Identification and validation of a prognostic anoikis-related gene signature in papillary thyroid carcinoma by integrated analysis of single-cell and bulk RNA-sequencing.

Papillary thyroid carcinoma (PTC) prognosis may be deteriorated due to the metastases, and anoikis palys an essential role in the tumor metastasis. However, the potential effect of anoikis-related genes on the prognosis of PTC was unclear. The mRNA and clinical information were obtained from the cancer genome atlas database. Hub genes were identified and risk model was constructed using Cox regression analysis. Kaplan-Meier (K-M) curve was applied for the survival analysis. Immune infiltration and immune therapy response were calculated using CIBERSORT and TIDE. The identification of cell types and cell interaction was performed by Seurat, SingleR and CellChat packages. GO, KEGG, and GSVA were applied for the enrichment analysis. Protein-protein interaction network was constructed in STRING and Cytoscape. Drug sensitivity was assessed in GSCA. Based on bulk RNA data, we identified 4 anoikis-related risk signatures, which were oncogenes, and constructed a risk model. The enrichment analysis found high risk group was enriched in some immune-related pathways. High risk group had higher infiltration of Tregs, higher TIDE score and lower levels of monocytes and CD8 T cells. Based on scRNA data, we found that 4 hub genes were mainly expressed in monocytes and macrophages, and they interacted with T cells. Hub genes were significantly related to immune escape-related genes. Drug sensitivity analysis suggested that cyclin dependent kinase inhibitor 2A may be a better chemotherapy target. We constructed a risk model which could effectively and steadily predict the prognosis of PTC. We inferred that the immune escape may be involved in the development of PTC.

Integrated single-cell and bulk RNA-Seq analysis enhances prognostic accuracy of PD-1/PD-L1 immunotherapy response in lung adenocarcinoma through necroptotic anoikis gene signatures.

In addition to presenting significant diagnostic and treatment challenges, lung adenocarcinoma (LUAD) is the most common form of lung cancer. Using scRNA-Seq and bulk RNA-Seq data, we identify three genes referred to as HMR, FAM83A, and KRT6A these genes are related to necroptotic anoikis-related gene expression. Initial validation, conducted on the GSE50081 dataset, demonstrated the model's ability to categorize LUAD patients into high-risk and low-risk groups with significant survival differences. This model was further applied to predict responses to PD-1/PD-L1 blockade therapies, utilizing the IMvigor210 and GSE78220 cohorts, and showed strong correlation with patient outcomes, highlighting its potential in personalized immunotherapy. Further, LUAD cell lines were analyzed using quantitative PCR (qPCR) and Western blot analysis to confirm their expression levels, further corroborating the model's relevance in LUAD pathophysiology. The mutation landscape of these genes was also explored, revealing their broad implication in various cancer types through a pan-cancer analysis. The study also delved into molecular subclustering, revealing distinct expression profiles and associations with different survival outcomes, emphasizing the model's utility in precision oncology. Moreover, the diversity of immune cell infiltration, analyzed in relation to the necroptotic anoikis signature, suggested significant implications for immune evasion mechanisms in LUAD. While the findings present a promising stride towards personalized LUAD treatment, especially in immunotherapy, limitations such as the retrospective nature of the datasets and the need for larger sample sizes are acknowledged. Prospective clinical trials and further experimental research are essential to validate these findings and enhance the clinical applicability of our prognostic model.

Identification and validation of an anoikis-related genes signature for prognostic implication in papillary thyroid cancer.

Thyroid cancer, notably papillary thyroid cancer (PTC), is a global health concern with increasing incidence. Anoikis, a regulator of programmed cell death, is pivotal in normal physiology and, when dysregulated, can drive cancer progression and metastasis. This study explored the impact of anoikis on PTC prognosis. Analyzing data from GEO, TCGA, and GeneCards, we identified a prognostic signature consisting of six anoikis-related genes (ARGs): EZH2, PRKCQ, CD36, INHBB, TDGF1, and MMP9. This signature independently predicted patient outcomes, with high-risk scores associated with worse prognoses. A robust predictive ability was confirmed via ROC analysis, and a nomogram achieved a C-index of 0.712. Differences in immune infiltration levels were observed between high- and low-risk groups. Importantly, the high-risk group displayed reduced drug sensitivity and poor responses to immunotherapy. This research provides insights into anoikis in PTC, offering a novel ARG signature for predicting patient prognosis and guiding personalized treatment strategies.

