LYL1 gene amplification predicts poor survival of patients with uterine corpus endometrial carcinoma: analysis of the Cancer genome atlas data

Novel targets and their functions in the prognosis of uterine corpus endometrial cancer patients

Aberrant mRNA expression is implicated in uterine corpus endometrial carcinoma (UCEC) oncogenesis and progression. However, effective prognostic biomarkers for UCEC remain limited. We aimed to construct a reliable multi-gene risk model using gene expression profiles. Utilizing TCGA data (543 UCEC samples, 35 controls), we identified 1517 differentially acting genes. Weighted gene co-expression complex analysis (WGCCA), hub gene screening, and risk regression analysis (RRA) were employed to determine prognosis-related genes and construct the risk model. Nomograms visualized risk scores and receiver operator characteristic (ROC) curves assessed model performance. Seven novel prognosis-related hub genes (ANGPT1, ASB2, GAL, GDF7, ONECUT2, SV2B, TRPC6) were identified. The model's concordance index (C index) by multivariate Cox regression analysis was 0.79. ROC curves yielded AUCs of 0.811 (3-year) and 0.79 (5-year), demonstrating the model's efficacy in predicting UCEC survival. Our study proposes a promising seven-biomarker risk model for predicting UCEC prognosis, offering potential clinical utility.

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders.

Super-enhancer profiling identifies novel critical and targetable cancer survival gene LYL1 in pediatric acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is a myeloid neoplasm makes up 7.6% of hematopoietic malignancies. Super-enhancers (SEs) represent a special group of enhancers, which have been reported in multiple cell types. In this study, we explored super-enhancer profiling through ChIP-Seq analysis of AML samples and AML cell lines, followed by functional analysis.

Methods

ChIP-seq analysis for H3K27ac was performed in 11 AML samples, 7 T-ALL samples, 8 B-ALL samples, and in NB4 cell line. Genes and pathways affected by GNE-987 treatment were identified by gene expression analysis using RNA-seq. One of the genes associated with super-enhancer and affected by GNE-987 treatment was LYL1 basic helix-loop-helix family member (LYL1). shRNA mediated gene interference was used to down-regulate the expression of LYL1 in AML cell lines, and knockdown efficiency was detected by RT-qPCR and western blotting. The effect of knockdown on the growth of AML cell lines was evaluated by CCK-8. Western blotting was used to detect PARP cleavage, and flow cytometry were used to determine the effect of knockdown on apoptosis of AML cells.

Results

We identified a total of 200 genes which were commonly associated with super-enhancers in ≧10 AML samples, and were found enriched in regulation of transcription. Using the BRD4 inhibitor GNE-987, we assessed the dependence of AML cells on transcriptional activation for growth and found GNE-987 treatment predominantly inhibits cell growth in AML cells. Moreover, 20 candidate genes were selected by super-enhancer profile and gene expression profile and among which LYL1 was observed to promote cell growth and survival in human AML cells.

Conclusions

In summary, we identified 200 common super-enhancer-associated genes in AML samples, and a series of those genes are cancer genes. We also found GNE-987 treatment downregulates the expression of super-enhancer-associated genes in AML cells, including the expression of LYL1. Further functional analysis indicated that LYL1 is required for AML cell growth and survival. These findings promote understanding of AML pathophysiology and elucidated an important role of LYL1 in AML progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02428-9.

Identification of Key Prognostic-Related miRNA-mRNA Pairs in the Progression of Endometrial Carcinoma

OBJECTIVES

Endometrial carcinoma (EC) is one of the leading causes of death from gynecological cancer due to the high recurrence rate. However, the molecular mechanisms of EC progression are not well understood. This study aimed to identify critical genes and miRNAs associated with the progression and prognosis of EC and find the potential mRNA-miRNA regulatory relationship.

