Identification of Novel Immunologic Checkpoint Gene Prognostic Markers for Ovarian Cancer

Xiao Huo; Xi Zhang; Shuhong Li; Shuzhen Wang; Hengzi Sun; Mo Yang

doi:10.1155/2022/8570882

Identification of Novel Immunologic Checkpoint Gene Prognostic Markers for Ovarian Cancer

J Oncol. 2022 Sep 15:2022:8570882. doi: 10.1155/2022/8570882. eCollection 2022.

Authors

Xiao Huo¹, Xi Zhang², Shuhong Li³, Shuzhen Wang³, Hengzi Sun³, Mo Yang⁴

Affiliations

¹ Center of Basic Medical Research, Institute of Medical Innovation and Research, Cancer Center, Peking University Third Hospital, Beijing, China PCODE: 100191.
² Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China PCODE: 100191.
³ Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China PCODE: 100025.
⁴ Medical Center for Human Reproduction, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China PCODE: 100025.

Abstract

Ovarian cancer has a higher resistance to chemotherapy, displaying the highest mortality rate among gynecological cancers. Recently, immune checkpoint inhibitor therapy is an effective treatment for selected patients. However, a low response rate for immune checkpoint treatment was observed for ovarian cancer patients. Therefore, it is necessary to identify ovarian cancer patients who might gain benefits from immune checkpoint treatment. Datasets containing ovarian cancer samples with mRNA-seq and clinical follow-up data were downloaded from different databases like The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The researchers applied the univariate analysis for selecting the immune checkpoint genes (ICGs) at a significance level of P < 0.05 as the candidate ICGs. The Spearman correlation coefficients were calculated to compare the correlation between tumor mutation burden and candidate ICGs, and the Kaplan-Meier plots were generated. They also assessed the external validation datasets and the results of immunohistochemical staining. 46 and 35 ICGs were extracted from the TCGA and GEO datasets, respectively, and we categorized the ICGs into 3 expression patterns. Nine (TCGA) and three (GEO) ICGs were significantly related to the prognosis. Univariate survival analysis indicated a significant prognostic relationship between the expression levels of ICOS, TIGIT, and TNFRSF8 and overall survival (OS). Moreover, the expression of ICOS and TIGIT also presented a significantly positive relationship with the CD8A expression. Importantly, patients with a higher CD8A and ICOS expression level (ICOS-H/CD8A-H) showed a better survival rate compared to other patients. Stratified analysis using TIGIT, TNFRSF8, and CD8A expression also showed an improved prognosis for the high TIGIT/high CD8A expression subgroup and the low TNFRSF8/low CD8A expression subgroup compared to the other subgroups. This study identified different immune subtypes that can predict the OS of ovarian cancer patients. This data could prove to be beneficial for making important clinical decisions and designing individual immunotherapeutic strategies.