Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma

Osteosarcoma-Associated Immune Genes as Potential Immunotherapy and Prognosis Biomarkers

Immune-modulating therapies exhibit abundant promise compared to traditional treatment for osteosarcoma. We aim to establish an immune-related prognostic signatures in osteosarcoma. We identified the differentially expressed genes in osteosarcoma compared with normal controls using the GEO dataset. The intersection with immune-related genes was considered as differentially expressed immune genes. Potential prognosis-related differentially expressed genes were first analyzed with the multifactor Cox regression and then the step function performed the iteration. The best model was finally chosen as the immunological risk score signature model. And finally, we evaluated the correlation of genes in the prognostic model with immune cells, common immune checkpoints, and immune checkpoint blockade responses. We identified 1527 significantly upregulated and 2407 significantly downregulated genes in osteosarcoma compared to normal samples. In the 258 differentially expressed immune genes, 20 genes with independent prognostic significance were included in the step function. Finally, we constructed a prognostic signature for overall survival based on five immune genes (JAG2, PLXNB1, CMKLR1, NUDT6, and PSMC4) in osteosarcoma. These five genes are closely related to immune infiltration. Osteosarcoma with high JAG2 expression or low CMKLR1 expression may be associated with better immune checkpoint blockade response. JAG2 overexpression or CMKLR1 inhibition induced sensitivity to PD-1 antibody in osteosarcoma cells. We constructed a prognosis prediction signature containing five immune-related genes (JAG2, PLXNB1, CMKLR1, NUDT6, and PSMC4) with a significant prognostic value in osteosarcoma. Significant differences in immune infiltration and immunotherapy responses were identified between groups with different levels of these genes.

Deciphering DNA repair gene mutational landscape in uterine corpus endometrial carcinoma patients using next generation sequencing

Uterine Corpus Endometrial Carcinoma (UCEC) is a significant health concern with a complex genetic landscape impacting disease susceptibility and progression. This study aimed to unravel the spectrum of DNA repair gene mutations in Pakistani UCEC patients through Next Generation Sequencing (NGS) and explore their potential functional consequences via downstream analyses. NGS analysis of genomic DNA from 30 UCEC patients was conducted to identify clinically significant pathogenic mutations in DNA repair genes. This analysis revealed mutations in 4 key DNA repair genes: BRCA1, BRCA2, APC, and CDH1. Kaplan-Meier (KM) analysis was employed to assess the prognostic value of these mutations on patient overall survival (OS) in UCEC. To delve into the functional impact of these mutations, we performed RT-qPCR, immunohistochemistry (IHC), and western blot analyses on the mutated UCEC samples compared to their non-mutated counterparts. These results unveiled the up-regulation in the expression of the mutated genes, suggesting a potential association between the identified mutations and enhanced gene activity. Additionally, targeted bisulfite sequencing analysis was utilized to evaluate DNA methylation patterns in the promoters of the mutated genes. Strikingly, hypomethylation in the promoters of BRCA1, BRCA2, APC, and CDH1 was observed in the mutated UCEC samples relative to the non-mutated, indicating the involvement of epigenetic mechanisms in the altered gene expression. In conclusion, this study offers insights into the genetic landscape of DNA repair gene mutations in Pakistani UCEC patients. The presence of pathogenic mutations in BRCA1, BRCA2, APC, and CDH1, coupled with their down-regulation and hypermethylation, suggests a convergence of genetic and epigenetic factors contributing to genomic instability in UCEC cells. These findings enhance our understanding of UCEC susceptibility and provide potential avenues for targeted therapeutic interventions in Pakistani UCEC patients.

Mechanism of action of icaritin on uterine corpus endometrial carcinoma based on network pharmacology and experimental evaluation

Background

Uterine corpus endometrial carcinoma (UCEC) belongs to a group of epithelial malignant tumors. Icaritin is the main active compound of Epimedii Folium. Icaritin has been utilized to induce UCEC cells to death.

Methods

We wished to identify potential targets for icaritin in the treatment of UCEC, as well as to provide a groundwork for future studies into its pharmacologic mechanism of action. Network pharmacology was employed to conduct investigations on icaritin. Target proteins were chosen from the components of icaritin for UCEC treatment. A protein-protein interaction (PPI) network was established using overlapping genes. Analyses of enrichment of function and signaling pathways were undertaken using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, respectively, to select "hub genes". Finally, experiments were carried out to ascertain the effect of icaritin on endometrial cancer (HEC-1-A) cells.

