The synergistic effects of anoikis-related genes and EMT-related genes in the prognostic prediction of Wilms tumor.

Wilms tumor (WT) is the most common type of malignant abdominal tumor in children; it exhibits a high degree of malignancy, grow rapidly, and is prone to metastasis. This study aimed to construct a prognosis model based on anoikis-related genes (ARGs) and epithelial-mesenchymal transition (EMT)-related genes (ERGs) for WT patients; we assessed the characteristics of the tumor microenvironment and treatment efficacy, as well as identifying potential therapeutic targets. To this end, we downloaded transcriptome sequencing data and clinical data for WT and normal renal cortices and used R to construct and validate the prognostic model based on ARGs and ERGs. Additionally, we performed clinical feature analysis, nomogram construction, mutation analysis, drug sensitivity analysis, Connectivity Map (cMAP) analysis, functional enrichment analysis, and immune infiltration analysis. Finally, we screened the hub gene using the STRING database and validated it via experiments. In this way, we constructed a model with good accuracy and robustness, which was composed of seven anoikis- and EMT-related genes. Paclitaxel and mesna were selected as potential chemotherapeutic drugs and adjuvant chemotherapeutic drugs for the WT high-risk group by using the Genomics of Drug Sensitivity in Cancer (GDSC) and cMAP compound libraries, respectively. We proved the existence of a strong correlation between invasive immune cells and prognostic genes and risk scores. Next, we selected NTRK2 as the hub gene, and in vitro experiments confirmed that its inhibition can significantly inhibit the proliferation and migration of tumor cells and promote late apoptosis. In summary, we screened out the potential biomarkers and chemotherapeutic drugs that can improve the prognosis of patients with WT.

GDF15 enhances anoikis resistance and metastasis of gastric cancer through protective autophagy.

Distant metastasis is a prevalent cause of mortality in gastric cancer (GC) patients. Anoikis, a process that induces cell death when cells get detached from the extracellular matrix (ECM), acts as a barrier to tumor metastasis. To survive in the circulatory system and metastasize, tumor cells must acquire anoikis resistance. It is crucial to identify the molecular processes that cause resistance to anoikis in GC since this might lead to the discovery of novel treatment targets and improve the long-term survival of GC patients. In this study, we employed quantitative proteomics to identify growth differentiation factor 15 (GDF15) as a key factor in GC anoikis resistance. We found that GDF15 enhances protective autophagy, thereby promoting anoikis resistance in GC cells. Furthermore, through DNA pull down assay, activating transcription factor 2 (ATF2) was found to be a critical regulator of GDF15 expression, acting as a transcriptional activator of GDF15. Collectively, these discoveries indicate that ATF2 and GDF15 have great potential as target candidates for developing therapeutic strategies to address the metastasis of GC.

Mesoporous cerium oxide nanoenzyme for Efficacious impeding tumor and metastasis via Conferring resistance to anoikis.

Tumor cells can survive when detached from the extracellular matrix or lose cell-to-cell connections, leading to a phenomenon known as anoikis resistance (AR). AR is closely associated with the metastasis and proliferation of tumor cells, enabling them to disseminate, migrate, and invade after detachment. Here, we have investigated a novel composite nanoenzyme comprising mesoporous silica/nano-cerium oxide (MSN-Ce@SP/PEG). This nanoenzyme exhibited satisfactory catalase (CAT) activity, efficiently converting high levels of H2O2 within tumor cells into O2, effectively alleviating tumor hypoxia. Furthermore, MSN-Ce@SP/PEG nanoenzyme demonstrated high peroxidase (POD) activity, elevating reactive oxygen species (ROS) levels and attenuating AR in hepatocellular carcinoma (HCC) cells. The MSN-Ce@SP/PEG nanoenzyme exhibited satisfactory dual bioactivity in CAT and POD and was significantly enhanced under favorable photothermal conditions. Through the synergistic effects of these capabilities, the nanoenzyme disrupted the epithelial-mesenchymal transition (EMT) process in detached HCC cells, ultimately inhibiting the recurrence and metastasis potential of anoikis-resistant HCC cells. This study represents the first report of a novel nanoenzyme based on mesoporous silica/nano-cerium oxide for treating AR in HCC cells, thereby suppressing HCC recurrence and metastasis. The findings of this work offer a pioneering perspective for the development of innovative strategies to prevent the recurrence and metastasis of HCC.

