Tumor mutation burden in connection with immune-related survival in uterine corpus endometrial carcinoma

Characteristics of the immune environment in prostate cancer as an adjunct to immunotherapy

Background and Aims

The tumor microenvironment (TME) exerts an important role in carcinogenesis and progression. Several investigations have suggested that immune cell infiltration (ICI) is of high prognostic importance for tumor progression and patient survival in many tumors, particularly prostate cancer. The pattern of immune infiltration of PCa, on the other hand, has not been thoroughly understood.

Methods

The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets on PCa were obtained, and several datasets were merged into one data set using the "ComBat" algorithm. The ICI profiles of PCa patients were then to be uncovered by two computer techniques. The unsupervised clustering method was utilized to identify three ICI patterns in tumor samples, and Principal Component Analysis (PCA) was conducted to estimate the ICI score.

Results

Three different clusters of three ICIs were identified in 1341 PCa samples, which also correlated with different clinical features/characteristics and biological pathways. Patients with PCa are classified into high and low subtypes based on the ICI scores extracted from immune-associated signature genes. High ICI score subtypes are associated with a worse prognosis, which may intrigue the activation of cancer-related and immune-related pathways such as pathways involving Toll-like receptors, T-cell receptors, JAK-STAT, and natural killer cells. The ICI score was linked to tumor mutation load and immune/cancer-relevant signaling pathways, which explain prostate cancer's poor prognosis.

Conclusion

The findings of this study not only advanced our knowledge of the mechanism of immune response in the prostate tumor microenvironment but also provided a novel biomarker, that is, the ICI score, for disease prognosis and guiding precision immunotherapy.

Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysis

BACKGROUND

In recent years, research on the pathogenesis of systemic lupus erythematosus (SLE) has made great progress. However, the prognosis of the disease remains poor, and high sensitivity and accurate biomarkers are particularly important for the early diagnosis of SLE.

METHODS

SLE patient information was acquired from three Gene Expression Omnibus (GEO) databases and used for differential gene expression analysis, such as weighted gene coexpression network (WGCNA) and functional enrichment analysis. Subsequently, three algorithms, random forest (RF), support vector machine-recursive feature elimination (SVM-REF) and least absolute shrinkage and selection operation (LASSO), were used to analyze the above key genes. Furthermore, the expression levels of the final core genes in peripheral blood from SLE patients were confirmed by real-time quantitative polymerase chain reaction (RT-qPCR) assay.

RESULTS

Five key genes (ABCB1, CD247, DSC1, KIR2DL3 and MX2) were found in this study. Moreover, these key genes had good reliability and validity, which were further confirmed by clinical samples from SLE patients. The receiver operating characteristic curves (ROC) of the five genes also revealed that they had critical roles in the pathogenesis of SLE.

CONCLUSION

In summary, five key genes were obtained and validated through machine-learning analysis, offering a new perspective for the molecular mechanism and potential therapeutic targets for SLE.

Comparative efficacy of Chinese herbal injections for treating endometrial carcinoma: A Bayesian network meta-analysis

BACKGROUND

In view of the limitations of chemotherapy (CT) for endometrial carcinoma (EC) and the extensive use of Chinese herbal injections (CHIs), this network meta-analysis (NMA) compared the efficacy and safety of 6 CHIs combined with CT for EC.

METHODS

Several electronic databases were searched for randomized controlled trials (RCTs). The retrieval period was from the establishment of the databases to September 18, 2022. The quality of the literature was assessed after data extraction using Review Manager version 5.4. The Stata 13.1 and OpenBUGS3.2.3 software were used for data analysis. Cluster analysis was performed to compare the effect of CHIs between 2 different outcomes.

RESULTS

A total of 25 RCTs with 2023 patients were included. The findings demonstrated that when combined with CT, Aidi, Compound Kushen, Kangai, Eshuyou injection, and Shenmai injection can increase clinical efficacy compared to chemotherapy alone. The KPS level can be raised with aidi injection. Combining CT with injections of Aidi, Shenmai, Huangqi, and Compound Kushen can improve immunological performance. Combining CT with injections of Aidi, Huangqi, and Compound Kushen can lower serum amounts of tumor markers. Kangai injection was regarded as a good option for minimizing negative responses. According to the cluster analysis, the clinical effective rate, the KPS score, the level of CA125, and the clinical effective rate of Kangai injection and Aidi injection combined with CT were all better.

CONCLUSION

Current evidence revealed that CHIs combined with CT have a better impact on patients with EC than CT alone. It's possible that KangAi, Aidi, and Eshuyou infusion are the best CHIs for EC. Additionally, more high-quality RCTs are required in order to further corroborate the findings due to NMA's limitations.

