Cervical cancer immune infiltration microenvironment identification, construction of immune scores, assisting patient prognosis and immunotherapy

The Prominence of the Broad-Spectrum Protease inhibitor gene A2ML1 as a potential biomarker in cervical cancer diagnostics using Immunotherapeutic and Multi-Omics approaches

One of the venereal tumors that threaten human life is cervical cancer. A2ML1 is detected in advanced-stage cancer patients and is found to be strongly associated with cervical cancer. A2ML1 was shown to be substantially expressed in cervical cancer in this study, which used data from the TCGA database. Those with high A2ML1 expression had a lower chance of survival than patients with low A2ML1 expression. Both univariate and multivariate Cox regression analyses were utilized to investigate the relationship between clinical variables and overall survival rates. An investigation into the link between A2ML1 and immune infiltration was subsequently conducted. Utilizing the immune cell database, research was conducted to investigate the dispersion of 24 immune cells and their correlation to A2ML1 expression. In addition to this, the favorable correlation between immune cells and A2ML1 was validated using all three immune cell methodologies. The Genomics of Drug Sensitivity in Cancer database was used to confirm the idea that there is a link between A2ML1 expression and the efficacy of chemotherapy or immunotherapy. The findings demonstrated that A2ML1 is a potential biomarker for cervical cancer diagnostics. This biomarker may be used to chaperone immunotherapy, as well as to explain the elucidates of cervical cancer caused by the immunological microenvironment.

KIF23 promotes cervical cancer progression via inhibiting NLRP3-mediated pyroptosis

BACKGROUND

Cervical cancer (CC), closely linked to persistent human papillomavirus infection, represents a major health problem for women worldwide. The objective of this study is to elucidate KIF23's role in the development of CC and its regulatory mechanism.

METHODS

The bioinformatics methods were utilized to extract pyroptosis-associated differentially expressed genes (DEGs) and pivot genes from the GSE9750 and GSE63678 datasets, followed by immune infiltration analysis and quantification of these genes' expression. The effects of kinesin family member 23 (KIF23) were verified through functional experiments in vitro and a mouse xenograft model. The NLPR3 activator, nigericin, was applied for further analyzing the potential regulatory mechanism of KIF23 in CC.

RESULTS

A total of 8 pyroptosis-related DEGs were screened out, among which 4 candidate core genes were identified as candidate hub genes and confirmed upregulation in CC tissues and cells. These genes respectively showed a positive correlation with the infiltration of distinct immune cells or tumor purity. Downregulation of KIF23 could suppress the proliferation, migration, and invasion abilities in CC cells and tumorigenesis through enhancing pyroptosis. Conversely, KIF23 overexpression accelerated the malignant phenotypes of CC cells and inhibited pyroptosis activation, which was blocked by nigericin treatment.

CONCLUSIONS

KIF23 may play an oncogenic role in CC progression via inhibition of the NLRP3-mediated pyroptosis pathway.

Development and validation of a prognostic model for cervical cancer by combination of machine learning and high-throughput sequencing

BACKGROUND

Cervical cancer holds the highest morbidity and mortality rates among female reproductive tract tumors. However, the curative outcomes for patients with persistent, recurrent, or metastatic cervical cancer remain unsatisfactory. There is a lack of comprehensive prognostic indicators for cervical cancer. This study aims to develop a model that evaluates the prognosis of cervical cancer in combination of high-throughput sequencing and various machine learning algorithms.

METHODS

In this study, we combined two single-cell RNA sequencing (scRNA-seq) projects and TCGA data for cervical cancer to obtain shared differentially expressed genes (DEGs). A LASSO regression and several learners were applied for signature feature selection. Six machine learning algorithms including Linear Discriminant Analysis, Naive Bayes, K Nearest Neighbors, Decision Tree, Random Forest, and eXtreme Gradient Boosting were utilized to construct a prognostic model for cervical cancer. External validation was conducted using the CGCI-HTMCP-CC dataset, and the accuracy of the model was assessed through ROC curve analysis.

RESULTS

The results demonstrated the successful construction of a prognostic model based on DEGs from bulk- and scRNA-seq data. Ten genes CXCL8, DLC1, GRN, MPLKIP, PRDX1, RUNX1, SNX3, TFRC, UBE2V2, and UQCRC1 were screened by feature selection and applied for model construction. Random Forest exhibited the best performance in predicting the risk of cervical cancer. Patients in the high-risk group presented worse overall survival compared to those in the low-risk group.

CONCLUSION

Conclusively, our model based on DEGs from bulk-seq and scRNA-seq data effectively evaluates the prognosis of cervical cancer and provides valuable insights for comprehensive clinical management.

Immune landscape and heterogeneity of cervical squamous cell carcinoma and adenocarcinoma

Despite the differences in disease outcomes and pathological features between cervical squamous cell carcinoma (CSCC) and adenocarcinoma (ADC), the molecular characteristics in immune heterogeneity of the tumor microenvironment remain unclear. Here, we explored the immune landscape and heterogeneity between CSCC and ADC. Gene expression and clinical characteristics of cervical carcinoma from The Cancer Genome Atlas (TCGA) were downloaded. Differentially expressed genes (DEGs), immune cell infiltration, and pathway enrichment analyses were used to explore the immune landscape and heterogeneity between CSCC and ADC. Furthermore, distinct immune signatures between CSCC and ADC were validated based on clinical samples. In total, 4,132 upregulated DEGs and 2,307 down-regulated DEGs were identified between CSCC and ADC, with enrichments in immune related-pathways in CSCC. In addition, 54 hub DEGs correlated with patients' prognosis and immunocytes infiltration were identified. The CSCC patients had a higher ImmuneScore and more abundant immunocytes infiltration compared to ADC patients, as validated by immunohistochemistry (IHC) and multicolor immunofluorescence (mIF) analyses of collected samples. Furthermore, CSCC displayed higher inhibitory immune checkpoints expression, tumor mutation burden (TMB), and microsatellite instability (MSI) compared to ADC, which indicated CSCC patients were more likely to benefit from immunotherapy. In summary, our results revealed the huge immune heterogeneity between CSCC and ADC, and provided guidance for immunotherapy selection for different pathological types of cervical cancer.

TIME Is Ticking for Cervical Cancer

Cervical cancer (CC) is a major health problem among reproductive-age females and comprises a leading cause of cancer-related deaths. Human papillomavirus (HPV) is the major risk factor associated with CC incidence. However, lifestyle is also a critical factor in CC pathogenesis. Despite HPV vaccination introduction, the incidence of CC is increasing worldwide. Therefore, it becomes critical to understand the CC tumor immune microenvironment (TIME) to develop immune cell-based vaccination and immunotherapeutic approaches. The current article discusses the immune environment in the normal cervix of adult females and its role in HPV infection. The subsequent sections discuss the alteration of different immune cells comprising CC TIME and their targeting as future therapeutic approaches.