|
1
|
Bray F, Laversanne M, Sung H, Ferlay J,
Siegel RL, Soerjomataram I and Jemal A: Global cancer statistics
2022: GLOBOCAN estimates of incidence and mortality worldwide for
36 cancers in 185 countries. CA Cancer J Clin. 74:229–263.
2024.PubMed/NCBI
|
|
2
|
Wisniak A, Yakam V, Bolo SE, Yakam V,
Schmidt NC, Kenfack B and Petignat P: Fertility and miscarriage
incidence after cervical intraepithelial neoplasia treatment by
thermal ablation: A cohort study. BMC Womens Health. 132:167–177.
2025.
|
|
3
|
Duenas-Gonzalez A, Serrano-Olvera A,
Cetina L and Coronel J: New molecular targets against cervical
cancer. Int J Women Health. 6:1023–1031. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Wei F, Georges D, Man I, Baussano I and
Clifford GM: Causal attribution of human papillomavirus genotypes
to invasive cervical cancer worldwide: A systematic analysis of the
global literature. Lancet. 404:435–444. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Malagón T, Franco EL, Tejada R and
Vaccarella S: Epidemiology of HPV-associated cancers past, present
and future: Towards prevention and elimination. Nat Rev, Clin
Oncol. 21:522–538. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Caruso G, Wagar MK, Hsu HC, Hoegl J, Rey
Valzacchi GM, Fernandes A, Cucinella G, Sahin Aker S, Jayraj AS,
Mauro J, et al: Cervical cancer: A new era. Int J Gynecol Cancer.
34:1946–1970. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Zhang Y, Lu Y, Li S, Zheng F, Dong Y, Tang
H, Wang X and Wang J: Precision theranostics in cervical Cancer:
Harnessing stimuli-responsive hydrogels for tumor
microenvironment-targeted therapy and diagnosis. Materials Today
Bio. 35:1023922025. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Tong H, Jiang Z, Song L, Tan K, Yin X, He
C, Huang J, Li X, Ling X, et al: Dual impacts of
serine/glycine-free diet in enhancing antitumor immunity and
promoting evasion via PD-L1 lactylation. Cell Metab. 36:2493–2510.
2024. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Sia TY, Wan V, Finlan M, Zhou QC, Iasonos
A, Zivanovic O, Sonoda Y, Chi DS, Long Roche K, Jewell E, et al:
Procedural interventions for oligoprogression during treatment with
immune checkpoint blockade in gynecologic malignancies: A case
series. Int J Gynecol Cancer. 34:594–601. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Pinheiro C, Garcia EA, Morais-Santos F,
Moreira MA, Almeida FM, Jubé LF, Queiroz GS, Paula ÉC, Andreoli MA,
Villa LL, et al: Reprogramming energy metabolism and inducing
angiogenesis: Co-expression of monocarboxylate transporters with
VEGF family members in cervical adenocarcinomas. BMC Cancer.
15:8352015. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Gao Y, Siyu Zhang, Zhang X, Du Y, Ni T and
Hao S: Crosstalk between metabolic and epigenetic modifications
during cell carcinogenesis. iScience. 27:1113592024. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Lin Y, Li L, Yuan B, Luo F, Zhang X, Yang
Y, Luo S, Lin J, Ye T, Zhang Y, et al: Phosphorylation determines
the glucose metabolism reprogramming and tumor-promoting activity
of sine oculis homeobox 1. Signal Transduct Target Ther. 9:3372024.
View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Ippolito L, Duatti A, Iozzo M, Comito G,
Pardella E, Lorito N, Bacci M, Pranzini E, Santi A, Sandrini G, et
al: Lactate supports cell-autonomous ECM production to sustain
metastatic behavior in prostate cancer. EMBO Rep. 25:3506–3531.
