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

Omics sciences for cervical cancer precision medicine from the perspective of the tumor immune microenvironment

Immunotherapies have demonstrated notable clinical benefits in the treatment of cervical cancer (CC). However, the development of therapeutic resistance and diverse adverse effects in immunotherapy stem from complex interactions among biological processes and factors within the tumor immune microenvironment (TIME). Advanced omic technologies offer novel insights into a more expansive and thorough layer of the TIME. Furthermore, integrating multidimensional omics within the frameworks of systems biology and computational methodologies facilitates the generation of interpretable data outputs to characterize the clinical and biological trajectories of tumor behavior. In this review, we present advanced omics technologies that utilize various clinical samples to address scientific inquiries related to immunotherapies for CC, highlighting their utility in identifying metastasis dissemination, recurrence risk, and therapeutic resistance in patients treated with immunotherapeutic approaches. This review elaborates on the strategy for integrating multi-omics data through artificial intelligence algorithms. Additionally, an analysis of the obstacles encountered in the multi-omics analysis process and potential avenues for future research in this domain are presented.

Identification of Biomarkers for Cervical Cancer Radiotherapy Sensitivity and Survival Prognosis

INTRODUCTION

Radiotherapy resistance leads to treatment failure and disease progression in patients with cervical cancer. This study aims to elucidate the molecular underpinnings of radiotherapy response in cervical cancer by identifying radiotherapy sensitivity genes (RSGs).

METHODS

We utilized two GEO expression profiling datasets (GSE3578 and GSE6213) comprising cervical cancer biopsy samples taken before and during radiotherapy to identify differentially expressed genes (DEGs) using the RankProd meta-analysis approach. Subsequent analysis was conducted using data from the TCGA-CESE project to further determine the RSGs and investigate their associations with survival prognosis, immune cell infiltration, and drug sensitivities. The differential expressions of the candidate RSGs were validated in an independent set of cervical cancer patients by qPCRs.

RESULTS

A total of 518 DEGs were identified, with 305 genes upregulated and 213 genes down-regulated during radiotherapy. Six key RSGs were identified as significantly associated with radiotherapy response. Cox regression analysis revealed that upregulations of IL1RAP and GPR15 were associated with an increased risk of poor survival prognosis. Functional enrichment analysis highlighted the involvement of these genes in critical biological processes such as cytokine signaling and immune regulation. Correlation analyses demonstrated significant associations between RSG expressions and M2 macrophage and γδT cell abundances in tumor microenvironment, as well as drug sensitivities. The expression of IL1RAP was significantly higher in the complete response group, supporting the bioinformatic finding.

CONCLUSION

Our findings on RSGs could potentially serve as potential biomarkers for predicting radiotherapy response and as therapeutic targets to enhance the efficacy of radiotherapy.

RNA biomarkers in cancer therapeutics: The promise of personalized oncology

Cancer therapy is a rapidly evolving and constantly expanding field. Current approaches include surgery, conventional chemotherapy and novel biologic agents as in immunotherapy, that together compose a wide armamentarium. The plethora of choices can, however, be clinically challenging in prescribing the most suitable treatment for any given patient. Fortunately, biomarkers can greatly facilitate the most appropriate selection. In recent years, RNA-based biomarkers have proven most promising. These molecules that range from small noncoding RNAs to protein coding gene transcripts can be valuable in cancer management and especially in cancer therapeutics. Compared to their DNA counterparts which are stable throughout treatment, RNA-biomarkers are dynamic. This allows prediction of success prior to treatment start and can identify alterations in expression that could reflect response. Moreover, improved nucleic acid technology allows RNA to be extracted from practically every biofluid/matrix and evaluated with exceedingly high analytic sensitivity. In addition, samples are largely obtained by minimally invasive procedures and as such can be used serially to assess treatment response real-time. This chapter provides the reader insight on currently known RNA biomarkers, the latest research employing Artificial Intelligence in the identification of such molecules and in clinical decisions driving forward the era of personalized oncology.