Development and verification of a newly established cuproptosis-associated lncRNA model for predicting overall survival in uterine corpus endometrial carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignant tumor with high recurrence and mortality rates. This study aimed to develop and validate a prognostic model for patients with UCEC based on cuproptosis-related long non-coding RNA (lncRNA) signature.

Methods

Transcriptome and clinical UCEC data were obtained from The Cancer Genome Atlas (TCGA) database. Correlation analysis was conducted to screen out the cuproptosis-related lncRNAs, and univariate regression analysis was performed to determine prognostic factors associated with overall survival (OS). A cuproptosis-related lncRNA risk model was constructed through least absolute shrinkage and selection operator (LASSO) regression and cross-validation. The accuracy and reliability of the model were verified through Kaplan-Meier (KM), proportional hazards model (Cox) regression, nomogram, principal component analysis (PCA), and stage analysis. Gene Ontology (GO) enrichment, immune function, and tumor mutation burden (TMB) analyses were conducted between low-risk and high-risk groups, and antineoplastic drugs were predicted.

Results

By correlation analysis, 155 cuproptosis-related lncRNAs were acquired, and 9 lncRNAs were identified as independent prognostic factors. A 6-cuproptosis-related lncRNA model was established. The results revealed that patients in the high-risk group were more inclined to have a poor OS than those in the low-risk group. Risk score was an independent prognostic factor and had a high accuracy and predictive value. The extracellular structure and anchored components of membrane-related GO terms were significantly enriched. Immune function and TMB results were assumed to be different from each other, which might explain a better outcome in the low-risk group than that in the high-risk group. Eighteen compounds were predicted as chemotherapy drugs with high half maximal inhibitory concentration (IC50) in the high-risk group.

Conclusions

We successfully developed a cuproptosis-related lncRNA risk model for the prediction of prognosis, while simultaneously providing insights on new approaches for immunotherapy and chemotherapy for patients with UCEC.

Analysis of endometrial carcinoma TCGA reveals differences in DNA methylation in tumors from Black and White women

OBJECTIVE

Racial disparities exist in cancer patients both in incidence and death rates. In endometrial cancer, Black patients are reported to have higher incidence of aggressive endometrial cancer subtypes and higher death rates than White women. To date, diagnostic and prognostic biomarkers associated with race-specific methylation driven genes have yet to be identified. The objective of this study was to explore DNA methylation patterns in endometrial tumor samples from White and Black women.

METHODS

Differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) were identified in White tumor samples compared to Black tumor samples using Endometrial Carcinoma (EC) methylation and clinical data from The Cancer Genome Atlas (TCGA). Survival analysis was performed using survival R package and results were visualized using Kaplan-Meier plots. To access the correlation between changes in methylation and gene expression, we downloaded raw RNA-sequencing by Expectation-Maximization (RSEM) counts data from The Cancer Genome Atlas (TCGA) using TCGABiolinks package (v2.18.0).

RESULTS

Our analysis revealed 704 differentially methylated CpGs in tumors from Black and White women. These differentially methylated genes showed strong negative correlation with gene expression and statistically significant enrichment in regulatory regions such as DNase I hypersensitivity sites (DHSs) and transcription factor binding sites (TFBSs). Increased variability in methylation occurred in genes such as the insulin signaling pathway in Black tumor samples.

CONCLUSION

By using two independent statistical method based on means (DMR, DMCs) and variances (DVCs) on the endometrial carcinoma TCGA data, we showed that endometrial tumors from Black women are hypomethylated and more hypervariable than tumors from White women. In-depth investigation of these methylation driven markers can aid in successful management of endometrial cancer disparities and improved overall survival in Black and White populations.

A comprehensive analysis of G-protein-signaling modulator 2 as a prognostic and diagnostic marker for pan-cancer

Background: G-protein signaling modulator 2 (GPSM2) maintains cell polarization and regulates the cell cycle. Recent studies have shown that it is highly expressed in various tumors, but its pan-cancer analysis has not been reported.

Methods: First, we analyzed the differential GPSM2 expression in normal and cancer tissues by the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Human Protein Atlas databases and investigated its expression effect on the survival of cancer patients by gene expression profiling interactive analysis 2 (GEPIA2). Second, we analyzed the GPSM2 phosphorylation level using the clinical proteomic tumor analysis consortium dataset. In addition, we investigated GPSM2 gene mutations in human tumor specimens and the impact of gene mutations on patient survival. Finally, we analyzed the relationship between GPSM2 expression and cellular immune infiltration through the TIMER 2.0 database. Meanwhile, the possible signaling pathway of the gene was analyzed by the Gene Ontology (GO)| Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to explore its potential mechanism.

