Prognostic Value of Tumor-microenvironment-associated Genes in Ovarian Cancer
Background: Ovarian cancer (OV) is the fifth leading cause of cancer death among women. Growing evidence supports a key role of the tumor microenvironment in the growth, progression, and metastasis of OV. However, the prognostic effects of gene expression signatures associated with the OV microenvir...
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| Format: | Article |
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Compuscript Ltd
2023-11-01
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| Series: | BIO Integration |
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| Online Access: | https://www.ingentaconnect.com/contentone/cscript/bioi/2023/00000004/00000003/art00002 |
| _version_ | 1797635307786469376 |
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| author | Shimei Li Jiyi Yao Shen Zhang Xinchuan Zhou Xinbao Zhao Na Di Shaoyun Hao Hui Zhi |
| author_facet | Shimei Li Jiyi Yao Shen Zhang Xinchuan Zhou Xinbao Zhao Na Di Shaoyun Hao Hui Zhi |
| author_sort | Shimei Li |
| collection | DOAJ |
| description | Background: Ovarian cancer (OV) is the fifth leading cause of cancer death among women. Growing evidence supports a key role of the tumor microenvironment in the growth, progression, and metastasis of OV. However, the prognostic effects of gene expression signatures associated with the OV microenvironment have not been well established. This study was aimed at applying the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm to identify tumor-microenvironment-associated genes that predict outcomes in patients with OV. Methods: The gene expression profiles of OV samples were downloaded from The Cancer Genome Atlas database. The immune and stromal scores of 469 OV samples on the basis of the ESTIMATE algorithm were available. To better understand the effects of gene expression signatures associated with the OV microenvironment on prognosis, we categorized these samples into groups with high and low ESTIMATE scores. A different OV cohort from the Gene Expression Omnibus (GEO) database and immunohistochemistry from The Human Protein Atlas database were used for external validation. Results: The molecular subtypes of patients with OV correlated with the stromal scores, and the mesenchymal subtype had the highest stromal scores. Patients with higher stromal scores had lower 5-year overall survival; 449 differentially expressed genes in the stromal score group were identified, 26 of which were significantly associated with poor prognosis in patients with OV (p < 0.05). In another OV cohort from the Gene Expression Omnibus database, six genes were further validated to be significantly associated with poor prognosis. Immunohistochemistry data from The Human Protein Atlas database confirmed the overexpression of CX3CR1, GFPT2, NBL1, TFPI2, and ZFP36 in OV tissues compared with normal tissues. Conclusion: Our findings suggest that CX3CR1, GFPT2, NBL1, TFPI2, and ZFP36 may be promising biomarkers for OV prognosis, with clinical implications for therapeutic strategies. |
| first_indexed | 2024-03-11T12:20:22Z |
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| id | doaj.art-fdd6c36dfddc4cb39b56ee985918d7a9 |
| institution | Directory Open Access Journal |
| issn | 2712-0082 |
| language | English |
| last_indexed | 2024-03-11T12:20:22Z |
| publishDate | 2023-11-01 |
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| record_format | Article |
| series | BIO Integration |
| spelling | doaj.art-fdd6c36dfddc4cb39b56ee985918d7a92023-11-07T05:02:31ZengCompuscript LtdBIO Integration2712-00822023-11-0143849610.15212/bioi-2022-0008Prognostic Value of Tumor-microenvironment-associated Genes in Ovarian CancerShimei Li0Jiyi Yao1Shen Zhang2Xinchuan Zhou3Xinbao Zhao4Na Di5Shaoyun Hao6Hui Zhi7Department of Ultrasound, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiangxi Rd, Guangzhou 510120, Guangdong Province, ChinaDepartment of Ultrasound, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiangxi Rd, Guangzhou 510120, Guangdong Province, ChinaDepartment of Ultrasound, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiangxi Rd, Guangzhou 510120, Guangdong Province, ChinaDepartment of Ultrasound, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiangxi Rd, Guangzhou 510120, Guangdong Province, ChinaDepartment of Ultrasound, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiangxi Rd, Guangzhou 510120, Guangdong Province, ChinaDepartment of Ultrasound, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiangxi Rd, Guangzhou 510120, Guangdong Province, ChinaDepartment of Ultrasound, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiangxi Rd, Guangzhou 510120, Guangdong Province, ChinaDepartment of Ultrasound, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiangxi Rd, Guangzhou 510120, Guangdong Province, ChinaBackground: Ovarian cancer (OV) is the fifth leading cause of cancer death among women. Growing evidence supports a key role of the tumor microenvironment in the growth, progression, and metastasis of OV. However, the prognostic effects of gene expression signatures associated with the OV microenvironment have not been well established. This study was aimed at applying the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm to identify tumor-microenvironment-associated genes that predict outcomes in patients with OV. Methods: The gene expression profiles of OV samples were downloaded from The Cancer Genome Atlas database. The immune and stromal scores of 469 OV samples on the basis of the ESTIMATE algorithm were available. To better understand the effects of gene expression signatures associated with the OV microenvironment on prognosis, we categorized these samples into groups with high and low ESTIMATE scores. A different OV cohort from the Gene Expression Omnibus (GEO) database and immunohistochemistry from The Human Protein Atlas database were used for external validation. Results: The molecular subtypes of patients with OV correlated with the stromal scores, and the mesenchymal subtype had the highest stromal scores. Patients with higher stromal scores had lower 5-year overall survival; 449 differentially expressed genes in the stromal score group were identified, 26 of which were significantly associated with poor prognosis in patients with OV (p < 0.05). In another OV cohort from the Gene Expression Omnibus database, six genes were further validated to be significantly associated with poor prognosis. Immunohistochemistry data from The Human Protein Atlas database confirmed the overexpression of CX3CR1, GFPT2, NBL1, TFPI2, and ZFP36 in OV tissues compared with normal tissues. Conclusion: Our findings suggest that CX3CR1, GFPT2, NBL1, TFPI2, and ZFP36 may be promising biomarkers for OV prognosis, with clinical implications for therapeutic strategies.https://www.ingentaconnect.com/contentone/cscript/bioi/2023/00000004/00000003/art00002ovarian cancertumor microenvironmentstromal scoresprognosistcga |
| spellingShingle | Shimei Li Jiyi Yao Shen Zhang Xinchuan Zhou Xinbao Zhao Na Di Shaoyun Hao Hui Zhi Prognostic Value of Tumor-microenvironment-associated Genes in Ovarian Cancer BIO Integration ovarian cancer tumor microenvironment stromal scores prognosis tcga |
| title | Prognostic Value of Tumor-microenvironment-associated Genes in Ovarian Cancer |
| title_full | Prognostic Value of Tumor-microenvironment-associated Genes in Ovarian Cancer |
| title_fullStr | Prognostic Value of Tumor-microenvironment-associated Genes in Ovarian Cancer |
| title_full_unstemmed | Prognostic Value of Tumor-microenvironment-associated Genes in Ovarian Cancer |
| title_short | Prognostic Value of Tumor-microenvironment-associated Genes in Ovarian Cancer |
| title_sort | prognostic value of tumor microenvironment associated genes in ovarian cancer |
| topic | ovarian cancer tumor microenvironment stromal scores prognosis tcga |
| url | https://www.ingentaconnect.com/contentone/cscript/bioi/2023/00000004/00000003/art00002 |
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