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] Machine learning-based screening and validation of liver metastasis-specific genes in colorectal cancer
    Zheng, Shiyao
    He, Hongxin
    Zheng, Jianfeng
    Zhu, Xingshu
    Lin, Nan
    Wu, Qing
    Wei, Enhao
    Weng, Caiming
    Chen, Shuqian
    Huang, Xinxiang
    Jian, Chenxing
    Guan, Shen
    Yang, Chunkang
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [42] An integrated machine learning-based model for joint diagnosis of ovarian cancer with multiple test indicators
    Yiwen Feng
    Journal of Ovarian Research, 17
  • [43] An integrated machine learning-based model for joint diagnosis of ovarian cancer with multiple test indicators
    Feng, Yiwen
    JOURNAL OF OVARIAN RESEARCH, 2024, 17 (01)
  • [44] Machine Learning-Based Chronic Kidney Cancer Prediction Application: A Predictive Analytics Approach
    Khandaker Mohammad Mohi Uddin
    Md. Nuzmul Hossain Nahid
    Md. Mehedi Hasan Ullah
    Badhan Mazumder
    Md. Saikat Islam Khan
    Samrat Kumar Dey
    Biomedical Materials & Devices, 2024, 2 (2): : 1028 - 1048
  • [45] A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework
    Masud, Mehedi
    Sikder, Niloy
    Nahid, Abdullah-Al
    Bairagi, Anupam Kumar
    AlZain, Mohammed A.
    SENSORS, 2021, 21 (03) : 1 - 21
  • [46] Exploring the Differential Expression and Prognostic Significance of the COL11A1 Gene in Human Colorectal Carcinoma: An Integrated Bioinformatics Approach
    Patra, Ritwik
    Das, Nabarun Chandra
    Mukherjee, Suprabhat
    FRONTIERS IN GENETICS, 2021, 12
  • [47] Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy
    Li, Linrui
    Qin, Zhihui
    Bo, Juan
    Hu, Jiaru
    Zhang, Yu
    Qian, Liting
    Dong, Jiangning
    INSIGHTS INTO IMAGING, 2024, 15 (01):
  • [48] 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
  • [49] Machine learning-based analysis identifies and validates serum exosomal proteomic signatures for the diagnosis of colorectal cancer
    Yin, Haofan
    Xie, Jinye
    Xing, Shan
    Lu, Xiaofang
    Yu, Yu
    Ren, Yong
    Tao, Jian
    He, Guirong
    Zhang, Lijun
    Yang, Xiaopeng
    Yang, Zheng
    Huang, Zhijian
    CELL REPORTS MEDICINE, 2024, 5 (08)
  • [50] Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system
    Zhang, Zhiqiao
    Huang, Liwen
    Li, Jing
    Wang, Peng
    BMC BIOINFORMATICS, 2022, 23 (01)