Results:GPSM2 is overexpressed in most cancers, which leads to reduced overall survival (OS) and disease-free survival in patients. The results of phosphorylation analysis suggest that tumor development involves a complex GPSM2 phosphorylation process. We identified GPSM2 mutation loci with the highest frequency of mutations in uterine corpus endometrial carcinoma (UCEC), and this mutation increased progression-free survival and overall survival in uterine corpus endometrial carcinoma patients. Finally, we found that the role of GPSM2 in tumors may be associated with cellular immune infiltration. Gene Ontology|KEGG pathway analysis showed that the enrichment pathways were mainly “mitotic nuclear division,” “chromosome segregation,” and “spindle.”

Conclusions: Our pan-cancer analysis provides a comprehensive overview of the oncogenic roles and potential mechanisms of GPSM2 in multiple human cancers.

Novel DNA Damage-Related Subtypes Characterization Identifies Uterine Corpus Endometrial Carcinoma (UCEC) Based on Machine Learning

Objective. Accumulating evidence suggests that DNA damage is associated with numerous gynecological illnesses, particularly advanced uterine corpus endometrial carcinoma (UCEC), illustrating the involvement of the DNA damage pathway in the advancement of UCEC. This research aimed to discover a robust subtype with the potential to contribute to the scientific treatment of UCEC. Methods. In this work, the expression patterns of prognostic DNA damage-related genes were curated, and consensus clustering analyses were undertaken to determine DNA damage subtypes in patients with UCEC in the TCGA cohort. Two DNA damage-related subtypes were identified for further investigation. Differentially expressed genes (DEGs) analysis, gene ontology analysis, mutation analysis, and immune cell infraction analysis were performed to find the molecular mechanism behind it. Finally, the polymerase chain reaction (PCR) was conducted to verify the correlation of the hub genes. Results. In total, 545 patients with UCEC were tested for two distinct DNA damage subtypes. The clinical prognosis was poorer among patients with DNA damage subtype 2 than those in subtype 1. The DEGs analysis and PPI analysis showed that ASMP, BUB1, CENPF, MAD2L1, NCAPG, SGO2, and TOP2A were expressed higher in UCEC tissues than in the normal tissues. Immune cell infraction analysis showed that hub genes were associated with the tumor microenvironment (TME). Conclusion. Altogether, our research identified two distinct DNA damage subtypes that are complicated and heterogeneous. A better knowledge of the characteristics of the TME may be gained by quantitative measurement of DNA damage subtypes in individual patients, which can also lead to the development of more successful treatment regimens.

MX2: Identification and systematic mechanistic analysis of a novel immune-related biomarker for systemic lupus erythematosus

Background

Systemic lupus erythematosus (SLE) is an autoimmune disease that involves multiple organs. However, the current SLE-related biomarkers still lack sufficient sensitivity, specificity and predictive power for clinical application. Thus, it is significant to explore new immune-related biomarkers for SLE diagnosis and development.

Methods

We obtained seven SLE gene expression profile microarrays (GSE121239/11907/81622/65391/100163/45291/49454) from the GEO database. First, differentially expressed genes (DEGs) were screened using GEO2R, and SLE biomarkers were screened by performing WGCNA, Random Forest, SVM-REF, correlation with SLEDAI and differential gene analysis. Receiver operating characteristic curves (ROCs) and AUC values were used to determine the clinical value. The expression level of the biomarker was verified by RT‒qPCR. Subsequently, functional enrichment analysis was utilized to identify biomarker-associated pathways. ssGSEA, CIBERSORT, xCell and ImmuCellAI algorithms were applied to calculate the sample immune cell infiltration abundance. Single-cell data were analyzed for gene expression specificity in immune cells. Finally, the transcriptional regulatory network of the biomarker was constructed, and the corresponding therapeutic drugs were predicted.

Results

Multiple algorithms were screened together for a unique marker gene, MX2, and expression analysis of multiple datasets revealed that MX2 was highly expressed in SLE compared to the normal group (all P < 0.05), with the same trend validated by RT‒qPCR (P = 0.026). Functional enrichment analysis identified the main pathway of MX2 promotion in SLE as the NOD-like receptor signaling pathway (NES=2.492, P < 0.001, etc.). Immuno-infiltration analysis showed that MX2 was closely associated with neutrophils, and single-cell and transcriptomic data revealed that MX2 was specifically expressed in neutrophils. The NOD-like receptor signaling pathway was also remarkably correlated with neutrophils (r >0.3, P < 0.001, etc.). Most of the MX2-related interacting proteins were associated with SLE, and potential transcription factors of MX2 and its related genes were also significantly associated with the immune response.

