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. 2024 May 28;49(5):758-774.
doi: 10.11817/j.issn.1672-7347.2024.230519.

Prognositic value of anoikis and tumor immune microenvironment-related gene in the treatment of osteosarcoma

[Article in English, Chinese]
Dong Wang  1 , Qing Deng  2 , Yi Peng  3 , Zhaochen Tong  3 , Zixin Li  3 , Liping Huang  3 , Jin Zeng  3 , Jinsong Li  3 , Jinglei Miao  3 , Shijie Chen  4   5
Affiliations

Prognositic value of anoikis and tumor immune microenvironment-related gene in the treatment of osteosarcoma

[Article in English, Chinese]
Dong Wang et al. Zhong Nan Da Xue Xue Bao Yi Xue Ban. .

Abstract
in English, Chinese, Chinese

Objectives: Osteosarcoma is a highly aggressive primary malignant bone tumor commonly seen in children and adolescents, with a poor prognosis. Anchorage-dependent cell death (anoikis) has been proven to be indispensable in tumor metastasis, regulating the migration and adhesion of tumor cells at the primary site. However, as a type of programmed cell death, anoikis is rarely studied in osteosarcoma, especially in the tumor immune microenvironment. This study aims to clarify prognostic value of anoikis and tumor immune microenvironment-related gene in the treatment of osteosarcoma.

Methods: Anoikis-related genes (ANRGs) were obtained from GeneCards. Clinical information and ANRGs expression profiles of osteosarcoma patients were sourced from the therapeutically applicable research to generate effective therapies and Gene Expression Omnibus (GEO) databases. ANRGs highly associated with tumor immune microenvironment were identified by the estimate package and the weighted gene coexpression network analysis (WGCNA) algorithm. Machine learning algorithms were performed to construct long-term survival predictive strategy, each sample was divided into high-risk and low-risk subgroups, which was further verified in the GEO cohort. Finally, based on single-cell RNA-seq from the GEO database, analysis was done on the function of signature genes in the osteosarcoma tumor microenvironment.

Results: A total of 51 hub ANRGs closely associated with the tumor microenvironment were identified, from which 3 genes (MERTK, BNIP3, S100A8) were selected to construct the prognostic model. Significant differences in immune cell activation and immune-related signaling pathways were observed between the high-risk and low-risk groups based on tumor microenvironment analysis (all P<0.05). Additionally, characteristic genes within the osteosarcoma microenvironment were identified in regulation of intercellular crosstalk through the GAS6-MERTK signaling pathway.

Conclusions: The prognostic model based on ANRGs and tumor microenvironment demonstrate good predictive power and provide more personalized treatment options for patients with osteosarcoma.

目的: 骨肉瘤是一种极具侵袭性的原发性恶性骨肿瘤，多见于儿童和青少年，预后差。矢巢凋亡(anchorage-dependent cell death，anoikis)已被证明在肿瘤转移中具有重要作用，可调节肿瘤细胞在原发部位的迁移和黏附。作为一种程序性细胞死亡的形式，anoikis在骨肉瘤中的研究较少，尤其是在肿瘤免疫微环境中的研究。本研究旨在阐明anoikis和肿瘤免疫微环境相关基因在骨肉瘤治疗中的预后价值。方法: 从GeneCards中获得anoikis相关基因(anoikis-related genes，ANRGs)；从产生有效疗法的治疗应用研究和基因表达综合(Gene Expression Omnibus，GEO)数据库中获取骨肉瘤患者的临床信息和ANRGs表达状况。利用估计包和加权基因共表达网络分析(weighted gene coexpression network analysis，WGCNA)算法识别出与肿瘤免疫微环境高度相关的ANRGs；采用机器学习算法构建长期生存预测模型，将样本分为高危组和低危组，并在GEO队列中进行进一步验证。最后，基于来自GEO数据库的单细胞RNA测序，分析骨肉瘤肿瘤微环境中特征基因的功能。结果: 51个与肿瘤微环境高度相关的枢纽ANRGs被证实，从中选择3个基因(MERTK、BNIP3、S100A8)构建预后模型。根据肿瘤微环境分析，在免疫细胞激活和免疫相关信号通路方面，高危组和低危组之间的差异均具有统计学意义(均P<0.05)。骨肉瘤微环境中的特征基因被证实通过参与GAS6-MERTK信号通路调控细胞间的串扰。结论: 基于ANRGs和肿瘤微环境的预后模型具有良好的预测能力，可为骨肉瘤患者提供更多个性化治疗方案。.

