A Novel Signature Based on Anoikis Associated with BCR-Free Survival for Prostate Cancer

Yang, Chen; Tian, Yu; Lin, Qin

doi:10.1007/s10528-023-10387-9

Cited by 3 publications

(4 citation statements)

References 60 publications

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“…This finding was also reported by Yang et al. ( 2023 ). C2 has a higher level of gamma delta T cell and activated B cell infiltration than C1.…”

Section: Discussionsupporting

confidence: 90%

“…Most immune cells were found to be enriched in subtype C2, indicating a possible association between immune cell enrichment and better prognosis in PCa. This finding was also reported by Yang et al (2023). C2 has a higher level of gamma delta T cell and activated B cell infiltration than C1.…”

Section: Discussionsupporting

confidence: 83%

“…The ROC curve analysis in our study demonstrated favorable stability and reliability for our anoikis-related gene signature, with AUC values of 0.82 in the training set and 0.723 in the validation set, over a five-year period. This contrasts with a previously established signature comprising TUBB3, FN1, LGALS3, and LMO3, which predicted biochemical relapse-free survival in PCa with AUC values of 0.867 in the training set and 0.57 in the validation set (Yang et al, 2023). Our signature exhibits superior generalization ability, indicating its potential for broader application in clinical prognostication of PCa.…”

Section: Discussioncontrasting

confidence: 77%

“…In various malignancies, the acquisition of anoikis resistance has been linked to poor clinical prognosis (Jin et al., 2018 ; Mak et al., 2017 ; Zhang, Li, et al., 2022 ). Recently, studies have explored the role of anoikis in PCa prognosis, suggesting that anoikis resistance is associated with Gleason grade and can predict biochemical recurrence in PCa patients (Yang et al., 2023 ). Although these insights mark significant advancements, the complex interplay between anoikis resistance and PCa prognosis warrants further investigation.…”

Section: Introductionmentioning

confidence: 99%

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Identification and validation of a novel anoikis‐related prognostic model for prostate cancer

Zhang,

Lv,

Luan

et al. 2024

Molec Gen & Gen Med

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BackgroundAnoikis resistance is a hallmark characteristic of oncogenic transformation, which is crucial for tumor progression and metastasis. The aim of this study was to identify and validate a novel anoikis‐related prognostic model for prostate cancer (PCa).MethodsWe collected a gene expression profile, single nucleotide polymorphism mutation and copy number variation (CNV) data of 495 PCa patients from the TCGA database and 140 PCa samples from the MSKCC dataset. We extracted 434 anoikis‐related genes and unsupervised consensus cluster analysis was used to identify molecular subtypes. The immune infiltration, molecular function, and genome alteration of subtypes were evaluated. A risk signature was developed using Cox regression analysis and validated with the MSKCC dataset. We also identify potential drugs for high‐risk group patients.ResultsTwo subtypes were identified. C1 exhibited a higher level of CNV amplification, immune score, stromal score, aneuploidy score, homologous recombination deficiency, intratumor heterogeneity, single‐nucleotide variant neoantigens, and tumor mutational burden compared to C2. C2 showed a better survival outcome and had a high level of gamma delta T cell and activated B cell infiltration. The risk signature consisting of four genes (HELLS, ZWINT, ABCC5, and TPSB2) was developed (area under the curve = 0.780) and was found to be an independent prognostic factor for overall survival in PCa patients. Four CTRP‐derived and four PRISM‐derived compounds were identified for high‐risk patients.ConclusionsThe anoikis‐related prognostic model developed in this study could be a useful tool for clinical decision‐making. This study may provide a new perspective for the treatment of anoikis‐related PCa.

