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Prediction of immune infiltration and prognosis for patients with gastric cancer based on the immune-related genes signature.
Li, Xianghui; Chen, Yuanyuan; Dong, Yuxiang; Ma, Zhongjin; Zheng, Wenjun; Lin, Youkun.
Affiliation
  • Li X; Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China.
  • Chen Y; Department of Dermatology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
  • Dong Y; Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China.
  • Ma Z; Second Clinical College of Nanjing Medical University, Nanjing, 210029, China.
  • Zheng W; Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China.
  • Lin Y; Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China.
Heliyon ; 9(12): e22433, 2023 Dec.
Article in En | MEDLINE | ID: mdl-38213590
ABSTRACT

Objective:

The immune microenvironment influenced clinical outcomes and treatment response of gastric cancer (GC) patients. Though thousands of immune-related genes (IRGs) have been identified, their effects on GC are not fully understood. The objective of the study is to analyze the correlations between the expression and effect of IRGs and clinical outcomes. Moreover, we evaluate the efficacy and value of utilizing the immune-related genes signature as a prognosis prediction model for GC patients.

Methods:

We identified the differentially expressed IRGs and systematically analyzed their functions. We constructed a novel GC prognostic signature and a new nomogram, Moreover, we explored the infiltrated immune cell types in the immune microenvironment and discussed the genetic variation in GC IRGs.

Results:

Eight IRGs, including CCL15, MSR1, GNAI1, NR3C1, ITGAV, NMB, AEN, and TGFBR1 were identified. Based on the prognostic signature, GC patients were distinguished into two subtype groups. As verified in multiple datasets, the prognostic signature exhibited good performance in predicting the prognosis (AUC = 0.803, P-value <0.001) and revealed the different clinical features and infiltrated immune cell types in the immune microenvironment.

Conclusions:

In summary, we found that IRGs contributed to GC prognosis prediction and constructed an IRGs-based GC prognostic signature, which could serve as an effective prognostic stratification tool.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Heliyon Year: 2023 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Heliyon Year: 2023 Document type: Article