Biomarker For Cervical Cancer Research Articles

JAM3 promotes cervical cancer metastasis by activating the HIF-1α/VEGFA pathway.

Cervical cancer is the fourth most common cancer and the leading cause of mortality among women worldwide. Tumor metastasis is an important cause of poor prognosis. Determining the exact mechanisms of metastasis and potential targeted therapies is urgently needed. Junctional adhesion molecule 3 (JAM3) is an important member of the TJ tight junction (TJ) family, and its biological function in cervical cancer needs to be further clarified. We found that JAM3 was highly expressed in cervical cancer patients with lymph node metastasis and that high expression of JAM3 promoted cervical cancer cell metastasis both in vitro and in vivo. In addition, overexpression of JAM3 induces epithelial-mesenchymal transition (EMT). Moreover, silencing JAM3 suppressed cervical cancer cell migration and invasion in vitro. Finally, JAM3 overexpression activated the HIF-1α/VEGFA pathway. In conclusion, our results suggested that JAM3 promotes cervical cancer cell migration and invasion by activating the HIF-1α/VEGFA pathway. JAM3 may be a promising biomarker and effective therapeutic target for cervical cancer.

Determination of the cervical cancer biomarker SCCA using CMK-3@UiO-66@AuNPs composite-modified glassy carbon electrodes

In order to detect the cervical cancer biomarker squamous cell carcinoma antigen (SCCA) in human serum, an electrochemical immunosensor was constructed. Zirconium 1,4-dicarboxybenzene MOF (UiO-66), with a high specific surface area, was coupled with gold nanoparticles (AuNPs), creating the UiO-66@AuNPs nanomaterial. This coupling increased the sensitivity of the electrochemical immunosensor by providing many biological binding sites for antibodies to bind to Au-S bonds. Then, mesoporous carbon (CMK-3) was added to the UiO-66@AuNPs nanomaterial to obtain a CMK-3@UiO-66@AuNPs nanocomposite. This further increased the electron transfer proficiency of the material and enhanced the electrochemical signal. Finally, the sensitive detection of squamous cell carcinoma antigen (SCCA) was made possible by unique immunological responses between antigens and antibodies. Transmission electron microscopy (TEM) analysis of CMK-3@UiO-66@AuNPs revealed embedded surfaces and voids; cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) confirmed the successful assembly of the electrochemical immunosensor. The developed electrochemical immunosensor demonstrated good linearity (R12 = 0.999) from 0.01 pg/mL to 10,000 pg/mL, which is the concentration range of SCCA in human serum. The limit of detection was 0.0034 pg/mL (at an S/N ratio of 3). The immunosensor exhibited excellent precision, stability and selectivity, with a high rate of recovery (98.26–101.8 %). The experimental validation studies demonstrated that the electrochemical immunosensor was effective for the detection of SCCA in human serum, offering a novel method for the early diagnosis and dynamic surveillance of cervical cancer.

Relationship between vaginal and oral microbiome in patients of human papillomavirus (HPV) infection and cervical cancer

BackgroundThe aim of this study was to assess the microbial variations and biomarkers in the vaginal and oral environments of patients with human papillomavirus (HPV) and cervical cancer (CC) and to develop novel prediction models.Materials and methodsThis study included 164 samples collected from both the vaginal tract and oral subgingival plaque of 82 women. The participants were divided into four distinct groups based on their vaginal and oral samples: the control group (Z/KZ, n = 22), abortion group (AB/KAB, n = 17), HPV-infected group (HP/KHP, n = 21), and cervical cancer group (CC/KCC, n = 22). Microbiota analysis was conducted using full-length 16S rDNA gene sequencing with the PacBio platform.ResultsThe vaginal bacterial community in the Z and AB groups exhibited a relatively simple structure predominantly dominated by Lactobacillus. However, CC group shows high abundances of anaerobic bacteria and alpha diversity. Biomarkers such as Bacteroides, Mycoplasma, Bacillus, Dialister, Porphyromonas, Anaerococcus, and Prevotella were identified as indicators of CC. Correlations were established between elevated blood C-reactive protein (CRP) levels and local/systemic inflammation, pregnancy, childbirth, and abortion, which contribute to unevenness in the vaginal microenvironment. The altered microbial diversity in the CC group was confirmed by amino acid metabolism. Oral microbial diversity exhibited an inverse pattern to that of the vaginal microbiome, indicating a unique relationship. The microbial diversity of the KCC group was significantly lower than that of the KZ group, indicating a link between oral health and cancer development. Several microbes, including Fusobacterium, Campylobacter, Capnocytophaga, Veillonella, Streptococcus, Lachnoanaerobaculum, Propionibacterium, Prevotella, Lactobacillus, and Neisseria, were identified as CC biomarkers. Moreover, periodontal pathogens were associated with blood CRP levels and oral hygiene conditions. Elevated oral microbial amino acid metabolism in the CC group was closely linked to the presence of pathogens. Positive correlations indicated a synergistic relationship between vaginal and oral bacteria.ConclusionHPV infection and CC impact both the vaginal and oral microenvironments, affecting systemic metabolism and the synergy between bacteria. This suggests that the use of oral flora markers is a potential screening tool for the diagnosis of CC.

