Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis

Predictors of para-aortic lymph node metastasis based on pathological diagnosis via surgical staging in patients with locally advanced cervical cancer: A multicenter study

Para-aortic lymph node (PALN) metastasis of patients with locally advanced cervical cancer (LACC) is associated with multiple risk factors. This study aimed to identify risk factors and develop a predictive model for PALN metastasis based on the pathological diagnosis via surgical staging to determine the patient-population suitable for extended-field irradiation (EFRT) and clarify the prognosis of patients with LACC. Five parameters were identified as predictors by logistic regression analysis. The predictive model was displayed as a nomogram and then modified into a simple scoring system. The concordance indices for the prediction nomogram were 0.939 in the training cohort, and 0.954 in the validation cohort, respectively. The scoring system consisted of tumor size, histological type, number of pelvic lymph nodes (PLNs), common iliac lymph node, and shorter diameter of the largest PLN. With a cutoff value of 8 points, the sensitivity and specificity of the predictive model were 91.04 % and 85.37 %, respectively, in the training cohort, and 89.47 % and 84.68 %, respectively, in the validation cohort. Using this system, patients were divided into high- and low-risk groups. Patients in the high-risk group showed a greater likelihood of PALN metastasis and worse PFS and OS than those in the low-risk group. The predictive model displays promise for the pathological diagnosis of PALN via surgical staging, offering good accuracy. It provides a non-invasive, practical tool to guide precise radiation strategy and stratify prognosis of patients with LACC.

Integrated analysis of N6-methyladenosine- and 5-methylcytosine-related long non-coding RNAs for predicting prognosis in cervical cancer

BACKGROUND

N6-methyladenosine (m6A) and 5-methylcytosine (m5C) play a role in modifying long non-coding RNAs (lncRNAs) implicated in tumorigenesis and progression. This study was performed to evaluate prognostic value of m6A- and m5C-related lncRNAs and develop an efficient model for prognosis prediction in cervical cancer (CC).

METHODS

Using gene expression data of TCGA set, we identified m6A- and m5C-related lncRNAs. Consensus Clustering Analysis was performed for samples subtyping based on survival-related lncRNAs, followed by analyzing tumor infiltrating immune cells (TIICs). Optimal signature lncRNAs were obtained using lasso Cox regression analysis for constructing a prognostic model and a nomogram to predict prognosis.

RESULTS

We built a co-expression network of 23 m6A-related genes, 15 m5C-related genes, and 62 lncRNAs. Based on 9 m6A- and m5C-related lncRNAs significantly associated with overall survival (OS) time, two molecular subtypes were obtained, which had significantly different OS time and fractions of TIICs. A prognostic model based on six m6A- and m5C-related signature lncRNAs was constructed, which could dichotomize patients into two risk subgroups with significantly different OS time. Prognostic power of the model was successfully validated in an independent dataset. We subsequently constructed a nomogram which could accurately predict survival probabilities. Drug sensitivity analysis found preferred chemotherapeutic agents for high and low-risk patients, respectively.

CONCLUSION

Our study reveals that m6A- and m5C-related lncRNAs are associated with prognosis and immune microenvironment of CC. The m6A- and m5C-related six-lncRNA signature may be a useful tool for survival stratification in CC and open new avenues for individualized therapies.

Novel biomarkers of inflammation-associated immunity in cervical cancer

Background

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.

Methods

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.

Results

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.

Conclusions

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.

A cellular senescence-related genes model allows for prognosis and treatment stratification of cervical cancer: a bioinformatics analysis and external verification

BACKGROUND

Cervical cancer (CC) is highly lethal and aggressive with an increasing trend of mortality for females. Molecular characterization-based methods hold great promise for improving the diagnostic accuracy and for predicting treatment response.

METHODS

The mRNAs expression data of CC patients and cellular senescence-related genes were obtained from the Cancer Genome Atlas (TCGA) and CellAge databases, respectively. Differentially expressed genes (DEGs) of senescence related genes between tumor and normal tissues were used for Least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model. Univariate and LASSO regression analyses were applied to establish a predictive nomogram. The performance of the nomogram were evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell's concordance index (C-index), and calibration curve. GSE44001 and GSE52903 were used for external validation.

