MiR-485-3p/MELK cascade mediates tumor progression in pancreatic cancer

Pancreatic cancer remains one of the leading causes of mortality worldwide, largely due to the limitations of current clinical strategies for its treatment. As a result, identifying genetic

alterations and potential therapeutic targets could offer new opportunities for improving the diagnosis and treatment of pancreatic cancer. The identification of differentially expressed

genes (DEGs) and subsequent analyses, including signaling pathway enrichment, functional classification, and protein-protein interaction (PPI) network construction, were conducted using

three public datasets: GSE32676, GSE71989, and GSE16515. Kaplan-Meier survival curves and receiver operating characteristic (ROC) curves were employed to investigate the correlation between

hub genes and clinicopathological features in pancreatic cancer patients. Genetic alterations were analyzed using the CBioPortal web tool. Cell proliferation was assessed through CCK-8,

colony formation, and EdU assays. Tumor migration, invasion, and angiogenesis were evaluated using transwell and tube formation assays, respectively. Protein and mRNA expression levels were

measured via western blot analysis and qPCR assays. The subcutaneous xenografted nude mice models were generated to evaluate the potential effect of miR-485-3p/MELK cascade on tumor growth

in vivo. Our analysis revealed that MELK expression is positively correlated with poor prognosis in patients with pancreatic cancer. The overexpression or knockdown of MELK significantly

influences cell proliferation, tumor metastasis, and angiogenesis across various pancreatic cancer cell lines. Furthermore, we identified that miR-485-3p regulates MELK expression by

directly targeting the MELK 3’UTR binding site in pancreatic cancer cells, which subsequently impacts tumor progression. Additionally, our findings demonstrate that the miR-485-3p/MELK

cascade is closely associated with tumor progression in pancreatic cancer cells. Mechanistically, the miR-485-3p/MELK cascade promotes the phosphorylation of Akt to regulate pancreatic

cancer cell progression, metastasis, and angiogenesis. Furthermore, overexpression of miR-485-3p inhibits the tumor growth induced by MELK overexpression in subcutaneous xenograft model.

MiR-485-3p/MELK cascade may serve as a promising biomarker and therapeutic target for the diagnosis and treatment of pancreatic cancer.

Pancreatic cancer (PC) is one of the most lethal forms of malignant cancer, characterized by high mortality rates and poor prognoses1. According to GLOBOCAN 2022, there were 510,566 newly

diagnosed cases of pancreatic cancer and 467,005 associated deaths, with an approximate 5-year survival rate of just 7%2. By 2030, pancreatic cancer is projected to become the second leading

cause of cancer-related deaths, with one of the highest incidence rates among malignant cancers3. Surgical resection, combined with subsequent chemo-radiotherapy, remains the only

potentially curative treatment for improving the prognosis of pancreatic cancer patients4. However, prognostic outcomes continue to be poor due to the limitations of current therapeutic

approaches. Consequently, the identification of potential therapeutic targets, which could significantly enhance diagnostic accuracy and prognostic prediction, may prove instrumental in

advancing clinical treatment options for pancreatic cancer.

Maternal embryonic leucine zipper kinase (MELK) is a Ser/Thr protein kinase, also known as a cell cycle-dependent protein kinase, and is categorized as an atypical member of the Snf1/AMPK

family5,6,7. Previous studies have reported that MELK is highly expressed in various malignant tumors, including breast, prostate, gastric, and cervical cancers8,9,10,11, suggesting its

significant role in tumor progression12. Importantly, further research has revealed that MELK mediates multiple biological events and signaling pathways to regulate various tumor-associated

processes, such as cell cycle progression, cell proliferation, apoptosis, tumor metastasis, and chemotherapy resistance13,14,15. Notably, MELK functions as a novel oncogenic regulator by

directly interacting with other factors or activating phosphorylation in these processes. For instance, one study demonstrated that MELK, as an upstream kinase of apoptosis signal-regulating

kinase 1 (ASK1), interacts with ASK1 to phosphorylate threonine 838 and enhance ASK1’s kinase activity16. Another report revealed that MELK inhibits breast cancer cell apoptosis by directly

binding to Bcl-G, a pro-apoptotic member of the Bcl-2 family8. Additionally, MELK has been shown to promote cell cycle arrest and apoptosis by interacting with the p53 protein through

direct phosphorylation at the Ser15 site17. The elevated expression of MELK in various tumors underscores its critical role in tumor progression while highlighting its potential as a

therapeutic target for cancer treatment. Nonetheless, the specific functions of MELK in the progression of pancreatic cancer remain insufficiently studied.

In this study, we initially identified MELK using bioinformatics techniques, which included analyzing differentially expressed genes (DEGs), performing functional enrichment analysis, and

constructing a protein-protein interaction (PPI) network for pancreatic cancer. The findings demonstrated that MELK is highly expressed in pancreatic cancer, suggesting it could serve as an

independent molecular prognostic factor for patients with this disease. Furthermore, we investigated the potential role of MELK in pancreatic cancer cells. Our results indicated that

overexpression of MELK significantly enhances the proliferation, invasion, metastasis, and angiogenesis of these cells. Additional research uncovered that miR-485-3p functions as an upstream

regulator of MELK and suppresses pancreatic cancer progression by modulating MELK expression. Collectively, our findings suggest that MELK could be a promising biomarker for predicting the

prognosis of pancreatic cancer patients and may offer a potential strategy for treating this condition.

The microarray gene expression datasets, including GSE32676, GSE71989, and GSE16515, were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/)18. All

three datasets were generated using the same platform, the Affymetrix Human Genome U133 Plus 2.0 Array (HG U133 Plus 2.0). Specifically, the GSE32676 dataset includes 25 samples from human

pancreatic cancer tissues and 7 samples from adjacent normal tissues. Similarly, the GSE71989 dataset comprises 14 pancreatic cancer samples and 8 adjacent normal pancreatic tissue samples,

while the GSE16515 dataset consists of 36 pancreatic tumor samples and 16 adjacent normal samples. The distribution of data for each sample is visualized using box plots. The effectiveness

of batch effect correction is evaluated through principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) plots. These visualizations were created using R

software with the `ggplot2` package.

Differential gene expression in pancreatic cancer samples was analyzed using the GEO2R web tool available on the NCBI Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/). The DEGs were

identified based on the following screening criteria: |log(FC)| > 1 and P  1 and P