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. 2022 Aug 31:13:921837.
doi: 10.3389/fgene.2022.921837. eCollection 2022.

Determination of a prediction model for therapeutic response and prognosis based on chemokine signaling-related genes in stage I-III lung squamous cell carcinoma

Jinzhi Lai  1 , Shiyu Yang  2 , Shuqiang Chu  3 , Tianwen Xu  1 , Jingshan Huang  2
Affiliations

Determination of a prediction model for therapeutic response and prognosis based on chemokine signaling-related genes in stage I-III lung squamous cell carcinoma

Jinzhi Lai et al. Front Genet. .

Abstract

Background: The chemokine signaling pathway plays an essential role in the development, progression, and immune surveillance of lung squamous cell carcinoma (LUSC). Our study aimed to systematically analyze chemokine signaling-related genes (CSRGs) in LUSC patients with stage I-III disease and develop a prediction model to predict the prognosis and therapeutic response. Methods: A total of 610 LUSC patients with stage I-III disease from three independent cohorts were included in our study. Least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses were used to develop a CSRG-related signature. GSVA and GSEA were performed to identify potential biological pathways. The ESTIMATE algorithm, ssGSEA method, and CIBERSORT analyses were applied to explore the correlation between the CSRG signature and the tumor immune microenvironment. The TCIA database and pRRophetic algorithm were utilized to predict responses to immunochemotherapy and targeted therapy. Results: A signature based on three CSRGs (CCL15, CXCL7, and VAV2) was developed in the TCGA training set and validated in the TCGA testing set and GEO external validation sets. A Kaplan-Meier survival analysis revealed that patients in the high-risk group had significantly shorter survival than those in the low-risk group. A nomogram combined with clinical parameters was established for clinical OS prediction. The calibration and DCA curves confirmed that the prognostic nomogram had good discrimination and accuracy. An immune cell landscape analysis demonstrated that immune score and immune-related functions were abundant in the high-risk group. Interestingly, the proportion of CD8 T-cells was higher in the low-risk group than in the high-risk group. Immunotherapy response prediction indicated that patients in the high-risk group had a better response to CTLA-4 inhibitors. We also found that patients in the low-risk group were more sensitive to first-line chemotherapeutic treatment and EGFR tyrosine kinase inhibitors. In addition, the expression of genes in the CSRG signature was validated by qRT‒PCR in clinical tumor specimens. Conclusion: In the present study, we developed a CSRG-related signature that could predict the prognosis and sensitivity to immunochemotherapy and targeted therapy in LUSC patients with stage I-III disease. Our study provides an insight into the multifaceted role of the chemokine signaling pathway in LUSC and may help clinicians implement optimal individualized treatment for patients.

Keywords: chemokine signaling-related genes; lung squamous cell carcinoma; prognosis; signature; therapy sensitivity.

