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A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma

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Abstract

Tumor microenvironment (TME) has been reported to exhibit a crucial effect in lung cancer. Therefore, this study was aimed to investigate the genes associated with TME and develop a risk score to predict the overall survival (OS) of patients with lung adenocarcinoma (LUAD) based on these genes. The immune and stromal scores were generated by the ESTIMATE algorithm for LUAD patients in The Cancer Genome Atlas (TCGA) database. Differentially expressed gene and weighted gene co-expression network analyses were used to derive immune- and stromal-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was applied for further selection and the selected genes were inputted into stepwise regression to develop TME-related risk score (TMErisk) which was further validated in Gene Expression Omnibus (GEO) datasets. TMErisk-related biological phenotypes were analyzed in function enrichment, tumor immune signature, and tumor mutation signature. The patient’s response to immunotherapy was inferred by the tumor immune dysfunction and exclusion (TIDE) score and immunophenoscore (IPS). According to our results, TMErisk was developed based on SERPINE1, CX3CR1, CD200R1, GBP1, IRF1, STAP1, LOX, and OR7E47P. Furthermore, high TMErisk was identified as a poor factor for OS in TCGA and GEO datasets, as well as in subgroup analysis with different gender, smoking status, age, race, anatomic site, therapies, and tumor-node-metastasis (TNM) stages. Higher TMErisk is also associated negatively with the abundance of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and other stromal or immune cells. Several genes of the human leukocyte antigen (HLA) family and immune checkpoints were less expressed in the high-TMErisk group. Mutations of 19 genes occurred more frequently in the high-TMErisk group. These mutations may be associated with TME change and indicate patients’ response to immunotherapy. According to our analyses, a lower TMErisk score may indicate better response and OS outcome of immunotherapy.

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Fig. 1: Screening for TME-related genes.
Fig. 2: Evaluation and validation for the prognostic value of the TMErisk score.
Fig. 3: Function enrichment analysis for TMErisk and correlation between TMErisk and expression of the HLA family genes/immune checkpoints.
Fig. 4: Landscape of immune and stromal cell infiltrations in the low- and high-TMErisk groups.
Fig. 5: TMErisk was related to tumor mutation status.

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Acknowledgements

This work was supported by grants from Wuhan University Medical Faculty Innovation Seed Fund Cultivation Project (grant no. TFZZ2018025), Chen xiao-ping foundation for the development of science and technology of Hubei province (grant no. CXPJJH12000001-2020313), and the National Natural Science Foundation of China (grant nos. 81670123 and 81670144).

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  1. These authors contributed equally: Jie Wu, Lan Li, Huibo Zhang

Authors and Affiliations

  1. Cancer Center, Renmin Hospital of Wuhan University, Wuhan, China

    Jie Wu, Lan Li, Huibo Zhang, Yaqi Zhao, Haohan Zhang, Siyi Wu & Bin Xu

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Contributions

Research design: Bin Xu and Jie Wu; data collection: Siyi Wu and Lan Li; data analysis: Jie Wu, Huibo Zhang and Lan Li; manuscript preparation: Jie Wu, Huibo Zhang, and Lan Li; chart preparation: Jie Wu and Siyi Wu; revisions: Jie Wu, Huibo Zhang, Lan Li, Yaqi Zhao, Haohan Zhang, and Bin Xu. All authors confirm that they contributed to manuscript reviews, critical revision for important intellectual content, and read and approved the final draft for submission. All authors are also responsible for the manuscript content.

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Supplement files-supplement figure legends

Figure S1. The flow diagram of TMErisk development.

Figure S2. Immune and stromal scores associate with clinical features and outcomes.

Figure S3. Analysis of network topology for various soft-thresholding powers.

Figure S4. Validation for prognostic value of alternative genes selected by LASSO analysis.

Figure S5. KM curve analysis for the low- and high-TMErisk in GSE3141, GSE31210, GSE81089, GSE30219, GSE37745 and TCGA.

Figure S6. Stratified analysis for the low- and high-TMErisk groups.

Figure S7. Difference analysis for the distribution of T helper cells and macrophage phenotypes.

Figure S8. Difference analysis for expression of 19 genes in wild type and mutant type.

Figure S9. TMErisk is a potential biomarker to predict patient’s benefits from drug therapies.

Table S1. The cut-off values used in the study.

Table S2. The epidemiological and clinical characteristics before and after propensity score matching.

Table S3. Results of GSEA for molecular function in GO database.

Table S4. Results of GSEA for biology process in GO database.

Table S5. Results of GSEA in KEGG database.

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Wu, J., Li, L., Zhang, H. et al. A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma. Oncogene 40, 4413–4424 (2021). https://doi.org/10.1038/s41388-021-01853-y

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