RESULTS

We established a cellular senescence-related genes-based stratified model, and a multivariable-based nomogram, which could accurately predict the prognosis of CC patients in the TCGA database. The Kaplan-Meier curve indicated that patients in the low-risk group had considerably better overall survival (OS, P =2.021e-05). The area under the ROC curve (AUC) of this model was 0.743 for OS. Multivariate analysis found that the 6-gene risk signature (HR=3.166, 95%CI: 1.660-6.041, P<0.001) was an independent risk factor for CC patients. We then designed an OS-associated nomogram that included the risk signature and clinicopathological factors. The AUC reached 0.860 for predicting 5-year OS. The nomogram showed excellent consistency between the predictions and actual survival observations. Two external GEO validations were corresponding to the gene expression pattern in TCGA.

CONCLUSIONS

Our results suggested a six-senescence related signature and established a prognostic nomogram that reliably predicted the overall survival for CC. These findings may be beneficial to personalized treatment and medical decision-making.

Construction of a prognostic signature of RFC5 immune-related genes in patients with cervical cancer

BACKGROUND

Cervical cancer (CC) is a malignant tumor threatening women's health. Replication factor C (RFC) 5 is significantly highly expressed in CC tissues, and the immune microenvironment plays a crucial role in tumor initiation, progression, and metastasis.

OBJECTIVE

To determine the prognostic role of RFC5 in CC, analyze the immune genes significantly associated with RFC5, and establish a nomogram to evaluate the prognosis of patients with CC.

METHODS

High RFC5 expression in patients with CC was analyzed and verified through TCGA GEO, TIMER2.0, and HPA databases. A risk score model was constructed using RFC5-related immune genes identified using R packages. Combining the risk score model and clinical information of patients with CC, a nomogram was constructed to evaluate the prognosis of patients with CC.

RESULTS

Comprehensive analysis showed that the risk score was a prognostic factor for CC. The nomogram could predict the 3-year overall survival of patients with CC.

CONCLUSIONS

RFC5 was validated as a biomarker for CC. The RFC5 related immune genes were used to establish a new prognostic model of CC.

SLAM-family receptors come of age as a potential molecular target in cancer immunotherapy

The signaling lymphocytic activation molecule (SLAM) family receptors were discovered in immune cells for the first time. The SLAM-family receptors are a significant player in cytotoxicity, humoral immune responses, autoimmune diseases, lymphocyte development, cell survival, and cell adhesion. There is growing evidence that SLAM-family receptors have been involved in cancer progression and heralded as a novel immune checkpoint on T cells. Previous studies have reported the role of SLAMs in tumor immunity in various cancers, including chronic lymphocytic leukemia, lymphoma, multiple myeloma, acute myeloid leukemia, hepatocellular carcinoma, head and neck squamous cell carcinoma, pancreas, lung, and melanoma. Evidence has deciphered that the SLAM-family receptors may be targeted for cancer immunotherapy. However, our understanding in this regard is not complete. This review will discuss the role of SLAM-family receptors in cancer immunotherapy. It will also provide an update on recent advances in SLAM-based targeted immunotherapies.

Transcriptome Analysis Reveals the Immune Infiltration Profiles in Cervical Cancer and Identifies KRT23 as an Immunotherapeutic Target

Cervical cancer (CC) is one of the most common malignancies in women worldwide. Dismal prognosis rates have been associated with conventional therapeutic approaches, emphasizing the need for new strategies. Recently, immunotherapy has been used to treat various types of solid tumors, and different subtypes of the tumor microenvironment (TME) are associated with diverse responses to immunotherapy. Accordingly, understanding the complexity of the TME is pivotal for immunotherapy. Herein, we used two methods, “ssGSEA” and “xCell,” to identify the immune profiles in CC and comprehensively assess the relationship between immune cell infiltration and genomic alterations. We found that more adaptive immune cells were found infiltrated in tumor tissues than in normal tissues, whereas the opposite was true for innate cells. Consensus clustering of CC samples based on the number of immune cells identified four clusters with different survival and immune statuses. Then, we subdivided the above four clusters into “hot” and “cold” tumors, where hot tumors exhibited higher immune infiltration and longer survival time. Enrichment analyses of differentially expressed genes (DEGs) revealed that the number of activated immune signaling pathways was higher in hot tumors than that in cold tumors. Keratin, type I cytoskeletal 23 (KRT23), was upregulated in cold tumors and negatively correlated with immune cell infiltration. In vitro experiments, real-time reverse transcription-quantitative polymerase chain reaction, cytometric bead arrays, and ELISA revealed that knockdown of KRT23 expression could promote the secretion of C-C motif chemokine ligand-5 and promote the recruitment of CD8⁺ T cells. We also constructed a model based on DEGs that exhibited a high predictive power for the survival of CC patients. Overall, our study provides deep insights into the immune cell infiltration patterns of CC. Moreover, KRT23 has huge prospects for application as an immunotherapeutic target. Finally, our model demonstrated a good predictive power for the prognosis of CC patients and may guide clinicians during immunotherapy.

