|
1
|
Khan MM, Yalamarty SSK, Rajmalani BA,
Filipczak N and Torchilin VP: Recent strategies to overcome breast
cancer resistance. Crit Rev Oncol Hematol. 197:1043512024.
View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Xiong X, Zheng LW, Ding Y, Chen YF, Cai
YW, Wang LP, Huang L, Liu CC, Shao ZM and Yu KD: Breast cancer:
Pathogenesis and treatments. Signal Transduct Target Ther.
10:492025. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Yuan J and Ofengeim D: A guide to cell
death pathways. Nat Rev Mol Cell Biol. 25:379–395. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Ames EG and Thoene JG: Programmed cell
death in cystinosis. Cells. 11:6702022. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Vu A, Glassman I, Campbell G, Yeganyan S,
Nguyen J, Shin A and Venketaraman V: Host cell death and modulation
of immune response against mycobacterium tuberculosis infection.
Int J Mol Sci. 25:62552024. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Liu J, Hong M, Li Y, Chen D, Wu Y and Hu
Y: Programmed cell death tunes tumor immunity. Front Immunol.
13:8473452022. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Huang X, Luo Z, Liang W, Xie G, Lang X,
Gou J, Liu C, Xu X and Fu D: Survival nomogram for young breast
cancer patients based on the SEER database and an external
validation cohort. Ann Surg Oncol. 29:5772–5781. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Zhu H, Hu H, Hao B, Zhan W, Yan T, Zhang
J, Wang S, Hu H and Zhang T: Insights into a machine Learning-Based
Palmitoylation-Related gene model for predicting the prognosis and
treatment response of breast cancer patients. Technol Cancer Res
Treat. 23:153303382412634342024. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Huang L, Zhang L, Shi X, Wang C, Chen X,
Li M, Ni N, Gao G, Wang T and Zhang X: Multi-cohort and single-cell
profiling of aging genes reveals prognostic and therapeutic targets
in breast cancer. iScience. 29:1148472026. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Zeng C, Wang J, Zhao S, Wei Y, Qi Y, Liu
S, Wang Y, Ge H, Yang X, Tan Y, et al: Multi-cohort validation of a
lipid metabolism and ferroptosis-associated index for prognosis and
immunotherapy response prediction in hormone receptor-positive
breast cancer. Int J Biol Sci. 21:3968–3992. 2025. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Zhang Z, Gao Z, Huang Q, Ling Z, Zhang L,
Li M, Xu Y and Liu M: Cuproptosis- and m6A-related lncRNA
prognostic signature for breast cancer, in which Z68871.1
contributes to triple-negative breast cancer progression. Int J
Biol Macromol. 321:1463212025. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Jiang B, Zhu H, Feng W, Wan Z, Qi X, He R,
Xie L and Li Y: Database mining detected a Cuproptosis-related
prognostic signature and a related regulatory axis in breast
cancer. Dis Markers. 2022:90048302022. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Zhang L, Cui Y, Zhou G, Zhang Z and Zhang
P: Leveraging mitochondrial-programmed cell death dynamics to
enhance prognostic accuracy and immunotherapy efficacy in lung
adenocarcinoma. J Immunother Cancer. 12:e0100082024. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Kao KJ, Chang KM, Hsu HC and Huang AT:
Correlation of microarray-based breast cancer molecular subtypes
and clinical outcomes: Implications for treatment optimization. BMC
Cancer. 11:1432011. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Dedeurwaerder S, Desmedt C, Calonne E,
Singhal SK, Haibe-Kains B, Defrance M, Michiels S, Volkmar M,
Deplus R, Luciani J, et al: DNA methylation profiling reveals a
predominant immune component in breast cancers. EMBO Mol Med.
3:726–741. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Clarke C, Madden SF, Doolan P, Aherne ST,
Joyce H, O'Driscoll L, Gallagher WM, Hennessy BT, Moriarty M, Crown
J, et al: Correlating transcriptional networks to breast cancer
survival: A large-scale coexpression analysis. Carcinogenesis.