Integrative analysis of anoikis-related genes prognostic signature with immunotherapy and identification of CDKN3 as a key oncogene in lung adenocarcinoma.

Anoikis, a form of programmed cell death induced by loss of cell contact, is closely associated with tumor invasion and metastasis, making it highly significant in lung cancer research. We examined the expression patterns and prognostic relevance of Anoikis-related genes (ARGs) in lung adenocarcinoma (LUAD) using the TCGA-LUAD database. This study identified molecular subtypes associated with Anoikis in LUAD and conducted functional enrichment analyses. We constructed an ARG risk score using univariate least absolute shrinkage and selection operator (LASSO) Cox regression, validated externally with GEO datasets and clinical samples. The clinical applicability of the prognostic model was evaluated using nomograms, calibration curves, decision curve analysis (DCA), and time-dependent AUC assessments. We identified four prognostically significant genes (PLK1, SLC2A1, CDKN3, PHLDA2) and two ARG-related molecular subtypes. ARGs were generally upregulated in LUAD and correlated with multiple pathways including the cell cycle and DNA replication. The prognostic model indicated that the low-risk group had better outcomes and significant correlations with clinicopathological features, tumor microenvironment, immune therapy responses, drug sensitivity, and pan-RNA epigenetic modification-related genes. Patients with low-risk LUAD were potential beneficiaries of immune checkpoint inhibitor (ICI) therapy. Prognostic ARGs' distribution and expression across various immune cell types were further analyzed using single-cell RNA sequencing. The pivotal role of CDKN3 in LUAD was confirmed through qRT-PCR and gene knockout experiments, demonstrating that CDKN3 knockdown inhibits tumor cell proliferation, migration, and invasion. Additionally, we constructed a ceRNA network involving CDKN3/hsa-miR-26a-5p/SNHG6, LINC00665, DUXAP8, and SLC2A1/hsa-miR-218-5p/RNASEH1-AS1, providing new insights for personalized and immune therapy decisions in LUAD patients.

Anoikis in prostate cancer bone metastasis gene signatures and therapeutic implications.

Background: Bone metastasis from prostate cancer severely impacts patient outcomes and quality of life. Anoikis, a form of programmed cell death triggered by the loss of cell-matrix interactions, plays a critical role in cancer progression. However, its precise relationship with prostate cancer-induced bone metastasis remains unclear. This study aims to elucidate this relationship, focusing on anoikis-related gene signatures, molecular pathways, and therapeutic implications. Methods: We used the TCGA-PRAD dataset for training, with MSKCC and GSE70769 as validation cohorts. To evaluate immunotherapy efficacy, we examined IMvigor 210 and GSE91016 datasets, and GSE137829 provided single-cell insights into prostate cancer. Specific anoikis-related genes (ARGs) were identified, and Random Survival Forest analysis and multivariate Cox regression were employed to develop anoikis-linked features. The 'clustanoikisProfilanoikis' and 'GSEA' packages were used to explore potential ARG-related pathways. Results: Analyzing 553 samples from TCGA, 231 from MSKCC, 94 from GSE70769, and single-cell data from 6 prostate cancer patients (GSE137829), we constructed a prognostic model based on 9 ARGs. GSVA revealed upregulation of carcinogenic pathways, including epithelial-mesenchymal transition, E2F targets, and angiogenesis, with downregulation of metabolic pathways. Significant differences in somatic mutations were observed between cohorts, with a positive correlation between anoikis scores and tumor mutational burden (TMB). Immune landscape analysis suggested high-risk patients might benefit more from chemotherapy than immunotherapy based on their risk score. Single-cell analysis indicated overactivation of carcinogenic pathways in the high anoikis score group. Conclusion: This study elucidates the complex interplay between anoikis and bone metastasis in prostate cancer. Our findings highlight the critical role of anoikis in metastatic progression, enhancing the understanding of key biomarkers and molecular dynamics. The identified anoikis-related gene signatures and disrupted pathways offer promising avenues for predictive and therapeutic strategies in prostate cancer management.

