Comprehensive analysis of anoikis-related long non-coding RNA immune infiltration in patients with bladder cancer and immunotherapy

Identification of a novel signature based on RNA methylation-associated anoikis-related genes for predicting prognosis and characterizing immune landscape in colorectal cancer

BACKGROUND

RNA methylation is a potential target for cancer therapy, while anoikis, a form of programmed cell death, is linked to cancer metastasis. However, the prognostic and immune significance of RNA methylation- and anoikis-related genes in colorectal cancer (CRC) remains unknown.

METHODS

Transcriptomic and clinicopathological data for CRC were obtained from TCGA and the GEO databases. A novel signature was constructed based on RNA methylation- and anoikis-related genes using univariate and multivariate Cox regression as well as LASSO Cox regression methods. CRC patients were stratified into low- and high-risk groups based on this signature. Differences in prognosis, immune infiltration, and drug sensitivity between two groups were analyzed. Finally, immunohistochemistry, western blot, and RT-qPCR were employed to validate the expression of the key gene SERPINE1 in CRC tissues and cells, as well as the effect of FTO on its expression.

RESULTS

We identified 79 differentially expressed RNA methylation-associated anoikis-related genes (RMRARGs) in both cancerous and normal tissues. A signature composed of 9 key genes (BID, FASN, PLK1, CDKN3, MYC, EPHA2, SERPINE1, CD36, PDK4) was established. Kaplan-Meier analysis revealed a poorer prognosis in the high-risk group. Compared to the other three published models, this signature demonstrated superior predictive performance based on the ROC curve analysis. Functional analyses highlighted differences in drug sensitivities and signaling pathways between risk groups. Furthermore, immune analysis results showed that risk score was associated with some immune cells and immune checkpoints. Immunohistochemistry showed high SERPINE1 expression in CRC tissues, with FTO expression positively correlated with SERPINE1. Furthermore, RT-qPCR and western blot indicated FTO knockdown markedly downregulated SERPINE1 levels.

CONCLUSION

Our findings underscore the prognostic value of this signature in CRC patients and its utility in assessing immune status. Additionally, the m6A demethylase FTO regulates the expression of the anoikis-related gene SERPINE1.

Identification of anoikis-related molecular patterns and the novel risk model to predict prognosis, tumor microenvironment infiltration and immunotherapy response in bladder cancer

Background

Anoikis, a unique form of cell death, serves as a vital part of the organism's defense by preventing shedding cells from re-attaching to the incorrect positions, and plays pivotal role in cancer metastasis. Nonetheless, the specific mechanisms among anoikis, the clinical prognosis and tumor microenvironment (TME) of bladder cancer (BLCA) are insufficiently understood.

Method

BLCA patients were classified into different anoikis subtypes based on the expression of candidate anoikis-related genes (ARGs), and differences in the clinicopathological features, TME, immune cell infiltration, and immune checkpoints between two anoikis subtypes were analyzed. Next, patients in the TCGA cohort were randomized into the train and test groups in a 1:1 ratio. Subsequently, the anoikis-related model was constructed to predict the prognosis via utilizing the univariate Cox, LASSO and multivariate Cox analyses, and validated internally and externally. Moreover, the relationships between the risk score and clinicopathologic features, immune cell infiltration, immunotherapy response, and antitumor drug sensitivity were also analyzed. In addition, representative genes were evaluated using immunohistochemistry in clinical specimens, and in BLCA cell lines, functional experiments were performed to determine the biological behavior of hub gene PLOD1.

Result

Two definite anoikis subgroups were identified. Compared to ARGcluster A, patients assigned to ARGcluster B were characterized by an immunosuppressive microenvironment and worse prognosis. Then, the anoikis-related model, including PLOD1, EHBP1, and CSPG4, was constructed, and BLCA patients in the low-risk group were characterized by a better prognosis. Next, the accurate nomogram was built to improve the clinical applicability by combining the age, tumor stage and risk Score. Moreover, immune infiltration and clinical features differed significantly between high- and low-risk groups. We also found that the low-risk group exhibited a lower tumor immune dysfunction and exclusion score, a higher immunophenoscore (IPS), had more sensitivity to immunotherapy. Eventually, the expression levels of three genes were verified by our experiment, and knockdown of PLOD1 could inhibit invasion and migration abilities in BLCA cell lines.

