Decreased PTGDS Expression Predicting Poor Survival of Endometrial Cancer by Integrating Weighted Gene Co-Expression Network Analysis and Immunohistochemical Validation

Characterization and validation of fatty acid metabolism-related genes predicting prognosis, immune infiltration, and drug sensitivity in endometrial cancer

Endometrial cancer is considered to be the second most common tumor of the female reproductive system, and patients diagnosed with advanced endometrial cancer have a poor prognosis. The influence of fatty acid metabolism in the prognosis of patients with endometrial cancer remains unclear. We constructed a prognostic risk model using transcriptome sequencing data of endometrial cancer and clinical information of patients from The Cancer Genome Atlas (TCGA) database via least absolute shrinkage and selection operator regression analysis. The tumor immune microenvironment was analyzed using the CIBERSORT algorithm, followed by functional analysis and immunotherapy efficacy prediction by gene set variation analysis. The role of model genes in regulating endometrial cancer in vitro was verified by CCK-8, colony formation, wound healing, and transabdominal invasion assays, and verified in vivo by subcutaneous tumor transplantation in nude mice. A prognostic model containing 14 genes was constructed and validated in 3 cohorts and clinical samples. The results showed differences in the infiltration of immune cells between the high-risk and low-risk groups, and that the high-risk group may respond better to immunotherapy. Experiments in vitro confirmed that knockdown of epoxide hydrolase 2 (EPHX2) and acyl-CoA oxidase like (ACOXL) had an inhibitory effect on EC cells, as did overexpression of hematopoietic prostaglandin D synthase (HPGDS). The same results were obtained in experiments in vivo. Prognostic models related to fatty acid metabolism can be used for the risk assessment of endometrial cancer patients. Experiments in vitro and in vivo confirmed that the key genes HPGDS, EPHX2, and ACOXL in the prognostic model may affect the development of endometrial cancer.

SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition

Cluster analysis, a pivotal step in single-cell sequencing data analysis, presents substantial opportunities to effectively unveil the molecular mechanisms underlying cellular heterogeneity and intercellular phenotypic variations. However, the inherent imperfections arise as different clustering algorithms yield diverse estimates of cluster numbers and cluster assignments. This study introduces Single Cell Consistent Clustering based on Spectral Matrix Decomposition (SCSMD), a comprehensive clustering approach that integrates the strengths of multiple methods to determine the optimal clustering scheme. Testing the performance of SCSMD across different distances and employing the bespoke evaluation metric, the methodological selection undergoes validation to ensure the optimal efficacy of the SCSMD. A consistent clustering test is conducted on 15 authentic scRNA-seq datasets. The application of SCSMD to human embryonic stem cell scRNA-seq data successfully identifies known cell types and delineates their developmental trajectories. Similarly, when applied to glioblastoma cells, SCSMD accurately detects pre-existing cell types and provides finer sub-division within one of the original clusters. The results affirm the robust performance of our SCSMD method in terms of both the number of clusters and cluster assignments. Moreover, we have broadened the application scope of SCSMD to encompass larger datasets, thereby furnishing additional evidence of its superiority. The findings suggest that SCSMD is poised for application to additional scRNA-seq datasets and for further downstream analyses.

The usefulness of p16 and COX-2 expression on the prediction of progression to endometrial cancer

OBJECTIVES

Endometrial cancer (EC) is the most commonly diagnosed gynecological cancer. Endometrial hyperplasia (EH) is a more common diagnosis than EC. Endometrial hyperplasia is found in approximately 1.5% of all women presenting with abnormal bleeding. Endometrial hyperplasia progresses to EC, and especially, cancer risk increases in cases with atypical hyperplasia. p16, one of the tumor suppressor proteins involved in the cell cycle, and COX-2, one of the key enzymes of prostaglandin synthesis, are important markers for the diagnosis of both EH and EC. There is lack of consensus in the classification, diagnosis and treatment of EH. The subject of changes in the cell cycle in the progression of endometrial pathologies may help to identify and prevent these affected pathways in the treatment stage. The aim of this study is to investigate the expression of p16 and COX-2 during the development of EC from EH.