Anoikis-related subtype and prognosis analyses based on bioinformatics, and an expression verification of ANGPTL4 based on experiments of lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high mortality. Anoikis resistance is an important mechanism of tumor cell proliferation and migration. Our research is devoted to exploring the role of anoikis in the diagnosis, classification, and prognosis of LUAD. Methods: We downloaded the expression profile, mutation, and clinical data of LUAD from The Cancer Genome Atlas (TCGA) database. The "ConsensusClusterPlus" package was then used for the cluster analysis, and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were used to establish the prognostic model. We verified the reliability of the model using a Gene Expression Omnibus (GEO) data set. A gene set variation analysis (GSVA) was conducted to investigate the functional enrichment differences in the different clusters and risk groups. The CIBERSORT algorithm and a single-sample gene set enrichment analysis (ssGSEA) were used to analyze immune cell infiltration. The tumor mutation burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) scores were used to evaluate the patients' sensitivity to immunotherapy. Immunohistochemical staining of tissue microarrays was used to verify the correlation between ANGPTL4 expression and the clinicopathological characteristics and prognosis of LUAD patients. Results: First, we screened 135 differentially expressed anoikis-related genes (ARGs) and 23 prognosis-related ARGs from TCGA-LUAD data set. Next, 494 LUAD samples were allocated to cluster A and cluster B based on the 23 prognosis-related ARGs. The Kaplan-Meier (K-M) analysis showed the overall survival (OS) of cluster B was better than that of cluster A. The clinicopathological characteristics and functional enrichment analyses revealed significant differences between clusters A and B. The tumor microenvironment (TME) analysis showed that cluster B had more immune cell infiltration and a higher TME score than cluster A. Subsequently, a LASSO Cox regression model of LUAD was constructed with ten ARGs. The K-M analysis showed that the low-risk patients had longer OS than the high-risk patients. The receiver operating characteristic curve, nomogram, and GEO data set verification results showed that the model had high accuracy and reliability. The level of immune cell infiltration and TME score were higher in the low-risk group than the high-risk group. The high-risk group had stronger sensitivity to immune checkpoint block therapy and weaker sensitivity to chemotherapy drugs than the low-risk group. ANGPTL4 expression was correlated with stage, tumor differentiation, tumor size, lymph node metastasis, and OS. Conclusions: We discovered novel molecular subtypes and constructed a novel prognostic model of LUAD. Our findings provide important insights into subtype classification and the accurate survival prediction of LUAD. We also identified ANGPTL4 as a prognostic indicator of LUAD.

FOSL1 promotes stem cell­like characteristics and anoikis resistance to facilitate tumorigenesis and metastasis in osteosarcoma by targeting SOX2.

Metastasis is the leading cause of cancer­related death in osteosarcoma (OS). OS stem cells (OSCs) and anoikis resistance are considered to be essential for tumor metastasis formation. However, the underlying mechanisms involved in the maintenance of a stem­cell phenotype and anoikis resistance in OS are mostly unknown. Fos­like antigen 1 (FOSL1) is important in maintaining a stem­like phenotype in various cancers; however, its role in OSCs and anoikis resistance remains unclear. In the present study, the dynamic expression patterns of FOSL1 were investigated during the acquisition of cancer stem­like properties using RNA sequencing, PCR, western blotting and immunofluorescence. Flow cytometry, tumor­sphere formation, clone formation assays, anoikis assays, western blotting and in vivo xenograft and metastasis models were used to further investigate the responses of the stem­cell phenotype and anoikis resistance to FOSL1 overexpression or silencing in OS cell lines. The underlying molecular mechanisms were evaluated, focusing on whether SOX2 is crucially involved in FOSL1­mediated stemness and anoikis in OS. FOSL1 expression was observed to be upregulated in OSCs and promoted tumor­sphere formation, clone formation and tumorigenesis in OS cells. FOSL1 expression correlated positively with the expression of stemness­related factors (SOX2, NANOG, CD117 and Stro1). Moreover, FOSL1 facilitated OS cell anoikis resistance and promoted metastases by regulating the expression of apoptosis related proteins BCL2 and BAX. Mechanistically, FOSL1 upregulated SOX2 expression by interacting with the SOX2 promoter and activating its transcription. The results also showed that SOX2 is critical for FOSL1­mediated stem­like properties and anoikis resistance. The current findings indicated that FOSL1 is an important regulator that promotes a stem cell­like phenotype and anoikis resistance to facilitate tumorigenesis and metastasis in OS by regulating the transcription of SOX2. Thus, FOSL1 might represent an attractive target for therapeutic interventions in OS.

