Development of a Prognostic Model Based on Lysosome-related Genes for Ovarian Cancer: Insights into Tumor Microenvironment, Mutation Patterns, and Personalized Treatment Strategies

Overview

Journal Cancer Cell Int

Publisher Biomed Central

Date 2024 Dec 20

PMID 39702158

Authors

Ran Sun

Siyi Li

Wanlu Ye

Yanming Lu

Affiliations

Soon will be listed here.

Abstract

Background: Ovarian cancer (OC) is often associated with an unfavorable prognosis. Given the crucial involvement of lysosomes in tumor advancement, lysosome-related genes (LRGs) hold promise as potential therapeutic targets.

Methods: To identify differentially expressed lysosome-related genes (DE-LRGs), we performed a matching analysis between differentially expressed genes (DEGs) in OC and the pool of LRGs. Genes with prognostic significance were analyzed using multiple regression analyses to construct a prognostic risk signature. The model's efficacy was validated through survival analysis in various cohorts. We further explored the model's correlation with clinical attributes, tumor microenvironment (TME), mutational patterns, and drug sensitivity. The quantitative real-time polymerase chain reaction (qRT-PCR) validated gene expression in OC cells.

Results: A 10-gene prognostic risk signature was established. Survival analysis confirmed its predictive accuracy across cohorts. The signature served as an independent prognostic element for OC. The high-risk and low-risk groups demonstrated notable disparities in terms of immune infiltration patterns, mutational characteristics, and sensitivity to therapeutic agents. The qRT-PCR results corroborated and validated the findings obtained from the bioinformatic analyses.

Conclusions: We devised a 10-LRG prognostic model linked to TME, offering insights for tailored OC treatments.

References

Jang J . Principal component analysis of hybrid functional and vector data. Stat Med. 2021; 40(24):5152-5173. PMC: 9084921. DOI: 10.1002/sim.9117. View

Jiao J, Jiang L, Luo Y . N6-Methyladenosine-Related RNA Signature Predicting the Prognosis of Ovarian Cancer. Recent Pat Anticancer Drug Discov. 2021; 16(3):407-416. DOI: 10.2174/1574892816666210615164645. View

Mayakonda A, Lin D, Assenov Y, Plass C, Koeffler H . Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018; 28(11):1747-1756. PMC: 6211645. DOI: 10.1101/gr.239244.118. View

Ma Y, Shurin G, Peiyuan Z, Shurin M . Dendritic cells in the cancer microenvironment. J Cancer. 2013; 4(1):36-44. PMC: 3564245. DOI: 10.7150/jca.5046. View

Galuppini F, Dal Pozzo C, Deckert J, Loupakis F, Fassan M, Baffa R . Tumor mutation burden: from comprehensive mutational screening to the clinic. Cancer Cell Int. 2019; 19:209. PMC: 6686509. DOI: 10.1186/s12935-019-0929-4. View

Goodman A, Kato S, Bazhenova L, Patel S, Frampton G, Miller V . Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers. Mol Cancer Ther. 2017; 16(11):2598-2608. PMC: 5670009. DOI: 10.1158/1535-7163.MCT-17-0386. View

Zhang Z, Yue P, Lu T, Wang Y, Wei Y, Wei X . Role of lysosomes in physiological activities, diseases, and therapy. J Hematol Oncol. 2021; 14(1):79. PMC: 8120021. DOI: 10.1186/s13045-021-01087-1. View

Gao L, Ying F, Cai J, Peng M, Xiao M, Sun S . Identification and validation of pyroptosis-related gene landscape in prognosis and immunotherapy of ovarian cancer. J Ovarian Res. 2023; 16(1):27. PMC: 9883900. DOI: 10.1186/s13048-022-01065-2. View

Bi F, Chen Y, Yang Q . Significance of tumor mutation burden combined with immune infiltrates in the progression and prognosis of ovarian cancer. Cancer Cell Int. 2020; 20:373. PMC: 7405355. DOI: 10.1186/s12935-020-01472-9. View

10.

Zhang Q, Li H, Mao Y, Wang X, Zhang X, Yu X . Apoptotic SKOV3 cells stimulate M0 macrophages to differentiate into M2 macrophages and promote the proliferation and migration of ovarian cancer cells by activating the ERK signaling pathway. Int J Mol Med. 2019; 45(1):10-22. PMC: 6889918. DOI: 10.3892/ijmm.2019.4408. View

11.

Mu L, Sun B, Kong Q, Wang J, Wang G, Zhang S . Disequilibrium of T helper type 1, 2 and 17 cells and regulatory T cells during the development of experimental autoimmune myasthenia gravis. Immunology. 2009; 128(1 Suppl):e826-36. PMC: 2753914. DOI: 10.1111/j.1365-2567.2009.03089.x. View

12.

Lim C, Zoncu R . The lysosome as a command-and-control center for cellular metabolism. J Cell Biol. 2016; 214(6):653-64. PMC: 5021098. DOI: 10.1083/jcb.201607005. View

13.

Kruiswijk F, Labuschagne C, Vousden K . p53 in survival, death and metabolic health: a lifeguard with a licence to kill. Nat Rev Mol Cell Biol. 2015; 16(7):393-405. DOI: 10.1038/nrm4007. View

14.

Jin M, Jin W . The updated landscape of tumor microenvironment and drug repurposing. Signal Transduct Target Ther. 2020; 5(1):166. PMC: 7447642. DOI: 10.1038/s41392-020-00280-x. View

15.

Goldenberg N, Grinstein S, Silverman M . Golgi-bound Rab34 is a novel member of the secretory pathway. Mol Biol Cell. 2007; 18(12):4762-71. PMC: 2096593. DOI: 10.1091/mbc.e06-11-0991. View

16.

Rendell A, Thomas-Bland I, McCuish L, Taylor C, Binju M, Yu Y . Targeting Tyrosine Kinases in Ovarian Cancer: Small Molecule Inhibitor and Monoclonal Antibody, Where Are We Now?. Biomedicines. 2022; 10(9). PMC: 9495728. DOI: 10.3390/biomedicines10092113. View

17.

Wang D, Cao X, Zhang Y, Liu Y, Yao C, Ge W . LAMP3 expression correlated with poor clinical outcome in human ovarian cancer. Tumour Biol. 2017; 39(3):1010428317695014. DOI: 10.1177/1010428317695014. View

18.

Yousefzadeh Y, Hallaj S, Moornani M, Asghary A, Azizi G, Hojjat-Farsangi M . Tumor associated macrophages in the molecular pathogenesis of ovarian cancer. Int Immunopharmacol. 2020; 84:106471. DOI: 10.1016/j.intimp.2020.106471. View

19.

Lheureux S, Gourley C, Vergote I, Oza A . Epithelial ovarian cancer. Lancet. 2019; 393(10177):1240-1253. DOI: 10.1016/S0140-6736(18)32552-2. View

20.

Piao S, Amaravadi R . Targeting the lysosome in cancer. Ann N Y Acad Sci. 2015; 1371(1):45-54. PMC: 4879098. DOI: 10.1111/nyas.12953. View