Prognostic Biomarker For Cervical Cancer Research Articles

HPV-ferroptosis related genes as biomarkers to predict the prognosis of cervical cancer

BackgroundFerroptosis can be used as a powerful predictor of cancer prognosis. HPV persistent infection is the main cause of cervical cancer, so it is very important to improve the prognosis of patients. Therefore, it is necessary to explore the value of HPV-ferroptosis related genes as prognostic biomarkers of cervical cancer patients.MethodsIn this study, differentially expressed HPV-ferroptosis related genes were obtained from GSE7410, HPV gene set crossed with iron death genes. Five HPV-ferroptosis related genes with prognostic features were finally identified: CYBB, VEGFA, CKB, EFNA1 and HELLS. Multifactorial Cox regression was applied to establish and validate the prognostic model, and drug susceptibility and immune infiltration analyses were also performed.ResultsThe prognostic model was validated in the training set (TCGA) and validation set (GSE44001). Kaplan–Meier curves reveal significant differences in overall survival (OS) between high-risk and low-risk groups. Receiver operating characteristic (ROC) curve reflects the stability and accuracy of the prognostic model established in this study. In terms of immune function, T cell costimulation was better in the low-risk group than in the high-risk group (P < 0.01). The therapeutic effects of cisplatin, paclitaxel, docetaxel and cyclophosphamide, commonly used chemotherapy drugs for cervical cancer, are better in the high-risk group than in the low-risk group (P < 0.001).ConclusionHPV-ferroptosis related gene prognostic model not only has good stability and accuracy in predicting the prognosis of cervical cancer patients, but also has certain guiding value for clinicians in terms of drug sensitivity and immune microenvironment.

SQLE—a promising prognostic biomarker in cervical cancer: implications for tumor malignant behavior, cholesterol synthesis, epithelial-mesenchymal transition, and immune infiltration

BackgroundCervical cancer, encompassing squamous cell carcinoma and endocervical adenocarcinoma (CESC), presents a considerable risk to the well-being of women. Recent studies have reported that squalene epoxidase (SQLE) is overexpressed in several cancers, which contributes to cancer development.MethodsRNA sequencing data for SQLE were obtained from The Cancer Genome Atlas. In vitro experiments, including colorimetry, colony formation, Transwell, RT-qPCR, and Western blotting were performed. Furthermore, a transplanted CESC nude mouse model was constructed to validate the tumorigenic activity of SQLE in vivo. Associations among the SQLE expression profiles, differentially expressed genes (DEGs), immune infiltration, and chemosensitivity were examined. The prognostic value of genetic changes and DNA methylation in SQLE were also assessed.ResultsSQLE mRNA expression was significantly increased in CESC. ROC analysis revealed the strong diagnostic ability of SQLE toward CESC. Patients with high SQLE expression experienced shorter overall survival. The promotional effects of SQLE on cancer cell proliferation, metastasis, cholesterol synthesis, and EMT were emphasized. DEGs functional enrichment analysis revealed the signaling pathways and biological processes. Notably, a connection existed between the SQLE expression and the presence of immune cells as well as the activation of immune checkpoints. Increased SQLE expressions exhibited increased chemotherapeutic responses. SQLE methylation status was significantly associated with CESC prognosis.ConclusionSQLE significantly affects CESC prognosis, malignant behavior, cholesterol synthesis, EMT, and immune infiltration; thereby offering diagnostic and indicator roles in CESC. Thus, SQLE can be a novel therapeutic target in CESC treatment.

Methylation-related differentially expressed genes as potential prognostic biomarkers for cervical cancer