Identification and validation of anoikis-related genes to clarify the prognosis and immune mechanisms of patients with low-grade glioma.

BACKGROUND: Low-grade glioma (LGG) has an extremely poor prognosis, and the mechanism leading to malignant development has not been determined. The aim of our study was to clarify the function and mechanism of anoikis and TIMP1 in the malignant progression of LGG. METHODS: We screened 7 anoikis-related genes from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to construct a prognostic-predicting model. The study assessed the clinical prognosis, pathological characteristics, and immune cell infiltration in both high- and low-risk groups. Additionally, the potential modulatory effects of TIMP1 on proliferation, migration, and anoikis in LGG were investigated both in vivo and in vitro. RESULTS: In this study, we identified seven critical genes, namely, PTGS2, CCND1, TIMP1, PDK4, LGALS3, CDKN1A, and CDKN2A. Kaplan‒Meier (K‒M) curves demonstrated a significant correlation between clinical features and overall survival (OS), and single-cell analysis and mutation examination emphasized the heterogeneity and pivotal role of hub gene expression imbalances in LGG development. Immune cell infiltration and microenvironment analysis further elucidated the relationships between key genes and immune cells. In addition, TIMP1 promoted the malignant progression of LGG in both in vitro and in vivo models. CONCLUSIONS: This study confirmed that TIMP1 promoted the malignant progression of LGG by inhibiting anoikis, providing insights into LGG pathogenesis and potential therapeutic targets.

Identification of an anoikis-related gene signature and characterization of immune infiltration in skin cutaneous melanoma.

Anoikis is considered strongly associated with a biological procession of tumors. Herein, we utilized anoikis-related genes (ARGs) to predict the prognosis and immunotherapeutic efficacy for skin cutaneous melanoma (SKCM). RNA-seq data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. After dividing patients into novel subtypes based on the expression of prognostic ARGs, K-M survival was conducted to compare the survival status. Subsequently, differentially expressed ARGs were identified and the predictive model was established. The predictive effects were validated using the areas under the curve about the receiver operating characteristic. Moreover, tumor mutation burden, the enriched functional pathway, immune cells and functions, and the immunotherapeutic response were also analyzed and compared. The distribution of model genes at cell level was visualized by the single-cell seq with tumor immune single-cell hub database. Patients of The Cancer Genome Atlas-SKCM cohort were divided into 2 clusters, the cluster 1 performed a better prognosis. Cluster 2 was more enriched in metabolism-related pathways whereas cluster 1 was more associated with immune pathways. A predictive risk model was established with 6 ARGs, showing the areas under the curves of 1-year, 3-year, and 5-year ROC were 0.715, 0,720, and 0.731, respectively. Moreover, risk score was negatively associated with tumor mutation burden and immune-related pathways enrichment. In addition, patients with high-risk scores performed immunosuppressive status but the decreasing scores enhanced immune cell infiltration, immune function activation, and immunotherapeutic response. In this study, we established a novel signature in predicting prognosis and immunotherapy. It can be considered reliable to formulate the complex treatment for SKCM patients.

Parkin Enhances Gefitinib-induced Anoikis in HeLa Cervical Cancer Cells.

