Comprehensive Analysis of the Glycolysis-Related Gene Prognostic Signature and Immune Infiltration in Endometrial Cancer

Establishment and verification of a prognostic signature associated with fatty acid metabolism in endometrial cancer

Endometrial carcinoma (EC) is one of the leading causes of mortality in women. Metabolic disorders, such as abnormal fatty acid metabolism (FAM), are considered to be indicators of tumorigenesis. However, to the best of our knowledge, the relationship between EC and FAM remains unclear. The process of FAM is associated with the function of immune cells, thus samples from The Cancer Genome Atlas were grouped according to immune infiltration levels. Subsequently, prognostic gene signatures were constructed based on selected FAM‑associated genes. The signature effect was validated, and enrichment analyses were conducted based on sample classification. Nomograms were used to predict survival, merging clinical data and the gene signature. Samples were divided into high‑ and low‑risk groups based on the gene signature. The survival status, clinical characteristics, enrichment analysis and immune infiltration were significantly different between high‑ and low‑risk groups. According to the nomogram, low microsatellite instability‑high as well as a high tumor mutation burden can be observed in the low‑nomo‑score group. Immune checkpoint inhibitor‑associated genes were differentially expressed between groups and 35 sensitive compounds were identified. Comprehensive bioinformatics analysis in EC revealed potential roles of FAM in tumorigenesis, the tumor microenvironment and prognosis, suggesting that FAM‑associated signatures are promising biomarkers for EC. These findings may improve the understanding of FAM in EC and pave the way for a more accurate assessment of prognosis and immunotherapy outcomes.

Glycometabolism and lipid metabolism related genes predict the prognosis of endometrial carcinoma and their effects on tumor cells

BACKGROUND

Glycometabolism and lipid metabolism are critical in cancer metabolic reprogramming. The primary aim of this study was to develop a prognostic model incorporating glycometabolism and lipid metabolism-related genes (GLRGs) for accurate prognosis assessment in patients with endometrial carcinoma (EC).

METHODS

Data on gene expression and clinical details were obtained from publicly accessible databases. GLRGs were obtained from the Genecards database. Through nonnegative matrix factorization (NMF) clustering, molecular groupings with various GLRG expression patterns were identified. LASSO Cox regression analysis was employed to create a prognostic model. Use rich algorithms such as GSEA, GSVA, xCELL ssGSEA, EPIC,CIBERSORT, MCPcounter, ESTIMATE, TIMER, TIDE, and Oncoppredict to analyze functional pathway characteristics of the forecast signal, immune status, anti-tumor therapy, etc. The expression was assessed using Western blot and quantitative real-time PCR techniques. A total of 113 algorithm combinations were combined to screen out the most significant GLRGs in the signature for in vitro experimental verification, such as colony formation, EdU cell proliferation, wound healing, apoptosis, and Transwell assays.

RESULTS

A total of 714 GLRGs were found, and 227 of them were identified as prognostic-related genes. And ten GLRGs (AUP1, ESR1, ERLIN2, ASS1, OGDH, BCKDHB, SLC16A1, HK2, LPCAT1 and PGR-AS1) were identified to construct the prognostic model of patients with EC. Based on GLRGs, the risk model's prognosis and independent prognostic value were established. The signature of GLRGs exhibited a robust correlation with the infiltration of immune cells and the sensitivity to drugs. In cytological experiments, we selected HK2 as candidate gene to verify its value in the occurrence and development of EC. Western blot and qRT-PCR revealed that HK2 was substantially expressed in EC cells. According to in vitro experiments, HK2 knockdown can increase EC cell apoptosis while suppressing EC cell migration, invasion, and proliferation.

CONCLUSION

The GLRGs signature constructed in this study demonstrated significant prognostic value for patients with endometrial carcinoma, thereby providing valuable guidance for treatment decisions.

SIM2: Its Prognostic Significance and Oncogenic Role in Endometrial Carcinoma

Background

Endometrial carcinoma ranks as the second most widespread malignancy affecting the reproductive system in females. Effective prognostic biomarkers are required to further improve survival rates for patients. Single-minded homolog 2 (SIM2) is known to participate in neurogenesis as a transcription factor. However, the potential role of SIM2 in endometrial carcinoma remains elusive.

Methods

Multiple public databases, including TIMER2.0, GEIPA2, UALCAN, LinkedOmics, BioGRID, DAVID and cBioPortal, were used to investigate SIM2 mRNA expression, SIM2-associated genes, PPI network, functional enrichment analysis, SIM2 gene alterations and methylation. The association between SIM2 expression and immune cell infiltrates was explored using GSVA. The effects of gene alterations and methylation on patient survival and CD8+T infiltration were examined using GSCA. Moreover, the prognostic potential of SIM2 was evaluated using COX regression, ROC curves and a nomogram model. Finally, the differential expression and function of SIM2 in UCEC were explored using qPCR, WB, CCK8 and Transwell assays.

