A novel anoikis-related signature predicts prognosis risk and treatment responsiveness in diffuse large B-cell lymphoma

ABSTRACT

Background

Although anoikis plays a role in cancer metastasis and aggressiveness, it has rarely been reported in diffuse large B cell lymphoma (DLBCL).

Methods

We obtained RNA sequencing data and matched clinical data from the GEO database. An anoikis-related genes (ARGs)-based risk signature was developed in GSE10846 training cohort and validated in three other cohorts. Additionally, we predicted half-maximal inhibitory concentration (IC50) of drugs based on bioinformatics method and obtained the actual IC50 to some chemotherapy drugs via cytotoxicity assay.

Results

The high-risk group, as determined by our signature, was associated with worse prognosis and an immunosuppressive environment in DLBCL. Meanwhile, the nomogram based on eight variables had more accurate ability in forecasting the prognosis than the international prognostic index in DLBCL. The prediction of IC50 indicated that DLBCL patients in the high-risk group were more sensitive to doxorubicin, IPA-3, lenalidomide, gemcitabine, and CEP.701, while patients in the low-risk group were sensitive to cisplatin and dasatinib. Consistent with the prediction, cytotoxicity assay suggested the higher sensitivity to doxorubicin and gemcitabine and the lower sensitivity to dasatinib in the high-risk group in DLBCL.

Conclusion

The ARG-based signature may provide a promising direction for prognosis prediction and treatment optimization for DLBCL patients.

KEYWORDS:

• Diffuse large B-cell lymphoma
• anoikis
• prognostic signature
• drug sensitivity
• immune infiltration

List of abbreviations

Table

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Author contributions

M Guan, B Tang and Q Zhang performed the data analysis and interpreted the data. H Zhao and L Li contributed to the experiments of this study. H Zhao prepared the draft. B Tang and X Wang performed the visualization and revised the draft. B Tang and X Wang designed the research and supervised all the work. All authors read and approved the final manuscript. All authors agree to take responsibility for the contents of the article and share responsibility to resolve any questions raised about the accuracy or integrity of the published work.

Availability of data and materials

The original datasets that we used can be obtained at Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). If you have any other questions, please contact the corresponding author directly.

Ethical approval

The study was approved by the Ethics Committee of Dalian Medical University (No. 2023–153). Informed consent was obtained from all subjects involved in the study.

Supplementary materials

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14737159.2024.2351465

Additional information

Funding

This manuscript was funded by National Natural Science Foundation of China [81800203], Doctoral Startup Scientific Research Foundation of Liaoning Province [20180540088] and 1+X program for clinical competency enhancement-improvement of MDT project, Dalian Medical University [2022MDTZY02].