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.

FASN promotes anoikis resistance in colorectal liver metastases through the ERK1/2 pathway.

PURPOSE: Colorectal cancer (CRC) is recognized as the third most common form of malignancy, with the liver frequently serving as the main site for metastasis. Anoikis resistance (AR) is critical in colorectal cancer liver metastases (CRLM). Fatty acid synthase (FASN), essential in lipid synthesis, mediates AR in many cancers. The present research examines the function of FASN in ERK1/2-mediated AR in CRLM and evaluates its therapeutic potential. METHODS: We performed scratch and migration experiment to evaluate the migration capacity of the LoVo cells. Flow cytometry was employed to identify cell apoptosis. The levels of FASN, p-ERK1/2, and proteins related to apoptosis was analyzed by Western blot. The mRNA level of FASN was determined by q-PCR after FASN silencing. In addition, we used an intrasplenic liver metastasis model of nude to assess the effect of FASN on CRLM. RESULTS: In vitro experiments showed that after FASN silencing, the cell apoptosis rate was increased, migration capability was notably decreased, the expression of p-ERK1/2, the proteins related to anti-apoptotic were significantly decreased, and the proteins related to apoptosis were significantly increased. In vivo experiments showed that AR significantly increased the number of liver metastatic foci, whereas FASN silencing significantly inhibited CRLM. CONCLUSION: These results suggest that FASN silencing suppressed AR through the ERK 1/2 pathway, which in turn suppressed CRLM.

Pokemon inhibits Bim transcription to promote the proliferation, anti-anoikis, invasion, histological grade, and dukes stage of colorectal neoplasms.

PURPOSE: This study aims to determine whether Pokemon regulates Bim activity in colorectal carcinoma (CRC) carcinogenesis. METHODS: Clinical tissue samples were analyzed to detect the expression and clinicopathological significance of Pokemon and Bim in CRC. Proliferation, apoptosis, and invasion assays were conducted to identify the regulatory effect of Pokemon on Bim. The combined treatment effects of Pokemon knockdown and diamminedichloroplatinum (DDP) were also examined. RESULTS: Immunohistochemical analysis of 80 samples of colorectal epithelia (CRE), 80 cases of colorectal adenoma (CRA), and 160 of CRC samples revealed protein expression rates of 23.8%, 38.8%, and 70.6% for Pokemon, and 88.8%, 73.8%, and 31.9% for Bim, respectively. A significant negative correlation was observed between Pokemon and Bim expression across the CRE, CRA, and CRC lesion stages. In CRC, higher Pokemon and lower Bim expression correlated with higher histological grades, advanced Dukes stages, and increased cancer invasion. In both LoVo and HCT116 cells, overexpression of Pokemon significantly reduced Bim expression, leading to increased proliferation, resistance to anoikis, and cell invasion. Additionally, Pokemon overexpression significantly decreased DDP-induced Bim expression, reduction of anti-apoptosis and invasion, whereas Pokemon knockdown resulted in the opposite effects. CONCLUSION: These findings suggest that Pokemon inhibits Bim transcription, thereby promoting CRC proliferation, resistance to apoptosis, invasion, and advancing histological grade and Dukes staging. Pokemon knockdown enhances the therapeutic efficacy of DDP in the treatment of CRC.

Exploring the prognostic value of BRMS1 + microglia based on single-cell anoikis regulator patterns in the immunologic microenvironment of GBM.

