Clinical value of anoikis-related genes and molecular subtypes identification in bladder urothelial carcinoma and in vitro validation

Development and Validation of an Anoikis-Related Gene Signature for Prognostic Prediction in Cervical Cancer

Background

Cervical cancer still has high incidence and mortality rates worldwide. This study aimed to evaluate the prognostic value of anoikis-related genes (ARGs) and develop a risk scoring model for accurate survival prediction in cervical cancer patients.

Methods

The expression profiles of cervical cancer tissue and survival data were downloaded from TCGA-CESC and CGCI-HTMCP-CC. We identified 83 ARGs significantly associated with patients' survival. Subsequently, we developed a risk-scoring model based on 10 key genes. We assessed the predictive performance of our model by survival analysis, ROC curve analysis, and a nomogram that incorporated clinical factors. Additionally, we validated the expression of Granzyme B (GZMB) by immunohistochemical staining. Furthermore, we compared the biological processes and pathway enrichment in high-risk and low-risk patient groups, using differential gene expression and functional enrichment analysis. Finally, we investigated the immune microenvironment of patients in both high-risk and low-risk groups.

Results

Patients in the high-risk group had significantly poorer survival compared to those in the low-risk group. The immunohistochemical results suggested that GZMB was associated with the prognosis of cervical cancer patients. The risk scoring model showed high accuracy in predicting the prognosis of cervical cancer patients. Differential gene expression analysis revealed enriched pathways related to tumor invasion and metastasis in the high-risk group. Conversely, the low-risk group showed a strong association with the activation of immune response pathways.

Conclusion

This study concluded that anoikis-related genes played a crucial role in determining the prognosis of individuals with cervical cancer. This discovery not only presented potential biomarkers but also provided valuable insights for informing treatment strategies. The risk scoring model may assist clinicians in better identifying high-risk patients and personalizing treatment plans.

Multi-cohort validation based on a novel prognostic signature of anoikis for predicting prognosis and immunotherapy response of esophageal squamous cell carcinoma

Immunotherapy is recognized as an effective and promising treatment modality that offers a new approach to cancer treatment. However, identifying responsive patients remains challenging. Anoikis, a distinct form of programmed cell death, plays a crucial role in cancer progression and metastasis. Thus, we aimed to investigate prognostic biomarkers based on anoikis and their role in guiding immunotherapy decisions for esophageal squamous cell carcinoma (ESCC). By consensus clustering, the GSE53624 cohort of ESCC patients was divided into two subgroups based on prognostic anoikis-related genes (ARGs), with significant differences in survival outcomes between the two subgroups. Subsequently, we constructed an ARGs signature with four genes, and its reliability and accuracy were validated both internally and externally. Additional, different risk groups showed notable variances in terms of immunotherapy response, tumor infiltration, functional enrichment, immune function, and tumor mutation burden. Notably, the effectiveness of the signature in predicting immunotherapy response was confirmed across multiple cohorts, including GSE53624, GSE53625, TCGA-ESCC, and IMvigor210, highlighting its potential utility in predicting immunotherapy response. In conclusion, the ARGs signature has the potential to serve as an innovative and dependable prognostic biomarker for ESCC, facilitating personalized treatment strategies in this field, and may represent a valuable new tool for guiding ESCC immunotherapy decision-making.

Integrating machine learning with mendelian randomization for unveiling causal gene networks in glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is a highly aggressive brain cancer with poor prognosis and limited treatment options. Despite advances in understanding its molecular mechanisms, effective therapeutic strategies remain elusive due to the tumor's genetic complexity and heterogeneity.

METHODS

This study employed a comprehensive analysis approach integrating 113 machine learning algorithms with Mendelian Randomization (MR) analysis to investigate the molecular underpinnings of GBM. Five publicly available gene expression datasets were analyzed to identify differentially expressed genes (DEGs) associated with GBM. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify GBM-related gene modules. Further, gene set enrichment and variation analyses were conducted to explore the biological pathways involved. The machine learning models were evaluated using Receiver Operating Characteristic (ROC) curves and confusion matrices to assess their predictive accuracy, with the best-performing model validated across external datasets. MR analysis was performed to establish causal relationships between genetically predicted gene expression levels and GBM outcomes.

