Risk prediction model of uterine corpus endometrial carcinoma based on immune-related genes.

BACKGROUND: Given the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and the long-term outcomes of patients, our objective was to develop a prognostic risk prediction model using immune-related genes to improve the accuracy of UCEC prognosis prediction. METHODS: The Limma, ESTIMATE, and CIBERSORT methods were used for cluster analysis, immune score calculation, and estimation of immune cell proportions. Univariate and multivariate analyses were utilized to develop a prognostic risk model for UCEC. Risk model scores and nomograms were used to evaluate the models. String constructs a protein-protein interaction (PPI) network of genes. The qRT-PCR, immunofluorescence, and immunohistochemistry (IHC) all confirmed the genes. RESULTS: Cluster analysis divided the immune-related genes into four subtypes. 33 immune-related genes were used to independently predict the prognosis of UCEC and construct the prognosis model and risk score. The analysis of the survival nomogram indicated that the model has excellent predictive ability and strong reliability for predicting the survival of patients with UCEC. The protein-protein interaction network analysis of key genes indicates that four genes play a pivotal role in interactions: GZMK, IL7, GIMAP, and UBD. The quantitative real-time polymerase chain reaction (qRT-PCR), immunofluorescence, and immunohistochemistry (IHC) all confirmed the expression of the aforementioned genes and their correlation with immune cell levels. This further revealed that GZMK, IL7, GIMAP, and UBD could potentially serve as biomarkers associated with immune levels in endometrial cancer. CONCLUSION: The study identified genes related to immune response in UCEC, including GZMK, IL7, GIMAP, and UBD, which may serve as new biomarkers and therapeutic targets for evaluating immune levels in the future.

CCNE1 is a predictive and immunotherapeutic indicator in various cancers including UCEC: a pan-cancer analysis.

BACKGROUND: CCNE1 plays an important oncogenic role in several tumors, especially high-stage serous ovarian cancer and endometrial cancer. Nevertheless, the fundamental function of CCNE1 has not been explored in multiple cancers. Therefore, bioinformatics analyses of pan-cancer datasets were carried out to explore how CCNE1 regulates tumorigenesis. METHODS: A variety of online tools and cancer databases, including GEPIA2, SangerBox, LinkedOmics and cBioPortal, were applied to investigate the expression of CCNE1 across cancers. The pan-cancer datasets were used to search for links between CCNE1 expression and prognosis, DNA methylation, m6A level, genetic alterations, CCNE1-related genes, and tumor immunity. We verified that CCNE1 has biological functions in UCEC cell lines using CCK-8, EdU, and Transwell assays. RESULTS: In patients with different tumor types, a high mRNA expression level of CCNE1 was related to a poor prognosis. Genes related to CCNE1 were connected to the cell cycle, metabolism, and DNA damage repair, according to GO and KEGG enrichment analyses. Genetic alterations of CCNE1, including duplications and deep mutations, have been observed in various cancers. Immune analysis revealed that CCNE1 had a strong correlation with TMB, MSI, neoantigen, and ICP in a variety of tumor types, and this correlation may have an impact on the sensitivity of various cancers to immunotherapy. CCK-8, EdU and Transwell assays suggested that CCNE1 knockdown can suppress UCEC cell proliferation, migration and invasion. CONCLUSION: Our study demonstrated that CCNE1 is upregulated in multiple cancers in the TCGA database and may be a promising predictive biomarker for the immunotherapy response in some types of cancers. Moreover, CCNE1 knockdown can suppress the proliferation, migration and invasion of UCEC cells.

A comprehensive prognostic and immune analysis of enhancer RNA identifies IGFBP7-AS1 as a novel prognostic biomarker in Uterine Corpus Endometrial Carcinoma.