Comprehensive analysis and experimental verification of the mechanism of anoikis related genes in pancreatic cancer.

Background: Pancreatic cancer (PC), characterized by its aggressive nature and low patient survival rate, remains a challenging malignancy. Anoikis, a process inhibiting the spread of metastatic cancer cells, is closely linked to cancer progression and metastasis through anoikis-related genes. Nonetheless, the precise mechanism of action of these genes in PC remains unclear. Methods: Study data were acquired from the Cancer Genome Atlas (TCGA) database, with validation data accessed at the Gene Expression Omnibus (GEO) database. Differential expression analysis and univariate Cox analysis were performed to determine prognostically relevant differentially expressed genes (DEGs) associated with anoikis. Unsupervised cluster analysis was then employed to categorize cancer samples. Subsequently, a least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted on the identified DEGs to establish a clinical prognostic gene signature. Using risk scores derived from this signature, patients with cancer were stratified into high-risk and low-risk groups, with further assessment conducted via survival analysis, immune infiltration analysis, and mutation analysis. External validation data were employed to confirm the findings, and Western blot and immunohistochemistry were utilized to validate risk genes for the clinical prognostic gene signature. Results: A total of 20 prognostic-related DEGs associated with anoikis were obtained. The TCGA dataset revealed two distinct subgroups: cluster 1 and cluster 2. Utilizing the 20 DEGs, a clinical prognostic gene signature comprising two risk genes (CDKN3 and LAMA3) was constructed. Patients with pancreatic adenocarcinoma (PAAD) were classified into high-risk and low-risk groups per their risk scores, with the latter exhibiting a superior survival rate. Statistically significant variation was noted across immune infiltration and mutation levels between the two groups. Validation cohort results were consistent with the initial findings. Additionally, experimental verification confirmed the high expression of CDKN3 and LAMA3 in tumor samples. Conclusion: Our study addresses the gap in understanding the involvement of genes linked to anoikis in PAAD. The clinical prognostic gene signature developed herein accurately stratifies patients with PAAD, contributing to the advancement of precision medicine for these patients.

Advancements in precision nanomedicine design targeting the anoikis-platelet interface of circulating tumor cells.

Tumor metastasis, the apex of cancer progression, poses a formidable challenge in therapeutic endeavors. Circulating tumor cells (CTCs), resilient entities originating from primary tumors or their metastases, significantly contribute to this process by demonstrating remarkable adaptability. They survive shear stress, resist anoikis, evade immune surveillance, and thwart chemotherapy. This comprehensive review aims to elucidate the intricate landscape of CTC formation, metastatic mechanisms, and the myriad factors influencing their behavior. Integral signaling pathways, such as integrin-related signaling, cellular autophagy, epithelial-mesenchymal transition, and interactions with platelets, are examined in detail. Furthermore, we explore the realm of precision nanomedicine design, with a specific emphasis on the anoikis‒platelet interface. This innovative approach strategically targets CTC survival mechanisms, offering promising avenues for combatting metastatic cancer with unprecedented precision and efficacy. The review underscores the indispensable role of the rational design of platelet-based nanomedicine in the pursuit of restraining CTC-driven metastasis.

