The necroptosis-related signature and tumor microenvironment immune characteristics associated with clinical prognosis and drug sensitivity analysis in stomach adenocarcinoma
Authors: Yang Biao , Zhang Meijing , Liu Tao , Luo Tianhang 
Published: August 2022

Abstract
Purpose
Necroptosis played an important role in the tumorigenesis, development, metastasis, and drug resistance of malignant tumors. This study explored the new model for assessing stomach adenocarcinoma (STAD) prognosis and immunotherapy by combining long noncoding RNAs associated with necroptosis.
Methods
Clinical data and gene expression information of STAD patients were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the molecular marker database. Perl software and R software were used for data processing and analysis. The correlation test of the R language was used to obtain necrotizing apoptosis-related lncRNAs in STAD. Combined with clinical data, univariate Cox regression and multivariate regression analysis were used to screen out immune-associated lncRNAs associated with STAD prognosis and construct the risk score model. The Kaplan-Meier (K-M) survival analysis and independent prognostic factor assessment were used to evaluate the model, and the model was combined with other clinical factors to construct the nomogram to predict the survival rate of individuals. Immune-related and drug sensitivity analysis of necroptosis-related lncRNA tumor clusters using TIMER2.0, GSVA, GSEA, PCA, and GDSC.
Results
Cox regression analysis determined that 21 necroptotic-related lncRNAs were used to construct the risk score model. With the median risk score as the cut-off, patients can be divided into high-risk and low-risk groups. The survival curve of the two groups of patients indicated a better prognosis for low-risk patients. In addition, the risk scoring model can be used as an independent prognostic factor for STAD. Combined with other clinical factors and risk scores of STAD, the nomogram was established to predict the survival rate of individuals, and the calibration plot showed that the predicted values were in good agreement with the actual observations. Through the single sample GSEA (ssGSEA) difference analysis, it was found that most immune cells had differences in the high-risk group and the low-risk group, whereas patients in the high-risk group had higher immune infiltration. Tumor microenvironment (TME) analysis showed that cluster-C3 had the highest score in the analysis of the three TMEs. Through the differential analysis of immune checkpoints, it was found that almost all immune checkpoint-related genes were expressed differently in various tumor clusters. Among them, CD44 expression was the highest. By comparing all drug sensitivities, we screened out 29 drugs with differences in drug sensitivity across different clusters.
Conclusions
By quantitatively evaluating the risk score of tumor clusters, the accuracy of STAD immunotherapy can be better predicted as a single drug or combination therapy. This study provided a new idea and basis for necrotizing apoptosis-related lncRNAs to achieve more efficient clinical translation and accurately guide immunotherapy in STAD patients.