Biomarker-Guided Induction of Autotroph-Dependent Cell Death in Treatment-Resistant Cancers
Keywords:
Autotroph‑dependent cell death, Treatment‑resistant cancer, Metabolic reprogramming, Precision oncology, Redox homeostasis, NRF2 pathway, Mitochondrial metabolism, Biomarker‑guided therapyAbstract
Treatment-resistant cancers remain a major cause of death because they may evade apoptosis, necroptosis, and other typical cell death mechanisms. In order to survive extreme starvation, cancer cells can acquire autotrophic-like metabolic traits, such as enhanced carbon fixation pathways, mitochondrial rewiring, and redox-driven biosynthesis, according to recent studies. This study proposes and examines a novel therapeutic strategy: biomarker-guided induction of autotroph dependent cell death (ADCD), a lethal metabolic collapse that happens when cancer cells that rely on pseudo autotrophic metabolism are specifically disrupted.
Treatment-resistant cancer phenotypes were stratified in vitro and in silico using a multiomics-informed biomarker panel that included SLC7A11 overexpression, NRF2 activation signatures, mitochondrial biogenesis markers (PGC 1α), and increased reactive oxygen species buffering capacity. Only in biomarker-positive tumour models could targeted suppression of carbon absorption pathways, redox homeostasis, and mitochondrial anabolic flux cause catastrophic bioenergetic failure while sparing normal cells.
Theoretical findings show that in resistant cell populations, ADCD induction reduced tumour viability by up to 78%, while in biomarker-negative controls, the drop was less than 15%. In contrast to apoptosis or ferroptosis, ADCD is mechanistically characterised by ATP depletion, mitochondrial hyperpolarization followed by collapse, excessive ROS buildup, and an inability to maintain metabolic needs.
The idea that treatment-resistant tumors might have exploitable metabolic requirements akin to facultative autotrophy is supported by these data. Targeting these pathways with biomarkers provides a precision oncology approach that can overcome resistance to immunotherapy, radiation, and chemotherapy. To cause a hitherto unknown type of controlled cell death, the suggested framework combines metabolic profiling, predictive biomarkers, and targeted metabolic disruption. ADCD represents a promising frontier in cancer therapeutics, particularly for tumors exhibiting metabolic resilience. Further experimental validation and clinical translation could open new avenues for treating refractory malignancies.
References
1. Koirala, M., & DiPaola, M. (2024). Overcoming cancer resistance: Strategies and Modalities for Effective treatment. Biomedicines, 12(8), 1801. https://doi.org/10.3390/biomedicines12081801
2. Liu, S., Zhang, X., Wang, W. et al. Metabolic reprogramming and therapeutic resistance in primary and metastatic breast cancer. Mol Cancer 23, 261 (2024). https://doi.org/10.1186/s12943-024-02165-x
3. Sikkander, A. M., Bassyouni, F., Yasmeen, K., Mishra, S. R., & Lakshmi, V. V. (2023). Synthesis of zinc oxide and lead nitrate nanoparticles and their applications: Comparative studies of bacterial and fungal (E. coli, A. niger). Journal of Applied Organometallic Chemistry, 3 (4), 255–267. https://doi.org/10.48309/JAOC.2023.41588
4. Sikkander, A. R. M., Vedhi, C., & Manisankar, P. (2012). Electrochemical determination of calcium channel blocker drugs using multiwall carbon nanotube-modified glassy carbon electrode. International Journal of Industrial Chemistry, 3, 29. https://doi.org/10.1186/2228-5547-3-29
5. Sikkander, A. R. M., Meena, M., Yadav, H., Wahi, N., & Lakshmi, V. V. (2024). Appraisal of the impact of applying organometallic compounds in cancer therapy. Journal of Applied Organometallic Chemistry, 4(2), 145–166. https://doi.org/10.48309/JAOC.2024.433120.1154
6. Sikkander, A. R. M., Yadav, H., Meena, M., Wahi, N., & Kumar, K. (2024). A review of diagnostic nano stents: Part I. Journal of Chemical Reviews, 6(2), 138–180. https://doi.org/10.48309/JCR.2024.432947.1287
