Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Cancer
Published in: BMC Cancer 1/2024

Open Access 01-12-2024 | Endometrial Cancer | Research

Glycometabolism and lipid metabolism related genes predict the prognosis of endometrial carcinoma and their effects on tumor cells

Authors: Xuefen Lin, Jianfeng Zheng, Xintong Cai, Li Liu, Shan Jiang, Qinying Liu, Yang Sun

Published in: BMC Cancer | Issue 1/2024

Login to get access

Abstract

Background

Glycometabolism and lipid metabolism are critical in cancer metabolic reprogramming. The primary aim of this study was to develop a prognostic model incorporating glycometabolism and lipid metabolism-related genes (GLRGs) for accurate prognosis assessment in patients with endometrial carcinoma (EC).

Methods

Data on gene expression and clinical details were obtained from publicly accessible databases. GLRGs were obtained from the Genecards database. Through nonnegative matrix factorization (NMF) clustering, molecular groupings with various GLRG expression patterns were identified. LASSO Cox regression analysis was employed to create a prognostic model. Use rich algorithms such as GSEA, GSVA, xCELL ssGSEA, EPIC,CIBERSORT, MCPcounter, ESTIMATE, TIMER, TIDE, and Oncoppredict to analyze functional pathway characteristics of the forecast signal, immune status, anti-tumor therapy, etc. The expression was assessed using Western blot and quantitative real-time PCR techniques. A total of 113 algorithm combinations were combined to screen out the most significant GLRGs in the signature for in vitro experimental verification, such as colony formation, EdU cell proliferation, wound healing, apoptosis, and Transwell assays.

Results

A total of 714 GLRGs were found, and 227 of them were identified as prognostic-related genes. And ten GLRGs (AUP1, ESR1, ERLIN2, ASS1, OGDH, BCKDHB, SLC16A1, HK2, LPCAT1 and PGR-AS1) were identified to construct the prognostic model of patients with EC. Based on GLRGs, the risk model’s prognosis and independent prognostic value were established. The signature of GLRGs exhibited a robust correlation with the infiltration of immune cells and the sensitivity to drugs. In cytological experiments, we selected HK2 as candidate gene to verify its value in the occurrence and development of EC. Western blot and qRT-PCR revealed that HK2 was substantially expressed in EC cells. According to in vitro experiments, HK2 knockdown can increase EC cell apoptosis while suppressing EC cell migration, invasion, and proliferation.

