Identification and validation of an anoikis-associated gene signature to predict clinical character, stemness, IDH mutation, and immune filtration in glioblastoma

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.

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.

Case report: A 53-year-old woman with synchronous WHO classification II and IV gliomas

Introduction

Glioma is the most common primary intracranial neoplasm with a relatively poor prognosis.

Case presentation

Here, we present a unique case of a 53-year-old woman with two histopathologically distinct gliomas at the initial diagnosis. She presented with headaches and left limb weakness before admission, and magnetic resonance imaging (MRI) showed right frontal and basal ganglia area involvement combined with hemorrhage. The patient underwent a navigation-guided craniotomy for tumor removal. Pathological examination revealed the right frontal lobe lesion as a WHO grade II IDH-NOS astrocytoma, but the right parietal lobe lesion was a WHO grade IV IDH-mutant diffuse astrocytoma. Molecular detection of the parietal lesion revealed a point mutation at the R132 locus of the IDH1 gene, no mutation in the TERT promoter, amplification of the epidermal growth factor receptor, and a non-homozygous CDKN2A/B deletion.

Discussion

In-depth epigenomic analysis and molecular examination revealed that one patient had two different brain tumors, underscoring the importance of performing a comprehensive brain tumor workup.

Conclusion

This unique case confirms that adjacent astrocytomas may have different molecular pathogenesis and provides novel insights into the development of gliomas.

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.

Themis: advancing precision oncology through comprehensive molecular subtyping and optimization

Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.

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.

Targeting Integrin α3 Blocks β1 Maturation, Triggers Endoplasmic Reticulum Stress, and Sensitizes Glioblastoma Cells to TRAIL-Mediated Apoptosis

Glioblastoma (GBM) is a devastating brain cancer for which new effective therapies are urgently needed. GBM, after an initial response to current treatment regimens, develops therapeutic resistance, leading to rapid patient demise. Cancer cells exhibit an inherent elevation of endoplasmic reticulum (ER) stress due to uncontrolled growth and an unfavorable microenvironment, including hypoxia and nutrient deprivation. Cancer cells utilize the unfolded protein response (UPR) to maintain ER homeostasis, and failure of this response promotes cell death. In this study, as integrins are upregulated in cancer, we have evaluated the therapeutic potential of individually targeting all αβ1 integrin subunits using RNA interference. We found that GBM cells are uniquely susceptible to silencing of integrin α3. Knockdown of α3-induced proapoptotic markers such as PARP cleavage and caspase 3 and 8 activation. Remarkably, we discovered a non-canonical function for α3 in mediating the maturation of integrin β1. In its absence, generation of full length β1 was reduced, immature β1 accumulated, and the cells underwent elevated ER stress with upregulation of death receptor 5 (DR5) expression. Targeting α3 sensitized TRAIL-resistant GBM cancer cells to TRAIL-mediated apoptosis and led to growth inhibition. Our findings offer key new insights into integrin α3's role in GBM survival via the regulation of ER homeostasis and its value as a therapeutic target.

Predicting the prognosis of glioma patients with TERT promoter mutations and guiding the specific immune profile of immune checkpoint blockade therapy

The telomerase reverse transcriptase promoter (TERTp) is frequently mutated in gliomas. This study sought to identify immune biomarkers of gliomas with TERTp mutations. Data from TCGA were used to identify and validate survival-associated gene signatures, and immune and stromal scores were calculated using the ESTIMATE algorithm. High stromal or immune scores in patients with TERTp-mutant gliomas correlated with shorter overall survival compared to cases with low stromal or immune scores. Among TERTp-mutant gliomas with both high immune and high stromal scores, 213 commonly shared DEGs were identified. Among 71 interacting DEGs representing candidate hub genes in a PPI network, HOXC6, WT1, CD70, and OTP showed significant ability in establishing subgroups of high- and low-risk patients. A risk model based on these 4 genes showed strong prognostic potential for gliomas with mutated TERTp, but was inapplicable for TERTp-wild-type gliomas. TERTp-mutant gliomas with high-risk scores displayed a greater percentage of naïve B cells, plasma cells, naïve CD4 T cells, and activated mast cells than low-risk score gliomas. TIDE analysis indicated that immune checkpoint blockade (ICB) therapy may benefit glioma patients with TERTp mutations. The present risk model can help predict prognosis of glioma patients with TERTp mutations and aid ICB treatment options.

Multi-cohort validation of Ascore: an anoikis-based prognostic signature for predicting disease progression and immunotherapy response in bladder cancer

Bladder cancer ranks as the 10th most common cancer worldwide, with deteriorating prognosis as the disease advances. While immune checkpoint inhibitors (ICIs) have shown promise in clinical therapy in both operable and advanced bladder cancer, identifying patients who will respond is challenging. Anoikis, a specialized form of cell death that occurs when cells detach from the extracellular matrix, is closely linked to tumor progression. Here, we aimed to explore the anoikis-based biomarkers for bladder cancer prognosis and immunotherapeutic decisions. Through consensus clustering, we categorized patients from the TCGA-BLCA cohort into two clusters based on anoikis-related genes (ARGs). Significant differences in survival outcome, clinical features, tumor immune environment (TIME), and potential ICIs response were observed between clusters. We then formulated a four-gene signature, termed "Ascore", to encapsulate this gene expression pattern. The Ascore was found to be closely associated with survival outcome and served as an independent prognosticator in both the TCGA-BLCA cohort and the IMvigor210 cohort. It also demonstrated superior predictive capacity (AUC = 0.717) for bladder cancer immunotherapy response compared to biomarkers like TMB and PD-L1. Finally, we evaluated Ascore's independent prognostic performance as a non-invasive biomarker in our clinical cohort (Gulou-Cohort1) using circulating tumor cells detection, achieving an AUC of 0.803. Another clinical cohort (Gulou-Cohort2) consisted of 40 patients undergoing neoadjuvant anti-PD-1 treatment was also examined. Immunohistochemistry of Ascore in these patients revealed its correlation with the pathological response to bladder cancer immunotherapy (P = 0.004). Impressively, Ascore (AUC = 0.913) surpassed PD-L1 (AUC = 0.662) in forecasting immunotherapy response and indicated better net benefit. In conclusion, our study introduces Ascore as a novel, robust prognostic biomarker for bladder cancer, offering a new tool for enhancing immunotherapy decisions and contributing to the tailored treatment approaches in this field.