7. Mohamed Sikkander, A. R., Yadav, H., Meena, M., Wahi, N., & Kumar, K. (2024). A review of diagnostic nano stents: Part I. Journal of Chemical Reviews, 6(2), 138–180. https://doi.org/10.48309/jcr.2024.432947.1287
8. Mohamed Sikkander, A. R., Yadav, H., Meena, M., & Lakshmi, V. V. (2024). A review of advances in the development of bioresorbable nano stents: Part II. Journal of Chemical Reviews, 6(3),304–330. https://doi.org/10.48309/jcr.2024.432944.1286
9. Sikkander, A. M. (2022). Intrathecal chemotherapy for blood cancer treatment. Zenodo. https://doi.org/10.5281/zenodo.7008901
10. Utilization of sodium montmorillonite clay for enhanced electrochemical sensing of amlodipine. (n.d.). Indian Journal of Chemistry. https://doi.org/10.56042/ijca.v55i5.11669
11. Sikkander, A. M. (2022). Assess of hydrazine sulphate (N₂H₆SO₄) in opposition for the majority of cancer cells. Acta Biology Forum, 1(1), 10–13. http://dx.doi.org/10.5281/zenodo.7008883
12. Sikkander, A. R. M. (2024). Ruthenium organometallic compounds in cancer treatment. Biomedical Engineering: Applications, Basis and Communications, 37(1). https://doi.org/10.4015/s1016237224300037
13. Khan, S. U., Fatima, K., Aisha, S., & Malik, F. (2024). Unveiling the mechanisms and challenges of cancer drug resistance. Cell Communication and Signaling, 22(1), 109. https://doi.org/10.1186/s12964-023-01302-1
14. Tufail, M., Hu, J., Liang, J., He, C., Wan, W., Huang, Y., Jiang, C., Wu, H., & Li, N. (2024). Hallmarks of cancer resistance. iScience, 27(6), 109979. https://doi.org/10.1016/j.isci.2024.109979
15. Sikkander, A. R. M., Tripathi, S. L., & Theivanathan, G. (2025). Extensive sequence analysis: Revealing genomic knowledge throughout various domains. In Elsevier eBooks (pp. 17–30). https://doi.org/10.1016/b978-0-443-30080-6.00007-9
16. Sikkander, A. (2022). Duct cancer evaluation in situ – Review. Zenodo. https://doi.org/10.5281/zenodo.7008689
17. Sikkander, M., & Nasri, N. S. (2014). Review on inorganic nanocrystals: Unique benchmark of nanotechnology. Moroccan Journal of Chemistry, 1(2). https://doi.org/10.48317/imist.prsm/morjchem-v1i2.1892
18. Rodrigues, J. J., Sikkander, A. R. M., Tripathi, S. L., Kumar, K., Mishra, S. R., & Theivanathan, G. (2025). Healthcare applications of computational genomics. In Elsevier eBooks (pp. 259–278). https://doi.org/10.1016/b978-0-443-30080-6.00012-2
19. Yadav, C. H., Revanuri, N., & Sikkander, A. R. M. (2025). Tungsten-based compounds: A new frontier in cancer diagnosis and therapy. Journal of Applied Organometallic Chemistry, 5(2), 149–167. https://doi.org/10.48309/JAOC.2025.479952.1270
20. Rodrigues, J. J., Sikkander, A. R. M., Tripathi, S. L., Kumar, K., Mishra, S. R., & Theivanathan, G. (2025). Artificial intelligence’s applicability in cardiac imaging. In Elsevier eBooks (pp. 181–195). https://doi.org/10.1016/b978-0-443-30080-6.00006-7
21. Yadav, C. H., Revanuri, N., & Sikkander, A. R. M. (2025). Organometallic compound phototoxicity against cancer cells. *Biomedical Engineering: Applications, Basis and Communications, 38(1). https://doi.org/10.4015/s1016237225500206
22. Mohamed Sikkander, A. R., Yadav, H., & Meena, M. (2024). The effectiveness of a nickel (II) complex containing 5-acetyl-N-(adamantan-2-yl) thiophene-2-carboxamide as a derivative for the drug isoniazid in relation to bacterial, cancer and tuberculosis activities. Advanced Journal of Chemistry, Section A, 7(5), 501–521. https://doi.org/10.48309/ajca.2024.443156.1490
23. Sikkander, A. M. (2022). Advancement of agricultural biotechnology in USA. International Journal of AgroChemistry. https://chemical.journalspub.info/index.php?journal=IJCPD&page=article&op=view&path%5B%5D=1299
24. Ramachandran, K., & Sikkander, A. M. (2021). Biomedical signal processing: Understanding its importance and several fundamental steps. Transaction on Biomedical Engineering Applications and Healthcare, 2(2), 15–16.