AimTo discover novel methylation-related differentially expressed genes (MRDEGs) for cervical cancer, with a focus on their potential for early diagnosis and prognostic assessment. Materials & methodsWe integrated data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. TCGA-MRDEGs were identified by analyzing differentially methylated genes (DMGs) and their correlation with gene expression. We examined GEO datasets GSE39001, GSE9750, and GSE46306 for GEO-MRDEGs. Overlapping MRDEGs were subjected to overall survival (OS) analysis to identify prognostic markers. The expression and methylation levels of these genes were validated in a total of 30 tissue samples, comprising 20 from cervical cancer patients and 10 from normal cervical tissues, using qRT-PCR and MassARRAY EpiTYPER Assay. ResultsA total of 314 TCGA-MRDEGs and 40 GEO-MRDEGs were identified. Intersection analysis yielded 10 overlapping MRDEGs. Notably, NOVA1, GSTM5, TRHDE, and CXCL12 were found to have reduced expression and increased methylation in cervical cancer, which correlated with poor prognosis. The methylation status and expression levels of these genes were confirmed in tissue specimens. ConclusionWe identified four MRDEGs as potential prognostic biomarkers for cervical cancer. Their clinical utility is highlighted, but further validation in larger cohorts is required to establish their clinical significance.

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.

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.

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.

SERPINB7 as a prognostic biomarker in cervical cancer: Association with immune infiltration and facilitation of the malignant phenotype

PurposeThe purpose of this study was to investigate the expression patterns, predictive significance, and roles in the immune microenvironment of Serpin Family-B Member 7 (SERPINB7) in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). MethodsThe expression of SERPINB7 and its prognostic relevance were evaluated using RNA-seq data from The Cancer Genome Atlas. SERPINB7 regulation of CESC cell growth and metastasis was investigated using MTT, scratch, and Transwell assays. In vivo effects of SERPINB7 were examined in xenograft model mice and differentially expressed genes (DEGs) associated with SERPINB7 were identified to explore its functional role in oncogenesis. Associations between SERPINB7 levels, chemosensitivity, and immune infiltration were assessed, and mutations and methylation of SERPINB7 were evaluated using the cBioPortal and MethSurv databases, respectively. ResultsSERPINB7 was up-regulated in CESC samples as well as in other tumors, and patients with higher SERPINB7A mRNA levels exhibited shorter overall survival. The area under the curve for the use of SERPINB7 in CESC diagnosis was above 0.9, and the gene was shown to regulate tumor cell proliferation and metastasis in vitro and in vivo. Overall, 398 DEGs enriched in key CESC progression-related signaling pathways were identified. SERPINB7 expression was additionally correlated with intratumoral immune infiltration and immune checkpoint activity. Patients expressing higher SERPINB7 levels exhibited distinct chemosensitivity profiles, and methylation of the SERPINB7 gene was linked to CESC patient prognostic outcomes. ConclusionSERPINB7 was found to be a crucial regulator of CESC progression, prognosis, and the tumor immune microenvironment, highlighting its potential as a diagnostic and prognostic biomarker and target for CESC immunotherapy.

In Silico Identification of Potential Quadruplex Forming Sequences in LncRNAs of Cervical Cancer.

Long non-coding RNAs (lncRNAs) have emerged as auxiliary regulators of gene expression influencing tumor microenvironment, metastasis and radio-resistance in cancer. The presence of lncRNA in extracellular fluids makes them promising diagnostic markers. LncRNAs deploy higher-order structures to facilitate a complex range of functions. Among such structures, G-quadruplexes (G4s) can be detected or targeted by small molecular probes to drive theranostic applications. The in vitro identification of G4 formation in lncRNAs can be a tedious and expensive proposition. Bioinformatics-driven strategies can provide comprehensive and economic alternatives in conjunction with suitable experimental validation. We propose a pipeline to identify G4-forming sequences, protein partners and biological functions associated with dysregulated lncRNAs in cervical cancer. We identified 17 lncRNA clusters which possess transcripts that can fold into a G4 structure. We confirmed in vitro G4 formation in the four biologically active isoforms of SNHG20, MEG3, CRNDE and LINP1 by Circular Dichroism spectroscopy and Thioflavin-T-assisted fluorescence spectroscopy and reverse-transcriptase stop assay. Gene expression data demonstrated that these four lncRNAs can be potential prognostic biomarkers of cervical cancer. Two approaches were employed for identifying G4 specific protein partners for these lncRNAs and FMR2 was a potential interacting partner for all four clusters. We report a detailed investigation of G4 formation in lncRNAs that are dysregulated in cervical cancer. LncRNAs MEG3, CRNDE, LINP1 and SNHG20 are shown to influence cervical cancer progression and we report G4 specific protein partners for these lncRNAs. The protein partners and G4s predicted in lncRNAs can be exploited for theranostic objectives.