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

BACKGROUND

Methods for predicting the outcome of lung adenocarcinoma (LUAD) in the clinic are limited. Anoikis is an important route to programmed cell death in LUAD, and the prognostic value of a model constructed with anoikis-related lncRNAs (ARlncRNAs) in LUAD is unclear.

METHODS

Transcriptome and basic information for LUAD patients was obtained from the Cancer Genome Atlas. Coexpression and Cox regression analyses were utilized to identify prognostically significant ARlncRNAs and construct a prognostic signature. Furthermore, the signature was combined with clinical characteristics to create a nomogram. Finally, we performed principal component, enrichment, tumor mutation burden (TMB), tumor microenvironment (TME) and drug sensitivity analyses to evaluate the basic research and clinical merit of the signature.

RESULTS

The prognostic signature developed with eleven ARlncRNAs can accurately predict that high-risk group patients have a worse prognosis, as proven by the receiver operating characteristic (ROC) curve (AUC: 0.718). Independent prognostic analyses indicated that the risk score is a significant independent prognostic element for LUAD (P<0.001). In the high-risk group, enrichment analysis demonstrated that glucose metabolism and DNA replication were the main enrichment pathways. TMB analysis indicated that the high-risk group had a high TMB (P<0.05). Drug sensitivity analyses can recognize drugs that are sensitive to different risk groups. Finally, 11 ARlncRNAs of this signature were verified by RT-qPCR analysis.

CONCLUSIONS

A novel prognostic signature developed with 11 ARlncRNAs can accurately predict the OS of LUAD patients and offer clinical guidance value for immunotherapy and chemotherapy treatment.

Identification and Validation of a Novel Anoikis-Related Gene Signature for Predicting Survival in Patients With Serous Ovarian Cancer

Background

Ovarian cancer is an extremely deadly gynecological malignancy, with a 5-year survival rate below 30%. Among the different histological subtypes, serous ovarian cancer (SOC) is the most common. Anoikis significantly contributes to the progression of ovarian cancer. Therefore, identifying an anoikis-related signature that can serve as potential prognostic predictors for SOC is of great significance.

Methods

We intersected 308 anoikis-related genes (ARGs) and identified those significantly associated with SOC prognosis using univariate Cox regression. A LASSO Cox regression model was constructed and evaluated using Kaplan-Meier and receiver operating characteristic (ROC) analyses in TCGA (The Cancer Genome Atlas) and GSE26193 cohorts. We conducted quantitative real-time polymerase chain reaction (qPCR) to assess mRNA levels and applied bioinformatics to investigate the correlation between risk groups and gene expression, mutations, pathways, tumor immune microenvironment (TIME), and drug sensitivity in SOC.

Results

Among 308 ARGs, 28 were significantly associated with SOC prognosis. A 13-gene prognostic model was established through LASSO Cox regression in TCGA cohort. High-risk group had poorer prognosis than low-risk group (median overall survival (mOS): 34.2 vs. 57.1 months, hazard ratio (HR): 2.590, 95% confidence interval (CI): 0.159 - 6.00, P < 0.001). The area under the curve (AUC) values of 0.63, 0.65, and 0.74 reflected the predictive performance for 3-, 5-, and 8-year overall survival (OS) in GSE26193 validation cohort. Functional enrichment, pathway analysis, and TIME analysis identified distinct characteristics between risk groups. Drug sensitivity analysis revealed potential drug advantages for each group. Furthermore, qPCR validation once again confirmed the effectiveness of the risk model in SOC patients.

Conclusions

We developed and validated a robust ARG model, which could be used to predict OS in SOC patients. By systematically analyzing the correlation between the risk score of the ARGs signature model and various patterns, including the TIME and drug sensitivity, our findings suggest that this prognostic model contributes to the advancement of personalized and precise therapeutic strategies. Nevertheless, further validation studies and investigations into the underlying mechanisms are warranted.

Deciphering hepatocellular carcinoma pathogenesis and therapeutics: a study on anoikis, ceRNA regulatory network and traditional Chinese medicine

Introduction: Hepatocellular carcinoma (HCC) is responsible for approximately 90% of liver malignancies and is the third most common cause of cancer-related mortality worldwide. However, the role of anoikis, a programmed cell death mechanism crucial for maintaining tissue equilibrium, is not yet fully understood in the context of HCC. Methods: Our study aimed to investigate the expression of 10 anoikis-related genes (ARGs) in HCC, including BIRC5, SFN, UBE2C, SPP1, E2F1, etc., and their significance in the disease. Results: Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, we discovered that these ARGs are involved in important processes such as tissue homeostasis, ion transport, cell cycle regulation, and viral infection pathways. Furthermore, we found a significant correlation between the prognostic value of five ARGs and immune cell infiltrates. Analysis of clinical datasets revealed a strong association between BIRC5 expression and HCC pathological progression, including pathological stage, T stage, overall survival (OS), and race. By constructing a competing endogenous RNA (ceRNA) network and using molecular docking, we identified ten bioactive compounds from traditional Chinese medicine (TCM) that could potentially modulate BIRC5. Subsequent in vitro experiments confirmed the influence of platycodin D, one of the identified compounds, on key elements within the ceRNA network. Discussion: In conclusion, our study presents a novel framework for an anoikis-centered prognostic model and an immune-involved ceRNA network in HCC, revealing potential regulatory targets. These insights contribute to our understanding of HCC pathology and may lead to improved therapeutic interventions.

Deciphering the significance of anoikis in bladder cancer and systematic analysis of S100A7 as a potential therapeutic target

BACKGROUND

Bladder cancer is an epidemic and life-threating urologic carcinoma. Anoikis is a unusual type of programmed cell death which plays a vital role in tumor survival, invasion and metastasis. Nevertheless, the relationship between anoikis and bladder cancer has not been understood thoroughly.

METHODS

We downloaded the transcriptome and clinical information of BLCA patients from TCGA and GEO databases. Then, we analyzed different expression of anoikis-related genes and established a prognostic model based on TCGA database by univariate Cox regression, lasso regression, and multivariate Cox regression. Then the Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves were performed. GEO database was used for external validation. BLCA patients in TCGA database were divided into two subgroups by non-negative matrix factorization (NMF) classification. Survival analysis, different gene expression, immune cell infiltration and drug sensitivity were calculated. Finally, we verified the function of S100A7 in two BLCA cell lines.

