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Evaluación multidimensional de la hipoxia tumoral, los resultados de supervivencia y la estratificación molecular en el cáncer de mama mediante habilidades computacionales
Resumen
La hipoxia tumoral desempeña un papel fundamental en la progresión del cáncer y la resistencia al tratamiento, y se han desarrollado sistemas de puntuación basados en la expresión génica como Buffa, Ragnum y Winter para cuantificar los niveles de hipoxia. Comprender la relación entre la hipoxia, los resultados de supervivencia y los estratificadores clínicos como el subtipo
molecular y la raza es esencial para avanzar en la atención personalizada en el cáncer de mama. Las puntuaciones de hipoxia se analizaron en todos los estadios tumorales utilizando los modelos Buffa, Ragnum y Winter. El análisis de supervivencia de KaplanMeier evaluó el impacto pronóstico de las puntuaciones Buffa altas frente a bajas en la supervivencia libre de progresión (SLP), la
supervivencia libre de enfermedad (SSE) y la supervivencia específica de la enfermedad (SEE). Se realizaron análisis estratificados por subtipo molecular, estadio AJCC y raza. La correlación de Pearson midió la concordancia entre las puntuaciones de hipoxia. La inestabilidad de microsatélites (MSI) se evaluó utilizando las puntuaciones MANTIS y MSI Sensor, y se exploró su asociación con la inestabilidad genómica (fracción del genoma alterado). Las puntuaciones de Buffa y Winter revelaron mayor hipoxia en estadios intermedios (IIA, IIB), mientras que Ragnum mostró niveles más uniformes. Las puntuaciones elevadas de Buffa se asociaron significativamente con peores PFS, DFS y DSS. El subtipo luminal A tuvo mejor pronóstico que Basal-like y Luminal B; los pacientes en estadios avanzados y de raza negra o afroamericana mostraron peores resultados. Se encontraron fuertes correlaciones entre las puntuaciones de hipoxia (r = 0,65–0,88). La mayoría de los tumores eran microsatélites estables, pero un subconjunto con puntuaciones altas del sensor MSI también mostró mayores alteraciones genómicas. Los niveles de hipoxia varían según el estadio y el sistema de puntuación y están fuertemente vinculados a los resultados de supervivencia. El subtipo molecular, el estadio del tumor y la raza afectan significativamente el pronóstico, lo que enfatiza la necesidad de una estratificación multidimensional. Las puntuaciones de hipoxia son concordantes y útiles, y el MSI puede contribuir a la inestabilidad genómica en subgrupos específicos de cáncer de mama.
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Ruan K, Song G, Ouyang G. Role of hypoxia in the hallmarks of human cancer. J Cell Biochem. 2009; 107: 1053-62. [PubMed] [Google Scholar]
Sarkar M, Niranjan N, Banyal PK. Mechanisms of hypoxemia. Lung India. R2017; 34: 47-60. [PubMed] [Google Scholar]
Semenza GL. The hypoxic tumor microenvironment: A driving force for breast cancer progression. Biochim Biophys Acta. 2016; 1863: 382-91.