Conclusion

The GLRGs signature constructed in this study demonstrated significant prognostic value for patients with endometrial carcinoma, thereby providing valuable guidance for treatment decisions.
Appendix
Available only for authorised users
Literature
1.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.PubMedCrossRef
2.
Lega IC, Lipscombe LL. Review: Diabetes, obesity, and cancer-pathophysiology and clinical implications. Endocr Rev. 2020;41(1):bnz014.PubMedCrossRef
3.
4.
Yang X, Li X, Dong Y, Fan Y, Cheng Y, Zhai L, Zhang S, Zhou J, Wang J. Effects of metabolic syndrome and its components on the prognosis of endometrial cancer. Front Endocrinol (Lausanne). 2021;12:780769.PubMedCrossRef
5.
Cai Y, Wang B, Xu W, Liu K, Gao Y, Guo C, Chen J, Kamal MA, Yuan C. Endometrial cancer: genetic, metabolic characteristics, therapeutic strategies and nanomedicine. Curr Med Chem. 2021;28(42):8755–81.PubMedCrossRef
6.
Bravo-Ruiz I, Medina M, Martínez-Poveda B. From food to genes: transcriptional regulation of metabolism by lipids and carbohydrates. Nutrients. 2021;13(5):1513.PubMedPubMedCentralCrossRef
7.
8.
9.
10.
11.
Huang P, Fan X, Yu H, Zhang K, Li H, Wang Y, Xue F. Glucose metabolic reprogramming and its therapeutic potential in obesity-associated endometrial cancer. J Transl Med. 2023;21(1):94.PubMedPubMedCentralCrossRef
12.
Murray M, Hraiki A, Bebawy M, Pazderka C, Rawling T. Anti-tumor activities of lipids and lipid analogues and their development as potential anticancer drugs. Pharmacol Ther. 2015;150:109–28.PubMedCrossRef
13.
Hanashima S, Yano Y, Murata M. Enantiomers of phospholipids and cholesterol: A key to decipher lipid-lipid interplay in membrane. Chirality. 2020;32(3):282–98.PubMedCrossRef
14.
Snaebjornsson MT, Janaki-Raman S, Schulze A. Greasing the wheels of the cancer machine: the role of lipid metabolism in cancer. Cell Metab. 2020;31(1):62–76.PubMedCrossRef
15.
16.
Liao C, Zhang D, Mungo C, Tompkins DA, Zeidan AM. Is diabetes mellitus associated with increased incidence and disease-specific mortality in endometrial cancer? A systematic review and meta-analysis of cohort studies. Gynecol Oncol. 2014;135(1):163–71.PubMedPubMedCentralCrossRef
17.
Saed L, Varse F, Baradaran HR, Moradi Y, Khateri S, Friberg E, Khazaei Z, Gharahjeh S, Tehrani S, Sioofy-Khojine AB, et al. The effect of diabetes on the risk of endometrial Cancer: an updated a systematic review and meta-analysis. BMC Cancer. 2019;19(1):527.PubMedPubMedCentralCrossRef
18.
Bian X, Liu R, Meng Y, Xing D, Xu D, Lu Z. Lipid metabolism and cancer. J Exp Med. 2021;218(1):e20201606.PubMedCrossRef
19.
Abu-Zaid A, Alomar O, Abuzaid M, Baradwan S, Salem H, Al-Badawi IA. Preoperative anemia predicts poor prognosis in patients with endometrial cancer: A systematic review and meta-analysis. Eur J Obstet Gynecol Reprod Biol. 2021;258:382–90.PubMedCrossRef
20.
Coll-de la Rubia E, Martinez-Garcia E, Dittmar G, Gil-Moreno A, Cabrera S, Colas E. Prognostic biomarkers in endometrial cancer: a systematic review and meta-Analysis. J Clin Med 2020;9(6).
21.
Hoang LN, Kinloch MA, Leo JM, Grondin K, Lee CH, Ewanowich C, Köbel M, Cheng A, Talhouk A, McConechy M, et al. Interobserver agreement in endometrial carcinoma histotype diagnosis varies depending on the cancer genome atlas (TCGA)-based molecular subgroup. Am J Surg Pathol. 2017;41(2):245–52.PubMedCrossRef
22.
Bendifallah S, Canlorbe G, Collinet P, Arsène E, Huguet F, Coutant C, Hudry D, Graesslin O, Raimond E, Touboul C, et al. Just how accurate are the major risk stratification systems for early-stage endometrial cancer? Br J Cancer. 2015;112(5):793–801.PubMedPubMedCentralCrossRef
23.
Murali R, Delair DF, Bean SM, Abu-Rustum NR, Soslow RA. Evolving roles of histologic evaluation and molecular/genomic profiling in the management of endometrial cancer. J Natl Compr Canc Netw. 2018;16(2):201–9.PubMedPubMedCentralCrossRef
24.
Voss MA, Ganesan R, Ludeman L, McCarthy K, Gornall R, Schaller G, Wei W, Sundar S. Should grade 3 endometrioid endometrial carcinoma be considered a type 2 cancer-a clinical and pathological evaluation. Gynecol Oncol. 2012;124(1):15–20.PubMedCrossRef
