Published online Jan 15, 2021. doi: 10.4251/wjgo.v13.i1.37
Peer-review started: September 17, 2020
First decision: November 3, 2020
Revised: November 8, 2020
Accepted: November 28, 2020
Article in press: November 28, 2020
Published online: January 15, 2021
Tumor mutational burden (TMB) is in the spotlight as a novel biomarker and a rational target for predicting response to immunotherapy in multiple cancers. Gastric cancer (GC) is one of the most common gastrointestinal malignant tumors worldwide. Accumulating evidence highlights that it is necessary to further explore clinical impact of TMB in GC.
The association of TMB with clinical outcomes and immune infiltration in the tumor microenvironment in GC patients has not yet been elucidated. MicroRNAs (miRNAs) have a crucial role in the carcinogenesis, migration, and invasion of tumor cells by regulating adaptive and innate immune responses, but the relationship between miRNA expression patterns and mutational load is not clear in GC.
This study aimed to explore the clinical impact of TMB and establish a miRNA-based signature for TMB prediction in GC patients.
The Kaplan-Meier analysis in the GC cohort from The Cancer Genome Atlas dataset was performed by defining the highest TMB quintile (top 20%) as the high-TMB group. The difference in immune infiltration between the high- and low-TMB subgroups was evaluated by Wilcoxon rank-sum test. The least absolute shrinkage and selection operator analysis was conducted to select parameters from differentially expressed miRNAs between the high- and low-TMB subgroups and construct a miRNA-based signature classifier for TMB prediction.
Higher mutational load in GC was significantly associated with better prognosis, older ages, female gender, earlier tumor stage, and lack of lymph node metastasis. Different mutational load levels exhibited different immune infiltration patterns and different miRNA expression patterns. In addition, we developed a miRNA-based signature using 23 differentially expressed miRNAs to predict TMB values of GC patients.
High TMB is notably correlated with good survival and might lead to the activation of antitumor immune cells in the tumor microenvironment in GC. The miRNA-based signature might be developed as a surrogate biomarker for TMB in GC.
The miRNA-based signature for TMB prediction might help develop treatment strategies for GC patients and have an impact on the clinical practice in the course of GC.