BACKGROUND: Anoikis is a specialized form of programmed cell death induced by the loss of cell adhesion to the extracellular matrix (ECM). Acquisition of anoikis resistance is a significant marker for cancer cell invasion, metastasis, therapy resistance, and recurrence. Although current research has identified multiple factors that regulate anoikis resistance, the pathological mechanisms of anoikis-mediated tumor microenvironment (TME) in glioblastoma (GBM) remain largely unexplored. METHODS: Utilizing single-cell RNA sequencing (scRNA-seq) data and employing non-negative matrix factorization (NMF), we identified and characterized TME cell clusters with distinct anoikis-associated gene signatures. Prognostic and therapeutic response analyses were conducted using TCGA and CGGA datasets to assess the clinical significance of different TME cell clusters. The spatial relationship between BRMS1 + microglia and tumor cells was inferred from spatial transcriptome RNA sequencing (stRNA-seq) data. To simulate the tumor immune microenvironment, co-culture experiments were performed with microglia (HMC3) and GBM cells (U118/U251), and microglia were transfected with a BRMS1 overexpression lentivirus. Western blot or ELISA were used to detect BRMS1, M2 macrophage-specific markers, PI3K/AKT signaling proteins, and apoptosis-related proteins. The proliferation and apoptosis capabilities of tumor cells were evaluated using CCK-8, colony formation, and apoptosis assays, while the invasive and migratory abilities of tumor cells were assessed using Transwell assays. RESULTS: NMF-based analysis successfully identified CD8 + T cell and microglia cell clusters with distinct gene signature characteristics. Trajectory analysis, cell communication, and gene regulatory network analyses collectively indicated that anoikis-mediated TME cell clusters can influence tumor cell development through various mechanisms. Notably, BRMS1 + AP-Mic exhibited an M2 macrophage phenotype and had significant cell communication with malignant cells. Moreover, high expression of BRMS1 + AP-Mic in TCGA and CGGA datasets was associated with poorer survival outcomes, indicating its detrimental impact on immunotherapy. Upregulation of BRMS1 in microglia may lead to M2 macrophage polarization, activate the PI3K/AKT signaling pathway through SPP1/CD44-mediated cell interactions, inhibit tumor cell apoptosis, and promote tumor proliferation and invasion. CONCLUSION: This pioneering study used NMF-based analysis to reveal the important predictive value of anoikis-regulated TME in GBM for prognosis and immunotherapeutic response. BRMS1 + microglial cells provide a new perspective for a deeper understanding of the immunosuppressive microenvironment of GBM and could serve as a potential therapeutic target in the future.

The resistance to anoikis, mediated by Spp1, and the evasion of immune surveillance facilitate the invasion and metastasis of hepatocellular carcinoma.

Anoikis-Related Genes (ARGs) lead to the organism manifesting resistance to anoikis and are associated with unfavorable prognostic outcomes across various malignancies.Therefore, it is crucial to identify the pivotal target genes related to anoikis in HCC .We found that ARGs were significantly correlated with prognosis and immune responses in HCC. The core gene, SPP1, notably promoted anoikis resistance and metastasis in HCC through both in vivo and in vitro studies. The PI3K-Akt-mTOR pathway played a critical role in anoikis suppression within HCC contexts. Our research unveiled SPP1's role in enhancing PKCα phosphorylation, which in turn activated the PI3K-Akt-mTOR cascade. Additionally, SPP1 was identified as a key regulator of MDSCs and Tregs migration, directly affecting their immunosuppressive capabilities.These findings indicate that in HCC, SPP1 promoted anoikis resistance and facilitated immune evasion by modulating MDSCs and Tregs.

An anoikis-related gene signature predicts prognosis, drug sensitivity, and immune microenvironment in cholangiocarcinoma.