BACKGROUND/AIM: Gefitinib exhibits anticancer activity against cervical cancer cells via anoikis, a type of apoptosis induced by cell detachment from the extracellular matrix. Previous studies have reported that Parkin expression affects the efficacy of anticancer drugs. However, the impact of Parkin expression on the therapeutic effects of gefitinib in human cervical cancer remains unclear. Thus, this study aimed to evaluate whether Parkin over-expression improves the therapeutic effects of gefitinib against HeLa cervical cancer cells. MATERIALS AND METHODS: Cell viability and apoptotic death of HeLa cells were measured by trypan blue dye exclusion assay and flow cytometry. Cell detachment, adhesion, spreading, and cell-cell interaction were observed by inverted microscopy. Alteration of adhesion-related molecules was evaluated by confocal microscopy and western blot assay. RESULTS: Parkin expression potentiated gefitinib-induced cell detachment by affecting the organization of the actin cytoskeleton. In addition, Parkin expression induced a further reduction in the reattachment of and interaction between detached cells. The therapeutic efficacy of low-dose gefitinib combined with Parkin expression was equivalent to that of high-dose gefitinib alone. CONCLUSION: Parkin expression promotes gefitinib-induced anoikis, consequently increasing the efficacy of gefitinib against HeLa human cervical cancer cells. Based on our results, we propose that Parkin can be used to increase the anti-cancer effect of gefitinib on cervical cancer cells.

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

BACKGROUND: Anoikis 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). METHODS: We 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. RESULTS: Two 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. CONCLUSIONS: The 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.

Identification and immunological characteristics of anoikis-associated molecular clusters in lung adenocarcinoma.

Anoikis plays a crucial role in the progression, prognosis, and immune response of lung adenocarcinoma (LUAD). However, its specific impact on LUAD remains unclear. In this study, we investigated the intricate interplay of nesting apoptotic factors in LUAD. By analyzing nine key nesting apoptotic factors, we categorized LUAD patients into two distinct clusters. Further examination of immune cell profiles revealed that Cluster A exhibited greater infiltration of innate immune cells than did Cluster B. Additionally, we identified two genes closely associated with prognosis and developed a predictive model to differentiate patients based on molecular clusters. Our findings suggest that the loss of specific anoikis-related genes could significantly influence the prognosis, tumor microenvironment, and clinical features of LUAD patients. Furthermore, we validated the expression and functional roles of two pivotal prognostic genes, solute carrier family 2 member 1 (SLC2A1) and sphingosine kinase 1 (SPHK1), in regulating tumor cell viability, migration, apoptosis, and anoikis. These results offer valuable insights for future mechanistic investigations. In conclusion, this study provides new avenues for advancing our understanding of LUAD, improving prognostic assessments, and developing more effective immunotherapy strategies.

Synergistic effects of photodynamic therapy and chemotherapy: Activating the intrinsic/extrinsic apoptotic pathway of anoikis for triple-negative breast cancer treatment.

Triple-negative breast cancer (TNBC) is a highly invasive and metastatic subtype of breast cancer that often recurs after surgery. Herein, we developed a cyclodextrin-based tumor-targeted nano delivery system that incorporated the photosensitizer chlorin e6 (Ce6) and the chemotherapeutic agent lonidamine (LND) to form the R6RGD-CMßCD-se-se-Ce6/LND nanoparticles (RCC/LND NPS). This nanosystem could target cancer cells, avoid lysosomal degradation and further localize within the mitochondria. The RCC/LND NPS had pH and redox-responsive to control the release of Ce6 and LND. Consequently, the nanosystem had a synergistic effect by effectively alleviating hypoxia, enhancing the production of cytotoxic reactive oxygen species (ROS) and amplifying the efficacy of photodynamic therapy (PDT). Furthermore, the RCC/LND NPS + light weakened anoikis resistance, disrupted extracellular matrix (ECM), activated both the intrinsic apoptotic pathway (mitochondrial pathway) and extrinsic apoptotic pathway (receptor death pathway) of anoikis. In addition, the nanosystem showed significant anti-TNBC efficacy in vivo. These findings collectively demonstrated that RCC/LND NPS + light enhanced the anticancer effects, induced anoikis and inhibited tumor cell migration and invasion through a synergistic effect of chemotherapy and PDT. Overall, this study highlighted the promising potential of the RCC/LND NPS + light for the treatment of TNBC.

MFG-E8 induces epithelial-mesenchymal transition and anoikis resistance to promote the metastasis of pancreatic cancer cells.

