Transcriptomic and immunologic implications of the epithelial-mesenchymal transition model reveal a novel role of SFTA2 in prognosis of non-small-cell lung carcinoma

Abstract

Non-small-cell lung cancer (NSCLC) is the second most common cancer worldwide, and most deaths are associated with epithelial-mesenchymal transition (EMT). Therefore, this study aimed to explore the role of EMT-related transcriptomic profiles in NSCLC and the effect of EMT-based signatures on clinical diagnosis, prognosis, and treatment responses for patients with NSCLC. After integrating the transcriptomics and clinicopathological data, we first constructed EMT clusters (C1 and C2) using machine learning algorithms, found the significant relationship between EMT clusters and survival outcomes, and then explored the impact of EMT clusters on the tumor heterogeneity, drug efficiency, and immune microenvironment of NSCLC. Prominently, differential-enriched tumor-infiltrated lymphocytes were found between EMT clusters, especially the macrophages and monocyte. Next, we identified the most significantly down-regulated gene SFTA2 in the EMT clusters C2 with poor prognosis. Using RT-qPCR and RNA-seq data from the public database, we found prominently elevated SFTA2 expression in NSCLC tissues compared with normal lung tissues, and the tumor suppressor role of SFTA2 in 82 Chinese patients with NSCLC. After Cox regression and survival analysis, we demonstrated that higher SFTA2 expression in tumor samples significantly predicts favorable prognosis of NSCLC based on multiple independent cohorts. In addition, the prognostic value of SFTA2 expression differs for patients with lung adenocarcinoma and squamous cell carcinoma. In conclusion, this study demonstrated that the EMT process is involved in the malignant progression and the constructed EMT clusters exerted significant predictive drug resistance and prognostic value for NSCLC patients. In addition, we first identified the high tumoral expression of SFTA2 correlated with better prognosis and could serve as a predictive biomarker for outcomes and treatment response of NSCLC patients.

Keywords: SFTA2; epithelial–mesenchymal transition (EMT); non-small-cell lung cancer (NSCLC); prognosis; tumor microenvironment.

FIGURE 1

Construction of EMT clusters (C1 and C2) using machine learning algorithms. (A) The principal component analysis (PCA) was applied to explore differences in the expressed genes between the two groups (C1 and C2). Unsupervised clustering was used to classify NSCLC patients into groups (C1 and C2). (B) The heatmap of consistent clustering results shows the expression of EMT in each sample in C1 and C2 groups. Red represents high expression and blue represents low expression. (C) We investigated the distribution of some clinical characteristics in the two groups and analyzed significant p-values by using the chi-square test. (D) Kaplan–Meier survival curve analysis using median patient samples.

FIGURE 2

EMT clusters divide differential tumor-infiltrated lymphocytes and immune microenvironment. (A) We found the connection between different immune cells and EMT. Red represents positive correlation while blue represents negative correlation. (B) Expression distribution of immune checkpoint molecules in C1 and C2 groups.

FIGURE 3

Intratumoral heterogeneity of NSCLC. The TIDE test was used to predict response to immune checkpoint inhibition therapy.

FIGURE 4

Function enrichment analysis of EMT clusters and the identification of tumor-suppressor SFTA2. (A) The graphs show the differentially expressed genes in the C1 and C2 groups, with up-regulated genes in red and down-regulated genes in blue. (B) We performed functional enrichment analysis to show the pathways involved in differentially up-regulated and down-regulated genes in the KEGG database.

FIGURE 5

Potential implications of SFTA2 expression in outcomes of NSCLC. (A) We compared the expression of SFTA2 in normal and tumor tissues in NSCLC and subgroups of LUAD and LUSC. (B) We performed univariate and multivariate regression analyses to find the factors related to the survival of NSCLC patients. (C) We utilized independent cohort samples to study the relationship between SFTA2 expression and survival prognosis.

FIGURE 6

Expression and prognostic validation of SFTA2 expression in outcomes of NSCLC patients from the real-world cohort. (A) We compared the expression of SFTA2 in normal tissues (n = 47) and NSCLC (n = 47) tissues using Student’s t test. (B) Kaplan–Meier survival analysis and log-rank test reveal the prognostic value of SFTA2 expression in 82 patients from the Jiaxing cohort.