Background: Cholangiocarcinoma is a malignant invasive biliary tract carcinoma with a poor prognosis. Anoikis-related genes are prognostic features of a variety of cancers. However, the value of prognostication and therapeutic effect of anoikis-related genes in cholangiocarcinoma have not been reported. The aim of this research was developing an ARGs signature associated with cholangiocarcinoma patients. Methods: We introduced transcriptome data to discover genes that were differentially expressed in cholangiocarcinoma. Subsequently, WGCNA was utilized to screen critical module genes in reference to anoikis. The univariate Cox, Lasso regression and Kaplan-Meier survival were executed to build a prognostic signature. We further performed gene functional enrichment, immune microenvironment and immunotherapy analysis between two risk subgroups. Finally, the pRRophetic algorithm was applied to compare the half inhibitory concentration value of several drugs. Results: A grand total of 1844 genes with differential expression related to the cholangiocarcinoma patients were identified. Furthermore, we obtained 2678 key module genes related to anoikis. Then, a prognostic signature was developed using the 6 prognostic genes (FXYD2, PCBD1, C1RL, GMNN, LAMA4 and HACL1). Independent prognostic analysis showed that risk score and alcohol could function as separate prognostic variables. We found cetain distinction in the immune microenvironment between the two risk subgroups. Moreover, immunotherapy evaluation showed that the anoikis-related gene signature could be applied as a therapy predictor. Finally, Chemotherapeutic drug sensitivity results showed that the low-risk group responded better to bosutinib, gefitinib, gemcitabine, and paclitaxel, while the high-risk group responded better to axitinib, cisplatin, and imatinib. Conclusion: The prognostic signature comprised of FXYD2, PCBD1, C1RL, GMNN, LAMA4 and HACL1 based on anoikis-related genes was established, which provided theoretical basis and reference value for the research and treatment of cholangiocarcinoma.

Integrative nomogram model based on anoikis-related genes enhances prognostic evaluation in colorectal cancer.

Background: Revealing the role of anoikis resistance plays in CRC is significant for CRC diagnosis and treatment. This study integrated the CRC anoikis-related key genes (CRC-AKGs) and established a novel model for improving the efficiency and accuracy of the prognostic evaluation of CRC. Methods: CRC-ARGs were screened out by performing differential expression and univariate Cox analysis. CRC-AKGs were obtained through the LASSO machine learning algorithm and the LASSO Risk-Score was constructed to build a nomogram clinical prediction model combined with the clinical predictors. In parallel, this work developed a web-based dynamic nomogram to facilitate the generalization and practical application of our model. Results: We identified 10 CRC-AKGs and a risk-related prognostic Risk-Score was calculated. Multivariate COX regression analysis indicated that the Risk-Score, TNM stage, and age were independent risk factors that significantly associated with the CRC prognosis(p < 0.05). A prognostic model was built to predict the outcome with satisfied accuracy (3-year AUC = 0.815) for CRC individuals. The web interactive nomogram (https://yuexiaozhang.shinyapps.io/anoikisCRC/) showed strong generalizability of our model. In parallel, a substantial correlation between tumor microenvironment and Risk-Score was discovered in the present work. Conclusion: This study reveals the potential role of anoikis in CRC and sets new insights into clinical decision-making in colorectal cancer based on both clinical and sequencing data. Also, the interactive tool provides researchers with a user-friendly interface to input relevant clinical variables and obtain personalized risk predictions or prognostic assessments based on our established model.

RAD51B-AS1 promotes the malignant biological behavior of ovarian cancer through upregulation of RAD51B. / RAD51B-AS1通过上调RAD51B促进卵巢癌的恶性生物学行为.

Long non-coding RNAs (lncRNAs) play an indispensable role in the occurrence and development of ovarian cancer (OC). However, the potential involvement of lncRNAs in the progression of OC is largely unknown. To investigate the detailed roles and mechanisms ofRAD51 homolog B-antisense 1 (RAD51B-AS1), a novel lncRNA in OC, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to verify the expression of RAD51B-AS1. Cellular proliferation, metastasis, and apoptosis were detected using the cell counting kit-8 (CCK-8), colony-formation, transwell, and flow cytometry assays. Mouse xenograft models were established for the detection of tumorigenesis. The results revealed that RAD51B-AS1 was significantly upregulated in a highly metastatic human OC cell line and OC tissues. RAD51B-AS1 significantly increased the proliferation and metastasis of OC cells and enhanced their resistance to anoikis. Biogenetics prediction analysis revealed that the only target gene of RAD51B-AS1 was RAD51B. Subsequent gene function experiments revealed that RAD51B exerts the same biological effects as RAD51B-AS1. Rescue experiments demonstrated that the malignant biological behaviors promoted by RAD51B-AS1 overexpression were partially or completely reversed by RAD51B silencing in vitro and in vivo. Thus, RAD51B-AS1 promotes the malignant biological behaviors of OC and activates the protein kinase B (Akt)/B cell lymphoma protein-2 (Bcl-2) signaling pathway, and these effects may be associated with the positive regulation of RAD51B expression. RAD51B-AS1 is expected to serve as a novel molecular biomarker for the diagnosis and prediction of poor prognosis in OC, and as a potential therapeutic target for disease management.