[PubMed] [Google Scholar]
Boudreau N, Myers C. Breast cancerinduced angiogenesis: multiple mechanisms and the role of the microenvironment. Breast Cancer Res. 2003, 5, 1–7. [PubMed] [Google Scholar]
Wang M, Zhang C, Song Y, Wang Z, Wang Y, Luo F, Xu Y, Zhao Y, Wu Z, Xu Y. Mechanism of immune evasion in breast cancer. Onco Targets Ther. 2017 Mar 14; 10: 1561–73. [PubMed] [Google Scholar]
Jing X, Yang F, Shao C, Wei K, Xie M, Shen H, Shu Y. Role of hypoxia in cancer therapy by regulating the tumor microenvironment. Mol Cancer. 2019; 18: 157: 1–15. [PubMed] [Google Scholar]
Zeng J, Wang J, Gao W, Mohammadreza A, Kelbauskas L, Zhang W, Johnson RH, Meldrum DR. Quantitative single-cell gene expression measurements of
multiple genes in response to hypoxia treatment. Anal Bioanal Chem. 2011; 401:3-13. [PubMed] [Google Scholar]
Badowska-Kozakiewicz AM, Budzik MP, Przybylski J. Hypoxia in breast cancer. Pol J Pathol. 2015; 66: 337-46. [PubMed] [Google Scholar]
Bracken, C., Whitelaw, M., Peet, D. The hypoxia-inducible factors: Key transcriptional regulators of hypoxic responses. Cellular and Molecular Life
Sciences (CMLS) 2003, 60, 1376–93. [PubMed] [Google Scholar]
Dengler, V.L., Galbraith, M.D., Espinosa, J.M. Transcriptional regulation by hypoxia inducible factors. Critical Reviews in Biochemistry and Molecular Biology 2014, 49, 1–15. [PubMed] [Google Scholar]
Liu L, Simon MC. Regulation of transcription and translation by hypoxia. Cancer Biol Ther. 2004 Jun;3(6):492-7. [PubMed] [Google Scholar]
Khan SU, Ullah Z, Shaukat H, Unab S, Jannat S, Ali W, Ali A, Irfan M, Khan MF, Cervantes-Villagrana RD. TP53 and its Regulatory Genes as Prognosis of Cutaneous Melanoma. Cancer Inform 2023; 22: 11769351231177267. [PubMed] [Google Scholar]
Haensgen G, Krause U, Becker A, Stadler P, Lautenschlaeger C, Wohlrab W, Rath FW, Molls M, Dunst J. Tumor hypoxia, p53, and prognosis in cervical cancers. Int J Radiat Oncol Biol Phys. 2001; 50: 865-72. [PubMed] [Google Scholar]
Regazzetti C, Peraldi P, Grémeaux T, Najem-Lendom R, Ben-Sahra I, Cormont M, Bost F, Le MarchandBrustel Y, Tanti JF, Giorgetti-Peraldi S.
Hypoxia decreases insulin signaling pathways in adipocytes. Diabetes. 2009; 58:95-103. [PubMed] [Google Scholar]
Alyas J, Rafiq A, Amir H, Khan SU, Sultana T, Ali A, Hameed A, Ahmad I, Kazmi A, Sajid TJBR. Human insulin: History, recent advances, and
expression systems for mass production. Biomed Res Ther. 2021, 8, 4540–4561. [Google Scholar]
Khan SU, Jannat S, Shaukat H, Unab S, Tanzeela, Akram M, Khan Khattak MN, Soto MV, Khan MF, Ali A, Rizvi SSR. Stress Induced Cortisol Release
Depresses The Secretion of Testosterone in Patients With Type 2 Diabetes Mellitus. Clin Med Insights Endocrinol Diabetes. 2023; 16: 11795514221145841. [PubMed] [Google Scholar]
Khan SU, Akram M, Iqbal Z, Javid M, Unab S, Jannat S, Ali A, Khan MF, Ali W, Rafi MJRJoDN, Diseases M. 2022. Relationship between testosterone
and cortisol levels and body mass index in men with type 2 diabetes mellitus. 29:341-348. [Google Scholar]
Ullah Khan S, Daniela HernándezGonzález K, Ali A, Shakeel Raza Rizvi S. Diabetes and the fabkin complex: A dual-edged sword. Biochem
Pharmacol. 2024; 223: 116196. [PubMed] [Google Scholar]
Catrina, S.-B., Zheng, X. Hypoxia and hypoxia-inducible factors in diabetes and its complications. Diabetologia 2021, 64, 709–716. [PubMed] [Google Scholar]
Javid M, Khan SU, Akram M, CervantesVillagrana RD, Rafi M, Khan MF, Raza Rizvi SS. Higher cortisol level and reduced circulating triiodothyronine in patients with cardiovascular diseases: A case-control study. JRSM Cardiovasc Dis. 2025, 14, 20480040251340609. [PubMed] [Google Scholar]
Giussani, D.A. Breath of life: Heart disease link to developmental hypoxia. Circulation 2021, 144, 1429–43. [PubMed] [Google Scholar]
Rabalais, N., Diaz, R.J., Levin, L., Turner, R.E., Gilbert, D., Zhang, J. Dynamics and distribution of natural and human-caused hypoxia. Biogeosciences 2010, 7, 585–619. [Google Scholar]
Saxena, K., Jolly, M.K. Acute vs. chronic vs. cyclic hypoxia: Their differential dynamics, molecular mechanisms, and effects on tumor progression.