RESULTS

The study identified 286 DEGs between GBM and adjacent normal tissues across five datasets. WGCNA highlighted the yellow module as the most relevant to GBM, containing key genes such as KLHL3, FOXO4, and MAP1A. Of the 113 machine learning models tested, Ridge regression achieved the highest area under the curve (AUC) of 0.92, demonstrating robust predictive accuracy. Validation using external datasets confirmed the model's reliability, with a classification accuracy of 89.5% in the training set and 85.3% in the validation sets. MR analysis provided strong evidence of a causal relationship between the expression levels of the identified genes and GBM risk.

CONCLUSIONS

This study demonstrates the power of combining machine learning and Mendelian Randomization to uncover novel genetic markers for GBM. The identified genes offer promising potential as biomarkers for GBM diagnosis and therapy, providing new avenues for personalized treatment strategies.

Role of anoikis-related gene RAC3 in prognosis, immune microenvironment, and contribution to malignant behavior in vitro and in vivo of bladder urothelial carcinoma

Background

Anoikis disrupts the normal apoptotic process in cells, leading to abnormal proliferation and migration, thereby promoting tumor formation and development. However, the role of anoikis in bladder urothelial carcinoma (BLCA) still requires further exploration.

Methods

Anoikis-related genes (ARGs) were retrieved from the GeneCards and Harmonizome databases to distinguish various subtypes of BLCA and develop a predictive model for BLCA. The immune microenvironment and enrichment pathways between various subtypes were also analyzed using consensus clustering. Potential medications were screened by utilizing drug sensitivity analysis. In vitro and vivo, the character of the independent prognostic gene in BLCA was confirmed through cell studies and mouse xenograft models.

Results

One hundred thirty differentially expressed genes (DEGs) were identified, and nine of them were chosen to construct predictive models that can accurately forecast the prognosis of BLCA patients. K = 2 was correctly identified as the optimal clustering type for BLCA, showing prominent differences in survival rates between the two subgroups. The immune-related functional studies manifested that the two subtypes' immune cell expressions differed. It was verified that RAC3 is an independent prognostic gene for BLCA. RAC3 shows high expression levels in BLCA, as indicated by its consistent mRNA and protein levels across different gene expressions. The functional verification results of RAC3 in BLCA showed that silencing RAC3 can significantly inhibit BLCA cell proliferation, colony formation, and migration. RAC3 knockdown inhibited the growth and migration of BLCA in vivo. SB505124 exhibited a significant inhibitory effect on the proliferation of BLCA cells.

Conclusion

Based on the predictive model developed in this study, BLCA patients' prognoses can be accurately predicted. SB505124 could become an important drug in the treatment of BLCA patients. RAC3 is essential in prognosis, immune microenvironment, and malignant behavior of BLCA in vitro and in vivo. It will also offer the potential for personalized treatment for BLCA patients and generate new research avenues for clinical investigators.

Identification of inflammation related gene signatures for bladder cancer prognosis prediction

Early diagnosis and treatment of bladder cancer are crucial, and since inflammation plays a role in all stages of bladder cancer, this study aims to develop a model based on inflammation-related genes to accurately predict patient prognosis. The data were initially processed through differential analysis and prognostic correlation analysis, then a Least absolute shrinkage and selection operator (LASSO) regression model was constructed by M-cohort and a nomogram was designed to increase the model readability. The T-cohort was used for internal validation, with the GSE32894 and Imvigor210 cohorts used as external data to verify the model's accuracy. The model's predictive ability was verified for the prognosis of patients of different ages, gender, tumor stage, and tumour grade. The GSE3167, GSE13507 and GeneExpression Profiling Interactive Analysis (GEPIA) datasets and Human Protein Atlas (HPA) database were used to verify the expression of the inflammation-related genes, which were confirmed by real-time Polymerase Chain Reaction (PCR). A comprehensive analysis of the model's inflammation-related genes, Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA) enrichment analysis, and immune-related analysis were also performed. Both internal and external data validations confirmed that the developed model can accurately predict the prognosis across different patient populations. Hierarchical validation results demonstrated that the model's predictive power is reliable for various patient stratifications. The expression of inflammation-related genes was consistent across The Cancer Genome Atlas (TCGA) database, GSE3167 dataset, GSE13507 dataset, Gene Expression Profiling Interactive Analysis (GEPIA) database, and the Human Protein Atlas (HPA) database, and was further validated by real-time Polymerase Chain Reaction (PCR). Pathway enrichment analysis indicated that patients in the high-risk (H-risk) group exhibited a variety of tumors. Meanwhile, patients in the low-risk (L-risk) group may be candidates for immunotherapy, whereas those in the high-risk group are more likely to benefit from chemotherapy. The model of inflammation-related genes can accurately predict bladder cancer patient prognosis, with MEST, FASN, KRT6B, and RGS2 anticipated to become new prognostic bladder cancer markers.