Uncovering the Role of Anoikis-Related Genes in Modulating Immune Infiltration and Pathogenesis of Diabetic Kidney Disease.

Background: Diabetic kidney disease (DKD) is an intricate complication of diabetes with limited treatment options. Anoikis, a programmed cell death activated by loss of cell anchorage to the extracellular matrix, participated in various physiological and pathological processes. Our study aimed to elucidate the role of anoikis-related genes in DKD pathogenesis. Methods: Differentially expressed genes (DEGs) associated with anoikis in DKD were identified. Weighted gene co-expression network analysis (WGCNA) was conducted to identify DKD-correlated modules and assess their functional implications. A diagnostic model for DKD was developed using LASSO regression and Gene set variation analysis (GSVA) was performed for enrichment analysis. Experimental validation was employed to validate the significance of selected genes in the progression of DKD. Results: We identified 10 anoikis-related DEGs involved in key signaling pathways impacting DKD progression. WGCNA highlighted the yellow module's significant enrichment in immune response and regulatory pathways. Correlation analysis further revealed the association between immune infiltration and anoikis-related DEGs. Our LASSO regression-based diagnostic model demonstrated a well-predictive efficacy with seven identified genes. GSVA indicated that gene function in the high-risk group was primarily associated with immune regulation. Further experimental validation using diabetic mouse models and data analysis in the single-cell dataset confirmed the significance of PYCARD and SFN in DKD progression. High glucose stimulation in RAW264.7 and TCMK-1 cells showed significantly increased expression levels of both Pycard and Sfn. Co-expression analysis demonstrated distinct functions of PYCARD and SFN, with KEGG pathway analysis showing significant enrichment in immune regulation and cell proliferation pathway. Conclusion: In conclusion, our study provides valuable insights into the molecular mechanisms involved in DKD pathogenesis, specifically highlighting the role of anoikis-related genes in modulating immune infiltration. These findings suggest that targeting these genes may hold promise for future diagnostic and therapeutic approaches in DKD management.

Integrative analysis of Anoikis-related genes reveals that FASN is a novel prognostic biomarker and promotes the malignancy of bladder cancer via Wnt/ß-catenin pathway.

Bladder cancer (BC) exhibits diversity in clinical outcomes and is characterized by heterogeneity. Anoikis, a form of programmed cell death, plays a crucial role in facilitating tumor invasion and metastasis. This study comprehensively investigated the genetic landscape of BC progression, identifying 300 differentially expressed Anoikis-related genes (DE-ARGs) through in-depth analysis of the GSE13507 datasets. Functional enrichment analysis revealed associations with diverse diseases and biological processes. Employing machine learning algorithms, a logistic regression model based on nine marker genes demonstrated superior accuracy in distinguishing BC from normal samples. Validation in TCGA datasets highlighted the prognostic significance of LRP1, FASN, and SIRT6, suggesting their potential as cancer biomarkers. Particularly, FASN emerged as an independent prognostic indicator, regulating BC cell proliferation and metastasis through the Wnt/ß-catenin pathway. The study provides crucial insights into altered genetic landscapes and potential therapeutic strategies for BC, emphasizing the significance of FASN in BC prognosis and progression.

Knockdown of Anoikis-Associated Gene OCIAD2 Reduces Proliferation and Migration of Glioblastoma Cell Lines.

