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. 2022 Dec 12;23(1):84.
doi: 10.1186/s12863-022-01096-0.

Identification of metabolism-related genes for predicting peritoneal metastasis in patients with gastric cancer

Chenyu Tian #  1 , Junjie Zhao #  1 , Dan Liu #  1 , Jie Sun  1 , Chengbo Ji  1 , Quan Jiang  1 , Haojie Li  2 , Xuefei Wang  3 , Yihong Sun  1
Affiliations

Identification of metabolism-related genes for predicting peritoneal metastasis in patients with gastric cancer

Chenyu Tian et al. BMC Genom Data. .

Abstract

Objective: The reprogramming of metabolism is an important factor in the metastatic process of cancer. In our study, we intended to investigate the predictive value of metabolism-related genes (MRGs) in recurrent gastric cancer (GC) patients with peritoneal metastasis.

Methods: The sequencing data of mRNA of GC patients were obtained from Asian Cancer Research Group (ACRG) and the GEO databases (GSE53276). The differentially expressed MRGs (DE-MRGs) between a cell line without peritoneal metastasis (HSC60) and one with peritoneal metastasis (60As6) were analyzed with the Limma package. According to the LASSO regression, eight MRGs were identified as crucially related to peritoneal seeding recurrence in patients. Then, disease free survival related genes were screened using Cox regression, and a promising prognostic model was constructed based on 8 MRGs. We trained and verified it in two independent cohort.

Results: We confirmed 713 DE-MRGs and the enriched pathways. Pathway analysis found that the MRG-related pathways were related to tumor metabolism development. With the help of Kaplan-Meier analysis, we found that the group with higher risk scores had worse rates of peritoneal seeding recurrence than the group with lower scores in the cohorts.

Conclusions: This study developed an eight-gene signature correlated with metabolism that could predict peritoneal seeding recurrence for GC patients. This signature could be a promising prognostic model, providing better strategy in treatment.

Keywords: Gastric cancer; Gene; Metabolism; Peritoneal metastasis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Differentially expressed MRGs between a highly peritoneal-metastatic GC cell line and a nonperitoneal-metastatic GC cell line. (A) Heatmap of the MRGs from the GSE53276 dataset; (B) volcano plot of the screened MRGs
Fig. 2
Fig. 2
Gene set enrichment analysis and Gene Ontology analysis of differentially expressed MRGs. (A) Gene set enrichment analysis of differentially expressed MRGs. (B) Significantly enriched Gene Ontology (GO) terms of the differentially expressed MRGs according to biological processes. (C) Significantly enriched GO pathways of the DE-MRGs
Fig. 3
Fig. 3
Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially expressed MRGs. (A) Chord plot indicates the relationships between the genes and KEGG pathways. (B) Significantly enriched KEGG pathways of the DE-MRGs
Fig. 4
Fig. 4
Establishment of the prognostic model for GC via LASSO regression analysis. (A) Correlation coefficients of the candidate genes. (B) A coefficient profile plot was generated against the log (lambda) sequence. (C) Univariate Cox regression analysis of the candidate genes. (D) ROC curve of the prediction model
Fig. 5
Fig. 5
Training and clinical use of the risk score based on the 8-MRG signature in ACRG cohort. (A) Kaplan–Meier curve of the group with high risk and the group with low risk in the ACRG cohort according to RS model. (B) The RS distribution and vital status of patients. (C)The heatmap of the 8 MRG expression profiles between the high-risk group and low-risk group
Fig. 6
Fig. 6
Validation and clinical use of the risk score based on the 8-MRG signature in Zhongshan GC cohort. (A) Kaplan–Meier curve of the group with high risk and the group with low risk in the Zhongshan GC cohort according to RS model. (B) The RS distribution and vital status of patients. (C)The heatmap of the 8 MRG expression profiles between the high-risk group and low-risk group
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