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. 2024 Jun 7;10(12):e32273.
doi: 10.1016/j.heliyon.2024.e32273. eCollection 2024 Jun 30.

Identification and validation of immunity- and disulfidptosis-related genes signature for predicting prognosis in ovarian cancer

Miaojia Jin  1 , Dan Ni  2 , Jianshu Cai  1 , Jianhua Yang  3
Affiliations

Identification and validation of immunity- and disulfidptosis-related genes signature for predicting prognosis in ovarian cancer

Miaojia Jin et al. Heliyon. .

Abstract

Background: Ovarian cancer (OC) ranks as the fifth most prevalent neoplasm in women and exhibits an unfavorable prognosis. To improve the OC patient's prognosis, a pioneering risk signature was formulated by amalgamating disulfidptosis-related genes.

Methods: A comparative analysis of OC tissues and normal tissues was carried out, and differentially expressed disulfidptosis-related genes (DRGs) were found using the criteria of |log2 (fold change) | > 0.585 and adjusted P-value <0.05. Subsequently, the TCGA training set was utilized to create a prognostic risk signature, which was validated by employing both the TCGA testing set and the GEO dataset. Moreover, the immune cell infiltration, mutational load, response to chemotherapy, and response to immunotherapy were analyzed. To further validate these findings, QRT-PCR analysis was conducted on ovarian tumor cell lines.

Results: A risk signature was created using fourteen differentially expressed genes (DEGs) associated with disulfidptosis, enabling the classification of ovarian cancer (OC) patients into high-risk group (HRG) and low-risk group (LRG). The HRG exhibited a lower overall survival (OS) compared to the LRG. In addition, the risk score remained an independent predictor even after incorporating clinical factors. Furthermore, the LRG displayed lower stromal, immune, and estimated scores compared to the HRG, suggesting a possible connection between the risk signature, immune cell infiltration, and mutational load. Finally, the QRT-PCR experiments revealed that eight genes were upregulated in the human OC cell line SKOV3 compared with the human normal OC line IOSE80, while six genes were down-regulated.

Conclusions: A fourteen-biomarker signature composed of disulfidptosis-related genes could serve as a valuable risk stratification tool in OC, facilitating the identification of patients who may benefit from individualized treatment and follow-up management.

Keywords: Bioinformatics; Disulfidptosis; Disulfidptosis-related genes (DRGs); Immunity; Ovarian cancer.

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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
Genetic and transcriptional alterations of DRGs in OC. (A) Distributions of 14 DRGs that differ in expression between the normal ovary and OC tissues (*p < 0.05, ***p < 0.001); (B) Expression correlation between 14 DRGs; (C) PPI network showing the interactions of the DRGs; (D) Frequencies of CNV gain, loss, and non-CNV among DRGs; (E) Locations of CNV alterations in DRGs on 23 chromosomes; (F) Mutation frequencies of 14 DRGs in OC patients from the TCGA cohort.
Fig. 2
Fig. 2
The prognosis significance of DRGs of OV patients. (A) Kaplan–Meier survival curves (KMSC) indicated that OC patients with high DSTN mRNA expression had a shorter OS; (B) KMSC manifested that OC patients with low CD2AP mRNA expression had a shorter OS; (C–L) KMSC manifested that OC patients with high ACTN4, FLNA, CAPZB, FLNB, TLN1, INF2, IQGAP1, MYH9, MYH10, and MYL6 mRNA expression had a shorter OS.
Fig. 3
Fig. 3
Landscape of the DRGs and biological characteristics of disulfidptosis subtypes in ovarian cancer. (A) Consensus matrix of OC patients, k = 2, using the unsupervised consensus clustering method; (B) Principal component analysis of 14 DRGs in all OC cohorts found two distinct subtypes; (C) Kaplan–Meier curves for overall survival of all OC patients; (D) Variations in clinicopathologic features and expression levels of 14 DRGs in all OC cohorts among the two distinct subtypes. Tumor stage, age, survival status, and cluster were used as patient annotations. Red and blue represent high and low expression of disulfidptosis genes, respectively; (E) Comparison of the ssGSEA scores for immune cells in the two OC subtypes. The line in the box represents the median value (*p < 0.05, **p < 0.01, ***p < 0.001).
Fig. 4
Fig. 4
Correlations of TME and biological characteristics in two OC subtypes. (A) a GSVA of KEGG biological pathways in two disulfidptosis subtypes; (B) KEGG enrichment analysis of DRGs; (C–D) GO enrichment analysis of DRGs.
Fig. 5
Fig. 5
Identification of gene subtypes based on the DEGs of Disulfidptosis-related clusters. (A–B) Identification of gene subtypes based on prognostic DEGs among TWO disulfidptosis subtypes in OC cohort. (C) Kaplan–Meier curves for overall survival of all OC patients with two gene subtypes. (D) Heat map showing the relationships between clinicopathologic features and the two gene subtypes. (E) Differences in the expression of 14 DRGs among the two gene clusters. (F) An alluvial diagram of the distribution of disulfidptosis cluster, gene cluster in two risk groups, as well as survival outcomes. (G) The Kaplan–Meier survival curves were stratified by disulfidptosis cluster and risk subgroup. (H) Difference of risk score among two cuproptosis clusters. (I) Difference of risk score among two gene clusters. (J) The differential analysis of DRGs expression in the collection file (*p < 0.05, **p < 0.01, ***p < 0.001).
Fig. 6
Fig. 6
The landscape of immune microenvironment with prognostic signature. (A) Correlations between risk score and immune score, stromal score, and ESTIMATE score. (B) Correlation between risk score and stem cell content (RNAss). (C–D) Correlations between risk score and different immune cells. (E–F) Mutation frequencies of the high- and low-risk groups. (G) The mRNA expression of fourteen genes in cell line SKOV3 and IOSE80 was measured by QRT-PCR (****P < 0.0001; ***P < 0.001; *P < 0.05; ns, not statistically different, Error bars are ± SEM).
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