Integrated bioinformatic and experimental study links cyclin B1/B2 to poor prognosis and immune infiltration in endometrial cancer

ABSTRACTHigh-risk endometrial cancer (EC) is an aggressive disease for which new therapeutic options are needed. Aims of this study were to validate the enhanced immune response in highly mutated ECs and to explore immune profiles in other EC subgroups. We evaluated immune infiltration in 116 high-risk ECs from the TransPORTEC consortium, previously classified into four molecular subtypes: (i) ultramutated POLE exonuclease domain-mutant ECs (POLE-mutant); (ii) hypermutated microsatellite unstable (MSI); (iii) p53-mutant; and (iv) no specific molecular profile (NSMP). Within The Cancer Genome Atlas (TCGA) EC cohort, significantly higher numbers of predicted neoantigens were demonstrated in POLE-mutant and MSI tumors compared with NSMP and p53-mutants. This was reflected by enhanced immune expression and infiltration in POLE-mutant and MSI tumors in both the TCGA cohort (mRNA expression) and the TransPORTEC cohort (immunohistochemistry) with high infiltration of CD8+ (90% and 69%), PD-1+ (73% and 69%) and PD-L1+ immune cells (100% and 71%). Notably, a subset of p53-mutant and NSMP cancers was characterized by signs of an antitumor immune response (43% and 31% of tumors with high infiltration of CD8+ cells, respectively), despite a low number of predicted neoantigens. In conclusion, the presence of enhanced immune infiltration, particularly high numbers of PD-1 and PD-L1 positive cells, in highly mutated, neoantigen-rich POLE-mutant and MSI endometrial tumors suggests sensitivity to immune checkpoint inhibitors.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

BackgroundFat mass and obesity-associated protein (FTO) is a critical N6-methyladenosine (m6A) demethylase that participates in tumorigenesis and is associated with the prognosis of patients in some cancers. However, the key roles of FTO in pan-cancer are still largely obscure.MethodsFTO expression levels in pan-cancer were estimated via the Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), and The Cancer Genome Atlas (TCGA) databases. Univariate survival analysis was used to estimate the effects of FTO on prognosis. In addition, we used the Tumor Immune Evaluation Resource (TIMER) to assess the immune cell infiltration of FTO gene across cancers. The association of FTO expression with immune checkpoint genes expression, DNA mismatch repair (MMR) gene mutation, DNA methyltransferases, microsatellite instability (MSI), and tumor mutational burden (TMB) was investigated using Spearman’s correlation analysis. Moreover, Gene Set Enrichment Analysis (GSEA) was utilized to identify critical pathways in cancers. The STRING website was used to reveal the protein–protein interaction (PPI) network of FTO.ResultsFTO was aberrantly expressed across cancers and survival analysis demonstrated that its expression was associated with clinical prognosis of many cancer patients. Specifically, FTO expression was significantly associated with immune infiltrating cells in colon adenocarcinoma, kidney renal clear cell carcinoma, and liver hepatocellular carcinoma. In addition, FTO expression was significantly associated with immune checkpoint genes expression, MMR, DNA methyltransferases levels, TMB, and MSI in multiple cancers. Moreover, the GSEA unveiled that FTO was involved in the regulation of tumors and immune-related signaling pathways. In addition, several m6A related genes were implicated in the PPI network of FTO.ConclusionFTO was related to patients’ prognosis and tumor immune infiltrates in various cancers, and may serve as a novel and potential prognostic and immune biomarker in human pan-cancer.

Screening and identification of potential hub genes and immune cell infiltration in the synovial tissue of rheumatoid arthritis by bioinformatic approach

Objective In this study, our goal was to explore the role of metabolism-associated genes in colorectal cancer (CRC) and construct a prognostic model for patients with CRC. Methods Differential expression analysis was conducted using RNA-sequencing data from The Cancer Genome Atlas (TCGA) dataset. Enrichment analyses were performed to determine the function of dysregulated metabolism-associated genes. The protein-protein interaction (PPI) network, Kaplan-Meier curves, and stepwise Cox regression analyses identified key metabolism-associated genes. A prognostic model was constructed using LASSO Cox regression analysis and visualized as a nomogram. Survival analyses were conducted in the TCGA and Expression Omnibus (GEO) cohorts to demonstrate the predictive ability of the model. Results A total of 332 differentially expressed metabolism-associated genes in CRC were screened from the TCGA cohort. Differentially expressed metabolism-associated genes mainly participate in the metabolism of nucleoside phosphate, ribose phosphate, lipids, and fatty acids. A PPI network was constructed out of 328 key genes. A prognostic model was established based on five prognostic genes (ALAD, CHDH, ISYNA1, NAT1, and P4HA1) and was demonstrated to predict survival in the TCGA and GEO cohorts accurately. Conclusion The metabolism-associated prognostic model can predict the survival of patients with CRC. Our work supplements previous work focusing on determining prognostic factors of CRC and lays a foundation for further mechanistic exploration.