DESIGN

The mRNA and miRNA data were downloaded from The Cancer Genome Atlas (TCGA) database. Next, differentially expressed genes (DEGs) were identified. Subsequently, prognosis-related genes and miRNAs were identified, followed by co-expression analysis of these mRNAs and miRNAs. Materials, Setting, and Methods: Samples in the mRNA microarray were divided into normal (n = 35), early stage (n = 385), and advanced stage (n = 153). Next, DEGs in normal versus early stage and early stage versus advanced stage were, respectively, identified, followed by Venn analysis to screen overlapping DEGs in 2 comparison groups. Based on the expression level of these DEGs, univariate Cox regression analysis and Kaplan-Meier method were performed to obtain prognosis-related genes. Moreover, genes-related miRNAs were predicted, and miRNA-mRNA co-expressed pairs were identified. Then, survival analysis of co-expressed miRNA was performed. Finally, co-expressed genes of key genes were identified, and then functional enrichment analysis was conducted.

RESULTS

After integrating analysis, 326 overlapping (309 upregulated and 17 downregulated) DEGs were obtained. Univariate Cox regression analysis showed that 44 mRNAs and 8 miRNAs were associated with the prognosis of EC. Combined with the co-expressed analysis, only one prognosis-related hsa-miR-326/ELFN2 axis was obtained. In addition, functional enrichment analysis showed that co-expressed genes of ELFN2 were mainly involved in the PI3K-Akt signaling pathway.

LIMITATIONS

These findings were obtained via bioinformatics analysis, and thus further experimental studies are urgently demanded to validate our results.

CONCLUSIONS

One key miRNA-mRNA regulatory pair (hsa-miR-326-ELFN2) was screened. This study provided a bioinformatics basis for the molecular mechanism of EC progression and might contribute to the identification of novel therapeutic targets.

Fertility-Sparing Approach in Women Affected by Stage I and Low-Grade Endometrial Carcinoma: An Updated Overview

Endometrial cancer (EC) is a deleterious condition which strongly affects a woman's quality of life. Although aggressive interventions should be considered to treat high-grade EC, a conservative approach should be taken into consideration for women wishing to conceive. In this scenario, we present an overview about the EC fertility-sparing approach state of art. Type I EC at low stage is the only histological type which can be addressed with a fertility-sparing approach. Moreover, no myometrium and/or adnexal invasion should be seen, and lymph-vascular space should not be involved. Regarding the pharmaceutical target, progestins, in particular medroxyprogesterone acetate (MPA) or megestrol acetate (MA), are the most employed agent in conservative treatment of early-stage EC. The metformin usage and hysteroscopic assessment is still under debate, despite promising results. Particularly strict and imperious attention should be given to the follow-up and psychological wellbeing of women, especially because of the double detrimental impairment: both EC and EC-related infertility consequences.

Association of laboratorial parameters and prognostic factors in uterine corpus cancer

OBJECTIVE

The aims were to compare the red blood cells, platelet count, neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, red cell distribution width, and fasting glucose in peripheral blood of patients with benign and malignant uterine neoplasms and to relate these laboratory parameters with prognostic factors and overall survival in cancer.

METHODS

The results of the laboratory parameters were analyzed using the Mann-Whitney U test. Receiver operating characteristic curves were used to find the cutoff values. Overall survival was estimated using the Kaplan-Meyer method.

RESULTS

Higher values of neutrophil-lymphocyte ratio and fasting glucose were found in cancer patients. Higher platelet-lymphocyte ratio values were associated with other subtypes when compared with endometrioid subtype; higher values of red cell distribution width were found in stage II/IV when compared with stage I; lower hemoglobin values were related to stage II/IV and nonendometrioid histological type. Platelet-lymphocyte ratio <145.56 was associated with longer overall survival.

CONCLUSION

Hemoglobin and platelet-lymphocyte ratio values are prognostic factors in uterine corpus cancer.

TF-RBP-AS Triplet Analysis Reveals the Mechanisms of Aberrant Alternative Splicing Events in Kidney Cancer: Implications for Their Possible Clinical Use as Prognostic and Therapeutic Biomarkers

Aberrant alternative splicing (AS) is increasingly linked to cancer; however, how AS contributes to cancer development still remains largely unknown. AS events (ASEs) are largely regulated by RNA-binding proteins (RBPs) whose ability can be modulated by a variety of genetic and epigenetic mechanisms. In this study, we used a computational framework to investigate the roles of transcription factors (TFs) on regulating RBP-AS interactions. A total of 6519 TF–RBP–AS triplets were identified, including 290 TFs, 175 RBPs, and 16 ASEs from TCGA–KIRC RNA sequencing data. TF function categories were defined according to correlation changes between RBP expression and their targeted ASEs. The results suggested that most TFs affected multiple targets, and six different classes of TF-mediated transcriptional dysregulations were identified. Then, regulatory networks were constructed for TF–RBP–AS triplets. Further pathway-enrichment analysis showed that these TFs and RBPs involved in triplets were enriched in a variety of pathways that were associated with cancer development and progression. Survival analysis showed that some triplets were highly associated with survival rates. These findings demonstrated that the integration of TFs into alternative splicing regulatory networks can help us in understanding the roles of alternative splicing in cancer.

