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

被引:3
|
作者
Asadnia, Alireza [1 ,2 ,3 ]
Nazari, Elham [4 ]
Goshayeshi, Ladan [5 ,6 ]
Zafari, Nima [1 ]
Moetamani-Ahmadi, Mehrdad [1 ,2 ]
Goshayeshi, Lena [6 ]
Azari, Haneih [1 ]
Pourali, Ghazaleh [1 ]
Khalili-Tanha, Ghazaleh [1 ]
Abbaszadegan, Mohammad Reza [2 ,3 ]
Khojasteh-Leylakoohi, Fatemeh [1 ,3 ]
Bazyari, Mohammadjavad [7 ]
Kahaei, Mir Salar [2 ]
Ghorbani, Elnaz [1 ]
Khazaei, Majid [1 ,3 ]
Hassanian, Seyed Mahdi [1 ,3 ]
Gataa, Ibrahim Saeed [8 ]
Kiani, Mohammad Ali [3 ]
Peters, Godefridus J. [9 ,10 ]
Ferns, Gordon A. [11 ]
Batra, Jyotsna [12 ]
Lam, Alfred King-yin [13 ]
Giovannetti, Elisa [10 ,14 ]
Avan, Amir [1 ,7 ,12 ]
机构
[1] Mashhad Univ Med Sci, Metab Syndrome Res Ctr, Mashhad 9177948564, Iran
[2] Mashhad Univ Med Sci, Med Genet Res Ctr, Mashhad 9188617871, Iran
[3] Mashhad Univ Med Sci, Basic Sci Res Inst, Mashhad 1394491388, Iran
[4] Shahid Beheshti Univ Med Sci, Sch Allied Med Sci, Dept Hlth Informat Technol & Management, Tehran 1983969411, Iran
[5] Mashhad Univ Med Sci, Fac Med, Dept Gastroenterol & Hepatol, Mashhad 9177948564, Iran
[6] Mashhad Univ Med Sci, Surg Oncol Res Ctr, Mashhad 9177948954, Iran
[7] Mashhad Univ Med Sci, Fac Med, Dept Med Biotechnol, Mashhad 9177948564, Iran
[8] Univ Warith Al Anbiyaa, Coll Med, Karbala 56001, Iraq
[9] Med Univ Gdansk, Dept Biochem, PL-80211 Gdansk, Poland
[10] VU Univ Med Ctr VUMC, Canc Ctr Amsterdam, Dept Med Oncol, Amsterdam UMC, NL-1081 HV Amsterdam, Netherlands
[11] Brighton & Sussex Med Sch, Dept Med Educ, Brighton BN1 9PH, Sussex, England
[12] Queensland Univ Technol QUT, Sch Biomed Sci, Fac Hlth, Brisbane, Qld 4059, Australia
[13] Griffith Univ, Sch Med & Dent, Pathol, Gold Coast Campus, Gold Coast, Qld 4222, Australia
[14] Fdn Pisana Sci, AIRC Startup Unit, Canc Pharmacol Lab, I-56017 Pisa, Italy
关键词
machine learning; colorectal cancer; bioinformatics; biomarker; prognosis; FINGER PROTEIN MAZ; PROLIFERATION; PRRC2A;
D O I
10.3390/cancers15174300
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
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.
引用
收藏
页数:18
相关论文
共 50 条
  • [41] Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system
    Zhiqiao Zhang
    Liwen Huang
    Jing Li
    Peng Wang
    BMC Bioinformatics, 23
  • [42] MACHINE LEARNING-BASED APPROACH FOR TILLAGE IDENTIFICATION USING SENTINEL-1 DATA
    Pandit, Ankur
    Bansal, Pradhyumn
    Sawant, Suryakant
    Mohite, Jayantrao
    Srinivasu, P.
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 3406 - 3409
  • [43] Development and evaluation of a colorectal cancer screening method using machine learning-based gut microbiota analysis
    Konishi, Yusuke
    Okumura, Shintaro
    Matsumoto, Tomonori
    Itatani, Yoshiro
    Nishiyama, Tsuyoshi
    Okazaki, Yuki
    Shibutani, Masatsune
    Ohtani, Naoko
    Nagahara, Hisashi
    Obama, Kazutaka
    Ohira, Masaichi
    Sakai, Yoshiharu
    Nagayama, Satoshi
    Hara, Eiji
    CANCER MEDICINE, 2022, 11 (16): : 3194 - 3206
  • [44] Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis
    Chen, De-Lun
    Cai, Jia-Hua
    Wang, Charles C. N.
    GENES, 2022, 13 (05)
  • [45] Machine learning-based prognostic modeling and surgical value analysis of de novo metastatic invasive ductal carcinoma of the breast
    Wei, Changlong
    Li, Honghui
    Li, Jinsong
    Liu, Yaxiong
    Zeng, Jinsheng
    Tian, Qiuhong
    UPDATES IN SURGERY, 2025,
  • [46] To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification
    Alsanea, Majed
    Dukyil, Abdulsalam S.
    Afnan
    Riaz, Bushra
    Alebeisat, Farhan
    Islam, Muhammad
    Habib, Shabana
    SENSORS, 2022, 22 (11)
  • [47] A light gradient boosting machine learning-based approach for predicting clinical data breast cancer
    Wang Qiuqian
    Gao Min
    Zhang KeZhu
    Chen Chen
    MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN, 2025, 8 (01)
  • [48] Identifying the tumor location-associated candidate genes in development of new drugs for colorectal cancer using machine-learning-based approach
    Bayrak, Tuncay
    Cetin, Zafer
    Saygili, E. Ilker
    Ogul, Hasan
    MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2022, 60 (10) : 2877 - 2897
  • [49] A machine learning-based prognostic signature utilizing MSC proteomics for predicting bladder cancer prognosis and treatment response
    Zhang, Xinyu
    Li, Pan
    Ji, Luhua
    Zhang, Yuanfeng
    Zhang, Ze
    Guo, Yufeng
    Zhang, Luyang
    Jing, Suoshi
    Dong, Zhilong
    Tian, Junqiang
    Yang, Li
    Ding, Hui
    Yang, Enguang
    Wang, Zhiping
    TRANSLATIONAL ONCOLOGY, 2025, 54
  • [50] Identification of thioredoxin-1 as a biomarker of lung cancer and evaluation of its prognostic value based on bioinformatics analysis
    Liu, Xiaoting
    Dong, Xilin
    Hu, Yaxin
    Fang, Yanan
    FRONTIERS IN ONCOLOGY, 2023, 13