Development of a prognostic model based on lysosome-related genes for ovarian cancer: insights into tumor microenvironment, mutation patterns, and personalized treatment strategies
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...
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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | Cancer Cell International |
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| Online Access: | https://doi.org/10.1186/s12935-024-03586-w |
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| author | Ran Sun Siyi Li Wanlu Ye Yanming Lu |
| author_facet | Ran Sun Siyi Li Wanlu Ye Yanming Lu |
| author_sort | Ran Sun |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-98c274f856584a8d8033dc65b359ea3c |
| institution | DOAJ |
| issn | 1475-2867 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Cell International |
| spelling | doaj-art-98c274f856584a8d8033dc65b359ea3c2025-08-20T02:40:17ZengBMCCancer Cell International1475-28672024-12-0124112010.1186/s12935-024-03586-wDevelopment of a prognostic model based on lysosome-related genes for ovarian cancer: insights into tumor microenvironment, mutation patterns, and personalized treatment strategiesRan Sun0Siyi Li1Wanlu Ye2Yanming Lu3Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical UniversityDepartment of Obstetrics and Gynecology, Shengjing Hospital of China Medical UniversityDepartment of Obstetrics and Gynecology, Shengjing Hospital of China Medical UniversityDepartment of Obstetrics and Gynecology, Shengjing Hospital of China Medical UniversityAbstract 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.https://doi.org/10.1186/s12935-024-03586-wOvarian cancerLysosomePrognosisTumor microenvironmentRisk signatureqRT-PCR |
| spellingShingle | Ran Sun Siyi Li Wanlu Ye Yanming Lu Development of a prognostic model based on lysosome-related genes for ovarian cancer: insights into tumor microenvironment, mutation patterns, and personalized treatment strategies Cancer Cell International Ovarian cancer Lysosome Prognosis Tumor microenvironment Risk signature qRT-PCR |
| title | Development of a prognostic model based on lysosome-related genes for ovarian cancer: insights into tumor microenvironment, mutation patterns, and personalized treatment strategies |
| title_full | Development of a prognostic model based on lysosome-related genes for ovarian cancer: insights into tumor microenvironment, mutation patterns, and personalized treatment strategies |
| title_fullStr | Development of a prognostic model based on lysosome-related genes for ovarian cancer: insights into tumor microenvironment, mutation patterns, and personalized treatment strategies |
| title_full_unstemmed | Development of a prognostic model based on lysosome-related genes for ovarian cancer: insights into tumor microenvironment, mutation patterns, and personalized treatment strategies |
| title_short | Development of a prognostic model based on lysosome-related genes for ovarian cancer: insights into tumor microenvironment, mutation patterns, and personalized treatment strategies |
| title_sort | development of a prognostic model based on lysosome related genes for ovarian cancer insights into tumor microenvironment mutation patterns and personalized treatment strategies |
| topic | Ovarian cancer Lysosome Prognosis Tumor microenvironment Risk signature qRT-PCR |
| url | https://doi.org/10.1186/s12935-024-03586-w |
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