Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Mar 3:8:625470.
doi: 10.3389/fmolb.2021.625470. eCollection 2021.

Exploration of a Robust and Prognostic Immune Related Gene Signature for Cervical Squamous Cell Carcinoma

Zhihua Zuo  1 , Junjun Xiong  2 , Chuyi Zeng  1 , Yao Jiang  1 , Kang Xiong  3 , Hualin Tao  1 , Yongcan Guo  4
Affiliations

Exploration of a Robust and Prognostic Immune Related Gene Signature for Cervical Squamous Cell Carcinoma

Zhihua Zuo et al. Front Mol Biosci. .

Abstract

Background: Cervical squamous cell carcinoma (CESC) is one of the most frequent malignancies in women worldwide. The level of immune cell infiltration and immune-related genes (IRGs) can significantly affect the prognosis and immunotherapy of CESC patients. Thus, this study aimed to identify an immune-related prognostic signature for CESC. Methods: TCGA-CESC cohorts, obtained from TCGA database, were divided into the training group and testing group; while GSE44001 dataset from GEO database was viewed as external validation group. ESTIMATE algorithm was applied to evaluate the infiltration levels of immune cells of CESC patients. IRGs were screened out through weighted gene co-expression network analysis (WGCNA). A multi-gene prognostic signature based on IRGs was constructed using LASSO penalized Cox proportional hazards regression, which was validated through Kaplan-Meier, Cox, and receiver operating characteristic curve (ROC) analyses. The abundance of immune cells was calculated using ssGSEA algorithm in the ImmuCellAI database, and the response to immunotherapy was evaluated using immunophenoscore (IPS) analysis and the TIDE algorithm. Results: In TCGA-CESC cohorts, higher levels of immune cell infiltration were closely associated with better prognoses. Moreover, a prognostic signature was constructed using three IRGs. Based on this given signature, Kaplan-Meier analysis suggested the significant differences in overall survival (OS) and the ROC analysis demonstrated its robust predictive potential for CESC prognosis, further confirmed by internal and external validation. Additionally, multivariate Cox analysis revealed that the three IRGs signature served as an independent prognostic factor for CESC. In the three-IRGs signature low-risk group, the infiltrating immune cells (B cells, CD4/8 + T cells, cytotoxic T cells, macrophages and so on) were much more abundant than that in high-risk group. Ultimately, IPS and TIDE analyses showed that low-risk CESC patients appeared to present with a better response to immunotherapy and a better prognosis than high-risk patients. Conclusion: The present prognostic signature based on three IRGs (CD3E, CD3D, LCK) was not only reliable for survival prediction but efficient to predict the clinical response to immunotherapy for CESC patients, which might assist in guiding more precise individual treatment in the future.