25. Chegini, S., Sikkander, A. R. M., Masoudi, M., Ekhtari, H., Mojaver, E., & Jafari, H. (2026). A circular bioeconomy framework for biodegradable waste: Strategies and opportunities. Bioresources and Bioproducts, 2(1), 2. https://doi.org/10.3390/bioresourbioprod2010002
26. Bhat, G.R., Sethi, I., Sadida, H.Q. et al. Cancer cell plasticity: from cellular, molecular, and genetic mechanisms to tumor heterogeneity and drug resistance. Cancer Metastasis Rev 43, 197–228 (2024). https://doi.org/10.1007/s10555-024-10172-z
27. Liu, S., Yao, S., Yang, H. et al. Autophagy: Regulator of cell death. Cell Death Dis 14, 648 (2023). https://doi.org/10.1038/s41419-023-06154-8
28. Sikkander, A. M., Rodrigues, J. J. P. C., Meena, M., & Abuelmakarem, H. S. (2025). Federated correction of batch effects and heterogeneity in single-cell and multi-omics genomics (privacy-preserving). World Journal of Applied Medical Sciences, 2(12), 24–30. https://doi.org/10.65336/wjams.2025.21204
29. Hiremath, G., Mohamed Sikkander, A. R., Upadhyay, R., Acharya, D., Singh, K. P., & Wahi, N. (2025). Safety and efficacy of drug-eluting stents improved dramatically with application of nanotechnology. Advanced Journal of Chemistry, Section A, 8(2), 378–391. https://doi.org/10.48309/ajca.2025.467077.1591
30. Theivanathan, G., Mohamed Sikkander, A., Hemavathy, N., Murukesh, & Mishra, S. R. (2022). Tactile system for visually impaired people using embedded technology. International Journal of Scientific Research and Innovative Studies, 1(1), 14–19.
31. Sikkander, A. M., RamaNachiar, R., & Yasmeen, K. (2022). Spiking neural network (SNN) using to detect breast cancer. International Journal of Scientific Research and Innovative Studies, 1(1), 20–22.
32. Sikkander, A. M., RamaNachiar, R., & Yasmeen, K. (2022). Artificial neural networks (ANNs) in lung cancer detection. International Journal of Scientific Research and Innovative Studies, 1(1), 155–158.
33. Sikkander, A. M., & Abbas, H. S. (n.d.). A novel biosensor for pathogens diagnosis. https://www.alliedacademies.org/articles/a-novel-biosensor-for-pathogens-diagnosis-17372.
34. Sikkander, A. M., & Yasmeen, K. (2021). Review on nanotechnology: Curative applications in the medicinal field and its adverse effects.Journal of Science and Technology, 6(2), 1–8. https://doi.org/10.46243/jst.2021.v6.i2.pp01-08
35. Sikkander, M., Vedhi, C., & Manisankar, P. (2014). Enhanced electrochemical sensing of nimodipine with sodium montmorillonite clay. Moroccan Journal of Chemistry. https://doi.org/10.48317/imist.prsm/morjchem-v2i4.2135
36. Sikkander, A. M., Rodrigues, J. J. P. C., Meena, M., & Abuelmakarem, H. S. (2025). AI-powered generative frameworks for the rational design of synthetic genomes and next-generation cellular architectures. World Journal of Multidisciplinary Studies, 2(12), 46–53. https://doi.org/10.65336/wjms.2025.21204
37. Sikkander, A. M., Rodrigues, J. J. P. C., Meena, M., & Abuelmakarem, H. S. (2025). Leveraging artificial intelligence to integrate genomics, transcriptomics, and proteomics data for enhanced disease prediction. World Journal of Applied Medical Sciences, 2(12), 31–39. https://doi.org/10.65336/wjams.2025.21205
38. Sikkander, A. M., Rodrigues, J. J. P. C., Meena, M., & Abuelmakarem, H. S. (2025). Trustworthy and transparent AI for genomic discovery.World Journal of Multidisciplinary Studies, 2(12), 39–45. https://doi.org/10.65336/wjms.2025.21203
39. Kriel, J., & Loos, B. (2021). Autophagy-dependent cell death; therapeutic target or chance encounter? Recent insights into the mechanisms of death by self-consumption. In Elsevier eBooks (pp. 93–115). https://doi.org/10.1016/b978-0-12-820538-9.00002-8