Exploring MiR-484 Regulation by Polyalthia longifolia: A Promising Biomarker and Therapeutic Target in Cervical Cancer through Integrated Bioinformatics and an In Vitro Analysis.

MiR-484, implicated in various carcinomas, holds promise as a prognostic marker, yet its relevance to cervical cancer (CC) remains unclear. Our prior study demonstrated the Polyalthia longifolia downregulation of miR-484, inhibiting HeLa cells. This study investigates miR-484's potential as a biomarker and therapeutic target in CC through integrated bioinformatics and an in vitro analysis. MiR-484 levels were analyzed across cancers, including CC, from The Cancer Genome Atlas. The limma R package identified differentially expressed genes (DEGs) between high- and low-miR-484 CC cohorts. We assessed biological functions, tumor microenvironment (TME), immunotherapy, stemness, hypoxia, RNA methylation, and chemosensitivity differences. Prognostic genes relevant to miR-484 were identified through Cox regression and Kaplan-Meier analyses, and a prognostic model was captured via multivariate Cox regression. Single-cell RNA sequencing determined cell populations related to prognostic genes. qRT-PCR validated key genes, and the miR-484 effect on CC proliferation was assessed via an MTT assay. MiR-484 was upregulated in most tumors, including CC, with DEGs enriched in skin development, PI3K signaling, and immune processes. High miR-484 expression correlated with specific immune cell infiltration, hypoxia, and drug sensitivity. Prognostic genes identified were predominantly epidermal and stratified patients with CC into risk groups, with the low-risk group showing enhanced survival and immunotherapeutic responses. qRT-PCR confirmed FGFR3 upregulation in CC cells, and an miR-484 mimic reversed the P. longifolia inhibitory effect on HeLa proliferation. MiR-484 plays a crucial role in the CC progression and prognosis, suggesting its potential as a biomarker for targeted therapy.

The differential expression of AFF3 in cervical cancer and its correlation with clinicopathological features and prognosis

Background Cervical cancer (CC) is the second most common malignancy in women, and identifying biomarkers of CC is crucial for prognosis prediction. Here, we investigated the expression of AF4/FMR2 Family Member 3 (AFF3) in CC and its association with clinicopathological features and prognosis. Methods Tumour and adjacent tissues, along with clinicopathological features and follow-up information, were collected from 78 patients. AFF3 expression was assessed using quantitative real-time polymerase chain reaction and Western blotting. The correlation between AFF3 expression and CC symptoms was using chi-square test. The 5-year overall survival (OS) was analysed using the Kaplan-Meier method. The Univariate analysis of prognostic risk factors was conducted using the COX proportional hazards model, followed by multivariate COX regression analysis including variables with p < 0.01. Results AFF3 expression was downregulated in CC, and its levels were correlated with lymph node metastasis (LNM) and International Federation of Gynaecology and Obstetrics (FIGO) stage. Patients with low AFF3 expression had a lower 5-year OS rate (52.78%, 19/36). Postoperative survival was reduced in patients with histological grade 3 (G3), myometrial invasion (depth ≥ 1/2), lymphovascular space invasion, LNM, and advanced FIGO stage. Low expression of AFF3 (HR: 2.848, 95% CI: 1.144–7.090) and histological grade G3 (HR: 4.393, 95% CI: 1.663–11.607) were identified as independent prognostic risk factors in CC patients. Conclusion Low expression of AFF3 and histological G3 are independent predictors of poor prognosis in CC patients, suggesting that AFF3 could serve as a potential biomarker for prognostic assessment in CC.

