Open Access

ARTICLE

A novel oxaliplatin-resistant gene signatures predicting survival of patients in colorectal cancer

QIOU GU¹, CHUILIN LAI¹, XIAO GUAN¹, JING ZHU², TIAN ZHAN¹, JIANPING ZHANG^1,*

1 Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
2 Department of Oncology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China

* Corresponding Author: JIANPING ZHANG. Email:

(This article belongs to the Special Issue: Frontiers in cancer: tumor microenvironment)

BIOCELL 2024, 48(2), 253-269. https://doi.org/10.32604/biocell.2023.028336

Received 15 December 2022; Accepted 01 June 2023; Issue published 23 February 2024

Abstract

Objectives: Colorectal cancer (CRC) is a serious threat to human health worldwide. Oxaliplatin is a platinum analog and is widely used to treat CRC. However, resistance to oxaliplatin restricts its effectiveness and application while its target recognition and mechanism of action also remain unclear. Therefore, we aimed to develop an oxaliplatin-resistant prognostic model to clarify these aspects. Methods: We first obtained oxaliplatin-resistant and parental cell lines, and identified oxaliplatin-resistant genes using RNA sequencing (RNA-seq) and differential gene analysis. We then acquired relevant data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to identify satisfactory resistance genes, and a prognostic model was established. Finally, small-molecule drugs targeting the high-risk (HR) and low-risk (LR) groups were predicted. Results: We identified 14 oxaliplatin-resistance genes in CRC. We built a model with these and used it to classify patients with CRC. Overall survival was better in the LR group than in the HR group (p < 0.001). Multivariate and univariate prognostic analyses revealed that this newly developed model had an independent prognostic value (p < 0.001). The risk score was found to be associated with the tumor microenvironment (TME) and 11 types of immune cells as per the CIBERSORT algorithm results. Finally, we screened 47 small-molecule drugs with half-maximal inhibitory concentration (IC50) values that were related to the risk scores. Conclusion: Our novel prognostic model for oxaliplatin resistance can be used to stratify the risk of CRC.

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