MATERIAL AND METHODS

We investigated COX-2 and P16 expressions in patients with proliferative endometrium, complex/simple endometrial hyperplasia and endometrioid adenocarcinoma.

RESULTS

p16 expression increased in EH and EC (p<0.001). COX-2 expression was increased in endometrial cancer compared to other groups, but this increase was not found to be statistically significant. Although p16 and COX-2 expression were increased in patients with advanced grade/stage, lymphovascular invasion, and >50% of myometrial invasion, this increase was not statistically significant.

CONCLUSIONS

More detailed studies are needed to investigate the prognostic significance of the COX-2 molecule. COX-2 might be a potential biomarker for the prognosis of endometrial cancer and a potential therapeutic target for EC treatment. Also, it might be used to prevent the progression of precursor lesions to invasive EC.

A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration

BACKGROUND

Diabetic foot ulcer (DFU) is one of the most common and severe complications of diabetes, with vascular changes, neuropathy, and infections being the primary pathological mechanisms. Glutamine (Gln) metabolism has been found to play a crucial role in diabetes complications. This study aims to identify and validate potential Gln metabolism biomarkers associated with DFU through bioinformatics and machine learning analysis.

METHODS

We downloaded two microarray datasets related to DFU patients from the Gene Expression Omnibus (GEO) database, namely GSE134431, GSE68183, and GSE80178. From the GSE134431 dataset, we obtained differentially expressed Gln-metabolism related genes (deGlnMRGs) between DFU and normal controls. We analyzed the correlation between deGlnMRGs and immune cell infiltration status. We also explored the relationship between GlnMRGs molecular clusters and immune cell infiltration status. Notably, WGCNA to identify differentially expressed genes (DEGs) within specific clusters. Additionally, we conducted GSVA to annotate enriched genes. Subsequently, we constructed and screened the best machine learning model. Finally, we validated the predictions' accuracy using a nomogram, calibration curves, decision curve analysis (DCA), and the GSE134431, GSE68183, and GSE80178 dataset.

RESULTS

In both the DFU and normal control groups, we confirmed the presence of deGlnMRGs and an activated immune response. From the GSE134431 dataset, we obtained 20 deGlnMRGs, including CTPS1, NAGS, SLC7A11, GGT1, GCLM, RIMKLA, ARG2, ASL, ASNS, ASNSD1, PPAT, GLS2, GLUD1, MECP2, ASS1, PRODH, CTPS2, ALDH5A1, DGLUCY, and SLC25A12. Furthermore, two clusters were identified in DFU. Immune infiltration analysis indicated the presence of immune heterogeneity in these two clusters. Additionally, we established a Support Vector Machine (SVM) model based on 5 genes (R3HCC1, ZNF562, MFN1, DRAM1, and PTGDS), which exhibited excellent performance on the external validation datasetGSE134431, GSE68183, and GSE80178 (AUC = 0.929).

CONCLUSION

This study has identified five Gln metabolism genes associated with DFU, revealing potential novel biomarkers and therapeutic targets for DFU. Additionally, the infiltration of immune-inflammatory cells plays a crucial role in the progression of DFU.

A Phase II Study of Epacadostat and Pembrolizumab in Patients with Advanced Sarcoma

PURPOSE

Epacadostat, an indole 2,3 dioxygenase 1 (IDO1) inhibitor, proposed to shift the tumor microenvironment toward an immune-stimulated state, showed early promise in melanoma but has not been studied in sarcoma. This study combined epacadostat with pembrolizumab, which has modest activity in select sarcoma subtypes.

PATIENTS AND METHODS

This phase II study enrolled patients with advanced sarcoma into five cohorts including (i) undifferentiated pleomorphic sarcoma (UPS)/myxofibrosarcoma, (ii) liposarcoma (LPS), (iii) leiomyosarcoma (LMS), (iv) vascular sarcoma, including angiosarcoma and epithelioid hemangioendothelioma (EHE), and (v) other subtypes. Patients received epacadostat 100 mg twice daily plus pembrolizumab at 200 mg/dose every 3 weeks. The primary endpoint was best objective response rate (ORR), defined as complete response (CR) and partial response (PR), at 24 weeks by RECIST v.1.1.

