Identification of Biomarkers for Cervical Cancer Radiotherapy Resistance Based on RNA Sequencing Data

Feng, Yue; Wang, Zhao; Yang, Nan; Liu, Sijia; Yan, Jiazhuo; Song, Jiayu; Yang, Shanshan; Zhang, Yunyan

doi:10.3389/fcell.2021.724172

Cited by 11 publications

(6 citation statements)

References 86 publications

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“…Recent years the net reclassification index (NRI), integrated discrimination improvement (IDI) have become the two popularity alternatives assessment in risk evaluation or prediction between different models (21,22). We searched ten recently published CESC prognostic signatures, and used NRI, IDI method in "survIDINRI" package to compare the clinical benefits of our signature and published signatures (23)(24)(25)(26)(27)(28)(29)(30)(31)(32).…”

Section: Clinical Benefits Evaluation Of Cesc Os Signaturesmentioning

confidence: 99%

A Novel Prognostic Risk Model for Cervical Cancer Based on Immune Checkpoint HLA-G-Driven Differentially Expressed Genes

Wang

Xing

et al. 2022

Front. Immunol.

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Human leukocyte antigen G (HLA-G) is a potential checkpoint molecule that plays a key role in cervical carcinogenesis. The purpose of this study was to construct and validate a prognostic risk model to predict the overall survival (OS) of cervical cancer patients, providing a reference for individualized clinical treatment that may lead to better clinical outcomes. HLA-G-driven differentially expressed genes (DEGs) were obtained from two cervical carcinoma cell lines, namely, SiHa and HeLa, with stable overexpression of HLA-G by RNA sequencing (RNA-seq). The biological functions of these HLA-G-driven DEGs were analysed by GO enrichment and KEGG pathway using the “clusterProfiler” package. The protein-protein interactions (PPIs) were assessed using the STRING database. The prognostic relevance of each DEG was evaluated by univariate Cox regression using the TCGA-CESC dataset. After the TCGA-CESC cohort was randomly divided into training set and testing set, and a prognostic risk model was constructed by LASSO and stepwise multivariate Cox regression analysis in training set and validated in testing set or in different types of cervical cancer set. The predictive ability of the prognostic risk model or nomogram was evaluated by a series of bioinformatics methods. A total of 1108 candidate HLA-G-driven DEGs, including 391 upregulated and 717 downregulated genes, were obtained and were enriched mostly in the ErbB pathway, steroid biosynthesis, and MAPK pathway. Then, an HLA-G-driven DEG signature consisting of the eight most important prognostic genes CD46, LGALS9, PGM1, SPRY4, CACNB3, PLIN2, MSMO1, and DAGLB was identified as a key predictor of cervical cancer. Multivariate Cox regression analysis showed that this signature is an independent risk factor for the overall survival of CESC patients. Kaplan-Meier survival analysis showed that the 5-year overall survival rate is 23.0% and 84.6% for the high-risk and low-risk patients, respectively (P<0.001). The receiver operating characteristic (ROC) curve of this prognostic model with an area under the curve (AUC) was 0.896 for 5 years, which was better than that of other clinical traits. This prognostic risk model was also successfully validated in different subtypes of cervical cancer, including the keratinizing squamous cell carcinoma, non-keratinizing squamous cell carcinoma, squamous cell neoplasms, non-squamous cell neoplasms set. Single-sample gene set enrichment (ssGSEA) algorithm and Tumor Immune Dysfunction and Exclusion (TIDE) analysis confirmed that this signature influence tumour microenvironment and immune checkpoint blockade. A nomogram that integrated risk score, age, clinical stage, histological grade, and pathological type was then built to predict the overall survival of CESC patients and evaluated by calibration curves, AUC, concordance index (C-index) and decision curve analysis (DCA). To summarize, we developed and validated a novel prognostic risk model for cervical cancer based on HLA-G-driven DEGs, and the prognostic signature showed great ability in predicting the overall survival of patients with cervical cancer.

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Section: Clinical Benefits Evaluation Of Cesc Os Signaturesmentioning

confidence: 99%

A Novel Prognostic Risk Model for Cervical Cancer Based on Immune Checkpoint HLA-G-Driven Differentially Expressed Genes

Wang

Xing

et al. 2022

Front. Immunol.