BACKGROUND: Long non-coding RNAs (lncRNA) have been implicated in a hand of studies that supported an involvement and co-operation in Uterine Corpus Endometrial Carcinoma (UCEC). Enhancer RNAs (eRNA), a functional subtype of lncRNA, have a key role throughout the genome to guide protein production, thus potentially associated with diseases. METHODS: In this study, we mainly applied the Cancer Genome Atlas (TCGA) dataset to systematically discover crucial eRNAs involving UCEC. For the key eRNAs in UCEC, we employed RT-qPCR to compare eRNA expression levels in tumor tissues and paired normal adjacent tissues from UCEC patients for validation. Furthermore, the relationships between the key eRNAs and immune activities were measured from several aspects, including the analysis for tumor microenvironment, immune infiltration cells, immune check point genes, tumor mutation burden, and microsatellite instability, as well as m6A related genes. Finally, the key eRNAs were verified by a comprehensive pan-cancer analysis. RESULTS: IGFBP7 Antisense RNA 1 (IGFBP7-AS1) was identified as the key eRNA for its expression patterns of low levels in tumor tissues and favorable prognostic value in UCEC correlated with its target gene IGFBP7. In RT-qPCR analysis, IGFBP7-AS1 and IGFBP7 had down-regulated expression in tumor tissues, which was consistent with previous analysis. Moreover, IGFBP7-AS1 was found closely related with immune response in relevant immune analyses. Besides, IGFBP7-AS1 and its target gene IGFBP7 correlated with a multi-omics pan-cancer analysis. CONCLUSIONS: Finally, we suggested that IGFBP7-AS1 played a key role in impacting on clinical outcomes of UCEC patients for its possible influence on immune activity.

m6A RNA methylation regulator-based signature for prognostic prediction and its potential immunological role in uterine corpus endometrial carcinoma.

BACKGROUND: Uterine corpus endometrial carcinoma (UCEC) is the most common female pelvic malignancy worldwide. N6-methyladenosine (m6A) plays an important role in various cellular responses, especially in cancer progression. However, the correlation between prognostic UCEC and m6A RNA methylation regulators remains unclear. METHODS: We used The Cancer Genome Atlas (TCGA) to provide a gene signature that could improve the prognostic evaluation of UCEC patients according to the distinct genetic trait of m6A RNA methylation regulators from a bioinformatics perspective. After comparing UCEC subgroups with different genetic profiles of m6A regulators, we identified 71 differentially expressed genes associated with overall survival (OS) and generated a nine-gene signature through least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Finally, we used in vitro and in vivo tumor cell experiments as well as the immune correlation analysis to verify the function of each gene in the proposed gene signature. RESULTS: Time-dependent receiver operating characteristic (ROC) curves revealed that the proposed gene signature could predict the outcome of UCEC patients accurately. We found that CDKN2A mainly acted from the perspective of tumor cells, while COL4A4, PXDN, TIGIT, CHODL, LMO3, KCNJ12, L1CAM, and EPHB1 might play a role in UCEC from an immunological point of view. CONCLUSIONS: From an epigenetics perspective, the m6A RNA methylation regulator-based gene signature can predict the prognosis of UCEC patients and immune therapeutic efficacy.

Identification of methylation-driven genes prognosis signature and immune microenvironment in uterus corpus endometrial cancer.

BACKGROUND: Uterus corpus endometrial cancer (UCEC) is the main malignant tumor in gynecology, with a high degree of heterogeneity, especially in terms of prognosis and immunotherapy efficacy. DNA methylation is one of the most important epigenetic modifications. Studying DNA methylation can help predict the prognosis of cancer patients and provide help for clinical treatment. Our research aims to discover whether abnormal DNA methylation can predict the prognosis of UCEC and reflect the patient's tumor immune microenvironment. PATIENTS AND METHODS: The clinical data, DNA methylation data, gene expression data and somatic mutation data of UCEC patients were all downloaded from the TCGA database. The MethylMix algorithm was used to integrate DNA methylation data and mRNA expression data. Univariate Cox regression analysis, Multivariate Cox regression analysis, and Lasso Cox regression analysis were used to determine prognostic DNA methylation-driven genes and to construct an independent prognostic index (MDS). ROC curve analysis and Kaplan-Meier survival curve analysis were used to evaluate the predictive ability of MDS. GSEA analysis was used to explore possible mechanisms that contribute to the heterogeneity of the prognosis of UCEC patients. RESULTS: 3 differential methylation-driven genes (DMDGs) (PARVG, SYNE4 and CDO1) were considered as predictors of poor prognosis in UCEC. An independent prognostic index was finally established based on 3 DMDGs. From the results of ROC curve analysis and survival curve analysis, MDS showed excellent prognostic ability in TCGA-UCEC. A new nomogram based on MDS and other prognostic clinical indicators has also been successfully established. The C-index of the nomogram for OS prediction was 0.764 (95% CI = 0.702-0.826). GSEA analysis suggests that there were differences in immune-related pathways among patients with different prognosis. The abundance of M2 macrophages and M0 macrophages were significantly enhanced in the high-risk group while T cells CD8, Eosinophils and Neutrophils were markedly elevated in the low-risk group. Meanwhile, patients in the low-risk group had higher levels of immunosuppressant expression, higher tumor mutational burden and immunophenoscore (IPS) scores. Joint survival analysis revealed that 7 methylation-driven genes could be independent prognostic factors for overall survival for UCEC. CONCLUSION: We have successfully established a risk model based on 3 DMDGs, which could accurately predict the prognosis of patients with UCEC and reflect the tumor immune microenvironment.