TMEM33 as a Prognostic Biomarker of Cervical Cancer and Its Correlation with Immune Infiltration.

In women all over the world, cervical cancer (CC) ranks as the fourth most common form of cancer to be diagnosed. It was previously reported that transmembrane protein 33(TMEM33) could report a poor prognosis in several cancers. The current study is aimed at investigating the potential prognostic value of TMEM33 and its relevance to the tumor microenvironment in CC in a comprehensive manner. In this study, CC specimens presented noticeably higher TMEM33 expression level in comparison to nontumor specimens. In pan-cancer assays, it was found that TMEM33 was present at a high level in many different kinds of tumors. We found that patients with CC patients who had a high TMEM33 expression presented worse overall survival (OS) and disease-free survival (DFS) relative to patients who had a low TMEM33 expression. According to the results of a multivariate analysis, a high level of TMEM33 expression can significantly and independently predict the prognosis of CC. The levels of TMEM33 were found to have a negative correlation with resting dendritic cells, resting mast cells, plasma cells, T cells CD8, T cells regulatory, and regulatory T cells. Finally, we confirmed that TMEM33 was overexpressed in CC cells, and its knockdown distinctly suppressed the proliferation and invasion of CC cells. Overall, we provided evidences that TMEM33 could be used as a potential biomarker to assess the prognosis and the level of immune infiltration in CC.

The pan cancer analysis identified DIAPH3 as a diagnostic biomarker of clinical cancer.

This study aimed to determine prognostic biomarkers of cervical cancer by pan-cancer analysis. Common differentially expressed genes in Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) database were demonstrated using R software analysis, and these genes were enriched by the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology. Genes with prognostic value were identified by least absolute contraction and selection regression, Cox regression, and survival analysis, and pan-cancer analysis was conducted using the Tumor Immune Estimation Resource database and TCGA database. Western blot, qRT-PCR, and immunohistochemistry were used to preliminarily verify its expression in cervical cancer (S1). The prognostic marker Diaphanous Related Formin 3 (DIAPH3) was obtained from us. The enrichment analysis revealed that DIAPH3 was involved in tumor proliferation, invasion, and inflammation. The pan-cancer analysis revealed that it was highly expressed in various cancers. Immune infiltration analysis revealed that its expression was related to B cells, effector T cells, and macrophage infiltration; however, immune checkpoint correlation analysis and tumor mutation burden analysis revealed the correlation between gene expression and immunotherapy. The expression of DIAPH3 in cervical cancer was significantly different from that in normal cervical tissues. The expression of DIAPH3 in cervical cancer was significantly increased, which may be related to the proliferation, metastasis, immune invasion, and immunotherapy of cervical cancer.

CCT3 as a Diagnostic and Prognostic Biomarker in Cervical Cancer.

The chaperonin-containing TCP1 complex subunit 3 (CCT3) has been reported to be involved in the development and prognosis of many tumors, including cervical cancer (CC). This study aimed to analyze the expression and prognostic value of CCT3 in CC by bioinformatics and retrospective study. CCT3 gene expression profiles and clinical information in CC were downloaded from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. CCT3 expression was verified by quantitative real-time polymerase chain reaction (RT-qPCR), Western blot, and immunohistochemistry (IHC). Logistic regression and chi-square testing were used to analyze the relationship between CCT3 expression and the clinical characteristics of CC. Kaplan-Meier and Cox analyses were used to evaluate whether CCT3 affects the prognosis of CC. Nomogram and calibration curves were used to test the predictive value of CCT3. The expression of CCT3 in CC tissues was significantly upregulated compared with that in adjacent benign tissues, and was related to HPV16/18 infection, grade, and positive lymph nodes. High expression of CCT3 is associated with poor prognosis of CC and can be used as an independent risk factor for CC. The prognostic model based on CCT3 and CC clinical features has good predictive ability. CCT3 is overexpressed in CC, which is related to poor prognosis and expected to become a biomarker for CC.