Predictive Value and Immunological Role of the HSPA5 Gene in Cervical Cancer.

Cervical cancer (CC) ranks fourth among women's malignancies worldwide and seriously affects women's health. HSPA5 is a heat shock protein, also known as glucose regulatory protein 78 (GRP78). Upregulation of HSPA5 has been reported to be closely associated with multiple types of tumors. However, the specific role of HSPA5 in cervical cancer has not been discovered. In our study, we explored the prognostic value of HSPA5 in CC. Here, we analyzed the (TCGA) and (UCSC) databases, the analysis of HSPA5 in many tumors types was conducted with the "wilcox. test" method. A False Discovery Rate (FDR) value < 0.05 and Log2 | (fold change, FC) |> 1 were set as the cutoffs. "*", "**", and "***" indicate FDR < 0.05, < 0.01, and < 0.001, respectively, and further used human cervical cancer cells for q-PCR and western blotting detection. q-PCR and western blotting results showed that HSPA5 was highly expressed in cervical cancer cells, while it was expressed at low levels in normal cells (P < 0.05).We also analyzed the immunohistochemical data. immunohistochemical analysis results showed that HSPA5 was highly expressed in human cervical cancer, while it was expressed at low levels in normal tissues (P < 0.05). Analysis in TCGA-UCSC showed that the proportion of G3 in the group with high expression of HSPA5 was relatively high (P < 0.05). Enrichment analysis and survival analysis showed that the increased expression of HSPA5 in cervical cancer was related to the survival of CC and was involved in the regulation of biological behavior and molecular signaling pathways of cervical cancer. The correlation analysis of immune checkpoint and immune infiltration showed that HSPA5 was involved in the regulation of immune process of cervical cancer (P < 0.05). Drug sensitivity correlation analysis showed that HSPA5 was a sensitive target for tumor drugs (P < 0.05). In brief, those results suggest that HSPA5 can act as an oncogene of CC development and can serve as an effective predictive biomarker in cervical cancer.

Progress in Cervical Cancer Biomarkers

Progress in Cervical Cancer Biomarkers

Identification and Verification of Potential Ferroptosis-Related Biomarkers in Cervical Cancer

This study screened important genes contributing to morbidity from differential ferroptosis-related genes (FRGs) in cervical cancer and to establish a risk assessment model with ferroptosis-related LncRNAs. Total RNA sequencing data were extracted from The cancer genome atlas (TCGA), Gene Expression Omnibus (GEO) and Genotype-Tissue Expression (GTEx). By differential analysis, we identified ferroptosis-related hub genes close to prevalence of cervical cancer. According to receiver operator curves (ROC) curves, hub genes have good diagnostic performance. The diagnostic potential of hub genes for occurrence of the disease was further assessed and verified. Further, a risk-assessing model based on ferroptosis-related LncRNAs was established. Finally, the differential expressions of hub genes were verified through qRT-PCR. Five hub genes were identified, and we found through GO, KEGG and immune infiltration, that the hub genes are connection with cervical cancer. The Area Under Curve (AUC) values were all greater than 0.8 in ROC curve, and the hub genes presented differences between disease and control groups in validation set GSE29570. We created a risk assessment model with 16 ferroptosis-related LncRNAs. There was a difference in survival between high-risk and low-risk groups. The AUC result for risk assessment model reached 0.792, and there were significant expression differences of Hub genes in Huvec and Hela cells. The study screened 5 hub genes and constructed the risk-assessment model based on 16 LncRNAs associated with ferroptosis genes.

