Computational Discovery of Colorectal Cancer Biomarkers through Multi-Platform Gene Expression Data

Haseeb Nisar; Saleh Alwahaishi; Atta Ur Rahman

doi:10.1109/BIBM66473.2025.11356523

Computational Discovery of Colorectal Cancer Biomarkers through Multi-Platform Gene Expression Data

Haseeb Nisar
, Saleh Alwahaishi
, Atta Ur Rahman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This study combines Colorectal cancer gene expression data from RNA-Seq and microarray platforms to improve statistical power and biological relevance. It merges the sensitivity of high-throughput sequencing with a large microarray data repository. LASSO regression identified strong colorectal cancer gene signatures from the combined data. This was followed by functional enrichment analysis and testing across five machine learning classifiers, using accuracy, F1score, sensitivity, and specificity metrics, with external validation from TCGA. Random Forest achieved the highest accuracy at 98.78%. All models achieved 90% accuracy on an external dataset for validation. Seven consensus biomarkers were identified as potential CRC prognostic markers: CA7, ABCA8, SST, MYOM1, CCL23, PCOLCE2, and CXCL10.

Original language	English
Title of host publication	Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025
Editors	Juan Liu, Jingshan Huang, Xiaowo Wang, Fa Zhang, Xiufen Zou, Tian Tian, Xiaohua Hu, Bin Hu, Yi Xiong
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	7798-7800
Number of pages	3
ISBN (Electronic)	9798331515577
DOIs	https://doi.org/10.1109/BIBM66473.2025.11356523
State	Published - 2025
Event	2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025 - Wuhan, China Duration: 15 Dec 2025 → 18 Dec 2025

Publication series

Name	Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025

Conference

Conference	2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025
Country/Territory	China
City	Wuhan
Period	15/12/25 → 18/12/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bioinformatics
Colorectal Cancer
Gene Expression
Machine Learning
Predictive Biomarker

ASJC Scopus subject areas

Artificial Intelligence
Computer Vision and Pattern Recognition
Biomedical Engineering
Modeling and Simulation
Medicine (miscellaneous)
Health Informatics

Access to Document

10.1109/BIBM66473.2025.11356523

Cite this

Nisar, H., Alwahaishi, S., & Ur Rahman, A. (2025). Computational Discovery of Colorectal Cancer Biomarkers through Multi-Platform Gene Expression Data. In J. Liu, J. Huang, X. Wang, F. Zhang, X. Zou, T. Tian, X. Hu, B. Hu, & Y. Xiong (Eds.), Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025 (pp. 7798-7800). (Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM66473.2025.11356523

Nisar, Haseeb ; Alwahaishi, Saleh ; Ur Rahman, Atta. / Computational Discovery of Colorectal Cancer Biomarkers through Multi-Platform Gene Expression Data. Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025. editor / Juan Liu ; Jingshan Huang ; Xiaowo Wang ; Fa Zhang ; Xiufen Zou ; Tian Tian ; Xiaohua Hu ; Bin Hu ; Yi Xiong. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 7798-7800 (Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025).

@inproceedings{c8c6b82d4224434c9f6881320fc2df47,

title = "Computational Discovery of Colorectal Cancer Biomarkers through Multi-Platform Gene Expression Data",

abstract = "This study combines Colorectal cancer gene expression data from RNA-Seq and microarray platforms to improve statistical power and biological relevance. It merges the sensitivity of high-throughput sequencing with a large microarray data repository. LASSO regression identified strong colorectal cancer gene signatures from the combined data. This was followed by functional enrichment analysis and testing across five machine learning classifiers, using accuracy, F1score, sensitivity, and specificity metrics, with external validation from TCGA. Random Forest achieved the highest accuracy at 98.78\%. All models achieved 90\% accuracy on an external dataset for validation. Seven consensus biomarkers were identified as potential CRC prognostic markers: CA7, ABCA8, SST, MYOM1, CCL23, PCOLCE2, and CXCL10.",

keywords = "Bioinformatics, Colorectal Cancer, Gene Expression, Machine Learning, Predictive Biomarker",

author = "Haseeb Nisar and Saleh Alwahaishi and \{Ur Rahman\}, Atta",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025 ; Conference date: 15-12-2025 Through 18-12-2025",

