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

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.955979

DOI=10.3389/fonc.2022.955979

ISSN=2234-943X

ABSTRACT=Background: One of the most common diseases that had a negative impact on women's health was endometrial carcinoma (EC). Advanced endometrial cancer has a dismal prognosis and lacks solid prognostic indicators. Interferon-gamma is a key cytokine in the inflammatory response, and it's also been suggested that it has a role in the tumor microenvironment. The significance of interferon-gamma-associated genes and long noncoding RNAs in endometrial cancer, however, is unknown.
Methods: The TCGA database was used to download RNA-seq data from endometrial cancer tissues and normal controls. Genes associated to interferon-gamma were retrieved from the GSEA website. Co-expression analysis was performed to find lncRNAs linked to the interferon-gamma 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 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 transcript expression of INF-gamma-related lncRNAs, two distinct subgroups of EC from the TCGA cohort were formed, each with different outcomes. Ten IFN-response 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.