RESULTS

Thirty patients were enrolled [60% male; median age 54 years (range, 24-78)]. The best ORR at 24 weeks was 3.3% [PR, n = 1 (leiomyosarcoma); two-sided 95% CI, 0.1%-17.2%]. The median PFS was 7.6 weeks (two-sided 95% CI, 6.9-26.7). Treatment was well tolerated. Grade 3 treatment-related adverse events occurred in 23% (n = 7) of patients. In paired pre- and post-treatment tumor samples, no association was found between treatment and PD-L1 or IDO1 tumor expression or IDO-pathway-related gene expression by RNA sequencing. No significant changes in serum tryptophan or kynurenine levels were observed after baseline.

CONCLUSIONS

Combination epacadostat and pembrolizumab was well tolerated and showed limited antitumor activity in sarcoma. Correlative analyses suggested that inadequate IDO1 inhibition was achieved.

Diagnosis of childhood and adolescent growth hormone deficiency using transcriptomic data

BACKGROUND

Gene expression (GE) data have shown promise as a novel tool to aid in the diagnosis of childhood growth hormone deficiency (GHD) when comparing GHD children to normal children. The aim of this study was to assess the utility of GE data in the diagnosis of GHD in childhood and adolescence using non-GHD short stature children as a control group.

METHODS

GE data was obtained from patients undergoing growth hormone stimulation testing. Data were taken for the 271 genes whose expression was utilized in our previous study. The synthetic minority oversampling technique was used to balance the dataset and a random forest algorithm applied to predict GHD status.

RESULTS

24 patients were recruited to the study and eight subsequently diagnosed with GHD. There were no significant differences in gender, age, auxology (height SDS, weight SDS, BMI SDS) or biochemistry (IGF-I SDS, IGFBP-3 SDS) between the GHD and non-GHD subjects. A random forest algorithm gave an AUC of 0.97 (95% CI 0.93 - 1.0) for the diagnosis of GHD.

CONCLUSION

This study demonstrates highly accurate diagnosis of childhood GHD using a combination of GE data and random forest analysis.

Prostanoid Signaling in Cancers: Expression and Regulation Patterns of Enzymes and Receptors

Cancer-associated disturbance of prostanoid signaling provides an aberrant accumulation of prostanoids. This signaling consists of 19 target genes, encoding metabolic enzymes and G-protein-coupled receptors, and prostanoids (prostacyclin, thromboxane, and prostaglandins E2, F2α, D2, H2). The study addresses the systems biology analysis of target genes in 24 solid tumors using a data mining pipeline. We analyzed differential expression patterns of genes and proteins, promoter methylation status as well as tissue-specific master regulators and microRNAs. Tumor types were clustered into several groups according to gene expression patterns. Target genes were characterized as low mutated in tumors, with the exception of melanoma. We found at least six ubiquitin ligases and eight protein kinases that post-translationally modified the most connected proteins PTGES3 and PTGIS. Models of regulation of PTGIS and PTGIR gene expression in lung and uterine cancers were suggested. For the first time, we found associations between the patient’s overall survival rates with nine multigene transcriptomics signatures in eight tumors. Expression patterns of each of the six target genes have predictive value with respect to cytostatic therapy response. One of the consequences of the study is an assumption of prostanoid-dependent (or independent) tumor phenotypes. Thus, pharmacologic targeting the prostanoid signaling could be a probable additional anticancer strategy.

