Methylation status of KLF4 and HS3ST2 genes as predictors of endometrial cancer and hyperplastic endometrial lesions

Identification of potential models for predicting progestin insensitivity in patients with endometrial atypical hyperplasia and endometrioid endometrial cancer based on ATAC-Seq and RNA-Seq integrated analysis

Objective: The aim of this study was to establish predictive models based on the molecular profiles of endometrial lesions, which might help identify progestin-insensitive endometrial atypical hyperplasia (EAH) or endometrioid endometrial cancer (EEC) patients before progestin-based fertility-preserving treatment initiation.

Methods: Endometrial lesions from progestin-sensitive (PS, n = 7) and progestin-insensitive (PIS, n = 7) patients were prospectively collected before progestin treatment and then analyzed by ATAC-Seq and RNA-Seq. Potential chromatin accessibility and expression profiles were compared between the PS and PIS groups. Candidate genes were identified by bioinformatics analyses and literature review. Then expanded samples (n = 35) were used for validating bioinformatics data and conducting model establishment.

Results: ATAC-Seq and RNA-Seq data were separately analyzed and then integrated for the subsequent research. A total of 230 overlapping differentially expressed genes were acquired from ATAC-Seq and RNA-Seq integrated analysis. Further, based on GO analysis, REACTOME pathways, transcription factor prediction, motif enrichment, Cytoscape analysis and literature review, 25 candidate genes potentially associated with progestin insensitivity were identified. Finally, expanded samples were used for data verification, and based on these data, three predictive models comprising 9 genes (FOXO1, IRS2, PDGFC, DIO2, SOX9, BCL11A, APOE, FYN, and KLF4) were established with an overall predictive accuracy above 90%.

Conclusion: This study provided potential predictive models that might help identify progestin-insensitive EAH and EEC patients before fertility-preserving treatment.

Identification of methylation-driven genes prognosis signature and immune microenvironment in uterus corpus endometrial cancer

Background

Uterus corpus endometrial cancer (UCEC) is the main malignant tumor in gynecology, with a high degree of heterogeneity, especially in terms of prognosis and immunotherapy efficacy. DNA methylation is one of the most important epigenetic modifications. Studying DNA methylation can help predict the prognosis of cancer patients and provide help for clinical treatment. Our research aims to discover whether abnormal DNA methylation can predict the prognosis of UCEC and reflect the patient's tumor immune microenvironment.

Patients and methods

The clinical data, DNA methylation data, gene expression data and somatic mutation data of UCEC patients were all downloaded from the TCGA database. The MethylMix algorithm was used to integrate DNA methylation data and mRNA expression data. Univariate Cox regression analysis, Multivariate Cox regression analysis, and Lasso Cox regression analysis were used to determine prognostic DNA methylation-driven genes and to construct an independent prognostic index (MDS). ROC curve analysis and Kaplan–Meier survival curve analysis were used to evaluate the predictive ability of MDS. GSEA analysis was used to explore possible mechanisms that contribute to the heterogeneity of the prognosis of UCEC patients.

Results

3 differential methylation-driven genes (DMDGs) (PARVG, SYNE4 and CDO1) were considered as predictors of poor prognosis in UCEC. An independent prognostic index was finally established based on 3 DMDGs. From the results of ROC curve analysis and survival curve analysis, MDS showed excellent prognostic ability in TCGA-UCEC. A new nomogram based on MDS and other prognostic clinical indicators has also been successfully established. The C-index of the nomogram for OS prediction was 0.764 (95% CI = 0.702–0.826). GSEA analysis suggests that there were differences in immune-related pathways among patients with different prognosis. The abundance of M2 macrophages and M0 macrophages were significantly enhanced in the high-risk group while T cells CD8, Eosinophils and Neutrophils were markedly elevated in the low-risk group. Meanwhile, patients in the low-risk group had higher levels of immunosuppressant expression, higher tumor mutational burden and immunophenoscore (IPS) scores. Joint survival analysis revealed that 7 methylation-driven genes could be independent prognostic factors for overall survival for UCEC.

Conclusion

We have successfully established a risk model based on 3 DMDGs, which could accurately predict the prognosis of patients with UCEC and reflect the tumor immune microenvironment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02038-z.

Identification of the miRNA signature and key genes in colorectal cancer lymph node metastasis

BACKGROUND

Because its metastasis to the lymph nodes are closely related to poor prognosis, miRNAs and mRNAs can serve as biomarkers for the diagnosis, prognosis, and therapy of colorectal cancer (CRC). This study aimed to identify novel gene signatures in the lymph node metastasis of CRC.

METHODS

GSE56350, GSE70574, and GSE95109 datasets were downloaded from the Gene Expression Omnibus (GEO) database, while data from 569 colorectal cancer cases were also downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs (DE-miRNAs) were calculated using R programming language (Version 3.6.3), while gene ontology and enrichment analysis of target mRNAs were performed using FunRich ( http://www.funrich.org ). Furthermore, the mRNA-miRNA network was constructed using Cytoscape software (Version 3.8.0). Gene expression levels were verified using the GEO datasets. Similarly, quantitative real-time PCR (qPCR) was used to examine expression profiles from 20 paired non-metastatic and metastatic lymph node tissue samples obtained from patients with CRC.

RESULTS

In total, five DE-miRNAs were selected, and 34 mRNAs were identified after filtering the results. Moreover, two key miRNAs (hsa-miR-99a, hsa-miR-100) and one gene (heparan sulfate-glucosamine 3-sulfotransferase 2 [HS3ST2]) were identified. The GEO datasets analysis and qPCR results showed that the expression of key miRNA and genes were consistent with that obtained from the bioinformatic analysis. A novel miRNA-mRNA network capable of predicting the prognosis and confirmed experimentally, hsa-miR-99a-HS3ST2-hsa-miR-100, was found after expression analysis in metastasized lymph node tissue from CRC samples.

