Dataset Information

Genomewide Expression Profiling Identifies a Novel miRNA-based Signature for the Detection of Peritoneal Metastasis in Patients With Gastric Cancer.

ABSTRACT:

Objective

This study aimed to conduct a genomewide transcriptomic profiling to develop a microRNA (miRNA)-based signature for the identification of peritoneal metastasis (PM) in patients with gastric cancer (GC).

Summary background data

Even though PM in patients with GC has long been recognized to associate with poor prognosis, currently there is lack of availability of molecular biomarkers for its robust diagnosis.

Methods

We performed a systematic biomarker discovery by analyzing miRNA expression profiles in primary tumors from GC patients with and without PM, and subsequently validated the expression of candidate miRNA biomarkers in 3 independent clinical cohorts of 354 patients with advanced GC.

Results

Five miRNAs (miR-30a-5p, -134-5p, -337-3p, -659-3p, and -3917) were identified during the initial discovery phase; three of which (miR-30a-5p, -659-3p, and -3917) were significantly overexpressed in the primary tumors from PM-positive patients in the testing cohort (P = 0.002, 0.04, and 0.007, respectively), and distinguished patients with versus without peritoneal metastasis with the value of area under the curve (AUC) of 0.82. Furthermore, high expression of these miRNAs also associated with poor prognosis (hazard ratio = 2.18, P = 0.04). The efficacy of the combination miRNA signature was subsequently validated in an independent validation cohort (AUC = 0.74). Finally, our miRNA signature when combined together with the macroscopic Borrmann's type score offered a much superior diagnostic in all 3 cohorts (AUC = 0.87, 0.76, and 0.79, respectively).

Conclusions

We have established an miRNA-based signature that have a potential to identify peritoneal metastasis in GC patients.

SUBMITTER: Shimura T

PROVIDER: S-EPMC7577555 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeGastric cancer peritoneal carcinomatosis is fatal. Delay in detection of peritoneal metastases contributes to high mortality, highlighting the need to develop biomarkers that can help identify patients at high risk for peritoneal recurrence or metastasis.Experimental designWe performed a systematic discovery and validation for the identification of peritoneal recurrence prediction and peritoneal metastasis detection biomarkers by analyzing expression profiling datasets from 249 patients with gastric cancer, followed by analysis of 426 patients from three cohorts for clinical validation.ResultsGenome-wide expression profiling identified a 12-gene panel for robust prediction of peritoneal recurrence in patients with gastric cancer (AUC = 0.95), which was successfully validated in a second dataset (AUC = 0.86). Examination of 216 specimens from a training cohort allowed us to establish a six gene-based risk-prediction model [AUC = 0.72; 95% confidence interval (CI): 0.66-0.78], which was subsequently validated in an independent cohort of 111 patients with gastric cancer (AUC = 0.76; 95% CI: 0.67-0.83). In both cohorts, combining tumor morphology and depth of invasion further improved the predictive accuracy of the prediction model (AUC = 0.84). Thereafter, we evaluated the performance of the identical six-gene panel for its ability to detect peritoneal metastasis by analyzing 210 gastric cancer specimens (prior 111 patients plus additional 99 cases), which discriminated patients with and without peritoneal metastasis (AUC = 0.72). Finally, our biomarker panel was also remarkably effective for identifying peritoneal micrometastasis (AUC = 0.72), and its diagnostic accuracy was significantly enhanced when depth of invasion was included in the model (AUC = 0.85).ConclusionsOur novel transcriptomic signature for risk stratification and identification of high-risk patients with peritoneal carcinomatosis might serve as an important clinical decision making in patients with gastric cancer.

Project description:BackgroundGastric cancer (GC) still represents the third leading cause of cancer-related death worldwide. Peritoneal relapse (PR) is the most frequent metastasis occurring among patients with advanced gastric cancer. Increasingly more evidence have clarified the tumor immune microenvironment (TIME) may predict survival and have clinical significance in GC. However, tumor-transcriptomics based immune signatures derived from immune profiling have not been established for predicting the peritoneal recurrence of the advanced GC.MethodsIn this study, we depict the immune landscape of GC by using transcriptome profiling and clinical characteristics retrieved from GSE62254 of Gene Expression Omnibus (GEO). Immune cell infiltration score was evaluated via single-sample gene set enrichment (ssGSEA) analysis algorithm. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was used to select the valuable immune cells and construct the final model for the prediction of PR. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to check the accuracy of PRIs. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to explore the molecular pathways associated with PRIs.ResultsA peritoneal recurrence related immune score (PRIs) with 10 immune cells was constructed. Compared to the low-PRIs group, the high-PRIs group had a greater risk. The upregulation of the focal adhesion signaling was observed in the high-PRIs subtype by GSEA and KEGG. Multivariate analysis found that both in the internal training cohort and the internal validation cohort, PRIs was a stable and independent predictor for PR. A nomogram that integrated clinicopathological features and PRIs to predict peritoneal relapse was constructed. Subgroup analysis indicated that the PRIs could obviously distinguish peritoneal recurrence in different molecular subtypes, pathological stages and Lauren subtypes, in which PRIs of Epithelial-Mesenchymal Transitions (EMT) subtype, III-IV stage and diffuse subtype are higher respectively.ConclusionOverall, we performed a comprehensive evaluation of the immune landscape of GC and constructed a predictive PR model based on the immune cell infiltration. The PRIs represents novel promising feature of predicting peritoneal recurrence of GC and sheds light on the improvement of the personalized management of GC patients after surgery.