Prognostic Model and Immune Response of Clear Cell Renal Cell Carcinoma Based on Co-Expression Genes Signature

BACKGROUND

The identification of reliable prognostic markers is crucial for optimizing patient management and improving clinical outcomes in clear cell renal cell carcinoma (ccRCC).

METHODS

We used the GSE89563 dataset from the GEO database and the Kidney Clear Cell Carcinoma (KIRC) dataset from the TCGA database to develop a prognostic model based on weighted gene co-expression network analysis (WGCNA) and non-negative matrix factorization (NMF) to predict disease progression and prognosis in ccRCC.

RESULT

We utilized WGCNA to identify risk genes and applied NMF to stratify high-risk populations in ccRCC. We characterized the immune gene features of these high-risk groups and ultimately developed a risk prediction model for ccRCC patients using a Lasso regression approach. The risk score was calculated as follows: Risk score = SUM (-0.136394797 ANK3 + 0.004238138 BIVM_ERCC5 - 0.046248451 C4orf19 - 0.036013206 F2RL3 - 0.125531316 GNG7 - 0.012698109 METTL7A + 0.078462369 MSTO1 - 0.050450656 PINK1 - 0.059446590 SLC16A12 - 0.039883686 SLC2A9 + 0.083310722 TLCD1 - 0.059801739 WDR72 + 0.071430088 ZNF117).

CONCLUSION

We develop a prognostic model for clear cell renal cell carcinoma and analyzed immune response in subgroups and confirmed protein-level expression concordance.

Fast-track surgery with three-port versus conventional perioperative management of bladder cancer associated laparoscopic radical cystectomy and Ileal conduit diversion: Chinese experience

OBJECTIVE

This study seeks to explore the impact of fast track surgery (FTS) with three-port in patients treated with laparoscopic radical cystectomy and ileal conduit on postoperative recovery, hospital stay and the complications.

METHODS

This retrospective study analyzed 230 patients with invasive bladder cancer who underwent laparoscopic radical cystectomy at the Second Hospital of Anhui Medical University between December 2011 to January 2023. 50 patients received conventional surgery (CS) and 180 patients received FTS with three-port. Patients were assessed for time to normal diet consumption, time to passing first flatus, number of postoperative recovery days and complications. Trends of serum C-reactive protein levels were monitored preoperatively and on postoperative days 1, 3 and 7.

RESULTS

Patients who underwent FTS with three-port had a shorter duration to first flatus (P < 0.05). And number of postoperative hospital days and the length of hospital stay were notably shorter in contrast to the CS group (P < 0.05). Serum CRP levels on postoperative day 7 were markedly reduced in those of the FTS group compared to the CS group (P < 0.05). Those of the CS group experienced more frequent rates of complications compared to those of the FTS with three-port group (P < 0.05).

CONCLUSION

Our findings demonstrate that the FTS with three-port program hastens postoperative recovery and reduces duration of hospital stay. It is safer and more effective than the CS program in the Chinese population undergoing laparoscopic radical cystectomy.

