Abstract
The existence of tumor heterogeneity and complex carcinogenic mechanisms in lung adenocarcinoma (LUAD) make the most commonly used TNM staging system unable to well-interpret the prognosis of patients. Using transcriptome profiling and clinical data from The Cancer Genome Atlas (TCGA) database, we constructed an immune signature based on a multivariate Cox analysis (stepwise model). We estimated the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs in patients according to the pRRophetic algorithm. Gene-set variation analysis (GSVA) was used to reveal pathway enrichment between groups. Moreover, immune microenvironment landscape was described by single-sample gene-set enrichment analysis (ssGSEA) and CIBERSORT and systematically correlated with genomic of these patients. A prognostic nomogram combining the immune signature and TNM stage to predict the prognosis was developed by multivariate Cox regression. The novel signature with four immune-related genes (MAL, MS4A1, OAS1, and WFDC2) had good robustness, which can accurately distinguish between high- and low-risk patients. Compared with low-risk patients, high-risk patients with a worse prognosis (5-year OS: 46.5 vs. 59.4%, p = 0.002) could benefit more from immunotherapy and the application of common chemotherapeutic agents such as cisplatin and paclitaxel (Wilcoxon test, all p < 0.05). There were significant differences in tumor immune microenvironment and metabolic pathways between the two groups. Additionally, the constructed nomogram had reliable predictive performance with the C-index of 0.725 (95% CI = 0.668–0.781) in the development set (n = 500), 0.793 (95% CI = 0.728–0.858) in the internal validation set (n = 250) and 0.679 (95% CI = 0.644–0.714) in the external validation set (n = 442). The corresponding calibration curves also showed good consistency. To sum up, we developed an immune-related gene signature and comprehensively evaluated LUAD immune landscape and metabolic pathways. Effective differentiation of high- and low-risk patients and accurate construction of nomogram would be helpful to the development of individualized treatment strategies.
Highlights
Lung cancer is the most common malignancy, with morbidity and mortality ranking first in the world, according to global data released by the International Center for Cancer Research in 2020 [1]
Tumor purity and immune score were considered to be important factors affecting the prognosis of cancer patients [30,31,32]
Some studies have reported that poor prognosis was closely related to low tumor purity in glioma [30] and colorectal cancer [31]
Summary
Lung cancer is the most common malignancy, with morbidity and mortality ranking first in the world, according to global data released by the International Center for Cancer Research in 2020 [1]. Lung cancer is divided into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). As the most common subtype of NSCLC [2], lung adenocarcinoma (LUAD) has complex carcinogenic mechanisms and obvious tumor heterogeneity. Due to the continuous improvement in the diagnosis and treatment of LUAD in recent years, especially the rise of immunotherapy, the prognosis of patients has improved significantly. The search for new models of diagnosis and treatment to benefit cancer patients has been the focus of oncologists.
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