Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan–Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein–protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC.

Thyroid cancer prognostic biomarker ARL4A and its relationship with immune infiltration

“…As part of the FERM family of proteins, FRMD4A is involved in cell polarity, migration, and signaling, crucial for cancer development [21]. It plays a key role in cytoskeleton remodeling and cell attachment sites, essential for the migration and invasion of cancer cells, such as in rectal [22] and pancreatic cancers [23]. By interacting with other proteins, FRMD4A influences various cell signaling pathways critical for tumor growth and survival, making it a potential therapeutic target [24].…”

The causal relationship between cathepsins and digestive system tumors: a Mendelian randomization study

“…A recent Mendelian randomization (MR) study revealed that elevated levels of cathepsin H may contribute to an increased risk of lung cancer ( 12 ). Previous observational studies have indicated a correlation between cathepsins and tumors in the digestive system, specifically cathepsin L ( 13 ) and cathepsin W ( 14 ), which have been linked to a poor prognosis for PCa. Cathepsin B has been linked to HCC ( 15 ), PCa ( 16 , 17 ), BTC ( 18 , 19 ), and CRC ( 20 , 21 ).…”