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“…This finding was also reported by Yang et al. ( 2023 ). C2 has a higher level of gamma delta T cell and activated B cell infiltration than C1.…”

Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 83%

Section: Discussioncontrasting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification and validation of a novel anoikis‐related prognostic model for prostate cancer

Zhang,

Lv,

Luan

et al. 2024

Molec Gen & Gen Med

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Identification of Novel Anoikis-Related Gene Signatures to Predict the Prognosis, Immune Microenvironment, and Drug Sensitivity of Breast Cancer Patients

Liu,

Wu,

Wang

et al. 2024

Cancer Control

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Introduction Breast cancer is one of the most prevalent types of cancer and a leading cause of cancer-related death among females worldwide. Anoikis, a specific type of apoptosis that is triggered by the loss of anchoring between cells and the native extracellular matrix, plays a vital role in cancer invasion and metastasis. However, studies that focus on the prognostic values of anoikis-related genes (ARGs) in breast cancer are scarce. Methods Gene expression data were obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Five anoikis-related signatures (ARS) were selected from ARGs through univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis. Subsequently, an ARGs risk score model was established, and breast cancer patients were divided into high and low risk groups. The correlation between risk groups and overall survival (OS), tumor mutation burden (TMB), tumor microenvironment (TME), stemness, and drug sensitivity were analyzed. Moreover, RT-qPCR was performed to verify the gene expression levels of the five ARS in breast cancer tissues. Furthermore, a nomogram model was constructed based on ARGs risk score and clinicopathological factors. Results A novel ARGs risk score model was constructed based on five ARS (CEMIP, LAMB3, CD24, PTK6, and PLK1), and breast cancer patients were divided into high and low risk groups. Correlation analysis showed that the high and low risk groups had different OS, TMB, TME, stemness, and drug sensitivity. Both the ARGs risk score model and the nomogram showed promising prognosis predictive value in breast cancer. Conclusion ARS could be used as promising biomarkers for breast cancer prognosis predication and treatment options selection.

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Prostate Cancer Prediction Model Based on Anoikis-Related Genes and Therapeutic Potential of BUB1 in Influencing Epithelial-Mesenchymal Transition

Luo,

Wang,

Zhang

et al. 2024

Preprint

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Background Prostate cancer is one of the most common malignancies among men worldwide. Anoikis is a form of programmed cell death that is potentially negatively correlated with tumor progression; however, its relationship with prostate cancer remains inconclusive. Methods The transcriptomic and clinical data for this study were obtained from the TCGA and GEO databases. The prediction model was established using univariate Cox, multivariate Cox, and LASSO regression. Receiver operating characteristic (ROC) curves determined the predictive performance, and the GEO database was used for external validation. Patients were stratified into different risk groups, and their prognoses were compared using Kaplan-Meier analysis. We also analyzed immune cell infiltration and sensitivity to immunotherapeutic drugs in prostate cancer patients. The BUB1 gene was selected for in vitro experimental validation. Results We constructed a prognostic risk prediction model using four ARGs: BUB1, PTGS2, RAC3, and IRX1. Patients in the high-risk group had worse overall survival than those in the low-risk group, with significant differences in immune cell infiltration, immune checkpoint expression, and sensitivity to immunotherapeutic drugs. Using NMF, we categorized TCGA prostate cancer patients into two subgroups, with cluster2 having better prognoses. Gene expression and immune cell infiltration were compared between the subgroups. Knocking down the BUB1 gene in PC3 and C4-2 cell lines reduced prostate cancer cell proliferation and invasion and altered EMT-related protein expression. Conclusion After external validation, our study shows that the ARG-based predictive model accurately forecasts prostate cancer prognosis. In vitro experiments revealed that the BUB1 gene significantly affects prostate cancer cell proliferation, invasion, and the expression of specific EMT-related proteins. Thus, BUB1 is a potential therapeutic target.

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A Novel Signature Based on Anoikis Associated with BCR-Free Survival for Prostate Cancer

Cited by 3 publications

References 60 publications

Identification and validation of a novel anoikis‐related prognostic model for prostate cancer

Identification and validation of a novel anoikis‐related prognostic model for prostate cancer

Identification of Novel Anoikis-Related Gene Signatures to Predict the Prognosis, Immune Microenvironment, and Drug Sensitivity of Breast Cancer Patients

Prostate Cancer Prediction Model Based on Anoikis-Related Genes and Therapeutic Potential of BUB1 in Influencing Epithelial-Mesenchymal Transition

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