目的: 骨肉瘤是一种极具侵袭性的原发性恶性骨肿瘤，多见于儿童和青少年，预后差。矢巢凋亡(anchorage-dependent cell death，anoikis)已被证明在肿瘤转移中具有重要作用，可调节肿瘤细胞在原发部位的迁移和黏附。作为一种程序性细胞死亡的形式，anoikis在骨肉瘤中的研究较少，尤其是在肿瘤免疫微环境中的研究。本研究旨在阐明anoikis和肿瘤免疫微环境相关基因在骨肉瘤治疗中的预后价值。

方法: 从GeneCards中获得anoikis相关基因(anoikis-related genes，ANRGs)；从产生有效疗法的治疗应用研究和基因表达综合(Gene Expression Omnibus，GEO)数据库中获取骨肉瘤患者的临床信息和ANRGs表达状况。利用估计包和加权基因共表达网络分析(weighted gene coexpression network analysis，WGCNA)算法识别出与肿瘤免疫微环境高度相关的ANRGs；采用机器学习算法构建长期生存预测模型，将样本分为高危组和低危组，并在GEO队列中进行进一步验证。最后，基于来自GEO数据库的单细胞RNA测序，分析骨肉瘤肿瘤微环境中特征基因的功能。

结果: 51个与肿瘤微环境高度相关的枢纽ANRGs被证实，从中选择3个基因( MERTK、 BNIP3、 S100A8)构建预后模型。根据肿瘤微环境分析，在免疫细胞激活和免疫相关信号通路方面，高危组和低危组之间的差异均具有统计学意义(均 P<0.05)。骨肉瘤微环境中的特征基因被证实通过参与GAS6-MERTK信号通路调控细胞间的串扰。

结论: 基于ANRGs和肿瘤微环境的预后模型具有良好的预测能力，可为骨肉瘤患者提供更多个性化治疗方案。

Keywords: anoikis; bioinformatics; osteosarcoma; prognosis; tumor immune microenvironment.

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Conflict of interest statement

The authors declare that they have no conflicts of interest to disclose.

