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

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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.

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Data Availability

Data will be made available on request.

Abbreviations

PDAC:

Pancreatic ductal adenocarcinoma

DEGs:

Differential expression genes

NCBI:

National Center for Biotechnology Information

RF:

Random forest

XGboost:

Extreme gradient boost

qPCR:

Quantitative polymerase chain reaction

RNA seq:

RNA sequencing

ML:

Machine learning

SVM:

Support vector machine

ROC:

Receiver operating characteristic curve

AUC:

Area under the curve

GEPIA:

Gene Expression Profiling Interactive Analysis

TCGA:

The Cancer Genome Atlas

GTEx:

Genotype-Tissue Expression

ERCC3:

Excision repair cross-complementation group 3

ACY3:

Aminoacylase 3

SCLC:

Squamous-cell lung cancer

NRL:

Neural retina- specific leucine

CCND2:

CyclinD2

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Funding

This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

Authors

Contributions

Pragya: Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Visualization, Writing - original draft, Writing - review & editing. Praveen Kumar Govarthan: Software, Investigation, Validation, Conceptualization. Malay Nayak: Methodology, Software, Validation, Visualization, Writing - original draft. Sudip Mukherjee: Conceptualization, Supervision, Writing - review & editing. Jac Fredo Agastinose Ronickom: Conceptualization, Supervision, Writing - review & editing.

Corresponding author

Correspondence to Jac Fredo Agastinose Ronickom.

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Pragya, P., Govarthan, P.K., Nayak, M. et al. Establishment of Three Gene Prognostic Markers in Pancreatic Ductal Adenocarcinoma Using Machine Learning Approach. J. Med. Biol. Eng. (2024). https://doi.org/10.1007/s40846-024-00859-7

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