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Identification of prognosis-related genes in the cervical cancer immune microenvironment
Lirong Yang1, Yang Yang1, Mingyao Meng2
1Central Laboratory of Yan'an Hospital Affiliated to Kunming Medical University, Kunming, Yunnan Province 650000, China; Key Laboratory of Tumor Immunological Prevention and Treatment of Yunnan Province, Kunming, Yunnan Province 650000, China; Kunming Medical University, Kunming, Yunnan Province 650500, China.
Background:
Cervical cancer is the fourth most commonly diagnosed cancer in women worldwide. The metastasis and invasion of this type of cancer are closely related to the tumor microenvironment. Immune cells and stromal cells dominate the tumor microenvironment in cervical cancer. Therefore, we should further investigate the complex interplay between the tumor progression with immune cells or stromal cells.
Methods:
We downloaded the gene expression profiles and clinical data of 307 patients with cervical cancers based on the TCGA database. Subsequently, the Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm was used to calculate the scores of stromal cells and immune cells in order to uncover differential expressed genes, and we analyzed the correlation between their scores and patient survival. Then the Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) deconvolution algorithm was applied to quantify the fraction and infiltration of 22 types of immune cells in cervical cancer. Moreover, we also used R language packs and network tools to analyze GO term, gene enrichment pathway, and protein-protein relationship to trace down genes related to inflammation and immune regulation.
Results:
The gene expression profiles and corresponding clinical data of 307 patients were obtained from TCGA database. The results showed that the scores were statistically significant between the high immunescore group and the low immunescore group. And the low immunescore group had shorter survival period than the high scores group (P = 0.035). Among the 22 types of immune cells, only T cells and mast cells were significantly related to the survival rate of cervical cancer patients. Moreover, PPI network analysis revealed that CCR5 and CXCL9, -10, -11/CXCR3 axis might be a new target for cervical cancer treatment. Finally, Kaplan-Meier survival curves found outnine representative genes significantly related to survival rate including BTNL8, CCR7, CD1E, CD6, CD27, CD79A, GRAP2, SP1B, LY9.
Conclusions:
These genes can be used as markers for the prognosis and diagnosis of cervical cancer and also might be used as treatment targets.
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