BACKGROUND: Glioblastoma (GBM) severely disrupts the quality of life of patients. Anoikis represents a significant mechanism in cancer invasion and metastasis. Our study focused on the prognostic relationship between the anoikis-associated gene and GBM and its effect on GBM cell progression. METHODS: We downloaded 656 and 979 GBM sample data from TCGA and CGGA cohort datasets, respectively. Fifteen anoikis-associated genes were obtained from the GeneCards database and were subsequently clustered to identify differential genes associated with them. After LAASO screening, the expression values of the 5 differential genes were the sum of LASSO regression coefficients. Survival analysis and ROC curve analysis of anoikis scores were performed using the TCGA training and CGGA validation sets. The prognostic factors were analyzed using Cox regression analysis in GBM. Moreover, CCK-8, colony formation, wound healing, and transwell assay were used to evaluate GBM cell proliferation and migration. RESULTS: Significant differences were observed in the 5-year survival of GBM patients between the two subgroups. Then, our analysis demonstrated that high OCIAD2, FTLP3, IGFBP2, and H19 levels were associated with lower 5-year GBM survival rates, whereas high SFRP2 levels were associated with higher survival rates. Univariate Cox analysis indicated that GBM risk was linked to both anoikis score and grade, while multivariate Cox analysis indicated that GBM risk was associated with both anoikis score and age. Additionally, OCIAD2 was highly expressed in U251MG and T98G cells. Moreover, OCIAD2 silencing inhibited GBM cell proliferation and migration. CONCLUSION: This study demonstrated the potential of the anoikis-associated gene OCIAD2 as a prognostic biomarker for GBM. Furthermore, we validated in vitro that OCIAD2 promoted GBM cell progression.

Anoikis resistance regulates immune infiltration and drug sensitivity in clear-cell renal cell carcinoma: insights from multi omics, single cell analysis and in vitro experiment.

Background: Anoikis is a form of programmed cell death essential for preventing cancer metastasis. In some solid cancer, anoikis resistance can facilitate tumor progression. However, this phenomenon is underexplored in clear-cell renal cell carcinoma (ccRCC). Methods: Using SVM machine learning, we identified core anoikis-related genes (ARGs) from ccRCC patient transcriptomic data. A LASSO Cox regression model stratified patients into risk groups, informing a prognostic model. GSVA and ssGSEA assessed immune infiltration, and single-cell analysis examined ARG expression across immune cells. Quantitative PCR and immunohistochemistry validated ARG expression differences between immune therapy responders and non-responders in ccRCC. Results: ARGs such as CCND1, CDKN3, PLK1, and BID were key in predicting ccRCC outcomes, linking higher risk with increased Treg infiltration and reduced M1 macrophage presence, indicating an immunosuppressive environment facilitated by anoikis resistance. Single-cell insights showed ARG enrichment in Tregs and dendritic cells, affecting immune checkpoints. Immunohistochemical analysis reveals that ARGs protein expression is markedly elevated in ccRCC tissues responsive to immunotherapy. Conclusion: This study establishes a novel anoikis resistance gene signature that predicts survival and immunotherapy response in ccRCC, suggesting that manipulating the immune environment through these ARGs could improve therapeutic strategies and prognostication in ccRCC.

A prognostic model for anoikis-related genes in pancreatic cancer.

Anoikis, a distinct form of programmed cell death, is crucial for both organismal development and maintaining tissue equilibrium. Its role extends to the proliferation and progression of cancer cells. This study aimed to establish an anoikis-related prognostic model to predict the prognosis of pancreatic cancer (PC) patients. Gene expression data and patient clinical profiles were sourced from The Cancer Genome Atlas (TCGA-PAAD: Pancreatic Adenocarcinoma) and the International Cancer Genome Consortium (ICGC-PACA: Pancreatic Ductal Adenocarcinoma). Non-cancerous pancreatic tissue gene expression data were obtained from the Genotype-Tissue Expression (GTEx) project. The R package was used to construct anoikis-related PC prognostic models, which were later validated with the ICGC-PACA database. Survival analyses demonstrated a poorer prognosis for patients in the high-risk group, consistent across both TCGA-PAAD and ICGC-PACA datasets. A nomogram was designed as a predictive tool to estimate patient mortality. The study also analyzed tumor mutations and immune infiltration across various risk groups, uncovering notable differences in tumor mutation patterns and immune landscapes between high- and low-risk groups. In conclusion, this research successfully developed a prognostic model centered on anoikis-related genes, offering a novel tool for predicting the clinical trajectory of PC patients.