Biomolecules 2019, 9, 339. [PubMed] [Google Scholar]
Ali A, Mashwani ZU, Ahmad I, Raja NI, Mohammad S, Khan SU. Plant in vitro cultures: A promising and emerging technology for the feasible production of antidiabetic metabolites in Caralluma tuberculata. Front Endocrinol (Lausanne). 2022, 13, 1029942. [PubMed] [Google Scholar]
Aravinth A, Dhanasundaram S, Perumal P, Kamaraj C, Khan SU, Ali A, Ragavendran C, Amutha V, Rajaram R, Santhanam P, Luna-Arias JP, Mashwani ZU. Evaluation of Brown and red seaweeds-extracts as a novel larvicidal agent against the deadly human diseases-vectors, Anopheles stephensi, Aedes aegypti and Culex quinquefasciatus. Exp Parasitol. 2024, 256, 108651. [PubMed] [Google Scholar]
Iftikhar F, Qureshi R, Siddiqa A, Anwar K, Arshad F, Mashwani Z-U-R, Riaz A, Khan SU, Ali A, Iqbal SJCJoFS. Harnessing nature’s secrets: Silver nanoparticles from Withania coagulans fruit and root extracts unveil exceptional antioxidant and antimicrobial properties. Czech Journal of Food Sciences 2024,42, 192–206. [Google Scholar]
Murugesan S, Ragavendran C, Ali A, Arumugam V, Lakshmanan DK, Palanichamy P, Venkatesan M, Kamaraj C, Luna-Arias JP, Fabián F-LJIJoTM.
Screening and druggability analysis of marine active metabolites against SARSCoV-2: An integrative computational approach. International Journal of
Translational Medicine 2022, 3, 27–41. [Google Scholar]
Thompson CM, Puterman AS, Linley LL, Hann FM, van der Elst CW, Molteno CD, Malan AF. The value of a scoring system for hypoxic ischaemic encephalopathy in predicting neurodevelopmental outcome. Acta Paediatr. 1997, 86, 757– 761. [PubMed] [Google Scholar]
Arjun R, Acharya S, Shender BS, Rorres C, Hrebien L, Kam M. Correlation of Cognitive Scores and the Onset of Hypoxia. Aerosp Med Hum Perform.