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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
Construction of a prognostic model for stage I–III LUSC patients according to three CSRGs. (A) Identifying 12 prognostic CSRGs using univariate Cox regression analysis. (B) Principal component analysis (PCA) based on the expression levels of three CSRGs in the TCGA training set. (C) Distribution of risk scores, survival status, and three CSRG expression panels in the training set. (D) Kaplan–Meier survival analysis of OS between the high-risk and low-risk groups. (E) Time-dependent ROC for 1-, 3-, and 5-year OS predictions for the CSRG prognostic signature in the training set.
FIGURE 2
FIGURE 2
Validation of the prognostic signature in TCGA internal testing and GEO external datasets. (A) Kaplan–Meier survival analysis of overall survival between the high- and low-risk groups in the validation datasets. (B) Time-dependent ROC for 1-, 3-, and 5-year OS predictions for the prognostic signature in validation datasets. (C) Time-dependent ROC curves for clinical characteristics and risk score for 3-year OS of LUSC in TCGA set and GEO datasets. (D) Multivariate Cox regression analyses of the risk score in the total TCGA cohort and GSE37745 and GSE30219 external validation cohorts.
FIGURE 3
FIGURE 3
Prognostic nomogram was constructed by combining clinical stage, T stage, and risk score in the TCGA cohort. (A) Prognostic nomogram for predicting the 1-, 3-, and 5-year survival rates of LUSC patients. (B) Corresponding calibration curve for 1-, 3-, and 5-year OS prediction. (C) DCA curve for the prediction of 3-year and 5-year overall survival.
FIGURE 4
FIGURE 4
Stratified Kaplan–Meier survival analysis of different clinical subgroups in the TCGA cohort. (A) Clinical I-II stage, T1-2, and N0 pathologic stage. (B) Smoking status, age, and sex. (C) Heatmap illustrating the associations between the risk score and clinicopathological characteristics.
FIGURE 5
FIGURE 5
Functional and biological pathway analysis of the CSRG prognostic signature. (A) Visualization of pathway enrichment analysis by GSVA between the high-risk and low-risk groups. (B) GSEA of biological pathways between the high-risk and low-risk groups.
FIGURE 6
FIGURE 6
Comparison of immune activity between the high-risk and low-risk groups. (A) Comparison of immune score, stromal score, and tumor purity between the low- and high-risk groups. (B) Comparison of the enrichment scores of 13 immune-related pathways between the low- and high-risk groups. (C) Correlation of the risk score and immune-related pathways. *p < 0.05, **p < 0.01, and ***p < 0.001.
FIGURE 7
FIGURE 7
Characteristics of immune infiltrating cells in the high-risk and low-risk groups. (A) Differences in the abundance of immune-infiltrating cells between the high-risk and low-risk groups. (B) Correlation analysis between immune-infiltrating cells and the risk score. (C) Kaplan–Meier analysis of prognosis according to the proportions of CD8 T-cells. (D) Correlation analysis of immune cells and three genes in the CSRG signature. *p < 0.05, **p < 0.01, and ***p < 0.001.
FIGURE 8
FIGURE 8
Characteristics of immune infiltrating cells in the high-risk and low-risk groups. (A) Box plots showing the relationship between the risk score and the expression level of coinhibitory immune checkpoint genes in the TCGA cohort. (B) TMB did not differ between the high-risk and low-risk groups. (C) Violin diagram showing the IPSs for CTLA-4 and PD-1 inhibitors for the two groups. *p < 0.05, **p < 0.01, and ***p < 0.001.
FIGURE 9
FIGURE 9
Association of risk score with chemotherapy and target therapy sensitivity in LUSC. (A) IC50 values of three chemotherapeutic drugs, cisplatin, etoposide, and vinorelbine, were calculated based on the pRRophetic algorithm. (B) IC50 values of three EGFR inhibitors, gefitinib, erlotinib, and afatinib, were calculated based on the pRRophetic algorithm. (C) Correlation between the risk score and cancer stemness scores of RNAss and DNAss.
FIGURE 10
FIGURE 10
Verification of the expression of CCL15 and PPBP in LUSC tissues. (A) qRT‒PCR analysis of the relative mRNA levels of CCL15 in tumor tissues compared with adjacent normal tissues. (B) qRT‒PCR analysis of the relative mRNA levels of PPBP in tumor tissues compared with adjacent normal tissues. *p < 0.05, **p < 0.01, and ***p < 0.001.
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References

    1. An N., Leng X., Wang X., Sun Y., Chen Z. (2020). Survival comparison of three histological subtypes of lung squamous cell carcinoma: A population-based propensity score matching analysis. Lung cancer 142, 13–19. Epub 2020/02/18PubMed PMID: 32062199. 10.1016/j.lungcan.2020.01.020 - DOI - PubMed
    1. Ashburner M., Ball C. A., Blake J. A., Botstein D., Butler H., Cherry J. M., et al. (2000). Gene ontology: Tool for the unification of biology. The gene Ontology consortium. Nat. Genet. 25 (1), 25–29. Epub 2000/05/10PubMed PMID: 10802651; PubMed Central PMCID: PMCPMC3037419. 10.1038/75556 - DOI - PMC - 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. Epub 2013/10/22PubMed PMID: 24138885. 10.1016/j.immuni.2013.10.003 - DOI - PubMed
    1. Bodelon C., Polley M. Y., Kemp T. J., Pesatori A. C., McShane L. M., Caporaso N. E., et al. (2013). Circulating levels of immune and inflammatory markers and long versus short survival in early-stage lung cancer. Ann. Oncol. 24 (8), 2073–2079. Epub 2013/05/18PubMed PMID: 23680692; PubMed Central PMCID: PMCPMC3718510. 10.1093/annonc/mdt175 - DOI - PMC - PubMed
    1. Botling J., Edlund K., Lohr M., Hellwig B., Holmberg L., Lambe M., et al. (2013). Biomarker discovery in non-small cell lung cancer: Integrating gene expression profiling, meta-analysis, and tissue microarray validation. Clin. Cancer Res. 19 (1), 194–204. Epub 2012/10/04PubMed PMID: 23032747. 10.1158/1078-0432.ccr-12-1139 - DOI - PubMed