Pyroptosis-related lncRNAs are potential biomarkers for predicting prognoses and immune responses in patients with UCEC

Uterine corpus endometrial carcinoma (UCEC) is a malignant disease globally, and there is no unified prognostic signature at present. In our study, two clusters were identified. Cluster 1 showed better prognosis and higher infiltration level, such as tumor microenvironment (TME), tumor mutation burden (TMB), and immune checkpoint genes expression. Gene set enrichment analysis (GSEA) indicated that some tumor-related pathways and immune-associated pathways were exposed. What is more, six pyroptosis-related long noncoding RNAs (lncRNAs) (PRLs) were applied to establish a prognostic signature through multiple Cox regression analysis. In both training and testing sets, patients with higher risk score had poorer survival than patients with low risk. The area under the curve (AUC) of receiver operating characteristic (ROC) curves performed that the survival probability was better in people with lower risk score. Mechanism analysis revealed that high risk score was correlated with reduced immune infiltration and T cells exhaustion, matching the definition of an "immune-desert" phenotype. Patients with lower risk score were characterized by higher immune checkpoint gene expression and TMB and have a sensitive response to immunotherapy and chemotherapy compared with patients with high risk score. The signature has accurate prediction ability of UCEC and is a promising therapeutic target to improve the effect of immunotherapy.

Recent Multiomics Approaches in Endometrial Cancer

Endometrial cancer is the most common gynecological cancers in developed countries. Many of the mechanisms involved in its initiation and progression remain unclear. Analysis providing comprehensive data on the genome, transcriptome, proteome, and epigenome could help in selecting molecular markers and targets in endometrial cancer. Multiomics approaches can reveal disturbances in multiple biological systems, giving a broader picture of the problem. However, they provide a large amount of data that require processing and further integration prior to analysis. There are several repositories of multiomics datasets, including endometrial cancer data, as well as portals allowing multiomics data analysis and visualization, including Oncomine, UALCAN, LinkedOmics, and miRDB. Multiomics approaches have also been applied in endometrial cancer research in order to identify novel molecular markers and therapeutic targets. This review describes in detail the latest findings on multiomics approaches in endometrial cancer.

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.

Prognostic marker identification based on weighted gene co-expression network analysis and associated in vitro confirmation in gastric cancer

The aim of this study was to explore the potential molecular mechanisms of Gastric cancer (GC) and identify new prognostic markers for GC. RNA sequencing data were downloaded from the Gene Expression Omnibus database, and 418 differentially expressed genes (DEGs) were screened. Weighted correlation network analysis (WGCNA) was performed to identify six hub modules related to the clinical features of GC. Cytoscape software was used to identify five hub genes in the co-expression network, including CST1, CEMIP, COL8A1, PMEPA1, and MSLN. The TCGA database was used to verify hub gene expression in GC. The overall survival in the high CEMIP expression group was significantly lower than that of patients in the low CEMIP expression group. CEMIP expression was also found to be negatively correlated with B cell and CD4 + T cell infiltration. Further, associated in vitro experiments confirmed that CEMIP downregulation suppressed the proliferation and migration of GC cells and impaired the chemoresistance of GC cells to 5-fluorouracil.

Our study effectively identified and validated prognostic biomarkers for GC, laying a new foundation for the therapeutic target, occurrence, and development of gastric cancer.

Identification of lncRNA Signature Associated With Pan-Cancer Prognosis

Long noncoding RNAs (lncRNAs) have emerged as potential prognostic markers in various human cancers as they participate in many malignant behaviors. However, the value of lncRNAs as prognostic markers among diverse human cancers is still under investigation, and a systematic signature based on these transcripts that related to pan-cancer prognosis has yet to be reported. In this study, we proposed a framework to incorporate statistical power, biological rationale, and machine learning models for pan-cancer prognosis analysis. The framework identified a 5-lncRNA signature (ENSG00000206567, PCAT29, ENSG00000257989, LOC388282, and LINC00339) from TCGA training studies (n = 1,878). The identified lncRNAs are significantly associated (all P ≤ 1.48E-11) with overall survival (OS) of the TCGA cohort (n = 4,231). The signature stratified the cohort into low- and high-risk groups with significantly distinct survival outcomes (median OS of 9.84 years versus 4.37 years, log-rank P = 1.48E-38) and achieved a time-dependent ROC/AUC of 0.66 at 5 years. After routine clinical factors involved, the signature demonstrated better performance for long-term prognostic estimation (AUC of 0.72). Moreover, the signature was further evaluated on two independent external cohorts (TARGET, n = 1,122; CPTAC, n = 391; National Cancer Institute) which yielded similar prognostic values (AUC of 0.60 and 0.75; log-rank P = 8.6E-09 and P = 2.7E-06). An indexing system was developed to map the 5-lncRNA signature to prognoses of pan-cancer patients. In silico functional analysis indicated that the lncRNAs are associated with common biological processes driving human cancers. The five lncRNAs, especially ENSG00000206567, ENSG00000257989 and LOC388282 that never reported before, may serve as viable molecular targets common among diverse cancers.