RESULTS

We developed a prognostic risk model based on three anoikis-related genes including TPM1, RAC3 and S100A7. The overall survival of BLCA patients in low-risk groups was significantly better than high-risk groups in training sets, test sets and external validation sets. Subsequently, the checkpoint and immune cell infiltration had significant difference between two groups. Then we identified two subtypes (CA and CB) through NMF analysis and found CA had better OS and PFS than CB. Besides, the accuracy of risk model was verified by ROC analysis. Finally, we identified that knocking down S100A7 gene expression restrained the proliferation and invasion of bladder cancer cells.

CONCLUSION

We established and validated a bladder cancer prognostic model consisting of three genes, which can effectively evaluate the prognosis of bladder cancer patients. Additionally, through cellular experiments, we demonstrated the significant role of S100A7 in the metastasis and invasion of bladder cancer, suggesting its potential as a novel target for future treatments.

Identification of prognostic value of anoikis-related gene score model combined with tumor microenvironment score models in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) has poor survival. Effective prognostic models with high application value remain lack.

METHODS

Bulk RNA seq and single cell RNA-seq data were retrieved from the XENA-TCGA-ESCC cohort and GSE188900. The anoikis-related gene score (ANO score) model and tumor microenvironment score (TME score) model were constructed and merged into three subgroups. Functional annotation was analyzed by Gene Ontology terms. Univariate and multivariate Cox regression analysis, least absolute shrinkage and selection operator regression analysis and weighted gene coexpression network analysis were performed to construct prognostic prediction models and identify prognostic value. Kaplan-Meier survival curves were drawn for evaluating the overall survival (OS) of patients classified by different score subgroups. Immunotherapy response and mutation analyses were also conducted.

RESULTS

In the ANO score model, TNFSF10 was an independent factor for the prognosis of ESCC patients. The area under the curve values of the ANO-TME score model in predicting the OS were 0.638 at 5 years and 0.632 at 7 years. Patients in the ANO low score-TME high score group had a much longer OS than patients in any other ANO-TME score subgroup (p < 0.001), suggesting a higher prognostic value. The differentially expressed genes of the ANO low score-TME high score group were mainly involved in cell adhesion molecules, nucleotide excision repair, the TGF-β signaling pathway and mismatch repair. TP53 (92%), TTN (38%) and NFE2L2 (31%) were the top genes with highest mutant frequency in the ANO low score-TME high score group.

CONCLUSIONS

A novel prognostic prediction model with high application value was constructed and identified for ESCC patients, which may provide evidence for immunotherapy in the treatment of ESCC.

A promising anoikis-related prognostic signature predicts prognosis of skin cutaneous melanoma

BACKGROUND

Skin cutaneous melanoma (SKCM) is a highly aggressive disease with a poor prognosis for advanced tumors. Anoikis is a caspase-dependent cell death process triggered by extracellular matrix (ECM) detachment, rectifies detachment-induced metabolic defects that compromise cell survival, recent study revealed the crucial role of anoikis for cancer cells to survive during metastasis. However, limited research focused on the role of anoikis in SKCM.

METHODS

Our study utilized the 27 anoikis-related genes (ARGs) to divide SKCM patients into two clusters, and obtain differentially expressed genes (DEGs) for each cluster. These DEGs were used in stepwise Cox regression analysis to develop a prediction model for SKCM patients consisting of nine ARGs, called the anoikis-related signature (ARS). Subsequently, we used the risk scores calculated from the ARS to divide SKCM patients into two groups and explored differences in immune microenvironment, immune checkpoint reactivity, and drug sensitivity between the groups.

RESULTS

Nine ARGs were identified to stratify SKCM patients into two risk groups, patients in the high-risk group had a poor prognosis and suppressed immune cell infiltration. Moreover, higher expression of immune checkpoint molecules and a greater sensitivity to immunotherapy and chemotherapy drugs were observed in the low-risk group. Finally, all of the ARS hub genes were found to be upregulated in SKCM tissues and cell lines.

CONCLUSION

A novel ARGs signature was identified for predicting the prognosis of SKCM. Based on the immune landscape associated with ARS discovered in our study, targeting ARS hub genes may be a promising treatment for SKCM.

Construction of stomach adenocarcinoma prognostic signature based on anoikis-related lncRNAs and clinical significance

As a dominant type of gastric cancer, stomach adenocarcinoma (STAD) is characterized by high morbidity and mortality rates. Anoikis factors participate in tumor metastasis and invasion. This study was designed to identify prognostic risk factors in anoikis-related long non-coding RNAs (lncRNAs) for STAD. First, with STAD expression datasets and anoikis-related gene sets downloaded from public databases, anoikis-related prognostic lncRNA signatures (AC091057.1, ADAMTS9.AS1, AC090825.1, AC084880.3, EMX2OS, HHIP.AS1, AC016583.2, EDIL3.DT, DIRC1, LINC01614, and AC103702.2) were screened by Cox regression to establish a prognostic risk model. Kaplan-Meier and receiver operating characteristic curves were used to evaluate the survival status of patients and verify predictive accuracy of the model. Besides, risk score could be an independent prognostic factor to assess the prognosis of STAD patients. Nomograms of the prognostic model that combined clinical information and risk score could effectively predict survival of STAD patients, as validated by calibration curve. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were performed for differentially expressed genes (DEGs) in high- and low-risk groups. These DEGs were related to neurotransmitter transmission, signal transmission, and endocytosis. Moreover, we analyzed immune status of different risk groups and found that STAD patients in low-risk group were more sensitive to immunotherapy. A prognostic risk assessment model for STAD using anoikis-related lncRNA genes was constructed here, which was proven to have high predictive accuracy and thus could offer a reference for prognostic evaluation and clinical treatment of STAD patients.

TJP3 promotes T cell immunity escape and chemoresistance in breast cancer: a comprehensive analysis of anoikis-based prognosis prediction and drug sensitivity stratification

BACKGROUND

Overcoming anoikis is a necessity during the metastasis and invasion of tumors. Recently, anoikis has been reported to be involved in tumor immunity and has been used to construct prognosis prediction models. However, the roles of anoikis in regulating tumor immunity and drug sensitivity in breast cancer are still not clear and therefore worth uncovering.

