Tumor microenvironment (TME) is emerging as an essential part of cervical cancer (CC) tumorigenesis and development, becoming a hotspot of research these years. However, comprehending the specific composition of TME is still facing enormous challenges, especially the immune and stromal components. In this study, we downloaded the RNA-seq profiles and somatic mutation data of 309 CC cases from The Cancer Genome Atlas (TCGA) database, which were analyzed by integrative bioinformatical methods. Initially, ESTIMATE computational method was employed to calculate the amount of immune and stromal components. Then, based on the high- and low-immunity cohorts, we recognized the differentially expressed genes (DEGs) as well as the differentially mutated genes (DMGs). Additionally, we conducted an intersection analysis of DEGs and DMGs, ultimately determining an immune-related prognostic signature, GTPase, IMAP Family Member 4 (GIMAP4). Moreover, sequential analyses demonstrated that GIMAP4 was a protective factor in CC, positively correlated with the overall survival (OS) and negatively with distant metastasis. Besides, we utilized the Gene Set Enrichment Analysis (GSEA) to explore the enrichment-pathways in high and low-expression cohorts of GIMAP4. The results indicated that the genes of the high-expression cohort had a high enrichment in immune-related biological processes and metabolic activities in the low one. Furthermore, CIBERSORT analysis was applied to evaluate the proportion of tumor-infiltrating immune cells (TICs), illustrating that several activated TICs were strongly associated with GIMAP4 expression, which suggested that GIMAP4 had the potential to be an indicator for the immune state in TME of CC. Hence, GIMAP4 contributed to predicting the CC patients’ clinical outcomes, such as survival rate, distant metastasis and immunotherapy response. Moreover, GIMAP4 could serve as a promising biomarker for TME remodeling, suggesting the possible underlying mechanisms of tumorigenesis and CC progression, which may provide different therapeutic perceptions of CC, and therefore improve treatment.

The cross-talk between tumor cells and the tumor microenvironment (TME) is an important factor in determining the tumorigenesis and progression of cervical cancer (CC). However, clarifying the potential mechanisms which trigger the above biological processes remains a challenge. The present study focused on immune-relevant differences at the transcriptome and somatic mutation levels through an integrative multi-omics analysis based on The Cancer Genome Atlas database. The objective of the study was to recognize the specific immune-related prognostic factors predicting the survival and response to immunotherapy of patients with CC. Firstly, eight hub immune-related prognostic genes were ultimately identified through construction of a protein–protein interaction network and Cox regression analysis. Secondly, 32 differentially mutated genes were simultaneously identified based on the different levels of immune infiltration. As a result, an immune gene-related prognostic model (IGRPM), including six factors (chemokine receptor 7 [CCR7], CD3d molecule [CD3D], CD3e molecule [CD3E], and integrin subunit beta 2 [ITGB2], family with sequence similarity 133 member A [FAM133A], and tumor protein p53 [TP53]), was finally constructed to forecast clinical outcomes of CC. Its predictive capability was further assessed and validated using the Gene Expression Omnibus validation set. In conclusion, IGRPM may be a promising prognostic signature to predict the prognoses and responses to immunotherapy of patients with CC. Moreover, the multi-omics study showed that IGRPM could be a novel therapeutic target for CC, which is a promising biomarker for indicating the immune-dominant status of the TME and revealing the potential mechanisms responsible for the tumorigenesis and progression of CC.

Ovarian cancer (OC) is one of the most malignant tumors whose mortality rate ranks first in gynecological tumors. Although immunotherapy sheds new light on clinical treatments, the low response still restricts its clinical use because of the unique characteristics of OC such as immunosuppressive microenvironment and unstable genomes. Further exploration on determining an efficient biomarker to predict the immunotherapy response of OC patients is of vital importance. In this study, integrative analyses were performed systematically using transcriptome profiles and somatic mutation data from The Cancer Genome Atlas (TCGA) based on the immune microenvironment and genomic instability of OC patients. Firstly, intersection analysis was conducted to identify immune-related differentially expressed genes (DEGs) and genomic instability-related DEGs. Secondly, Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3A (APOBEC3A) was recognized as a protective factor for OC, which was also verified through basic experiments such as quantitative reverse transcription PCR (RT-qPCR), immunohistochemistry (IHC), Cell Counting Kit-8 (CCK-8), and transwell assays. Thirdly, the correlation analyses of APOBEC3A expression with tumor-infiltrating immune cells (TICs), inhibitory checkpoint molecules (ICPs), Immunophenoscores (IPS), and response to anti-PD-L1 immunotherapy were further applied along with single-sample GSEA (ssGSEA), demonstrating APOBEC3A as a promising biomarker to forecast the immunotherapy response of OC patients. Last, the relationship between APOBEC3A expression with tumor mutation burden (TMB), DNA damage response (DDR) genes, and m6A-related regulators was also analyzed along with the experimental verification of immunofluorescence (IF) and RT-qPCR, comprehensively confirming the intimate association of APOBEC3A with genomic instability in OC. In conclusion, APOBEC3A was identified as a protective signature and a promising prognostic biomarker for forecasting the survival and immunotherapy effect of OC patients, which might accelerate the clinical application and improve immunotherapy effect.

The results suggest that HER-2/neu amplification does exist in a subgroup of invasive cervical cancer and may play a role in cervical carcinogenesis. The role as independent prognostic factor has to be evaluated by further prospective studies.

Background. Cervical cancer (CC) has long been a concern, as a gynecological cancer type of high-risk. At present, there are few studies on the early detection of CC at the genetic level. The breakthrough is to recognize CC patients tending to have a worse prognosis by checking the expression pattern of ferroptosis-related genes, which enjoy a great potential of being applied to cancer treatment. Methods. Data used in this study was obtained from a series of public online databases, integrated with ferroptosis-related gene collection stored from the FerrDb database and GeneCards database. The least absolute shrinkage and selection operator- (LASSO-) penalized analysis was taken for modeling, and before, univariate Cox regression analysis got done to shrink the candidates’ range. Several analyses were made for the evaluation of the efficacy of the new model, based on CC patients’ overall survival (OS). Tumor microenvironment- (TME-) related analyses were conducted by various algorithms on different populations, comprising CIBERSORT, ssGSEA, XCELL, etc. Nonnegative matrix factorization (NMF) clustering got applied to find that ferroptosis-marker genes affect prognosis more than “driver” and “suppressor”. Hub-gene PTGS2 was screened out by protein-protein interaction analysis and real-time qPCR after ferroptosis induction, and ELISA was conducted for further verification on the correlation between ferroptosis and M1 polarization. Results. The twenty-five ferroptosis-related genes model can estimate the prognosis of patients independently of other clinical factors, and the low-risk score group shows higher expression of immune-enhancing cells, noteworthily for M1 macrophages. It is experimentally validated that the M1 marker TNF-α significantly increased after coculturing M1 macrophages and SiHa cells processed with ferroptosis inductor before. The key gene to the model, PTGS2, presented to be a risk factor in cervical cancer, and its low-expression group has stronger immune activity and higher tumor mutation burden, with the significantly highly mutated gene TENM2 in it showing high drug sensitivity and neoantigen for patients with its mutant-type. Meanwhile, it influences macrophage polarization. Conclusion. Prognosis of early-stage cervical cancer patients can be exactly predicted on ferroptosis-related genes. Among model genes, PTGS2 may have a major impact by affecting macrophage polarization and mutation effects.