Transcriptional factor III A promotes colorectal cancer progression by upregulating cystatin A

KRAS/PI3K axis driven GTF3C6 expression and promotes LUAD via FAK pathway

INTRODUCTION

Effective targeting drugs for KRAS mutation-mediated Lung Adenocarcinoma (LUAD) are currently are limited.

OBJECTIVES

Investigating and intervening in the downstream key target genes of KRAS is crucial for clinically managing KRAS mutant-driven LUAD. GTF3C6, a newly identified member of the general transcription factor III (GTF3) family, plays a role in the transcription of RNA polymerase III (pol III)-dependent genes. However, its involvement in cancer remains unexplored.

METHODS

This study examined the expression, roles, and potential molecular mechanisms of GTF3C6 in LUAD tissues, LSL-KrasG12D/+;LSL-p53-/- LUAD mouse models, and LUAD patients-derived organoid using Western blot, qRT-PCR, immunofluorescence, immunohistochemistry, and gene manipulation assays.

RESULTS

We present the first evidence that GTF3C6 is highly expressed in LUAD tissues, LSL-KrasG12D/+;LSL-p53-/- LUAD mouse models, and LUAD organoids, correlating with poor clinical prognosis. Furthermore, GTF3C6 was found to promote anchorage-independent proliferation, migration, and invasion of LUAD cells. Mechanistically, KRAS mutation drives GTF3C6 expression through the PI3K pathway, and GTF3C6 knockdown reverses the malignant phenotype of KRAS mutation-driven LUAD cells. Additionally, the FAK pathway emerged as a crucial downstream signaling pathway through which GTF3C6 mediates the malignant phenotype of LUAD. Finally, GTF3C6 knockdown suppresses LUAD organoid formation and inhibits tumor growth in vivo.

CONCLUSION

Our findings demonstrate that GTF3C6, driven by KRAS mutation, promotes LUAD development by regulating FAK phosphorylation, suggesting its potential as a biomarker and therapeutic target in KRAS mutant-driven LUAD.

Investigation of the shared molecular mechanisms and hub genes between myocardial infarction and depression

Background

The pathogenesis of myocardial infarction complicating depression is still not fully understood. Bioinformatics is an effective method to study the shared pathogenesis of multiple diseases and has important application value in myocardial infarction complicating depression.

Methods

The differentially expressed genes (DEGs) between control group and myocardial infarction group (M-DEGs), control group and depression group (D-DEGs) were identified in the training set. M-DEGs and D-DEGs were intersected to obtain DEGs shared by the two diseases (S-DEGs). The GO, KEGG, GSEA and correlation analysis were conducted to analyze the function of DEGs. The biological function differences of myocardial infarction and depression were analyzed by GSVA and immune cell infiltration analysis. Four machine learning methods, nomogram, ROC analysis, calibration curve and decision curve were conducted to identify hub S-DEGs and predict depression risk. The unsupervised cluster analysis was constructed to identify myocardial infarction molecular subtype clusters based on hub S-DEGs. Finally, the value of these genes was verified in the validation set, and blood samples were collected for RT-qPCR experiments to further verify the changes in expression levels of these genes in myocardial infarction and depression.

Results

A total of 803 M-DEGs, 214 D-DEGs, 13 S-DEGs and 6 hub S-DEGs (CD24, CSTA, EXTL3, RPS7, SLC25A5 and ZMAT3) were obtained in the training set and they were all involved in immune inflammatory response. The GSVA and immune cell infiltration analysis results also suggested that immune inflammation may be the shared pathogenesis of myocardial infarction and depression. The diagnostic models based on 6 hub S-DEGs found that these genes showed satisfactory combined diagnostic performance for depression. Then, two molecular subtypes clusters of myocardial infarction were identified, many differences in immune inflammation related-biological functions were found between them, and the hub S-DEGs had satisfactory molecular subtypes identification performance. Finally, the analysis results of the validation set further confirmed the value of these hub genes, and the RT-qPCR results of blood samples further confirmed the expression levels of these hub genes in myocardial infarction and depression.

Conclusion

Immune inflammation may be the shared pathogenesis of myocardial infarction and depression. Meanwhile, hub S-DEGs may be potential biomarkers for the diagnosis and molecular subtype identification of myocardial infarction and depression.