1,180
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Construction of a ferroptosis and hypoxia-related gene signature in cervical cancer to assess tumour immune microenvironment and predict prognosis

, , , , &
Article: 2321323 | Received 27 Apr 2023, Accepted 15 Feb 2024, Published online: 29 Feb 2024

Figures & data

Figure 1. Construction of risk model in training set. (A) Gene Venn diagram after univariate Cox screening in training set and test set; (B) forest map of 12 prognostic genes in the training set; (C) forest map of 12 prognostic genes in the test set; (D) locus of coefficients for each gene with value log (lambda). The horizontal axis and vertical axis represent the log value of the independent variable lambda and the coefficient of the independent variable, respectively. (E) Confidence intervals under each lambda; (F) Venn diagram of distribution of signature genes.

Figure 1. Construction of risk model in training set. (A) Gene Venn diagram after univariate Cox screening in training set and test set; (B) forest map of 12 prognostic genes in the training set; (C) forest map of 12 prognostic genes in the test set; (D) locus of coefficients for each gene with value log (lambda). The horizontal axis and vertical axis represent the log value of the independent variable lambda and the coefficient of the independent variable, respectively. (E) Confidence intervals under each lambda; (F) Venn diagram of distribution of signature genes.

Figure 2. Effect of risk score on patient survival in training set. (A) Distribution map of risk score in training set; (B) survival status of patients at high and low risk score in training set; (C) expression heat map of eight signature genes in high and low risk groups in training set; (D) Kaplan–Meier’s survival curves of patients with high and low risk score in training set; (E) time-dependent ROC analysis of risk score in training set.

Figure 2. Effect of risk score on patient survival in training set. (A) Distribution map of risk score in training set; (B) survival status of patients at high and low risk score in training set; (C) expression heat map of eight signature genes in high and low risk groups in training set; (D) Kaplan–Meier’s survival curves of patients with high and low risk score in training set; (E) time-dependent ROC analysis of risk score in training set.

Figure 3. Relationship between risk score and clinical features in training set. (A) Multivariate Cox analysis of clinical features and risk score; (B) nomogram of clinical features and risk score; (C) calibration chart in 1-, 3- and 5-year calibration chart; (D) differences in risk score among different clinical subgroups.

Figure 3. Relationship between risk score and clinical features in training set. (A) Multivariate Cox analysis of clinical features and risk score; (B) nomogram of clinical features and risk score; (C) calibration chart in 1-, 3- and 5-year calibration chart; (D) differences in risk score among different clinical subgroups.

Figure 4. Comparison of TIME in high and low risk groups. (A) Differences in the degree of immune cell infiltration between high and low risk groups; (B) differences in immune score between high and low risk groups; (C) differences in stromal score between high and low risk groups; (D) differences in ESTIMATE score between high and low risk groups; (E) differences in tumour purity between high and low risk groups.

Figure 4. Comparison of TIME in high and low risk groups. (A) Differences in the degree of immune cell infiltration between high and low risk groups; (B) differences in immune score between high and low risk groups; (C) differences in stromal score between high and low risk groups; (D) differences in ESTIMATE score between high and low risk groups; (E) differences in tumour purity between high and low risk groups.

Figure 5. Comparison of TMB in high and low risk groups and real-time PCR validation. (A) Differential analysis of TMB in high and low risk groups; (B) correlations between TMB, risk score and signature genes; (C) expression validation of ANO6, PGK1, SLC7A5 and TAZ in cervical cancer samples by real-time PCR.

Figure 5. Comparison of TMB in high and low risk groups and real-time PCR validation. (A) Differential analysis of TMB in high and low risk groups; (B) correlations between TMB, risk score and signature genes; (C) expression validation of ANO6, PGK1, SLC7A5 and TAZ in cervical cancer samples by real-time PCR.
Supplemental material

Supplemental Material

Download Zip (41.3 MB)

Data availability statement

All data generated or analysed during this study are included in this published article.