Results

Our findings revealed the heightened expression of SIM2 in endometrial carcinoma, and that its DNA methylation and CNV alterations were correlated with immune infiltration and patients' prognosis. Additionally, functional enrichment revealed the involvement of SIM2 in transcription regulation and signal transduction. Moreover, we performed cell-based experiments to corroborate the oncogenic function of SIM2 in facilitating cell proliferation, migration and invasion.

Conclusion

Collectively, these results suggest that SIM2 holds promise as both a potential prognostic indicator and a viable treatment target for endometrial carcinoma.

The role of microbiota in esophageal squamous cell carcinoma: A review of the literature

Esophageal squamous cell carcinoma (ESCC) exhibits high incidence with poor prognosis. Alcohol drinking, cigarette smoking, and betel nut chewing are well-known risk factors. Dysbiosis, an imbalance of the microbiota residing in a local environment, is known to be associated with human diseases, especially cancer. This article reviews the current evidence of esophageal microbiota in ESCC carcinogenesis, including initiation, progression, and drug resistance. Articles involving the esophageal microbiota, diagnosis, treatment, and the progression of esophageal cancer were acquired using a comprehensive literature search in PubMed in recent 10 years. Based on 16S rRNA sequencing of human samples, cell, and animal studies, current evidence suggests dysbiosis of the esophagus promotes ESCC progression and chemotherapy resistance, leading to a poor prognosis. Smoking and drinking are associated with esophageal dysbiosis. Specific bacteria have been reported to promote carcinogenesis, involving either progression or drug resistance in ESCC, for example Porphyromonas gingivalis and Fusobacterium nucleatum. These bacteria promote ESCC cell proliferation and migration via the TLR4/NF-κB and IL-6/STAT3 pathways. F. nucleatum induces cisplatin resistance via the enrichment of immunosuppressive myeloid-derived suppressor cells (MDSCs). Correcting the dysbiosis and reducing the abundance of specific esophageal pathogens may help in suppressing cancer progression. In conclusion, esophageal dysbiosis is associated with ESCC progression and chemoresistance. Screening the oral and esophageal microbiota is a potential diagnostic tool for predicting ESCC development or drug-resistance. Repairing esophageal dysbiosis is a novel treatment for ESCC. Clinical trials with probiotics in addition to current chemotherapy are warranted to study the therapeutic effects.

Glutamine and amino acid metabolism as a prognostic signature and therapeutic target in endometrial cancer

INTRODUCTION

Endometrial cancer (EC) is the most common female reproductive system cancer in developed countries with growing incidence and associated mortality, which may be due to the growing prevalence of obesity. Metabolism reprogramming including glucose, amino acid, and lipid remodeling is a hallmark of tumors. Glutamine metabolism has been reported to participate in tumor proliferation and development. This study aimed to develop a glutamine metabolism-related prognostic model for EC and explore potential targets for cancer treatment.

METHOD

Transcriptomic data and survival outcome of EC were retrieved from The Cancer Genome Atlas (TCGA). Differentially expressed genes related to glutamine metabolism were recognized and utilized to build a prognostic model by univariate and multivariate Cox regressions. The model was confirmed in the training, testing, and the entire cohort. A nomogram combing prognostic model and clinicopathologic features was established and tested. Moreover, we explored the effect of a key metabolic enzyme, PHGDH, on the biological behavior of EC cell lines and xenograft model.

RESULTS

Five glutamine metabolism-related genes, including PHGDH, OTC, ASRGL1, ASNS, and NR1H4, were involved in prognostic model construction. Kaplan-Meier curve suggested that patients recognized as high risk underwent inferior outcomes. The receiver operating characteristic (ROC) curve showed the model was sufficient to predict survival. Enrichment analysis recognized DNA replication and repair dysfunction in high-risk patients whereas immune relevance analysis revealed low immune scores in the high-risk group. Finally, a nomogram integrating the prognostic model and clinical factors was created and verified. Further, knockdown of PHGDH showed cell growth inhibition, increasing apoptosis, and reduced migration. Promisingly, NCT-503, a PHGDH inhibitor, significantly repressed tumor growth in vivo (p = 0.0002).

CONCLUSION

Our work established and validated a glutamine metabolism-related prognostic model that favorably evaluates the prognosis of EC patients. DNA replication and repair may be the crucial point that linked glutamine metabolism, amino acid metabolism, and EC progression. High-risk patients stratified by the model may not be sufficient for immune therapy. PHGDH might be a crucial target that links serine metabolism, glutamine metabolism as well as EC progression.