METHODS

TCGA and GEO data are the source of gene expression profiles, which are used to identify anoikis-related-gene (ARG)-based subtypes. R4.2 is used for data analysis.

RESULTS

Breast cancer is divided into three subgroups, amongst which shows prognosis differences in pan-cancer cohort, ACC, BLCA, BRCA, LUAD, MESO, PAAD, and SKCM. In breast cancer, it shows significant differences in clinical features, immune cell infiltration and drug sensitivity. Machine learning constructs prognosis prediction model, which is useful to perform chemotherapy sensitivity stratification. Following, TJP3 is identified and verified as the key ARG, up-regulation of which increases tolerance of paclitaxel-induced cell toxicity, accompanied with increased expression of caspas3 and cleaved-caspase3. In addition, Down-regulation of TJP3 weakens the cell migration, which accompanied with increased expression of E-cad and decreased expression of vimentin, twist1, zeb1, and MMP7. Furthermore, the expression level of PD-L1 is negative correlated with TJP3.

CONCLUSION

ARGs-based subgroup stratification is useful to recognize chemotherapy sensitive cohort, and also is useful to predict clinical outcome. TJP3 promotes chemoresistance, tumor metastasis and potential immunotherapy escape in breast cancer.

Identification of a Novel Oxidative Stress- and Anoikis-Related Prognostic Signature and Its Immune Landscape Analysis in Non-Small Cell Lung Cancer

The objective of this study was to identify a kind of prognostic signature based on oxidative stress- and anoikis-related genes (OARGs) for predicting the prognosis and immune landscape of NSCLC. Initially, We identified 47 differentially expressed OARGs that primarily regulate oxidative stress and epithelial cell infiltration through the PI3K-Akt pathway. Subsequently, 10 OARGs related to prognosis determined two potential clusters. A cluster was associated with a shorter survival level, lower immune infiltration, higher stemness index and tumor mutation burden. Next, The best risk score model constructed by prognostic OARGs was the Random Survival Forest model, and it included SLC2A1, LDHA and PLAU. The high-risk group was associated with cluster A and poor prognosis, with a higher tumor mutation burden, stemness index and proportion of M0-type macrophages, and a lower immune checkpoint expression level, immune function score and IPS score. The calibration curve and decision-making curve showed that the risk score combined with clinical pathological characteristics could be used to construct a nomogram for guiding the clinical treatment strategies. Finally, We found that all three hub genes were highly expressed in tumor tissues, and LDHA expression was mainly regulated by has-miR-338-3p, has-miR-330-5p and has-miR-34c-5p. Altogether, We constructed an OARG-related prognostic signature to reveal potential relationships between the signature and clinical characteristics, TME, stemness, tumor mutational burden, drug sensitivity and immune landscape in NSCLC patients.

Identification and validation of a novel anoikis-related long non-coding RNA signature for pancreatic adenocarcinoma to predict the prognosis and immune response

OBJECTIVE

To provide more precise treatment options for pancreatic adenocarcinoma (PAAD) patients and improve their prognosis,we established a novel anoikis-related long non-coding RNA signature (ARLSig) to predict the prognosis and immune response for PAAD patients.

METHODS

We downloaded information on PAAD from The Cancer Genome Atlas (TCGA) database, and screened long non-coding RNA (lncRNA) linked with anoikis, and prognostic signatures with these lncRNAs. After that, ARLSig was verified using receiver operating characteristic (ROC) and C-index curves. To further investigate the role of ARLSig, we also performed enrichment analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). Additionally, using immunological correlation analysis and single-sample genetic enrichment analysis, we investigated the effectiveness of PAAD immunotherapy.

RESULTS

We screened 7 lncRNAs to construct a novel ARLSig and utilized it to predict the efficacy of immunotherapy and the prognosis of PAAD patients.

CONCLUSION

ARLSig can identify patients who will benefit from immunotherapy and improve the prediction of PAAD patient prognosis.

ANRGs impact on gastric cancer progression and drug efficacy: A comprehensive study

Gastric cancer (GC) is a significant contributor to cancer-related mortality globally, with the heterogeneity of metastasis and treatment impacting patient prognosis. Currently, the treatment of GC still relies on early surgical resection, and comprehensive treatment is needed for patients with metastatic GC. Anikis-related genes (ANRGs) have been shown to affect tumor metastasis. Exploring the role of ANRGs in GC will help us understand the mechanism of tumor metastasis; screening precise targets and selecting appropriate chemotherapeutics will help individualize the treatment of GC patients. In this study, we established a prognostic scoring model based on ANRGs and explored their association with GC patient prognosis, immune microenvironment, chemotherapeutic drug sensitivity, and small molecule compounds. Our findings revealed that a gene signature composed of ANXA5, CCN1, EGF, VTN, and ZBTB7A accurately predicted GC patient prognosis. Patients in the low-risk group had better outcomes, higher macrophage M1 infiltration, and higher tumor mutation burden. The half maximal inhibitory concentration (IC50) values of Ponatinib (ap.24534), Motesanib (amg.706), and Navitoclax (abt.263) were lower in the high-risk group, indicating that patients in the high-risk group were more sensitive to these chemotherapy drugs, meaning with better clinical outcomes. In addition, we screened the small molecule compound SGC-CBP30 that can inhibit ANXA5 and CCN1, and these results help individualized treatment of GC patients. Our study identified key genes based on ANRGs and developed a novel gene signature for predicting the prognosis of GC patients and understanding the relationship between immunity and tumor mutation burden. Additionally, we identified chemotherapeutic drugs that can guide GC treatment and elucidated the binding affinity between specific targeted drugs and distinct protein sites, providing novel insights for the precise treatment of GC patients.

