Human leukocyte antigen G (HLA-G) is a potential checkpoint molecule that plays a key role in cervical carcinogenesis. The purpose of this study was to construct and validate a prognostic risk model to predict the overall survival (OS) of cervical cancer patients, providing a reference for individualized clinical treatment that may lead to better clinical outcomes. HLA-G-driven differentially expressed genes (DEGs) were obtained from two cervical carcinoma cell lines, namely, SiHa and HeLa, with stable overexpression of HLA-G by RNA sequencing (RNA-seq). The biological functions of these HLA-G-driven DEGs were analysed by GO enrichment and KEGG pathway using the “clusterProfiler” package. The protein-protein interactions (PPIs) were assessed using the STRING database. The prognostic relevance of each DEG was evaluated by univariate Cox regression using the TCGA-CESC dataset. After the TCGA-CESC cohort was randomly divided into training set and testing set, and a prognostic risk model was constructed by LASSO and stepwise multivariate Cox regression analysis in training set and validated in testing set or in different types of cervical cancer set. The predictive ability of the prognostic risk model or nomogram was evaluated by a series of bioinformatics methods. A total of 1108 candidate HLA-G-driven DEGs, including 391 upregulated and 717 downregulated genes, were obtained and were enriched mostly in the ErbB pathway, steroid biosynthesis, and MAPK pathway. Then, an HLA-G-driven DEG signature consisting of the eight most important prognostic genes CD46, LGALS9, PGM1, SPRY4, CACNB3, PLIN2, MSMO1, and DAGLB was identified as a key predictor of cervical cancer. Multivariate Cox regression analysis showed that this signature is an independent risk factor for the overall survival of CESC patients. Kaplan-Meier survival analysis showed that the 5-year overall survival rate is 23.0% and 84.6% for the high-risk and low-risk patients, respectively (P<0.001). The receiver operating characteristic (ROC) curve of this prognostic model with an area under the curve (AUC) was 0.896 for 5 years, which was better than that of other clinical traits. This prognostic risk model was also successfully validated in different subtypes of cervical cancer, including the keratinizing squamous cell carcinoma, non-keratinizing squamous cell carcinoma, squamous cell neoplasms, non-squamous cell neoplasms set. Single-sample gene set enrichment (ssGSEA) algorithm and Tumor Immune Dysfunction and Exclusion (TIDE) analysis confirmed that this signature influence tumour microenvironment and immune checkpoint blockade. A nomogram that integrated risk score, age, clinical stage, histological grade, and pathological type was then built to predict the overall survival of CESC patients and evaluated by calibration curves, AUC, concordance index (C-index) and decision curve analysis (DCA). To summarize, we developed and validated a novel prognostic risk model for cervical cancer based on HLA-G-driven DEGs, and the prognostic signature showed great ability in predicting the overall survival of patients with cervical cancer.

Purpose: Chronic inflammation has been proven to be an important factor in carcinogenesis. Cytokines are the central mediators in the inflammatory microenvironment, and their release may be influenced by soluble HLA-G (sHLA-G). The aim of this study was to monitor the dynamic process of these soluble factors in patients with cervical cancer at Taizhou Hospital of Zhejiang Province, trying to understand their relationship with diagnosis, treatment, and prognosis. Methods: We quantified plasma levels of sHLA-G and 12 cytokines using ELISA and flow cytometry, respectively, in the peripheral blood of patients with cervical cancer divided into three groups: preoperation, postoperation and clinical relapse. Healthy women were used as the control group. Data were analysed by non-parametric tests, receiver-operating characteristic (ROC) curves, and Kaplan–Meier plotter (log-rank test). Results: In this study, our findings showed that preoperation plasma levels of sHLA-G and the cytokines IL-6, IL-10, and IFN-γ in cervical cancer patients had a good discriminatory effect between cervical cancer patients and healthy women. It should be noted that plasma levels of sHLA-G, IL-6, and IL-10 were significantly decreased within 30 days after radical hysterectomy (P < 0.05). A positive correlation was observed between IL-6 and IL-10, IL-8 and IL-17 levels preoperatively. In contrast, sHLA-G levels were negatively correlated with IL-10 but not with other cytokines. An increased survival rate in patients with cervical cancer was associated with IL-5 < 1.70 pg/mL, IL-17 < 2.30 pg/mL, and IFN-α < 2.26 pg/mL preoperatively. In addition, our findings showed that the levels of cytokines IL-6, IL-8, IL-12p70, IL-17, and IFN-γ may be related to 5-year relapse rates and/or the metastasis of cervical cancer. Conclusion: The current findings enhance our understanding of the dynamic process (preoperation, postoperation and clinical relapse) of sHLA-G and these cytokines in the plasma of patients with cervical cancer from diagnosis to prognosis. These biomarkers may play a potential therapeutic target role of such dynamic changes in the immunotherapy for cervical cancer.