year = "2025",

doi = "10.1109/BIBM66473.2025.11356523",

language = "English",

series = "Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "7798--7800",

editor = "Juan Liu and Jingshan Huang and Xiaowo Wang and Fa Zhang and Xiufen Zou and Tian Tian and Xiaohua Hu and Bin Hu and Yi Xiong",

booktitle = "Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025",

address = "United States",

}

Nisar, H , Alwahaishi, S & Ur Rahman, A 2025, Computational Discovery of Colorectal Cancer Biomarkers through Multi-Platform Gene Expression Data. in J Liu, J Huang, X Wang, F Zhang, X Zou, T Tian, X Hu, B Hu & Y Xiong (eds), Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025. Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025, Institute of Electrical and Electronics Engineers Inc., pp. 7798-7800, 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025, Wuhan, China, 15/12/25. https://doi.org/10.1109/BIBM66473.2025.11356523

Computational Discovery of Colorectal Cancer Biomarkers through Multi-Platform Gene Expression Data. / Nisar, Haseeb ; Alwahaishi, Saleh; Ur Rahman, Atta.
Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025. ed. / Juan Liu; Jingshan Huang; Xiaowo Wang; Fa Zhang; Xiufen Zou; Tian Tian; Xiaohua Hu; Bin Hu; Yi Xiong. Institute of Electrical and Electronics Engineers Inc., 2025. p. 7798-7800 (Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Computational Discovery of Colorectal Cancer Biomarkers through Multi-Platform Gene Expression Data

AU - Nisar, Haseeb

AU - Alwahaishi, Saleh

AU - Ur Rahman, Atta

PY - 2025

Y1 - 2025

N2 - This study combines Colorectal cancer gene expression data from RNA-Seq and microarray platforms to improve statistical power and biological relevance. It merges the sensitivity of high-throughput sequencing with a large microarray data repository. LASSO regression identified strong colorectal cancer gene signatures from the combined data. This was followed by functional enrichment analysis and testing across five machine learning classifiers, using accuracy, F1score, sensitivity, and specificity metrics, with external validation from TCGA. Random Forest achieved the highest accuracy at 98.78%. All models achieved 90% accuracy on an external dataset for validation. Seven consensus biomarkers were identified as potential CRC prognostic markers: CA7, ABCA8, SST, MYOM1, CCL23, PCOLCE2, and CXCL10.

AB - This study combines Colorectal cancer gene expression data from RNA-Seq and microarray platforms to improve statistical power and biological relevance. It merges the sensitivity of high-throughput sequencing with a large microarray data repository. LASSO regression identified strong colorectal cancer gene signatures from the combined data. This was followed by functional enrichment analysis and testing across five machine learning classifiers, using accuracy, F1score, sensitivity, and specificity metrics, with external validation from TCGA. Random Forest achieved the highest accuracy at 98.78%. All models achieved 90% accuracy on an external dataset for validation. Seven consensus biomarkers were identified as potential CRC prognostic markers: CA7, ABCA8, SST, MYOM1, CCL23, PCOLCE2, and CXCL10.

KW - Bioinformatics

KW - Colorectal Cancer

KW - Gene Expression

KW - Machine Learning

KW - Predictive Biomarker

UR - https://www.scopus.com/pages/publications/105033551769

U2 - 10.1109/BIBM66473.2025.11356523

DO - 10.1109/BIBM66473.2025.11356523

M3 - Conference contribution

AN - SCOPUS:105033551769

T3 - Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025

SP - 7798

EP - 7800

BT - Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025

A2 - Liu, Juan

A2 - Huang, Jingshan

A2 - Wang, Xiaowo

A2 - Zhang, Fa

A2 - Zou, Xiufen

A2 - Tian, Tian

A2 - Hu, Xiaohua

A2 - Hu, Bin

A2 - Xiong, Yi

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025

Y2 - 15 December 2025 through 18 December 2025

ER -

Nisar H , Alwahaishi S, Ur Rahman A. Computational Discovery of Colorectal Cancer Biomarkers through Multi-Platform Gene Expression Data. In Liu J, Huang J, Wang X, Zhang F, Zou X, Tian T, Hu X, Hu B, Xiong Y, editors, Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025. Institute of Electrical and Electronics Engineers Inc. 2025. p. 7798-7800. (Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025). doi: 10.1109/BIBM66473.2025.11356523

Computational Discovery of Colorectal Cancer Biomarkers through Multi-Platform Gene Expression Data

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this