Identification of a new anoikis-related gene signature for prognostic significance in head and neck squamous carcinomas

Anoikis, a mode of programmed cell death, is essential for normal development and homeostasis in the organism and plays an important role in the onset and progression of cancers. The authors of this research sought to establish a gene signature associated with anoikis to predict therapy outcomes and patient prognosis for individuals with head and neck squamous cell carcinoma (HNSCC). Transcriptome data of anoikis-related genes (ARGs) in individuals with HNSCC were retrieved from public databases to aid in the formulation of the gene signature. A novel ARG signature was then created using a combination of the Least Absolute Shrinkage and Selection Operator regression and Cox regression analysis. The relationship between ARGs and tumor immune microenvironment in HNSCC was explored using single-cell analysis. HNSCC individuals were classified into high-risk and low-risk groups as per the median value of risk score. The study also investigated the variations in the infiltration status of immune cells, tumor microenvironment, sensitivity to immunotherapy and chemotherapeutics, as well as functional enrichment between the low-risk and high-risk categories. A total of 18 ARGs were incorporated in the formulation of the signature. Our signature's validity as a standalone predictive predictor was validated by multivariate Cox regression analysis and Kaplan-Meier survival analysis. Generally, the prognosis was worse for high-risk individuals. Subjects in the low-risk groups had a better prognosis and responded in a better way to combination immunotherapy, had higher immunological ratings and activity levels, and had more immune cell infiltration. In addition, gene set enrichment analysis findings showed that the low-risk subjects exhibited heightened activity in several immune-related pathways. However, the high-risk patients responded better to chemotherapy. The aim of this research was to develop a new ARG signature to predict the prognosis and sensitivity to immunotherapeutic and chemotherapeutic schemes for HNSCC patient. As a result, this could help spur the creation of new chemotherapeutics and immunotherapeutic approaches for patients with HNSCC.

Comparative analysis of α-pinene alone and combined with temozolomide in human glioblastoma cells

α-Pinene (PEN) is a phyto compound present in terpene plants. In traditional medicine, PEN has been used for its anti-inflammatory, pain-relieving, and bronchodilator properties. The effect of PEN in combination with temozolomide (TMZ) in glioblastoma multiforme (GBM) cells has been evaluated. The action of the PEN + TMZ combination on cell migration, soft-agar, and cell death was determined in LN229 and U87MG human glioblastoma cells. In combination, PEN with TMZ showed a synergistic inhibitory effect in the GBM cells. The PEN + TMZ treatment showed a higher fluorescent intensity and reduced the percentage of wound area closure compared to the compound alone. The compounds in combination also resulted in a reduction in single-cell colony formation. To conclude, the study showed that plant-derived PEN enhanced the effectiveness of standard chemotherapeutic, TMZ, in LN229 and U87MG cells.

Development of the prognostic value in lung adenocarcinoma based on anoikis-related genes and initial experimental validation

BACKGROUND

Lung adenocarcinoma (LUAD) is a highly aggressive cancer in advanced stages and has the highest cancer-related death across the world. Anoikis has emerged as a specific form of apoptotic cell death that may play a vital role in the formation and development of tumors.

METHODS

Based on The Cancer Genome Atlas dataset, we developed a novel anoikis-related genes (ARGs) signature in LUAD and evaluated the differences between low and high-risk groups in clinical characteristics, expression patterns, immune cell infiltration, and drug sensitivity, etc. According to multivariate Cox regression analysis, the risk score was identified as a significant independent prognostic factor. The possible biological pathways of ARGs' were assessed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. The immune infiltration landscape and risk score of ARGs were analyzed by ESTIMATE and CIBERSORT analysis. A nomogram grounded on six key ARGs and clinicopathological features was provided. Moreover, experiment validation of the expression patterns of six hub ARGs in lung cancer cell lines was conducted.

RESULTS

We identified 53 survival-related LUAD anoikis-related differentially expressed genes and finally six hub anoikis genes (LDHA, SLC2A1, SERPINB5, ITGB4, BRCA2, and PIK3R1) were selected to construct an ARG model. The risk model could efficiently cluster the patients into low- and high-risk groups which could accurately predict clinical outcomes for LUAD patients. There is evidence that the prognostic risk score is a remarkable prognostic factor in determining overall survival. Different immune statuses and drug sensitivity between low- and high-risk groups were explored according to functional analysis. On the basis of risk scores and LUAD clinicopathological features, a novel nomogram was developed. Ultimately, all six key genes except for PIK3R1 were proved to be upregulated in LUAD tissues and cell lines by bioinformatics analysis and experimental validation.

CONCLUSIONS

The result of the present study suggest that ARGs could be carcinogenic to LUAD and could be used as an effective stratification factor to customize therapies and forecast the survival rate in LUAD patients.

Development of a novel, clinically relevant anoikis-related gene signature to forecast prognosis in patients with prostate cancer

Introduction: Anoikis is a specific form of programmed cell death and is related to prostate cancer (PC) metastasis. This study aimed to develop a reliable anoikis-related gene signature to accurately forecast PC prognosis. Methods: Based on anoikis-related genes and The Cancer Genome Atlas (TCGA) data, anoikis-related molecular subtypes were identified, and their differences in disease-free survival (DFS), stemness, clinical features, and immune infiltration patterns were compared. Differential expression analysis of the two subtypes and weighted gene co-expression network analysis (WGCNA) were employed to identify clinically relevant anoikis-related differentially expressed genes (DEGs) between subtypes, which were then selected to construct a prognostic signature. The clinical utility of the signature was verified using the validation datasets GSE116918 and GSE46602. A nomogram was established to predict patient survival. Finally, differentially enriched hallmark gene sets were revealed between the different risk groups. Results: Two anoikis-related molecular subtypes were identified, and cluster 1 had poor prognosis, higher stemness, advanced clinical features, and differential immune cell infiltration. Next, 13 clinically relevant anoikis-related DEGs were identified, and five of them (CKS2, CDC20, FMOD, CD38, and MSMB) were selected to build a prognostic signature. This gene signature had a high prognostic value. A nomogram that combined Gleason score, T stage, and risk score could accurately predict patient survival. Furthermore, gene sets closely related with DNA repair were differentially expressed in the different risk groups. Conclusion: A novel, clinically relevant five-anoikis-related gene signature was a powerful prognostic biomarker for PC.

Predicting the immune microenvironment and prognosis with a anoikis - related signature in breast cancer

Background

Tumor heterogeneity is widely recognized as a crucial factor impacting the prognosis of breast cancer (BC) patients. However, there remains an insufficient understanding of the underlying impact of anoikis on the prognosis of BC patients.

Methods

The researchers utilized the TCGA-BRCA dataset to screen and analyze the differentially expressed genes of anoikis-related genes (ARGs) in BC and normal breast tissue. Prognostic gene signatures were established through univariate, LASSO, and multivariate Cox regression analyses. These signatures were evaluated using Kaplan-Meier curve and receiver operating characteristic (ROC) analyses, resulting in the development of an anoikis-related index (ACI). The training dataset was TCGA-BRCA, while METABRIC and GSE96058 were used for external validation. Additionally, nomograms were developed by combining risk scores and clinical parameters, enabling gene set enrichment analysis (GSEA) and tumor immunoassay. Furthermore, an exploration of small molecule compounds was conducted to identify potential therapeutic benefits.

