Anoikis patterns exhibit distinct prognostic and immune landscapes in Osteosarcoma

Zhang, Zhao; Zhu, Zhijie; Fu, Jun; Liu, Xincheng; Mi, Zhenzhou; Tao, Huiren; Fan, Hongbin

doi:10.1016/j.intimp.2023.109684

Cited by 14 publications

(7 citation statements)

References 35 publications

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“…Further, the Anoikis score may also serve as an indicator for the response of immunotherapy. Similar findings were also reported in other cancer types, as ARGs were significantly associated with TME (Guizhen et al, 2022;Zhang et al, 2023). Although the anoikis scoring system achieved good predictive performance, high intratumor heterogeneity between samples may limit further application of this tool.…”

Section: Discussionsupporting

confidence: 82%

Identification of anoikis-related molecular patterns to define tumor microenvironment and predict immunotherapy response and prognosis in soft-tissue sarcoma

Chen

Wang

et al. 2023

Front. Pharmacol.

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Background: Soft-tissue sarcoma (STS) is a massive threat to human health due to its high morbidity and malignancy. STS also represents more than 100 histologic and molecular subtypes, with different prognosis. There is growing evidence that anoikis play a key role in the proliferation and invasion of tumors. However, the effects of anoikis in the immune landscape and the prognosis of STS remain unclear.Methods: We analyzed the genomic and transcriptomic profiling of 34 anoikis-related genes (ARGs) in patient cohort of pan-cancer and STS from The Cancer Genome Atlas (TCGA) database. Single-cell transcriptome was used to disclose the expression patterns of ARGs in specific cell types. Gene expression was further validated by real-time PCR and our own sequencing data. We established the Anoikis cluster and Anoikis subtypes by using unsupervised consensus clustering analysis. An anoikis scoring system was further built based on the differentially expressed genes (DEGs) between Anoikis clusters. The clinical and biological characteristics of different groups were evaluated.Results: The expressions of most ARGs were significantly different between STS and normal tissues. We found some common ARGs profiles across the pan-cancers. Network of 34 ARGs demonstrated the regulatory pattern and the association with immune cell infiltration. Patients from different Anoikis clusters or Anoikis subtypes displayed distinct clinical and biological characteristics. The scoring system was efficient in prediction of prognosis and immune cell infiltration. In addition, the scoring system could be used to predict immunotherapy response.Conclusion: Overall, our study thoroughly depicted the anoikis-related molecular and biological profiling and interactions of ARGs in STS. The Anoikis score model could guide the individualized management.

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Section: Discussionsupporting

confidence: 82%

Identification of anoikis-related molecular patterns to define tumor microenvironment and predict immunotherapy response and prognosis in soft-tissue sarcoma

Chen

Wang

et al. 2023

Front. Pharmacol.

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“…Two compounds (doxorubicin and staurosporine) selectively target OS cells, induce caspase 3 and 7 activity in U3OS cells, and promote apoptosis in OS cell line 30 . In addition, various cell death-related gene signatures have been constructed to predict the prognosis of patients with OS, including proptosis-related lncRNA-based and anoiki-based prognostic signatures 31 , 32 .…”

Section: Discussionmentioning

confidence: 99%

Transcriptome and single-cell analysis reveal disulfidptosis-related modification patterns of tumor microenvironment and prognosis in osteosarcoma

Wang,

Liu,

Tai

et al. 2024

Sci Rep

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Osteosarcoma (OS) is the most common malignant bone tumor with high pathological heterogeneity. Our study aimed to investigate disulfidptosis-related modification patterns in OS and their relationship with survival outcomes in patients with OS. We analyzed the single-cell-level expression profiles of disulfidptosis-related genes (DSRGs) in both OS microenvironment and OS subclusters, and HMGB1 was found to be crucial for intercellular regulation of OS disulfidptosis. Next, we explored the molecular clusters of OS based on DSRGs and related immune cell infiltration using transcriptome data. Subsequently, the hub genes of disulfidptosis in OS were screened by applying multiple machine models. In vitro and patient experiments validated our results. Three main disulfidptosis-related molecular clusters were defined in OS, and immune infiltration analysis suggested high immune heterogeneity between distinct clusters. The in vitro experiment confirmed decreased cell viability of OS after ACTB silencing and higher expression of ACTB in patients with lower immune scores. Our study systematically revealed the underlying relationship between disulfidptosis and OS at the single-cell level, identified disulfidptosis-related subtypes, and revealed the potential role of ACTB expression in OS disulfidptosis.

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“…Overall, these findings suggested that the high levels of eosinophils and neutrophils may predict a poorer tumor prognosis, as they can promote tumor migration and metastasis formation by releasing cytokines. Finally, recent studies have reported that the correlation between anoikis score and sensitivity to chemotherapy drugs in several cancers [ 33 , 34 ]. Liu et al revealed that a synergistic lethal effect upon co-administration of AZD5153 and palbociclib, resulting in cell cycle arrest and heightened apoptosis, even in cell lines with impaired RB functionality in patients with ovarian cancer [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

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

Deng,

Zhang,

Tang

et al. 2024

World J Oncol

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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.

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Anoikis patterns exhibit distinct prognostic and immune landscapes in Osteosarcoma

Cited by 14 publications

References 35 publications

Identification of anoikis-related molecular patterns to define tumor microenvironment and predict immunotherapy response and prognosis in soft-tissue sarcoma

Identification of anoikis-related molecular patterns to define tumor microenvironment and predict immunotherapy response and prognosis in soft-tissue sarcoma

Transcriptome and single-cell analysis reveal disulfidptosis-related modification patterns of tumor microenvironment and prognosis in osteosarcoma

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

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