Results

We demonstrated that icaritin has bioactive components and putative targets that are therapeutically important. Icaritin treatment induced sustained activation of the phosphoinositide 3-kinase/protein kinase B (PI3K/Akt pathway) and inhibited growth of HEC-1-A cells.

Conclusion

Our data provide a rationale for preclinical and clinical evaluations of icaritin for UCEC therapy.

The prognostic value of 19S ATPase proteasome subunits in acute myeloid leukemia and other forms of cancer

Introduction

The ubiquitin-proteasome system (UPS) is an intracellular organelle responsible for targeted protein degradation, which represents a standard therapeutic target for many different human malignancies. Bortezomib, a reversible inhibitor of chymotrypsin-like proteasome activity, was first approved by the FDA in 2003 to treat multiple myeloma and is now used to treat a number of different cancers, including relapsed mantle cell lymphoma, diffuse large B-cell lymphoma, colorectal cancer, and thyroid carcinoma. Despite the success, bortezomib and other proteasome inhibitors are subject to severe side effects, and ultimately, drug resistance. We recently reported an oncogenic role for non-ATPase members of the 19S proteasome in chronic myeloid leukemia (CML), acute myeloid leukemia (AML), and several different solid tumors. In the present study, we hypothesized that ATPase members of the 19S proteasome would also serve as biomarkers and putative therapeutic targets in AML and multiple other cancers.

Methods

We used data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) available at UALCAN and/or GEPIA2 to assess the expression and prognostic value of proteasome 26S subunit, ATPases 1-6 (PSMC1-6) of the 19S proteasome in cancer. UALCAN was also used to associate PSMC1-6 mRNA expression with distinct clinicopathological features. Finally, cBioPortal was employed to assess genomic alterations of PSMC genes across different cancer types.

Results

The mRNA and protein expression of PSMC1-6 of the 19S proteasome were elevated in several cancers compared with normal controls, which often correlated with worse overall survival. In contrast, AML patients demonstrated reduced expression of these proteasome subunits compared with normal mononuclear cells. However, AML patients with high expression of PSMC2-5 had worse outcomes.

Discussion

Altogether, our data suggest that components of the 19S proteasome could serve as prognostic biomarkers and novel therapeutic targets in AML and several other human malignancies.

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.

CXCL9 correlates with antitumor immunity and is predictive of a favorable prognosis in uterine corpus endometrial carcinoma

Background

The C-X-C motif chemokine ligand-9 (CXCL9) is related to the progression of multiple neoplasms. Yet, its biological functions in uterine corpus endometrioid carcinoma (UCEC) remain shrouded in confusion. Here, we assessed the prognostic significance and potential mechanism of CXCL9 in UCEC.

Methods

Firstly, bioinformatics analysis of the public cancer database, including the Cancer Genome Atlas / the Genotype-Tissue Expression project (TCGA+ GTEx, n=552) and Gene Expression Omnibus (GEO): GSE63678 (n=7), were utilized for the CXCL9 expression-related analysis in UCEC. Then, the survival analysis of TCGA-UCEC was performed. Futher, the gene set enrichment analysis (GSEA) was carried out to reveal the potential molecular signaling pathway in UCEC associated with CXCL9 expression. Moreover, the immunohistochemistry (IHC) assay of our validation cohort (n=124) from human specimens were used to demonstrate the latent significance of CXCL9 in UCEC.

Results

The bioinformatics analysis suggested that CXCL9 expression was significantly upregulated in UCEC patients; and hyper-expression of CXCL9 was related to prolonged survival. the GSEA enrichment analysis showed various immune response-related pathways, including T/NK cell, lymphocyte activation, cytokine-cytokine receptor interaction network, and chemokine signaling pathway, mediated by CXCL9. In addition, the cytotoxic molecules (IFNG, SLAMF7, JCHAIN, NKG7, GBP5, LYZ, GZMA, GZMB, and TNF3F9) and the immunosuppressive genes (including PD-L1) were positively related to the expression of CXCL9. Further, the IHC assay indicated that the CXCL9 protein expression was mainly located in intertumoral and significantly upregulated in the UCEC patients; UCEC with high intertumoral CXCL9 cell abundance harbored an improved prognosis; a higher ratio of anti-tumor immune cells (CD4⁺, CD8⁺, and CD56⁺ cell) and PD-L1 was found in UCEC with CXCL9 high expression.