High-throughput metabolomics identifies new biomarkers for cervical cancer

BackgroundCervical cancer (CC) is a danger to women’s health, especially in many developing countries. Metabolomics can make the connection between genotypes and phenotypes. It provides a wide spectrum profile of biological processes under pathological or physiological conditions.MethodIn this study, we conducted plasma metabolomics of healthy volunteers and CC patients and integratively analyzed them with public CC tissue transcriptomics from Gene Expression Omnibus (GEO).ResultHere, we screened out a panel of 5 metabolites to precisely distinguish CC patients from healthy volunteers. Furthermore, we utilized multi-omics approaches to explore patients with stage I-IIA1 and IIA2-IV4 CC and comprehensively analyzed the dysregulation of genes and metabolites in CC progression. We identified that plasma levels of trimethylamine N-oxide (TMAO) were associated with tumor size and regarded as a risk factor for CC. Moreover, we demonstrated that TMAO could promote HeLa cell proliferation in vitro. In this study, we delineated metabolic profiling in healthy volunteers and CC patients and revealed that TMAO was a potential biomarker to discriminate between I-IIA1 and IIA2-IV patients to indicate CC deterioration.ConclusionOur study identified a diagnostic model consisting of five metabolites in plasma that can effectively distinguish CC from healthy volunteers. Furthermore, we proposed that TMAO was associated with CC progression and might serve as a potential non-invasive biomarker to predict CC substage.ImpactThese findings provided evidence of the important role of metabolic molecules in the progression of cervical cancer disease, as well as their ability as potential biomarkers.

Abstract 4930: Identification of CBX7 and PCDHB18 as novel prognostic biomarkers of cervical cancer: RNA-sequencing and machine learning analysis

Abstract Background- Cervical cancer, ranking fourth in prevalence among women worldwide, is highly preventable when diagnosed early. The advent of innovative technologies including bioinformatics and machine learning, is revolutionizing the discovery and development of novel biomarkers for cancer. Our study uniquely integrates artificial neural networks with RNA-sequencing data of cervical cancer patients. This combination enhances the accuracy and reliability of biomarker predictions, providing a better understanding of the potential clinical utility of the identified biomarkers. Methods- RNA-sequencing data and clinicopathological details of 304 Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) were obtained from the GDAC database (https://gdac.broadinstitute.org/). Differentially expressed genes (DEGs) were identified (P&amp;lt;0.05, |log2 fold change (FC)| &amp;gt; 1.5, false discovery rate (FDR) &amp;lt; 0.05). Pathway enrichment analysis was performed and protein-protein interactions of DEGs were constructed using the STRING database. Prognostic biomarkers were identified using Kaplan-Meier and Cox proportional hazard methods adjusted for potential confounders such as age, disease stage, and comorbidities (HR&amp;lt;1, p&amp;lt;0.05). Deep learning algorithms were applied for predictive marker identification, utilizing Weight by Correlation feature selection. The model used AUC (Area Under the Curve), accuracy, MSE (Mean Squared Error), and R2 (R-squared) as evaluation metrics in a 70-30 training-test split. A combined ROC curve assessed diagnostic biomarkers, validated externally with a GEO dataset on cervical cancer patients. Results- In our study, 4153 DEGs were identified. Pathway analysis revealed that key dysregulated genes play a pivotal role in extracellular matrix organization. The survival analysis identified that six upregulated genes (CT62, SLC7A5P1, C10orf110, FDPSL2A, TNFSF15, and TTLL13) and seven downregulated genes (BAI3, CBX7, DKFZp566F0947, PCDHB18, GRAPL, PCDHB19P, and C4orf38) decreased the overall survival in patients. The machine learning model demonstrated high predictive accuracy (AUC=1, accuracy=99.02%, R2=0.99), identifying twenty genes with a positive correlation to cervical cancer risk. Notably, CBX7 emerged as a prognostic and diagnostic biomarker (AUC=0.99, sensitivity=0.93, specificity=1.00). Conclusion- Our study uncovers the prognostic significance of two novel genes in cervical cancer: CBX7, a vital regulator of tumor suppression, and PCDHB18, a member of the protocadherin beta gene cluster functioning as a cell adhesion molecule. Downregulation of these genes is associated with decreased overall survival. Further functional analyses and validation of these candidate biomarkers are crucial to fully assess their potential clinical value in cervical cancer. Citation Format: Ghazaleh Pourali, Mohsen Zeinali, Mahshid Arastonejad, Nima Khalili-Tanha, Elham Nazari, Ghazaleh Khalili-Tanha, Adetunji T. Toriola. Identification of CBX7 and PCDHB18 as novel prognostic biomarkers of cervical cancer: RNA-sequencing and machine learning analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4930.