Conclusion

These results demonstrated a new direction in precision therapy for BLCA, and indicated that the ARGs might be helpful to in predicting prognosis and as therapeutic targets in BLCA.

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.

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.

Integrated Analysis Reveals COL4A3 as a Novel Diagnostic and Therapeutic Target in UV-Related Skin Cutaneous Melanoma

Background

High levels of UV exposure are a significant factor that can trigger the onset and progression of SKCM. Moreover, this exposure is closely linked to the malignancy of the tumor and the prognosis of patients. Our objective is to identify a tumor biomarker database associated with UV exposure, which can be utilized for prognostic analysis and diagnosis and treatment of SKCM.

Methods

This study used the weighted gene co-expression network analyses (WGCNA) and gene mutation frequency analyses to screen for UV-related target genes using the GSE59455 and the cancer genome atlas databases (TCGA). The prognostic model was created using Cox regression and least absolute shrinkage and selection operator analyses (LASSCO). Furthermore, in vitro experiments further validated that the overexpression or knockdown of COL4A3 could regulate the proliferation and migration abilities of SKMEL28 and A357 melanoma cells.

Results

A prognostic model was created that included six genes with a high UV-related mutation in SKCM: COL4A3, CHRM2, DSC3, GIMAP5, LAMC2, and PSG7. The model had a strong patient survival correlation (P˂0.001, hazard ratio (HR) = 1.57) and significant predictor (P˂0.001, HR = 3.050). Furthermore, the model negatively correlated with immune cells, including CD8+ T cells (Cor=-0.408, P˂0.001), and M1-type macrophages (Cor=-0.385, P˂0.001), and immune checkpoints, including programmed cell death ligand-1. Moreover, we identified COL4A3 as a molecule with significant predictive functionality. Overexpression of COL4A3 significantly inhibited the proliferation, migration, and invasion abilities of SKMEL28 and A357 melanoma cells, while knockdown of COL4A3 yielded the opposite results. And overexpression of COL4A3 enhanced the inhibitory effects of imatinib on the proliferation, migration, and invasion abilities of SKMEL28 and A357 cells.

Conclusion

The efficacy of the prognostic model was validated by analyzing the prognosis, immune infiltration, and immune checkpoint profiles. COL4A3 stands out as a novel diagnostic and therapeutic target for SKCM, offering new strategies for small-molecule targeted drug therapies.

Deciphering the prognostic features of bladder cancer through gemcitabine resistance and immune-related gene analysis and identifying potential small molecular drug PIK-75

BACKGROUND

Bladder cancer (BCa) stands out as a prevalent and highly lethal malignancy worldwide. Chemoresistance significantly contributes to cancer recurrence and progression. Traditional Tumor Node Metastasis (TNM) stage and molecular subtypes often fail to promptly identify treatment preferences based on sensitivity.

METHODS

In this study, we developed a prognostic signature for BCa with uni-Cox + LASSO + multi-Cox survival analysis in multiple independent cohorts. Six machine learning algorithms were adopted to screen out the hub gene, RAC3. IHC staining was used to validate the expression of RAC3 in BCa tumor tissue. RT-qPCR and Western blot were performed to detect and quantify the mRNA and protein levels of RAC3. CCK8, colony formation, wound healing, and flow cytometry analysis of apoptosis were employed to determine cell proliferation, migration, and apoptosis. Molecular docking was used to find small target drugs, PIK-75. 3D cell viability assay was applied to evaluate the ATP viability of bladder cancer organoids before and after PIK-75 treated.

RESULTS

The established clinical prognostic model, GIRS, comprises 13 genes associated with gemcitabine resistance and immunology. This model has demonstrated robust predictive capabilities for survival outcomes across various independent public cohorts. Additionally, the GIRS signature shows significant correlations with responses to both immunotherapy and chemotherapy. Leveraging machine learning algorithms, the hub gene, RAC3, was identified, and potential upstream transcription factors were screened through database analysis. IHC results showed that RAC3 was higher expressed in GEM-resistant BCa patients. Employing molecular docking, the small molecule drug PIK-75, as binding to RAC3, was identified. Experiments on cell lines, organoids and animals validated the biological effects of PIK-75 in bladder cancer.