Conclusion

Overexpressed CXCL9 correlates with antitumor immunity and is predictive of a favorable prognosis in UCEC. It hinted that CXCL9 may serve as an independent prognostic biomarker or therapeutic target in UCEC patients, which augmented anti-tumor immune effects to furnish survival benefits.

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.

Integrated bioinformatics analysis and experimental validation reveals fatty acid metabolism-related prognostic signature and immune responses for uterine corpus endometrial carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is the third most common gynecologic malignancy. Fatty acid metabolism (FAM) is an essential metabolic process in the immune microenvironment that occurs reprogramming in the presence of tumor signaling and nutrient competition. This study aimed to identify the fatty acid metabolism-related genes (FAMGs) to develop a risk signature for predicting UCEC.

Methods

The differentially expressed FAMGs between UCEC samples and controls from TCGA database were discovered. A prognostic signature was then constructed by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Based on the median risk score, UCEC samples were categorized into high- and low-FAMGs groups. Kaplan-Meier (K-M) curve was applied to determine patients’ overall survival (OS). The independent prognostic value was assessed by uni- and multivariate analyses. The associations between the risk score and immune status, immune score, and drug resistance were evaluated. Quantitative Real-time PCR (qRT-PCR) was utilized to confirm FAMGs expression levels in UCEC cells.

Results

We built a 10-FAMGs prognostic signature and examined the gene mutation and copy number variations (CNV). Patients with a high-FAMGs had a worse prognosis compared to low-FAMGs patients in TCGA train and test sets. We demonstrated that FAMGs-based risk signature was a significant independent prognostic predictor of UCEC. A nomogram was also created incorporating this risk model and clinicopathological features, with high prognostic performance for UCEC. The immune status of each group was varied, and immune score was higher in a low-FAMGs group. HLA-related genes such as DRB1, DMA, DMB, and DQB2 had higher expression levels in the low-FAMGs group. Meanwhile, high-FAMGs patients were likely to response more strongly to the targeted drugs Bortezomib, Foretinib and Gefitinib. The qRT-PCR evidence further verified the significant expression of FAMGs in this signature.

Conclusions

A FAMGs-based risk signature might be considered as an independent prognostic indicator to predict UCEC prognosis, evaluate immune status and provide a new direction for therapeutic strategies.

Development of a 4-miRNA prognostic signature for endometrial cancer

To develop an effective uterine corpus endometrial carcinoma (UCEC) risk assessment tool to monitor treatment outcomes. Limma package was used to analyze differentially expressed microRNAs (miRNAs) between UCEC tissues and normal tissues in the TCGA database. According to univariate Cox risk regression, least absolute shrinkage, and selection operator (LASSO) Cox analysis were performed to screen prognostic miRNAs and construct a risk scoring model. The prognostic performance of signature was evaluated by Kaplan-Meier and receiver operating characteristic. Multivariate Cox regression analysis was used to determine the independent prognostic factors of UCEC. Nomogram was constructed according to age, clinical stage, and risk score. A 4-miRNA signature based on miR-31-5p, miR-34a-5p, miR-26a-1-3p and miR-4772-3p was established. Risk scores of each patient were calculated by the 4-miRNA signature. After z-score, the patients were divided into high- and low-risk groups. The overall survival of high-risk patients was significantly shorter than that of low-risk patients, pointing to the high performance and independence of the 4-miRNA signature in predicting UCEC prognosis. The nomogram showed a high accuracy in predicting overall survival of UCEC patients. We developed a 4-miRNA signature that could effectively predict the prognosis of UCEC.

Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.

Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.

Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.

Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.