Novel biomarkers of inflammation-associated immunity in cervical cancer.

Cervical cancer (CC) is a highly malignant gynecological cancer with a direct causal link to inflammation, primarily resulting from persistent high-risk human papillomavirus (HPV) infection. Given the challenges in early detection and mid to late-stage treatment, our research aims to identify inflammation-associated immune biomarkers in CC. Using a bioinformatics approach combined with experimental validation, we integrated two CC datasets (GSE39001 and GSE63514) in the Gene Expression Omnibus (GEO) to eliminate batch effects. Immune-related inflammation differentially expressed genes (DGEs) were obtained by R language identification. This analysis identified 37 inflammation-related DEGs. Subsequently, we discussed the different levels of immune infiltration between CC cases and controls. Weighted gene co-expression network analysis (WGCNA) identified seven immune infiltration-related modules in CC. We identified 15 immune DEGs associated with inflammation at the intersection of these findings. In addition, we constructed a protein interaction network using the String database and screened five hub genes using "CytoHubba": CXC chemokine ligand 8 (CXCL8), CXC chemokine ligand 10 (CXCL10), CX3C chemokine receptor 1 (CX3CR1), Fc gamma receptors 3B (FCGR3B), and SELL. The expression of these five genes in CC was determined by PCR experiments. In addition, we assessed their diagnostic value and further analyzed the association of immune cells with them. Five inflammation- and immune-related genes were identified, aiming to provide new directions for early diagnosis and mid to late-stage treatment of CC from multiple perspectives.

Calcium-Activated Potassium Channel (KCNMA1) as Biomarker of Pre-Invasive and Invasive Cervical Cancer

Calcium-Activated Potassium Channel (KCNMA1) as Biomarker of Pre-Invasive and Invasive Cervical Cancer

Biomarker cystatin B expression correlates with pathogenesis in cervical cancer.

Cervical cancer (CC) is one of the most common gynecologic malignancies worldwide. Although rapid improvements have been made regarding its prevention and treatment, little is known about disease pathogenesis and the clinical relevance of reliable biomarkers. The present study evaluated the expression of cystatin B (CSTB) as a potential biomarker of CC. Tissue microarray analysis and immunohistochemical staining were performed to detect CSTB expression, while CSTB mRNA and protein expression levels of freshly isolated CC tissue were measured by quantitative real-time PCR and western blot, respectively. Bioinformatics were used to analyze the CSTB co-expression network and functional enrichments. We observed high CSTB mRNA and protein expression levels in CC tissues, which was confirmed by tissue microarray in a comparison with paired adjacent non-cancerous cervical tissue samples. CSTB gene enrichments and associations with co-expressed genes were also observed. Further analysis showed that elevated CSTB expression was associated with pathological progress in CC. Our data demonstrate that CSTB has the potential to be used as a tissue biomarker with clinical value in patients with CC, which may aid the development of intervention strategies.

Advanced detection of cervical cancer biomarkers using engineered filamentous phage nanofibers.