Elucidating prognosis in cervical squamous cell carcinoma and endocervical adenocarcinoma: a novel anoikis-related gene signature model.

Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are among the most prevalent gynecologic malignancies globally. The prognosis is abysmal once cervical cancer progresses to lymphatic metastasis. Anoikis, a specialized form of apoptosis induced by loss of cell adhesion to the extracellular matrix, plays a critical role. The prediction model based on anoikis-related genes (ARGs) expression and clinical data could greatly aid clinical decision-making. However, the relationship between ARGs and CESC remains unclear. Methods: ARGs curated from the GeneCards and Harmonizome portals were instrumental in delineating CESC subtypes and in developing a prognostic framework for patients afflicted with this condition. We further delved into the intricacies of the immune microenvironment and pathway enrichment across the identified subtypes. Finally, our efforts culminated in the creation of an innovative nomogram that integrates ARGs. The utility of this prognostic tool was underscored by Decision Curve Analysis (DCA), which illuminate its prospective benefits in guiding clinical interventions. Results: In our study, We discerned a set of 17 survival-pertinent, anoikis-related differentially expressed genes (DEGs) in CESC, from which nine were meticulously selected for the construction of prognostic models. The derived prognostic risk score was subsequently validated as an autonomous prognostic determinant. Through comprehensive functional analyses, we observed distinct immune profiles and drug response patterns among divergent prognostic stratifications. Further, we integrated the risk scores with the clinicopathological characteristics of CESC to develop a robust nomogram. DCA corroborated the utility of our model, demonstrating its potential to enhance patient outcomes through tailored clinical treatment strategies. Conclusion: The predictive signature, encompassing nine pivotal genes, alongside the meticulously constructed nomogram developed in this research, furnishes clinicians with a sophisticated tool for tailoring treatment strategies to individual patients diagnosed with CESC.

Identification of anoikis-related gene signatures and construction of the prognosis model in prostate cancer.

Background: One of the primary reasons for tumor invasion and metastasis is anoikis resistance. Biochemical recurrence (BCR) of prostate cancer (PCa) serves as a harbinger of its distant metastasis. However, the role of anoikis in PCa biochemical recurrence has not been fully elucidated. Methods: Differential expression analysis was used to identify anoikis-related genes based on the TCGA and GeneCards databases. Prognostic models were constructed utilizing LASSO regression, univariate and multivariate Cox regression analyses. Moreover, Gene Expression Omnibus datasets (GSE70770 and GSE46602) were applied as validation cohorts. Gene Ontology, KEGG and GSVA were utilized to explore biological pathways and molecular mechanisms. Further, immune profiles were assessed using CIBERSORT, ssGSEA, and TIDE, while anti-cancer drugs sensitivity was analyzed by GDSC database. In addition, gene expressions in the model were examined using online databases (Human Protein Atlas and Tumor Immune Single-Cell Hub). Results: 113 differentially expressed anoikis-related genes were found. Four genes (EEF1A2, RET, FOSL1, PCA3) were selected for constructing a prognostic model. Using the findings from the Cox regression analysis, we grouped patients into groups of high and low risk. The high-risk group exhibited a poorer prognosis, with a maximum AUC of 0.897. Moreover, larger percentage of immune infiltration of memory B cells, CD8 Tcells, neutrophils, and M1 macrophages were observed in the high-risk group than those in the low-risk group, whereas the percentage of activated mast cells and dendritic cells in the high-risk group were lower. An increased TIDE score was founded in the high-risk group, suggesting reduced effectiveness of ICI therapy. Additionally, the IC50 results for chemotherapy drugs indicated that the low-risk group was more sensitive to most of the drugs. Finally, the genes EEF1A2, RET, and FOSL1 were expressed in PCa cases based on HPA website. The TISCH database suggested that these four ARGs might contribute to the tumor microenvironment of PCa. Conclusion: We created a risk model utilizing four ARGs that effectively predicts the risk of BCR in PCa patients. This study lays the groundwork for risk stratification and predicting survival outcomes in PCa patients with BCR.