25.
Pecorelli S. Revised FIGO staging for carcinoma of the vulva, cervix, and endometrium. Int J Gynaecol Obstet. 2009;105(2):103–4.PubMedCrossRef
26.
Gilks CB, Oliva E, Soslow RA. Poor interobserver reproducibility in the diagnosis of high-grade endometrial carcinoma. Am J Surg Pathol. 2013;37(6):874–81.PubMedCrossRef
27.
Han G, Sidhu D, Duggan MA, Arseneau J, Cesari M, Clement PB, Ewanowich CA, Kalloger SE, Köbel M. Reproducibility of histological cell type in high-grade endometrial carcinoma. Mod Pathol. 2013;26(12):1594–604.PubMedCrossRef
28.
Clarke BA, Gilks CB. Endometrial carcinoma: controversies in histopathological assessment of grade and tumour cell type. J Clin Pathol. 2010;63(5):410–5.PubMedCrossRef
29.
Stelloo E, Nout RA, Osse EM, Jürgenliemk-Schulz IJ, Jobsen JJ, Lutgens LC, van der Steen-Banasik EM, Nijman HW, Putter H, Bosse T, et al. Improved risk assessment by integrating molecular and clinicopathological factors in early-stage endometrial cancer-combined analysis of the PORTEC cohorts. Clin Cancer Res. 2016;22(16):4215–24.PubMedCrossRef
30.
Talhouk A, McConechy MK, Leung S, Yang W, Lum A, Senz J, Boyd N, Pike J, Anglesio M, Kwon JS, et al. Confirmation of ProMisE: A simple, genomics-based clinical classifier for endometrial cancer. Cancer. 2017;123(5):802–13.PubMedCrossRef
31.
Zheng J, Cai X, Zhang Y, Wang H, Liu L, Tang F, Liu L, Sun Y. A comprehensive pan-cancer analysis of necroptosis molecules in four gynecologic cancers. BMC Cancer. 2022;22(1):1160.PubMedPubMedCentralCrossRef
32.
Zheng J, Jiang S, Lin X, Wang H, Liu L, Cai X, Sun Y. Comprehensive analyses of mitophagy-related genes and mitophagy-related lncRNAs for patients with ovarian cancer. BMC Womens Health. 2024;24(1):37.PubMedPubMedCentralCrossRef
33.
Przybyla L, Gilbert LA. A new era in functional genomics screens. Nat Rev Genet. 2022;23(2):89–103.PubMedCrossRef
34.
Wang Z, Jensen MA, Zenklusen JC. A Practical guide to the cancer genome atlas (TCGA). Methods Mol Biol. 2016;1418:111–41.PubMedCrossRef
35.
36.
Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S, et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 2012;22(9):1760–74.PubMedPubMedCentralCrossRef
37.
Safran M, Dalah I, Alexander J, Rosen N, Iny Stein T, Shmoish M, Nativ N, Bahir I, Doniger T, Krug H, et al. GeneCards Version 3: the human gene integrator. Database (Oxford). 2010;2010:baq020.PubMedCrossRef
38.
Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28(11):1747–56.PubMedPubMedCentralCrossRef
39.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.PubMedPubMedCentralCrossRef
40.
Shi Y, Wang Y, Dong H, Niu K, Zhang W, Feng K, Yang R, Zhang Y. Crosstalk of ferroptosis regulators and tumor immunity in pancreatic adenocarcinoma: novel perspective to mRNA vaccines and personalized immunotherapy. Apoptosis. 2023;28(9–10):1423–35.PubMedPubMedCentralCrossRef
41.
Hamamoto R, Takasawa K, Machino H, Kobayashi K, Takahashi S, Bolatkan A, Shinkai N, Sakai A, Aoyama R, Yamada M, et al. Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine. Brief Bioinform. 2022;23(4):bbac246.PubMedPubMedCentralCrossRef
42.
Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16(4):385–95.PubMedCrossRef
43.
Hong K, Cen K, Chen Q, Dai Y, Mai Y, Guo Y. Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy. Front Immunol. 2023;14:1128390.PubMedPubMedCentralCrossRef
44.
Ning J, Sun K, Fan X, Jia K, Meng L, Wang X, Li H, Ma R, Liu S, Li F, et al. Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer. Sci Rep. 2023;13(1):7019.PubMedPubMedCentralCrossRef
45.
Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26(8):1364–70.PubMedCrossRef
46.
47.
Cheng Y, Wang X, Xia Y. Supervised t-distributed stochastic neighbor embedding for data visualization and classification. INFORMS J Comput. 2021;33(2):419–835.PubMed
48.
49.
50.
Subramanian A, Kuehn H, Gould J, Tamayo P, Mesirov JP. GSEA-P: a desktop application for Gene Set Enrichment Analysis. Bioinformatics. 2007;23(23):3251–3.PubMedCrossRef
51.
Zeng D, Ye Z, Shen R, Yu G, Wu J, Xiong Y, Zhou R, Qiu W, Huang N, Sun L, et al. IOBR: Multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures. Front Immunol. 2021;12:687975.PubMedPubMedCentralCrossRef