HAMP as a Prognostic Biomarker for Colorectal Cancer Based on Tumor Microenvironment Analysis

Colorectal cancer (CRC) is the most common digestive tumor in the world and has a high mortality rate. The development and treatment of CRC are related to the immune microenvironment, but immune response-related prognostic biomarkers are lacking. In this study, we used The Cancer Genome Atlas (TCGA) to explore the tumor microenvironment (TME) and weighted gene coexpression network analysis (WGCNA) to identify significant prognostic genes. We also identified differentially expressed genes in the TCGA data and explored immune-related genes and transcription factors (TFs). Then, we built a TF regulatory network and performed a comprehensive prognostic analysis of an lncRNA-associated competitive endogenous RNA network (ceRNA network) to build a prognostic model. CCR8 and HAMP were identified both in the WGCNA key module and as immune-related genes. HAMP had good prognostic value for CRC and was highly expressed in CRC tissues and had a negative correlation with CD4⁺ T cells and M0 macrophages based on immunohistochemistry and immunofluorescence staining of clinical specimens.We found that HAMP had high prognostic and therapeutic target value for CRC and was associated with liver metastasis. These analysis results revealed that HAMP may be a candidate immune-related prognostic biomarker for CRC.

A 13-Gene Signature Based on Estrogen Response Pathway for Predicting Survival and Immune Responses of Patients With UCEC

Background: Accumulating evidence suggests that anti-estrogens have been effective against multiple gynecological diseases, especially advanced uterine corpus endometrial carcinoma (UCEC), highlighting the contribution of the estrogen response pathway in UCEC progression. This study aims to identify a reliable prognostic signature for potentially aiding in the comprehensive management of UCEC.

Methods: Firstly, univariate Cox and LASSO regression were performed to identify a satisfying UCEC prognostic model quantifying patients’ risk, constructed from estrogen-response-related genes and verified to be effective by Kaplan-Meier curves, ROC curves, univariate and multivariate Cox regression. Additionally, a nomogram was constructed integrating the prognostic model and other clinicopathological parameters. Next, UCEC patients from the TCGA dataset were divided into low- and high-risk groups according to the median risk score. To elucidate differences in biological characteristics between the two risk groups, pathway enrichment, immune landscape, genomic alterations, and therapeutic responses were evaluated to satisfy this objective. As for treatment, effective responses to anti-PD-1 therapy in the low-risk patients and sensitivity to six chemotherapy drugs in the high-risk patients were demonstrated.

Results: The low-risk group with a relatively favorable prognosis was marked by increased immune cell infiltration, higher expression levels of HLA members and immune checkpoint biomarkers, higher tumor mutation burden, and lower copy number alterations. This UCEC prognostic signature, composed of 13 estrogen-response-related genes, has been identified and verified as effective.

Conclusion: Our study provides molecular signatures for further functional and therapeutic investigations of estrogen-response-related genes in UCEC and represents a potential systemic approach to characterize key factors in UCEC pathogenesis and therapeutic responses.

Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification

Ribonucleic acid Sequencing (RNA-Seq) analysis is particularly useful for obtaining insights into differentially expressed genes. However, it is challenging because of its high-dimensional data. Such analysis is a tool with which to find underlying patterns in data, e.g., for cancer specific biomarkers. In the past, analyses were performed on RNA-Seq data pertaining to the same cancer class as positive and negative samples, i.e., without samples of other cancer types. To perform multiple cancer type classification and to find differentially expressed genes, data for multiple cancer types need to be analyzed. Several repositories offer RNA-Seq data for various cancer types. In this paper, data from the Mendeley data repository for five cancer types are analyzed. As a first step, RNA-Seq values are converted to 2D images using normalization and zero padding. In the next step, relevant features are extracted and selected using Deep Learning (DL). In the last phase, classification is performed, and eight DL algorithms are used. Results and discussion are based on four different splitting strategies and k-fold cross validation for each DL classifier. Furthermore, a comparative analysis is performed with state of the art techniques discussed in literature. The results demonstrated that classifiers performed best at 70–30 split, and that Convolutional Neural Network (CNN) achieved the best overall results. Hence, CNN is the best DL model for classification among the eight studied DL models, and is easy to implement and simple to understand.

Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with low survival rate and poor prognosis. The traditional clinicopathological staging is insufficient to estimate the prognosis of UCEC. It is necessary to select a more effective prognostic signature of UCEC to predict the prognosis and immunotherapy effect of UCEC.