Human leukocyte antigen (HLA)-G is an immune checkpoint molecule, which expression in cervical cancer cells enables them to escape immunosurveillance. To date, limited information has been published on the association of HLA-G genetic background in malignant cells with levels of HLA-G expression and the clinical outcome of patients. We investigated the influence of the HLA-G 14 bp In/Del (rs66554220) and + 3142C/G (rs1063320) polymorphisms in 130 cases of HPV16 infection, 130 cases of HPV18 infection and 185 age-matched, unrelated, HPV-negative, and cytologically normal Chinese Han women. Case-matched cervical biopsy tissues were evaluated by immunohistochemistry. Our findings show that the frequency of alleles, 14 bp In (38.5% vs 29.2%, OR = 1.52, P < 0.05) and + 3142G (72.7% vs 57.0%, OR = 2.01, P < 0.05), were significantly increased in the HPV18-infected group compared with the control group. The HLA-G polymorphisms (alleles 14 bp In and + 3142G) are also associated with the progression of HPV18-related cervical lesions. Moreover, HLA-G expression increased from CIN1 to CIN2/3 lesions and was highest in patients with adenocarcinoma; however, a significant association between these characteristics and the HLA-G polymorphisms was not observed. Our results support that the HLA-G 14 bp In and + 3142G alleles are risk factors for HPV18 infections and influence the progression of HPV18-related cervical lesions. This suggests that HLA-G-driven immune mechanisms play an important role in cervical carcinogenesis.

Human leukocyte antigen G (HLA-G) is known as a novel immune checkpoint molecule in cancer; thus, HLA-G and its receptors might be targets for immune checkpoint blockade in cancer immunotherapy. The aim of this study was to systematically identify the roles of checkpoint HLA-G molecules across various types of cancer. ONCOMINE, GEPIA, CCLE, TRRUST, HAP, PrognoScan, Kaplan-Meier Plotter, cBioPortal, LinkedOmics, STRING, GeneMANIA, DAVID, TIMER, and CIBERSORT were utilized. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. In this study, we comprehensively analysed the heterogeneous expression of HLA-G molecules in various types of cancer and focused on genetic alterations, coexpression patterns, gene interaction networks, HLA-G interactors, and the relationships between HLA-G and pathological stage, prognosis, and tumor-infiltrating immune cells. We first identified that the mRNA expression levels of HLA-G were significantly upregulated in both most tumor tissues and tumor cell lines on the basis of in-depth analysis of RNAseq data. The expression levels of HLA-G were positively associated with those of the other immune checkpoints PD-1 and CTLA-4. Abnormal expression of HLA-G was significantly correlated with the pathological stage of some but not all tumor types. There was a significant difference between the high and low HLA-G expression groups in terms of overall survival (OS) or disease-free survival (DFS). The results showed that HLA-G highly expressed have positive associations with tumor-infiltrating immune cells in the microenvironment in most types of tumors (P<0.05). Additionally, we identified the key transcription factor (TF) targets in the regulation of HLA-G expression, including HIVEP2, MYCN, CIITA, MYC, and IRF1. Multiple mutations (missense, truncating, etc.) and the methylation status of the HLA-G gene may explain the differential expression of HLA-G across different tumors. Functional enrichment analysis showed that HLA-G was primarily related to T cell activation, T cell regulation, and lymphocyte-mediated immunity. The data may provide novel insights for blockade of the HLA-G/ILT axis, which holds potential for the development of more effective antitumour treatments.

The tumor microenvironment (TME) is characterized by discrete changes in metabolic features of cancer and immune cells, with various implications. Cancer cells take up most of the available glucose to support their growth, thereby leaving immune cells with insufficient nutrients to expand. In the relative absence of glucose, T cells switch the metabolic program to lipid-based sources, which is pivotal to T-cell differentiation and activation in nutrient-stressed TME. Although consumption of lipids should provide an alternative energy source to starving T cells, a literature survey has revealed that it may not necessarily lead to antitumor responses. Different subtypes of T cells behave differently in various lipid overload states, which widely depends upon the kind of free fatty acids (FFA) engulfed. Key lipid metabolic genes provide cytotoxic T cells with necessary nutrients for proliferation in the absence of glucose, thereby favoring antitumor immunity, but the same genes cause immune evasion in T_mem and T_reg. This review aims to detail the complexity of differential lipid metabolism in distinct subtypes of T cells that drive the antitumor or pro-tumor immunity in specific TME states. We have identified key drug targets related to lipid metabolic rewiring in TME.