Prognostic value of anoikis-related genes revealed using multi-omics analysis and machine learning based on lower-grade glioma features and tumor immune microenvironment.

Background: The investigation explores the involvement of anoikis-related genes (ARGs) in lower-grade glioma (LGG), seeking to provide fresh insights into the disease's underlying mechanisms and to identify potential targets for therapy. Methods: We applied unsupervised clustering techniques to categorize LGG patients into distinct molecular subtypes based on ARGs with prognostic significance. Additionally, various machine learning algorithms were employed to pinpoint genes most strongly correlated with patient outcomes, which were then used to develop and assess risk profiles. Results: Our analysis identified two distinct molecular subtypes of LGG, each with significantly different prognoses. Patients in Cluster 2 had a median survival of 2.036 years, markedly shorter than the 7.994 years observed in Cluster 1 (P < 0.001). We also constructed a six-gene ARG signature that efficiently classified patients into two risk categories, showing median survival durations of 4.084 years for the high-risk group and 10.304 years for the low-risk group (P < 0.001). Significantly, the immune profiles, tumor mutation characteristics, and drug sensitivity varied greatly among these risk groups. The high-risk group was characterized by a "cold" tumor microenvironment (TME), a lower IDH1 mutation rate (61.7 % vs. 91.4 %), a higher TP53 mutation rate (53.7 % vs. 38.9 %), and greater sensitivity to targeted therapies such as QS11 and PF-562271. Furthermore, our nomogram, integrating risk scores with clinicopathological features, demonstrated strong predictive accuracy for clinical outcomes in LGG patients, with an AUC of 0.903 for the first year. The robustness of this prognostic model was further validated through internal cross-validation and across three external cohorts. Conclusions: The evidence from our research suggests that ARGs could potentially serve as reliable indicators for evaluating immunotherapy effectiveness and forecasting clinical results in patients with LGG.

Protein Structure Inspired Discovery of a Novel Inducer of Anoikis in Human Melanoma.

Drug discovery historically starts with an established function, either that of compounds or proteins. This can hamper discovery of novel therapeutics. As structure determines function, we hypothesized that unique 3D protein structures constitute primary data that can inform novel discovery. Using a computationally intensive physics-based analytical platform operating at supercomputing speeds, we probed a high-resolution protein X-ray crystallographic library developed by us. For each of the eight identified novel 3D structures, we analyzed binding of sixty million compounds. Top-ranking compounds were acquired and screened for efficacy against breast, prostate, colon, or lung cancer, and for toxicity on normal human bone marrow stem cells, both using eight-day colony formation assays. Effective and non-toxic compounds segregated to two pockets. One compound, Dxr2-017, exhibited selective anti-melanoma activity in the NCI-60 cell line screen. In eight-day assays, Dxr2-017 had an IC50 of 12 nM against melanoma cells, while concentrations over 2100-fold higher had minimal stem cell toxicity. Dxr2-017 induced anoikis, a unique form of programmed cell death in need of targeted therapeutics. Our findings demonstrate proof-of-concept that protein structures represent high-value primary data to support the discovery of novel acting therapeutics. This approach is widely applicable.

Anoikis-Related Long Non-Coding RNA Signatures to Predict Prognosis and Immune Infiltration of Gastric Cancer.

Anoikis is a distinct type of programmed cell death and a unique mechanism for tumor progress. However, its exact function in gastric cancer (GC) remains unknown. This study aims to investigate the function of anoikis-related lncRNA (ar-lncRNA) in the prognosis of GC and its immunological infiltration. The ar-lncRNAs were derived from RNA sequencing data and associated clinical information obtained from The Cancer Genome Atlas. Pearson correlation analysis, differential screening, LASSO and Cox regression were utilized to identify the typical ar-lncRNAs with prognostic significance, and the corresponding risk model was constructed, respectively. Comprehensive methods were employed to assess the clinical characteristics of the prediction model, ensuring the accuracy of the prediction results. Further analysis was conducted on the relationship between immune microenvironment and risk features, and sensitivity predictions were made about anticancer medicines. A risk model was built according to seven selected ar-lncRNAs. The model was validated and the calibration plots were highly consistent in validating nomogram predictions. Further analyses revealed the great accuracy of the model and its ability to serve as a stand-alone GC prognostic factor. We subsequently disclosed that high-risk groups display significant enrichment in pathways related to tumors and the immune system. Additionally, in tumor immunoassays, notable variations in immune infiltrates and checkpoints were noted between different risk groups. This study proposes, for the first time, that prognostic signatures of ar-lncRNA can be established in GC. These signatures accurately predict the prognosis of GC and offer potential biomarkers, suggesting new avenues for basic research, prognosis prediction and personalized diagnosis and treatment of GC.