CONCLUSIONS

The GIRS signature offers a valuable complement to the conventional anatomic TNM staging system and molecular subtype stratification in bladder cancer. The hub gene, RAC3, plays a crucial role in BCa and is significantly associated with resistance to gemcitabine. The small molecular drug, PIK-75 having the potential as a therapeutic agent in the context of gemcitabine-resistant and immune-related pathways.

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 anoikis-related lncRNAs for prognostic significance and immune microenvironment characterization in ovarian cancer

Anoikis, a form of apoptotic cell death resulting from inadequate cell-matrix interactions, has been implicated in tumor progression by regulating tumor angiogenesis and metastasis. However, the potential roles of anoikis-related long non-coding RNAs (arlncRNAs) in the tumor microenvironment are not well understood. In this study, five candidate lncRNAs were screened through least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis based on differentially expressed lncRNAs associated with anoikis-related genes (ARGs) from TCGA and GSE40595 datasets. The prognostic accuracy of the risk model was evaluated using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) analyses revealed significant differences in immune-related hallmarks and signal transduction pathways between the high-risk and low-risk groups. Additionally, immune infiltrate analysis showed significant differences in the distribution of macrophages M2, follicular T helper cells, plasma cells, and neutrophils between the two risk groups. Lastly, silencing the expression of PRR34_AS1 and SPAG5_AS1 significantly increased anoikis-induced cell death in ovarian cancer cells. In conclusion, our study constructed a risk model that can predict clinicopathological features, tumor microenvironment characteristics, and prognosis of ovarian cancer patients. The immune-related pathways identified in this study may offer new treatment strategies for ovarian cancer.

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.

Deciphering the prognostic significance of anoikis-related lncRNAs in invasive breast cancer: from comprehensive bioinformatics analysis to functional experimental validation

The global prevalence of breast cancer necessitates the development of innovative prognostic markers and therapeutic strategies. This study investigated the prognostic implications of anoikis-related long non-coding RNAs (ARLs) in invasive breast cancer (IBC), which is an area that has not been extensively explored. By integrating the RNA sequence transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database and employing advanced regression analyses, we devised a novel prognostic model based on ARL scores. ARL scores correlated with diverse clinicopathological parameters, cellular pathways, distinct mutation patterns, and immune responses, thereby affecting both immune cell infiltration and anticipated responses to chemotherapy and immunotherapy. Additionally, the overexpression of a specific lncRNA, AL133467.1, significantly impeded the proliferation and migration, as well as possibly the anoikis resistance of breast cancer cells. These findings highlight the potential of the ARL signature as a robust prognostic tool and a promising basis for personalized IBC treatment strategies.

Construction of an anoikis‒related prognostic signature to predict immunotherapeutic response and prognosis in hepatocellular carcinoma

PURPOSE

Anoikis resistance is an important inducer of tumor metastasis. The role of anoikis-related genes (ARGs) in hepatocellular carcinoma (HCC) remains unclear.

METHODS

A list of ARGs was obtained and regression analysis was employed to assemble an anoikis-related prognostic signature and risk score formula from mRNA data and clinical prognostic data downloaded from The Cancer Genome Atlas database. Quantitative real-time PCR (qRT-PCR) was performed on clinical samples to validate the selected ARGs expressions. Kaplan‒Meier curves, ROC curves, and Cox regression analyses were used to demonstrated the prognostic value of this signature. Biological functional enrichment analysis and immune infiltration analysis were utilized to analyze the differences in potential biological functions, immune cell infiltration, immune functions, and immunotherapeutic responses.

RESULTS

A prognostic signature based on 6 ARGs and corresponding prognostic nomogram were established. Our qRT-PCR results showed a higher expression of 6 ARGs in HCC tissues (p value < 0.05). Kaplan‒Meier curves, ROC curves, and Cox regression analyses demonstrated good prognostic value of the signature in HCC (p value < 0.05). Significant differences between the enriched biological functions and immune landscapes of patients in different risk groups were discovered (p value < 0.05). In addition, patients with higher risk scores possibly had poor therapeutic response to transhepatic arterial chemotherapy and embolization or sorafenib, but their responses to immunotherapy might be more effective.