Conclusion

Our study found that MX2 can serve as an immune-related biomarker for predicting the diagnosis and disease activity of SLE. It activates the NOD-like receptor signaling pathway and promotes neutrophil infiltration to aggravate SLE.

Pan-cancer analysis of tumor mutation burden sensitive tumors reveals tumor-specific subtypes and hub genes related to immune infiltration

BACKGROUND

High tumor mutation burden (TMB) failed to serve as a favorable prognostic biomarker for immunotherapy across all tumors. This study aimed to explore TMB-sensitive tumors on a pan-cancer level and construct their immune infiltration phenotypes in TMB-high groups.

METHODS

Pan-cancer patients were separated into TMB-high and TMB-low groups based on the median TMB values per tumor. TMB-related genes were identified using differently expressed genes (DEGs) and differently mutated genes (DMGs) between the above two TMB groups. CIBERSORT algorithm was used to estimate the abundance of 22 tumor immune infiltrating cells (TIICs). Consensus clustering analysis was applied to predict molecular subtypes. Cox regression analysis was performed to evaluate the correlations between hub genes and TIICs and immunomodulator genes.

RESULTS

Nine TMB-sensitive tumors were identified by high-frequency of TMB-related genes. A total of 126 tumor-specific hub genes (1 in BLCA, 19 in BRCA, 4 in COAD, 4 in HNSC, 25 in LUAD, 2 in LUSC, 27 in SKCM, 37 in STAD, and 7 UCEC) were identified. In five out of nine TMB-sensitive tumors, the molecular subtypes based on hub gene expression were characterized by TMB values, prognostic values and tumor-specific TIICs levels. In TMB-high groups, hub genes associated immune infiltration phenotypes were constructed with key TIICs and immunomodulators spanning TMB-sensitive tumors.

CONCLUSIONS

Our tumor-specific analysis revealed hub genes associated immune infiltration features may serve as potential therapeutic targets and prognostic markers of immunotherapy, providing the potential underlying mechanism of immune infiltration in TMB-high groups across TMB-sensitive tumors.

Relationship between Prognosis, Immune Infiltration Level, and Differential Expression of PARVG Gene in Uterine Corpus Endometrial Carcinoma

Endometrial cancer (UCEC) is very common in gynecological diseases and ranks second in the death cause of gynecological cancer in developed countries. The connection between the overall survival of UCEC patients and immune invasion of the tumor microenvironment is positive. The PARVG gene has not been given notice in cancer, and its mechanism is unknown. The research utilized TCGA data to test the function of PARVG in UCEC. The manifestation of PARVG in UCEC was studied by GEPIA. By assessing the survival module, the authors learned the impact of PARVG on the survival of people with UCEC and then obtained UCEC information from TCGA. This study uses logistic regression to prove the possible relationship between PARVG expression and clinical information. From the research of Cox regression, clinicopathological characteristics of people with TCGA were connected with overall survival. Furthermore, the “correlation” module of GEPIA and CIBERSORT was used to study the association between cancer immune invasion and PARVG. Using univariate logistic regression analysis with PARVG expression as a categorical variable (median expression value of 2.5), the result suggested that raised PARVG expression was considerably connected with tumor status, pathological stage, and lymph nodes. Multiple factor studies have shown that upregulation of PARVG, distant metastasis, and negative pathological stage are absolute elements of excellent prognosis. In addition, CIBERSORT analysis was utilized to determine that raised PARVG expression has a positive connection with immune infiltration by T cells, mast cells, neutrophils, and B cells. This is recognized in GEPIA's “correlation” module. The above outcomes show us that the raised expression of PARVG is associated with a good prognosis and it raises the proportion of immune cells (such as T cells, mast cells, neutrophils, and B cells) in UCEC. These outcomes tell us that PARVG can be utilized as a possible biomarker to evaluate UCEC's immune infiltration levels and prognosis.

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.