Identification of the prognostic value of a 2-gene signature of the WNT gene family in UCEC using bioinformatics and real-world data.

BACKGROUND: The WNT gene family plays an important role in the occurrence and development of malignant tumors, but its involvement has not been systematically analyzed in uterine corpus endometrial carcinoma (UCEC). This study aimed to evaluate the prognostic value of the WNT gene family in UCEC. METHODS: Pan-cancer transcriptome data of the UCSC Xena database and Genotype-Tissue Expression (GTEx) normal tissue data were downloaded to analyze the expression and prognosis of 19 WNT family genes in UCEC. A cohort from The Cancer Genome Atlas-Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) was used to analyze the expression of the WNT gene family in different immune subtypes and clinical subgroups. The STRING database was used to analyze the interaction of the WNT gene family and its biological function. Univariate Cox regression analysis and Lasso cox analysis were used to identify the genes associated with significant prognosis and to construct multi signature prognosis model. An immunohistochemical assay was used to verify the predictive ability of the model. Risk score and the related clinical features were used to construct a nomogram. RESULTS: The expression levels of WNT2, WNT3, WNT3A, WNT5A, WNT7A, and WNT10A were significantly different among different immune subtypes and correlated with TP53 mutation. According to the WNT family genes related to the prognosis of UCEC, UCEC was classified into two subtypes (C1, C2). The prognosis of subtype C1 was significantly better than that of subtype C2. A 2-gene signature (WNT2 and WNT10A) was constructed and the two significantly prognostic groups can be divided based on median Risk score. These results were verified using real-world data, and the nomogram constructed using clinical features and Risk score had good prognostic ability. CONCLUSIONS: The 2-gene signature including WNT2 and WNT10A can be used to predict the prognosis of patients with UCEC, which is important for clinical decision-making and individualized therapy for patients with UCEC.

Good or not good: Role of miR-18a in cancer biology.

miR-18a is a member of primary transcript called miR-17-92a (C13orf25 or MIR17HG) which also contains five other miRNAs: miR-17, miR-19a, miR-20a, miR-19b and miR-92a. This cluster as a whole shows specific characteristics, where miR-18a seems to be unique. In contrast to the other members, the expression of miR-18a is additionally controlled and probably functions as its own internal controller of the cluster. miR-18a regulates many genes involved in proliferation, cell cycle, apoptosis, response to different kinds of stress, autophagy and differentiation. The disturbances of miR-18a expression are observed in cancer as well as in different diseases or pathological states. The miR-17-92a cluster is commonly described as oncogenic and it is known as 'oncomiR-1', but this statement is a simplification because miR-18a can act both as an oncogene and a suppressor. In this review we summarize the current knowledge about miR-18a focusing on its regulation, role in cancer biology and utility as a potential biomarker.

A comprehensive analysis and experimental validation of TK1 in uterine corpus endometrial carcinoma.

Uterine corpus endometrial carcinoma (UCEC) is becoming a main malignant cancer that threaten to women's health. Thymidine kinase 1 (TK1) is considering to be associated with tumorigenesis and development. Nevertheless, the function of TK1 in UCEC is still unclear. Herein, we analyzed the TK1 expression level in pan-cancer and found that TK1 was upregulated in a variety of cancers including UCEC. Patients of UCEC with high expression of TK1 were related to poor outcome. TK1 was also related to clinical stage, histologic grade and lymph node metastasis. Abnormal expression of TK1 in UCEC was related to promoter methylation while gene mutation was not frequent. TK1 and its associated genes appeared to be prominent in cell cycle and DNA replication, according to GO and KEGG analysis. Analysis of immune infiltration revealed a negative correlation between TK1 and CD8 + T cells, macrophages, and dendritic cells. In vitro experiments, TK1 knockdown resulted in the inhibition of proliferation, migration, invasion and EMT in UCEC cell lines.

Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma.

Background: Uterine Corpus Endometrial Carcinoma (UCEC) stands as one of the prevalent malignancies impacting women globally. Given its heterogeneous nature, personalized therapeutic approaches are increasingly significant for optimizing patient outcomes. This study investigated the prognostic potential of cellular senescence genes(CSGs) in UCEC, utilizing machine learning techniques integrated with large-scale genomic data. Methods: A comprehensive analysis was conducted using transcriptomic and clinical data from 579 endometrial cancer patients sourced from the Cancer Genome Atlas (TCGA). A subset of 503 CSGs was assessed through weighted gene co-expression network analysis (WGCNA) alongside machine learning algorithms, including Gaussian Mixture Model (GMM), support vector machine - recursive feature elimination (SVM-RFE), Random Forest, and eXtreme Gradient Boosting (XGBoost), to identify key differentially expressed cellular senescence genes. These genes underwent further analysis to construct a prognostic model. Results: Our analysis revealed two distinct molecular clusters of UCEC with significant differences in tumor microenvironment and survival outcomes. Utilizing cellular senescence genes, a prognostic model effectively stratified patients into high-risk and low-risk categories. Patients in the high-risk group exhibited compromised overall survival and presented distinct molecular and immune profiles indicative of tumor progression. Crucially, the prognostic model demonstrated robust predictive performance and underwent validation in an independent patient cohort. Conclusion: The study emphasized the significance of cellular senescence genes in UCEC progression and underscored the efficacy of machine learning in developing reliable prognostic models. Our findings suggested that targeting cellular senescence holds promise as a strategy in personalized UCEC treatment, thus warranting further clinical investigation.

A comprehensive analysis of CEBPA on prognosis and function in uterine corpus endometrial carcinoma.

Uterine corpus endometrial carcinoma (UCEC) is one of the most common tumours of the female reproductive system. CCAAT enhancer-binding protein alpha (CEBPA) is a member of the transcription factor family involved in regulating processes such as cell proliferation, differentiation, metabolism, and the immune response. However, the role of CEBPA in UCEC has not been clarified. Here, we performed a comprehensive analysis to explore the expression level, prognostic value, immune infiltration and biological function of CEBPA in UCEC. In this study, we found that CEBPA expression was upregulated and associated with poor prognosis in UCEC patients. KEGG and GO analyses revealed that the genes positively correlated with CEBPA were enriched primarily in immune regulation and oxidative phosphorylation. Immune infiltration analysis revealed that CEBPA is strongly correlated with immune cell infiltration in UCEC. RT-qPCR indicated that CEBPA may regulate the OXPHOS level in Ishikawa cells. CCK-8, cell cycle, Transwell and scratch wound healing assays revealed that CEBPA promoted Ishikawa cell proliferation, invasion and migration. In addition, PPI and survival analyses suggested that CEBPG may be a potential target of CEBPA in UCEC. These results demonstrated that CEBPA may be a potential therapeutic target in UCEC.

Dachsous cadherin related 1 (DCHS1) is a novel biomarker for immune infiltration and epithelial-mesenchymal transition in endometrial cancer via pan-cancer analysis.

BACKGROUND: Dachsous cadherin related 1 (DCHS1) is one of calcium-dependent adhesion membrane proteins and is mainly involved in the development of mammalian tissues. There is a lack of more detailed research on the biological function of DCHS1 in pan-cancer. MATERIALS AND METHODS: We evaluated the expression, the prognostic value, the diagnostic value and genomic alterations of DCHS1 by using the databases, including TCGA, UALCAN, HPA, GEPIA2.0 and GSCA. We employed the databases of UCSC, TIMER2.0, TISIDB, GSCA to analyze the association between DCHS1 expression and the immune microenvironment, stemness, TMB, MSI and anticancer drug sensitivity. BioGRID, STRING and GEPIA2.0 were used to perform protein interaction and functional enrichment analysis. Real-time quantitative PCR, CCK8, Transwell assay and Western blot were performed to determine the function of DCHS1 in UCEC. RESULTS: DCHS1 is differentially expressed in many cancers and its expression is significantly associated with tumor prognosis and diagnosis. DCHS1 expression was significantly correlated with the infiltration of cancer-associated fibroblasts (CAFs), Endothelial cell (ECs), and Hematopoietic stem cell in most cancers. In addition, DCHS1 was significantly associated with sensitivity to many antitumor drugs. Functional enrichment analysis revealed that DCHS1-related proteins were involved in Focal adhesion, Endometrial cancer and Wnt signaling pathway. GSEA results showed that DCHS1 was related to epithelial-mesenchymal transition (EMT) in many cancers. In vitro experiments in UCEC showed that DCHS1 regulated cell proliferation, migration and EMT. CONCLUSIONS: Our findings indicated that DCHS1 might be a novel prognostic and diagnostic biomarker and immunotherapy target, and plays an important role in the proliferation, migration and EMT in UCEC.