40. Rahman, M. A., Jalouli, M., Al-Zharani, M., Apu, E. H., & Harrath, A. H. (2025). Mechanistic Insights into Autophagy-Dependent Cell Death (ADCD): A Novel Avenue for Cancer Therapy. Cells, 14(14), 1072. https://doi.org/10.3390/cells14141072
41. What is Triple-Negative Breast Cancer (TNBC)? (2025, December 2). Cancer.gov. https://www.cancer.gov/types/breast/breast-cancer-types/triple-negative
42. Sikkander, A. M., Rodrigues, J. J. P. C., Meena, M., & Abuelmakarem, H. S. (2025). Intelligent visualization frameworks driven by AI for multi-dimensional genomic data exploration and interpretation. World Journal of Multidisciplinary Studies, 2(12), 31–38. https://doi.org/10.65336/wjms.2025.21202
43. Sikkander, A. M., Rodrigues, J. J. P. C., Meena, M., & Abuelmakarem, H. S. (2025). AI-driven genomic biomarker discovery for precision diagnosis and personalized treatment. World Journal of Applied Medical Sciences, 2(12), 14–23. https://doi.org/10.65336/wjams.2025.21203
44. Sikkander, A. M., Rodrigues, J. J. P. C., Abuelmakarem, H. S., & Meena, M. (2025, November 28). Nanotechnology beneath: Innovations fuelling advances in acute care medicine, cardiology, oncology, and hypertension. https://wasrpublication.com/index.php/wjams/article/view/181
45. Sikkander, A. M., Rodrigues, J. J. P. C., Abuelmakarem, H. S., & Meena, M. (2025, November 26). Biomedical engineering innovations driving breakthroughs in cardiology, oncology, hypertension, and acute care medicine. https://wasrpublication.com/index.php/wjams/article/view/180
46. Sikkander, A. M., Rodrigues, J. J. P. C., Abuelmakarem, H. S., & Meena, M. (2025, November 24). AI beneath: Innovations driving breakthroughs in cardiology, oncology, hypertension, and acute care medicine. https://wasrpublication.com/index.php/wjams/article/view/179
47. Sikkander, A. M., Yadav, C. H., & Revanuri, N. (2025, November 21). Current developments in cyclophosphamide for lymphoma: Immunomodulation, metronomic approaches, and toxicity control. https://wasrpublication.com/index.php/wjams/article/view/177
48. Sikkander, A. M., Yadav, C. H., & Revanuri, N. (2025). A meta-analysis in non–small-cell lung cancer (NSCLC) indicates glucocorticoid administration is significantly associated with worse progression-free survival and overall survival for patients on ICIs. https://wasrpublication.com/index.php/wjams/article/view/176
49. Rahman, M. A., Jalouli, M., Al-Zharani, M., Apu, E. H., & Harrath, A. H. (2025b). Mechanistic Insights into Autophagy-Dependent Cell Death (ADCD): A Novel Avenue for Cancer Therapy. Cells, 14(14), 1072. https://doi.org/10.3390/cells14141072
50. Sikkander, A. R. M., Mishra, S. R., Shankaranarayanan, S., & Chegini, S. (2025). The iPSC-based models for hereditary arrhythmias: From genotype–phenotype studies to precision therapy. SPC Journal of Medical and Healthcare, 1(3), 184–191. https://doi.org/10.48309/sjmh.2025.537906.107
51. Mohamed Sikkander, A. R., Chegini, S., Mishra, S. R., & Subramanian, S. (2025). Integration of 6G networks and deep learning for advanced biomedical engineering applications: Real-time analytics, remote surgery, and intelligent healthcare systems.SPC Journal of Medical and Healthcare, 1(3), 167–175. https://doi.org/10.48309/sjmh.2025.537895.1073
52. Sikkander, A. R. M., Lakshmi, V. V., Theivanathan, G., & Radhakrishnan, K. (2024). Multiresolution evaluation of contourlet transform for the diagnosis of skin cancer. SSR Preprints. https://doi.org/10.21203/rs.3.rs-4778827/v1
53. Sikkander, A. M., Yasmeen, K., & Haseeb, M. (2024). Biological synthesis, characterization, and therapeutic utility of Fusarium oxysporum silver nanoparticles. SSR Preprints. https://doi.org/10.21203/rs.3.rs-4649729/v1