52.
Fu J, Li K, Zhang W, Wan C, Zhang J, Jiang P, Liu XS. Large-scale public data reuse to model immunotherapy response and resistance. Genome Med. 2020;12(1):21.PubMedPubMedCentralCrossRef
53.
Kuehn H, Liberzon A, Reich M, Mesirov JP. Using GenePattern for gene expression analysis. Curr Protoc Bioinformatics. 2008;Chapter 7:7.12.11-17.12.39.
54.
Maeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 2021;22(6):bbab260.PubMedPubMedCentralCrossRef
55.
Liu Z, Liu L, Weng S, Guo C, Dang Q, Xu H, Wang L, Lu T, Zhang Y, Sun Z, et al. Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer. Nat Commun. 2022;13(1):816.PubMedPubMedCentralCrossRef
56.
Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the international diabetes federation diabetes atlas, 9(th) edition. Diabetes Res Clin Pract. 2019;157:107843.PubMedCrossRef
57.
Nead KT, Sharp SJ, Thompson DJ, Painter JN, Savage DB, Semple RK, Barker A, Perry JR, Attia J, Dunning AM, et al. Evidence of a causal association between insulinemia and endometrial cancer: a mendelian randomization analysis. J Natl Cancer Inst. 2015;107(9):djv178.PubMedPubMedCentralCrossRef
58.
Sidorkiewicz I, Jóźwik M, Niemira M, Krętowski A. Insulin resistance and endometrial cancer: emerging role for microRNA. Cancers (Basel). 2020;12(9):2559.PubMedCrossRef
59.
Kahn CR, Wang G, Lee KY. Altered adipose tissue and adipocyte function in the pathogenesis of metabolic syndrome. J Clin Invest. 2019;129(10):3990–4000.PubMedPubMedCentralCrossRef
60.
Gong TT, Wu QJ, Wang YL, Ma XX. Circulating adiponectin, leptin and adiponectin-leptin ratio and endometrial cancer risk: Evidence from a meta-analysis of epidemiologic studies. Int J Cancer. 2015;137(8):1967–78.PubMedCrossRef
61.
Boroń D, Nowakowski R, Grabarek BO, Zmarzły N, Opławski M. Expression pattern of leptin and its receptors in endometrioid endometrial cancer. J Clin Med. 2021;10(13):2787.PubMedPubMedCentralCrossRef
62.
Dąbruś D, Kiełbasiński R, Grabarek BO, Boroń D. Evaluation of the impact of cisplatin on variances in the expression pattern of leptin-related genes in endometrial cancer cells. Int J Mol Sci. 2020;21(11):4135.PubMedPubMedCentralCrossRef
63.
Yen TT, Wang TL, Fader AN, Shih IM, Gaillard S. Molecular classification and emerging targeted therapy in endometrial cancer. Int J Gynecol Pathol. 2020;39(1):26–35.PubMedPubMedCentralCrossRef
64.
Zhang Y, Zhang Z. The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications. Cell Mol Immunol. 2020;17(8):807–21.PubMedPubMedCentralCrossRef
65.
Mahdi H, Chelariu-Raicu A, Slomovitz BM. Immunotherapy in endometrial cancer. Int J Gynecol Cancer. 2023;33(3):351–7.PubMedCrossRef
66.
Ngu SF, Ngan HY, Chan KK. Role of adjuvant and post-surgical treatment in gynaecological cancer. Best Pract Res Clin Obstet Gynaecol. 2022;78:2–13.PubMedCrossRef
67.
Tubridy EA, Taunk NK, Ko EM. Treatment of node-positive endometrial cancer: chemotherapy, radiation, immunotherapy, and targeted therapy. Curr Treat Options Oncol. 2024;25(3):330–45.PubMedPubMedCentralCrossRef
68.
Droog M, Nevedomskaya E, Kim Y, Severson T, Flach KD, Opdam M, Schuurman K, Gradowska P, Hauptmann M, Dackus G, et al. Comparative cistromics reveals genomic cross-talk between FOXA1 and ERα in tamoxifen-associated endometrial carcinomas. Cancer Res. 2016;76(13):3773–84.PubMedCrossRef
69.
Wightman B, Ha I, Ruvkun G. Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell. 1993;75(5):855–62.PubMedCrossRef
70.
Patra KC, Wang Q, Bhaskar PT, Miller L, Wang Z, Wheaton W, Chandel N, Laakso M, Muller WJ, Allen EL, et al. Hexokinase 2 is required for tumor initiation and maintenance and its systemic deletion is therapeutic in mouse models of cancer. Cancer Cell. 2013;24(2):213–28.PubMedPubMedCentralCrossRef
71.
Gagliardi PA, Puliafito A, Primo L. PDK1: At the crossroad of cancer signaling pathways. Semin Cancer Biol. 2018;48:27–35.PubMedCrossRef
72.
73.
Zhang Y, Zhang X, Wang X, Gan L, Yu G, Chen Y, Liu K, Li P, Pan J, Wang J, et al. Inhibition of LDH-A by lentivirus-mediated small interfering RNA suppresses intestinal-type gastric cancer tumorigenicity through the downregulation of Oct4. Cancer Lett. 2012;321(1):45–54.PubMedCrossRef