Overexpression of zinc finger DHHC-type containing 1 is associated with poor prognosis and cancer cell growth and metastasis in uterine corpus endometrial carcinoma.

The zinc finger DHHC-type containing 1 (ZDHHC1) gene is implicated in the pathogenesis and progression of various malignant tumors, but its precise involvement in uterine corpus endometrial carcinoma (UCEC) remains unknown. Thus, this study investigated ZDHHC1 expression in UCEC using publicly available TCGA and Xena databases and elucidated the functions and mechanisms of the ZDHHC1 gene in UCEC progression using bioinformatics and in vitro experiments. The correlation between ZDHHC1 expression and prognosis, clinical features, immune cells, and RNA modifications of UCEC was evaluated using nomograms, correlation, ROC, and survival analyses. The impacts of ZDHHC1 overexpression on UCEC progression and mechanisms were explored with bioinformatics and in vitro experiments. Our study revealed that ZDHHC1 expression was significantly downregulated in UCEC and correlated with poor prognosis, cancer diagnosis, clinical stage, age, weight, body mass index, histological subtypes, residual tumor, tumor grade, and tumor invasion. Notably, Cox regression analysis and constructed nomograms showed that downregulated ZDHHC1 expression was a prognostic factor associated with poor prognosis in patients with UCEC. Conversely, above-normal ZDHHC1 expression inhibited the cell growth, cell cycle transition, migration, and invasion of UCEC cells, which may be related to the cell cycle, DNA replication, PI3K-AKT, and other pathways that promote tumor progression. Altered ZDHHC1 expression in UCEC was significantly associated with RNA modifications and the changes in cancer immune cell populations, such as CD56 bright NK cells, eosinophils, Th2 cells, and cell markers. In conclusion, considerably reduced ZDHHC1 expression in UCEC is associated with cancer cell growth, metastasis, poor prognosis, immune infiltration, and RNA modifications, revealing the promising potential of ZDHHC1 as a prognostic marker for UCEC.

Systematic pan-cancer analysis of RNF186 with potential implications in progression and prognosis in human cancer.

AIMS: Cancer remains a significant global public health issue. There is growing proof that Ring Finger Protein 186 (RNF186) may play a function in pan-cancer, however, this has not yet been thoroughly determined. This study aims to analyze RNF186 with potential implications in progression and prognosis in human cancer. MATERIALS AND METHODS: A comprehensive bioinformatics approaches combined with experimental verification were used across 33 types of cancers in this study to conduct a pan-cancer investigation of RNF186 from the perspectives of gene expression, prognosis, genomic alterations, immunological markers, gene set, and function. KEY FINDINGS: RNF186 is a valuable prognostic biomarker in several cancer types, especially breast invasive carcinoma (BRCA) and uterine corpus endometrial carcinoma (UCEC). The levels of RNF186 promoter methylation and genetic alterations may be responsible for some cancers' abnormal expression. Furthermore, RNF186 expression was determined to be associated with immune checkpoint genes. Analysis of RNF186-related genes revealed that proteasome and PI3K-AKT signaling pathway were primarily involved in the cellular function of RNF186. Additionally, our research first confirmed that RNF186 may function as an oncogene and contribute to cancer proliferation, migration and invasion in UCEC. In contrast, RNF186 may play an inhibitory role in BRCA progression. This function depends on the ligase activity of RNF186. SIGNIFICANCE: This study suggests that RNF186 is a novel critical target for tumor progression in BRCA and UCEC. It reveals that RNF186 may be associated with tumor immunotherapy, which may provide an effective predictive evaluation of the prognosis of immunotherapy.

