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Identification of Novel Biomarkers for Response to Preoperative Chemoradiation in Locally Advanced Rectal Cancer with Genetic Algorithm–Based Gene Selection

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Abstract

Background

The conventional treatment for patients with locally advanced colorectal tumors is preoperative chemo-radiotherapy (PCRT) preceding surgery. This treatment strategy has some long-term side effects, and some patients do not respond to it. Therefore, an evaluation of biomarkers that may help predict patients’ response to PCRT is essential.

Methods

We took advantage of genetic algorithm to search the space of possible combinations of features to choose subsets of genes that would yield convenient performance in differentiating PCRT responders from non-responders using a logistic regression model as our classifier.

Results

We developed two gene signatures; first, to achieve the maximum prediction accuracy, the algorithm yielded 39 genes, and then, aiming to reduce the feature numbers as much as possible (while maintaining acceptable performance), a 5-gene signature was chosen. The performance of the two gene signatures was (accuracy = 0.97 and 0.81, sensitivity = 0.96 and 0.83, and specificity = 86 and 0.77) using a logistic regression classifier. Through analyzing bias and variance decomposition of the model error, we further investigated the involved genes by discovering and validating another 28-gene signature which possibly points towards two different sub-systems involved in the response of the patients to treatment.

Conclusions

Using genetic algorithm as our gene selection method, we have identified two groups of genes that can differentiate PCRT responders from non-responders in patients of the studied dataset with considerable performance.

Impact

After passing standard requirements, our gene signatures may be applicable as a robust and effective PCRT response prediction tool for colorectal cancer patients in clinical settings and may also help future studies aiming to further investigate involved pathways gain a clearer picture for the course of their research.

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Acknowledgements

This study has been adapted from the M.Sc. thesis of the first author at Bu-Ali Sina University, Hamadan, Iran.

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Authors and Affiliations

  1. Department of Biology, Faculty of Science, Lund University, Skåne, Sweden

    Nima Mohseni

  2. Department of Electrical Engineering, Bu-Ali Sina University, Hamadan, Iran

    Majid Ghaniee Zarich & Manouchehr Hosseini

  3. Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran

    Saeid Afshar

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  1. Nima Mohseni
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  3. Saeid Afshar
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  4. Manouchehr Hosseini
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Contributions

Conceptualization, N.M. and S.A. Methodology, N.M., S.A., and M.G.Z. Software, N.M. Data curation, N.M. Validation, N.M., S.A., and M.G.Z. Formal analysis, N.M. and S.A. Writing—original draft, N.M. Visualization, N.M. Writing—review and editing, M.G.Z., S.A., and M.H. Project administration, S.A. Supervision, M.G.Z. and M.H. Resources, S.A.

Corresponding author

Correspondence to Saeid Afshar.

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Mohseni, N., Ghaniee Zarich, M., Afshar, S. et al. Identification of Novel Biomarkers for Response to Preoperative Chemoradiation in Locally Advanced Rectal Cancer with Genetic Algorithm–Based Gene Selection. J Gastrointest Canc 54, 937–950 (2023). https://doi.org/10.1007/s12029-022-00873-5

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