SIRT6 Inhibits Anoikis of Colorectal Cancer Cells by Down-Regulating NDRG1.

Anoikis, a form of apoptosis resulting from the loss of cell-extracellular matrix interaction, is a significant barrier to cancer cell metastasis. However, the epigenetic regulation of this process remains to be explored. Here, we demonstrate that the histone deacetylase sirtuin 6 (SIRT6) plays a pivotal role in conferring anoikis resistance to colorectal cancer (CRC) cells. The protein level of SIRT6 is negatively correlated with anoikis in CRC cells. The overexpression of SIRT6 decreases while the knockdown of SIRT6 increases detachment-induced anoikis. Mechanistically, SIRT6 inhibits the transcription of N-myc downstream-regulated gene 1 (NDRG1), a negative regulator of the AKT signaling pathway. We observed the up-regulation of SIRT6 in advanced-stage CRC samples. Together, our findings unveil a novel epigenetic program regulating the anoikis of CRC cells.

Construction and validation of a prognostic signature based on anoikis-related lncRNAs in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and is characterized by a high death rate and a poor prospect for survival. Anoikis, which is a kind of programmed cell apoptosis, is an important factor in the advancement of tumors. Nonetheless, the function of anoikis-related lncRNAs (ARLRs) in LUAD is still not well understood. The TCGA database was queried for genomic and clinical information. A prognostic signature for ARLRs was established via the use of coexpression analysis and Cox regression. Validation of the model's accuracy was conducted utilizing K-M curves and receiver operating characteristic (ROC) curves, and the signature was utilized to develop a nomogram. LncRNAs were implicated in the progression of tumors, as determined by functional enrichment analysis. There was an improvement in prognosis, increased immune cell infiltration, and higher immune scores among the low-risk patients. Additionally, we found that the two groups had varied anticancer drug sensitivities, which could help guide treatment. The impact of one ARLR, AC026355.2, on migration and invasion was validated by in vitro experiments in LUAD cells. Herein, a new lncRNA signature associated with anoikis was identified and estimated, potentially serving as a prognostic indicator for LUAD patients.

System analysis based on Anoikis-related genes identifies MAPK1 as a novel therapy target for osteosarcoma with neoadjuvant chemotherapy.

BACKGROUND: Osteosarcoma (OS) is the most common bone malignant tumor in children, and its prognosis is often poor. Anoikis is a unique mode of cell death.However, the effects of Anoikis in OS remain unexplored. METHOD: Differential analysis of Anoikis-related genes was performed based on the metastatic and non-metastatic groups. Then LASSO logistic regression and SVM-RFE algorithms were applied to screen out the characteristic genes. Later, Univariate and multivariate Cox regression was conducted to identify prognostic genes and further develop the Anoikis-based risk score. In addition, correlation analysis was performed to analyze the relationship between tumor microenvironment, drug sensitivity, and prognostic models. RESULTS: We established novel Anoikis-related subgroups and developed a prognostic model based on three Anoikis-related genes (MAPK1, MYC, and EDIL3). The survival and ROC analysis results showed that the prognostic model was reliable. Besides, the results of single-cell sequencing analysis suggested that the three prognostic genes were closely related to immune cell infiltration. Subsequently, aberrant expression of two prognostic genes was identified in osteosarcoma cells. Nilotinib can promote the apoptosis of osteosarcoma cells and down-regulate the expression of MAPK1. CONCLUSIONS: We developed a novel Anoikis-related risk score model, which can assist clinicians in evaluating the prognosis of osteosarcoma patients in clinical practice. Analysis of the tumor immune microenvironment and chemotherapeutic drug sensitivity can provide necessary insights into subsequent mechanisms. MAPK1 may be a valuable therapeutic target for neoadjuvant chemotherapy in osteosarcoma.

