AUTHOR=Xing Xuewei , Tian Yanan , Jin Xuan 

TITLE=Immune infiltration and a necroptosis-related gene signature for predicting the prognosis of patients with cervical cancer

JOURNAL=Frontiers in Genetics

VOLUME=13

YEAR=2023

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.1061107

DOI=10.3389/fgene.2022.1061107

ISSN=1664-8021

ABSTRACT=<p><bold>Background:</bold> Cervical cancer (CC), the fourth most common cancer among women worldwide, has high morbidity and mortality. Necroptosis is a newly discovered form of cell death that plays an important role in cancer development, progression, and metastasis. However, the expression of necroptosis-related genes (NRGs) in CC and their relationship with CC prognosis remain unclear. Therefore, we screened the signature NRGs in CC and constructed a risk prognostic model.</p><p><bold>Methods:</bold> We downloaded gene data and clinical information of patients with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) from The Cancer Genome Atlas (TCGA) database. We performed functional enrichment analysis on the differentially expressed NRGs (DENRGs). We constructed prognostic models and evaluated them by Cox and LASSO regressions for DENRGs, and validated them using the International Cancer Genome Consortium (ICGC) dataset. We used the obtained risk score to classify patients into high- and low-risk groups. We employed the ESTIMATE and single sample gene set enrichment analysis (ssGSEA) algorithms to explore the relationship between the risk score and the clinical phenotype and the tumor immune microenvironment.</p><p><bold>Results:</bold> With LASSO regression, we established a prognostic model of CC including 16 signature DENRGs (<italic>TMP3</italic>, <italic>CHMP4C</italic>, <italic>EEF1A1</italic>, <italic>FASN</italic>, <italic>TNF</italic>, <italic>S100A10</italic>, <italic>IL1A</italic>, <italic>H1.2</italic>, <italic>SLC25A5</italic>, <italic>GLTP</italic>, <italic>IFNG</italic>, <italic>H2AC13</italic>, <italic>TUBB4B</italic>, <italic>AKNA</italic>, <italic>TYK2</italic>, and <italic>H1.5</italic>). The risk score was associated with poor prognosis in CC. Survival was lower in the high-risk group than the low-risk group. The nomogram based on the risk score, T stage, and N stage showed good prognostic predictive power. We found significant differences in immune scores, immune infiltration analysis, and immune checkpoints between the high- and low-risk groups (<italic>p</italic> &lt; 0.05).</p><p><bold>Conclusion:</bold> We screened for DENRGs based on the TCGA database by using bioinformatics methods, and constructed prognostic models based on the signature DENRGs, which we confirmed as possibly having important biological functions in CC. Our study provides a new perspective on CC prognosis and immunity, and offers a series of new targets for future treatment.</p>