2024. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Zhao Y, Liu MJ, Zhang L, Yang Q, Sun QH,
Guo JR, Lei XY, He KY, Li JQ, Yang JY, et al: High mobility group
A1 (HMGA1) promotes the tumorigenesis of colorectal cancer by
increasing lipid synthesis. Nat Commun. 15:99092024. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Patil K, Johnston E, Novack J, Wallace G,
Lin M and Pai SB: Multifaceted impact of HIV inhibitor dapivirine
on triple negative breast cancer cells reveals potential entities
as targets for novel therapy. Sci Rep. 14:301032024. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Lin C, Ye J, Xu C, Zheng Y, Xu Y, Chen Y,
Chi L, Lin J, Li F, Lin Y and Wang Q: Evaluating lactate metabolism
for prognostic assessment and therapy response prediction in
gastric cancer with emphasis on the oncogenic role of SLC5A12.
Biochim Biophys Acta Gen Subj. 1869:1307392024. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Sun D, Lu J, Zhao W, Chen X, Xiao C, Hua
F, Hydbring P, Gabazza EC, Tartarone A, Zhao X and Yang W:
Construction and validation of a prognostic model based on
oxidative stress-related genes in non-small cell lung cancer
(NSCLC): Predicting patient outcomes and therapy responses. Transl
Lung Cancer Res. 13:3152–3174. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Zhang H, Yang Y, Xing W, Li Y and Zhang S:
Expression and gene regulatory network of S100A16 protein in
cervical cancer cells based on data mining. BMC Cancer.
23:11242023. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW,
Shi W and Smyth GK: Limma powers differential expression analyses
for RNA-sequencing and microarray studies. Nucleic Acids Res.
43:e472015. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Roychowdhury A, Samadder S, Das P,
Mazumder DI, Chatterjee A, Addya S, Mondal R, Roy A, Roychoudhury S
and Panda CK: Deregulation of H19 is associated with cervical
carcinoma. Genomics. 112:961–970. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Fjeldbo CS, Hompland T, Hillestad T,
Aarnes EK, Günther CC, Kristensen GB, Malinen E and Lyng H:
Combining imaging- and gene-based hypoxia biomarkers in cervical
cancer improves prediction of chemoradiotherapy failure independent
of intratumour heterogeneity. EBioMedicine. 57:1028412020.
View Article : Google Scholar : PubMed/NCBI
|
|
22
|
den Boon JA, Pyeon D, Wang SS, Horswill M,
Schiffman M, Sherman M, Zuna RE, Wang Z, Hewitt SM, Pearson R, et
al: Molecular transitions from papillomavirus infection to cervical
precancer and cancer: Role of stromal estrogen receptor signaling.
Proc Natl Acad Sci USA. 112:E3255–E3264. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Lee YY, Kim TJ, Kim JY, Choi CH, Do IG,
Song SY, Sohn I, Jung SH, Bae DS, Lee JW and Kim BG: Genetic
profiling to predict recurrence of early cervical cancer. Gynecol
Oncol. 131:650–654. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Teschendorff AE, Jones A and Widschwendter
M: Stochastic epigenetic outliers can define field defects in
cancer. BMC Bioinformatics. 17:1782016. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Zhai Y, Kuick R, Nan B, Ota I, Weiss SJ,
Trimble CL, Fearon ER and Cho KR: Gene expression analysis of
preinvasive and invasive cervical squamous cell carcinomas
identifies HOXC10 as a key mediator of invasion. Cancer Res.
67:10163–10172. 2007. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
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
|
|
27
|
Cheng Z, Huang H, Li M, Liang X, Tan Y and
Chen Y: Lactylation-related gene signature effectively predicts
prognosis and treatment responsiveness in hepatocellular carcinoma.
Pharmaceuticals. 16:6442023. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Zhang D, Tang Z, Huang H, Zhou G, Cui C,
Weng Y, Liu W, Kim S, Lee S, Perez-Neut M, et al: Metabolic
regulation of gene expression by histone lactylation. Nature.
574:575–580. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Moreno-Yruela C, Zhang D, Wei W, Bæk M,
Liu W, Gao J, Danková D, Nielsen AL, Bolding JE, Yang L, et al:
Class I histone deacetylases (HDAC1-3) are histone lysine
delactylases. Sci Adv. 8:eabi66962022. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Mariathasan S, Turley SJ, Nickles D,
Castiglioni A, Yuen K, Wang Y, Kadel EE III, Koeppen H, Astarita
JL, Cubas R, et al: TGFβ attenuates tumour response to PD-L1
blockade by contributing to exclusion of T cells. Nature.