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

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

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

The oncogenic ADAMTS1-VCAN-EGFR cyclic axis drives anoikis resistance and invasion in renal cell carcinoma.

BACKGROUND: Metastasis, the leading cause of renal cell carcinoma (RCC) mortality, involves cancer cells resisting anoikis and invading. Until now, the role of the matrix metalloproteinase (MMP)-related enzyme, A disintegrin and metalloprotease with thrombospondin motifs 1 (ADAMTS1), in RCC anoikis regulation remains unclear. METHODS: The clinical significance of ADAMTS1 and its associated molecules in patients with RCC was investigated using data from the Gene Expression Omnibus (GEO) and TCGA datasets. Human phosphoreceptor tyrosine kinase (RTK) array, luciferase reporter assays, immunoprecipitation (IP) assays, western blotting, and real-time reverse-transcription quantitative polymerase chain reaction (RT-qPCR) were used to elucidate the underlying mechanisms of ADAMTS1. Functional assays, including anoikis resistance assays, invasion assays, and a Zebrafish xenotransplantation model, were conducted to assess the roles of ADAMTS1 in conferring resistance to anoikis in RCC. RESULTS: This study found elevated ADAMTS1 transcripts in RCC tissues that were correlated with a poor prognosis. ADAMTS1 manipulation significantly affected cell anoikis through the mitochondrial pathway in RCC cells. Human receptor tyrosine kinase (RTK) array screening identified that epidermal growth factor receptor (EGFR) activation was responsible for ADAMTS1-induced anoikis resistance and invasion. Further investigations revealed that enzymatically active ADAMTS1-induced versican V1 (VCAN V1) proteolysis led to EGFR transactivation, which in turn, through positive feedback, regulated ADAMTS1. Additionally, ADAMTS1 can form a complex with p53 to influence EGFR signaling. In vivo, VCAN or EGFR knockdown reversed ADAMTS1-induced prometastatic characteristics of RCC. A clinical analysis revealed a positive correlation between ADAMTS1 and VCAN or the EGFR and patients with RCC with high ADAMTS1 and VCAN expression had the worst prognoses. CONCLUSIONS: Our results collectively uncover a novel cyclic axis involving ADAMTS1-VCAN-EGFR, which significantly contributes to RCC invasion and resistance to anoikis, thus presenting a promising therapeutic target for RCC metastasis.

Anoikis in cell fate, physiopathology, and therapeutic interventions.

The extracellular matrix (ECM) governs a wide spectrum of cellular fate processes, with a particular emphasis on anoikis, an integrin-dependent form of cell death. Currently, anoikis is defined as an intrinsic apoptosis. In contrast to traditional apoptosis and necroptosis, integrin correlates ECM signaling with intracellular signaling cascades, describing the full process of anoikis. However, anoikis is frequently overlooked in physiological and pathological processes as well as traditional in vitro research models. In this review, we summarized the role of anoikis in physiological and pathological processes, spanning embryonic development, organ development, tissue repair, inflammatory responses, cardiovascular diseases, tumor metastasis, and so on. Similarly, in the realm of stem cell research focused on the functional evolution of cells, anoikis offers a potential solution to various challenges, including in vitro cell culture models, stem cell therapy, cell transplantation, and engineering applications, which are largely based on the regulation of cell fate by anoikis. More importantly, the regulatory mechanisms of anoikis based on molecular processes and ECM signaling will provide new strategies for therapeutic interventions (drug therapy and cell-based therapy) in disease. In summary, this review provides a systematic elaboration of anoikis, thus shedding light on its future research.