Pancreatic cancer is an extremely malignant tumor, and only a few clinical treatment options exist. MFG-E8 and kindlin-2 all play an important role in cancer progression. However, the specific mechanism occurring between MFG-E8, kindlin-2 and the migration and invasion of pancreatic cancer cells remains unelucidated. To unravel the specific mechanism, this study assessed the potential association between MFG-E8 and kindlin-2 as well as the involvement of MFG-E8 in pancreatic cancer using two pancreatic cancer cell lines (MiaPaCa-2 and PANC-1). Pancreatic cancer cells were treated with 0, 250, and 500 ng/ml MFG-E8, and the effects of MFG-E8 on the migration, invasion, and anoikis of pancreatic cancer cells were observed. To investigate the role of kindlin-2 in pancreatic cancer, kindlin-2-shRNAi was transfected to knock down its expression level in the two pancreatic cancer cell lines. Furthermore, cilengitide, a receptor blocker of MFG-E8, was used to explore the relationship between MFG-E8, kindlin-2, and pancreatic cancer progression. Our findings demonstrated that MFG-E8 promotes the migration and invasion of pancreatic cancer cells and induces cell anoikis resistance in a dose-dependent manner, which was effectively counteracted by cilengitide, a receptor blocker. Additionally, the knockdown of kindlin-2 expression nullified the effect of MFG-E8 on the migration and invasion of pancreatic cancer cells. Consequently, this study provides insights into the specific mechanism underlying the interplay between MFG-E8 and kindlin-2 in the progression of pancreatic cancer cells.

Identification of important genes related to anoikis in acute myocardial infarction.

Acute myocardial infarction (AMI) increasingly precipitates severe heart failure, with diagnoses now extending to progressively younger demographics. The focus of this study was to pinpoint critical genes linked to both AMI and anoikis, thereby unveiling potential novel biomarkers for AMI detection and intervention. Differential analysis was performed to identify significant differences in expression, and gene functionality was explored. Weighted gene coexpression network analysis (WGCNA) was used to construct gene coexpression networks. Immunoinfiltration analysis quantified immune cell abundance. Protein-protein interaction (PPI) analysis identified the proteins that interact with theanoikis. MCODE identified key functional modules. Drug enrichment analysis identified relevant compounds explored in the DsigDB. Through WGCNA, 13 key genes associated with anoikis and differentially expressed genes were identified. GO and KEGG pathway enrichment revealed the regulation of apoptotic signalling pathways and negative regulation of anoikis. PPI network analysis was also conducted, and 10 hub genes, such as IL1B, ZAP70, LCK, FASLG, CD4, LRP1, CDH2, MERTK, APOE and VTN were identified. IL1B were correlated with macrophages, mast cells, neutrophils and Tcells in MI, and the most common predicted medications were roxithromycin, NSC267099 and alsterpaullone. This study identified key genes associated with AMI and anoikis, highlighting their role in immune infiltration, diagnosis and medication prediction. These findings provide valuable insights into potential biomarkers and therapeutic targets for AMI.

Pan-cancer analysis reveals a regulatory pattern of anoikis in human cancers.

Anoikis emerges when a cell finds itself extricated from the appropriate extracellular matrix, leading to an interruption in integrin ligation and thus triggering programmed cellular demise. The cardinal role of Anoikis in the realms of tumor invasion and metastasis is undeniable, although our grasp on its precise influence within the convoluted landscape of cancer biology remains somewhat circumscribed. Notably, both the immune milieu of the tumor and its inherent aggression are correlated with the fluctuating variables of Anoikis. We conducted a thorough evaluation of the genes associated with anoikis and studied the regulatory patterns of these genes as well as the prognostic impact of anoikis in 33 different types of tumors. We provided functional annotations for the regulatory patterns linked to Anoikis. Additionally, we described the associations between immunological factors and genes associated with Anoikis. By applying gene set variation analysis (GSVA), we utilized the inherent abilities of 34 basic genes to calculate the Anoikis index. The Anoikis index is closely related to prognosis, immune microenvironment, immunotherapy, and other aspects. Our functional research revealed a correlation between immune cell infiltration, EMT, and a regulatory gene that is synonymous with adverse survival outcomes. In addition, our observations revealed a direct relationship between the expression of CEACAM5 and CEACAM6,the amplification of epithelial mesenchymal transition (EMT) phenomenon, and a decrease in survival outcomes.The potential therapeutic utility of anoikis-related genes was highlighted by the possible links between TME, clinical samples, genetic mutations, drug resistance, and immunotherapy.