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“…91 Radiation therapy is used to treat gynecological malignancies. 92 Biological effects of therapeutic radiation exposure: The ionizing radiation used in radiation therapy kills the tumor but affects surrounding normal tissue. However, lung cancer patients undergoing radiotherapy experienced secondary morbidity, both acute and chronic toxicity to normal tissues.…”

Section: Prostate Cancermentioning

confidence: 99%

Radiation induced therapeutic effects in cancerous and tumor cells: A review

Upadhyay¹,

Rai²

2023

JSRT

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Present review article describes use of radiation and radionuclides on cancer and cancer cell therapeutics. It also sketches out cumulative effects of radiation exposure received by the patients during cancer diagnostics. Though, in cancer therapeutics a selected and permissible dose is provided in several cycles to ablate the neoplastic cells and improve the condition of patient, but radiation harms surrounding cells and imparts negative effects on biology of cells. Ionizing radiation (IR) promotes cancer cell death through cytotoxicity. This article emphasizes both remedial effects and biological effects of radiation and radio-resistance in cells. It suggests safe use of radionucleides by encapsulating them in nanomaterials so as to use it alternate to chemotherapy to destroy various cancer types to enhance the survival of normal cells. This article explains effect of ionizing and non-ionizing radiation on cellular metabolism and genetics.

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“…Integrated bioinformatics analysis on RNA sequencing has identified ten potential biomarkers related to radiotherapy resistance in cervical cancer. The results have indicated overexpression in tumor immune process pathways, including cellular defense response, negative regulation of the immunity, T cell and neutrophil activation, regulation of antigen presentation, and peptidyl-tyrosine autophosphorylation [ 140 ]. Other biomarkers, such as HIF-1 could enhance hypoxia-induced radio-resistance via targeting NDRG2 [ 141 ].…”

Section: Inferring Heterogeneity With Therapeutic Diversitymentioning

confidence: 99%

Cervical cancer heterogeneity: a constant battle against viruses and drugs

et al. 2022

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Cervical cancer is the first identified human papillomavirus (HPV) associated cancer and the most promising malignancy to be eliminated. However, the ever-changing virus subtypes and acquired multiple drug resistance continue to induce failure of tumor prevention and treatment. The exploration of cervical cancer heterogeneity is the crucial way to achieve effective prevention and precise treatment. Tumor heterogeneity exists in various aspects including the immune clearance of viruses, tumorigenesis, neoplasm recurrence, metastasis and drug resistance. Tumor development and drug resistance are often driven by potential gene amplification and deletion, not only somatic genomic alterations, but also copy number amplifications, histone modification and DNA methylation. Genomic rearrangements may occur by selection effects from chemotherapy or radiotherapy which exhibits genetic intra-tumor heterogeneity in advanced cervical cancers. The combined application of cervical cancer therapeutic vaccine and immune checkpoint inhibitors has become an effective strategy to address the heterogeneity of treatment. In this review, we will integrate classic and recently updated epidemiological data on vaccination rates, screening rates, incidence and mortality of cervical cancer patients worldwide aiming to understand the current situation of disease prevention and control and identify the direction of urgent efforts. Additionally, we will focus on the tumor environment to summarize the conditions of immune clearance and gene integration after different HPV infections and to explore the genomic factors of tumor heterogeneity. Finally, we will make a thorough inquiry into completed and ongoing phase III clinical trials in cervical cancer and summarize molecular mechanisms of drug resistance among chemotherapy, radiotherapy, biotherapy, and immunotherapy.

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Identification of Biomarkers for Cervical Cancer Radiotherapy Resistance Based on RNA Sequencing Data

Cited by 11 publications

References 86 publications

A Novel Prognostic Risk Model for Cervical Cancer Based on Immune Checkpoint HLA-G-Driven Differentially Expressed Genes

A Novel Prognostic Risk Model for Cervical Cancer Based on Immune Checkpoint HLA-G-Driven Differentially Expressed Genes

Radiation induced therapeutic effects in cancerous and tumor cells: A review

Cervical cancer heterogeneity: a constant battle against viruses and drugs

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