Multicenter cohort analysis of anoikis and EMT: implications for prognosis and therapy in lung adenocarcinoma.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Anoikis-related genes in breast cancer patients: reliable biomarker of prognosis.

BACKGROUND: Breast cancer (BC) is the most common cancer in women, and its progression is closely related to the phenomenon of anoikis. Anoikis, the specific programmed death resulting from a lack of contact between cells and the extracellular matrix, has recently been recognized as playing a critical role in tumor initiation, maintenance, and treatment. The ability of cancer cells to resist anoikis leads to cancer progression and metastatic colonization. However, the impact of anoikis on the prognosis of BC patients remains unclear. METHOD: This study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect transcriptome and clinical data of BC patients. Anoikis-related genes (ARGs) were classified into subtypes A and B through consensus clustering. Subsequently, survival prognosis analysis, immune cell infiltration analysis, and functional enrichment analysis were performed for both subtypes. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a set of 10 ARGs related to prognosis was identified. Immune cell infiltration and tumor microenvironment analyses were conducted on these 10 ARGs to develop a prognostic model. Furthermore, single-cell data analysis and real-time polymerase chain reaction (RT-PCR) analysis were employed to study the expression of the 10 identified prognostic ARGs in BC cells. RESULTS: One hundred thirty-five ARGs were identified as differentially expressed genes in the TCGA and GEO databases, with 42 of them associated with the survival prognosis of BC patients. Analyses involving Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) revealed distinct expression patterns of ARGs between types A and B. Patients in type A exhibited worse survival prognosis and lower immune cell infiltration compared to type B. Subsequent analyses identified 10 key ARGs (YAP1, PIK3R1, BAK1, PHLDA2, EDA2R, LAMB3, CD24, SLC2A1, CDC25C, and SLC39A6) relevant to BC prognosis. Kaplan-Meier analysis indicated that high-risk patients based on these ARGs had a poorer BC prognosis. Additionally, Cox regression analysis established gender, age, T (tumor), N (nodes), and risk score as predictive factors in a nomogram model for BC. The model demonstrated diagnostic value for BC patients at 1, 3, and 5 years. Decision curve analysis (DCA) verified the risk score as a reliable predictor of BC patient survival rates. Moreover, RT-PCR results confirmed differential expressions of YAP1, PIK3R1, BAK1, PHLDA2, CD24, SLC2A1, and CDC25C in BC cells, with SLC39A6, EDA2R, and LAMB3 showing low expression levels. CONCLUSION: ARGs markers can be used as BC biomarkers for risk stratification and survival prediction in BC patients. Besides, ARGs can be used as stratification factors for individualized and precise treatment of BC patients.

Unveiling an anoikis-related risk model and the role of RAD9A in colon cancer.

OBJECTIVE: Colorectal cancer (CRC), specifically colon adenocarcinoma, is the third most prevalent and the second most lethal form of cancer. Anoikis is found to be specialized form of programmed cell death (PCD), which plays a pivotal role in tumor progression. This study aimed to investigate the role of the anoikis related genes (ARGs) in colon cancer. METHODS: Consensus unsupervised clustering, differential expression analysis, tumor mutational burden analysis, and analysis of immune cell infiltration were utilized in the study. For the analysis of RNA sequences and clinical data of COAD patients, data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were obtained. A prognostic scoring system for overall survival (OS) prediction was developed using Cox regression and LASSO regression analysis. Furthermore, loss-of-function assay was utilized to explore the role of RAD9A played in the progression of colon cancer. RESULTS: The prognostic value of a risk score composed of NTRK2, EPHA2, RAD9A, CDC25C, and SNAI1 genes was significant. Furthermore, these findings suggested potential mechanisms that may influence prognosis, supporting the development of individualized treatment plans and management of patient outcomes. Further experiments confirmed that RAD9A could promote proliferation and metastasis of colon cancer cells. These effects may be achieved by affecting the phosphorylation of AKT. CONCLUSION: Differences in survival time and the tumor immune microenvironment (TIME) were observed between two gene clusters associated with ARGs. In addition, a prognostic risk model was established and confirmed as an independent risk factor. Furthermore, our data indicated that RAD9A promoted tumorigenicityby activating AKT in colon cancer.