Anoikis-related lncRNA signature predicts prognosis and is associated with immune infiltration in hepatocellular carcinoma.

Anoikis is a programmed cell death process triggered when cells are dislodged from the extracellular matrix. Numerous long noncoding RNAs (lncRNAs) have been identified as significant factors associated with anoikis resistance in various tumor types, including glioma, breast cancer, and bladder cancer. However, the relationship between lncRNAs and the prognosis of hepatocellular carcinoma (HCC) has received limited research attention. Further research is needed to investigate this potential link and understand the role of lncRNAs in the progression of HCC. We developed a prognostic signature based on the differential expression of lncRNAs implicated in anoikis in HCC. A co-expression network of anoikis-related mRNAs and lncRNAs was established using data obtained from The Cancer Genome Atlas (TCGA) for HCC. Cox regression analyses were conducted to formulate an anoikis-related lncRNA signature (ARlncSig) in a training cohort, which was subsequently validated in both a testing cohort and a combined dataset comprising the two cohorts. Receiver operating characteristic curves, nomograms, and decision curve analyses based on the ARlncSig score and clinical characteristics demonstrated robust predictive ability. Moreover, gene set enrichment analysis revealed significant enrichment of several immune processes in the high-risk group compared to the low-risk group. Furthermore, significant differences were observed in immune cell subpopulations, expression of immune checkpoint genes, and response to chemotherapy and immunotherapy between the high- and low-risk groups. Lastly, we validated the expression levels of the five lncRNAs included in the signature using quantitative real-time PCR. In conclusion, our ARlncSig model holds substantial predictive value regarding the prognosis of HCC patients and has the potential to provide clinical guidance for individualized immunotherapy. In this study, we obtained 36 genes associated with anoikis from the Gene Ontology and Gene Set Enrichment Analysis databases. We also identified 22 differentially expressed lncRNAs that were correlated with these genes using data from TCGA. Using Cox regression analyses, we developed an ARlncSig in a training cohort, which was then validated in both a testing cohort and a combined cohort comprising data from both cohorts. Additionally, we collected eight pairs of liver cancer tissues and adjacent tissues from the Affiliated Tumor Hospital of Nantong University for further analysis. The aim of this study was to investigate the potential of ARlncSig as a biomarker for liver cancer prognosis. The study developed a risk stratification system called ARlncSig, which uses five lncRNAs to categorize liver cancer patients into low- and high-risk groups. Patients in the high-risk group exhibited significantly lower overall survival rates compared to those in the low-risk group. The model's predictive performance was supported by various analyses including the receiver operating characteristic curve, nomogram calibration, clinical correlation analysis, and clinical decision curve. Additionally, differential analysis of immune function, immune checkpoint, response to chemotherapy, and immune cell subpopulations revealed significant differences between the high- and low-risk groups. Finally, quantitative real-time PCR validated the expression levels of the five lncRNAs. In conclusion, the ARlncSig model demonstrates critical predictive value in the prognosis of HCC patients and may provide clinical guidance for personalized immunotherapy.

Identification and clinical validation of the role of anoikis-related genes in diabetic foot.

This study aims to investigate the role of anoikis-related genes in diabetic foot (DF) by utilizing bioinformatics analysis to identify key genes associated with anoikis in DF. We selected the GEO datasets GSE7014, GSE80178 and GSE68183 for the extraction and analysis of differentially expressed anoikis-related genes (DE-ARGs). GO analysis and KEGG analysis indicated that DE-ARGs in DF were primarily enriched in apoptosis, positive regulation of MAPK cascade, anoikis, focal adhesion and the PI3K-Akt signalling pathway. Based on the LASSO and SVM-RFE algorithms, we identified six characteristic genes. ROC curve analysis revealed that these six characteristic genes had an area under the curve (AUC) greater than 0.7, indicating good diagnostic efficacy. Expression analysis in the validation set revealed downregulation of CALR in DF, consistent with the training set results. GSEA results demonstrated that CALR was mainly enriched in blood vessel morphogenesis, endothelial cell migration, ECM-receptor interaction and focal adhesion. The HPA database revealed that CALR was moderately enriched in endothelial cells, and CALR was found to interact with 63 protein-coding genes. Functional analysis with DAVID suggested that CALR and associated genes were enriched in the phagosome component. CALR shows promise as a potential marker for the development and treatment of DF.