Direct Detection of MiRNAs miR-34a, -145, and -218 with CRISPR/Cas13a-nuclease

Using CRISPR/Cas13a-nuclease we have demonstrated a feasibility of direct detection of three miRNAs, miR-34a, -145, and -218 (their molecular signature is suggested as a diagnostic and prognostic biomarker in cervical cancer),. The detection is based on registration of a cleavage of molecular reporters bearing a fluorophore and a quencher by the complex of CRISPR/Cas13a-nuclease and guide RNA (gRNA) with a spacer of 21-23 nucleotides long. The detection sensitivity varied among miRNAs tested by 10-fold, presumably due to the unwanted intramolecular partial base paring of gRNA. The miRNA detection with Cas13a nuclease strongly depended on the presence of background RNA thus potentially compromising its direct application to complex media in a general case. Further optimization of measurement conditions including probably an additional amplification of the signal generated by collateral activity of Cas13a nuclease is necessary to directly detect miR-34a, -145, and -218 in biological samples.

Targeting the Transforming Growth Factor-beta Signaling Pathway in the Treatment of Gynecologic Cancer.

The transforming growth factor-beta (TGF-β) signaling pathway has been reported to be dysregulated in the pathogenesis of several malignancies, including gynecologic cancers. This provides proof of concept of its potential value as a therapeutic target and prognostic biomarker in cervical cancer. Here we provide an overview of the biological role and clinical impact of TGF-β inhibitors either as a single agent or as a combinatorial therapy in gynecological cancers, concentrating on phase I to phase II/III clinical trials. Aberrant TGF-β signaling may lead to carcinogenesis. Inhibition of TGF-β represents an interesting area of focus for the treatment of gynecological cancer. Several TGF-β inhibitors are potential anticancer agents and are undergoing clinical trials in cancer, including galunisertib, dalantercept, and vigil. There is a growing body of data showing the potential therapeutic impact of targeting the TGF-β pathway in different cancer types, although further studies are still warranted to explore the value of this strategy and finding the most appropriate patients who could most benefit from therapy.

A novel ten-gene prognostic signature for cervical cancer based on CD79B-related immunomodulators.

The identification of immune-related prognostic biomarkers opens up the possibility of developing new immunotherapy strategies against tumors. In this study, we investigated immune-related biomarkers in the tumor microenvironment to predict the prognosis of cervical cancer (CC) patients. ESTIMATE and CIBERSORT algorithms were used to calculate the abundance of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal components in cervical samples (n = 309) from The Cancer Genome Atlas. Ten immune-related differentially expressed genes associated with CC survival were identified via intersection analyses of multivariate Cox regression and protein-protein interactions. CD79B was chosen for further study, and its prognostic value and role in anti-CC immune functions were analyzed. Differential expression analysis and qRT-PCR validation both revealed that CD79B expression was down-regulated in CC tissues. Survival analysis suggested that a high level of CD79B expression was associated with good prognosis. In the clinical correlation analysis, CD79B expression was found to be related to primary therapy outcome, race, histological type, degree of cell differentiation, disease-specific survival, and progression-free interval. GSEA showed that the function and pathway of CD79B were mainly related to immune activities. Meanwhile, CD79B expression was correlated with 10 types of TICs. Based on CD79B-associated immunomodulators, a novel immune prognostic signature consisting of 10 genes (CD96, LAG3, PDCD1, TIGIT, CD27, KLRK1, LTA, PVR, TNFRSF13C, and TNFRSF17) was established and validated as possessing good independent prognostic value for CC patients. Finally, a nomogram to predict personalized 3- and 5-year overall survival probabilities in CC patients was built and validated. In summary, our findings demonstrated that CD79B might be a potential prognostic biomarker for CC. The 10-gene prognostic signature independently predicted the overall survival of patients with CC, which could improve individualized treatment and aid clinical decision-making.

MiR-95-3p acts as a prognostic marker and promotes cervical cancer progression by targeting VCAM1.