34:2300–2308. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Jézéquel P, Loussouarn D,
Guérin-Charbonnel C, Campion L, Vanier A, Gouraud W, Lasla H,
Guette C, Valo I, Verrièle V and Campone M: Gene-expression
molecular subtyping of triple-negative breast cancer tumours:
Importance of immune response. Breast cancer Res. 17:432015.
View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Brueffer C, Vallon-Christersson J, Grabau
D, Ehinger A, Häkkinen J, Hegardt C, Malina J, Chen Y, Bendahl PO,
Manjer J, et al: Clinical value of RNA Sequencing-based classifiers
for prediction of the five conventional breast cancer biomarkers: A
report from the Population-Based multicenter Sweden Cancerome
analysis Network-breast initiative. JCO Precis Oncol. 22018.doi:
10.1200/PO.17.00135. PubMed/NCBI
|
|
19
|
Rosenberg JE, Galsky MD, Powles T,
Petrylak DP, Bellmunt J, Loriot Y, Necchi A, Hoffman-Censits J,
Perez-Gracia JL, van der Heijden MS, et al: Atezolizumab
monotherapy for metastatic urothelial carcinoma: Final analysis
from the phase II IMvigor210 trial. ESMO Open. 9:1039722024.
View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Riaz N, Havel JJ, Makarov V, Desrichard A,
Urba WJ, Sims JS, Hodi FS, Martín-Algarra S, Mandal R, Sharfman WH,
et al: Tumor and microenvironment evolution during immunotherapy
with nivolumab. Cell. 171:934–949.e16. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Hugo W, Zaretsky JM, Sun L, Song C, Moreno
BH, Hu-Lieskovan S, Berent-Maoz B, Pang J, Chmielowski B, Cherry G,
et al: Genomic and transcriptomic features of response to Anti-PD-1
therapy in metastatic melanoma. Cell. 165:35–44. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Liberzon A, Subramanian A, Pinchback R,
Thorvaldsdóttir H, Tamayo P and Mesirov JP: Molecular signatures
database (MSigDB) 3.0. Bioinformatics. 27:1739–1740. 2011.
View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Kanehisa M, Furumichi M, Tanabe M, Sato Y
and Morishima K: KEGG: New perspectives on genomes, pathways,
diseases and drugs. Nucleic Acids Res. 45:D353–D361. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Wang Y and Zhang Q: Leveraging programmed
cell death signature to predict clinical outcome and immunotherapy
benefits in postoperative bladder cancer. Sci Rep. 14:229762024.
View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Ding D, Wang L, Zhang Y, Shi K and Shen Y:
Machine learning developed a programmed cell death signature for
predicting prognosis and immunotherapy benefits in lung
adenocarcinoma. Transl Oncol. 38:1017842023. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Stelzer G, Rosen N, Plaschkes I, Zimmerman
S, Twik M, Fishilevich S, Stein TI, Nudel R, Lieder I, Mazor Y, et
al: The GeneCards Suite: From gene data mining to disease genome
sequence analyses. Curr Protoc Bioinformatics. 54:1.30.1–1.30.33.
2016. View
Article : Google Scholar : PubMed/NCBI
|
|
27
|
Love MI, Huber W and Anders S: Moderated
estimation of fold change and dispersion for RNA-seq data with
DESeq2. Genome Biol. 15:5502014. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Leek JT, Johnson WE, Parker HS, Jaffe AE
and Storey JD: The sva package for removing batch effects and other
unwanted variation in high-throughput experiments. Bioinformatics.
28:882–883. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Ishwaran H, Gerds TA, Kogalur UB, Moore
RD, Gange SJ and Lau BM: Random survival forests for competing
risks. Biostatistics. 15:757–773. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Stocking JC, Taylor SL, Fan S, Wingert T,
Drake C, Aldrich JM, Ong MK, Amin AN, Marmor RA, Godat L, et al: A
least absolute shrinkage and selection Operator-derived predictive
model for postoperative respiratory failure in a heterogeneous
adult elective surgery patient population. CHEST Crit Care.