Immune Infiltrates of m5C RNA Methylation-Related LncRNAs in Uterine Corpus Endometrial Carcinoma

Aberrant 5-methylcytidine (m5C) modification plays an essential role in the progression of different cancers. More and more researchers are focusing on developing a lncRNA-based risk model to assess the clinical prognosis of cancer patients. However, the impact of m5C-related lncRNAs on the prognosis of patients with uterine corpus endometrial carcinoma (UCEC), as well as the immune microenvironment of UCEC, remains unclear. Here, we comprehensively analyzed the predictive value of m5C-associated lncRNAs in UCEC and their association with the tumor immune microenvironment, according to the information extracted from the TCGA-UCEC dataset. We identified a total of 32 m5C-associated lncRNAs that were significantly correlated with the prognosis of UCEC patients. Two molecular subtypes were determined by consensus clustering analysis of these 32 m5C-associated prognostic lncRNAs. Further data showed that cluster 1 was associated with poor clinical prognosis, advanced tumor grade, higher PD-L1 expression levels, higher ESTIMATEScore, and higher immuneScore, as well as the immune cell infiltration. Then, 17 m5C-associated lncRNAs with prognostic values were obtained using LASSO regression analysis. And a risk model was constructed based on these 17 lncRNAs. It was revealed that the risk model could be used as an independent factor for UCEC prognosis. In addition, patients with UCEC in the high-risk group had higher tumor grades and immune scores. The risk model based on m5C-related lncRNAs was also closely associated with infiltrating immune cells. In conclusion, our study elucidated the crucial roles of the identified m5C-related lncRNAs in the UCEC patients' prognoses, as well as in the immune microenvironment in UCEC. The results suggest that the components of risk models based on the m5C-related lncRNAs may serve as important mediators of the immune microenvironment in UCEC.

Screening differentially expressed genes between endometriosis and ovarian cancer to find new biomarkers for endometriosis

AIM

Endometriosis is one of the most common reproductive system diseases, but the mechanisms of disease progression are still unclear. Due to its high recurrence rate, searching for potential therapeutic biomarkers involved in the pathogenesis of endometriosis is an urgent issue.

METHODS

Due to the similarities between endometriosis and ovarian cancer, four endometriosis datasets and one ovarian cancer dataset were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, followed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) analyses. Then, we validated gene expression and performed survival analysis with ovarian serous cystadenocarcinoma (OV) datasets in TCGA/GTEx database, and searched for potential drugs in the Drug-Gene Interaction Database. Finally, we explored the miRNAs of key genes to find biomarkers associated with the recurrence of endometriosis.

RESULTS

In total, 104 DEGs were identified in the endometriosis datasets, and the main enriched GO functions included cell adhesion, extracellular exosome and actin binding. Fifty DEGs were identified between endometriosis and ovarian cancer datasets including 11 consistently regulated genes, and nine DEGs with significant expression in TCGA/GTEx. Only IGHM had both significant expression and an association with survival, three module DEGs and two significantly expressed DEGs had drug associations, and 10 DEGs had druggability.

CONCLUSIONS

ITGA7, ITGBL1 and SORBS1 may help us understand the invasive nature of endometriosis, and IGHM might be related to recurrence; moreover, these genes all may be potential therapeutic targets.KEY MESSAGEThis manuscript used a bioinformatics approach to find target genes for the treatment of endometriosis.This manuscript used a new approach to find target genes by drawing on common characteristics between ovarian cancer and endometriosis.We screened relevant therapeutic agents for target genes in the drug database, and performed histological validation of target genes with both expression and survival analysis difference in cancer databases.

Multiomics analysis of tumor mutational burden across cancer types

Whether tumor mutational burden (TMB) is related to improved survival outcomes or the promotion of immunotherapy in various malignant tumors remains controversial, and we lack a comprehensive understanding of TMB across cancers. Based on the data obtained from The Cancer Genome Atlas (TCGA), we conducted a multiomics analysis of TMB across 21 cancer types to identify characteristics related to TMB and determine the mechanism as it relates to prognosis, gene expression, gene mutation and signaling pathways. In our study, TMB was found to have a significant relationship with prognosis for 21 tumors, and the relationship was different in different tumors. TMB may also be related to different outcomes for patients with different tumor subtypes. TMB was confirmed to be correlated with clinical information, such as age and sex. Mutations in GATA3 and MAP3K1 in beast invasive carcinoma (BRCA), TCF7L2 in colon adenocarcinoma (COAD), NFE2L2 in esophageal carcinoma (ESCA), CIC and IDH1 in brain lower grade glioma (LGG), CDH1 in stomach adenocarcinoma (STAD), and TP53 in uterine corpus endometrial carcinoma (UCEC) were demonstrated to be correlated with lower TMB. Moreover, we identified differentially expressed genes (DEGs) and differentially methylated regions (DMRs) according to different TMB levels in 21 cancers. We also investigated the correlation between enrichment of signaling pathways, immune cell infiltration and TMB. In conclusion, we identified multiomic characteristics related to the TMB in 21 tumors, providing support for a comprehensive understanding of the role of TMB in different tumors.