Methods

CIBERSORT and weighted correlation network analysis (WGCNA) algorithms were combined to screen modules related to regulatory T (Treg) cells. Subsequently, univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the genes in key modules. The difference in overall survival (OS) between high- and low-risk patients was analyzed by Kaplan-Meier analysis. The Tregs-related risk signature (TRRS) was screened by uni- and multivariate Cox analyses. Afterward, we analyzed the expression difference of TRRS and verified its ability to predict the prognosis of UCEC and the effect of immunotherapy.

Results

Red module has the highest correlation with Tregs among all clustered modules. Pathways enrichment indicated that the related processes of UCEC were primarily associated to the immune system. Eight genes (ZSWIM1, NPRL3, GOLGA7, ST6GALNAC4, CDC16, ITPK1, PCSK4, and CORO1B) were selected to construct TRRS. We found that this TRRS is a significantly independent prognostic factor of UCEC. Low-risk patients have higher overall survival than high-risk patients. The immune status of different groups was different, and tumor-related pathways were enriched in patients with higher risk score. Low-risk patients are more likely take higher tumor mutation burden (TMB). Meanwhile, they are more sensitive to chemotherapy than patients with high-risk score, which indicated a superior prognosis. Immune checkpoints such as PD-1, CTLA4, PD-L1, and PD-L2 all had a higher expression level in low-risk group. TRRS expression really has a relevance with the sensitivity of UCEC patients to chemotherapeutic drugs.

Conclusion

We developed and validated a TRRS to estimate the prognosis and reflect the immune status of UCEC, which could accurately assess the prognosis of patients with UCEC and supply personalized treatments for them.

Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer

Background

Infiltrating immune and stromal cells are important components of the endometrial cancer (EC) microenvironment, which has a significant effect on the biological behavior of EC, suggesting that unique immune-related genes may be associated with the prognosis of EC. However, the association of immune-related genes with the prognosis of EC has not been elucidated. We attempted to identify immune-related genes with potentially prognostic value in EC using The Cancer Genome Atlas database and the relationship between immune microenvironment and EC.

Methods

We analyzed 578 EC samples from TCGA database and used weighted gene co-expression network analysis to screen out immune-related genes. We constructed a protein–protein interaction network and analyzed it using STRING and Cytoscape. Immune-related genes were analyzed through conjoint Cox regression and random forest algorithm analysis were to identify a multi-gene prediction model and stratify low-risk and high-risk groups of EC patients. Based on these data, we constructed a nomogram prediction model to improve prognosis assessment. Evaluation of Immunological, gene mutations and gene enrichment analysis were applied on these groups to quantify additional differences.

Results

Using conjoint Cox regression and random forest algorithm, we found that TRBC2, TRAC, LPXN, and ARHGAP30 were associated with the prognosis of EC and constructed four gene risk models for overall survival and a consistent nomogram. The time-dependent receiver operating characteristic curve analysis revealed that the area under the curve for 1-, 3-, and 5-y overall survival was 0.687, 0.699, and 0.76, respectively. These results were validated using a validation cohort. Immune-related pathways were mostly enriched in the low-risk group, which had higher levels of immune infiltration and immune status.

Conclusion

Our study provides new insights for novel biomarkers and immunotherapy targets in EC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08935-w.

Identification of the Ferroptosis-Associated Gene Signature to Predict the Prognostic Status of Endometrial Carcinoma Patients

Endometrial carcinoma (EC) is one of the most common gynecological carcinomas. As previously described, ferroptosis was reported to exhibit a significant association with the development of malignant neoplasms. Nevertheless, there are few studies towards the association between the implication of ferroptosis-related genes (FRGs) and the prognostic status of patients with EC. Our study demonstrated that ferroptosis-related genes were evidently differently expressed in EC. Further analysis showed that SLC7A11, SAT1, CDKN1A, and TP5MC3 expression was linked to the low stage, grade of pTNM, and longer survival time. Bioinformatics analysis demonstrated that these ferroptosis-related regulators played a crucial role in EC by modulating multiple biological processes, such as cell cycle, citrate cycle (TCA cycle), metabolism-related pathways, ERK activation, p53 signaling pathway, cellular senescence, TAp63 pathway, and Notch signaling pathway. Of note, our results showed that ATP5MC3, CDKN1A, and SLC7A11 expression was dramatically positively related with the tumor mutational burden (TMB) score in EC. However, we did not observe a significant correlation between SAT1 and the TMB score in EC. These findings for the first time demonstrated that ferroptosis was displayed crucially in EC progression. We speculated that our findings offered novel targets and strategies for personalized treatment.

