Development and clinical validation of a seven-gene signature based on tumor stem cell-related genes to predict ovarian cancer prognosis
Abstract Objective Tumors are highly heterogeneous, and within their parenchyma, a small population of tumor-stem cells possessing differentiation potential, high oncogenicity, and self-renewal capabilities exists. These cells are pivotal in mediating tumor development, chemotherapy resistance, and...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | Journal of Ovarian Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13048-023-01326-8 |
| _version_ | 1797259077847351296 |
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| author | Guangwei Wang Xiaofei Liu Yue You Silei Chen Xiaohan Chang Qing Yang |
| author_facet | Guangwei Wang Xiaofei Liu Yue You Silei Chen Xiaohan Chang Qing Yang |
| author_sort | Guangwei Wang |
| collection | DOAJ |
| description | Abstract Objective Tumors are highly heterogeneous, and within their parenchyma, a small population of tumor-stem cells possessing differentiation potential, high oncogenicity, and self-renewal capabilities exists. These cells are pivotal in mediating tumor development, chemotherapy resistance, and recurrence. Ovarian cancer shares characteristics with tumor stem cells, making it imperative to investigate molecular markers associated with these cells. Methods Stem cell-related genes were collected, and molecular subtypes were established based on gene expression profiles from The Cancer Genome Atlas using the R package tool “ConsensusClusterPlus.” Multi-gene prognostic markers were identified using LASSO regression analysis. Gene set enrichment analysis was employed to gain insights into the potential molecular mechanisms of these identified markers. The robustness of these prognostic markers was analyzed across different cohorts, and their clinical independence was determined through multivariate Cox analysis. A nomogram was constructed to assess the model’s clinical applicability. Immunohistochemistry was performed to validate the expression of hub genes. Results Utilizing 49 tumor stem cell-related genes associated with prognosis, 362 ovarian cancer samples were divided into two distinct clusters, revealing significant prognostic disparities. A seven-gene signature (GALP, CACNA1C, COL16A1, PENK, C4BPA, PSMA2, and CXCL9), identified through LASSO regression, exhibited stability and robustness across various platforms. Multivariate Cox regression analysis confirmed the signature’s independence in predicting survival in patients with ovarian cancer. Furthermore, a nomogram combining the gene signature demonstrated strong predictive abilities. Immunohistochemistry results indicated significantly elevated GALP, CACNA1C, COL16A1, PENK, C4BPA, PSMA2, and CXCL9 expression in cancer tissues. Conclusion The seven-gene signature holds promise as a valuable tool for decision-making and prognosis prediction in patients with ovarian cancer. |
| first_indexed | 2024-04-24T23:03:42Z |
| format | Article |
| id | doaj.art-775ea3afcdba49b98d350e1391305fb0 |
| institution | Directory Open Access Journal |
| issn | 1757-2215 |
| language | English |
| last_indexed | 2024-04-24T23:03:42Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Ovarian Research |
| spelling | doaj.art-775ea3afcdba49b98d350e1391305fb02024-03-17T12:35:06ZengBMCJournal of Ovarian Research1757-22152024-03-0117112010.1186/s13048-023-01326-8Development and clinical validation of a seven-gene signature based on tumor stem cell-related genes to predict ovarian cancer prognosisGuangwei Wang0Xiaofei Liu1Yue You2Silei Chen3Xiaohan Chang4Qing Yang5Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical UniversityShenyang Women’s and Children’s HospitalDepartment 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 Objective Tumors are highly heterogeneous, and within their parenchyma, a small population of tumor-stem cells possessing differentiation potential, high oncogenicity, and self-renewal capabilities exists. These cells are pivotal in mediating tumor development, chemotherapy resistance, and recurrence. Ovarian cancer shares characteristics with tumor stem cells, making it imperative to investigate molecular markers associated with these cells. Methods Stem cell-related genes were collected, and molecular subtypes were established based on gene expression profiles from The Cancer Genome Atlas using the R package tool “ConsensusClusterPlus.” Multi-gene prognostic markers were identified using LASSO regression analysis. Gene set enrichment analysis was employed to gain insights into the potential molecular mechanisms of these identified markers. The robustness of these prognostic markers was analyzed across different cohorts, and their clinical independence was determined through multivariate Cox analysis. A nomogram was constructed to assess the model’s clinical applicability. Immunohistochemistry was performed to validate the expression of hub genes. Results Utilizing 49 tumor stem cell-related genes associated with prognosis, 362 ovarian cancer samples were divided into two distinct clusters, revealing significant prognostic disparities. A seven-gene signature (GALP, CACNA1C, COL16A1, PENK, C4BPA, PSMA2, and CXCL9), identified through LASSO regression, exhibited stability and robustness across various platforms. Multivariate Cox regression analysis confirmed the signature’s independence in predicting survival in patients with ovarian cancer. Furthermore, a nomogram combining the gene signature demonstrated strong predictive abilities. Immunohistochemistry results indicated significantly elevated GALP, CACNA1C, COL16A1, PENK, C4BPA, PSMA2, and CXCL9 expression in cancer tissues. Conclusion The seven-gene signature holds promise as a valuable tool for decision-making and prognosis prediction in patients with ovarian cancer.https://doi.org/10.1186/s13048-023-01326-8Tumor stem cellOvarian cancerMarkerPrognosis |
| spellingShingle | Guangwei Wang Xiaofei Liu Yue You Silei Chen Xiaohan Chang Qing Yang Development and clinical validation of a seven-gene signature based on tumor stem cell-related genes to predict ovarian cancer prognosis Journal of Ovarian Research Tumor stem cell Ovarian cancer Marker Prognosis |
| title | Development and clinical validation of a seven-gene signature based on tumor stem cell-related genes to predict ovarian cancer prognosis |
| title_full | Development and clinical validation of a seven-gene signature based on tumor stem cell-related genes to predict ovarian cancer prognosis |
| title_fullStr | Development and clinical validation of a seven-gene signature based on tumor stem cell-related genes to predict ovarian cancer prognosis |
| title_full_unstemmed | Development and clinical validation of a seven-gene signature based on tumor stem cell-related genes to predict ovarian cancer prognosis |
| title_short | Development and clinical validation of a seven-gene signature based on tumor stem cell-related genes to predict ovarian cancer prognosis |
| title_sort | development and clinical validation of a seven gene signature based on tumor stem cell related genes to predict ovarian cancer prognosis |
| topic | Tumor stem cell Ovarian cancer Marker Prognosis |
| url | https://doi.org/10.1186/s13048-023-01326-8 |
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