, 90, 429–39. [PubMed] [Google Scholar]
Jubb, A.M., Buffa, F.M., Harris, A.L. Assessment of tumour hypoxia for prediction of response to therapy and cancer prognosis. Journal of Cellular and Molecular Medicine 2010, 14, 18–29. [PubMed] [Google Scholar]
Liu Z, Tang Q, Qi T, Othmane B, Yang Z, Chen J, Hu J, Zu X. A Robust Hypoxia Risk Score Predicts the Clinical Outcomes and Tumor Microenvironment Immune Characters in Bladder Cancer. Front Immunol. 2021, 12, 725223. [PubMed] [Google Scholar]
McGranahan, N., Swanton, C. Clonal heterogeneity and tumor evolution: Past, present, and the future. Cell 2017, 168, 613–28. [PubMed] [Google Scholar]
Amirouchene-Angelozzi, N., Swanton, C., Bardelli, A. Tumor evolution as a therapeutic target. Cancer Discovery 2017, 7, 805–17. [PubMed] [Google
Scholar]
Galon, J., Bruni, D. Tumor immunology and tumor evolution: Intertwined histories. Immunity 2020, 52, 55–81. [PubMed] [Google Scholar]
Lorusso, G., Ru¨egg, C. The tumor microenvironment and its contribution to tumor evolution toward metastasis. Histochemistry and Cell Biology 2008, 130, 1091–103. [PubMed] [Google Scholar]
Graham NA, Minasyan A, Lomova A, Cass A, Balanis NG, Friedman M, Chan S, Zhao S, Delgado A, Go J, Beck L, Hurtz C, Ng C, Qiao R, Ten Hoeve J, Palaskas N, Wu H, Müschen M, Multani AS, Port E, Larson SM, Schultz N, Braas D, Christofk HR, Mellinghoff IK, Graeber TG. Recurrent
patterns of DNA copy number alterations in tumors reflect metabolic selection pressures. Mol Syst Biol. 2017, 13, 914. [PubMed] [Google Scholar]
Broglio KR, Berry DA. Detecting an overall survival benefit that is derived from progression-free survival. J Natl Cancer Inst. 2009, 101, 1642–9. [PubMed][Google Scholar]
Halabi, S., Vogelzang, N.J., Ou, S.-S., Owzar, K., Archer, L., Small, E.J. Progression-free survival as a predictor of overall survival in men with castrateresistant prostate cancer. Journal of Clinical Oncology 2009, 27, 2766–71. [PubMed] [Google Scholar]
Fleischer, F., Gaschler-Markefski, B., Bluhmki, E. A statistical model for the dependence between progression-free survival and overall survival. Statistics in Medicine 2009, 28, 2669–86. [PubMed] [Google Scholar]
Araldi, E., Jutzeler, C.R., Ristow, M. Lithium treatment extends human lifespan: Findings from the UK Biobank. Aging (Albany NY) 2023, 15, 421.
[PubMed] [Google Scholar]
Bader, S.B., Dewhirst, M.W., Hammond, E.M. Cyclic hypoxia: An update on its characteristics, methods to measure it and biological implications in cancer. Cancers 2020, 13, 23. [PubMed] [Google Scholar]
Chi JT, Wang Z, Nuyten DS, Rodriguez EH, Schaner ME, Salim A, Wang Y, Kristensen GB, Helland A, BørresenDale AL, Giaccia A, Longaker MT, Hastie
T, Yang GP, van de Vijver MJ, Brown PO. Gene expression programs in response to hypoxia: cell type specificity and prognostic significance in human cancers. PLoS Med. 2006, 3, e47. [PubMed] [Google Scholar]
Ragnum HB, Vlatkovic L, Lie AK, Axcrona K, Julin CH, Frikstad KM, Hole KH, Seierstad T, Lyng H. The tumour hypoxia marker pimonidazole reflects
a transcriptional programme associated with aggressive prostate cancer. Br J Cancer. 2015, 112, 382–90. [PubMed] [Google Scholar]
Ragnum HB, Røe K, Holm R, Vlatkovic L, Nesland JM, Aarnes EK, Ree AH, Flatmark K, Seierstad T, Lilleby W, Lyng H. Hypoxia-independent