554:544–548. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Kurki MI, Karjalainen J, Palta P, Sipilä
TP, Kristiansson K, Donner KM, Reeve MP, Laivuori H, Aavikko M,
Kaunisto MA, et al: FinnGen provides genetic insights from a
well-phenotyped isolated population. Nature. 613:508–518. 2023.
View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Stuart T, Butler A, Hoffman P, Hafemeister
C, Papalexi E, Mauck WM III, Hao Y, Stoeckius M, Smibert P and
Satija R: Comprehensive Integration of Single-Cell Data. Cell.
177:1888–1902.e21. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Korsunsky I, Millard N, Fan J, Slowikowski
K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh PR and Raychaudhuri
S: Fast, sensitive and accurate integration of single-cell data
with Harmony. Nat Methods. 16:1289–1296. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Jin S, Guerrero-Juarez CF, Zhang L, Chang
I, Ramos R, Kuan CH, Myung P, Plikus MV and Nie Q: Inference and
analysis of cell-cell communication using CellChat. Nat Commun.
12:10882021. View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Langfelder P and Horvath S: WGCNA: An R
package for weighted correlation network analysis. BMC
Bioinformatics. 9:5592008. View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Wyss R, Van Der Laan M, Gruber S, Shi X,
Lee H, Dutcher SK, Nelson JC, Toh S, Russo M, Wang SV, et al:
Targeted learning with an undersmoothed LASSO propensity score
model for large-scale covariate adjustment in health-care database
studies. Am J Epidemiol. 193:1632–1640. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Binder H and Schumacher M: Allowing for
mandatory covariates in boosting estimation of sparse
high-dimensional survival models. BMC Bioinformatics. 9:142008.
View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Friedman J, Hastie T and Tibshirani R:
Regularization paths for generalized linear models via coordinate
descent. J Stat Soft. 33:1–22. 2010.umerical. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Chen D, Lin D, Li H, Yang J, Liu L, Zhang
H, Tang D and Wang K: The glycolytic characteristics of
hepatocellular carcinoma and its interaction with the
microenvironment: A comprehensive omics study. J Transl Med.
23:4242025. View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Blanche P, Dartigues J and Jacqmin-Gadda
H: Estimating and comparing time-dependent areas under receiver
operating characteristic curves for censored event times with
competing risks. Statistics in Medicine. 32:5381–5397. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
41
|
Restaino S, Pellecchia G, Arcieri M,
Bogani G, Taliento C, Greco P, Driul L, Chiantera V, Ercoli A,
Fanfani F, et al: Management for cervical cancer patients: A
comparison of the guidelines from the international scientific
societies (ESGO-NCCN-ASCO-AIOM-FIGO-BGCS-SEOM-ESMO-JSGO). Cancers.
16:25412024. View Article : Google Scholar : PubMed/NCBI
|
|
42
|
Tang Z, Kang B, Li C, Chen T and Zhang Z:
GEPIA2: An enhanced web server for large-scale expression profiling
and interactive analysis. Nucleic Acids Res. 47:W556–W560. 2019.
View Article : Google Scholar : PubMed/NCBI
|
|
43
|
Subramanian A, Tamayo P, Mootha VK,
Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub
TR, Lander ES and Mesirov JP: Gene set enrichment analysis: A
knowledge-based approach for interpreting genome-wide expression
profiles. Proc Natl Acad Sci USA. 102:15545–15550. 2005. View Article : Google Scholar : PubMed/NCBI
|
|
44
|
Hänzelmann S, Castelo R and Guinney J:
GSVA: Gene set variation analysis for microarray and RNA-seq data.
