Development and Validation of a Three-Gene Prognostic Signature Based on Tumor Microenvironment for Gastric Cancer

Abstract

Gastric cancer (GC), which has high morbidity and low survival rate, is one of the most common malignant tumors in the world. The increasing evidences show that the tumor microenvironment (TME) is related to the occurrence and progression of tumors and the prognosis of patients. In this study, we aimed to develop a TME-based prognostic signature for GC. We first identified the differentially expressed genes (DEGs) related to the TME using the Wilcoxon rank-sum test in a training set of GC. Univariate Cox regression analysis was used to identify prognostic-related DEGs. To decrease the overfitting, we performed the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of signature genes and obtained three genes (LPPR4, ADAM12, NOX4). Next, the multivariate Cox regression was performed to construct the risk score model, and a three-gene prognostic signature was developed. According to the signature, patients were classified into high-risk and low-risk groups with significantly different survival. The signature was then applied to three independent validated sets and obtained the same results. We conducted the time-dependent Receiver Operating Characteristic (ROC) curve analysis to evaluate our signature. We further evaluated the differential immune characters between high-risk and low-risk patients to reveal the potential immune mechanism of the impact on the prognosis of the model. Overall, we identified a three-gene prognostic signature based on TME to predict the prognosis of patients with GC and facilitate the development of a precise treatment strategy.

A novel immune-related radioresistant lncRNAs signature based model for risk stratification and prognosis prediction in esophageal squamous cell carcinoma

“…The GSE 45670 dataset was utilized to evaluate immunity infiltration via the CIBERSORT algorithm. Growing evidence pointed to the underlying mechanisms by which the local immune microenvironment and immune cells drive tumorigenesis in many cancers ( Greten and Grivennikov, 2019 ; Wang Q. et al, 2021 ; Kumar et al, 2022 ). Previous studies have shown that DNA damage repair-related genes, apoptosis-related genes, cellular hypoxia-related genes, cell cycle-related genes, and autophagy genes play important roles in radiosensitivity by changing the microenvironment ( Chen et al, 2017 ; Tang et al, 2018 ; Zhang H. et al, 2021 ).…”

Development and Validation of an Immunotherapy-Related Prognostic Signature Based on Lymph Node Ratio for Gastric Cancer

“…Univariate Cox regression analysis was applied to assess the prognostic value of LNR-related genes, and genes with P <0:01 were identified as prognostic genes and were incorporated into least absolute shrinkage and selection operator (LASSO) regression. LASSO regression is an algorithm usually used for fitting selecting variables in high-dimensional generalized linear models [16]. We then constructed a risk score model on the basis of the expression levels and the risk coefficients (β) of prognostic genes identified by LASSO regression.…”

Plasma membrane phospholipid phosphatase-related proteins as pleiotropic regulators of neuron growth and excitability

“…The mechanism by which PLPPR4 affects tumorigenesis appears to involve cell adhesion. Recently, PLPPR4 was identified as a tumor microenvironment-based prognostic marker for gastric cancer ( Wang et al, 2022 ). Overall, these data indicate aberrant expression of some PLPPRs in cancer lines and tissue suggesting a link to tumorigenesis.…”