The sorafenib resistance-related gene signature predicts prognosis and indicates immune activity in hepatocellular carcinoma

ABSTRACT

Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related death worldwide. Most patients with advanced HCC acquire sorafenib resistance. Drug resistance reflects the heterogeneity of tumors and is the main cause of tumor recurrence and death.We identified and validated sorafenib resistance related-genes (SRGs) as prognostic biomarkers for HCC. We obtained SRGs from the Gene Expression Omnibus and selected four key SRGs using the least absolute shrinkage and selection operator, random forest, and Support Vector Machine-Recursive feature elimination machine learning algorithms. Samples from the The Cancer Genome Atlas (TCGA)-HCC were segregated into two groups by consensus clustering. Following difference analysis, 19 SRGs were obtained through univariate Cox regression analysis, and a sorafenib resistance model was constructed for risk stratification and prognosis prediction. In multivariate Cox regression analysis, the risk score was an independent predictor of overall survival (OS). Patients classified as high-risk were more sensitive to other chemotherapy drugs and showed a higher expression of the common immune checkpoints. Additionally, the expression of drug-resistance genes was verified in the International Cancer Genome Consortium cohort. A nomogram model with a risk score was established, and its prediction performance was verified by calibration chart analysis of the TCGA-HCC cohort. We conclude that there is a significant correlation between sorafenib resistance and the tumor immune microenvironment in HCC. The risk score could be used to identify a reliable prognostic biomarker to optimize the therapeutic benefits of chemotherapy and immunotherapy, which can be helpful in the clinical decision-making for HCC patients.

KEYWORDS:

• Hepatocellular carcinoma
• sorafenib resistant-related gene
• prognostic model
• immune infiltration

Acknowledgements

We acknowledge TCGA, GEO and ICGC database for providing their platforms and contributors for uploading their meaningful datasets.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Authors’ contributions

SZ and JH designed the study. TL and XC performed the data analysis and drafted the manuscript. SZ and JH improved the language of the manuscript and analyzed subsequent related data sets. SZ and WP reviewed and modified the manuscript. The final version of the manuscript was reviewed by all authors. The author(s) read and approved the final manuscript.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15384101.2024.2309020

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [No. 81860723 and 81960589], the Science and Technology Foundation of Guizhou Health and Family Planning Commission [number gzwjkj2018-1-073], and the Science and Technology Foundation of Traditional Chinese Medicine and Ethnic Medicine [number QZYY-2018-019] and Guizhou Science and Technology Plan Project [Guizhou Science and Technology Support [2021] General 097].