Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma
Abstract Background Uterine corpus endometrial carcinoma (UCEC) is a frequent gynecological malignancy with a poor prognosis particularly at an advanced stage. Herein, this study aims to construct prognostic markers of UCEC based on immune-related genes to predict the prognosis of UCEC. Methods We a...
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| Format: | Article |
| Language: | English |
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BMC
2020-11-01
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| Series: | Cancer Cell International |
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| Online Access: | http://link.springer.com/article/10.1186/s12935-020-01560-w |
| _version_ | 1819172445277388800 |
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| author | Cankun Zhou Chaomei Li Fangli Yan Yuhua Zheng |
| author_facet | Cankun Zhou Chaomei Li Fangli Yan Yuhua Zheng |
| author_sort | Cankun Zhou |
| collection | DOAJ |
| description | Abstract Background Uterine corpus endometrial carcinoma (UCEC) is a frequent gynecological malignancy with a poor prognosis particularly at an advanced stage. Herein, this study aims to construct prognostic markers of UCEC based on immune-related genes to predict the prognosis of UCEC. Methods We analyzed expression data of 575 UCEC patients from The Cancer Genome Atlas database and immune genes from the ImmPort database, which were used for generation and validation of the signature. We constructed a transcription factor regulatory network based on Cistrome databases, and also performed functional enrichment and pathway analyses for the differentially expressed immune genes. Moreover, the prognostic value of 410 immune genes was determined using the Cox regression analysis. We then constructed and verified a prognostic signature. Finally, we performed immune infiltration analysis using TIMER-generating immune cell content. Results The immune cell microenvironment as well as the PI3K-Akt, and MARK signaling pathways were involved in UCEC development. The established prognostic signature revealed a ten-gene prognostic signature, comprising of PDIA3, LTA, PSMC4, TNF, SBDS, HDGF, HTR3E, NR3C1, PGR, and CBLC. This signature showed a strong prognostic ability in both the training and testing sets and thus can be used as an independent tool to predict the prognosis of UCEC. In addition, levels of B cells and neutrophils were significantly correlated with the patient’s risk score, while the expression of ten genes was associated with immune cell infiltrates. Conclusions In summary, the ten-gene prognostic signature may guide the selection of the immunotherapy for UCEC. |
| first_indexed | 2024-12-22T20:07:18Z |
| format | Article |
| id | doaj.art-9b360f3f5dfe4d18a0394d08d68dab03 |
| institution | Directory Open Access Journal |
| issn | 1475-2867 |
| language | English |
| last_indexed | 2024-12-22T20:07:18Z |
| publishDate | 2020-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Cell International |
| spelling | doaj.art-9b360f3f5dfe4d18a0394d08d68dab032022-12-21T18:14:07ZengBMCCancer Cell International1475-28672020-11-0120111710.1186/s12935-020-01560-wIdentification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinomaCankun Zhou0Chaomei Li1Fangli Yan2Yuhua Zheng3Department of Gynecology, Southern Medical University Affiliated Maternal & Child Health Hospital of FoshanDepartment of Obstetrics and Gynecology, Southern Medical University Affiliated Maternal & Child Health Hospital of FoshanDepartment of Gynecology, Shaoxing People’s HospitalDepartment of Gynecology, Southern Medical University Affiliated Maternal & Child Health Hospital of FoshanAbstract Background Uterine corpus endometrial carcinoma (UCEC) is a frequent gynecological malignancy with a poor prognosis particularly at an advanced stage. Herein, this study aims to construct prognostic markers of UCEC based on immune-related genes to predict the prognosis of UCEC. Methods We analyzed expression data of 575 UCEC patients from The Cancer Genome Atlas database and immune genes from the ImmPort database, which were used for generation and validation of the signature. We constructed a transcription factor regulatory network based on Cistrome databases, and also performed functional enrichment and pathway analyses for the differentially expressed immune genes. Moreover, the prognostic value of 410 immune genes was determined using the Cox regression analysis. We then constructed and verified a prognostic signature. Finally, we performed immune infiltration analysis using TIMER-generating immune cell content. Results The immune cell microenvironment as well as the PI3K-Akt, and MARK signaling pathways were involved in UCEC development. The established prognostic signature revealed a ten-gene prognostic signature, comprising of PDIA3, LTA, PSMC4, TNF, SBDS, HDGF, HTR3E, NR3C1, PGR, and CBLC. This signature showed a strong prognostic ability in both the training and testing sets and thus can be used as an independent tool to predict the prognosis of UCEC. In addition, levels of B cells and neutrophils were significantly correlated with the patient’s risk score, while the expression of ten genes was associated with immune cell infiltrates. Conclusions In summary, the ten-gene prognostic signature may guide the selection of the immunotherapy for UCEC.http://link.springer.com/article/10.1186/s12935-020-01560-wUterine corpus endometrial carcinomaTCGAImmune genePrognosis |
| spellingShingle | Cankun Zhou Chaomei Li Fangli Yan Yuhua Zheng Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma Cancer Cell International Uterine corpus endometrial carcinoma TCGA Immune gene Prognosis |
| title | Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma |
| title_full | Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma |
| title_fullStr | Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma |
| title_full_unstemmed | Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma |
| title_short | Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma |
| title_sort | identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma |
| topic | Uterine corpus endometrial carcinoma TCGA Immune gene Prognosis |
| url | http://link.springer.com/article/10.1186/s12935-020-01560-w |
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