Aldolase A promotes cervical cancer cell radioresistance by regulating the glycolysis and DNA damage after irradiation

Radioresistance is the major obstacle that affects the efficacy of radiotherapy which is an important treatment for cervical cancer. By analyzing the databases, we found that aldolase A (ALDOA), which is a key enzyme in metabolic reprogramming, has a higher expression in cervical cancer patients and is associated with poor prognosis. We detected the expression of ALDOA in the constructed cervical cancer radioresistance (RR) cells by repetitive irradiation and found that it was upregulated compared to the control cells. Functional assays were conducted and the results showed that the knockdown of ALDOA in cervical cancer RR cells inhibited the proliferation, migration, and clonogenic abilities by regulating the cell glycolysis. In addition, downregulation of ALDOA enhanced radiation-induced apoptosis and DNA damage by causing G2/M phase arrest and further promoted radiosensitivity of cervical cancer cells. The functions of ALDOA in regulating tumor radiosensitivity were also verified by the mouse tumor transplantation model in vivo. Therefore, our study provides new insights into the functions of ALDOA in regulating the efficacy of radiotherapy and indicates that ALDOA might be a promising target for enhancing radiosensitivity in treating cervical cancer patients.

IRAK2, an Immune and Radiation-Response Gene, Correlates with Advanced Disease Features but Predicts Higher Post-Irradiation Local Control in Non-Metastatic and Resected Oral Cancer Patients

Gene Ontology (GO) analysis can provide a comprehensive function analysis for investigating genes, allowing us to identify the potential biological roles of genes. The present study conducted GO analysis to explore the biological function of IRAK2 and performed a case analysis to define its clinical role in disease progression and mediating tumor response to RT. Methods: We performed a GO enrichment analysis on the RNA-seq data to validate radiation-induced gene expression. A total of 172 I-IVB specimens from oral squamous cell carcinoma patients were collected for clinical analysis, from which IRAK2 expression was analyzed by immunohistochemistry. This was a retrospective study conducted between IRAK2 expression and the outcomes of oral squamous cell carcinoma patients after radiotherapy treatment. We conducted Gene Ontology (GO) analysis to explore the biological function of IRAK2 and performed a case analysis to define its clinical role in mediating tumor response to radiotherapy. GO enrichment analysis to validate radiation-induced gene expression was performed. Clinically, 172 stage I-IVB resected oral cancer patients were used to validate IRAK2 expression in predicting clinical outcomes. GO enrichment analysis showed that IRAK2 is involved in 10 of the 14 most enriched GO categories for post-irradiation biological processes, focusing on stress response and immune modulation. Clinically, high IRAK2 expression was correlated with adverse disease features, including pT3-4 status (p = 0.01), advanced overall stage (p = 0.02), and positive bone invasion (p = 0.01). In patients who underwent radiotherapy, the IRAK2-high group was associated with reduced post-irradiation local recurrence (p = 0.025) compared to the IRAK2-low group. IRAK2 plays a crucial role in the radiation-induced response. Patients with high IRAK2 expression demonstrated more advanced disease features but predicted higher post-irradiation local control in a clinical setting. These findings support IRAK2 as a potential predictive biomarker for radiotherapy response in non-metastatic and resected oral cancer patients.

Identification of prognostic and therapeutic biomarkers in type 2 papillary renal cell carcinoma

BACKGROUND

Papillary renal cell carcinoma (PRCC) can be divided into type 1 (PRCC1) and type 2 (PRCC2) and PRCC2 share a more invasive phenotype and worse prognosis. This study aims to identify potential prognostic and therapeutic biomarkers in PRCC2.

METHODS

A cohort from The Cancer Genome Atlas and two datasets from Gene Expression Omnibus were examined. Common differentially expressed genes (DEGs) were screened and potential biomarkers were explored by using Kaplan-Meier method and cox regression analysis. Functional enrichment analysis was utilized to evaluate the potential biological functions. Tumor infiltrating immune cells were estimated by CIBERSORT algorithm. Ninety-two PRCC2 samples from Fudan University Shanghai Cancer Center were obtained, and immunostaining was performed to validate prognostic and therapeutic significance of the potential biomarker.