Prognostic analysis of anoikis-related genes in bladder cancer: An observational study

Anoikis is proved to play a crucial role in the development of cancers. However, the impact of anoikis on the prognosis of bladder cancer (BLCA) is currently unknown. Thus, this study aimed to find potential effect of anoikis in BLCA. The Cancer Genome Atlas (TCGA)-BLCA and GSE13507 cohorts were downloaded from TCGA and the Gene Expression Omnibus (GEO) databases, respectively. Differentially expressed genes (DEGs) were screened between BLCA and normal groups, which intersected with anoikis-related genes to yield anoikis-related DEGs (AR DEGs). Univariate COX, rbsurv, and multivariate COX analyses were adopted in order to build a prognostic risk model. The differences of risk score in the different clinical subgroups and the relevance between survival rate and clinical characteristics were explored as well. Finally, chemotherapy drug sensitivity in different risk groups was analyzed. In total, 78 AR DEGs were acquired and a prognostic signature was build based on the 6 characteristic genes (CALR, FASN, CSPG4, HGF, INHBB, SATB1), where the patients of low-risk group had longer survival time. The survival rate of BLCA patients was significantly differential in different groups of age, stage, smoking history, pathologic-T, and pathologic-N. The IC50 of 56 drugs showed significant differences between 2 risk groups, such as imatinib, docetaxel, and dasatinib. At last, the results of real time quantitative-polymerase chain reaction (RT-qPCR) demonstrated that the expression trend of CALR, HGF, and INHBB was consistent with the result obtained previously based on public databases. Taken together, this study identified 6 anoikis-related characteristic genes (CALR, FASN, CSPG4, HGF, INHBB, SATB1) for the prognosis of BLCA patients, providing a scientific reference for further research on BLCA.

Multiple programmed cell death patterns and immune landscapes in bladder cancer: Evidence based on machine learning and multi-cohorts

BACKGROUND

Bladder cancer (BLCA) is the most prevalent malignant neoplasm of the urinary tract, and ranks seventh as the most frequent systemic neoplasm in males. Dysregulation of programmed cell death (PCD) has been implicated in various stages of cancer progression, including tumorigenesis, invasion, and metastasis. However, the correlation between multiple PCD modes and BLCA is lacking. Thus, a risk prediction model was built based on 12 models of PCD to predict prognosis and immunotherapy response in patients with BLCA.

METHODS

The RNA sequencing transcriptome data of BLCA were collected from the Cancer Genome Atlas Program (TCGA) and GEO datasets. Univariate Cox and LASSO regression analyzes were performed to identify PCD-related genes (PCDRGs) significant for prognosis. Multivariate Cox regression analysis was used to develop a prognostic model for PCD. Survival analysis and chi-squared test were employed to analyze the survival variations between different risk groups. Univariate and multivariate Cox analyses were performed to evaluate the model as an independent prognostic predictor. A nomogram was formulated using both clinical data and the model to predict the survival rates of BLCA patients. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were performed to analyze and elucidate the molecular mechanisms and pathways operating within different risk score groups. Furthermore, the immune landscape was investigated and the efficacy of various anti-tumor drugs was evaluated for BLCA. Finally, consensus clustering analysis was adopted to explore the association between different PCD clusters and clinical characteristics.

RESULTS

Assessment of the public datasets and multivariate Cox analysis yielded 1254 PCDRGs, of which 10 PCDRGs for BLCA were identified. Based on the PCDRGs, a prognostic model was built for BLCA patient prognosis. Compared with the low-risk group, the high-risk group had a poorer prognosis. The model predicted area under the curve (AUC) values of 0.751, 0.753, and 0.763, respectively, for 1-, 3-, and 5-year survival of BLCA patients. The nomogram further demonstrated the credibility of the prognosis model. The low-risk group patients exhibited lower TIDE scores and higher TMB scores, implying better response of the low-risk group to immunotherapy. The consensus clustering analysis indicated that compared with PCD cluster A, PCD cluster B was significantly more expressed in PCDRGs, suggesting a closer relation of PCD cluster B to PCDRGs. Patients in PCD cluster B had lower risk scores.

CONCLUSION

To summarize, the effects of 12 PCD patterns on BLCA were synthesized and the correlation between PCD and BLCA was explored. These findings provide new and convincing evidence for individualized treatment of BLCA, and help guide the treatment strategy and improve the prognosis of BLCA patients.