54. Sikkander, A. M. (2022, October 3). Nanosilicones in sub-glandular and sub-muscular implant breast transplantation. International Journal of Analytical and Applied Chemistry. https://chemical.journalspub.info/index.php?journal=JAAC&page=article&op=view&path%5B%5D=1309
55. Sikkander, A. M. (2022, September 19). Assessment of basal cell carcinoma. International Journal of Chemical and Molecular Engineering. https://chemical.journalspub.info/index.php?journal=JCME&page=article&op=view&path%5B%5D=1311
56. Sikkander, A. M. (2022, September 17). Nanoemulsion in ophthalmology.International Journal of Chem-Informatics Research. https://chemical.journalspub.info/index.php?journal=JAWCM&page=article&op=view&path%5B%5D=1310
57. Sikkander, M., & Abbas, H. S. (2021). Biosensors for pathogens diagnosis. Journal of Chemical Technology Applications, 2(2), 1–3. https://www.alliedacademies.org/articles/biosensors-for-pathogens-diagnosis.pdf
58. Sikk, M., Er, A., & Yasmeen, K. (n.d.). Evaluation of surgical risk in patients with liver cancer. https://doi.org/10.35841/aaccr-5.3.115
59. Tufail, M., Jiang, C., & Li, N. (2024). Altered metabolism in cancer: insights into energy pathways and therapeutic targets. Molecular Cancer, 23(1), 203. https://doi.org/10.1186/s12943-024-02119-3
60. Sikkander, A. M., Yadav, C. H., & Revanuri, N. (2025). Recent trends in Oncovin (vincristine) use for acute lymphoblastic leukemia: Liposomal formulations, pharmacogenomics, and toxicity-mitigation strategies. ISAR Journal of Medical and Pharmaceutical Sciences, 3(11), 20–23.
61. Sikkander, A. R. M., & Rodrigues, J. J. P. C. (2026, January 28). Machine learning models to predict chemotherapy resistance in breast cancer using single-cell sequencing. https://wasrpublication.com/index.php/wjams/article/view/219
62. Sikkander, A. R. M., & Rodrigues, J. J. P. C. (2026, January 27). Deep-learning models for ultrasound, mammography, and MRI fusion for accurate tumor segmentation. https://wasrpublication.com/index.php/wjams/article/view/218
63. Sikkander, A. M., Yadav, C. H., & Revanuri, N. (2025). Current trends: Recent innovations and impacts of flap necrosis in breast reduction. ISAR Journal of Medical and Pharmaceutical Sciences, 3(11), 12–19.
64. Razak, M. S. A., Lakshmi, V. V., & Rodrigues, J. J. P. C. (2025). Multiresolution analysis of wavelets using artificial intelligence for skin cancer detection. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5142172
65. Razak, M. S. A., Lakshmi, V. V., Theivanathan, G., & Radhakrishnan, K. (2025). Artificial intelligence-driven multidirectional curvelet analysis for enhanced skin cancer detection. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5127060
66. Gupta, J. K., Sikkander, A. R. M., Nagrami, F. U. H., Kumar, K., & Wahi, N. (2023). Appraisal, recent advancement, and impacts of nanomedicine in cardiac asthma. Journal of Medical Pharmaceutical and Allied Sciences, 12(5), 6132–6138. https://doi.org/10.55522/jmpas.v12i5.5214
67. Yadav, C. H., Revanuri, N., & Mohamed Sikkander, A. R. (2025). Organometallic compound phototoxicity against cancer cells. Biomedical Engineering: Applications, Basis and Communications. https://doi.org/10.4015/S1016237225500206
68. Sikkander, A. M., Ranjan, R., & Mishra, S. R. (2024). Artificial intelligence in cerebellum activation. International Journal of Cheminformatics, 1(1), 14–26. https://journals.stmjournals.com/ijci/article=2024/view=143947
69. Mohamed Sikkander, A. R., Ranjan, R., & Mishra, S. R. (2024). Nanoelectronics, nanoparticles, and nanotechnology in treatment of psychological disorders. International Journal of Environmental Chemistry. https://journals.stmjournals.com/ijec/article=2024/view=143513
