The IFN-γ-related long non-coding RNA signature predicts prognosis and indicates immune microenvironment infiltration in uterine corpus endometrial carcinoma

Chunyan Gu; Chen Lin; Zheng Zhu; Li Hu; Fengxu Wang; Xuehai Wang; Junpu Ruan; Xinyuan Zhao; Sen Huang

doi:10.3389/fonc.2022.955979

The IFN-γ-related long non-coding RNA signature predicts prognosis and indicates immune microenvironment infiltration in uterine corpus endometrial carcinoma

Front Oncol. 2022 Jul 26:12:955979. doi: 10.3389/fonc.2022.955979. eCollection 2022.

Authors

Chunyan Gu¹, Chen Lin², Zheng Zhu³, Li Hu⁴, Fengxu Wang⁵, Xuehai Wang⁵, Junpu Ruan⁵, Xinyuan Zhao⁵, Sen Huang¹

Affiliations

¹ Department of Obstetrics and Gynecology, Nantong Haimen People's Hospital, Nantong, China.
² Vectors and Parasitosis Control and Prevention Section, Center of Disease Prevention and Control in Pudong New Area of Shanghai, Shanghai, China.
³ Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
⁴ Department of Medicine, Kangda College of Nanjing Medical University, Lianyungang, China.
⁵ Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong, China.

Abstract

Background: One of the most common diseases that have a negative impact on women's health is endometrial carcinoma (EC). Advanced endometrial cancer has a dismal prognosis and lacks solid prognostic indicators. IFN-γ is a key cytokine in the inflammatory response, and it has also been suggested that it has a role in the tumor microenvironment. The significance of IFN-γ-related genes and long non-coding RNAs in endometrial cancer, however, is unknown.

Methods: The Cancer Genome Atlas (TCGA) database was used to download RNA-seq data from endometrial cancer tissues and normal controls. Genes associated with IFN-γ were retrieved from the gene set enrichment analysis (GSEA) website. Co-expression analysis was performed to find lncRNAs linked to IFN-γ gene. The researchers employed weighted co-expression network analysis (WGCNA) to find lncRNAs that were strongly linked to survival. The prognostic signature was created using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. The training cohort, validation cohort, and entire cohort of endometrial cancer patients were then split into high-risk and low-risk categories. To investigate variations across different risk groups, we used survival analysis, enrichment analysis, and immune microenvironment analysis. The platform for analysis is R software (version X64 3.6.1).

Results: Based on the transcript expression of IFN-γ-related lncRNAs, two distinct subgroups of EC from TCGA cohort were formed, each with different outcomes. Ten IFN-γ-related lncRNAs were used to build a predictive signature using Cox regression analysis and the LASSO regression, including CFAP58, LINC02014, UNQ6494, AC006369.1, NRAV, BMPR1B-DT, AC068134.2, AP002840.2, GS1-594A7.3, and OLMALINC. The high-risk group had a considerably worse outcome (p < 0.05). In the immunological microenvironment, there were also substantial disparities across different risk categories.

Conclusion: Our findings give a reference for endometrial cancer prognostic type and immunological status assessment, as well as prospective molecular markers for the disease.

Keywords: bioinformatics; endometrial carcinoma; immunology; interferon-gamma; signature.