CONCLUSION

A successful anoikis-related prognostic signature and corresponding clinical nomogram were established, which might facilitate better predictions of prognosis and therapeutic responses for HCC patients.

Comprehensive analysis of anoikis-related lncRNAs for predicting prognosis and response of immunotherapy in hepatocellular carcinoma

Nowadays, primary liver cancer is still a major threat to human health. Anoikis is a particular form of programed cell death that has an inhibitory effect on neoplasm metastasis. Although several prognostic models based on anoikis-related genes for Hepatocellular carcinoma (HCC) have been established, signatures associated with anoikis-related lncRNAs have not been identified. To fill this blank space, the authors built up a prognostic signature and appraised its value in guiding immunotherapy. Eleven prognostic anoikis-related lncRNAs were identified through Least Absolute Shrinkage and Selection Operator Cox analysis. The accuracy of the risk signature in predicting prognosis was verified by K-M survival analysis and Receiver operating characteristic analysis. We further discovered that the high-risk group was often enriched in signal pathways related to cell growth and death and immune response; in addition, in the low-risk group, cells often undergo metabolic changes through gene set enrichment analysis. Finally, we realised that HCC patients in the high-risk group were upregulated in immune-checkpoint molecules and tend to have a higher tumour mutation burden level which indicated a higher sensitivity to immunotherapy. All in all, the anoikis-related lncRNAs risk signature showed excellent ability in predicting prognosis and may guide the application of immunotherapy in future clinical practice.

Roles of non-coding RNAs in the metabolism and pathogenesis of bladder cancer

Bladder cancer (BC) is featured as the second most common malignancy of the urinary tract worldwide with few treatments leading to high incidence and mortality. It stayed a virtually intractable disease, and efforts to identify innovative and effective therapies are urgently needed. At present, more and more evidence shows the importance of non-coding RNA (ncRNA) for disease-related study, diagnosis, and treatment of diverse types of malignancies. Recent evidence suggests that dysregulated functions of ncRNAs are closely associated with the pathogenesis of numerous cancers including BC. The detailed mechanisms underlying the dysregulated role of ncRNAs in cancer progression are still not fully understood. This review mainly summarizes recent findings on regulatory mechanisms of the ncRNAs, long non-coding RNAs, microRNAs, and circular RNAs, in cancer progression or suppression and focuses on the predictive values of ncRNAs-related signatures in BC clinical outcomes. A deeper understanding of the ncRNA interactive network could be compelling framework for developing biomarker-guided clinical trials.

Prognosis Prediction of Disulfidptosis-Related Genes in Bladder Cancer and a Comprehensive Analysis of Immunotherapy

As a newly discovered mechanism of cell death, disulfidptosis is expected to help diagnose and treat bladder cancer patients. First, data obtained from public databases were analyzed using bioinformatics techniques. SVA packages were used to combine data from different databases to remove batch effects. Then, the differential analysis and COX regression analysis of ten disulfidptosis-related genes identified four prognostically relevant differentially expressed genes which were subjected to Lasso regression for further screening to obtain model-related genes and output model formulas. The predictive power of the prognostic model was verified and the immunohistochemistry of model-related genes was verified in the HPA database. Pathway enrichment analysis was performed to identify the mechanism of bladder cancer development and progression. The tumor microenvironment and immune cell infiltration of bladder cancer patients with different risk scores were analyzed to personalize treatment. Then, information from the IMvigor210 database was used to predict the responsiveness of different risk patients to immunotherapy. The oncoPredict package was used to predict the sensitivity of patients at different risk to chemotherapy drugs, and its results have some reference value for guiding clinical use. After confirming that our model could reliably predict the prognosis of bladder cancer patients, the risk scores were combined with clinical information to create a nomogram to accurately calculate the patient survival rate. A prognostic model containing three disulfidptosis-related genes (NDUFA11, RPN1, SLC3A2) was constructed. The functional enrichment analysis and immune-related analysis indicated patients in the high-risk group were candidates for immunotherapy. The results of drug susceptibility analysis can guide more accurate treatment for bladder cancer patients and the nomogram can accurately predict patient survival. NDUFA11, RPN1, and SLC3A2 are potential novel biomarkers for the diagnosis and treatment of bladder cancer. The comprehensive analysis of tumor immune profiles indicated that patients in the high-risk group are expected to benefit from immunotherapy.