Multi-cohort validation of Ascore: an anoikis-based prognostic signature for predicting disease progression and immunotherapy response in bladder cancer

Bladder cancer ranks as the 10th most common cancer worldwide, with deteriorating prognosis as the disease advances. While immune checkpoint inhibitors (ICIs) have shown promise in clinical therapy in both operable and advanced bladder cancer, identifying patients who will respond is challenging. Anoikis, a specialized form of cell death that occurs when cells detach from the extracellular matrix, is closely linked to tumor progression. Here, we aimed to explore the anoikis-based biomarkers for bladder cancer prognosis and immunotherapeutic decisions. Through consensus clustering, we categorized patients from the TCGA-BLCA cohort into two clusters based on anoikis-related genes (ARGs). Significant differences in survival outcome, clinical features, tumor immune environment (TIME), and potential ICIs response were observed between clusters. We then formulated a four-gene signature, termed "Ascore", to encapsulate this gene expression pattern. The Ascore was found to be closely associated with survival outcome and served as an independent prognosticator in both the TCGA-BLCA cohort and the IMvigor210 cohort. It also demonstrated superior predictive capacity (AUC = 0.717) for bladder cancer immunotherapy response compared to biomarkers like TMB and PD-L1. Finally, we evaluated Ascore's independent prognostic performance as a non-invasive biomarker in our clinical cohort (Gulou-Cohort1) using circulating tumor cells detection, achieving an AUC of 0.803. Another clinical cohort (Gulou-Cohort2) consisted of 40 patients undergoing neoadjuvant anti-PD-1 treatment was also examined. Immunohistochemistry of Ascore in these patients revealed its correlation with the pathological response to bladder cancer immunotherapy (P = 0.004). Impressively, Ascore (AUC = 0.913) surpassed PD-L1 (AUC = 0.662) in forecasting immunotherapy response and indicated better net benefit. In conclusion, our study introduces Ascore as a novel, robust prognostic biomarker for bladder cancer, offering a new tool for enhancing immunotherapy decisions and contributing to the tailored treatment approaches in this field.

Identification and Validation of a Novel Anoikis-Related Gene Signature for Predicting Survival in Patients With Serous Ovarian Cancer

Background

Ovarian cancer is an extremely deadly gynecological malignancy, with a 5-year survival rate below 30%. Among the different histological subtypes, serous ovarian cancer (SOC) is the most common. Anoikis significantly contributes to the progression of ovarian cancer. Therefore, identifying an anoikis-related signature that can serve as potential prognostic predictors for SOC is of great significance.

Methods

We intersected 308 anoikis-related genes (ARGs) and identified those significantly associated with SOC prognosis using univariate Cox regression. A LASSO Cox regression model was constructed and evaluated using Kaplan-Meier and receiver operating characteristic (ROC) analyses in TCGA (The Cancer Genome Atlas) and GSE26193 cohorts. We conducted quantitative real-time polymerase chain reaction (qPCR) to assess mRNA levels and applied bioinformatics to investigate the correlation between risk groups and gene expression, mutations, pathways, tumor immune microenvironment (TIME), and drug sensitivity in SOC.

Results

Among 308 ARGs, 28 were significantly associated with SOC prognosis. A 13-gene prognostic model was established through LASSO Cox regression in TCGA cohort. High-risk group had poorer prognosis than low-risk group (median overall survival (mOS): 34.2 vs. 57.1 months, hazard ratio (HR): 2.590, 95% confidence interval (CI): 0.159 - 6.00, P < 0.001). The area under the curve (AUC) values of 0.63, 0.65, and 0.74 reflected the predictive performance for 3-, 5-, and 8-year overall survival (OS) in GSE26193 validation cohort. Functional enrichment, pathway analysis, and TIME analysis identified distinct characteristics between risk groups. Drug sensitivity analysis revealed potential drug advantages for each group. Furthermore, qPCR validation once again confirmed the effectiveness of the risk model in SOC patients.

Conclusions

We developed and validated a robust ARG model, which could be used to predict OS in SOC patients. By systematically analyzing the correlation between the risk score of the ARGs signature model and various patterns, including the TIME and drug sensitivity, our findings suggest that this prognostic model contributes to the advancement of personalized and precise therapeutic strategies. Nevertheless, further validation studies and investigations into the underlying mechanisms are warranted.

Deciphering the significance of anoikis in bladder cancer and systematic analysis of S100A7 as a potential therapeutic target

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.