BMC Bioinf. 14:72013. View Article : Google Scholar
|
|
45
|
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
|
|
46
|
Mroz EA and Rocco JW: MATH, a novel
measure of intratumor genetic heterogeneity, is high in
poor-outcome classes of head and neck squamous cell carcinoma. Oral
Oncology. 49:211–215. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
47
|
Geeleher P, Cox N and Huang RS:
pRRophetic: An R package for prediction of clinical
chemotherapeutic response from tumor gene expression levels. PLoS
One. 9:e1074682014. View Article : Google Scholar : PubMed/NCBI
|
|
48
|
Walker VM, Davies NM, Hemani G, Zheng J,
Haycock PC, Gaunt TR, Davey Smith G and Martin RM: Using the
MR-Base platform to investigate risk factors and drug targets for
thousands of phenotypes. Wellcome Open Res. 4:1132019. View Article : Google Scholar : PubMed/NCBI
|
|
49
|
Verbanck M, Chen CY, Neale B and Do R:
Detection of widespread horizontal pleiotropy in causal
relationships inferred from Mendelian randomization between complex
traits and diseases. Nat Genet. 50:693–698. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
50
|
Cho Y, Haycock PC, Sanderson E, Gaunt TR,
Zheng J, Morris AP, Davey Smith G and Hemani G: Exploiting
horizontal pleiotropy to search for causal pathways within a
mendelian randomization framework. Nat Commun. 11:10102020.
View Article : Google Scholar : PubMed/NCBI
|
|
51
|
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
|
|
52
|
Yordanov A, Damyanova P, Vasileva-Slaveva
M, Hasan I, Kostov S and Shivarov V: Integrated analysis of
phagocytic and immunomodulatory markers in cervical cancer reveals
constellations of potential prognostic relevance. Int J Mol Sci.
25:91172024. View Article : Google Scholar : PubMed/NCBI
|
|
53
|
Allemani C, Minicozzi P, Morawski B, Lima
CA, Bennett D, Pongnikorn D, Petrova D, Innos K, Girardi F, Galán
Alvarez Y, et al: Global variation in patterns of care and time to
initial treatment for breast, cervical, and ovarian cancer from
2015 to 2018 (VENUSCANCER): A secondary analysis of individual
records for 275 792 women from 103 population-based cancer
registries in 39 countries and territories. Lancet. 406:2325–2348.
2025. View Article : Google Scholar : PubMed/NCBI
|
|
54
|
Andersen K, Bonde J, Waldstrøm M, Jakobsen
MV, Lamy P, Pedersen H, Bønløkke S, Stougaard M and Steiniche T:
Evaluation of targeted next-generation sequencing for detection of
HPV genotypes and sublineages in cervical liquid-based cytology
SurePath samples from the Danish screening program. Int J Cancer.
158:193–201. 2026. View Article : Google Scholar : PubMed/NCBI
|
|
55
|
Jian X, Cheng C, Lu W, Peng H and Yang D:
Histone lactylation: Unveiling a novel pathway for the impact of
lactate on physiological and pathological processes (review). Int J
Mol Med. 57:1–12. 2025. View Article : Google Scholar
|
|
56
|
Dang T, You Y, Wei L, Li Q, Sun H, Sun M,
Li X, Yang S, Zeng T, Zhang L, et al: ICAT drives lactylation of
tumor-associated macrophages via the c-myc-ENO1 axis to promote
cervical cancer progression. Free Radic Biol Med. 241:316–329.
2025. View Article : Google Scholar : PubMed/NCBI
|
|
57
|
Xue ZR, Xin YY and Jin WL: Exploiting
metabolic vulnerabilities in cancer: From mechanisms to therapeutic
opportunities. Cancer Lett. 634:2180672025. View Article : Google Scholar : PubMed/NCBI
|
|
58
|
Sarwar F, Ashhad S, Vimal A and
Vishvakarma R: Small molecule inhibitors of the VEGF and tyrosine
kinase for the treatment of cervical cancer. Med Oncol. 41:1992024.
View Article : Google Scholar : PubMed/NCBI
|
|
59
|
Mokoala KMG, Lawal IO, Maserumule LC, Bida
M, Maes A, Ndlovu H, Reed J, Mahapane J, Davis C, Van de Wiele C,
et al: Correlation between [68Ga]Ga-FAPI-46 PET imaging and HIF-1α
immunohistochemical analysis in cervical cancer: Proof-of-concept.
Cancers. 15:39532023. View Article : Google Scholar : PubMed/NCBI
|
|
60
|
Nasioudis D, Fernandez ML, Wong N, Powell
DJ Jr, Mills GB, Westin S, Fader AN, Carey MS and Simpkins F: The
spectrum of MAPK-ERK pathway genomic alterations in gynecologic
malignancies: Opportunities for novel therapeutic approaches.