Comprehensive Molecular Analyses of a TNF Family-Based Gene Signature as a Potentially Novel Prognostic Biomarker for Cervical Cancer

Background

Increasing evidence suggests that tumour necrosis factor (TNF) family genes play important roles in cervical cancer (CC). However, whether TNF family genes can be used as prognostic biomarkers of CC and the molecular mechanisms of TNF family genes remain unclear.

Methods

A total of 306 CC and 13 normal samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. We identified differentially expressed TNF family genes between CC and normal samples and subjected them to univariate Cox regression analysis for selecting prognostic TNF family genes. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses were performed to screen genes to establish a TNF family gene signature. Gene set enrichment analysis (GSEA) was performed to investigate the biological functions of the TNF family gene signature. Finally, methylation and copy number variation data of CC were used to analyse the potential molecular mechanisms of TNF family genes.

Results

A total of 26 differentially expressed TNF family genes were identified between the CC and normal samples. Next, a TNF family gene signature, including CD27, EDA, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 was constructed based on univariate Cox, LASSO, and multivariate Cox regression analyses. The TNF family gene signature was related to age, pathological stages M and N, and could predict patient survival independently of clinical factors. Moreover, KEGG enrichment analysis suggested that the TNF family gene signature was mainly involved in the TGF-β signaling pathway, and the TNF family gene signature could affect the immunotherapy response. Finally, we confirmed that the mRNA expressions of CD27, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 were upregulated in CC, while that of EDA was downregulated. The mRNA expressions of CD27, EDA, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 might be influenced by gene methylation and copy number variation.

Conclusion

Our study is the first to demonstrate that CD27, EDA, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 might be used as prognostic biomarkers of CC and are associated with the immunotherapy response of CC.

Metabolic Molecule PLA2G2D Is a Potential Prognostic Biomarker Correlating With Immune Cell Infiltration and the Expression of Immune Checkpoint Genes in Cervical Squamous Cell Carcinoma

Cervical squamous cell carcinoma (CSCC) is the major pathological type of cervical cancer (CC), the second most prevalent reproductive system malignant tumor threatening the health of women worldwide. The prognosis of CSCC patients is largely affected by the tumor immune microenvironment (TIME); however, the biomarker landscape related to the immune microenvironment of CSCC and patient prognosis is less characterized. Here, we analyzed RNA-seq data of CSCC patients from The Cancer Genome Atlas (TCGA) database by dividing it into high- and low-immune infiltration groups with the MCP-counter and ESTIMATE R packages. After combining weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis, we found that PLA2G2D, a metabolism-associated gene, is the top gene positively associated with immune infiltration and patient survival. This finding was validated using data from The Cancer Genome Characterization Initiative (CGCI) database and further confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Finally, multiplex immunohistochemistry (mIHC) was performed to confirm the differential infiltration of immune cells between PLA2G2D-high and PLA2G2D-low tumors at the protein level. Our results demonstrated that PLA2G2D expression was significantly correlated with the infiltration of immune cells, especially T cells and macrophages. More importantly, PLA2G2D-high tumors also exhibited higher infiltration of CD8+ T cells inside the tumor region than PLA2G2D-low tumors. In addition, PLA2G2D expression was found to be positively correlated with the expression of multiple immune checkpoint genes (ICPs). Moreover, based on other immunotherapy cohort data, PLA2G2D high expression is correlated with increased cytotoxicity and favorable response to immune checkpoint blockade (ICB) therapy. Hence, PLA2G2D could be a novel potential biomarker for immune cell infiltration, patient survival, and the response to ICB therapy in CSCC and may represent a promising target for the treatment of CSCC patients.

