Towards precision oncology discovery: four less known genes and their unknown interactions as highest-performed biomarkers for colorectal cancer

Yongjun Liu; Yuqing Xu; Xiaoxing Li; Mengke Chen; Xueqin Wang; Ning Zhang; Heping Zhang; Zhengjun Zhang

doi:10.1038/s41698-024-00512-1

Towards precision oncology discovery: four less known genes and their unknown interactions as highest-performed biomarkers for colorectal cancer

NPJ Precis Oncol. 2024 Jan 20;8(1):13. doi: 10.1038/s41698-024-00512-1.

Authors

Yongjun Liu¹, Yuqing Xu², Xiaoxing Li³, Mengke Chen⁴, Xueqin Wang⁵, Ning Zhang⁶, Heping Zhang⁷, Zhengjun Zhang^{8

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Affiliations

¹ Department of Laboratory Medicine and Pathology, University of Washington Medical Center, Seattle, WA, USA.
² Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA.
³ State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China. Lixiaox23@mail.sysu.edu.cn.
⁴ State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China.
⁵ Department of Statistics and Finance, University of Science and Technology of China, Hefei, China.
⁶ Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
⁷ Yale School of Public Health, Yale University, New Haven, CT, USA.
⁸ Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA. zjz@stat.wisc.edu.
⁹ Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA. zjz@stat.wisc.edu.
¹⁰ School of Economics and Management, and MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation, University of Chinese Academy of Sciences, Center for Forecasting Sciences, Chinese Academy of Sciences, Beijing, China. zjz@stat.wisc.edu.

Abstract

The goal of this study was to use a new interpretable machine-learning framework based on max-logistic competing risk factor models to identify a parsimonious set of differentially expressed genes (DEGs) that play a pivotal role in the development of colorectal cancer (CRC). Transcriptome data from nine public datasets were analyzed, and a new Chinese cohort was collected to validate the findings. The study discovered a set of four critical DEGs - CXCL8, PSMC2, APP, and SLC20A1 - that exhibit the highest accuracy in detecting CRC in diverse populations and ethnicities. Notably, PSMC2 and CXCL8 appear to play a central role in CRC, and CXCL8 alone could potentially serve as an early-stage marker for CRC. This work represents a pioneering effort in applying the max-logistic competing risk factor model to identify critical genes for human malignancies, and the interpretability and reproducibility of the results across diverse populations suggests that the four DEGs identified can provide a comprehensive description of the transcriptomic features of CRC. The practical implications of this research include the potential for personalized risk assessment and precision diagnosis and tailored treatment plans for patients.

Abstract

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