The Prognostic Value of ASPHD1 and ZBTB12 in Colorectal Cancer: A Machine Learning-Based Integrated Bioinformatics Approach

Alireza Asadnia; Elham Nazari; Ladan Goshayeshi; Nima Zafari; Mehrdad Moetamani-Ahmadi; Lena Goshayeshi; Haneih Azari; Ghazaleh Pourali; Ghazaleh Khalili-Tanha; Mohammad Reza Abbaszadegan; Fatemeh Khojasteh-Leylakoohi; MohammadJavad Bazyari; Mir Salar Kahaei; Elnaz Ghorbani; Majid Khazaei; Seyed Mahdi Hassanian; Ibrahim Saeed Gataa; Mohammad Ali Kiani; Godefridus J Peters; Gordon A Ferns; Jyotsna Batra; Alfred King-Yin Lam; Elisa Giovannetti; Amir Avan

doi:10.3390/cancers15174300

The Prognostic Value of ASPHD1 and ZBTB12 in Colorectal Cancer: A Machine Learning-Based Integrated Bioinformatics Approach

Cancers (Basel). 2023 Aug 28;15(17):4300. doi: 10.3390/cancers15174300.

Authors

Alireza Asadnia^{1

2

3}, Elham Nazari⁴, Ladan Goshayeshi^{5

6}, Nima Zafari¹, Mehrdad Moetamani-Ahmadi^{1

2}, Lena Goshayeshi⁶, Haneih Azari¹, Ghazaleh Pourali¹, Ghazaleh Khalili-Tanha¹, Mohammad Reza Abbaszadegan^{2

3}, Fatemeh Khojasteh-Leylakoohi^{1

3}, MohammadJavad Bazyari⁷, Mir Salar Kahaei², Elnaz Ghorbani¹, Majid Khazaei^{1

3}, Seyed Mahdi Hassanian^{1

3}, Ibrahim Saeed Gataa⁸, Mohammad Ali Kiani³, Godefridus J Peters^{9

10}, Gordon A Ferns¹¹, Jyotsna Batra¹², Alfred King-Yin Lam¹³, Elisa Giovannetti^{10

14}, Amir Avan^{1

7

12}

Affiliations

¹ Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran.
² Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad 91886-17871, Iran.
³ Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad 13944-91388, Iran.
⁴ Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran 19839-69411, Iran.
⁵ Department of Gastroenterology and Hepatology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran.
⁶ Surgical Oncology Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48954, Iran.
⁷ Department of Medical Biotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran.
⁸ College of Medicine, University of Warith Al-Anbiyaa, Karbala 56001, Iraq.
⁹ Department of Biochemistry, Medical University of Gdansk, 80-211 Gdansk, Poland.
¹⁰ Cancer Center Amsterdam, Amsterdam U.M.C., VU University Medical Center (VUMC), Department of Medical Oncology, 1081 HV Amsterdam, The Netherlands.
¹¹ Brighton & Sussex Medical School, Department of Medical Education, Falmer, Brighton, Sussex BN1 9PH, UK.
¹² Faculty of Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia.
¹³ Pathology, School of Medicine and Dentistry, Gold Coast Campus, Griffith University, Gold Coast, QLD 4222, Australia.
¹⁴ Cancer Pharmacology Lab, AIRC Start Up Unit, Fondazione Pisana per La Scienza, 56017 Pisa, Italy.

Abstract

Introduction: Colorectal cancer (CRC) is a common cancer associated with poor outcomes, underscoring a need for the identification of novel prognostic and therapeutic targets to improve outcomes. This study aimed to identify genetic variants and differentially expressed genes (DEGs) using genome-wide DNA and RNA sequencing followed by validation in a large cohort of patients with CRC. Methods: Whole genome and gene expression profiling were used to identify DEGs and genetic alterations in 146 patients with CRC. Gene Ontology, Reactom, GSEA, and Human Disease Ontology were employed to study the biological process and pathways involved in CRC. Survival analysis on dysregulated genes in patients with CRC was conducted using Cox regression and Kaplan-Meier analysis. The STRING database was used to construct a protein-protein interaction (PPI) network. Moreover, candidate genes were subjected to ML-based analysis and the Receiver operating characteristic (ROC) curve. Subsequently, the expression of the identified genes was evaluated by Real-time PCR (RT-PCR) in another cohort of 64 patients with CRC. Gene variants affecting the regulation of candidate gene expressions were further validated followed by Whole Exome Sequencing (WES) in 15 patients with CRC. Results: A total of 3576 DEGs in the early stages of CRC and 2985 DEGs in the advanced stages of CRC were identified. ASPHD1 and ZBTB12 genes were identified as potential prognostic markers. Moreover, the combination of ASPHD and ZBTB12 genes was sensitive, and the two were considered specific markers, with an area under the curve (AUC) of 0.934, 1.00, and 0.986, respectively. The expression levels of these two genes were higher in patients with CRC. Moreover, our data identified two novel genetic variants-the rs925939730 variant in ASPHD1 and the rs1428982750 variant in ZBTB1-as being potentially involved in the regulation of gene expression. Conclusions: Our findings provide a proof of concept for the prognostic values of two novel genes-ASPHD1 and ZBTB12-and their associated variants (rs925939730 and rs1428982750) in CRC, supporting further functional analyses to evaluate the value of emerging biomarkers in colorectal cancer.

Keywords: bioinformatics; biomarker; colorectal cancer; machine learning; prognosis.

Abstract

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