Results

A six-gene anoikis-related signature was constructed, which divided BC patients into high- and low-ACI groups based on median ACI scores. The ACI accurately predicted prognosis and acted as an independent prognostic factor for BC patients. Patients in the high-ACI group exhibited poorer overall survival (OS) across all cohorts and showed more severe clinical manifestations compared to the low-ACI group. The study also explored the potential impacts of anoikis on immune cells infiltrating tumors, immune checkpoints, growth factors, and cytokine levels. Additionally, the potential implications of anoikis in BC immunotherapy were discussed, along with highlighting small molecule compounds that could offer therapeutic benefits.

Conclusions

Anoikis was found to hold significant prognostic value in breast cancer, providing a novel approach for managing patients with different prognoses and implementing more precise immunotherapy strategies.

A signature based on anoikis-related genes for the evaluation of prognosis, immunoinfiltration, mutation, and therapeutic response in ovarian cancer

Background

Ovarian cancer (OC) is a highly lethal and aggressive gynecologic cancer, with an overall survival rate that has shown little improvement over the decades. Robust models are urgently needed to distinguish high-risk cases and predict reliable treatment options for OC. Although anoikis-related genes (ARGs) have been reported to contribute to tumor growth and metastasis, their prognostic value in OC remains unknown. The purpose of this study was to construct an ARG pair (ARGP)-based prognostic signature for patients with OC and elucidate the potential mechanism underlying the involvement of ARGs in OC progression.

Methods

The RNA-sequencing and clinical information data of OC patients were obtained from The Center Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A novel algorithm based on pairwise comparison was utilized to select ARGPs, followed by the Least Absolute Shrinkage and Selection Operator Cox analysis to construct a prognostic signature. The predictive ability of the model was validated using an external dataset, a receiver operating characteristic curve, and stratification analysis. The immune microenvironment and the proportion of immune cells were analyzed in high- and low-risk OC cases using seven algorithms. Gene set enrichment analysis and weighted gene co-expression network analysis were performed to investigate the potential mechanisms of ARGs in OC occurrence and prognosis.

Results

The 19-ARGP signature was identified as an important prognostic predictor for 1-, 2-, and 3-year overall survival of patients with OC. Gene function enrichment analysis showed that the high-risk group was characterized by the infiltration of immunosuppressive cells and the enrichment of adherence-related signaling pathway, suggesting that ARGs were involved in OC progression by mediating immune escape and tumor metastasis.

Conclusion

We constructed a reliable ARGP prognostic signature of OC, and our findings suggested that ARGs exerted a vital interplay in OC immune microenvironment and therapeutic response. These insights provided valuable information regarding the molecular mechanisms underlying this disease and potential targeted therapies.

Development and validation of a novel anoikis-related gene signature for predicting prognosis in ovarian cancer

Anoikis plays a critical role in variable cancer types. However, studies that focus on the prognostic values of anoikis-related genes (ANRGs) in OV are scarce. Cohorts with transcriptome data and corresponding clinicopathologic data of OV patients were collected and consolidated from public databases. Multiple bioinformatics approaches were used to screen key genes from 446 anoikis-related genes, including Cox regression analysis, random survival forest analysis, and Kaplan-Meier analysis of best combinations. A five-gene signature was constructed in the discovery cohort (TCGA) and validated in four validation cohorts (GEO). Risk score of the signature stratified patients into high-risk (HRisk) and low-risk (LRisk) subgroups. Patients in the HRisk group were associated with worse OS than those in the LRisk group in both the TCGA cohort (p<0.0001, HR=2.718, 95%CI:1.872-3.947) and the four GEO cohorts (p<0.05). Multivariate Cox regression analyses confirmed that the risk score served as an independent prognostic factor in both cohorts. The signature's predictive capacity was further demonstrated by the nomogram analysis. Pathway enrichment analysis revealed that immunosuppressive and malignant progression-related pathways were enriched in the HRisk group, including TGF-β, WNT and ECM pathways. The LRisk group was characterized by immune-active signaling pathways (interferon-gamma, T cell activation, etc.) and higher proportions of anti-tumor immune cells (NK, M1, etc.) while HRisk patients were associated with higher stromal scores and less TCR richness. In conclusion, the signature reveals a close relationship between the anoikis and prognosis and may provide a potential therapeutic target for OV patients.

Establishment and validation of a novel anoikis-related prognostic signature of clear cell renal cell carcinoma

Background

Despite progression in its treatment, the clinical outcome of patients with clear cell renal cell carcinoma (ccRCC) remains not ideal. Anoikis is a unique form of programmed apoptosis, owing to insufficient cell-matrix interactions. Anoikis plays a crucial role in tumor migration and invasion, and tumor cells could protect themselves through the capacity of anoikis resistance.

Methods

Anoikis-related genes (ARGs) were obtained from Genecards and Harmonizome portals. The ARGs related to ccRCC prognosis were identified through univariate Cox regression analysis, then we utilized these ARGs to construct a novel prognostic model for ccRCC patients. Moreover, we explored the expression profile of ARGs in ccRCC using the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. We also conducted Real-Time Polymerase Chain Reaction (RT-PCR) to probe ARGs expression of the risk score. Finally, we performed correlation analysis between ARGs and tumor immune microenvironment.

Results

We identified 17 ARGs associated with ccRCC survival, from which 7 genes were chosen to construct a prognostic model. The prognostic model was verified as an independent prognostic indicator. The expression of most ARGs was higher in ccRCC samples. These ARGs were closely correlated with immune cell infiltration and immune checkpoint members, and had independent prognostic value respectively. Functional enrichment analysis demonstrated that these ARGs were significantly associated with multiple types of malignances.

Conclusion

The prognostic signature was identified to be highly efficient in predicting ccRCC prognosis, and these ARGs were closely related to tumor microenvironment.