DNA Methylation Haplotype Block Markers Efficiently Discriminate Follicular Thyroid Carcinoma from Follicular Adenoma

CONTEXT

Follicular thyroid carcinoma (FTC) is the second most common type of thyroid carcinoma and must be pathologically distinguished from benign follicular adenoma (FA). Additionally, the clinical assessment of thyroid tumors with uncertain malignant potential (TT-UMP) demands effective indicators.

OBJECTIVE

We aimed to identify discriminating DNA methylation markers between FA and FTC.

METHODS

DNA methylation patterns were investigated in 33 FTC and 33 FA samples using reduced representation bisulfite sequencing and methylation haplotype block-based analysis. A prediction model was constructed and validated in an independent cohort of 13 FTC and 13 FA samples. Moreover, 36 TT-UMP samples were assessed using this model.

RESULTS

A total of 70 DNA methylation markers, approximately half of which were located within promoters, were identified to be significantly different between the FTC and FA samples. All the Gene Ontology terms enriched among the marker-associated genes were related to "DNA binding," implying that the inactivation of DNA binding played a role in FTC development. A random forest model with an area under the curve of 0.994 was constructed using those markers for discriminating FTC from FA in the validation cohort. When the TT-UMP samples were scored using this model, those with fewer driver mutations also exhibited lower scores.

CONCLUSION

An FTC-predicting model was constructed using DNA methylation markers, which distinguished between FA and FTC tissues with a high degree of accuracy. This model can also be used to help determine the potential of malignancy in TT-UMP.

Fertility preservation in endometrial cancer patients: options, challenges and perspectives

Several different approaches have been designed by physicians in order to preserve fertility in younger patients with endometrial carcinoma. There are various options offering different advantages, but hysteroscopic resection of pathologic endometrial tissue with placement of a Levonorgestrel Intrauterine Device has proven to be the most successful in allowing patients to conceive and give birth afterwards. However, conservative treatments should only be considered in patients with low-grade and low-stage endometrial tumours. There are many published studies which have sought out a preferable approach to treating endometrial cancer whilst preserving fertility. However, more research on this matter is needed to allow a better understanding as to which techniques/approaches are optimal. In this review, we will summarise the current available treatment options for endometrial cancer in patients of reproductive age.

Identification of hub genes and key pathways associated with the progression of gynecological cancer

Gynecological cancer is the leading cause of cancer mortality in women. However, the mechanisms underlying gynecological cancer progression have remained largely unclear. In the present study, 799 dysregulated genes were identified in ovarian serous cystadenocarcinoma (OV), 488 dysregulated genes in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), and 621 dysregulated genes in uterine corpus endometrial carcinoma (UCEC). Bioinformatics analysis revealed that mRNA splicing and cell proliferation-associated biological processes served important roles in OV progression. Metabolism-associated biological processes played important roles in CESC progression, and protein phosphorylation and small GTPase-mediated signal transduction served important roles in UCEC progression. The present study also constructed OV, CESC and UCEC progression-associated protein-protein interaction networks to reveal the associations among these genes. Furthermore, Kaplan-Meier curve analysis showed that progression-related genes were associated with the duration of overall survival. Finally, NARS2 and TPT1 in OV, SMYD2, EGLN1, TNFRSF10D, FUT11, SYTL3, MMP8 and EREG in CESC, and SLC5A1, TXN, KDM4B, TXNDC11, HSDL2, COX16, MGAT4A, DAGLA, ELOVL7, THRB and PCOLCE2 in UCEC were identified as hub genes in cancer progression. Therefore, this study may assist in the identification of novel mechanisms underlying cancer progression and new biomarkers for gynecological cancer prognosis and therapy.