SPC25 Functions as a Prognostic-Related Biomarker, and Its High Expression Correlates with Tumor Immune Infiltration and UCEC Progression.

BACKGROUND: Most tumor tissues expressed spindle pole body component 25 (SPC25), one of the four subunits of the NDC80 complex, at greater levels compared to surrounding normal tissues. According to earlier researches, this subunit strongly encouraged tumor cell proliferation and tumor growth, which resulted in worse prognoses in patients with hepatocellular, breast, lung, and prostate cancer. Precisely because SPC25's role in uterine corpus endometrial carcinoma (UCEC) is understudied, we chose to concentrate on UCEC for gaining a more scientific and thorough understanding of SPC25. METHODS: Along with examining SPC25's differential expression, prognostic significance, and biological function in UCEC, our research sought to clarify the underlying mechanism by which SPC25 influences the course of UCEC and patient prognosis from the viewpoints of methylation and immune infiltration. RESULTS: We observed differential expression of SPC25 gene in different clinicopathological features of UCEC and identified SPC25 as a hazard factor for poorer overall survival (OS), disease-specific survival (DSS), and progress free interval (PFI) in UCEC, particularly in its multiple clinical subtypes. In addition, we also discovered that SPC25 and its co-expressed genes mostly engaged in biological processes and signal transduction routes linked to cell cycle and cell division in UCEC. After investigating SPC25's methylation status, we discovered that patients with UCEC had elevated SPC25 expression and a poor prognosis due to hypomethylation of CpG sites in the SPC25 gene sequence. Finally, we investigated SPC25's potential role in immunotherapy and discovered that SPC25 might alter the major immune cell infiltration levels in the tumor microenvironment (TME) by regulating the expression of immunoregulatory molecules and chemokines, which would be beneficial for SPC25 to control the progression of UCEC. CONCLUSIONS: In conclusion, SPC25 was a useful predictive biomarker as well as a possible therapeutic target for UCEC.

SLC8A1, a novel prognostic biomarker and immunotherapy target in RSA and UCEC based on scRNA-seq and pan-cancer analysis.

Background: The field of gynaecological immunology has increasingly focused on recurrent spontaneous abortion (RSA). The complex mechanisms underlying the interaction between RSA and cancer are not well understood. Methods: Weighted gene coexpression network analysis (WGCNA), single-cell RNA sequencing (scRNA-seq), and machine learning algorithms were used for the analysis of RSA decidua samples to identify the hub genes. The expression and distribution of the hub genes were subsequently investigated via the pancancer database TCGA. A prognostic prediction was made to assess the impact of the hub genes on the cancer response, mutation burden, immune microenvironment, immune checkpoint, and chemotherapy. In vitro assays were performed to determine whether SLC8A1 influences HTR-8/SVneo cell proliferation, apoptosis and the concentration of calcium ions. Results: SLC8A1 was identified as a hub gene within RSA and was highly expressed in uterine corpus endometrial carcinoma (UCEC). The efficacy of SLC8A1 as a predictive marker was substantiated by calibration curves and the concordance index. The mutation rate of SLC8A1 was found to be 6 % on the basis of the waterfall plot. Immune analysis revealed notable differences in the fractions of T cells and macrophages between the high- and low-expression groups. Patients classified in the low-risk group exhibited enhanced responsiveness to osimertinib, dasatinib, and ibrutinib. The results of in vitro experiments revealed that SLC8A1 promotes proliferation and inhibits the apoptosis and concentration of calcium ions in HTR-8/SVneo cells. Conclusion: These findings suggest that SLC8A1 may serve as a promising prognostic biomarker and potential target for immunotherapy in the context of RSA and UCEC.

Exploring prognostic microbiota markers in patients with endometrial carcinoma: Intratumoral insights.