Figures

Figure 1
Figure 1. Whole analysis process of this research
TARGET: Therapeutically Applicable Research to Generate Effective Therapies databases; OS: Osteosarcoma; ANRGs: Anoikis-related genes; TIME: Tumor immune microenvironment; LASSO: Least absolute shrinkage and selection operator regression; KM: Kaplan-Meier; ROC: Receiver operator characteristic; DCA: Decision curve analysis; GSEA: Gene set enrichment analysis; GSVA: Gene set variation analysis; TIMER: Tumor immune estimation resource.
Figure 2
Figure 2. Identification of hub ANRGs highly associated with TIME
A and D: Clustering tree of dissimilar ANRGs based on the topological overlap, and specified merge module colors and original module colors in TARGET (A) and GSE21257 (D) cohort, respectively. B: Heat map of module-trait correlations in TARGET cohort. The hub (148) ANRGs in the brown module presented the strongest association with immune score and stromal score. C: Network diagram shows the relationship between these hub genes and the prognosis of OS. Purple is the risk factor, and green is the benefit factor ( P<0.001). E: Heat map of module-trait correlations in the GEO cohort. Regard to immune score and stromal score in the TIME, the gene set (110 ANRGs) in the blue module had the most vital connection. F: Final 51 ANRGs most relevant to the TIME of OS through the intersection of hub genes from the TARGET and the GEO databases. ANRGs: Anoikis-related genes; GEO: Gene Expression Omnibus; TIME: Tumor immune microenvironment; OS: Osteosarcoma.
Figure 3
Figure 3. Functional enrichment analysis of Hub ANRGs and selection of prognostic genes
A and B: Fifth-one genes are shown to be powerfully relevant to the TIME by GO functional enrichment analysis. C and D: Fifth-one genes are shown to be strongly relevant to the TIME and PCD by KEGG functional enrichment analysis. E and F: Three ANRGs with prognosis were screened by LASSO regression and multivariate analysis. ANRGs: Anoikis-related genes; TIME: Tumor immune microenvironment; GO: Gene Ontology; PCD: Programmed cell death; KEGG: Kyoto Encyclopedia of Genes and Genomes.
Figure 4
Figure 4. Constructed the prognostic risk model
A-C: KM curve and time-dependent ROC curve in all TARGET queue (A), training queue (B), and test queue (C); D: Nomogram for 1, 3, and 5-year overall survival of specimens combined risk model with clinical features; E: Calibration curves showing association between the model prediction probability and the observed probability, and the dotted line referring to the ideal nomogram; F: Cumulative risk curve based on the nomogram. G: DCA curves based on the risk model and nomo-risk model. KM: Kaplan-Meie; ROC: Receiver operator characteristic; DCA: Decision curve analysis.
Figure 5
Figure 5. Prognostic risk model for predicting immune response
A: GSEA analysis in the low-risk group; B: Heatmap of GSVA analysis in both groups; C: Heatmap showing correlations between major signaling pathways and model genes or risk scores; D: Spearman rank correlation coefficient between the risk score and immune cells in TIME; E: Barplot displaying the TIME composition of each sample; F: Correlation heatmap of immune cell in TIME; G: Box plots showing the correlation in immune function between the low-risk and high-risk groups; H: Heatmap showing correlations between immune checkpoint-related genes and signature genes and risk scores. GSEA: Gene set enrichment analysis; TIME: Tumor immune microenvironment.
Figure 6
Figure 6. Validation of prognostic model
A and B: KM curve (A) and time-dependent ROC curve (B) in the GSE21257 cohort; C: Nomogram plot based on ANRG score and clinical factors; D: Validation of the nomogram; E: Cumulative risk curve based on nomogram. KM: Kaplan-Meie; ROC: Receiver operator characteristic; ANRG: Anoikis-related gene.
Figure 7
Figure 7. Comparison of prognostic models
A: Multivariate analysis of risk-score and clinical characteristics; B: ROC curve based on the clinical features and the risk level; C and D: KM survival curves for different ages (C) and genders (D); E: Concordant index of different prognostic models; F: Restricted mean survival (RMS) time for the different models. KM: Kaplan-Meier; ROC: Receiver operator characteristic
Figure 8
Figure 8. Crosstalk between prognostic model genes and TME
A: Ten types of cells identified in TME; B: Scatter plot of prognostic gene expression; C: Violin plot of prognostic gene expression; D: Two ligand receptor (L-R) pairs of the GAS6/TAM signaling pathway in the tumor microenvironment; E: Hierarchy plot of GAS6/TAM signaling pathway between intercellular cross-talk; F: Violin plot of 2 L-R pairs expressed in different cells; G: Chord plot of GAS6-MERTK L-R pair involved in intercellular cross-talk. TME: Tumor microenvironment.
Figure 9
Figure 9. Pan-cancer analysis of MERTK
A: Boxplot shows the expression of MERTK in different tumors. *P<0.05, **P<0.01, ***P<0.001. B: Differences are showed in MERTK expression between the tumor tissues and the para-cancer tissues.
Figure 10
Figure 10. Correlation between the risk score and the drug sensitivity
See this image and copyright information in PMC

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