Deciphering comprehensive features of tumor microenvironment controlled by chromatin regulators to predict prognosis and guide therapies in uterine corpus endometrial carcinoma

BACKGROUND

Dysregulation of chromatin regulators (CRs) can perturb the tumor immune microenvironment, but the underlying mechanism remains unclear. We focused on uterine corpus endometrial carcinoma (UCEC) and used gene expression data from TCGA-UCEC to investigate this mechanism.

METHODS

We used weighted gene co-expression network analysis (WGCNA) and consensus clustering algorithm to classify UCEC patients into Cluster_L and Cluster_H. TME-associated CRs were identified using WGCNA and differential gene expression analysis. A CR risk score (CRRS) was constructed using univariate Cox and LASSO-Cox regression analyses. A nomogram was developed based on CRRS and clinicopathologic factors to predict patients' prognosis.

RESULTS

Lower CRRS was associated with lower grade, more benign molecular subtypes, and improved survival. Patients with low CRRS showed abundant immune infiltration, a higher mutation burden, fewer CNVs, and better response to immunotherapy. Moreover, low CRRS patients were more sensitive to 24 chemotherapeutic agents.

CONCLUSION

A comprehensive assessment of CRRS could identify immune activation and improve the efficacy of UCEC treatments.

A comprehensive analysis focusing on cuproptosis to investigate its clinical and biological relevance in uterine corpus endometrial carcinoma and its potential in indicating prognosis

Cuproptosis, a novel copper-dependent cell death involving mitochondrial respiration, is distinct from other known death mechanisms, which inspires us to study further in uterine corpus endometrial carcinoma (UCEC). Herein, leveraging comprehensive data from TCGA-UCEC, we conducted transcriptional and genetic analyses of 13 recently identified cuproptosis genes. We discovered severe genetic instability of cuproptosis genes, extensive positive correlations among those genes with each other at the mRNA level, and their involvement in oncogenic pathways in UCEC samples. Next, WGCNA was performed to identify a potential module regulating cuproptosis, in which the hub genes, in addition to 13 cuproptosis genes, were drawn to construct a scoring system termed Cu. Score. Furthermore, its clinical and biological relevance and tumor immune landscape, genetic alterations, as well as predicted sensitivity of chemotherapy drugs in different Cu. Score subgroups had been discussed extensively and in detail. Additionally, univariate Cox and LASSO regression were performed to identify 13 cuproptosis-related prognostic genes to establish a prognostic signature, the Risk. Score. Integrating the Risk. Score and clinical parameters, we established a nomogram with excellent performance to predict the 1-/3-/5-year survival probabilities of UCEC patients. To conclude, we conducted a comprehensive analysis encompassing cuproptosis and developed a cuproptosis scoring system and a prognostic prediction model for UCEC, which may offer help with individualized assessment and treatment for UCEC patients from the perspective of a novel death mechanism.

N7-Methylguanosine Regulatory Genes Profoundly Affect the Prognosis, Progression, and Antitumor Immune Response of Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is a common abdominal cancer with poor survival outcomes. Although there is growing evidence that N7-methylguanosine (m7G) is closely associated with tumor prognosis, development, and immune response, few studies focus on this topic.

Methods

The novel m7G risk signature was constructed through the Lasso regression analysis. Its prognostic value was evaluated through a series of survival analyses and was tested in ICGC-LIRI, GSE14520, and GSE116174 cohorts. CIBERSORT, ssGSEA, and ESTIMATE methods were applied to explore the effects of the m7G risk score on tumor immune microenvironment (TIM). The GSEA method was used to evaluate the impacts of the m7G risk score on glycolysis, ferroptosis, and pyroptosis. The human protein atlas (HPA) database was used to clarify the histological expression levels of five m7G signature genes. The biofunctions of NCBP2 in hepatocellular cancer (HC) cells were confirmed through qPCR, CCK8, and transwell assays.

Results

Five m7G regulatory genes comprised the novel risk signature. The m7G risk score was identified as an independent prognostic factor of HCC and could increase the decision-making benefit of traditional prognostic models. Besides, we established a nomogram containing the clinical stage and m7G risk score to predict the survival rates of HCC patients. The prognostic value of the m7G model was successfully validated in ICGC and GSE116174 cohorts. Moreover, high m7G risk led to a decreased infiltration level of CD8+ T cells, whereas it increased the infiltration levels of Tregs and macrophages. The glycolysis and pyroptosis processes were found to be enriched in the HCC patients with high m7G risk. Finally, overexpression of NCBP2 could promote the proliferation, migration, and invasion of HC cells.

Conclusions

The m7G risk score was closely related to the prognosis, antitumor immune process, glycolysis, and malignant progression of HCC. NCBP2 has pro-oncogenic abilities, showing promise as a novel treatment target.

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.