Immune and inflammation: related factor alterations as biomarkers for predicting prognosis and responsiveness to PD-1 monoclonal antibodies in cervical cancer

Abstract

Purpose We aimed to elucidate the potential mechanisms of effective responsiveness to PD-1 monoclonal antibody and evaluate more reliable biomarkers to improve the ability to predict the populations of cervical cancer (CC) suitable for immunotherapy. Methods Peripheral blood samples of CC patients undergoing anti-PD-1 therapy were collected before and after treatment. Differentially expressed genes (DEGs) were analyzed between partial response (PR) and progressive disease (PD) patients. A novel prognostic inflammation and immune–related response gene (IRRG) model was constructed and its prognostic role, correlation with tumor immunity and tumor mutation were evaluated. Results DEGs in PR patient after treatment could predict the response to PD-1 monoclonal antibodies. Among PR-specific pathways, tumor immunity, leukocyte migration, and cytokine activities were prominently enriched. Additionally, an IRRG signature comprising CTLA4, AZU1, C5, LAT, CXCL2, GDF7, MPL, PPARG and CELA1 was established and validated to predict the prognosis of CC with great accuracy and specificity. This signature could reflect the tumor microenvironment (TME) and tumor mutational burden (TMB). We also found stimulated adaptive immunity and downregulated inflammation at baseline in patients with sensitive responses to PD-1 monoclonal antibody. Conclusion We developed an IRRG signature and verified that it was an independent prognostic factor for predicting survival and could reflect a sensitive response to PD-1 monoclonal antibody, which plays a nonnegligible role in the TME of CC. Further investigations are warranted to confirm that patients with stimulated adaptive immunity and downregulated inflammation at baseline could achieve a better survival benefit from PD-1 monoclonal antibody.

A Risk Score Based on Immune- and Oxidative Stress-Related LncRNAs Predicts Prognosis in Lung Adenocarcinoma: Insights from in vitro Experiments and Large-Scale Transcriptome Analysis

“…26 However, previous studies mainly focused on constructing prognostic models using single oxidative stress or immune features, which often suffer from limitations in robustness and generalizability. [27][28][29][30] In order to address these limitations, our study takes a comprehensive approach by incorporating both immune-related and oxidative stress-related lncRNAs. By encompassing multiscale clinical features, we aim to improve the accuracy and robustness of prognostic signatures.…”

Cervical cancer‐specific long non‐coding RNA landscape reveals the favorable prognosis predictive performance of an ion‐channel‐related signature model

“…In this study, 119 raw transcriptome data from CC patients, including 107 tissue samples and 12 peripheral blood samples, were obtained from the Gene Expression Omnibus. 14 , 15 , 16 The statistic of public datasets used in this study are shown in Table S1 . The gene expression data and corresponding clinical information of CC patients were downloaded from The Cancer Genome Atlas database (TCGA, https://xenabrowser.net or https://portal.gdc.cancer.gov ).…”

“…In this study, 119 raw transcriptome data from CC patients, including 107 tissue samples and 12 peripheral blood samples, were obtained from the Gene Expression Omnibus 14–16 . The statistic of public datasets used in this study are shown in Table S1.…”

Senataxin and R-loops homeostasis: multifaced implications in carcinogenesis

“…Inflammation signaling represents an essential physiological cellular response of the innate immunity involved in many biological processes, such as tissue repairing and defense against pathogens. However, chronic inflammation response has been largely proved to be related to pathogenesis of several human diseases, including cancer [ 85 – 89 ]. In the last years, several observations have functionally linked R-loops homeostasis to the activation of the inflammation response (Fig.…”