Prognositic value of anoikis and tumor immune microenvironment-related gene in the treatment of osteosarcoma. / Anoikiså’Œè‚¿ç˜¤å ç–«å¾®çŽ¯å¢ƒç›¸å ³åŸºå› åœ¨éª¨è‚‰ç˜¤æ²»ç–—ä¸­çš„é¢„åŽä»·å€¼ï¼ˆè‹±æ–‡ï¼‰.

OBJECTIVES: Osteosarcoma is a highly aggressive primary malignant bone tumor commonly seen in children and adolescents, with a poor prognosis. Anchorage-dependent cell death (anoikis) has been proven to be indispensable in tumor metastasis, regulating the migration and adhesion of tumor cells at the primary site. However, as a type of programmed cell death, anoikis is rarely studied in osteosarcoma, especially in the tumor immune microenvironment. This study aims to clarify prognostic value of anoikis and tumor immune microenvironment-related gene in the treatment of osteosarcoma. METHODS: Anoikis-related genes (ANRGs) were obtained from GeneCards. Clinical information and ANRGs expression profiles of osteosarcoma patients were sourced from the therapeutically applicable research to generate effective therapies and Gene Expression Omnibus (GEO) databases. ANRGs highly associated with tumor immune microenvironment were identified by the estimate package and the weighted gene coexpression network analysis (WGCNA) algorithm. Machine learning algorithms were performed to construct long-term survival predictive strategy, each sample was divided into high-risk and low-risk subgroups, which was further verified in the GEO cohort. Finally, based on single-cell RNA-seq from the GEO database, analysis was done on the function of signature genes in the osteosarcoma tumor microenvironment. RESULTS: A total of 51 hub ANRGs closely associated with the tumor microenvironment were identified, from which 3 genes (MERTK, BNIP3, S100A8) were selected to construct the prognostic model. Significant differences in immune cell activation and immune-related signaling pathways were observed between the high-risk and low-risk groups based on tumor microenvironment analysis (all P<0.05). Additionally, characteristic genes within the osteosarcoma microenvironment were identified in regulation of intercellular crosstalk through the GAS6-MERTK signaling pathway. CONCLUSIONS: The prognostic model based on ANRGs and tumor microenvironment demonstrate good predictive power and provide more personalized treatment options for patients with osteosarcoma.

Anoikis-related genes linked with patient outcome in pancreatic cancer.

Anoikis is programmed cell death occurring upon cell detachment from the extracellular matrix. Cancer cells need to evade anoikis to be able to metastasize to distant sites. However, the molecular features and prognostic value of anoikis-related genes (ARGs) in pancreatic cancer remain unclear. In this study, we utilized transcriptome data from the TCGA and GSE102238 databases to identify 64 ARGs significantly associated with prognosis. We used the "ConsensusClusterPlus" R package to stratify patients into high and low-risk prognostic subgroups. The KEGG and GSEA analyses revealed that the clusters with poor prognosis were enriched for the ECM receptor interaction pathway, the TP53 signaling pathway, and the galactose metabolism pathway, and that the cell cycle pathway was upregulated. A prognostic model consisting of seven ARGs (SERPINE1, EGF, E2F1, MSLN, RAB27B, ETV7, MST1) was constructed using LASSO regression and when combined with clinicopathological parameters using Cox regression, a prognostic Nomogram was created, which demonstrated high prognostic utility. Among the biomarker candidates, we report ETV7 as a novel, independent prognostic marker in pancreatic cancer. ETV7 was highly expressed in KRAS and TP53 co-occurrent mutant TCGA patients, indicating that it may be regulated by the two major driver genes of pancreatic cancer. Therefore, targeting ETV7 could be a potential focus for future therapeutic studies.