HK2 promotes migration and invasion of intrahepatic cholangiocarcinoma via enhancing cancer stem-like cells' resistance to anoikis.

Cancer stem-like cells (CSLCs) and anoikis resistance play crucial roles in the metastasis of cancers. However, it remains unclear whether CSLCs are related to anoikis resistance in intrahepatic cholangiocarcinoma (ICC). Here we identified a group of stemness-related anoikis genes (SRAGs) via bioinformatic analysis of public data. Accordingly, a novel anoikis-related classification was established and it divided ICC into C1 and C2 type. Different type ICC displayed distinct prognosis, molecular as well immune characteristics. Furthermore, we found one key SRAGs via several machine learning algorithms. HK2 was up-regulated in tumor-repopulating cells (TRCs) of ICC, a kind of CSLCs with a potent resistance to anoikis. Its up-regulation may be caused by the activation of MTORC1 signaling in ICC-TRCs. And inhibition of HK2 significantly increased anoikis and decreased migration as well invasion in ICC-TRCs. Our studies provide an insight into the molecular mechanism underlying the resistance of ICC-TRCs to anoikis and enhance the evidences for targeting HK2 in ICC.

Circ_0007534 promotes cholangiocarcinoma stemness and resistance to anoikis through DDX3X-mediated positive feedback regulation of parental gene DDX42.

Cholangiocarcinoma (CCA) is a malignancy with an extremely poor prognosis, and much remains unknown about its pathogenesis and treatment modalities. Circular RNA (circRNA) has been proven to play regulatory roles in various tumorigenesis, yet its potential function and mechanism in cholangiocarcinoma require further investigation. This study is the first to identify the aberrant expression and functional role of a novel circRNA, circ_0007534, derived from the DDX42 gene, in cholangiocarcinoma. Compared to the normal control group, the expression of circ_0007534 was significantly elevated in the tissues and cells with CCA and that high expression correlated with lymph node invasion and poor prognosis. Functional experiments indicated that downregulating circ_0007534 markedly inhibited the proliferation, migration, invasion, stemness, and anti-anoikis ability of CCA cells, as well as the tumor growth and liver and lung metastasis in nude mice. Mechanistic studies revealed that DDX42, as the parent gene of circ_0007534, can mutually regulate each other's expression. Predominantly located in the cytoplasm, circ_0007534 can form a complex with the RNA-binding protein DDX3X, which enhances the stability of DDX42 mRNA, thereby upregulating the expression of DDX42. This creates a positive feedback loop among the three, collectively promoting the progression of cholangiocarcinoma. In conclusion, this study sheds light on the pivotal role and molecular mechanism of circ_0007534 in the development of CCA, offering potential new targets for early diagnosis and treatment.

Clinical value of molecular subtypes identification based on anoikis-related lncRNAs in castration-resistant prostate cancer.