Cervical cancer patients have a high risk of metastasis and a poor prognosis with shorter disease-free survival. Thus, novel biomarkers and feasible therapies urgently need to be discovered. Previous studies have shown that miR-95-3p plays crucial roles in several cancer types. However, the roles of miR-95-3p in cervical cancer remain unknown. The micro ribonucleic acid (miRNA) expression data and clinical characteristics of cervical cancer samples were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses were conducted to identify the prognostic-related miRNAs. The potential target genes of miR-95-3p were predicted by the TargetScan database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to explore the target gene of miR-95-3p. The effects of miR-95-3p inhibition and overexpression on cell proliferation were inspected by cell counting kit-8 (CCK-8) assays and cell colony formation assays. Wound-healing assays and transwell assays were also used to examine cell migration ability in HeLa and SiHa cells. MiR-95-3p was the only miRNA significantly associated with the poor prognosis of cervical squamous cell carcinoma. A further analysis suggested that vascular cell adhesion molecule 1 (VCAM1) is a target gene of miR-95-3p in cervical cancer, and miR-95-3p promotes the malignant behavior of cervical cancer cells by inhibiting the expression of VCAM1. The CCK-8 and cell colony assays showed that miR-95-3p downregulation significantly suppressed cell proliferation in the HeLa and SiHa cells. The transwell and wound-healing assays showed that miR-95-3p inhibition suppressed cell migration in the HeLa and SiHa cells. Further the Western blot analysis and the quantitative real-time-polymerase chain reaction (qRT-PCR) showed that the knockdown of miR-95-3p in HeLa cells resulted in increased VCAM1 expression. And VCAM1 was highly expressed in the paired adjacent normal cervical epithelium tissue samples, but lowly expressed in the cervical tumor tissue samples. Our study was the first to show that miR-95-3p could serve as a prognostic biomarker of cervical cancer. Mechanistically, we discovered that miR-95-3p inhibited the expression of the cell adhesion molecule VCAM1 and thus promoted further tumor progression.

Low Level FLT3LG is a Novel Poor Prognostic Biomarker for Cervical Cancer with Immune Infiltration.

IntroductionThe FMS-related tyrosine kinase 3 (FLT3) ligand (FLT3LG), a growth factor, binds to FLT3 on dendritic cell (DCs) to enhance their differentiation and expansion. It has shown great potential as an immunotherapy target for cancers. However, the expression and function of FLT3LG in cervical cancer remain largely unknown.Materials and MethodsIn this study, we obtained the expression of FLT3LG, the clinical prognosis in cervical cancer, via multiple databases, including The Cancer Genome Atlas (TCGA), the TISIDB database, and Tumor Immune Estimate Resource (TIMER). The results were further investigated using real-time quantitative PCR (qPCR) cytology specimens in 489 patients. Furthermore, Kaplan-Meier Cox regression and prognostic nomogram analyses were used to assess FLT3LG’s clinical significance in cervical cancer patients. All calculations used the R package.ResultsAs a result, FLT3LG expression decreased in cervical cancer compared with standard samples. And the low expression of FLT3LG was associated with a poor prognosis. Furthermore, Receiver Operating Characteristics (ROC) analysis indicated that FLT3LG might serve as a valuable diagnostic biomarker for cervical cancer. Additionally, it indicated that the FLT3LG had the highest odds ratio (OR=10.519; (7.371–27.071)) for detecting CIN 2+. In addition, our result also demonstrated that expression of FLT3LG was closely related to immune cells, immune inhibitors, immunostimulators, receptors, and chemokines in CESC.ConclusionResearch on FLT3LG provided insight into its critical function. Hence, the low expression of FLT3LG may be a valuable biomarker in CESC patients linked with immune infiltration.

LncRNA LAMTOR5-AS1 sponges miR-210-3p and regulates cervical cancer progression.

Cervical cancer has attracted increasing attention in recent years, and the incidence has shown a trend of younger age. Therefore, it is an effective method to regulate the progression of cervical cancer through new prognostic biomarkers. The purpose of this study was to evaluate the potential of lncRNA LAMTOR5-AS1 (LAMTOR5-AS1) as a prognostic biomarker and reveal its regulatory role in cervical cancer. A total of 120 patients with cervical cancer were selected as research subjects to verify the prognostic effect of LAMTOR5-AS1 in a series of experiments. The expression of LAMTOR5-AS1 in cervical cancer tissues and cells was determined by polymerase chain reaction assay. The proliferation, migration, and invasion ability of cervical cancer cells were evaluated by Cell Counting Kit-8 (CCK-8) and Transwell assay. Luciferase reporter gene detection was used to determine the mechanism of LAMTOR5-AS1 targeting miR-210-3p, and to reflect the prognostic value of LAMTOR5-AS1 according to statistical methods. LAMTOR5-AS1 decreased in cervical cancer tissues, while miR-210-3p expression increased. In the study of cervical cancer cells, it was found that the LAMTOR5-AS1 sponge miR-210-3p was associated with the malignant progression of cervical cancer. Overexpression of LAMTOR5-AS1 could effectively inhibit the development of cervical cancer cells and might be chosen as a prognostic biomarker of cervical cancer. LAMTOR5-AS1 sponges miR-210-3p and modulates the progression of cervical cancer, which predict the prognosis of cervical cancer patients.