Construction of circRNA-miRNA-mRNA network in the pathogenesis of recurrent implantation failure using integrated bioinformatics study

This research attempted to elucidate the molecular components are involved in the pathogenesis of recurrent implantation failure (RIF). We initially identified that 386 mRNAs, 144 miRNAs and 2548 circRNAs were differentially expressed (DE) in RIF and then investigated the genetic cause of the observed abnormal expression by constructing a circRNA‐miRNA‐mRNA network considering the competing endogenous RNA theory. We further analysed the upstream transcription factors and related kinases of DEmRNAs (DEMs) and demonstrated that SUZ12, AR, TP63, NANOG, and TCF3 were the top five TFs binding to these DEMs. Besides, protein‐protein interaction analysis disclosed that ACTB, CXCL10, PTGS2, CXCL12, GNG4, AGT, CXCL11, SST, PENK, and FOXM1 were the top 10 hub genes in the acquired network. Finally, we performed the functional enrichment analysis and found that arrhythmogenic right ventricular cardiomyopathy (ARVC), hypertrophic cardiomyopathy (HCM), pathways in cancer, TNF signalling pathway and steroid hormone biosynthesis were the potentially disrupted pathways in RIF patients. Optimistically, our findings may deepen our apprehensions about the underlying molecular and biological causes of RIF and provide vital clues for future laboratory and clinical experiments that will ultimately bring a better outcome for patients with RIF.

Identification of a MicroRNA Signature Associated With Lymph Node Metastasis in Endometrial Endometrioid Cancer

Background

Lymph node metastasis (LNM) is an important prognostic factor in endometrial cancer. Anomalous microRNAs (miRNAs) are associated with cell functions and are becoming a powerful tool to characterize malignant transformation and metastasis. The aim of this study was to construct a miRNA signature to predict LNM in endometrial endometrioid carcinoma (EEC).

Method

Candidate target miRNAs related to LNM in EEC were screened by three methods including differentially expressed miRNAs (DEmiRs), weighted gene co-expression network analysis (WGCNA), and decision tree algorithms. Samples were randomly divided into the training and validation cohorts. A miRNA signature was built using a logistic regression model and was evaluated by the area under the curve (AUC) of receiver operating characteristic curve (ROC) and decision curve analysis (DCA). We also conducted pathway enrichment analysis and miRNA-gene regulatory network to look for potential genes and pathways engaged in LNM progression. Survival analysis was performed, and the miRNAs were tested whether they expressed differently in another independent GEO database.

Result

Thirty-one candidate miRNAs were screened and a final 15-miRNA signature was constructed by logistic regression. The model showed good calibration in the training and validation cohorts, with AUC of 0.824 (95% CI, 0.739-0.912) and 0.821 (95% CI, 0.691-0.925), respectively. The DCA demonstrated the miRNA signature was clinically useful. Hub miRNAs in signature seemed to contribute to EEC progression via mitotic cell cycle, cellular protein modification process, and molecular function. MiR-34c was statistically significant in survival that a higher expression of miR-34c indicated a higher survival time. MiR-34c-3p, miR-34c-5p, and miR-34b-5p were expressed differentially in GSE75968.

Conclusion

The miRNA signature could work as a noninvasive method to detect LNM in EEC with a high prediction accuracy. In addition, miR-34c cluster may be a key biomarker referring LNM in endometrial cancer.