BACKGROUND: Anoikis is a distinctive type of apoptosis. It is involved in tumor progression and metastasis. But its function in castration-resistant prostate cancer (CRPC) remains veiled. We aimed to develop a prognostic indicator based on anoikis-related long non-coding RNAs (arlncRNAs) and to investigate their biological function in CRPC. MATERIAL AND METHOD: Differentially expressed anoikis-related genes were extracted from two CRPC datasets, GSE51873, and GSE78201. Four lncRNAs associated with the anoikis-related genes were selected. A risk model based on these lncRNAs was developed and validated in The Cancer Genome Atlas (TCGA) and the Memorial Sloan-Kettering Cancer Center (MSKCC) prostate cancer cohorts. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, immune infiltration, immune checkpoints expression, and drug susceptibility were performed based on the model. To identify the biofunction of anoikis-related lncRNA, CCK-8 assays, colony formation assays, and flow cytometry were used. RESULT: Twenty-nine anoikis-related genes were differentially expressed in the CRPC datasets. And 36 prognostic arlncRNAs were selected for the LASSO Cox analysis. Patients were subsequently classified into two subtypes by constructing an anoikis-related lncRNA based prognostic index (ARPI). The accuracy of this index was validated. KEGG enrichment analysis revealed that the high-ARPI group was enriched in cancer-related and immune-related pathways. Immune infiltration analysis has indicated a positive association between high-ARPI groups and increased immune infiltration. Fulvestrant, OSI-027, Lapatinib, Dabrafenib, and Palbociclib were identified as potential sensitive drugs for high-ARPI patients. In vitro experiments exhibited that silencing LINC01138 dampened the proliferation, migration and enzalutamide resistance in CRPC. Furthermore, it stimulated apoptosis and inhibited the eithelial-mesenchymal transition process. CONCLUSION: Four arlncRNAs were identified and a risk model was established to predict the prognosis of patients with prostate cancer. Immune infiltration and drug susceptibility analysis revealed a potential therapeutic strategy for patients with castration-resistant prostate cancer.

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.

Comprehensive analysis of anoikis-related genes in diagnosis osteoarthritis: based on machine learning and single-cell RNA sequencing data.

Osteoarthritis (OA) is a degenerative disease closely associated with Anoikis. The objective of this work was to discover novel transcriptome-based anoikis-related biomarkers and pathways for OA progression.The microarray datasets GSE114007 and GSE89408 were downloaded using the Gene Expression Omnibus (GEO) database. A collection of genes linked to anoikis has been collected from the GeneCards database. The intersection genes of the differential anoikis-related genes (DEARGs) were identified using a Venn diagram. Infiltration analyses were used to identify and study the differentially expressed genes (DEGs). Anoikis clustering was used to identify the DEGs. By using gene clustering, two OA subgroups were formed using the DEGs. GSE152805 was used to analyse OA cartilage on a single cell level. 10 DEARGs were identified by lasso analysis, and two Anoikis subtypes were constructed. MEgreen module was found in disease WGCNA analysis, and MEturquoise module was most significant in gene clusters WGCNA. The XGB, SVM, RF, and GLM models identified five hub genes (CDH2, SHCBP1, SCG2, C10orf10, P FKFB3), and the diagnostic model built using these five genes performed well in the training and validation cohorts. analysing single-cell RNA sequencing data from GSE152805, including 25,852 cells of 6 OA cartilage.

Prognostic value and immune landscapes of anoikis-associated lncRNAs in lung adenocarcinoma.

BACKGROUND: Methods for predicting the outcome of lung adenocarcinoma (LUAD) in the clinic are limited. Anoikis is an important route to programmed cell death in LUAD, and the prognostic value of a model constructed with anoikis-related lncRNAs (ARlncRNAs) in LUAD is unclear. METHODS: Transcriptome and basic information for LUAD patients was obtained from the Cancer Genome Atlas. Coexpression and Cox regression analyses were utilized to identify prognostically significant ARlncRNAs and construct a prognostic signature. Furthermore, the signature was combined with clinical characteristics to create a nomogram. Finally, we performed principal component, enrichment, tumor mutation burden (TMB), tumor microenvironment (TME) and drug sensitivity analyses to evaluate the basic research and clinical merit of the signature. RESULTS: The prognostic signature developed with eleven ARlncRNAs can accurately predict that high-risk group patients have a worse prognosis, as proven by the receiver operating characteristic (ROC) curve (AUC: 0.718). Independent prognostic analyses indicated that the risk score is a significant independent prognostic element for LUAD (P<0.001). In the high-risk group, enrichment analysis demonstrated that glucose metabolism and DNA replication were the main enrichment pathways. TMB analysis indicated that the high-risk group had a high TMB (P<0.05). Drug sensitivity analyses can recognize drugs that are sensitive to different risk groups. Finally, 11 ARlncRNAs of this signature were verified by RT-qPCR analysis. CONCLUSIONS: A novel prognostic signature developed with 11 ARlncRNAs can accurately predict the OS of LUAD patients and offer clinical guidance value for immunotherapy and chemotherapy treatment.