Integrative Analysis Constructs an Extracellular Matrix-Associated Gene Signature for the Prediction of Survival and Tumor Immunity in Lung Adenocarcinoma

Abstract

Background: Lung adenocarcinoma (LUAD) accounts for the majority of lung cancers, and the survival of patients with advanced LUAD is poor. The extracellular matrix (ECM) is a fundamental component of the tumor microenvironment (TME) that determines the oncogenesis and antitumor immunity of solid tumors. However, the prognostic value of extracellular matrix-related genes (ERGs) in LUAD remains unexplored. Therefore, this study is aimed to explore the prognostic value of ERGs in LUAD and establish a classification system to predict the survival of patients with LUAD.Methods: LUAD samples from The Cancer Genome Atlas (TCGA) and GSE37745 were used as discovery and validation cohorts, respectively. Prognostic ERGs were identified by univariate Cox analysis and used to construct a prognostic signature by Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. The extracellular matrix-related score (ECMRS) of each patient was calculated according to the prognostic signature and used to classify patients into high- and low-risk groups. The prognostic performance of the signature was evaluated using Kaplan–Meier curves, Cox regression analyses, and ROC curves. The relationship between ECMRS and tumor immunity was determined using stepwise analyses. A nomogram based on the signature was established for the convenience of use in the clinical practice. The prognostic genes were validated in multiple databases and clinical specimens by qRT-PCR.Results: A prognostic signature based on eight ERGs (FERMT1, CTSV, CPS1, ENTPD2, SERPINB5, ITGA8, ADAMTS8, and LYPD3) was constructed. Patients with higher ECMRS had poorer survival, lower immune scores, and higher tumor purity in both the discovery and validation cohorts. The predictive power of the signature was independent of the clinicopathological parameters, and the nomogram could also predict survival precisely.Conclusions: We constructed an ECM-related gene signature which can be used to predict survival and tumor immunity in patients with LUAD. This signature can serve as a novel prognostic indicator and therapeutic target in LUAD.

Single‐cell transcriptomics reveal metastatic CLDN4+ cancer cells underlying the recurrence of malignant pleural effusion in patients with advanced non‐small‐cell lung cancer

“…Genes like Annexin A2 ( ANXA2 ), Ezrin ( EZR ), Secretory Leukocyte Peptidase Inhibitor ( SLPI ), Integrin Subunit Beta 1 ( ITGB1 ) and Epithelial Membrane Protein 1 ( EMP1 ), which were extracellular matrix related genes, showed a positive correlation with CLDN4 (Figure 3E). Previous studies have suggested that the extracellular matrix related genes determined the oncogenesis in lung cancer, and could be used as predictive indicators 42,43 . The GO enrichment analysis was performed to identify the transcriptomic signature between recurrent verses non‐recurrent status in C3 cluster, indicating the typical characteristics of the expression of ameboidal‐type cell migration related genes among C3 subset in recurrent MPE (Figure 3F).…”

“…Previous studies have suggested that the extracellular matrix related genes determined the oncogenesis in lung cancer, and could be used as predictive indicators. 42 , 43 The GO enrichment analysis was performed to identify the transcriptomic signature between recurrent verses non‐recurrent status in C3 cluster, indicating the typical characteristics of the expression of ameboidal‐type cell migration related genes among C3 subset in recurrent MPE (Figure 3F ). Ameboidal‐type cell migration was reported to be one of the typical migration modes of the invasive tumour cells.…”

Identification and validation of a 4-extracellular matrix gene signature associated with prognosis and immune infiltration in lung adenocarcinoma

“…We also assessed the efficacy of the risk score against three established models, including Sun et al [ 6 ], Zou et al [ 7 ] and Xiao et al [ 9 ]. The time-dependent ROC analysis indicated that the predictive ability of this model was not inferior to the previous models ( Fig.…”

“…The prognostic ability of the risk score was compared with three published models, including Sun et al [ 6 ], Zou et al [ 7 ] and Xiao et al [ 9 ], using “timeROC” package [ 21 ] and Harrell's concordance index (C-index).…”

“…Previous research has found that signature derived from bioinformatics analysis has better prognostic performance than single gene. Some studies have also found more effective prognostic models based on different gene sets in LUAD, such as immune checkpoints [ 6 ], inflammatory response [ 7 ], TME [ 8 ] and ECM [ 9 ]. However, some studies used only two datasets in the analysis, so their prediction performance may be limited and cannot be well applied to other data.…”