Abstract

We investigated the local immune status and its prognostic value in lung adenocarcinoma. In total, 513 lung adenocarcinoma samples from TCGA and ImmPort databases were collected and analyzed. The R package coxph was employed to mine immune-related genes that were significant prognostic indicators using both univariate and multivariate analyses. The R software package glmnet was then used for Lasso Cox regression analysis, and a prognosis prediction model was constructed for lung adenocarcinoma; clusterProfiler was selected for functional gene annotations and KEGG enrichment analysis. Finally, correlations between the RiskScore and clinical features or signaling pathways were established. Sixty-four immune-related genes remarkably correlated with patient prognosis and were further applied. Samples were hierarchically clustered into two subgroups. Accordingly, the LASSO regression algorithm was employed to screen the 14 most representative immune-related genes (PSMD11, PPIA, MIF, BMP5, DKK1, PDGFB, ANGPTL4, IL1R2, THRB, LTBR, TNFRSF1, TNFRSF17, IL20RB, and MC1R) with respect to patient prognosis. Then, the prognosis prediction model for lung adenocarcinoma patients (namely, the RiskScore equation) was constructed, and the training set samples were incorporated to evaluate the efficiency of this model to predict and classify patient prognosis. Subsequently, based on functional annotations and KEGG pathway analysis, the 14 immune-related genes were mainly enriched in pathways closely associated with lung adenocarcinoma and its immune microenvironment, such as cytokine–cytokine receptor interaction and human T-cell leukemia virus 1 infection. Furthermore, correlations between the RiskScore and clinical features of the training set samples and signaling pathways (such as p53, cell cycle, and DNA repair) were also demonstrated. Finally, the test set sample data were employed for independent testing and verifying the model. We established a prognostic prediction RiskScore model based on the expression profiles of 14 immune-related genes, which shows high prediction accuracy and stability in identifying immune features. This could provide clinical guidance for the diagnosis and prognosis of different immunophenotypes, and suggest multiple targets for precise advanced lung adenocarcinoma therapy based on subtype-specific immune molecules.

Highlights

  • Lung adenocarcinoma is one of the most commonly encountered malignant tumors in the clinic, and is characterized by its high rate of metastasis and marked invasiveness

  • Mining of Characteristic Immune-Related Genes Based on the Survival and Prognosis Results of Lung Adenocarcinoma Patients

  • There was a significant difference in their prognoses (p < 0.001). These results suggested that immune-related genes could be used to predict prognosis in lung adenocarcinoma patients

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Summary

Introduction

Lung adenocarcinoma is one of the most commonly encountered malignant tumors in the clinic, and is characterized by its high rate of metastasis and marked invasiveness. Biomarkers can reliably estimate disease prognosis and patient survival, which is of great value to guide the clinical treatment of lung adenocarcinoma [2, 3]. In recent years, increasing studies have reported methods to predict and stratify survival and prognosis for lung adenocarcinoma patients based on gene expression. Such studies have not been translated to routine clinical practice, which can be ascribed to small sample sizes, excessive data fitting, or inadequate evidence [5,6,7,8]. The molecular events underlying tumor cell–immunocyte interactions in lung adenocarcinoma microenvironments need to be further explored and summarized, which will determine their potential to predict the prognosis of patients with lung adenocarcinoma

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