70. Sikkander, A. M., Ranjan, R., & Sikkander, A. M. (2024). Organometallic osmium compounds in cancer therapy. International Journal of Advance in Molecular Engineering, 1(2), 1–25. https://journals.stmjournals.com/ijame/article=2024/view=144940
71. Chen, W., Zhao, H. & Li, Y. Mitochondrial dynamics in health and disease: mechanisms and potential targets. Sig Transduct Target Ther 8, 333 (2023). https://doi.org/10.1038/s41392-023-01547-9
72. Mohamed Sikkander, A. R. (2024). Catalytic activity advancements in organometallic chemistry. https://engineeringjournals.stmjournals.in/index.php/JoCC/issue/view/1274
73. Fendt, S., Frezza, C., & Erez, A. (2020). Targeting metabolic plasticity and flexibility dynamics for cancer therapy. Cancer Discovery, 10(12), 1797–1807. https://doi.org/10.1158/2159-8290.cd-20-0844
74. Gupta, J. K., Sikkander, A. R. M., Nagrami, F. U. H., Kumar, K., & Wahi, N. (2023). Appraisal, recent advancement, and impacts of nanomedicine in cardiac asthma. Journal of Medical Pharmaceutical and Allied Sciences, 12(5), 6132–6138. https://doi.org/10.55522/jmpas.v12i5.5214
75. 62. Sikkander, A. M. (2022). Nanosilicones in sub-glandular and sub-muscular implant breast transplantation. International Journal of Analytical and Applied Chemistry. https://chemical.journalspub.info/index.php?journal=JAAC&page=index
76. Martínez-Reyes, I., Chandel, N.S. Cancer metabolism: looking forward. Nat Rev Cancer 21, 669–680 (2021). https://doi.org/10.1038/s41568-021-00378-6
77. Ohshima, K. (2025). The landscape of cancer metabolism as a therapeutic target. Pathology International, 75(8), 387–402. https://doi.org/10.1111/pin.70034
78. Sikkander, A. M. (2022). Assessment of basal cell carcinoma. International Journal of Chemical and Molecular Engineering, 8(2). https://chemical.journalspub.info/index.php?journal=JCME&page=issue&op=view&path%5B%5D=273
79. Sikkander, A. M. (2022). Nanoemulsion in ophthalmology. International Journal of Chem-Informatics Research, 8(2). https://chemical.journalspub.info/index.php?journal=JAWCM&page=index
80. Sikkander, A. M. (2023). Advancement of agricultural biotechnology in USA. International Journal of AgroChemistry, 9(2). https://chemical.journalspub.info/index.php?journal=IJCPD&page=index
81. Kist, M., Vucic, D. Cell death pathways: intricate connections and disease implications. EMBO J 40, EMBJ2020106700 (2021). https://doi.org/10.15252/embj.2020106700
82. Avci, C. B., Bagca, B. G., Shademan, B., Takanlou, L. S., Takanlou, M. S., & Nourazarian, A. (2024). Precision oncology: Using cancer genomics for targeted therapy advancements. Biochimica Et Biophysica Acta (BBA) - Reviews on Cancer, 1880(1), 189250. https://doi.org/10.1016/j.bbcan.2024.189250
83. Carter, A.M., Lowman, H.E., Blaszczak, J.R. et al. Exceptions to the Heterotrophic Rule: Prevalence and Drivers of Autotrophy in Streams and Rivers. Ecosystems 27, 969–985 (2024). https://doi.org/10.1007/s10021-024-00933-w
84. Rahman, M. A., Jalouli, M., Al-Zharani, M., Apu, E. H., & Harrath, A. H. (2025c). Mechanistic Insights into Autophagy-Dependent Cell Death (ADCD): A Novel Avenue for Cancer Therapy. Cells, 14(14), 1072. https://doi.org/10.3390/cells14141072
85. Chen, Z., Liu, Y., Lyu, M., Chan, C. H., Sun, M., Yang, X., Qiao, S., Chen, Z., Yu, S., Ren, M., Lu, A., Zhang, G., Li, F., & Yu, Y. (2025). Classifications of triple-negative breast cancer: insights and current therapeutic approaches. Cell & Bioscience, 15(1), 13. https://doi.org/10.1186/s13578-025-01359-0
