Establishment of Three Gene Prognostic Markers in Pancreatic Ductal Adenocarcinoma Using Machine Learning Approach

Abstract

Purpose

Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent form of pancreatic cancer, accounting for about 85% of all occurrences. It is highly challenging to treat PDAC because of its extreme aggressiveness and lack of therapeutic options. Identifying new gene markers can help in the design of novel targeted therapeutics.

Methods

In this study, we identified three different gene prognostic markers in PDAC using a machine learning approach. Initially, the differential expression genes (DEGs) profile of accession number GSE183795 was downloaded from the gene expression omnibus database of the National Center for Biotechnology Information (NCBI), which consists of the expression profile of the 244 patients with PDAC (139 pancreatic tumors, 102 adjacent non-tumors and 3 normal). Then, the expression dataset was preprocessed using different packages of R programming, such as GEOquery, Affy, and Limma. Further, DEGs were identified by the machine learning algorithms, including random forest (RF) and extreme gradient boost (XGboost). Finally, survival analysis was performed to identify DEGs using GEPIA software (TCGA database).

Results

Our results revealed that 6 out of 25 DEGs (ERCC3, ACY3, ATP2A3, MW-TW1879, MW-TW3829, and ZBTB7A) identified by RF and XGBoost algorithm were the same, indicating their feature importance. Moreover, three genes, including ATP2A3 (p = 0.029), NRL (p = 0.012), and FBXO45 (p = 0.013), were statistically significant when tested for survival analysis and may be utilized as the prognostic marker genes for PDAC.

Conclusion

These findings provide valuable insights into the molecular characteristics of PDAC and can potentially guide future research on cancer theranostics interventions for this devastating disease.