1:1000252023. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Arashi M, Roozbeh M, Hamzah NA and
Gasparini M: Ridge regression and its applications in genetic
studies. PLoS One. 16:e02453762021. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Xu QF, Ding XH, Jiang CX, Yu KM and Shi L:
An elastic-net penalized expectile regression with applications. J
Appl Stat. 48:2205–2230. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Liu Y, Zhou C, Shen T, Yu X, Li Q, Jiang
T, Li W and Zhu Y: Identification of prognostic markers related to
homologous recombination deficiency in cholangiocarcinoma using
CoxBoost and LASSO machine learning techniques. Front Immunol.
17:16156572026. View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Li S, Xiao Y, Wang Y, Bai M, Du F and
Zhang H: Exploration of influencing factors for postoperative
recurrence in patients with Madelung's disease on the basis of
multivariate stepwise cox regression analysis. Clin Cosmet Investig
Dermatol. 16:103–110. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Sadiq M, Alnagar DKF, Abdulrahman AT and
Alharbi R: The partial least squares spline model for public health
surveillance data. Comput Math Methods Med. 2022:87747422022.
View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Pan PJ, Lee CH, Hsu NW and Sun TL:
Combining principal component analysis and logistic regression for
multifactorial fall risk prediction among community-dwelling older
adults. Geriatr Nurs. 57:208–216. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Schmid M, Wickler F, Maloney KO, Mitchell
R, Fenske N and Mayr A: Boosted beta regression. PLoS One.
8:e616232013. View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Turki T and Wei Z: Boosting support vector
machines for cancer discrimination tasks. Comput Biol Med.
101:236–249. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Van Oirbeek R and Lesaffre E: An
application of Harrell's C-index to PH frailty models. Stat Med.
29:3160–3171. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Iasonos A, Schrag D, Raj GV and Panageas
KS: How to build and interpret a nomogram for cancer prognosis. J
Clin Oncol. 26:1364–1370. 2008. View Article : Google Scholar : PubMed/NCBI
|
|
41
|
Findlay JW and Dillard RF: Appropriate
calibration curve fitting in ligand binding assays. AAPS J.
9:E260–E267. 2007. View Article : Google Scholar : PubMed/NCBI
|
|
42
|
Racle J and Gfeller D: EPIC: A tool to
estimate the proportions of different cell types from bulk gene
expression data. Methods Mol Biol. 2120:233–248. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
43
|
Aran D, Hu Z and Butte AJ: xCell:
Digitally portraying the tissue cellular heterogeneity landscape.
Genome Biol. 18:2202017. View Article : Google Scholar : PubMed/NCBI
|
|
44
|
Becht E, Giraldo NA, Lacroix L, Buttard B,
Elarouci N, Petitprez F, Selves J, Laurent-Puig P, Sautès-Fridman
C, Fridman WH and de Reyniès A: Estimating the population abundance
of tissue-infiltrating immune and stromal cell populations using
gene expression. Genome Biol. 17:2182016. View Article : Google Scholar : PubMed/NCBI
|
|
45
|
Li T, Fan J, Wang B, Traugh N, Chen Q, Liu
JS, Li B and Liu XS: TIMER: A web server for comprehensive analysis
of Tumor-infiltrating immune cells. Cancer Res. 77:e108–e110. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
46
|
Sturm G, Finotello F and List M:
Immunedeconv: An R package for unified access to computational
methods for estimating immune cell fractions from bulk
RNA-Sequencing data. Methods Mol Biol. 2120:223–232. 2020.
