Cell Death Analysis: A Comprehensive R Package for Multi-pathway Cell Death Analysis in Transcriptomic Data

Abstract

Background: Cell death pathways have crucial roles in cancer development, immune responses, and disease progression. With the discovery of 14 distinct types of regulated cell death, there is a critical need for integrated analytical tools that can simultaneously evaluate multiple cell death pathways in transcriptomic data.

Methods: We developed CellDeathAnalysis (v0.4.0), an R package providing a unified framework for analyzing 14 cell death pathways in bulk RNA-seq data. The package introduces two novel algorithms: Crosstalk-Aware Pathway Scoring, which uses gene specificity weighting (inverse document frequency-inspired) and residual debiasing to reduce redundancy from inter-pathway gene overlap; and Cell Death Subtype Classification, which uses consensus clustering on pathway score profiles to identify biologically meaningful patient subtypes. The package integrates curated gene sets from FerrDb, MSigDB, KEGG, and the primary literature, and implements multiple scoring methods (z-score, ssGSEA, GSVA, and AUCell, and the novel crosstalk-aware method), survival analysis, enrichment analysis, and publication-ready visualizations.

Results: By applying CellDeathAnalysis to 2704 The Cancer Genome Atlas (TCGA) samples across four cancer types (BRCA, LUAD, LIHC, and STAD), the crosstalk-aware method reduced inter-pathway correlation (mean reduction = 0.69) compared to z-score scoring. The disulfidptosis score in LUAD exhibited a significant survival association (HR = 2.19, P_adj = 0.037). Consensus clustering identified clinically meaningful subtypes in LIHC ( P = 0.017) and STAD ( P = 0.028).

Conclusions: CellDeathAnalysis provides the first dedicated toolkit for multi-pathway cell death analysis with novel crosstalk-aware scoring and subtype classification capabilities. The package addresses the critical challenge of gene overlap between cell death pathways and enables discovery of clinically meaningful patient subtypes.

Keywords

Cancer genomics / cell death / consensus clustering / crosstalk-aware / cuproptosis / ferroptosis / pathway scoring / R package / survival analysis / transcriptomics

References

[1] Dixon SJ, Lemberg KM, Lamprecht MR, Skouta R, Zaitsev EM, et al. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell 2012; 149(5): 1060-72. [PMID: 22632970 DOI: 10.1016/j.cell.2012.03.042]

[2] Degterev A, Huang Z, Boyce M, Li Y, Jagtap P, et al. Chemical inhibitor of nonapoptotic cell death with therapeutic potential for ischemic brain injury. Nat Chem Biol 2005; 1(2): 112-9. [PMID: 16408008 DOI: 10.1038/nchembio711]

[3] Shi J, Zhao Y, Wang K, Shi X, Wang Y, et al. Cleavage of GSDMD by inflammatory caspases determines pyroptotic cell death. Nature 2015; 526(7575): 660-5. [PMID: 26375003 DOI: 10.1038/nature15514]

[4] Tsvetkov P, Coy S, Petrova B, Dreishpoon M, Verma A, et al. Copper induces cell death by targeting lipoylated TCA cycle proteins. Science 2022; 375(6586): 1254-61. [PMID: 35298263 DOI: 10.1126/science.abf0529]

[5] Liu X, Nie L, Zhang Y, Yan Y, Wang C, et al. Actin cytoskeleton vulnerability to disulfide stress mediates disulfidptosis. Nat Cell Biol 2023; 25(3): 404-14. [PMID: 36747082 DOI: 10.1038/s41556-023-01091-2]

[6] Galluzzi L, Vitale I, Aaronson SA, Abrams JM, Adam D, et al. Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018. Cell Death Differ 2018; 25(3): 486-541. [PMID: 29362479 DOI: 10.1038/s41418-017-0012-4]

[7] Tang D, Kang R, Berghe TV, Vandenabeele P, Kroemer G. The molecular machinery of regulated cell death. Cell Res 2019; 29(5): 347-64. [PMID: 30948788 DOI: 10.1038/s41422-019-0164-5]