Endometrial cancer, a leading gynecological malignancy, is profoundly influenced by the uterine microbiota, a key factor in disease prognosis and treatment. Our study underscores the distinct microbial compositions in endometrial cancer compared to adjacent non-cancerous tissues, revealing a dominant presence of p_Actinobacteria in cancerous tissues as opposed to p_Firmicutes in surrounding areas. Through comprehensive analysis, we identified 485 unique microorganisms in cancer tissues, 26 of which correlate with patient prognosis. Employing univariate Cox regression and LASSO regression analyses, we devised a microbial risk scoring model, effectively stratifying patients into high and low-risk categories, thereby providing predictive insights into their overall survival. We further developed a nomogram that incorporates the microbial risk score along with age, grade, and clinical stage, significantly enhancing the accuracy of our clinical prediction model for endometrial cancer. Moreover, our study delves into the differential immune landscapes of high-risk and low-risk patients. The low-risk group displayed a higher prevalence of activated B cells and increased T cell co-stimulation, indicative of a robust immune response. Conversely, high-risk patients showed elevated tumor immune dysfunction and exclusion scores, suggesting less favorable outcomes in immunotherapy. Notably, the efficacy of IPS-CTLA4 and PD1/PD-L1/PD-L2 blockers was substantially higher in the low-risk group, pointing to a more responsive immunotherapeutic approach. In summary, our research elucidates the unique microbial patterns in endometrial cancer and adjacent tissues, and establishes both a microbial risk score model and a clinical prediction nomogram. These findings highlight the potential of uterine microbiota as a biomarker for customizing treatment strategies, enabling precise interventions for high-risk patients while preventing overtreatment in low-risk cases. This study emphasizes the microbiota's role in tailoring immunotherapy, offering a novel perspective in the treatment and prognosis of endometrial cancer. Significantly, our study's expansive sample analysis from the TCGA-UCEC cohort, employing linear discriminant analysis effect size methodology, not only validates but also enhances our understanding of the microbiota's role in endometrial cancer, paving the way for novel diagnostic and therapeutic approaches in its management.

Investigating novel biomarkers in uterine corpus endometrial carcinoma: in silico analysis and clinical specimens validation via RT-qPCR and immunohistochemistry.

The rising incidence and mortality rate of Uterine Corpus Endometrial Carcinoma (UCEC) pose significant health concerns. CC and CXC chemokines have been linked to tumorigenesis and cancer progression. Recognizing the growing significance of CC and CXC chemokines' diagnostic and prognostic significance in diverse cancer types, our objective was to comprehensively analyze the diagnostic and prognostic values of hub genes from the CC and CXC chemokines in UCEC, utilizing both in silico and clinical samples and cell lines-based approaches. In silico analyses include STRING, Cytoscape, Cytohubba, The Cancer Genome Atlas (TCGA) datasets analysis via the UALCAN, GEPIA, OncoDB, and MuTarget, SurvivalGenie, MEXPRESS, cBioPoratal, TIMER, ENCORI, and DrugBank. Meanwhile, clinical samples and cell lines based analyses include Reverse transcription-quantitative polymerase chain reaction (RT-qPCR), targeted bisulfite sequencing (bisulfite-seq) analysis, and immunohistochemistry (IHC). Through present study, we identified CCL25 (CC motif chemokine ligand 25), CXCL10 (C-X-C motif chemokine ligand 10), CXCL12 (C-X-C motif chemokine ligand 12), and CXCL16 (C-X-C motif chemokine ligand 16) as crucial hub genes among the CC and CXC chemokines. Analyzing the expression data from TCGA, we observed a significant up-regulation of CCL25, CXCL10, and CXCL16 in UCEC samples compared to controls. In contrast, we noted a significant down-regulation of CXCL12 expression in UCEC samples. On clinical UCEC samples and cell lines analysis, the significant higher expression of CCL25, CXCL10, and CXCL16 and significant lower expression of CXCL12 were also denoted in UCEC samples than the controls via RT-qPCR and IHC analyses. Moreover, in silico analysis also confirmed the abnormal promoter methylation levels of the hub genes in TCGA UCEC samples, which was later validated by the clinical samples using targeted based bisulfite-seq analysis. In addition, various additional aspects of the CCL25, CXCL10, CXCL12, and CXCL16 have also been uncovered in UCEC during the present study. Our findings offer novel insights that contribute to the clinical utility of CCL25, CXCL10, CXCL12, and CXCL16 chemokines as potential diagnostic and prognostic biomarkers in UCEC.

Necroptosis-Related LncRNA Signatures for Prognostic Prediction in Uterine Corpora Endometrial Cancer.