View Article : Google Scholar : PubMed/NCBI
|
|
47
|
Chen B, Khodadoust MS, Liu CL, Newman AM
and Alizadeh AA: Profiling tumor infiltrating immune cells with
CIBERSORT. Methods Mol Biol. 1711:243–259. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
48
|
Yoshihara K, Shahmoradgoli M, Martínez E,
Vegesna R, Kim H, Torres-Garcia W, Treviño V, Shen H, Laird PW,
Levine DA, et al: Inferring tumour purity and stromal and immune
cell admixture from expression data. Nat Commun. 4:26122013.
View Article : Google Scholar : PubMed/NCBI
|
|
49
|
Song D and Wang X: DEPTH2: An mRNA-based
algorithm to evaluate intratumor heterogeneity without reference to
normal controls. J Transl Med. 20:1502022. View Article : Google Scholar : PubMed/NCBI
|
|
50
|
Fu J, Li K, Zhang W, Wan C, Zhang J, Jiang
P and Liu XS: Large-scale public data reuse to model immunotherapy
response and resistance. Genome Med. 12:212020. View Article : Google Scholar : PubMed/NCBI
|
|
51
|
Charoentong P, Finotello F, Angelova M,
Mayer C, Efremova M, Rieder D, Hackl H and Trajanoski Z: Pan-cancer
immunogenomic analyses reveal Genotype-immunophenotype
relationships and predictors of response to checkpoint blockade.
Cell Rep. 18:248–262. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
52
|
Samstein RM, Lee CH, Shoushtari AN,
Hellmann MD, Shen R, Janjigian YY, Barron DA, Zehir A, Jordan EJ,
Omuro A, et al: Tumor mutational load predicts survival after
immunotherapy across multiple cancer types. Nat Genet. 51:202–206.
2019. View Article : Google Scholar : PubMed/NCBI
|
|
53
|
Maeser D, Gruener RF and Huang RS:
oncoPredict: An R package for predicting in vivo or cancer patient
drug response and biomarkers from cell line screening data. Brief
Bioinform. 22:bbab2602021. View Article : Google Scholar : PubMed/NCBI
|
|
54
|
Yang W, Soares J, Greninger P, Edelman EJ,
Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, et
al: Genomics of drug sensitivity in cancer (GDSC): A resource for
therapeutic biomarker discovery in cancer cells. Nucleic Acids Res.
41:D955–D961. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
55
|
Colwill K and Gräslund S: A roadmap to
generate renewable protein binders to the human proteome. Nat
Methods. 8:551–558. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
56
|
Livak KJ and Schmittgen TD: Analysis of
relative gene expression data using real-time quantitative PCR and
the 2(−Delta Delta C(T)) method. Methods. 25:402–408. 2001.
View Article : Google Scholar : PubMed/NCBI
|
|
57
|
Li X, Chen G, Liu B, Tao Z, Wu Y, Zhang K,
Feng Z, Huang Y and Wang H: PLK1 inhibition promotes apoptosis and
DNA damage in glioma stem cells by regulating the nuclear
translocation of YBX1. Cell Death Discov. 9:682023. View Article : Google Scholar : PubMed/NCBI
|
|
58
|
Chen Z, Cai H, Ye W, Wu J, Liu J, Xie Y,
Feng S, Jin Y, Lv Y, Ye H, et al: TP63 transcriptionally regulates
SLC7A5 to suppress ferroptosis in head and neck squamous cell
carcinoma. Front Immunol. 15:14454722024. View Article : Google Scholar : PubMed/NCBI
|
|
59
|
Cai Z, Zhang R, Liu R, Zhao L and Zhou L:
Plumbagin ameliorates ferroptosis of ovarian granulosa cells in
polycystic ovary syndrome by down-regulating SLC7A5 m6A methylation
modification through inhibition of YTHDF1. J Ovarian Res.
