Association Mining Identifies MAL2 as a Novel Tumor Suppressor in Colorectal Cancer

Abstract

Introduction

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. However, the driver genes that promote CRC metastasis remain poorly understood. Association mining mines and extracts the repeated correlations and relevance in a dataset to predict the appearance of other data items according to the appearance of one item.

Methods

Here, the Apriori algorithm was used to find the frequent mutational gene sets (FMGSs) and hidden association rules (ARs) within these FMGSs from 383 CRCs with whole exome sequencing datasets. The weighted correlation network analysis (WGCNA) was used to identify the hub genes in CRC. CCK8, colony formation, cell migration and invasion assays were adopted to detect the roles of hub genes in CRC.

Results

Intriguingly, we found that MAL2 (myelin and lymphocyte protein 2) was associated with TP53 and APC in stage IV of CRC, and further subnetwork exploration based on WGCNA identified MAL2 as a potent hub gene. To validate the metastasis-related role of MAL2 in CRC, a lentivirus-based overexpression system was utilized to construct MAL2-overexpressing human CRC LOVO cells. Overexpression of MAL2 remarkably inhibited CRC cell proliferation and invasion.

Conclusion

Our results highlighted that MAL2 acts as a tumor suppressor in CRC and could serve as a potential therapeutic target.

Keywords:

• Apriori
• CRC
• frequent mutational gene sets
• MAL2

Data Sharing Statement

All datasets presented in this study are included in the article.

Ethics Approval and Consent to Participate

This study was approved by the Ethics Committee of the Second Affiliated Hospital, Zhejiang University School of Medicine.

Disclosure

The authors declare that they have no competing interests in this work.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (grant number 81802883) and Fundamental Research Funds for the Central Universities (grant number 2018FZA7012) to Wangxiong Hu.