Development of a Novel Immune Infiltration-Based Gene Signature to Predict Prognosis and Immunotherapy Response of Patients With Cervical Cancer

Predictive models could indicate the clinical outcome of patients with carcinoma. Cervical cancer is one of the most frequently diagnosed female malignancies. Herein, we proposed an immune infiltration-related gene signature that predicts prognosis of patients with cervical cancer and depicts the immune landscape as well. We utilized the transcriptome data of The Cancer Genome Atlas (TCGA) and estimated the infiltration level of 28 immune cell types. We screened out four immune cell types conducive to patient survival and recognized their shared differentially expressed genes (DEGs). Four core genes (CHIT1, GTSF1L, PLA2G2D, and GNG8) that composed the ultimate signature were identified via univariate and multivariate Cox regression. The optimal model we built up could distinguish patients with cervical cancer into high-score and low-score subgroups. These two subgroups showed disparity in aspects of patient survival, immune infiltration landscape, and response to immune checkpoint inhibitors. Additionally, we found that GTSF1L was decreased gradually along with the severity of cervical lesions, and its potential role in immune contexture and clinical practice were also demonstrated. Our results suggested that the Immunoscore based on four immune-related genes could serve as a supplementary criterion to effectively foresee the survival outcome, tumor infiltration status, and immunotherapy efficacy of cervical cancer patients.

Elevated Ras related GTP binding B (RRAGB) expression predicts poor overall survival and constructs a prognostic nomogram for colon adenocarcinoma

Currently, no articles have explored the roles of RRAGB gene in the occurrence and development of cancer. By means of The Cancer Genome Atlas (TCGA) data mining, we found that this gene might be a novel prognostic predictor for colon adenocarcinoma (COAD). Hence, this article was carried out to explore its roles in COAD and associations with immunity. RRAGB single-gene expression matrix and corresponding clinical information were extracted from TCGA database. Univariate/multivariate cox regression analyses and gene set enrichment analysis (GSEA) were utilized to identify independent prognostic factors and RRAGB related pathways, respectively. Relationships between RRAGB and immunity were also analyzed. Boxplot and K-M survival analysis indicated that RRAGB was not only differently expressed in COAD (P < 0.05), but also significantly associated with overall survival (OS; P < 0.05). Univariate and multivariate Cox hazard regression analyses indicated that RRAGB could serve as an independent prognostic factor for COAD (both P < 0.05). GSEA identified five signaling pathways significantly enriched in the high-RRAGB expression phenotype. Moreover, a RRAGB-based nomogram was successfully constructed and displayed a satisfactory performance. In addition, RRAGB expression was found to be significantly associated with microsatellite instability (MSI), tumor mutational burden (TMB) and immunity. Our results revealed that RRAGB could be a prognostic biomarker for COAD in terms of OS and markedly related to MSI, TMB, and immunity. We also constructed an RRAGB-based nomogram with a satisfactory performance. Further researches should be carried out to validate our findings.

Constructe a novel 5 hypoxia genes signature for cervical cancer

Background

Hypoxia, which affects the development, metastasis and prognosis of cancer, represents a key feature of cancer. This study describe a hypoxia risk factor model, with predicting the prognosis of cervical cancer.

Methods

Based on hypoxia pathway related genes, we divided cervical cancer samples into high and low expression groups. A cox analysis was then performed. Genes from these cervical cancer samples showing a significant impact on OS were selected for cluster analysis to obtain two subtypes. The TPM dataset of TCGA was divided into training and validation sets. For the training set, a lasso analysis was conducted as based on cox analysis of meaningful genes and a risk factor model was constructed. The constructed model was verified in internal and external data sets. Finally, RT-PCR, immunohistochemistry were used to detect the expression of relative genes or proteins and functional assays were used to evaluate the biological function of signature genes.

Results

Two molecular subtypes were obtained, Cluster2 vs Cluster1.These subtypes were obtained by clustering with a total of 149 DEGs (Differential expressed genes) being in line with this standard, of which 27 were up-regulated and 122 were down-regulated. The five genes with lambda = 0.0571 were selected to construct the model, the RiskScore = AK4*0.042 + HK2*0.021 + P4HA1*0.22 + TGFBI*0.1 + VEGFA*0.077. Further, in order to verify the signature, we used TCGA-test and GSE44001 chip datasets to test, and finally got a good risk prediction effect in those datasets. Moreover, the result of RT-PCR and immunohistochemistry demonstrated that AK4, HK2, P4HA1, TGFBI and VEGFA were all highly expressed in these cervical cancer tissue samples. The functional study shown that expression of AK4, HK2, P4HA1, TGFBI and VEGFA can regulate the proliferation, migration, and invasion ability of cervical cancer cells in vitro.

Conclusions

In summary, we developed a 5-gene signature prognostic hierarchical system based on the hypoxic pathway of cervical cancer, which is independent of clinical characteristics. And also conducted experimental verifications on these signature gene. Therefore, we propose that use of this classifier as a molecular diagnostic test can provide an effective means for evaluating the prognostic risk of cervical cancer patients, and provide potential targets for the treatment of cervical cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02050-3.