ANXA5: A Key Regulator of Immune Cell Infiltration in Hepatocellular Carcinoma.

BACKGROUND Hepatocellular carcinoma (HCC) poses a significant threat to human life and is the most prevalent form of liver cancer. The intricate interplay between apoptosis, a common form of programmed cell death, and its role in immune regulation stands as a crucial mechanism influencing tumor metastasis. MATERIAL AND METHODS Utilizing HCC samples from the TCGA database and 61 anoikis-related genes (ARGs) sourced from GeneCards, we analyzed the relationship between ARGs and immune cell infiltration in HCC. Subsequently, we identified long non-coding RNAs (lncRNAs) associated with ARGs, using the least absolute shrinkage and selection operator (LASSO) regression analysis to construct a robust prognostic model. The predictive capabilities of the model were then validated through examination in a single-cell dataset. RESULTS Our constructed prognostic model, derived from lncRNAs linked to ARGs, comprised 11 significant lncRNAs: NRAV, MCM3AP-AS1, OTUD6B-AS1, AC026356.1, AC009133.1, DDX11-AS1, AC108463.2, MIR4435-2HG, WARS2-AS1, LINC01094, and HCG18. The risk score assigned to HCC samples demonstrated associations with immune indicators and the infiltration of immune cells. Further, we identified Annexin A5 (ANXA5) as the pivotal gene among ARGs, with it exerting a prominent role in regulating the lncRNA gene signature. Our validation in a single-cell database elucidated the involvement of ANXA5 in immune cell infiltration, specifically in the regulation of mononuclear cells. CONCLUSIONS This study delves into the intricate correlation between ARGs and immune cell infiltration in HCC, culminating in the development of a novel prognostic model reliant on 11 ARGs-associated lncRNAs. Furthermore, our findings highlight ANXA5 as a promising target for immune regulation in HCC, offering new perspectives for immune therapy in the context of HCC.

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.

Development of an anoikis-related gene signature and prognostic model for predicting the tumor microenvironment and response to immunotherapy in colorectal cancer.

The effect of anoikis-related genes (ARGs) on clinicopathological characteristics and tumor microenvironment remains unclear. We comprehensively analyzed anoikis-associated gene signatures of 1057 colorectal cancer (CRC) samples based on 18 ARGs. Anoikis-related molecular subtypes and gene features were identified through consensus clustering analysis. The biological functions and immune cell infiltration were assessed using the GSVA and ssGSEA algorithms. Prognostic risk score was constructed using multivariate Cox regression analysis. The immunological features of high-risk and low-risk groups were compared. Finally, DAPK2-overexpressing plasmid was transfected to measure its effect on tumor proliferation and metastasis in vitro and in vivo. We identified 18 prognostic ARGs. Three different subtypes of anoikis were identified and demonstrated to be linked to distinct biological processes and prognosis. Then, a risk score model was constructed and identified as an independent prognostic factor. Compared to the high-risk group, patients in the low-risk group exhibited longer survival, higher enrichment of checkpoint function, increased expression of CTLA4 and PD-L1, higher IPS scores, and a higher proportion of MSI-H. The results of RT-PCR indicated that the expression of DAPK2 mRNA was significantly downregulated in CRC tissues compared to normal tissues. Increased DAPK2 expression significantly suppressed cell proliferation, promoted apoptosis, and inhibited migration and invasion. The nude mice xenograft tumor model confirmed that high expression of DAPK2 inhibited tumor growth. Collectively, we discovered an innovative anoikis-related gene signature associated with prognosis and TME. Besides, our study indicated that DAPK2 can serve as a promising therapeutic target for inhibiting the growth and metastasis of CRC.

Identification of the molecular subtypes and signatures to predict the prognosis, biological functions, and therapeutic response based on the anoikis-related genes in colorectal cancer.

