Machine learning-enabled multi-omics discovery of prognostic biomarkers and signaling targets in pancreatic cancer.

Shafiul Haque¹, Darin Mansor Mathkor², Mohd Wahid³, Raju K Mandal⁴, Hifzur R Siddique⁵, Dharmendra K Yadav⁶

¹Department of Nursing, College of Nursing and Health Sciences, Jazan University, Jazan 82911, Saudi Arabia; School of Medicine, Universidad Espiritu Santo, Samborondon 091952, Ecuador. Electronic address: shhaque@jazanu.edu.sa.

²Department of Nursing, College of Nursing and Health Sciences, Jazan University, Jazan 82911, Saudi Arabia. Electronic address: darin.mathkor@gmail.com.

³Department of Nursing, College of Nursing and Health Sciences, Jazan University, Jazan 82911, Saudi Arabia. Electronic address: wahidbiochem@gmail.com.

⁴Department of Nursing, College of Nursing and Health Sciences, Jazan University, Jazan 82911, Saudi Arabia. Electronic address: rajmandalbiot@gmail.com.

⁵Molecular Cancer Genetics & Translational Research Lab, Section of Genetics, Department of Zoology, Aligarh Muslim University, Aligarh 202002, Uttar Pradesh, India. Electronic address: hifzur.zo@amu.ac.in.

⁶College of Pharmacy, Gachon University, Hambakmoeiro 191, Yeonsu-gu, Incheon 21924, Republic of Korea. Electronic address: dharmendra@gachon.ac.kr.

PubMed DOI

Abstract

Pancreatic ductal adenocarcinoma (PDAC) remains difficult to subtype using single omics layers. We conducted an exploratory investigation integrating reverse-phase protein array (RPPA) and DNA methylation data from the cancer genome atlas (TCGA)- pancreatic adenocarcinoma (PAAD) to assess the feasibility of multi-omics subtyping, alongside a supervised machine learning analysis of a small gene exp…

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