18:1152025. View Article : Google Scholar : PubMed/NCBI
|
|
60
|
Wang H, Zhao W, Wang D and Chen J: ANO6
(TMEM16F) inhibits gastrointestinal stromal tumor growth and
induces ferroptosis. Open Med (Wars). 19:202409412024. View Article : Google Scholar : PubMed/NCBI
|
|
61
|
Liu G, Liu G, Chen H, Borst O, Gawaz M,
Vortkamp A, Schreiber R, Kunzelmann K and Lang F: Involvement of
Ca2+ activated Cl-channel Ano6 in platelet activation and
apoptosis. Cell Physiol Biochem. 37:1934–1944. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
62
|
Liu YB, Dai WH, Chang JJ and Wei K:
CircRNA TUBA1C promotes proliferation and glucose metabolism, and
blocks apoptosis of osteosarcoma cells through sponging miR-143-3p.
Pol J Pathol. 75:215–227. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
63
|
Liu GJ, Wang YJ, Yue M, Zhao LM, Guo YD,
Liu YP, Yang HC, Liu F, Zhang X, Zhi LH, et al: High expression of
TCN1 is a negative prognostic biomarker and can predict neoadjuvant
chemosensitivity of colon cancer. Sci Rep. 10:119512020. View Article : Google Scholar : PubMed/NCBI
|
|
64
|
Zhu X, Jiang X, Zhang Q, Huang H, Shi X,
Hou D and Xing C: TCN1 deficiency inhibits the malignancy of
colorectal cancer cells by regulating the ITGB4 pathway. Gut Liver.
17:412–429. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
65
|
Jhunjhunwala S, Hammer C and Delamarre L:
Antigen presentation in cancer: Insights into tumour immunogenicity
and immune evasion. Nat Rev Cancer. 21:298–312. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
66
|
Li Y, Li Z, Tang Y, Zhuang X, Feng W, Boor
PPC, Buschow S, Sprengers D and Zhou G: Unlocking the therapeutic
potential of the NKG2A-HLA-E immune checkpoint pathway in T cells
and NK cells for cancer immunotherapy. J Immunother Cancer.
12:e0099342024. View Article : Google Scholar : PubMed/NCBI
|
|
67
|
Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X,
Li Z, Traugh N, Bu X, Li B, et al: Signatures of T cell dysfunction
and exclusion predict cancer immunotherapy response. Nat Med.
24:1550–1558. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
68
|
Chen A, Yang C and Wang J: Multiple roles
of ANO6 in tumors, molecular mechanism and its potential
therapeutic value. Biochem Biophys Rep. 44:1022302025.PubMed/NCBI
|
|
69
|
Kandala S, Ramos M, Voith von Voithenberg
L, Diaz-Jimenez A, Chocarro S, Keding J, Brors B, Imbusch CD and
Sotillo R: Chronic chromosome instability induced by Plk1 results
in immune suppression in breast cancer. Cell Rep. 42:1132662023.
View Article : Google Scholar : PubMed/NCBI
|
|
70
|
Lavallée É, Roulet-Matton M, Giang V,
Cardona Hurtado R, Chaput D and Gravel SP: Mitochondrial signatures
shape phenotype switching and apoptosis in response to PLK1
inhibitors. Life Sci Alliance. 8:e2024029122024. View Article : Google Scholar : PubMed/NCBI
|
|
71
|
Kong Y, Li C, Liu J, Wu S, Zhang M,
Allison DB, Hassan F, He D, Wang X, Mao F, et al: Single-cell
analysis identifies PLK1 as a driver of immunosuppressive tumor
microenvironment in LUAD. PLoS Genet. 20:e10113092024. View Article : Google Scholar : PubMed/NCBI
|
|
72
|
Sokolov AM, Holmberg JC and Feliciano DM:
The amino acid transporter Slc7a5 regulates the mTOR pathway and is
required for granule cell development. Hum Mol Genet. 29:3003–3013.
2020. View Article : Google Scholar : PubMed/NCBI
|
|
73
|
Zhang H, Su X, Burley SK and Zheng XFS:
mTOR regulates aerobic glycolysis through NEAT1 and nuclear
paraspeckle-mediated mechanism in hepatocellular carcinoma.