downregulation of hypoxia-inducible factor 1 targets by androgen deprivation therapy in prostate cancer. Int J Radiat Oncol Biol Phys.2013, 87,
–60. [PubMed] [Google Scholar]
Torrecilla S, Sia D, Harrington AN, Zhang Z, Cabellos L, Cornella H, Moeini A, Camprecios G, Leow WQ, Fiel MI, Hao K, Bassaganyas L, Mahajan M,
Thung SN, Villanueva A, Florman S, Schwartz ME, Llovet JM. Trunk mutational events present minimal intra- and inter-tumoral heterogeneity in hepatocellular carcinoma. J Hepatol. 2017, 67, 1222–31. [PubMed] [Google Scholar]
Jeantet M, Tougeron D, Tachon G, Cortes U, Archambaut C, Fromont G, KarayanTapon L. High Intra- and Inter-Tumoral Heterogeneity of RAS Mutations in Colorectal Cancer. Int J Mol Sci. 2016, 17, 2015. [PubMed] [Google Scholar]
Chiu AM, Mitra M, Boymoushakian L, Coller HA. Integrative analysis of the inter-tumoral heterogeneity of triplenegative breast cancer. Sci Rep. 2018, 8, 11807. [PubMed] [Google Scholar]
Vaupel P. Hypoxia and aggressive tumor phenotype: implications for therapy and prognosis. Oncologist. 2008, 13, 21–6. [PubMed] [Google Scholar]
Muz, B., De La Puente, P., Azab, F., Kareem Azab, A. The role of hypoxia in cancer progression, angiogenesis, metastasis, and resistance to therapy. Hypoxia 2015, 83–92. [PubMed] [Google Scholar]
Karakashev SV, Reginato MJ. Progress toward overcoming hypoxia-induced resistance to solid tumor therapy. Cancer Manag Res. 2015, 253– 64. [PubMed] [Google Scholar]
Vaupel P. The role of hypoxia-induced factors in tumor progression. Oncologist. 2004, 9, 10–7. [PubMed] [Google Scholar]
Vasseur, S., Tomasini, R., Tournaire, R., Iovanna, J.L. Hypoxia induced tumor metabolic switch contributes to pancreatic cancer aggressiveness.
Cancers 2010, 2, 2138–52. [PubMed] [Google Scholar]
Wu S, Zhu W, Thompson P, Hannun YA. Evaluating intrinsic and non-intrinsic cancer risk factors. Nat Commun. 2018, 9, 3490. [PubMed] [Google Scholar]
Sun XX, Yu Q. Intra-tumor heterogeneity of cancer cells and its implications for cancer treatment. Acta Pharmacol Sin. 2015, 36, 1219–27. [PubMed] [Google Scholar]
Yuan R, Zhu X, Wang G, Li S, Ao P. Cancer as robust intrinsic state shaped by evolution: a key issues review. Rep Prog Phys. 2017, 80, 042701. [PubMed] [Google Scholar]
Abbas T, Keaton MA, Dutta A. Genomic instability in cancer. Cold Spring Harb Perspect Biol. 2013, 5, a012914. [PubMed] [Google Scholar]
Charames GS, Bapat B. Genomic instability and cancer. Curr Mol Med. 2003, 3, 589–97. [PubMed] [Google Scholar]
Yao Y, Dai W. Genomic Instability and Cancer. J Carcinog Mutagen. 2014, 5, 1000165. [PubMed] [Google Scholar]
Shen, Z. Genomic Instability and Cancer: An Introduction. Oxford University Press: 2011; Vol. 3, pp. 1–3. [Google Scholar]
Halford SE, Sawyer EJ, Lambros MB, Gorman P, Macdonald ND, Talbot IC, Foulkes WD, Gillett CE, Barnes DM, Akslen LA, Lee K, Jacobs IJ, Hanby AM,
Ganesan TS, Salvesen HB, Bodmer WF, Tomlinson IP, Roylance RR. MSI-low, a real phenomenon which varies in frequency among cancer types. J Pathol.
, 201, 389–94. [PubMed] [Google Scholar]
Ashktorab H, Ahuja S, Kannan L, Llor X, Ellis NA, Xicola RM, Laiyemo AO, Carethers JM, Brim H, Nouraie M. A metaanalysis of MSI frequency and race in colorectal cancer. Oncotarget. 2016, 7, 34546. [PubMed] [Google Scholar]
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