BACKGROUND: Tumors that resist anoikis, a programmed cell death triggered by detachment from the extracellular matrix, promote metastasis; however, the role of anoikis-related genes (ARGs) in colorectal cancer (CRC) stratification, prognosis, and biological functions remains unclear. METHODS: We obtained transcriptomic profiles of CRC and 27 ARGs from The Cancer Genome Atlas, the Gene Expression Omnibus, and MSigDB databases, respectively. CRC tissue samples were classified into two clusters based on the expression pattern of ARGs, and their functional differences were explored. Hub genes were screened using weighted gene co-expression network analysis, univariate analysis, and least absolute selection and shrinkage operator analysis, and validated in cell lines, tissues, or the Human Protein Atlas database. We constructed an ARG-risk model and nomogram to predict prognosis in patients with CRC, which was validated using an external cohort. Multifaceted landscapes, including stemness, tumor microenvironment (TME), immune landscape, and drug sensitivity, between high- and low-risk groups were examined. RESULTS: Patients with CRC were divided into C1 and C2 clusters. Cluster C1 exhibited higher TME scores, whereas cluster C2 had favorable outcomes and a higher stemness index. Eight upregulated hub ARGs (TIMP1, P3H1, SPP1, HAMP, IFI30, ADAM8, ITGAX, and APOC1) were utilized to construct the risk model. The qRT-PCR, Western blotting, and immunohistochemistry results were consistent with those of the bioinformatics analysis. Patients with high risk exhibited worse overall survival (p < 0.01), increased stemness, TME, immune checkpoint expression, immune infiltration, tumor mutation burden, and drug susceptibility compared with the patients with low risk. CONCLUSION: Our results offer a novel CRC stratification based on ARGs and a risk-scoring system that could predict the prognosis, stemness, TME, immunophenotypes, and drug susceptibility of patients with CRC, thereby improving their prognosis. This stratification may facilitate personalized therapies.

Anoikis-related gene signatures in colorectal cancer: implications for cell differentiation, immune infiltration, and prognostic prediction.

Colorectal cancer (CRC) is a malignant tumor originating from epithelial cells of the colon or rectum, and its invasion and metastasis could be regulated by anoikis. However, the key genes and pathways regulating anoikis in CRC are still unclear and require further research. The single cell transcriptome dataset GSE221575 of GEO database was downloaded and applied to cell subpopulation type identification, intercellular communication, pseudo time cell trajectory analysis, and receptor ligand expression analysis of CRC. Meanwhile, the RNA transcriptome dataset of TCGA, the GSE39582, GSE17536, and GSE17537 datasets of GEO were downloaded and merged into one bulk transcriptome dataset. The differentially expressed genes (DEGs) related to anoikis were extracted from these data sets, and key marker genes were obtained after feature selection. A clinical prognosis prediction model was constructed based on the marker genes and the predictive effect was analyzed. Subsequently, gene pathway analysis, immune infiltration analysis, immunosuppressive point analysis, drug sensitivity analysis, and immunotherapy efficacy based on the key marker genes were conducted for the model. In this study, we used single cell datasets to determine the anoikis activity of cells and analyzed the DEGs of cells based on the score to identify the genes involved in anoikis and extracted DEGs related to the disease from the transcriptome dataset. After dimensionality reduction selection, 7 marker genes were obtained, including TIMP1, VEGFA, MYC, MSLN, EPHA2, ABHD2, and CD24. The prognostic risk model scoring system built by these 7 genes, along with patient clinical data (age, tumor stage, grade), were incorporated to create a nomogram, which predicted the 1-, 3-, and 5-years survival of CRC with accuracy of 0.818, 0.821, and 0.824. By using the scoring system, the CRC samples were divided into high/low anoikis-related prognosis risk groups, there are significant differences in immune infiltration, distribution of immune checkpoints, sensitivity to chemotherapy drugs, and efficacy of immunotherapy between these two risk groups. Anoikis genes participate in the differentiation of colorectal cancer tumor cells, promote tumor development, and could predict the prognosis of colorectal cancer.

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.

Functional Role of DUSP6 in the EGFR-mediated Progression of Head and Neck Squamous Cell Carcinoma.

BACKGROUND/AIM: Epidermal growth factor receptor (EGFR) over-expression is commonly observed in advanced head and neck squamous cell carcinoma (HNSCC) and is correlated with poor patient outcomes. However, the role of dual-specificity phosphatase 6 (DUSP6) in EGFR-associated HNSCC progression remains poorly understood. This study aimed to investigate the correlation between DUSP6 expression and EGFR signaling in malignant HNSCC tissues. MATERIALS AND METHODS: Data mining and in vitro assays were employed to assess DUSP6 expression levels in HNSCC tissues compared to normal tissues. Additionally, the correlation between DUSP6 and EGFR expression was examined. Functional assays were conducted to investigate the modulation of DUSP6 expression by EGFR signaling and its involvement in EGF-induced cell migration and anoikis resistance. RESULTS: Our analysis revealed a significant elevation in DUSP6 expression in HNSCC tissues compared to normal tissues and a strong correlation between DUSP6 and EGFR expression. EGFR signaling modulated DUSP6 expression in a dose- and time-dependent manner, primarily through the extracellular signal-regulated kinase (ERK) pathway. Knockdown experiments demonstrated the functional role of DUSP6 in EGF-induced cell migration and anoikis resistance. CONCLUSION: The findings of this study elucidate the intricate signaling networks governing DUSP6 expression and its interplay with EGFR signaling in HNSCC. Moreover, the results provide insights into the potential role of DUSP6 as a therapeutic target and highlight the importance of personalized treatment strategies in HNSCC management.