74.
Yakisich JS, Azad N, Kaushik V, Iyer AKV. The Biguanides metformin and buformin in combination with 2-Deoxy-glucose or WZB-117 inhibit the viability of highly resistant human lung cancer cells. Stem Cells Int. 2019;2019:6254269.PubMedPubMedCentralCrossRef
75.
Abdel-Wahab AF, Mahmoud W, Al-Harizy RM. Targeting glucose metabolism to suppress cancer progression: prospective of anti-glycolytic cancer therapy. Pharmacol Res. 2019;150:104511.PubMedCrossRef
76.
Liu Y, Li M, Zhang Y, Wu C, Yang K, Gao S, Zheng M, Li X, Li H, Chen L. Structure based discovery of novel hexokinase 2 inhibitors. Bioorg Chem. 2020;96:103609.PubMedCrossRef
77.
Yoshino H, Enokida H, Itesako T, Kojima S, Kinoshita T, Tatarano S, Chiyomaru T, Nakagawa M, Seki N. Tumor-suppressive microRNA-143/145 cluster targets hexokinase-2 in renal cell carcinoma. Cancer Sci. 2013;104(12):1567–74.PubMedPubMedCentralCrossRef
78.
Gershon TR, Crowther AJ, Tikunov A, Garcia I, Annis R, Yuan H, Miller CR, Macdonald J, Olson J, Deshmukh M. Hexokinase-2-mediated aerobic glycolysis is integral to cerebellar neurogenesis and pathogenesis of medulloblastoma. Cancer Metab. 2013;1(1):2.PubMedPubMedCentralCrossRef
79.
Wolf A, Agnihotri S, Micallef J, Mukherjee J, Sabha N, Cairns R, Hawkins C, Guha A. Hexokinase 2 is a key mediator of aerobic glycolysis and promotes tumor growth in human glioblastoma multiforme. J Exp Med. 2011;208(2):313–26.PubMedPubMedCentralCrossRef
80.
Anderson M, Marayati R, Moffitt R, Yeh JJ. Hexokinase 2 promotes tumor growth and metastasis by regulating lactate production in pancreatic cancer. Oncotarget. 2017;8(34):56081–94.PubMedCrossRef
81.
Garcia SN, Guedes RC, Marques MM. Unlocking the potential of HK2 in cancer metabolism and therapeutics. Curr Med Chem. 2019;26(41):7285–322.PubMedCrossRef
82.
Meireles CG, Pereira SA, Valadares LP, Rêgo DF, Simeoni LA, Guerra ENS, Lofrano-Porto A. Effects of metformin on endometrial cancer: Systematic review and meta-analysis. Gynecol Oncol. 2017;147(1):167–80.PubMedCrossRef
83.
Tang YL, Zhu LY, Li Y, Yu J, Wang J, Zeng XX, Hu KX, Liu JY, Xu JX. Metformin Use Is Associated with reduced incidence and improved survival of endometrial cancer: A Meta-Analysis. Biomed Res Int. 2017;2017:5905384.PubMedPubMedCentralCrossRef
84.
Ciscato F, Ferrone L, Masgras I, Laquatra C, Rasola A. Hexokinase 2 in Cancer: A prima donna playing multiple characters. Int J Mol Sci. 2021;22(9):4716.PubMedPubMedCentralCrossRef
85.
Yang X, Li X, Cheng Y, Zhou J, Shen B, Zhao L, Wang J. Comprehensive analysis of the glycolysis-related gene prognostic signature and immune infiltration in endometrial cancer. Front Cell Dev Biol. 2021;9:797826.PubMedCrossRef
Metadata
Title
Glycometabolism and lipid metabolism related genes predict the prognosis of endometrial carcinoma and their effects on tumor cells
Authors
Xuefen Lin
Jianfeng Zheng
Xintong Cai
Li Liu
Shan Jiang
Qinying Liu
Yang Sun
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2024
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-024-12327-1

Other articles of this Issue 1/2024

BMC Cancer 1/2024 Go to the issue

Review

The role of the methyltransferase METTL3 in prostate cancer: a potential therapeutic target

Research

Clinical significance of low expression of CADM3 in breast cancer and preliminary exploration of related mechanisms

Research

Tumor burden score and carcinoembryonic antigen predict outcomes in patients with intrahepatic cholangiocarcinoma following liver resection: a multi‑institutional analysis

Research

Evaluation of the diagnostic performance of colposcopy in the detection of cervical high-grade squamous intraepithelial lesions among women with transformation zone type 3

Research

Effect of educational intervention on the knowledge, attitude and practice of breast self-examination among female students at a private university in Southern Nigeria

Research

UTP11 promotes the growth of hepatocellular carcinoma by enhancing the mRNA stability of Oct4
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine
Image Credits