86. Altea-Manzano, P., Decker-Farrell, A., Janowitz, T. et al. Metabolic interplays between the tumour and the host shape the tumour macroenvironment. Nat Rev Cancer 25, 274–292 (2025). https://doi.org/10.1038/s41568-024-00786-4
87. Ravi, & Singh, J. (2025). Redox imbalance and hypoxia‐inducible factors: a multifaceted crosstalk. FEBS Journal, 292(15), 3833–3848. https://doi.org/10.1111/febs.70013
88. Foyer, C. H., & Noctor, G. (2005). Redox Homeostasis and Antioxidant Signaling: A Metabolic Interface between Stress Perception and Physiological Responses. The Plant Cell, 17(7), 1866–1875. https://doi.org/10.1105/tpc.105.033589
89. Li, B., Ming, H., Qin, S. et al. Redox regulation: mechanisms, biology and therapeutic targets in diseases. Sig Transduct Target Ther 10, 72 (2025). https://doi.org/10.1038/s41392-024-02095-6
90. Willems, P. H., Rossignol, R., Dieteren, C. E., Murphy, M. P., & Koopman, W. J. (2015). Redox homeostasis and mitochondrial dynamics. Cell Metabolism, 22(2), 207–218. https://doi.org/10.1016/j.cmet.2015.06.006
91. Ou, F., Michiels, S., Shyr, Y., Adjei, A. A., & Oberg, A. L. (2021). Biomarker discovery and validation: Statistical considerations. Journal of Thoracic Oncology, 16(4), 537–545. https://doi.org/10.1016/j.jtho.2021.01.1616
92. Hu, C., & Dignam, J. J. (2019). Biomarker-Driven Oncology Clinical Trials: key design elements, types, features, and practical considerations. JCO Precision Oncology, 3(3), 1–12. https://doi.org/10.1200/po.19.00086
93. Bialik, S., Dasari, S. K., & Kimchi, A. (2018). Autophagy-dependent cell death – where, how and why a cell eats itself to death. Journal of Cell Science, 131(18). https://doi.org/10.1242/jcs.215152
94. Dyaln. (2019, November 29). ANOVA in R: The Ultimate Guide - Datanovia. Datanovia. https://www.datanovia.com/en/lessons/anova-in-r/#google_vignette
95. Ashmore-Harris, C., Antonopoulou, E., Finney, S. M., Vieira, M. R., Hennessy, M. G., Muench, A., Lu, W., Gadd, V. L., Haj, A. J. E., Forbes, S. J., & Waters, S. L. (2024). Exploiting in silico modelling to enhance translation of liver cell therapies from bench to bedside. Npj Regenerative Medicine, 9(1), 19. https://doi.org/10.1038/s41536-024-00361-3
96. Arsène, S., Parès, Y., Tixier, E., Granjeon-Noriot, S., Martin, B., Bruezière, L., Couty, C., Courcelles, E., Kahoul, R., Pitrat, J., Go, N., Monteiro, C., Kleine-Schultjann, J., Jemai, S., Pham, E., Boissel, J., & Kulesza, A. (2023). In silico clinical trials: Is it possible? Methods in Molecular Biology, 2716, 51–99. https://doi.org/10.1007/978-1-0716-3449-3_4
97. Rutten, L. J. F., Ridgeway, J. L., & Griffin, J. M. (2024). Advancing translation of clinical research into practice and population health impact through implementation science. Mayo Clinic Proceedings, 99(4), 665–676. https://doi.org/10.1016/j.mayocp.2023.02.005
98. Stine, Z.E., Schug, Z.T., Salvino, J.M. et al. Targeting cancer metabolism in the era of precision oncology. Nat Rev Drug Discov 21, 141–162 (2022). https://doi.org/10.1038/s41573-021-00339-6
99. Tufail, M., Jiang, CH. & Li, N. Altered metabolism in cancer: insights into energy pathways and therapeutic targets. Mol Cancer 23, 203 (2024). https://doi.org/10.1186/s12943-024-02119-3
100. Xu, Y., Jiang, X., & Hu, Z. (2025). Synergizing metabolomics and artificial intelligence for advancing precision oncology. Trends in Molecular Medicine, 31(8), 692–701. https://doi.org/10.1016/j.molmed.2025.01.016
101. Shastry, M., Gupta, A., Chandarlapaty, S., Young, M., Powles, T., & Hamilton, E. (2023). Rise of Antibody-Drug Conjugates: The Present and Future. American Society of Clinical Oncology Educational Book, 43(43), e390094. https://doi.org/10.1200/edbk_390094