BackgroundProstate cancer (PCa) is one of the most common cancers and the fifth leading cause of cancer-related death in men. Immune responses in the tumor microenvironment are hypothesized to be related to the prognosis of PCa patients; however, no studies are available to confirm the same. In this study, we aimed to explore the potential link between these two factors and identify new biomarkers to estimate the survival rate of PCa patients.MethodsA total of 490 cases were obtained from The Cancer Genome Atlas (TCGA) database. The gene expression data were analyzed by the ESTIMATE algorithm to evaluate the immune and stromal scores. The survival rate was calculated according to the case-specific clinical data. Enrichment analysis was performed to discover the main biological processes and signaling pathways of immune responses. We further identified and analyzed hub genes in the protein-protein interaction (PPI) network and evaluated their prognostic values.ResultsImmune score significantly correlated with immune cell infiltration and overall survival of PCa patients. The genes CXCR4 and GPR183, identified as hub genes in the PPI network, correlated with immune cell infiltration and prognosis of PCa patients.ConclusionCXCR4 and GPR183 participate in immune cell infiltration and function in PCa patients. The immune score, as well as the expression of CXCR4 and GPR183 in prostate cancer tissues, could be potential indexes for the prognosis of prostate cancer.

BackgroundThe occurrence and progression of hepatic fibrosis (HF) is accompanied by inflammatory damage. Immune genes play a pivotal role in fibrogenesis and inflammatory damage in HF by regulating immune cell infiltration. However, the immune mechanisms of HF are inadequately studied. Therefore, this research aims to identify the immune genes and biological pathway which involved in fibrosis formation and inflammatory damage in HF and explore immune target-based therapeutics for HF.MethodsThe expression dataset GSE84044 of HF was downloaded from the GEO database. The crucial module genes for HF were screened according to weighted gene co-expression network analysis (WGCNA). The crucial module genes were mapped to immune-related genes obtained from the ImmPort database to obtain the hepatic fibrosis immune genes (HFIGs). In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed on HFIGs. Then, the protein-protein interaction (PPI) network was conducted on HFIGs and hub genes were identified from the PPI network. Moreover, immune infiltration analysis was performed to identified correlation between hub gene and immune cell infiltration. To verify the reliability of the GSE84044 expression profile data analysis, a rat model of CCl4-induced HF was established, followed by transcriptome sequencing and immunofluorescence analysis and quantitative reverse transcription (q-PCR) experiments were performed in HF rats and normal rat liver tissues. Finally, CMAP platform was used to explore immune target-based therapeutics for HF.ResultsIn the bioinformatics analysis of GSE84044 data, 98 HFIGs were screened. These genes were mainly involved in inflammation-related biological pathways such as NOD-like receptor signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway and PI3K-Akt signaling pathway. From the PPI network, 10 hub genes were identified, including CXCL8, IL18, CXCL10, CD8A, IL7, PTPRC, CCL5, IL7R, CXCL9 and CCL2. Immune infiltration analysis showed that immune cells like neutrophils, natural killer (NK) cells, macrophages M1 and macrophages M2 were significantly correlated with the hepatic fibrosis process and hub gene expression was significantly correlated with these immune cells. Notably, most of the biological pathways HFIGs riched and all the hub gene expression except CXCL8 were validated in subsequent transcriptome and qRCR experiments. Finally, 15 small molecule compounds with the potential to reverse the high expression of hub genes were screen out as potential therapeutic agents for HF.ConclusionThe immune genes CXCL8, IL18, CXCL10, CD8A, IL7, PTPRC, CCL5, IL7R, CXCL9 and CCL2 may play an essential role in the fibrosis formation and inflammatory damage in HF. The outcomes of this research provide a basis for the study of the immune mechanisms of HF and contribute to the diagnosis and prevention and treatment of HF in clinical practice.

