Figure - available from: Frontiers in Oncology
This content is subject to copyright.
Source publication
Purpose
This 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).
Methods
In this study, we utilised data from TCGA-PARD and GEO databases to identify specific ARGs in prostate cancer. We established a s...
Citations
... Therefore, there is an urgent need to find new and effective biomarkers or treatment targets for LUAD patients. Anoikis resistance is a critical factor influencing the progression and metastasis of various cancers, including lung cancer, gastric cancer, colorectal cancer, breast cancer, and prostate cancer, among others [26][27][28]. This resistance enables tumor cells to survive detachment from the extracellular matrix, facilitating their dissemination to distant sites. ...
Anoikis and epithelial-mesenchymal transition (EMT) are pivotal in the distant metastasis of lung adenocarcinoma (LUAD). A detailed understanding of their interplay and the identification of key genes is vital for effective therapeutic strategies against LUAD metastasis.
Key prognostic genes related to anoikis and EMT were identified through univariate Cox regression analysis. We utilized ten machine learning algorithms to develop the Anoikis and EMT-Related Optimal Model (AEOM). The TCGA-LUAD dataset served as the training cohort, while six additional international multicenter LUAD datasets were employed as validation cohorts. The average concordance index (c-index) was used to evaluate model performance and identify the most effective model. Subsequent multi-omics analyses were conducted to explore differences in pathway enrichment, immune infiltration, and mutation landscapes between high and low AEOM groups. Experimental validation demonstrated that RHPN2, a key biomarker within the model, acts as an oncogene facilitating LUAD progression.
The AEOM displayed superior prognostic predictive performance for LUAD patients, outperforming numerous previously published LUAD signatures. Biologically, the AEOM was notably associated with immune features; the high AEOM group exhibited decreased immune activity and a tendency towards immune-cold tumors, as well as a higher tumor mutational burden (TMB). Subgroup analysis revealed that the low AEOM + high TMB group had the most favorable prognosis. The high AEOM group was primarily enriched in cell cycle-related pathways, promoting cancer cell proliferation. RHPN2, a crucial gene within the AEOM (correlation = 0.85, P < 0.05), was linked to poorer prognosis in LUAD patients with elevated RHPN2 expression. Further in vitro experiments showed that RHPN2 modulates LUAD cell proliferation and invasion.
The AEOM provides a robust prognostic model for LUAD, uncovering critical immune and biological pathways, with RHPN2 identified as a key oncogenic driver. These findings offer valuable insights for targeted therapies and enhanced patient outcomes.
... The identification of new specific markers associated with cancer recurrence is critical in the management of PCa patients. The autophagy-related gene expression levels have great potential in predicting tumor recurrence risk and evaluating the response to treatment in PCa patients (6,7). Kang et al., built a risk model using four anoikis-related genes that effectively predict the risk of recurrence and survival outcomes in PCa patients, confirming the clinical value of in-depth investigation of anoikis-related genes in PCa. ...
... These ndings indicate that anoikis is related to prostate cancer prognosis. Previous studies have explored the relationship between anoikis and prostate cancer prognosis (22,33,34), but these studies were limited to bioinformatic analyses without further exploration of the genes' roles in prostate cancer. ...
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.
... These findings underscored the substantial impact of anoikis on the overall survival of cancer patients. Indeed, the role of anoikis has been extensively investigated in patients with hepatocellular carcinoma [30], prostate cancer [31,32], gastric cancer [33,34], and other malignancies [35][36][37]. This study aims to further contribute to the understanding of the role of anoikis in bladder cancer patients, thereby addressing a gap in the current research on this topic in bladder cancer. ...
Background
Bladder cancer is an epidemic and life-threating urologic carcinoma. Anoikis is a unusual type of programmed cell death which plays a vital role in tumor survival, invasion and metastasis. Nevertheless, the relationship between anoikis and bladder cancer has not been understood thoroughly.
Methods
We downloaded the transcriptome and clinical information of BLCA patients from TCGA and GEO databases. Then, we analyzed different expression of anoikis-related genes and established a prognostic model based on TCGA database by univariate Cox regression, lasso regression, and multivariate Cox regression. Then the Kaplan–Meier survival analysis and receiver operating characteristic (ROC) curves were performed. GEO database was used for external validation. BLCA patients in TCGA database were divided into two subgroups by non-negative matrix factorization (NMF) classification. Survival analysis, different gene expression, immune cell infiltration and drug sensitivity were calculated. Finally, we verified the function of S100A7 in two BLCA cell lines.
Results
We developed a prognostic risk model based on three anoikis-related genes including TPM1, RAC3 and S100A7. The overall survival of BLCA patients in low-risk groups was significantly better than high-risk groups in training sets, test sets and external validation sets. Subsequently, the checkpoint and immune cell infiltration had significant difference between two groups. Then we identified two subtypes (CA and CB) through NMF analysis and found CA had better OS and PFS than CB. Besides, the accuracy of risk model was verified by ROC analysis. Finally, we identified that knocking down S100A7 gene expression restrained the proliferation and invasion of bladder cancer cells.
Conclusion
We established and validated a bladder cancer prognostic model consisting of three genes, which can effectively evaluate the prognosis of bladder cancer patients. Additionally, through cellular experiments, we demonstrated the significant role of S100A7 in the metastasis and invasion of bladder cancer, suggesting its potential as a novel target for future treatments.