CONCLUSION

In summary, miRNAs and genes with potential as biomarkers were found and a novel miRNA-mRNA network was established for CRC lymph node metastasis by systematic bioinformatic analysis and experimental validation. This network may be used as a potential biomarker in the development of lymph node metastatic CRC.

HS3ST2 and Its Related Molecules as Potential Biomarkers for Predicting Lymph Node Metastasis in Patients with Colorectal Cancer

Background

Lymph node metastasis is a major cause of cancer-related death in patients with colorectal cancer (CRC), but current strategies are limited to predicting this clinical behavior. Our study aims to establish a lymph node metastasis prediction model based on miRNA and mRNA to improve the accuracy of prediction.

Methods

GSE56350, GSE70574, and GSE95109 were downloaded from the Gene Expression Omnibus (GEO) database and 569 colorectal cancer statistics were also downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs were calculated by using R software. Besides, gene ontology and enriched pathway analysis of target mRNAs were analyzed by using FunRich. Furthermore, the mRNA–miRNA network was constructed using Cytoscape software. Gene expression level was also detected by performing qRT-PCR (quantitative real-time PCR) in colorectal cancer and lymph node tissues.

Results

In total, 5 differentially expressed miRNAs were selected, and 34 mRNAs were identified after filtering. The research of KEGG indicated that mRNAs are enriched in many cancer pathways. Differentially expressed miRNAs were most enriched in the cytoplasm, nucleoside, transcription factor activity, and RNA binding. KEGG pathway analysis of these target genes was mainly enriched in 5 pathways including fatty acid elongation, MAPK signaling pathway, autophagy, signaling pathways regulating pluripotency of stem cells, and Th17 cell differentiation. The results of qRT-PCR indicated that hsa-miR-100 and hsa-miR-99a were differentially expressed in lymph node metastatic colorectal cancer tissues and lymph node non-metastasis tissues which all target HS3ST2. Besides, we also found they have a significant difference in colorectal cancer tissues compared with normal tissues.

Conclusion

By using microarray and bioinformatics analyses, differentially expressed miRNAs were identified and a complete gene network was constructed. To our knowledge, HS3ST2 and related molecules including hsa-miR-100 and hsa-miR-99a were firstly identified as potential biomarkers in the development of lymph node metastatic colorectal cancer.

Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma

Background

Osteosarcoma (OS) is a common malignant bone tumor originating in the interstitial tissues and occurring mostly in adolescents and young adults. Energy metabolism is a prerequisite for cancer cell growth, proliferation, invasion, and metastasis. However, the gene signatures associated with energy metabolism and their underlying molecular mechanisms that drive them are unknown.

Methods

Energy metabolism-related genes were obtained from the TARGET database. We applied the “NFM” algorithm to classify putative signature gene into subtypes based on energy metabolism. Key genes related to progression were identified by weighted co-expression network analysis (WGCNA). Based on least absolute shrinkage and selection operator (LASSO) Cox proportional regression hazards model analyses, a gene signature for the predication of OS progression and prognosis was established. Robustness and estimation evaluations and comparison against other models were used to evaluate the prognostic performance of our model.

Results

Two subtypes associated with energy metabolism was determined using the “NFM” algorithm, and significant modules related to energy metabolism were identified by WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the genes in the significant modules were enriched in kinase, immune metabolism processes, and metabolism-related pathways. We constructed a seven-gene signature consisting of SLC18B1, RBMXL1, DOK3, HS3ST2, ATP6V0D1, CCAR1, and C1QTNF1 to be used for OS progression and prognosis. Upregulation of CCAR1, and C1QTNF1 was associated with augmented OS risk, whereas, increases in the expression SCL18B1, RBMXL1, DOK3, HS3ST2, and ATP6VOD1 was correlated with a diminished risk of OS. We confirmed that the seven-gene signature was robust, and was superior to the earlier models evaluated; therefore, it may be used for timely OS diagnosis, treatment, and prognosis.

Conclusions

The seven-gene signature related to OS energy metabolism developed here could be used in the early diagnosis, treatment, and prognosis of OS.

ESRRG promoter hypermethylation as a diagnostic and prognostic biomarker in laryngeal squamous cell carcinoma

Background

Estrogen‐related receptor gamma (ESRRG) has been identified as a tumor suppressor gene in several cancers. We aimed to evaluate ESRRG promoter methylation in laryngeal squamous cell carcinoma (LSCC) and its relative clinical value in LSCC.

Methods

Bisulfite pyrosequencing assays were performed on 91 pairs of tumor and paracancer tissues from LSCC patients in China. The diagnostic value and overall survival (OS) were analyzed descriptively by receiver operating characteristic (ROC) curves and the Kaplan‐Meier methods, respectively.

Results

The ESRRG promoter was more frequently hypermethylated in tumor tissues than in adjacent tissues (P < 0.01). ESRRG promoter methylation was significantly increased in advanced T stage tumors (P < 0.01) and advanced clinical stage patients (P < 0.01). Moreover, the area under the ROC curve (AUC) value (0.81) indicated high discrimination accuracy. Furthermore, ESRRG hypermethylation was associated with poor OS, as confirmed by Kaplan‐Meier survival curves (P < 0.01).

Conclusion

Our study indicated that ESRRG promoter hypermethylation contributed to LSCC‐related risks, primarily tumor progression and survival prognosis, in patients. ESRRG promoter methylation could, therefore, be a diagnostic and prognostic biomarker in LSCC.