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. 2023 Jul 26;9(8):e18708.
doi: 10.1016/j.heliyon.2023.e18708. eCollection 2023 Aug.

Identification and validation of a novel anoikis-related signature for predicting prognosis and immune landscape in ovarian serous cystadenocarcinoma

Yu-Ting Zhu  1   2 , Shuang-Yue Wu  1   2 , Song Yang  3 , Jie Ying  1   2 , Lu Tian  1   2 , Hong-Liang Xu  4 , He-Ping Zhang  4 , Hui Yao  1   2 , Wei-Yu Zhang  1   2 , Qin-Qin Jin  1   2 , Yin-Ting Yang  1   2 , Xi-Ya Jiang  1   2 , Nan Zhang  1   2 , Shun Yao  1   2 , Shu-Guang Zhou  1   2 , Guo Chen  1   2
Affiliations

Identification and validation of a novel anoikis-related signature for predicting prognosis and immune landscape in ovarian serous cystadenocarcinoma

Yu-Ting Zhu et al. Heliyon. .

Abstract

Background: Ovarian serous cystadenocarcinoma (OSC) is the most prevalent histological subtype of ovarian cancer (OV) and presents a serious threat to women's health. Anoikis is an essential component of metastasis, and tumor cells can get beyond it to become viable. The impact of anoikis on OSC, however, has only been the topic of a few studies.

Methods: The mRNA sequencing and clinical information of OSC came from The Cancer Genome Atlas Target Genotype-Tissue Expression (TCGA TARGET GTEx) dataset. Anoikis-related genes (ARGs) were collected by Harmonizome and GeneCards websites. Centered on these ARGs, we used unsupervised consensus clustering to explore potential tumor typing and filtered hub ARGs to create a model of predictive signature for OSC patients. Furthermore, we presented clinical specialists with a novel nomogram based on ARGs, revealing the underlying clinical relevance of this signature. Finally, we explored the immune microenvironment among various risk groups.

Results: We identified 24 ARGs associated with the prognosis of OSC and classified OSC patients into three subtypes, and the subtype with the best prognosis was more enriched in immune-related pathways. Seven ARGs (ARHGEF7, NOTCH4, CASP2, SKP2, PAK4, LCK, CCDC80) were chosen to establish a risk model and a nomogram that can provide practical clinical decision support. Risk scores were found to be an independent and significant prognostic factor in OSC patients. The CIBERSORTx result revealed an inflammatory microenvironment is different for risk groups, and the proportion of immune infiltrates of Macrophages M1 is negatively correlated with risk score (rs = -0.21, P < 0.05). Ultimately, quantitative reverse transcription polymerase chain reaction (RT-PCR) was utilized to validate the expression of the seven pivotal ARGs.

Conclusion: In this study, based on seven ARGs, a risk model and nomogram established can be used for risk stratification and prediction of survival outcomes in patients with OSC, providing a reliable reference for individualized therapy of OSC patients.

Keywords: Anoikis; Immune landscape; Ovarian serous cystadenocarcinoma; Prognosis; Tumor metastasis.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Differences and characteristics of ARGs in OSC. (A) Heatmap and (B) volcano plot of gene expression in OSC and normal ovarian samples. (C) Differential expression of 313 ARGs in OSC and normal ovarian samples. (D) The forest plot suggested that 24 ARGs were associated with OSC prognosis. (E) The circle plot suggested GO analysis of 24 ARGs.
Fig. 2
Fig. 2
Subtypes of OSC related by 24 ARGs. (A) The consensus matrix for k = 3 was derived by consensus clustering. (B) OS for three subtypes. (C) UMAP and (D) tSNE differentiate three subtypes. (E) GSVA analyses focused on KEGG differences between Cluster A and Cluster C. (F) GSVA analyses focused on KEGG differences between Cluster B and Cluster C.
Fig. 3
Fig. 3
Identify ARGs. (A) Lasso coefficient values were calculated regarding 24 ARGs in OSC. (B) The overview of Lasso coefficients. (C) OS of risk groups in TCGA OV set (p < 0.0001). (D) OS of risk groups in the Test set. (E, F) The ROC curves of OS at 1-, 3-, 5-, and 7-year intervals. (G) Risk score in 3 clusters. (H) The Sankey diagram demonstrates clusters, risk scores, and life status.
ig. 4
ig. 4
Establishment and validation of prognostic nomogram. (A) Multivariate Cox regression forest plot of risk scores and clinical features in OSC patients. (B) Nomogram plot. (C, D) Calibration plot for the nomogram in TCGA OV set and Test set. (E, F) DCA curves of the nomogram and risk score for 1-,3-year OS in OSC.
Fig. 5
Fig. 5
Immunological microenvironment of OSC in risk groups. (A) Proportions of infiltrating immune cells in 424 OSC patients. (B) Correlation analysis between risk scores and the abundance of five immune cells. (C) Disparities in immune infiltrate proportions between risk groups.(D) Correlation among 22 immune cell types.
Fig. 6
Fig. 6
Association between immune infiltration and ARGs signatures. (A)The box plots depict the expression patterns of seven hub genes. (B) Association among seven hub genes and 22 Immune cells.
Fig. 7
Fig. 7
Results of RT-PCR. The expression of ARHGEF7 (A), NOTCH4 (B), CASP2 (C), SKP2 (D), PAK4 (E), LCK (F), and CCDC80(G) in the three cell lines. *p < 0.05, **p < 0.01, ***p < 0.001.
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