MAD2L1 is transcriptionally regulated by TEAD4 and promotes cell proliferation and migration in colorectal cancer

TEAD4: A key regulator of tumor metastasis and chemoresistance - Mechanisms and therapeutic implications

Cancer metastasis is a complex process influenced by various factors, including epithelial-mesenchymal transition (EMT), tumor cell proliferation, tumor microenvironment, and cellular metabolic status, which remains a significant challenge in clinical oncology, accounting for a majority of cancer-related deaths. TEAD4, a key mediator of the Hippo signaling pathway, has been implicated in regulating these factors that are all critical in the metastatic cascade. TEAD4 drives tumor metastasis and chemoresistance, and its upregulation is associated with poor prognosis in many types of cancers, making it an attractive target for therapeutic intervention. TEAD4 promotes EMT by interacting with coactivators and activating the transcription of genes involved in mesenchymal cell characteristics and extracellular matrix remodeling. Additionally, TEAD4 enhances the stemness of cancer stem cells (CSCs) by regulating the expression of genes associated with CSC maintenance. TEAD4 contributes to metastasis by modulating the secretion of paracrine factors and promoting heterotypic cellular communication. In this paper, we highlight the central role of TEAD4 in cancer metastasis and chemoresistance and its impact on various aspects of tumor biology. Understanding the mechanistic basis of TEAD4-mediated processes can facilitate the development of targeted therapies and combination approaches to combat cancer metastasis and improve treatment outcomes.

Analysis of RNA-Seq data using self-supervised learning for vital status prediction of colorectal cancer patients

BACKGROUND

RNA sequencing (RNA-Seq) is a technique that utilises the capabilities of next-generation sequencing to study a cellular transcriptome i.e., to determine the amount of RNA at a given time for a given biological sample. The advancement of RNA-Seq technology has resulted in a large volume of gene expression data for analysis.

RESULTS

Our computational model (built on top of TabNet) is first pretrained on an unlabelled dataset of multiple types of adenomas and adenocarcinomas and later fine-tuned on the labelled dataset, showing promising results in the context of the estimation of the vital status of colorectal cancer patients. We achieve a final cross-validated (ROC-AUC) Score of 0.88 by using multiple modalities of data.

CONCLUSION

The results of this study demonstrate that self-supervised learning methods pretrained on a vast corpus of unlabelled data outperform traditional supervised learning methods such as XGBoost, Neural Networks, and Decision Trees that have been prevalent in the tabular domain. The results of this study are further boosted by the inclusion of multiple modalities of data pertaining to the patients in question. We find that genes such as RBM3, GSPT1, MAD2L1, and others important to the computation model's prediction task obtained through model interpretability corroborate with pathological evidence in current literature.