Xanthine dehydrogenase as a prognostic biomarker related to tumor immunology in hepatocellular carcinoma

Background

Xanthine dehydrogenase (XDH) is a critical enzyme involved in the oxidative metabolism of purines, pterin and aldehydes and a central component of the innate immune system. However, the prognostic value of XDH in predicting tumor-infiltrating lymphocyte abundance, the immune response, and survival in different cancers, including hepatocellular carcinoma (HCC), is still unclear.

Methods

XDH expression was analyzed in multiple databases, including Oncomine, the Tumor Immune Estimation Resource (TIMER), the Kaplan–Meier plotter database, the Gene Expression Profiling Interactive Analysis (GEPIA) database, and The Cancer Genome Atlas (TCGA). XDH-associated transcriptional profiles were detected with an mRNA array, and the levels of infiltrating immune cells were validated by immunohistochemistry (IHC) of HCC tissues. A predictive signature containing multiple XDH-associated immune genes was established using the Cox regression model.

Results

Decreased XDH mRNA expression was detected in human cancers originating from the liver, bladder, breast, colon, bile duct, kidney, and hematolymphoid system. The prognostic potential of XDH mRNA expression was also significant in certain other cancers, including HCC, breast cancer, kidney or bladder carcinoma, gastric cancer, mesothelioma, lung cancer, and ovarian cancer. In HCC, a low XDH mRNA level predicted poorer overall survival, disease-specific survival, disease-free survival, and progression-free survival. The prognostic value of XDH was independent of the clinical features of HCC patients. Indeed, XDH expression in HCC activated several immune-related pathways, including the T cell receptor, PI3K-AKT, and MAPK signaling pathways, which induced a cytotoxic immune response. Importantly, the microenvironment of XDH^high HCC tumors contained abundant infiltrating CD8 + T cells but not exhausted T cells. A risk prediction signature based on multiple XDH-associated immune genes was revealed as an independent predictor in the TCGA liver cancer cohort.

Conclusion

These findings suggest that XDH is a valuable prognostic biomarker in HCC and other cancers and indicate that it may function in tumor immunology. Loss of XDH expression may be an immune evasion mechanism for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02173-7.

An immune-related pseudogene signature to improve prognosis prediction of endometrial carcinoma patients

Background

Pseudogenes show multiple functions in various cancer types, and immunotherapy is a promising cancer treatment. Therefore, this study aims to identify immune-related pseudogene signature in endometrial cancer (EC).

Methods

Gene transcriptome data of EC tissues and corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) through UCSC Xena browser. Spearman correlation analysis was performed to identify immune-related pseudogenes (IRPs) between the immune genes and pseudogenes. Univariate Cox regression, LASSO, and multivariate were performed to develop a risk score signature to investigate the different overall survival (OS) between high- and low-risk groups. The prognostic significance of the signature was assessed by the Kaplan–Meier curve, time-dependent receiver operating characteristic (ROC) curve. The abundance of 22 immune cell subtypes of EC patients was evaluated using CIBERSORT.

Results

Nine IRPs were used to build a prognostic signature. Survival analysis revealed that patients in the low-risk group presented longer OS than those in the high-risk group as well as in multiple subgroups. The signature risk score was independent of other clinical covariates and was associated with several clinicopathological variables. The prognostic signature reflected infiltration by multiple types of immune cells and revealed the immunotherapy response of patients with anti-programmed death-1 (PD-1) and anti-programmed cell death 1 ligand 1 (PD-L1) therapy. Function enrichment analysis revealed that the nine IRPs were mainly involved in multiple cancer-related pathways.

Conclusion

We identified an immune-related pseudogene signature that was strongly correlated with the prognosis and immune response to EC. The signature might have important implications for improving the clinical survival of EC patients and provide new strategies for cancer treatment.