Cervical cancer is a major global health concern, characterized by its high incidence and mortality rates. The detection of tumor markers is crucial for managing cancer, making treatment decisions, and monitoring disease progression. Vascular endothelial growth factor (VEGF) and programmed death-ligand 1 (PDL-1) are key targets in cervical cancer therapy and valuable biomarkers in predicting treatment response and prognosis. In this study, we found that combining the measurement of VEGF and soluble PDL-1 can be used for diagnosing and evaluating the progression of cervical cancer. To explore a more convenient approach for detecting and assessing cervical cancer, we designed and prepared an engineered fd bacteriophage, a human-safe viral nanofiber, equipped with two peptides targeting VEGF and PD-L1. The dual-display phage nanofiber specifically recognizes and binds to both proteins. Utilizing this nanofiber as a novel capture agent, we developed a new enzyme-linked immunosorbent assay (ELISA) method. This method shows significantly enhanced detection sensitivity compared to conventional ELISA methods, which use either anti-VEGF or anti-PD-L1 antibodies as capture agents. Therefore, the phage dual-display nanofiber presents significant potential in detecting cancer markers, evaluating medication efficacy, and advancing immunotherapy drug development. KEY POINTS: • The combined measurement of VEGF and soluble Programmed Death-Ligand 1(sPD-L1) demonstrates an additive effect in the diagnosis of cervical cancer. Fd phage nanofibers have been ingeniously engineered to display peptides that bind to VEGF and PD-L1, enabling the simultaneous detection of both proteins within a single assay • Genetically engineered phage nanofibers, adorned with two distinct peptides, can be utilized for the diagnosis and prognosis of cancer and can be mass-produced cost-effectively through bacterial infections • Employing dual-display fd phage nanofibers as capture probes, the phage ELISA method exhibited significantly enhanced detection sensitivity compared to traditional sandwich ELISA. Furthermore, phage ELISA facilitates the detection of a single protein or the simultaneous detection of multiple proteins, rendering them powerful tools for protein analysis and diagnosis across various fields, including cancer research.

The role of long non-coding RNAs and circular RNAs in cervical cancer: modulating miRNA function.

Cervical cancer (CC) is a primary global health concern, ranking as the fourth leading cause of cancer-related death in women. Despite advancements in prognosis, long-term outcomes remained poor. Beyond HPV, cofactors like dietary deficiencies, immunosuppression, hormonal contraceptives, co-infections, and genetic variations are involved in CC progression. The pathogenesis of various diseases, including cancer, has brought to light the critical regulatory roles of microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). The aberrant expression of these miRNAs, lncRNAs, and circRNAs plays a pivotal role in the initiation and progression of CC. This review provides a comprehensive summary of the recent literature regarding the involvement of lncRNAs and circRNAs in modulating miRNA functions in cervical neoplasia and metastasis. Studies have shown that lncRNAs and circRNAs hold great potential as therapeutic agents and innovative biomarkers in CC. However, more clinical research is needed to advance our understanding of the therapeutic benefits of circRNAs and lncRNAs in CC.

Screening and Identification of Prognostic Biomarkers of Cervical Cancer Based on Bioinformatics Analysis

Screening and Identification of Prognostic Biomarkers of Cervical Cancer Based on Bioinformatics Analysis

Identification of prognostic biomarkers for cervical cancer based on programmed cell death-related genes and assessment of their immune profile and response to drug therapy.

Programmed cell death (PCD) has been widely investigated in various human diseases. The present study aimed to identify a novel PCD-related genetic signature in cervical squamous cell carcinoma (CESC) to provide clues for survival, immunotherapy and drug sensitization prediction. Single-sample gene set enrichment analysis (ssGSEA) was used to quantify the PCD score and assess the distribution of PCD in clinicopathological characteristics in The Cancer Genome Atlas (TCGA)-CESC samples. Then, the ConsensusClusterPlus method was used to identify molecular subtypes in the TCGA-CESC database. Genomic mutation analysis, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment, as well as tumor microenvironment (TME) infiltration analysis, were performed for each molecular subtype group. Finally, a prognostic model by Uni-Cox and least absolute shrinkage and selection operator-Cox analysis was established based on differentially expressed genes from molecular subtypes. ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) and ssGSEA were performed to assess the correlation between the model and TME. Drug sensitization prediction was carried out with the oncoPredict package. Preliminary analysis indicated that PCD had a potential association clinical characteristics of the TCGA-CESC cohort, and PCD-related genes mutated in 289 (70.59%) CESC patients. Next, four groups of CESC molecular typing were clustered based on 63 significantly prognostic PCD-related genes. Among four subtypes, C1 group displayed the worst prognosis combined with over expressed PCD genes and enriched cell cycle-related pathways. C4 group exhibited the best prognosis accompanied with high degree of immune infiltration. Finally, a five-gene (SERPINE1, TNF, CA9, CX3CL1 and JAK3) prognostic model was constructed. Patients in the high-risk group displayed unfavorable survival. Immune infiltration analysis found that the low-risk group had significantly higher levels of immune cell infiltration such as T cells, Macrophages_M1, relative to the high-risk group, and were significantly enriched in apoptosis-associated pathways, which predicted a higher level of immunity. Drug sensitivity correlation analysis revealed that the high-risk group was resistant to conventional chemotherapeutic drugs and sensitive to the Food and Drug Administration-approved drugs BI.2536_1086 and SCH772984_1564. In the present study, we first found that PCD-related gene expression patterns were correlated with clinical features of CESC patients, which predicts the feasibility of subsequent mining of prognostic features based on these genes. The five-PCD-associated-gene prognostic model showed good assessment ability in predicting patient prognosis, immune response and drug-sensitive response, and provided guidance for the elucidation of the mechanism by which PCD affects CESC, as well as for the clinical targeting of drugs.