Endometriosis (EMs) is a chronic inflammatory disease characterized by the presence of endometrial tissue in the non-uterine cavity, resulting in dysmenorrhea, pelvic pain, and infertility. Epidemiologic data have suggested the correlation between EMs and recurrent pregnancy loss (RPL), but the pathological mechanism is unclear. This study aims to investigate the potential biomarkers and immune infiltration in EMs and RPL, providing a basis for early detection and treatment of the two diseases. Two RPL and six EMs transcriptomic datasets from the Gene Expression Omnibus (GEO) database were used for differential analysis via limma package, followed by weighted gene co-expression network analysis (WGCNA) for key modules screening. Protein-protein interaction (PPI) network and two machine learning algorithms were applied to identify the common core genes in both diseases. The diagnostic capabilities of the core genes were assessed by receiver operating characteristic (ROC) curves. Moreover, immune cell infiltration was estimated using CIBERSORTx, and the Cancer Genome Atlas (TCGA) database was employed to elucidate the role of key genes in endometrial carcinoma (EC). 26 common differentially expressed genes (DEGs) were screened in both diseases, three of which were identified as common core genes (MAN2A1, PAPSS1, RIBC2) through the combination of WGCNA, PPI network, and machine learning-based feature selection. The area under the curve (AUC) values generated by the ROC indicates excellent diagnostic powers in both EMs and RPL. The key genes were found to be significantly associated with the infiltration of several immune cells. Interestingly, MAN2A1 and RIBC2 may play a predominant role in the development and prognostic stratification of EC. We identified three key genes linking EMs and RPL, emphasizing the heterogeneity of immune infiltration in the occurrence of both diseases. These findings may provide new mechanistic insights or therapeutic targets for further research of EMs and RPL.

Diabetic retinopathy (DR), a microvascular disorder linked to diabetes, is on the rise globally. Oxidative stress and immune cell infiltration are linked to illness initiation and progression, according to recent study. This study investigated biomarkers connected to DR and oxidative stress and their connection with immune cell infiltration using bioinformatics analysis and found possible therapeutic medications. The Gene Expression Omnibus (GEO) database was used to obtain the gene expression data for DR. Differentially expressed genes (DEGs) and oxidative stress (OS)-related genes were intersected. The Enrichment analyses concentrate on OS-related differentially expressed genes (DEOSGs). Analysis of protein-protein interaction (PPI) networks and machine learning algorithms were used to identify hub genes. Single-gene Gene Set Enrichment Analysis (GSEA) identified biological functions, while nomograms and ROC curves assessed diagnostic potential. Immune infiltration analysis and regulatory networks were constructed. Drug prediction was validated through molecular docking, and hub gene expression was confirmed in dataset and animal models. Compared to the control group, 91 DEOSGs were found. Enrichment analyses showed that these DEOSGs were largely connected to oxidative stress response, PI3K/Akt pathway, inflammatory pathways, and immunological activation. Four hub genes were found via PPI networks and machine learning. These hub genes were diagnostically promising according to nomogram and ROC analysis. Analysis of immune cell infiltration highlighted the role of immune cells. Gene regulatory networks for transcription factor (TF) and miRNA were created. Using structural data, molecular docking shows potential drugs and hub genes have high binding affinity. Dataset analysis, Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) and Western Blot (WB) confirmed the CCL4 expression difference between DR and controls. CCL4 was identified as potential oxidative stress-related biomarker in DR, providing new insights for DR diagnosis and treatment.

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.

IntroductionEndometrial hyperplasia (EH) is the recognized precursor lesion of most endometrial endometrioid cancers, but it remains unclear if having a prior EH diagnosis impacts upon survival. We aimed to quantify...

Increasing numbers of biomarkers have been identified in various cancers. However, biomarkers associated with endometrial carcinoma (EC) remain largely to be explored. In the current research, we downloaded the RNA-seq data and corresponding clinicopathological features from the Cancer Genome Atlas (TCGA) database. We conducted an expression analysis, which resulted in RILPL2 as a novel diagnostic biomarker in EC. The dysregulation of RILPL2 in EC was also validated in multiple datasets. The correlations between clinical features and RILPL2 expression were assessed by logistic regression analysis. Then, Kaplan-Meier analysis, univariate and multivariate Cox regression analysis were performed to estimate prognostic values of RILPL2 in the TCGA cohort, which revealed that increased level of RILPL2 was remarkably associated with better prognosis and could act as an independent prognostic biomarker in patients with EC. Moreover, correlation analysis of RILPL2 and tumor-infiltrating immune cells (TIICs) indicated that RILPL2 might play a critical role in regulating immune cell infiltration in EC and is related to immune response. Besides, high methylation level was a significant cause of low RILPL2 expression in EC. Subsequently, weighted gene co-expression network analysis (WGCNA) and enrichment analysis were conducted to explore the RILPL2-involved underlying oncogenic mechanisms, and the results indicated that RILPL2 mainly regulated cell cycle. In conclusion, our findings provided evidence that downregulation of RILPL2 in EC is an indicator of adverse prognosis and RILPL2 may act as a promising target for the therapeutics of EC.