O-Glycosylating Enzyme GALNT2 Predicts Worse Prognosis in Cervical Cancer.

Identification of novel biomarkers is helpful for the diagnosis and treatment of cervical cancer. Mucin glycosylating enzyme GALNT2 modulates mucin O-glycosylation, and has been revealed as a regulator of tumorigenesis in various cancers. However, the expression pattern of GALNT2 in cervical cancer is still unclear. In this study, we demonstrated that the mRNA expression and protein level of GALNT2 were increased in cervical high-grade intraepithelial neoplasia and tumor tissues compared with normal cervix tissues. Kaplan-Meier plotter showed that overexpression of GALNT2 was associated with worse overall survival in TCGA cohort (p < 0.001, HR = 2.65, 95% CI = 1.62–4.34) and poor disease free survival in GSE44001 cohort (p = 0.0218, HR = 2.15, 95% CI = 1.14–4.06). In addition, GSEA analysis showed that various immune-related pathways were closely related to the expression of GALNT2 in cervical cancer. Moreover, co-expression of GALNT2 and IL1A, IL1B, IL11, CXCL1, CXCL2, CXCL5, CXCL6, CXCR1, or CCR3 predicted poor overall survival, and the expression of GALNT2 also affected the prognostic value of CD47, CD274, CD276, CSF1R, TNFSF9, and TNFSF11 in cervical cancer patients. These findings suggest that GALNT2 might be used as a prognostic biomarker in cervical cancer.

Comprehensive analyses identify RIPOR2 as a genomic instability-associated immune prognostic biomarker in cervical cancer

Cervical cancer (CC) is a malignancy that tends to have a poor prognosis when detected at an advanced stage; however, there are few studies on the early detection of CC at the genetic level. The tumor microenvironment (TME) and genomic instability (GI) greatly affect the survival of tumor patients via effects on carcinogenesis, tumor growth, and resistance. It is necessary to identify biomarkers simultaneously correlated with components of the TME and with GI, as these could predict the survival of patients and the efficacy of immunotherapy. In this study, we extracted somatic mutational data and transcriptome information of CC cases from The Cancer Genome Atlas, and the GSE44001 dataset from the Gene Expression Omnibus database was downloaded for external verification. Stromal components differed most between genomic unstable and genomic stable groups. Differentially expressed genes were screened out on the basis of GI and StromalScore, using somatic mutation information and ESTIMATE methods. We obtained the intersection of GI- and StromalScore-related genes and used them to establish a four-gene signature comprising RIPOR2, CCL22, PAMR1, and FBN1 for prognostic prediction. We described immunogenomic characteristics using this risk model, with methods including CIBERSORT, gene set enrichment analysis (GSEA), and single-sample GSEA. We further explored the protective factor RIPOR2, which has a close relationship with ImmuneScore. A series of in vitro experiments, including immunohistochemistry, immunofluorescence, quantitative reverse transcription PCR, transwell assay, CCK8 assay, EdU assay, cell cycle detection, colony formation assay, and Western blotting were performed to validate RIPOR2 as an anti-tumor signature. Combined with integrative bioinformatic analyses, these experiments showed a strong relationship between RIPOR2 with tumor mutation burden, expression of genes related to DNA damage response (especially PARP1), TME-related scores, activation of immune checkpoint activation, and efficacy of immunotherapy. To summarize, RIPOR2 was successfully identified through comprehensive analyses of the TME and GI as a potential biomarker for forecasting the prognosis and immunotherapy response, which could guide clinical strategies for the treatment of CC patients.