RESULTS

PRCC2 has worse overall survival and shares distinct molecular characteristics from PRCC1. There was significant higher expression level of Targeting protein for Xklp2 (TPX2) in PRCC2 compared with normal tissues. Higher expression level of TPX2 was significantly associated with worse overall survival in PRCC2 and kinesin family genes expression were found significantly elevated in high risk PRCC2. Abundance of tumor infiltrating M1 macrophage was significantly higher in PRCC2 and it was also associated with worse overall survival. In the FUSCC cohort, higher TPX2 expression was significantly correlated with worse overall and progression-free survival. Retrospective analysis indicated that mTOR inhibitor (everolimus) had greater efficacy in the high-risk group than in the low-risk group (overall response rate: 28.6% vs. 16.7%) and that everolimus had greater efficacy than sunitinib in the high-risk group (overall response rate: 28.6% vs. 20%).

CONCLUSIONS

TPX2 was a prognostic and therapeutic biomarker in PRCC2. Higher abundance of tumor infiltrating M1 macrophage was significantly associated with worse overall survival in PRCC2. mTOR inhibitors may have good efficacy in patients with high-risk PRCC2.

Cervical cancer heterogeneity: a constant battle against viruses and drugs

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.

Recent progress of the tumor microenvironmental metabolism in cervical cancer radioresistance

Radiotherapy is widely used as an indispensable treatment option for cervical cancer patients. However, radioresistance always occurs and has become a big obstacle to treatment efficacy. The reason for radioresistance is mainly attributed to the high repair ability of tumor cells that overcome the DNA damage caused by radiotherapy, and the increased self-healing ability of cancer stem cells (CSCs). Accumulating findings have demonstrated that the tumor microenvironment (TME) is closely related to cervical cancer radioresistance in many aspects, especially in the metabolic processes. In this review, we discuss radiotherapy in cervical cancer radioresistance, and focus on recent research progress of the TME metabolism that affects radioresistance in cervical cancer. Understanding the mechanism of metabolism in cervical cancer radioresistance may help identify useful therapeutic targets for developing novel therapy, overcome radioresistance and improve the efficacy of radiotherapy in clinics and quality of life of patients.

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

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.

Long non-coding RNA KCNQ1OT1 facilitates the progression of cervical cancer and tumor growth through modulating miR-296-5p/HYOU1 axis

Literature reports that lncRNA KCNQ1OT1 is markedly up-regulated in cervical cancer (CC) tissues and cell lines, and KCNQ1OT1 can promote the proliferation and metastasis of CC cells. This current work was designed to investigate the molecular mechanism underlying the participation of KCNQ1OT1 in CC progression. Herein, RT-qPCR was utilized for determining the levels of KCNQ1OT1, miR-296-5p and HYOU1 in clinical tumor tissue specimens and CC cell lines. Then, starBase predicted the complementary binding sites of KCNQ1OT1 and miR-296-5p or miR-296-5p and HYOU1. Dual-luciferase reporter assay/RIP assay validated the interplays among KCNQ1OT1/miR-296-5p/HYOU1. In addition, CCK-8, wound healing and transwell assays were employed to assess the proliferative, migrative and invasive properties of CC cells. Moreover, nude mice xenograft model was established by subcutaneously injection with SiHa cells in order to validate the precise functions of KCNQ1OT1/miR-296-5p/HYOU1 axis in CC in vivo. Besides, Immunohistochemical staining examined Ki-67 expression in xenograft tumors and western blotting analysis detected expressions of MMP2/9 and Wnt/β-catenin signaling pathway in CC cells and xenograft tumors. Elevated KCNQ1OT1 and HYOU1 as well as reduced miR-296-5p were observed in clinical tumor tissue specimens and CC cell lines. Results revealed that upregulation of miR-296-5p counteracted the enhancing effects of overexpressed KCNQ1OT1 on the proliferative, migrative and invasive abilities of CC cells. Additionally, HYOU1 overexpression abolished the suppressing effects of silenced KCNQ1OT1 on the malignant behaviors of CC cells and tumor growth. To conclude, KCNQ1OT1 could aggravate the malignant behaviors of CC and facilitate tumor growth through modulating miR-296-5p/HYOU1 axis.