BackgroundThreonine and tyrosine kinase (TTK) is associated with invasion and metastasis in various tumors. However, the prognostic importance of TTK and its correlation with immune infiltration in endometrial cancer (EC) remain unclear.MethodsThe expression profile of TTK was analyzed using data from The Cancer Genome Atlas (TCGA) and the Clinical Proteome Cancer Analysis Consortium (CPTAC). TTK protein and mRNA levels were verified in EC cell lines. Receiver operating characteristic (ROC) curve analysis was used to evaluate the ability of TTK to distinguish between normal and EC tissues. K-M survival analysis was also conducted to evaluate the impact of TTK on survival outcomes. Protein‒protein interaction (PPI) networks associated with TTK were explored using the STRING database. Functional enrichment analysis was performed to elucidate the biological functions of TTK. TTK mRNA expression and immune infiltration correlations were examined using the Tumor Immune Estimation Resource (TIMER) and the Tumor-Immune System Interaction Database (TISIDB).ResultsTTK expression was significantly greater in EC tissues than in adjacent normal tissues. Higher TTK mRNA expression was associated with tumor metastasis and advanced TNM stage. The protein and mRNA expression of TTK was significantly greater in tumor cell lines than in normal endometrial cell lines. ROC curve analysis revealed high accuracy (94.862%), sensitivity (95.652%), and specificity (94.894%) of TTK in differentiating EC from normal tissues. K-M survival analysis demonstrated that patients with high TTK expression had worse overall survival (OS) and disease-free survival (DFS) rates. Correlation analysis revealed that TTK mRNA expression was correlated with B cells and neutrophils.ConclusionTTK upregulation is significantly associated with poor survival outcomes and immune infiltration in patients with EC. TTK can serve as a potential biomarker for poor prognosis and a promising immunotherapy target in EC. Further investigation of the role of TTK in EC may provide valuable insights for therapeutic interventions and personalized treatment strategies.

PurposeThe aim of our study was to assess overall survival and cancer-specific survival in endometrial cancer patients with type 2 diabetes mellitus (T2DM) using metformin.MethodsPatients with endometrial cancer and T2DM during 2000–2012 period were identified from the Lithuanian Cancer Registry and the National Health Insurance Fund database. Cancer-specific and overall survival were primary outcomes.ResultsIn our study we included 6287 women with endometrial cancer out of whom 664 were diagnosed with T2DM (598 metformin users and 66 never users). During follow-up (mean follow-up time was 8.97 years), no differences in risk of endometrial cancer specific mortality was observed in diabetic patients treated with metformin (Hazard Ratio (HR) 0.87, 95% Confidence Interval (CI) 0.70–1.07). Overall mortality in the diabetic metformin ever users’ group was significantly higher compared with the non-diabetic endometrial cancer women (HR 1.17, 95% CI 1.03–1.32) and in the group of metformin never users with T2DM (HR 1.42, 95% CI 1.07–1.87).ConclusionOur study results suggest no beneficial impact on overall and cancer-specific survival in endometrial cancer patients who were treated with metformin as part of their diabetes treatment.

Background: Endometrial cancer (EC) is one of the most common type of female genital malignancies. The purpose of the present study was to reveal the underlying oncogene and mechanism that played a pivotal role in postmenopausal EC patients.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted using the microarray dataset and clinical data of EC patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify significant gene modules and hub genes associated with postmenopausal status in EC patients. LASSO regression was conducted to build and validate the risk model. Finally, expression of hub gene was validated in pre- and post-menopausal EC patients in our center.Results: 1240 common genes were used to construct the WGCNA model. According to the WGCNA results, we identified a brown module with 471 genes which was significantly associated with postmenopausal status in EC patients. Furthermore, we constructed an 11-gene risk signature to predict the overall survival of EC patients. The Kaplan–Meier curve and area under the ROC curve (AUC) of this model showed high accuracy in prediction. We also validate the risk model in patients in our center and it also has a high accuracy. Among the 11 genes, PKD1 was recognized as a potential biomarker in the progression of EC patients with postmenopausal status.Conclusion: Taken together, we uncovered a common PKD1-mediated mechanism underlying postmenopausal EC patients’ progression by integrated analyses. This finding may improve targeted therapy for EC patients.