Theranostics. 12:3518–3533. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
74
|
Huang R, Wang H, Hong J, Wu J, Huang O, He
J, Chen W, Li Y, Chen X, Shen K and Wang Z: Targeting glutamine
metabolic reprogramming of SLC7A5 enhances the efficacy of
anti-PD-1 in triple-negative breast cancer. Front Immunol.
14:12516432023. View Article : Google Scholar : PubMed/NCBI
|
|
75
|
Moore XTR, Gheghiani L and Fu Z: The role
of Polo-like kinase 1 in regulating the forkhead box family
transcription factors. Cells. 12:13442023. View Article : Google Scholar : PubMed/NCBI
|
|
76
|
Zhang Z, Cheng L, Li J, Qiao Q, Karki A,
Allison DB, Shaker N, Li K, Utturkar SM, Atallah Lanman NM, et al:
Targeting Plk1 sensitizes pancreatic cancer to immune checkpoint
therapy. Cancer Res. 82:3532–3548. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
77
|
Lu JV, Chen HC and Walsh CM: Necroptotic
signaling in adaptive and innate immunity. Semin Cell Dev Biol.
35:33–39. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
78
|
Wang M, Yu F, Zhang Y and Li P: Programmed
cell death in tumor immunity: Mechanistic insights and clinical
implications. Front Immunol. 14:13096352023. View Article : Google Scholar : PubMed/NCBI
|
|
79
|
Tong X, Tang R, Xiao M, Xu J, Wang W,
Zhang B, Liu J, Yu X and Shi S: Targeting cell death pathways for
cancer therapy: Recent developments in necroptosis, pyroptosis,
ferroptosis, and cuproptosis research. J Hematol Oncol. 15:1742022.
View Article : Google Scholar : PubMed/NCBI
|
|
80
|
Chen X, Li J, Kang R, Klionsky DJ and Tang
D: Ferroptosis: Machinery and regulation. Autophagy. 17:2054–2081.
2021. View Article : Google Scholar : PubMed/NCBI
|
|
81
|
Efimova I, Catanzaro E, Van der Meeren L,
Turubanova VD, Hammad H, Mishchenko TA, Vedunova MV, Fimognari C,
Bachert C, Coppieters F, et al: Vaccination with early ferroptotic
cancer cells induces efficient antitumor immunity. J Immunother
Cancer. 8:e0013692020. View Article : Google Scholar : PubMed/NCBI
|
|
82
|
Liu WQ, Lin WR, Yan L, Xu WH and Yang J:
Copper homeostasis and cuproptosis in cancer immunity and therapy.
Immunol Rev. 321:211–227. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
83
|
Zhao P, Yin S, Qiu Y, Sun C and Yu H:
Ferroptosis and pyroptosis are connected through autophagy: A new
perspective of overcoming drug resistance. Mol Cancer. 24:232025.
View Article : Google Scholar : PubMed/NCBI
|
|
84
|
Su L, Chen Y, Huang C, Wu S, Wang X, Zhao
X, Xu Q, Sun R, Kong X, Jiang X, et al: Targeting Src reactivates
pyroptosis to reverse chemoresistance in lung and pancreatic cancer
models. Sci Transl Med. 15:eabl78952023. View Article : Google Scholar : PubMed/NCBI
|
|
85
|
Wan H, Yang X, Sang G, Ruan Z, Ling Z,
Zhang M, Liu C, Hu X, Guo T, He J, et al: CDKN2A was a
cuproptosis-related gene in regulating chemotherapy resistance by
the MAGE-A family in breast cancer: Based on artificial
intelligence (AI)-constructed pan-cancer risk model. Aging (Albany
NY). 15:11244–11267. 2023.PubMed/NCBI
|
|
86
|
Li S, Liu S, Zheng Y, Hong W, Du Y, Liu X,
Tang H, Meng X and Zheng Q: Machine learning analysis of
coagulation-related genes for breast cancer diagnosis and prognosis
prediction. Sci Rep. 15:354292025. View Article : Google Scholar : PubMed/NCBI
|