ZEB family is a prognostic biomarker and correlates with anoikis and immune infiltration in kidney renal clear cell carcinoma.

BACKGROUND: Zinc finger E-box binding homEeobox 1 (ZEB1) and ZEB2 are two anoikis-related transcription factors. The mRNA expressions of these two genes are significantly increased in kidney renal clear cell carcinoma (KIRC), which are associated with poor survival. Meanwhile, the mechanisms and clinical significance of ZEB1 and ZEB2 upregulation in KIRC remain unknown. METHODS: Through the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, expression profiles, prognostic value and receiver operating characteristic curves (ROCs) of ZEB1 and ZEB2 were evaluated. The correlations of ZEB1 and ZEB2 with anoikis were further assessed in TCGA-KIRC database. Next, miRTarBase, miRDB, and TargetScan were used to predict microRNAs targeting ZEB1 and ZEB2, and TCGA-KIRC database was utilized to discern differences in microRNAs and establish the association between microRNAs and ZEBs. TCGA, TIMER, TISIDB, and TISCH were used to analyze tumor immune infiltration. RESULTS: It was found that ZEB1 and ZEB2 expression were related with histologic grade in KIRC patient. Kaplan-Meier survival analyses showed that KIRC patients with low ZEB1 or ZEB2 levels had a significantly lower survival rate. Meanwhile, ZEB1 and ZEB2 are closely related to anoikis and are regulated by microRNAs. We constructed a risk model using univariate Cox and LASSO regression analyses to identify two microRNAs (hsa-miR-130b-3p and hsa-miR-138-5p). Furthermore, ZEB1 and ZEB2 regulate immune cell invasion in KIRC tumor microenvironments. CONCLUSIONS: Anoikis, cytotoxic immune cell infiltration, and patient survival outcomes were correlated with ZEB1 and ZEB2 mRNA upregulation in KIRC. ZEB1 and ZEB2 are regulated by microRNAs.

Protein Structure Inspired Drug Discovery.

Drug discovery starts with known function, either of a compound or a protein, in-turn prompting investigations to probe 3D structure of the compound-protein interface. As protein structure determines function, we hypothesized that unique 3D structural motifs represent primary information denoting unique function that can drive discovery of novel agents. Using a physics-based protein structure analysis platform developed by us, designed to conduct computationally intensive analysis at supercomputing speeds, we probed a high-resolution protein x-ray crystallographic library developed by us. We selected 3D structural motifs whose function was not otherwise established, that offered environments supporting binding of drug-like chemicals and were present on proteins that were not established therapeutic targets. For each of eight potential binding pockets on six different proteins we accessed a 60 million compound library and used our analysis platform to evaluate binding. Using eight-day colony formation assays acquired compounds were screened for efficacy against human breast, prostate, colon and lung cancer cells and toxicity against human bone marrow stem cells. Compounds selectively inhibiting cancer growth segregated to two pockets on separate proteins. The compound, Dxr2-017, exhibited selective activity against human melanoma cells in the NCI-60 cell line screen, had an IC50 of 19 nM against human melanoma M14 cells in our eight-day assay, while over 2100-fold higher concentrations inhibited stem cells by less than 30%. We show that Dxr2-017 induces anoikis, a unique form of programmed cell death in need of targeted therapeutics. The predicted target protein for Dxr2-017 is expressed in bacteria, not in humans. This supports our strategy of focusing on unique 3D structural motifs. It is known that functionally important 3D structures are evolutionarily conserved. Here we demonstrate proof-of-concept that protein structure represents high value primary data to support discovery of novel therapeutics. This approach is widely applicable.