Necroptosis is one of the common modes of apoptosis, and it has an intrinsic association with cancer prognosis. However, the role of the necroptosis-related long non-coding RNA LncRNA (NRLncRNAs) in uterine corpora endometrial cancer (UCEC) has not yet been fully elucidated at present. Therefore, the present study is designed to investigate the potential prognostic value of necroptosis-related LncRNAs in UCEC. In the present study, the expression profiles and clinical data of UCEC patients were downloaded from TCGA database to identify the differentially expressed NRLncRNAs associated with overall survival. A LncRNA risk model was constructed via Cox regression analysis, and its prognostic value was evaluated. We have also further evaluated the relationships between the LncRNA features and the related cellular function, related pathways, immune status, and immune checkpoints m6A-related genes. Seven signatures, including PCAT19, CDKN2B-AS1, LINC01936, LINC02178, BMPR1B-DT, LINC00237, and TRPM2-AS, were established to assess the overall survival (OS) of the UCEC in the present study. Survival analysis and ROC curves indicated that the correlated signature has good predictable performance. The normogram could accurately predict the overall survival of the patients with an excellent clinical practical value. Enrichment analysis of gene sets indicated that risk signals were enriched in several immune-related pathways. In addition, the risk characteristics were significantly correlated with immune cells, immune function, immune cell infiltration, immune checkpoints, and some m6A-related genes. This study has identified seven necroptosis-related LncRNA signatures for the first time, providing a valuable basis for a more accurate prognostic prediction of UCEC.

A novel cuproptosis-related gene signature for overall survival prediction in uterine corpus endometrial carcinoma (UCEC).

Cuproptosis is a copper-dependent model of cell death involved in tumor genesis and progression. Its roles in uterine corpus endometrial carcinoma (UCEC) remains elusive. Here, we aimed to explore the expression and prognostic values of cuprotosis-related genes (CRGs) in UCEC. Expression profiles and clinical data of UCEC were downloaded from The Cancer Genome Atlas (TCGA), and randomly divided into testing or training cohort (1:1 ratio). The CRG signature was identified by LASSO regression analysis. The differentially expressed genes and their functional enrichment analysis were performed by the "limma" R package and Metascape, respectively. The immunocytes infiltration was measured by TIMER, and "GSVA" R package. In total, seven differentially expressed prognostic genes of CRGs in UCEC were identified, and four genes (GLS, CDKN2A, PC, and SUCLG1) were selected to construct a predictive model in training cohort. UCEC patients from training and testing cohorts were further divided into high- or low-risk groups according to the median risk score. High-risk group favored poor prognosis compared to low-risk group. Functional enrichment analysis revealed this CRG signature were got involved in the process of cell-cell adhesion and immune activities (e.g., IL-1 signaling pathway, cellular response to cytokine stimulus). Further analyses revealed there were significant differences between high- and low-risk patients regarding immunocytes infiltration, chemokines, and chemokine receptors. Finally, the expression and biological functions of identified CRGs were confirmed by UCEC samples and experimental methods in vitro. In summary, the CRG signature was significantly correlated with patients' overall survival, which could provide insights into the diagnosis and prognosis prediction for UCEC.

Metabolism-associated molecular classification of uterine corpus endometrial carcinoma.

Uterine corpus endometrial carcinoma (UCEC) is one of the most common gynecologic malignancies. Currently, for UCEC cancer, molecular classification based on metabolic gene characteristics is rarely established. Here, we describe the molecular subtype features of UCEC by classifying metabolism-related gene profiles. Therefore, integrative analysis was performed on UCEC patients from the TCGA public database. Consensus clustering of RNA expression data on 2,752 previously reported metabolic genes identified two metabolic subtypes, namely, C1 and C2 subtypes. Two metabolic subtypes for prognostic characteristics, immune infiltration, genetic alteration, and responses to immunotherapy existed with distinct differences. Then, differentially expressed genes (DEGs) among the two metabolic subtypes were also clustered into two subclusters, and the aforementioned features were similar to the metabolic subtypes, supporting that the metabolism-relevant molecular classification is reliable. The results showed that the C1 subtype has high metabolic activity, high immunogenicity, high gene mutation, and a good prognosis. The C2 subtype has some features with low metabolic activity, low immunogenicity, high copy number variation (CNV) alteration, and poor prognosis. Finally, a model was identified, with three gene metabolism-related signatures, which can predict the prognosis. These findings of this study demonstrate a new classification in UCEC based on the metabolic pattern, thereby providing valuable information for understanding UCEC's molecular characteristics.