Keywords: cervical squamous cell carcinoma; immune cells infiltration; immunotherapy sensitivity; prognosis; weighted gene co-expression network analysis.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The flow diagram of this study. TCGA, the Cancer Genome Atlas; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; ROC curve, receiver operating characteristic curve; IPS, Immunophenoscore; TIDE, the Tumor Immune Dysfunction and Exclusion.
FIGURE 2
FIGURE 2
Associations between immune/stromal/ESTIMATE scores and CESC patients’ prognosis.
FIGURE 3
FIGURE 3
The cluster dendrogram of module eigengenes.
FIGURE 4
FIGURE 4
Analysis of key immune-related modules. (A), The correlation between modules and traits was displayed. (B–C) The correlation between GS and MM in the green and dark-turquoise modules. GS, gene significance; MM, module membership.
FIGURE 5
FIGURE 5
GO enrichment and KEGG pathway analyses of significant module genes. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.
FIGURE 6
FIGURE 6
PPI network and biological process analyses of hub IRGs related to prognosis. (A) The common genes between modules genes and IRGs from ImmPort database. (B) PPI network of 20 hub IRGs. (C) Correlation analysis of 20 hub IRGs. (D) Biological process analysis of 20 hub IRGs. PPI network, protein-protein interaction network; IRGs, immune-related genes.
FIGURE 7
FIGURE 7
Construction of prognostic signature based on four hub IRGs in training group. (A) The distribution of risk scores and survival status between low- and high-risk groups, and mean level of risk score was set as the cut-off value. (B) The overall survival analysis of patients in two subgroups. (C) ROC curve analysis for the prediction of 1-, 3-, and 5-year OS as the defining point of the four-hub IRGs signature. (D) Heatmap of four prognostic IRGs. IRGs, immune-related genes; ROC curve, receiver operating characteristic curve; OS, overall survival.
FIGURE 8
FIGURE 8
Validation of prognostic signature based on four hub IRGs in testing group. (A) The distribution of risk scores and survival status between low- and high-risk groups, and mean level of risk score was set as the cut-off value. (B) The overall survival analysis of patients in two subgroups. (C) ROC curve analysis for the prediction of 1-, 3-, and 5-year OS as the defining point of the four-hub IRGs signature. (D) Heatmap of four prognostic IRGs. IRGs, immune-related genes; ROC curve, receiver operating characteristic curve; OS, overall survival.
FIGURE 9
FIGURE 9
Validation of prognostic signature based on four hub IRGs in external group. (A) The distribution of risk scores and survival status between low- and high-risk groups, and mean level of risk score was set as the cut-off value. (B) The overall survival analysis of patients in two subgroups. (C) ROC curve analysis for the prediction of 1-, 3-, and 5-year OS as the defining point of the four-hub IRGs signature. (D) Heatmap of four prognostic IRGs. IRGs, immune-related genes; ROC curve, receiver operating characteristic curve; OS, overall survival.
FIGURE 10
FIGURE 10
The correlation of CD3D, CD3E, LCK expression with immune checked molecular, including PDCD1(PD1), CD274(PD-L1), and CTLA4.
FIGURE 11
FIGURE 11
Comparison of immune infiltration patterns of CESC patients between low- and high-risk groups. CESC, cervical squamous cell carcinoma.
FIGURE 12
FIGURE 12
Immunogenicity and immunotherapeutic sensitivity with prognostic signature. (A–D) the IPS, IPS-CTLA4 blocker, IPS-PD1-PD-L1-PD-L2 blocker, and IPS-PD1-PD-L1-PD-L2-CTLA4 blocker scores between low- and high-risk groups. (E) Immune infiltrating cell profile of tumor microenvironment of CESC patients. (F) The differences of immunotherapy sensitivity between low- and high-risk groups. (G) Survival analysis of different immunotherapy sensitivity. IPS, Immunophenoscore; ICIs, immune check inhibitors; CESC, cervical squamous cell carcinoma.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Alvarez M., Simonetta F., Baker J., Morrison A. R., Wenokur A. S., Pierini A., et al. (2020). Indirect impact of PD-1/PD-L1 blockade on a murine model of NK cell exhaustion. Front. Immunol. 11, 7. 10.3389/fimmu.2020.00007 - DOI - PMC - PubMed
    1. Bhattacharya S., Andorf S., Gomes L., Dunn P., Schaefer H., Pontius J., et al. (2014). ImmPort: disseminating data to the public for the future of immunology. Immunol. Res. 58 (2–3), 234–239. 10.1007/s12026-014-8516-1 - DOI - PubMed
    1. Bindea G., Mlecnik B., Tosolini M., Kirilovsky A., Waldner M., Obenauf A. C., et al. (2013). Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39 (4), 782–795. 10.1016/j.immuni.2013.10.003 - DOI - PubMed
    1. Bommhardt U., Schraven B., Simeoni L. (2019). Beyond TCR signaling: emerging functions of lck in cancer and immunotherapy. Int. J. Mol. Sci. 20 (14), 3500. 10.3390/ijms20143500 - DOI - PMC - PubMed
    1. Bray F., Ferlay J., Soerjomataram I., Siegel R. L., Torre L. A., Jemal A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68 (6), 394–424. 10.3322/caac.21492 - DOI - PubMed