MicroRNAs Expression Patterns Predict Tumor Mutational Burden in Colorectal Cancer.

Tumor mutational burden (TMB) could be a measure of response to immune checkpoint inhibitors therapy for patients with colorectal cancer (CRC). MicroRNAs (miRNAs) participate in anticancer immune responses. In the present study, we determined miRNA expression patterns in patients with CRC and built a signature that predicts TMB. Next generation sequencing (NGS) on formalin-fixed paraffin-embedded samples from CRC patients was performed to measure TMB levels. We used datasets from The Cancer Genome Atlas to compare miRNA expression patterns in samples with high and low TMB from patients with CRC. We created an miRNA-based signature index using the selection operator (LASSO) and least absolute shrinkage method from the training set. We used an independent test set as internal validation. We used real-time polymerase chain reaction (RT-PCR) to validate the miRNA-based signature classifier. Twenty-seven samples from CRC patients underwent NGS to determine the TMB level. We identified four miRNA candidates in the training set for predicting TMB (N = 311). We used the test set (N = 204) for internal validation. The four-miRNA-based signature classifier was an accurate predictor of TMB, with accuracy 0.963 in the training set. In the test set, it was 0.902; and it was 0.946 in the total set. The classifier was superior to microsatellite instability (MSI) for predicting TMB in TCGA dataset. In the validation cohort, MSI status more positively correlated with TMB levels than did the classifier. Validation from RT-qPCR showed good target discrimination of the classifier for TMB prediction. To our knowledge, this is the first miRNA-based signature classifier validated using high quality clinical data to accurately predict TMB level in patients with CRC.

Huang J ，Liu H ，Zhao Y ，Luo T ，Liu J ，Liu J ，Pan X ，Tang W ... -  《Frontiers in Oncology》

MiRNA expression patterns are associated with tumor mutational burden in lung adenocarcinoma.

Background: Tumor mutational burden (TMB) has emerged as an independent biomarker to predict patient responses to treatment with immune checkpoint inhibitors (ICIs) for lung adenocarcinoma (LUAD). MicroRNAs (miRNAs) have a crucial role in the regulation of anticancer immune responses, but the association of miRNA expression patterns and TMB is not clear in LUAD. Methods: Differentially expressed miRNAs in samples with high TMB and low TMB samples were screened in the LUAD dataset in The Cancer Genome Atlas. The least absolute shrinkage and selection operator (LASSO) method was applied to develop a miRNA-based signature classifier for predicting TMB levels in the training set. An test set was used to validate this classifier. The correlation between the miRNA-based classifier index and the expression of three immune checkpoints (PD-1, PD-L1, and CTLA-4) were explored. Functional enrichment analysis was carried out of the miRNAs included in the miRNA-based signature classifier. Results: Twenty-five differentially expressed miRNAs were used to establish a miRNA-based signature classifier for predicting TMB level. The accuracy of the 25-miRNA-based signature classifier was 0.850 in the training set, 0.810 in the test set and 0.840 in the total set. This miRNA-based signature classifier index showed a low correlation with PD-1 and PD-L1, and no correlation with CTLA-4. Enrichment analysis for these 25 miRNA revealed they are involved in many immune-related biological processes and cancer-related pathways. Conclusion: MiRNA expression patterns are associated with tumor mutational burden and a miRNA-based signature classifier may serve as a biomarker for prediction of TMB levels in LUAD.

Lv Y ，Huang Z ，Lin Y ，Fang Y ，Chen Z ，Pan L ，Zhang Y ，Xu Z ... -  《OncoImmunology》

Identification and validation of a miRNA-related expression signature for tumor mutational burden in colorectal cancer.

Xu L ，Zheng Q 《World Journal of Surgical Oncology》

A next-generation sequencing-based strategy combining microsatellite instability and tumor mutation burden for comprehensive molecular diagnosis of advanced colorectal cancer.

Xiao J ，Li W ，Huang Y ，Huang M ，Li S ，Zhai X ，Zhao J ，Gao C ，Xie W ，Qin H ，Cai S ，Bai Y ，Lan P ，Zou Y ... -  《BMC CANCER》

miRNA-Based Feature Classifier Is Associated with Tumor Mutational Burden in Head and Neck Squamous Cell Carcinoma.

Tumor mutation burden (TMB) is considered to be an independent genetic biomarker that can predict the tumor patient's response to immune checkpoint inhibitors (ICIs). Meanwhile, microRNA (miRNA) plays a key role in regulating the anticancer immune response. However, the correlation between miRNA expression patterns and TMB is not elucidated in HNSCC. In the HNSCC cohort of the TCGA dataset, miRNAs that were differentially expressed in high TMB and low TMB samples were screened. The least absolute contraction and selection operator (LASSO) method is used to construct a miRNA-based feature classifier to predict the TMB level in the training set. The test set is used to verify the classifier. The correlation between the miRNA-based classifier index and the expression of three immune checkpoints (PD1, PDL1, and CTLA4) was explored. We further perform functional enrichment analysis on the miRNA contained in the miRNA-based feature classifier. Twenty-five differentially expressed miRNAs are used to build miRNA-based feature classifiers to predict TMB levels. The accuracy of the 25-miRNA-based signature classifier is 0.822 in the training set, 0.702 in the test set, and 0.774 in the total set. The miRNA-based feature classifier index showed a low correlation with PD1 and PDL1, but no correlation with CTLA4. The enrichment analysis of these 25 miRNAs shows that they are involved in many immune-related biological processes and cancer-related pathways. The miRNA expression patterns are related to tumor mutation burden, and miRNA-based feature classifiers can be used as biomarkers to predict TMB levels in HNSCC.

Xia Y ，Wang Q ，Huang X ，Yin X ，Song J ，Ke Z ，Duan X ... -  《-》