Proteomic analysis of small extracellular vesicles unique to cervical cancer.

Cervical cancer (CC) is the fourth most common cancer in females worldwide. Existing biomarkers for CC, such as squamous cell carcinoma antigens, show low specificity. Hence, a novel biomarker for the diagnosis of CC is required. Through proteomic analysis, this study aimed to distinguish between the small extracellular vesicle (sEV) protein profiles of healthy controls (HC) and CC sera and to identify potential sEV proteins that can serve as biomarkers for CC diagnosis. The number and size distribution of sEVs in HC and CC sera were measured using nanoparticle tracking analysis. Differential ultracentrifugation combined with size-exclusion chromatography was used to isolate and purify sEVs. Liquid chromatography-tandem mass spectrometry was used to identify and compare the protein profiles between patients with CC and HC. Differentially expressed extracellular vesicle (EV) proteins were validated using The Cancer Genome Atlas database. The EV particle concentration in patients with CC was marginally higher than that in HC. Proteomic and functional protein analyses revealed a difference in the EV protein profiles between HC and CC and identified proteins that can serve as biomarkers for CC. This study provides insights into the potential of sEVs as less invasive biomarkers for CC diagnosis. Validation with a well-designed cohort should be performed to determine the clinical diagnostic value of specific protein markers for CC.

RPL24 as a potential prognostic biomarker for cervical cancer treated by Cisplatin and concurrent chemoradiotherapy.

Cervical carcinoma (CC) is the one of most common gynecologic cancers worldwide. The ribosomal proteins (RPs) are essential for ribosome assembly and function, and it has been verified that the abnormal expression of RPs was closely associated with tumorigenesis. In this study, we found that the RP large subunit 24 (RPL24) expression level was upregulated after the CC cell lines SiHa and HeLa were treated with Cisplatin (CDDP) in vitro. Simultaneously, a nude mouse xenograft model was used to examine the effect of RPL24 on tumor growth in vivo, which showed that overexpression of RPL24 can suppress tumor growth. Furthermore, we proved that RPL24 expression increased after CC patients were treated with concurrent chemoradiotherapy (CCRT), and the higher expression of RPL24 predicted a better prognosis using clinical data from 40 CC patients, verified via the Kaplan-Meier Plotter and LOGpc. These results revealed that RPL24 can be considered a potential biomarker to predict the prognosis of CC patients and assess CCRT efficacy.

Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers.

Cervical cancer (CC) is one of the world's most common and severe cancers. This cancer includes two histological types: squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The current study aims at identifying novel potential candidate mRNA and miRNA biomarkers for SCC based on a protein-protein interaction (PPI) and miRNA-mRNA network analysis. The current project utilized a transcriptome profile for normal and SCC samples. First, the PPI network was constructed for the 1335 DEGs, and then, a significant gene module was extracted from the PPI network. Next, a list of miRNAs targeting module's genes was collected from the experimentally validated databases, and a miRNA-mRNA regulatory network was formed. After network analysis, four driver genes were selected from the module's genes including MCM2, MCM10, POLA1, and TONSL and introduced as potential candidate biomarkers for SCC. In addition, two hub miRNAs, including miR-193b-3p and miR-615-3p, were selected from the miRNA-mRNA regulatory network and reported as possible candidate biomarkers. In summary, six potential candidate RNA-based biomarkers consist of four genes containing MCM2, MCM10, POLA1, and TONSL, and two miRNAs containing miR-193b-3p and miR-615-3p are opposed as potential candidate biomarkers for CC.