Empagliflozin drives ferroptosis in anoikis-resistant cells by activating miR-128-3p dependent pathway and inhibiting CD98hc in breast cancer.

A tumour suppressor miRNA, miR-128-3p, is widely involved in various biological processes and has been found to get downregulated in breast cancer patients. We previously published that ectopically expressed miR-128-3p suppressed migration, invasion, cell cycle arrest, and breast cancer stem cells. In the present study, we explored the role of Empagliflozin (EMPA) as a miR-128-3p functionality-mimicking drug in inducing ferroptosis by inhibiting CD98hc. Given that CD98hc is one of the proteins critical in triggering ferroptosis, we confirmed that miR-128-3p and EMPA inhibited SP1, leading to inhibition of CD98hc expression. Further, transfection with siCD98hc, miR-128-3p mimics, and inhibitors was performed to assess their involvement in the ferroptosis of anoikis-resistant cells. We proved that anoikis-resistant cells possess high ROS and iron levels. Further, miR-128-3p and EMPA treatments induced ferroptosis by inhibiting GSH and enzymatic activity of GPX4 and also induced lipid peroxidation. Moreover, EMPA suppressed bioluminescence of 4T1-Red-FLuc induced thoracic cavity, peritoneal tumour burden and lung nodules in an in-vivo metastatic model of breast cancer. Collectively, we revealed that EMPA sensitized the ECM detached cells to ferroptosis by synergically activating miR-128-3p and lowering the levels of SP1 and CD98hc, making it a potential adjunct drug for breast cancer chemotherapy.

Deciphering anoikis resistance and identifying prognostic biomarkers in clear cell renal cell carcinoma epithelial cells.

This study tackles the persistent prognostic and management challenges of clear cell renal cell carcinoma (ccRCC), despite advancements in multimodal therapies. Focusing on anoikis, a critical form of programmed cell death in tumor progression and metastasis, we investigated its resistance in cancer evolution. Using single-cell RNA sequencing from seven ccRCC patients, we assessed the impact of anoikis-related genes (ARGs) and identified differentially expressed genes (DEGs) in Anoikis-related epithelial subclusters (ARESs). Additionally, six ccRCC RNA microarray datasets from the GEO database were analyzed for robust DEGs. A novel risk prognostic model was developed through LASSO and multivariate Cox regression, validated using BEST, ULCAN, and RT-PCR. The study included functional enrichment, immune infiltration analysis in the tumor microenvironment (TME), and drug sensitivity assessments, leading to a predictive nomogram integrating clinical parameters. Results highlighted dynamic ARG expression patterns and enhanced intercellular interactions in ARESs, with significant KEGG pathway enrichment in MYC + Epithelial subclusters indicating enhanced anoikis resistance. Additionally, all ARESs were identified in the spatial context, and their locational relationships were explored. Three key prognostic genes-TIMP1, PECAM1, and CDKN1A-were identified, with the high-risk group showing greater immune infiltration and anoikis resistance, linked to poorer prognosis. This study offers a novel ccRCC risk signature, providing innovative approaches for patient management, prognosis, and personalized treatment.

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 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 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.

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 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.

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.

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.

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 genes as potential prognostic biomarkers in gastric cancer: A multilevel integrative analysis and predictive therapeutic value.

BACKGROUND: Gastric cancer (GC) is a frequent malignancy of the gastrointestinal tract. Exploring the potential anoikis mechanisms and pathways might facilitate GC research. PURPOSE: The authors aim to determine the significance of anoikis-related genes (ARGs) in GC prognosis and explore the regulatory mechanisms in epigenetics. METHODS: After describing the genetic and transcriptional alterations of ARGs, we searched differentially expressed genes (DEGs) from the cancer genome atlas and gene expression omnibus databases to identify major cancer marker pathways. The non-negative matrix factorisation algorithm, Lasso, and Cox regression analysis were used to construct a risk model, and we validated and assessed the nomogram. Based on multiple levels and online platforms, this research evaluated the regulatory relationship of ARGs with GC. RESULTS: Overexpression of ARGs is associated with poor prognosis, which modulates immune signalling and promotes anti-anoikis. The consistency of the DEGs clustering with weighted gene co-expression network analysis results and the nomogram containing 10 variable genes improved the clinical applicability of ARGs. In anti-anoikis mode, cytology, histology, and epigenetics could facilitate the analysis of immunophenotypes, tumour immune microenvironment (TIME), and treatment prognosis. CONCLUSION: A novel anoikis-related prognostic model for GC is constructed, and the significance of anoikis-related prognostic genes in the TIME and the metabolic pathways of tumours is initially explored.

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.