[8] Carneiro BA, El-Deiry WS. Targeting apoptosis in cancer therapy. Nat Rev Clin Oncol 2020; 17(7): 395-417. [PMID: 32203277 DOI: 10.1038/s41571-020-0341-y]

[9] Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011; 144(5): 646-74. [PMID: 21376230 DOI: 10.1016/j.cell.2011.02.013]

[10] Green DR, Llambi F. Cell death signaling. Cold Spring Harb Perspect Biol 2015; 7(12): a006080. [PMID: 26626938 DOI: 10.1101/cshperspect.a006080]

[11] Pfeffer CM, Singh ATK. Apoptosis: a target for anticancer therapy. Int J Mol Sci 2018; 19(2): 448. [PMID: 29393886 DOI: 10.3390/ijms19020448]

[12] Wu T, Hu E, Xu S, Chen M, Guo P, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb) 2021; 2(3): 100141. [PMID: 34557778 DOI: 10.1016/j.xinn.2021.100141]

[13] Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013; 14: 7. [PMID: 23323831 DOI: 10.1186/1471-2105-14-7]

[14] Cookson BT, Brennan MA. Pro-inflammatory programmed cell death. Trends Microbiol 2001; 9(3): 113-4. [PMID: 11303500 DOI: 10.1016/S0966-842X(00)01936-3]

[15] Kerr JF, Wyllie AH, Currie AR. Apoptosis: a basic biological phenomenon with wide-ranging implications in tissue kinetics. Br J Cancer 1972; 26(4): 239-57. [PMID: 4561027 DOI: 10.1038/bjc.1972.33]

[16] Bedoui S, Herold MJ, Strasser A. Emerging connectivity of programmed cell death pathways and its physiological implications. Nat Rev Mol Cell Biol 2020; 21(11): 678-95. [PMID: 32873928 DOI: 10.1038/s41580-020-0270-8]

[17] Stockwell BR, Friedmann Angeli JP, Bayir H, Bush AI, Conrad M, et al. Ferroptosis: a regulated cell death nexus linking metabolism, redox biology, and disease. Cell 2017; 171(2): 273-85. [PMID: 28985560 DOI: 10.1016/j.cell.2017.09.021]

[18] Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 2009; 462(7269): 108-12. [PMID: 19847166 DOI: 10.1038/nature08460]

[19] Sparck Jones K. A statistical interpretation of term specificity and its application in retrieval. J Doc 1972; 28(1): 11-21. [DOI: 10.1108/eb026526]

[20] Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn 2003; 52(1-2): 91-118. [DOI: 10.1023/A:1023949509487]

[21] Hoadley KA, Yau C, Hinoue T, Wolf DM, Lazar AJ, et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 2018; 173(2): 291-304.e6. [PMID: 29625048 DOI: 10.1016/j.cell.2018.03.022]

[22] Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, et al. Comprehensive characterization of cancer driver genes and mutations. Cell 2018; 173(2): 371-385.e18. [PMID: 29625053 DOI: 10.1016/j.cell.2018.02.060]

[23] Jiang X, Stockwell BR, Conrad M. Ferroptosis: mechanisms, biology and role in disease. Nat Rev Mol Cell Biol 2021; 22(4): 266-82. [PMID: 33495651 DOI: 10.1038/s41580-020-00324-8]

[24] Chen X, Kang R, Kroemer G, Tang D. Broadening horizons: the role of ferroptosis in cancer. Nat Rev Clin Oncol 2021; 18(5): 280-96. [PMID: 33514910 DOI: 10.1038/s41571-020-00462-0]

[25] Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R Package for comparing biological themes among gene clusters. OMICS 2012; 16(5): 284-7. [PMID: 22455463 DOI: 10.1089/omi.2011.0118]

[26] Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005; 102(43): 15545-50. [PMID: 16199517 DOI: 10.1073/pnas.0506580102]

[27] Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods 2017; 14(11): 1083-6. [PMID: 28991892 DOI: 10.1038/nmeth.4463]

[28] Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature 2003; 422(6928): 198-207. [PMID: 12634793 DOI: 10.1038/nature01511]

[29] Marx V. Method of the year: spatially resolved transcriptomics. Nat Methods 2021; 18(1): 9-14. [PMID: 33408395 DOI: 10.1038/s41592-020-01033-y]