A novel risk model based on anoikis: Predicting prognosis and immune infiltration in cutaneous melanoma

Cutaneous melanoma (CM) is a highly aggressive malignancy with a dimal prognosis and limited treatment options. Anoikis is believed to involve in the regeneration, migration, and metastasis of tumor. The exact role of anoikis-related genes (ARGs) in the development and progression of cutaneous melanoma, however, remains elusive. Four ARGs (SNAI2, TFDP1, IKBKG, and MCL1) with significant differential expression were selected through Cox regression and LASSO analyses. Data for internal and external cohorts validated the accuracy and clinical utility of the prognostic risk model based on ARGs. The Kaplan-Meier curve indicated a much better overall survival rate of low-risk patients. Notably, we also found that the action of ARGs in the CM was mediated by immune-related signaling pathways. Consensus clustering and TIME landscape analysis also indicated that the low-risk score patients have excellent immune status. Moreover, the results of immunotherapy response and drug sensitivity also confirmed the potential implications of informing individualized immune therapeutic strategies for CM. Collectively, the predictive risk model constructed based on ARGs provides an excellent and accurate prediction tool for CM patients. This present research provides a rationale for the joint application of targeted therapy and immunotherapy in CM treatment. The approach could have great therapeutic value and make a contribution to personalized medicine therapy.

An anoikis-based signature for predicting prognosis in hepatocellular carcinoma with machine learning

Background: Hepatocellular carcinoma (HCC) is a common malignancy with high mortality worldwide. Despite advancements in diagnosis and treatment in recent years, there is still an urgent unmet need to explore the underlying mechanisms and novel prognostic markers. Anoikis has received considerable attention because of its involvement in the progression of human malignancies. However, the potential mechanism of anoikis-related genes (ANRGs) involvement in HCC progression remains unclear. Methods: We use comprehensive bioinformatics analyses to determine the expression profile of ANRGs and their prognostic implications in HCC. Next, a risk score model was established by least absolute shrinkage and selection operator (Lasso) Cox regression analysis. Then, the prognostic value of the risk score in HCC and its correlation with clinical characteristics of HCC patients were further explored. Additionally, machine learning was utilized to identify the outstanding ANRGs to the risk score. Finally, the protein expression of DAP3 was examined on a tissue microarray (TMA), and the potential mechanisms of DAP3 in HCC was explored. Results: ANRGs were dysregulated in HCC, with a low frequency of somatic mutations and associated with prognosis of HCC patients. Then, nine ANRGs were selected to construct a risk score signature based on the LASSO model. The signature presented a strong ability of risk stratification and prediction for overall survival in HCC patients.Additionally, high risk scores were closely correlated with unfavorable clinical features such as advanced pathological stage, poor histological differentiation and vascular invasion. Moreover, The XGBoost algorithm verified that DAP3 was an important risk score contributor. Further immunohistochemistry determined the elevated expression of DAP3 in HCC tissues compared with nontumor tissues. Finally, functional analyses showed that DAP3 may promote HCC progression through multiple cancer-related pathways and suppress immune infiltration. Conclusion: In conclusion, the anoikis-based signature can be utilized as a novel prognostic biomarker for HCC, and DAP3 may play an important role in the development and progression of HCC.

An anoikis-related gene signature predicts prognosis and reveals immune infiltration in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a global health burden with poor prognosis. Anoikis, a novel programmed cell death, has a close interaction with metastasis and progression of cancer. In this study, we aimed to construct a novel bioinformatics model for evaluating the prognosis of HCC based on anoikis-related gene signatures as well as exploring the potential mechanisms.

Materials and methods

We downloaded the RNA expression profiles and clinical data of liver hepatocellular carcinoma from TCGA database, ICGC database and GEO database. DEG analysis was performed using TCGA and verified in the GEO database. The anoikis-related risk score was developed via univariate Cox regression, LASSO Cox regression and multivariate Cox regression, which was then used to categorize patients into high- and low-risk groups. Then GO and KEGG enrichment analyses were performed to investigate the function between the two groups. CIBERSORT was used for determining the fractions of 22 immune cell types, while the ssGSEA analyses was used to estimate the differential immune cell infiltrations and related pathways. The "pRRophetic" R package was applied to predict the sensitivity of administering chemotherapeutic and targeted drugs.

Results

A total of 49 anoikis-related DEGs in HCC were detected and 3 genes (EZH2, KIF18A and NQO1) were selected out to build a prognostic model. Furthermore, GO and KEGG functional enrichment analyses indicated that the difference in overall survival between risk groups was closely related to cell cycle pathway. Notably, further analyses found the frequency of tumor mutations, immune infiltration level and expression of immune checkpoints were significantly different between the two risk groups, and the results of the immunotherapy cohort showed that patients in the high-risk group have a better immune response. Additionally, the high-risk group was found to have higher sensitivity to 5-fluorouracil, doxorubicin and gemcitabine.

Conclusion

The novel signature of 3 anoikis-related genes (EZH2, KIF18A and NQO1) can predict the prognosis of patients with HCC, and provide a revealing insight into personalized treatments in HCC.

Identification of anoikis-related genes classification patterns and immune infiltration characterization in ischemic stroke based on machine learning

Introduction

Ischemic stroke (IS) is a type of stroke that leads to high mortality and disability. Anoikis is a form of programmed cell death. When cells detach from the correct extracellular matrix, anoikis disrupts integrin junctions, thus preventing abnormal proliferating cells from growing or attaching to an inappropriate matrix. Although there is growing evidence that anoikis regulates the immune response, which makes a great contribution to the development of IS, the role of anoikis in the pathogenesis of IS is rarely explored.

Methods

First, we downloaded GSE58294 set and GSE16561 set from the NCBI GEO database. And 35 anoikis-related genes (ARGs) were obtained from GSEA website. The CIBERSORT algorithm was used to estimate the relative proportions of 22 infiltrating immune cell types. Next, consensus clustering method was used to classify ischemic stroke samples. In addition, we used least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms to screen the key ARGs in ischemic stroke. Next, we performed receiver operating characteristics (ROC) analysis to assess the accuracy of each diagnostic gene. At the same time, the nomogram was constructed to diagnose IS by integrating trait genes. Then, we analyzed the correlation between gene expression and immune cell infiltration of the diagnostic genes in the combined database. And gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on these genes to explore differential signaling pathways and potential functions, as well as the construction and visualization of regulatory networks using NetworkAnalyst and Cytoscape. Finally, we investigated the expression pattern of ARGs in IS patients across age or gender.

