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. 2021 Feb 28;22(5):2442.
doi: 10.3390/ijms22052442.

Immunogenomic Identification for Predicting the Prognosis of Cervical Cancer Patients

Qun Wang  1 , Aurelia Vattai  1 , Theresa Vilsmaier  1 , Till Kaltofen  1 , Alexander Steger  2 , Doris Mayr  3 , Sven Mahner  1 , Udo Jeschke  1   4 , Helene Hildegard Heidegger  1
Affiliations

Immunogenomic Identification for Predicting the Prognosis of Cervical Cancer Patients

Qun Wang et al. Int J Mol Sci. .

Abstract

Cervical cancer is primarily caused by the infection of high-risk human papillomavirus (hrHPV). Moreover, tumor immune microenvironment plays a significant role in the tumorigenesis of cervical cancer. Therefore, it is necessary to comprehensively identify predictive biomarkers from immunogenomics associated with cervical cancer prognosis. The Cancer Genome Atlas (TCGA) public database has stored abundant sequencing or microarray data, and clinical data, offering a feasible and reliable approach for this study. In the present study, gene profile and clinical data were downloaded from TCGA, and the Immunology Database and Analysis Portal (ImmPort) database. Wilcoxon-test was used to compare the difference in gene expression. Univariate analysis was adopted to identify immune-related genes (IRGs) and transcription factors (TFs) correlated with survival. A prognostic prediction model was established by multivariate cox analysis. The regulatory network was constructed and visualized by correlation analysis and Cytoscape, respectively. Gene functional enrichment analysis was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A total of 204 differentially expressed IRGs were identified, and 22 of them were significantly associated with the survival of cervical cancer. These 22 IRGs were actively involved in the JAK-STAT pathway. A prognostic model based on 10 IRGs (APOD, TFRC, GRN, CSK, HDAC1, NFATC4, BMP6, IL17RD, IL3RA, and LEPR) performed moderately and steadily in squamous cell carcinoma (SCC) patients with FIGO stage I, regardless of the age and grade. Taken together, a risk score model consisting of 10 novel genes capable of predicting survival in SCC patients was identified. Moreover, the regulatory network of IRGs associated with survival (SIRGs) and their TFs provided potential molecular targets.

Keywords: KEGG; TCGA; bioinformatics analysis; cervical cancer; tumor immune.

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Conflict of interest statement

Sven Mahner reports grants and personal fees from AstraZeneca, personal fees from Clovis, grants, and personal fees from Medac, grants, and personal fees from MSD. He also reports personal fees from Novartis, grants and personal fees from PharmaMar, grants and personal fees from Roche, personal fees from Sensor Kinesis, grants, and personal fees from Tesaro, grants and personal fees from Teva, outside the submitted work. All other authors declare no conflict of interest.

Figures

Figure 1
Figure 1
DEGs. (a) Volcano plot of DEGs between primary cervical cancer and para-tumor tissues. (b) Volcano plot of differentially expressed IRGs. Green dots, down—regulated genes; red dots, up-regulated genes, black dots, no DEGs. (c) The results of KEGG analysis. IRGs, Immune—related genes.
Figure 2
Figure 2
Identification of SIRGs. (a) The most significant KEGG pathways for SIRGs. (b) A forest plot of hazard ratios. The left is the list of SIRGs and their prognostic values showing as name, p-value, and the hazard ratio (95% CI), and the right is the relevant forest plot; Green bar, protective factor; red bar, adverse factor. (c) PPI network. The left is the PPI network, and the right is the number of interactive genes for each gene. (d) The most significant KEGG pathways for the hub SIRGs. SIRGs, immune-related genes associated with survival.
Figure 2
Figure 2
Identification of SIRGs. (a) The most significant KEGG pathways for SIRGs. (b) A forest plot of hazard ratios. The left is the list of SIRGs and their prognostic values showing as name, p-value, and the hazard ratio (95% CI), and the right is the relevant forest plot; Green bar, protective factor; red bar, adverse factor. (c) PPI network. The left is the PPI network, and the right is the number of interactive genes for each gene. (d) The most significant KEGG pathways for the hub SIRGs. SIRGs, immune-related genes associated with survival.
Figure 3
Figure 3
Verification of the efficacy of the prognostic model. (a) Kaplan-Meier plots demonstrated that the prognostic model could distinguish different clinical outcomes from cervical cancer patients (p < 0.05). Blue represents the low-risk group; red represents the high-risk group. (b) The ROC for verifying the accuracy of the predictive model and AUC for the risk score model displayed moderately accuracy in the cancer Genome Atlas (TCGA) dataset. (c) Value of risk score in cervical cancer patients. Both the horizontal axis and the vertical axis represent risk score. From left to right, the risk score is increasing; red dot represents the high- risk case; green dot represents the low-risk case; (d) survival status and time in the two risk groups. From left to right, the risk score is increasing. The vertical axis represents the survival time. (e) Heatmap of the differentially expressed SIRGs involved in the prognostic model. From left to right, the risk score is increasing. Blue represents a high-risk case. Red represents a low-risk case.
Figure 3
Figure 3
Verification of the efficacy of the prognostic model. (a) Kaplan-Meier plots demonstrated that the prognostic model could distinguish different clinical outcomes from cervical cancer patients (p < 0.05). Blue represents the low-risk group; red represents the high-risk group. (b) The ROC for verifying the accuracy of the predictive model and AUC for the risk score model displayed moderately accuracy in the cancer Genome Atlas (TCGA) dataset. (c) Value of risk score in cervical cancer patients. Both the horizontal axis and the vertical axis represent risk score. From left to right, the risk score is increasing; red dot represents the high- risk case; green dot represents the low-risk case; (d) survival status and time in the two risk groups. From left to right, the risk score is increasing. The vertical axis represents the survival time. (e) Heatmap of the differentially expressed SIRGs involved in the prognostic model. From left to right, the risk score is increasing. Blue represents a high-risk case. Red represents a low-risk case.
Figure 4
Figure 4
The clinical significance of IIRGs. The box plots showed that the expressions of TFRC, IL17RD, and HDAC1 were significantly different in subgroups of age and grade. (a,b) Blue represents the group of age ≤ 45years, and red represents the group of age > 45 years. (c) Blue represents the group of grades 1 and 2, and red represents the group of grades 3 and 4.
Figure 5
Figure 5
The construction of a regulatory network between SDETFs and SIRGs. (a) Heatmap of the SDETFs. (b) A forest plot of hazard ratios. The left is the list of SDETFs and their prognostic values showing as name, p-value, and the hazard ratio (95% CI), and the right is the relevant forest plot. Green bar, protective factor; red bar, adverse factor. (c) A regulatory network between SDETFs and SIRGs. Triangle, TFs; Roundness, SIRGs; Red roundness, the overexpressed SIRGs; Green roundness, down-expressed SIRGs. Red line, the TFs up-regulate SIRGs; Green line, the TFs down-regulate SIRGs.
Figure 5
Figure 5
The construction of a regulatory network between SDETFs and SIRGs. (a) Heatmap of the SDETFs. (b) A forest plot of hazard ratios. The left is the list of SDETFs and their prognostic values showing as name, p-value, and the hazard ratio (95% CI), and the right is the relevant forest plot. Green bar, protective factor; red bar, adverse factor. (c) A regulatory network between SDETFs and SIRGs. Triangle, TFs; Roundness, SIRGs; Red roundness, the overexpressed SIRGs; Green roundness, down-expressed SIRGs. Red line, the TFs up-regulate SIRGs; Green line, the TFs down-regulate SIRGs.
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