, thereby leading to large amounts of under- and over-treatment. Thus, we expect the development of novel prediction approaches for the prevention of inadequate therapies. The emerging evidence demonstrates the pivotal role of lysosome-related mechanisms in the prognosis of PCa. In this study, we aimed to identify a lysosome-related prognostic predictor in PCa for future therapies. Methods: The PCa samples involved in this study were gathered from The Cancer Genome Atlas database (TCGA) (n = 552) and cBioPortal database (n = 82). During screening, we categorized PCa patients into two immune groups based on median ssGSEA scores. Then, the Gleason score and lysosome-related genes were included and screened out by using a univariate Cox regression analysis and the least absolute shrinkage and selection operation (LASSO) analysis. Following further analysis, the probability of progression free interval (PFI) was modeled by using unadjusted Kaplan-Meier estimation curves and a multivariable Cox regression analysis. A receiver operating characteristic (ROC) curve, nomogram and calibration curve were used to examine the predictive value of this model in discriminating progression events from non-events. The model was trained and repeatedly validated by creating a training set (n = 400), an internal validation set (n = 100) and an external validation (n = 82) from the cohort. Results: Following grouping by ssGSEA score, the Gleason score and two LRGs-neutrophil cytosolic factor 1 (NCF1) and gamma-interferon-inducible lysosomal thiol reductase (IFI30)-were screened out to differentiate patients with or without progression (1-year AUC = 0.787; 3-year AUC = 0.798; 5-year AUC = 0.772; 10-year AUC = 0.832). Patients with a higher risk showed poorer outcomes (p < 0.0001) and a higher cumulative hazard (p < 0.0001). Besides this, our risk model combined LRGs with the Gleason score and presented a more accurate prediction of PCa prognosis than the Gleason score alone. In three validation sets, our model still achieved high prediction rates. Conclusion: In conclusion, this novel lysosome-related gene signature, coupled with the Gleason score, works well in PCa for prognosis prediction.

SUBMITTER: Huang Y

PROVIDER: S-EPMC10098196 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A novel lysosome-related gene signature coupled with gleason score for prognosis prediction in prostate cancer.

Huang Ying Y Yang Fan F Zhang Wenyi W Zhou Yupeng Y Duan Dengyi D Liu Shuang S Li Jianmin J Zhao Yang Y

Frontiers in genetics 20230330

<b>Background:</b> Prostate cancer (PCa) is highly heterogeneous, which makes it difficult to precisely distinguish the clinical stages and histological grades of tumor lesions, thereby leading to large amounts of under- and over-treatment. Thus, we expect the development of novel prediction approaches for the prevention of inadequate therapies. The emerging evidence demonstrates the pivotal role of lysosome-related mechanisms in the prognosis of PCa. In this study, we aimed to identify a lysoso ...[more]

PMID: 37065491

Similar Datasets

Project description:Background and aimsThe biological function of lysosomes has been increasingly appreciated in cancer. However, the relationship between lysosome and lung adenocarcinoma (LUAD) was not well understood. In this study, a lysosome-related signature was developed for LUAD risk stratification and prognosis prediction.MethodsDownload RNA-seq data of LUAD and clinical information of corresponding samples from the UCSC-Xena platform. GSE31210 databases is used as a validation cohort. The lysosome-related genes was obtained from molecular signature database. The differentially expressed genes (DEGs) as well as lysosome-associated prognosis signatures were identified by using univariate, multivariate cox, and Lasso regression. A nomogram was constructed and evaluated using ROC and DCA.ResultsA total of 109 lysosome-related DEGs were identified and 30 prognostic related DEGs were subsequently screened. Cluster analysis further divides the TCGA cohort into clusters 1 and 2. Patients in cluster 2 had a worse prognosis (p = 0.016), lower LYSOSOME score. Enrichment analysis showed that 21 significantly enriched gene sets in the cluster 2 were activated. And 10 pathways, such as E2F_TARGETS, G2M_CHECKPOINT were upregulated. Multivariate Cox regression analysis identified 17 best prognostic genes as risk signature. An independent prognostic factor, the risk signature, was identified. A prognostic nomogram including risk signature, age, TNM stage, and gender was constructed, and ROC and DCA curves proved its excellent performance. We examined CTSZ and AP3S2 protein expression in 48 stage 3-4 NSCLC samples. Low AP3S2 expression was associated with better prognosis (median overall survival: 37.87 vs. 8.53 months, p = 0.0211). Increased CTSZ expression also indicated better prognosis (median overall survival: 6.77 vs. 30.50 months, p = 0.0306).ConclusionWe identified molecular subtypes and lysosomal-based prognostic signatures for LUAD patients, as well as 17 genes that serve as a biomarker for evaluating the prognosis of LUAD patients.

Project description:PurposeThis study presents a novel approach to predict postoperative biochemical recurrence (BCR) in prostate cancer (PCa) patients which involves constructing a signature based on anoikis-related genes (ARGs).MethodsIn this study, we utilised data from TCGA-PARD and GEO databases to identify specific ARGs in prostate cancer. We established a signature of these ARGs using Cox regression analysis and evaluated their clinical predictive efficacy and immune-related status through various methods such as Kaplan-Meier survival analysis, subject work characteristics analysis, and CIBERSORT method. Our findings suggest that these ARGs may have potential as biomarkers for prostate cancer prognosis and treatment. To investigate the biological pathways of genes associated with anoikis, we utilised GSVA, GO, and KEGG. The expression of ARGs was confirmed by the HPA database. Furthermore, we conducted PPI analysis to identify the core network of ARGs in PCa.ResultsBased on analysis of the TCGA database, a set of eight ARGs were identified as prognostic signature genes for prostate cancer. The reliability and validity of this signature were well verified in both the TCGA and GEO codifications. Using this signature, patients were classified into two groups based on their risk for developing BCR. There was a significant difference in BCR-free time between the high and low risk groups (P < 0.05).This signature serves as a dependable and unbiased prognostic factor for predicting biochemical recurrence (BCR) in prostate cancer (PCa) patients. It outperforms clinicopathological characteristics in terms of accuracy and reliability. PLK1 may play a potential regulatory role as a core gene in the development of prostate cancer.ConclusionThis signature suggests the potential role of ARGs in the development and progression of PCa and can effectively predict the risk of BCR in PCa patients after surgery. It also provides a basis for further research into the mechanism of ARGs in PCa and for the clinical management of patients with PCa.

Dataset Information

A novel lysosome-related gene signature coupled with gleason score for prognosis prediction in prostate cancer.

Publications

A novel lysosome-related gene signature coupled with gleason score for prognosis prediction in prostate cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets