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. 2020 Aug 24:8:e9627.
doi: 10.7717/peerj.9627. eCollection 2020.

Mining of prognosis-related genes in cervical squamous cell carcinoma immune microenvironment

Jiong Ma #  1 , Pu Cheng #  1   2 , Xuejun Chen  1 , Chunxia Zhou  1 , Wei Zheng  1
Affiliations

Mining of prognosis-related genes in cervical squamous cell carcinoma immune microenvironment

Jiong Ma et al. PeerJ. .

Abstract

Purpose: The aim of this study was to explore the effective immune scoring method and mine the novel and potential immune microenvironment-related diagnostic and prognostic markers for cervical squamous cell carcinoma (CSSC).

Materials and methods: The Cancer Genome Atlas (TCGA) data was downloaded and multiple data analysis approaches were initially used to search for the immune-related scoring system on the basis of Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data (ESTIMATE) algorithm. Afterwards, the representative genes in the gene modules correlated with immune-related scores based on ESTIMATE algorithm were further screened using Weighted Gene Co-expression Network Analysis (WGCNA) and network topology analysis. Gene functions were mined through enrichment analysis, followed by exploration of the correlation between these genes and immune checkpoint genes. Finally, survival analysis was applied to search for genes with significant association with overall survival and external database was employed for further validation.

Results: The immune-related scores based on ESTIMATE algorithm was closely associated with other categories of scores, the HPV infection status, prognosis and the mutation levels of multiple CSCC-related genes (HLA and TP53). Eighteen new representative immune microenvironment-related genes were finally screened closely associated with patient prognosis and were further validated by the independent dataset GSE44001.

Conclusion: Our present study suggested that the immune-related scores based on ESTIMATE algorithm can help to screen out novel immune-related diagnostic indicators, therapeutic targets and prognostic predictors in CSCC.

Keywords: Cervical carcinoma; Immune; Prognosis; TCGA.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Flow diagram of methods for mining of prognostic immune-related genes in CSCC.
Figure 2
Figure 2. The correlations of immune-related scoring system based on ESTIMATE algorithm with other categories of scores among CSCC samples.
(A) The correlations of various immune scoring systems among CSCC samples. Spearman correlation coefficients are shown color-coded to illustrate positive (red) or negative (green) associations. (B) The clustering heat maps of various types of scoring systems. (C) The relationships among immune scores according to four different algorithms. Mete, metagene immune score; Est, ESTIMATE immune score; Timer, Timer immune score; Mcp, MCPcounter immune score.
Figure 3
Figure 3. StromalScore (A), ImmuneScore (B) and ESTIMATEScore (C) distribution among CSCC patients with or without HPV infection.
Figure 4
Figure 4. The relationships between levels of StromalScore (A), ImmuneScore (B) or ESTIMATEScore (C) and prognosis for CSCC patients.
H, High immune score; L, Low immune score.
Figure 5
Figure 5. The correlations of immune-related scores based on ESTIMATE algorithm with gene mutations.
The StromalScore (A, D, G, J), ImmuneScore (B, E, H, K) and ESTIMATEScore (C, F, I, L) were calculated respectively in HLA-A (A, B, C), HLA-B (D, E, F), HLA-C (G, H, I) and TP53 (J, K, L) mutation and non-mutation groups. Green represents the mutant group and red represents the wild type. Mut, Mutant; WT, Wild type.
Figure 6
Figure 6. Immune scores-related gene modules mined through WGCNA.
(A) Sample clustering analysis. (B, C) Analysis of network topology for various soft-thresholding powers. (D) Gene dendrogram and module colors. (E) Correlation between each module and three immune-related scores.
Figure 7
Figure 7. The KEGG pathway and GO enrichment analysis of the genes in yellow module.
(A) Top 20 KEGG pathways enriched by the genes in yellow module. (B) Top 20 GO BP terms enriched by the genes in yellow module. (C) Top 20 GO CC terms enriched by the genes in yellow module. (D) Top 20 GO MF terms enriched by the genes in yellow module. GO, Gene Ontology; BP: biological process; CC, cellular component; MF, molecular function.
Figure 8
Figure 8. Construction of co-expression network of yellow module-related genes.
(A) Co-expression network of weights between genes in yellow module. (B) The degree distribution of nodes in yellow module. (C) The correlation of genes and module in the network.
Figure 9
Figure 9. The KEGG pathway and GO enrichment analysis of 18 novel representative immune microenvironment-related genes for CSCC patients.
(A) Top 20 KEGG pathways enriched by 18 novel representative immune microenvironment-related genes. (B) Top 20 GO BP terms enriched by 18 novel representative immune microenvironment-related genes. (C) Top 20 GO CC terms enriched by 18 novel representative immune microenvironment-related genes. (D) Top 20 GO MF terms enriched by 18 novel representative immune microenvironment-related genes. GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function.
Figure 10
Figure 10. The association between 18 novel representative immune microenvironment-related genes for CSCC patients and immune checkpoints.
Apart from CD276 and VTCN1, the other 6 immune checkpoints were significantly related to 18 immune microenvironment-related genes.
Figure 11
Figure 11. The relationship between 18 novel representative immune microenvironment-related genes and prognosis (A–R).
Apart from DOCK2 (P = 0.12765), SIRPG (P = 0.0658), CD4 (P = 0.29192), NCKAP1L (P = 0.12765) and CCR5 (P = 0.06736), high expression of other 13 genes were significantly associated with better overall survival.
Figure 12
Figure 12. The correlations of 18 immune microenvironment-related genes with ImmuneScore for CSCC patients in independent dataset (A–R).
Apart from CCR5 (P = 0.867, R = 0.01), the other 17 genes were significantly associated with the ImmuneScore.
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