Gynecol Oncol. 177:86–94. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
61
|
Del Dotto V, Grillini S, Righetti R,
Grandi M, Giorgio V, Solaini G and Baracca A: Bioenergetics of
cancer cells: Insights into the warburg effect and regulation of
ATP synthase. Mol Med. 31:3112025. View Article : Google Scholar : PubMed/NCBI
|
|
62
|
Wen B, Luo L, Zeng Z and Luo X: MYL9
promotes squamous cervical cancer migration and invasion by
enhancing aerobic glycolysis. J Int Med Res.
51:30006052312085822023. View Article : Google Scholar : PubMed/NCBI
|
|
63
|
Robledo-Cadena DX, Pacheco-Velázquez SC,
Vargas-Navarro JL, Padilla-Flores JA, López-Marure R, Pérez-Torres
I, Kaambre T, Moreno-Sánchez R and Rodríguez-Enríquez S:
Synergistic celecoxib and dimethyl-celecoxib combinations block
cervix cancer growth through multiple mechanisms. PLoS One.
19:e03082332024. View Article : Google Scholar : PubMed/NCBI
|
|
64
|
Sheng B, Pan S, Ye M, Liu H, Zhang J, Zhao
B, Ji H and Zhu X: Single-cell RNA sequencing of cervical
exfoliated cells reveals potential biomarkers and cellular
pathogenesis in cervical carcinogenesis. Cell Death Dis.
15:1302024. View Article : Google Scholar : PubMed/NCBI
|
|
65
|
Limones-Gonzalez JE, Aguilar Esquivel P,
Vazquez-Santillan K, Castro-Oropeza R, Lizarraga F, Maldonado V,
Melendez-Zajgla J, Piña-Sanchez P and Mendoza-Almanza G: Changes in
the molecular nodes of the notch and NRF2 pathways in cervical
cancer tissues from the precursor stages to invasive carcinoma.
Oncol Lett. 28:5222024. View Article : Google Scholar : PubMed/NCBI
|
|
66
|
Hazazi A, Khan FR, Albloui F, Arif S,
Abdulaziz O, Alhomrani M, Sindi AAA, Abu-Alghayth MH, Abalkhail A,
Nassar SA and Binshaya AS: Signaling pathways in HPV-induced
cervical cancer: Exploring the therapeutic promise of RNA
modulation. Pathol Res Pract. 263:1556122024. View Article : Google Scholar : PubMed/NCBI
|
|
67
|
Yan Y, Dai T, Guo M, Zhao X, Chen C, Zhou
Y, Qin M, Xu L and Zhao J: A review of non-classical MAPK family
member, MAPK4: A pivotal player in cancer development and
therapeutic intervention. Int J Biol Macromol. 271:1326862024.
View Article : Google Scholar : PubMed/NCBI
|
|
68
|
Zheng X, Tong T, Duan L, Ma Y, Lan Y, Shao
Y, Liu H, Chen W, Yang T and Yang L: VSIG4 induces the
immunosuppressive microenvironment by promoting the infiltration of
M2 macrophage and Tregs in clear cell renal cell carcinoma. Int
Immunopharmacol. 142:1131052024. View Article : Google Scholar : PubMed/NCBI
|
|
69
|
Miao C, You X, Zhang Z, Jiang Z, Liu L,
Jia Y, Bai J, Gao Y, Ye L, Cao Y, et al: SCG2 mediates HNSCC
progression with CCL2/TGFβ1 M2 macrophage infiltration. Oral Dis.
31:782–795. View Article : Google Scholar : PubMed/NCBI
|
|
70
|
Feng L, Shi Q, Wang S, Zhao Y, Wu H, Wei
L, Hao Q, Cui Z, Wang L, Zhang J, et al: The outcome of advanced
and recurrent cervical cancer patients treated with First-line
platinum and paclitaxel with or without indication for immune
checkpoint inhibitors: The comparative study. BMC Cancer.
24:12672024. View Article : Google Scholar : PubMed/NCBI
|