Results

Our study comprehensively analyzed the role of ARGs in IS for the first time. We revealed the expression profile of ARGs in IS and the correlation with infiltrating immune cells. And The results of consensus clustering analysis suggested that we can classify IS patients into two clusters. The machine learning analysis screened five signature genes, including AKT1, BRMS1, PTRH2, TFDP1 and TLE1. We also constructed nomogram models based on the five risk genes and evaluated the immune infiltration correlation, gene-miRNA, gene-TF and drug-gene interaction regulatory networks of these signature genes. The expression of ARGs did not differ by sex or age.

Discussion

This study may provide a beneficial reference for further elucidating the pathogenesis of IS, and render new ideas for drug screening, individualized therapy and immunotherapy of IS.

Identification and Validation of Anoikis-Related Signatures for Predicting Prognosis in Lung Adenocarcinoma with Machine Learning

Background

Lung adenocarcinoma (LUAD) is an aggressive cancer that has an extremely poor prognosis. As well as facilitating the detachment of cancer cells from the primary tumor site, anoikis plays an important role in cancer metastasis. Few studies to date, however, have examined the role of anoikis in LUAD, in patient prognosis.

Methods

A total of 316 anoikis-related genes (ANRGs) integrated from Genecards and Harmonizome portals. LUAD transcriptome data were retrieved from the Genotype-Tissue Expression Project (GEO) and The Cancer Genome Atlas (TCGA). Anoikis-related prognostic genes (ANRGs) were primarily screened by univariate Cox regression. All ANRGs were included in the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model to construct the powerful prognostic signature. This signature was validated and assessed using the Kaplan-Meier method as well as univariate and multivariate Cox regression analyses. Anoikis-related regulators of risk score were identified using a XG-boost machine learning model. The expression of ITGB4 protein was examined in a ZhengZhou University (ZZU) tissue cohort by immunohistochemistry, and the potential mechanisms of action of ITGB4 in LUAD were explored by GO, KEGG, and ingenuity pathway analyses and by GSEA.

Results

A risk score signature was constructed based on eight ANRGs, with high risk scores found to closely correlate with unfavorable clinical features. ITGB4 expression may be associated with 5-year over survival, with immunohistochemistry showed that the expression of ITGB4 was higher in LUAD than in nontumor tissues. Enrichment analysis suggested that ITGB4 may promote LUAD development by targeting E2F, MYC, and oxidative phosphorylation signaling pathways.

Conclusion

Our anoikis-related signature from RNA-seq data may be a novel prognostic biomarker in patients with LUAD. It may help physicians develop personalized LUAD treatments in clinical practice. Moreover, ITGB4 may affect the development of LUAD through the oxidative phosphorylation pathway.

Construction of a novel molecular typing and scoring system for anoikis distinguishes between different prognostic risks and treatment responsiveness in low-grade glioma

Background

The main factors responsible for low-grade glioma (LGG)s' poor prognosis and treatment effectiveness include recurrence and malignant progression. A specific type of programmed cell death, known as anoikis, which is crucial for tumor invasion and metastasis, however, has not yet been investigated in LGGs.

Methods

We downloaded data of 509 samples from the TCGA-LGG cohort, carried out cluster analysis for typing twice on the basis of 19 anoikis-associated genes, and the subtypes were evaluated the differences in clinicopathological and biological features. ESTIMATE and single-sample gene set enrichment analysis were employed to examine the immunological milieu of LGGs, and enrichment analysis was used to look into the underlying biological mechanisms in LGGs. Cox regression analysis and the Least Absolute Shrinkage and Selection Operator regression algorithm were used to create a prediction scoring system. The scoring system was used for classifying LGG into high- and low- anoikis riskscore (anoiS) groups. The impact of the anoiS on the prognosis, standard treatment, and immunotherapy of patients with LGG was assessed using survival analysis and drug sensitivity analysis. Cell experiments were employed for the verification of the differential expression between LGG cells and normal cells of the anoikis gene team that regard CCT5 as the core.

Results

Based on the expression profiles of the 19 anoikis-associated genes, all individuals with LGG were classified into four subtypes and two macrosubtypes. The different macrosubtypes had significantly different biological characteristics, and the anoirgclusterBD subtype manifested a significantly bad prognosis and a high immune level of infiltration. And subsequent secondary genotyping also showed good prognostic discrimination. We further constructed an anoikis scoring system, anoiS. LGG patients having a high anoiS had a worse prognosis in comparison to those having a low anoiS. The high anoiS group exhibited larger levels of immune infiltration and superior immunotherapy efficacy than the low anoiS group. The high anoiS group was also more susceptible to temozolomide (TMZ) than the low anoiS group, according to a drug sensitivity analysis of TMZ.

Conclusion

This study constructed a scoring system for predicting the prognosis of patients with LGG and their responsive to TMZ and immunotherapy.

Identification of a novel anoikis-related gene signature to predict prognosis and tumor microenvironment in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most prevalent histotype of non-small cell lung cancer. Anoikis, an alternative form of programmed cell death, plays a pivotal role in cancer invasion and metastasis, preventing the detached cancer cells from readhering to other substrates for abnormal proliferation. The aim of this study was to conduct a comprehensive analyses of the prognostic implications of anoikis-related genes (ARGs) in LUAD.

METHODS

ARGs were selected from The Cancer Genome Atlas (TCGA) database and Genecards dataset using differential expression analysis. The signature incorporating ARGs was identified using univariate Cox regression analysis and LASSO regression analysis. Furthermore, a nomogram containing the signature and clinical information was developed through univariate and multivariate Cox regression analysis. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves were applied to evaluate the predictive validity of these risk models. Finally, functional analysis of the selected ARGs in signature and analysis of immune landscape were also conducted.

RESULTS

A 16-gene signature was integrated to stratify LUAD patients into different survival risk groups. The prognostic risk score generated from the signature and TNM stage were identified as independent prognostic factors and utilized to develop a nomogram. Both the signature and the nomogram showed satisfactory prediction performance in predicting overall survival (OS) of LUAD patients. The ARGs were enriched in several biological functions and signaling pathways. Finally, differences of immune landscape were investigated among the high- and low-risk groups stratified by the signature.

CONCLUSIONS

This study revealed potential relationships between ARGs and prognosis of LUAD. The prognostic predictors identified in present study could be utilized as potential biomarkers for clinical applications.