4.4 Article

Construction of an immune-related gene signature to predict survival and treatment outcome in gastric cancer

期刊

SCIENCE PROGRESS
卷 104, 期 1, 页码 -

出版社

SAGE PUBLICATIONS LTD
DOI: 10.1177/0036850421997286

关键词

Gastric cancer; immune-related genes; prognosis; drug resistance

资金

  1. National Natural Science Foundation of China [81673025]
  2. National Key Research and Development Program of China [2017YFC1308900]
  3. National Science and Technology Major Project of the Ministry of Science and Technology of China [2017ZX09304025]
  4. Key Research and Development Program of Liaoning Province [2018225060]

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The study identified 155 differentially expressed immune-related genes in patients with gastric cancer, with four genes closely associated with overall survival. These genes were found to be predictive of patient outcomes.
Immune cells have emerged as key regulators in the occurrence and development of multiple tumor types. However, it is unclear whether immune-related genes (IRGs) and the tumor immune microenvironment can predict prognosis for patients with gastric cancer (GC). The mRNA expression data in GC tissues (n = 368) were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs in patients with GC were determined using a computational difference algorithm. A prognostic signature was constructed using COX regression and random survival forest (RSF) analyses. In addition, datasets related to gemcitabine resistance and trastuzumab resistance (GSE58118 and GSE77346) were obtained for GEO database, and DEGs associated with drug-resistance were screened. Then, we analyzed correlations between gene expression and cancer immune infiltrates via Tumor Immune Estimation Resource (TIMER) site. The cBioportal database was used to analyze drug-resistant gene mutation status and survival. One hundred and fifty-five differentially expressed IRGs were screened between GC and normal tissues, and a prognostic signature consisting of four IRGs (NRP1, PPP3R1, IL17RA, and FGF16) was closely related to the overall survival (OS). According to cutoff value of risk score, patients were divided into high-risk and low-risk group. Patients in the high-risk group had shorter OS compared to the low-risk group in both the training (p < 0.0001) and testing sets (p = 0.0021). In addition, we developed a 5-IRGs (LGR6, DKK1, TNFRSF1B, NRP1, and CXCR4) signature which may participate in drug resistance processes in GC. Survival analysis showed that patients with drug-resistant gene mutations had shorter OS (p = 0.0459) and DFS (p < 0.001). We constructed four survival-related IRGs and five IRGs related to drug resistance which may contribute to predict the prognosis of GC.

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