Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer

Investigating the Role of Telomere and Telomerase Associated Genes and Proteins in Endometrial Cancer

Endometrial cancer (EC) is the commonest gynaecological malignancy. Current prognostic markers are inadequate to accurately predict patient survival, necessitating novel prognostic markers, to improve treatment strategies. Telomerase has a unique role within the endometrium, whilst aberrant telomerase activity is a hallmark of many cancers. The aim of the current in silico study is to investigate the role of telomere and telomerase associated genes and proteins (TTAGPs) in EC to identify potential prognostic markers and therapeutic targets. Analysis of RNA-seq data from The Cancer Genome Atlas identified differentially expressed genes (DEGs) in EC (568 TTAGPs out of 3467) and ascertained DEGs associated with histological subtypes, higher grade endometrioid tumours and late stage EC. Functional analysis demonstrated that DEGs were predominantly involved in cell cycle regulation, while the survival analysis identified 69 DEGs associated with prognosis. The protein-protein interaction network constructed facilitated the identification of hub genes, enriched transcription factor binding sites and drugs that may target the network. Thus, our in silico methods distinguished many critical genes associated with telomere maintenance that were previously unknown to contribute to EC carcinogenesis and prognosis, including NOP56, WFS1, ANAPC4 and TUBB4A. Probing the prognostic and therapeutic utility of these novel TTAGP markers will form an exciting basis for future research.

Development and Validation of a Five-immune Gene Pair Signature in Endometrial Carcinoma

BACKGROUND

Endometrial cancer (EC) is a common gynecological malignancy worldwide. Immunity is closely related to the occurrence and prognosis of EC. At the same time, immune-related genes have great potential as prognostic markers in many types of cancer.

OBJECTIVE

Therefore, we attempt to develop immune-related gene markers to enhance prognosis prediction of EC.

METHODS

542 samples of EC gene expression data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA). The samples were randomly divided into two groups, one group as a training set (N=271), and one set as a validation set. (N=271). In the training set, the gene pairs were established based on the relative expression levels of 271 immune genes, and the prognosis-related gene pairs were screened. The lasso was used to select the features, and finally, the robust biomarkers were screened. Finally, the prognostic model of the immune gene pair was established and verified by the validation data set.

RESULTS

10030 immune gene pair (IRGPs) were obtained, and univariate survival analysis was used to identify 1809 prognostic-related IRGPs (p<0.05). 5-IRGPs were obtained by lasso regression feature selection, and multivariate regression was used to establish 5-IRGPs signature, 5-IRGPs signature is an independent prognostic factor for EC patients, and could be risk stratified in patients with TCGA datasets, age, ethnicity, stage, and histological classification (p<0.05). The mean AUC of survival in both the training set and the validation set was greater than 0.7, indicating that 5-IRGPs signature has superior classification performance in patients with EC. In addition, 5-IRGPs have the highest average C index (0.795) compared to the prognostic characteristics of the three endometrial cancers reported in the past and Stage and Age.

CONCLUSION

This study constructed a 5-IRGPs signature as a novel prognostic marker for predicting survival in patients with EC.

Identification of potential crucial genes associated with the pathogenesis and prognosis of pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is a type of malignant tumor with the highest mortality rate among all neoplasms worldwide, and its exact pathogenesis is still poorly understood. Timely diagnosis and treatment are of great importance in order to decrease the mortality rate of PAAD. Therefore, identifying new biomarkers for diagnosis and prognosis is essential to enable early detection of PAAD and to improve the overall survival (OS) rate. In order to screen and integrate differentially expressed genes (DEGs) between PAAD and normal tissues, a total of seven datasets were downloaded from the Gene Expression Omnibus database and the ‘limma’ and ‘robustrankggreg’ packages in R software were used. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of the DEGs was performed using the Database for Annotation, Visualization and Integrated Discovery website, and the protein-protein interaction network analysis was performed using the Search Tool for the Retrieval of Interacting Genes/Proteins database. A gene prognostic signature was constructed using the Cox regression model. A total of 10 genes (CDK1, CCNB1, CDC20, ASPM, UBE2C, TPX2, TOP2A, NUSAP1, KIF20A and DLGAP5) that may be associated with pancreatic adenocarcinoma were identified. According to the differentially expressed genes in The Cancer Genome Atlas, the present study set up four prognostic signatures (matrix metalloproteinase 12, sodium voltage-gated channel α subunit 11, tetraspanin 1 and SH3 domain and tetratricopeptide repeats-containing 2), which effectively predicted OS. The hub genes that were highly associated with the occurrence, development and prognosis of PAAD were identified, which may be helpful to further understand the molecular basis of pancreatic cancer and guide the synthesis of drugs for PPAD.

Decreased expression of TRPM4 is associated with unfavorable prognosis and aggressive progression of endometrial carcinoma

Transient Receptor Potential Melastatin 4 (TRPM4) is a nonselective channel conducting monovalent ions and indirectly regulates intracellular Ca²⁺. Aberrant expression has been reported in a number of cancers. However, the biological function of TRPM4 in endometrial carcinoma (EC) is still unknown. We find that decreased TRPM4 expression is significantly correlated with a poor prognosis, overall survival (OS, P<0.001) and recurrence-free survival (P=0.002) through The Cancer Genome Atlas (TCGA) datasets in mRNA level. Multivariate Cox regression analysis suggests that TRPM4 is an independent prognostic factor for OS in EC patients. In vitro assays show that TRPM4-deletion results in significant promotion of proliferation and migration in EC cells. We then conducted a gene set enrichment analysis (GSEA) and according to the results, the expression of TRPM4 is modulated by estrogen, which is inhibited by ER antagonist. Furthermore, the silencing of TRPM4 causes a decreased p53 and hyper-activation of EMT, PI3K/AKT/mTOR signaling pathway in EC, as demonstrated in vitro. Overall, these results indicate that TRPM4 is clinically useful in predicting EC prognosis and represent a potential candidate as a new therapeutic target.

m6A RNA methylation regulators have prognostic value in papillary thyroid carcinoma

BACKGROUND

N⁶-Methyladenosine (m⁶A) is a ubiquitous RNA modification with vital roles in various cancers, but little is known about its role in papillary thyroid carcinoma (PTC), a common endocrine malignancy.

METHODS

In this study, an m⁶A RNA methylation regulator-based biomarker signature was developed for the effective prediction of prognosis in patients with PTC. The gene expression profiles of m⁶A RNA methylation regulators and the corresponding clinical information was downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed m⁶A RNA methylation regulators between tumor and normal control samples, and correlation expression levels, clinical parameters, and outcomes were evaluated. And a prognostic signature was built using a PTC cohort from TCGA.

RESULTS

The expression level of HNRNPC was remarkably upregulated in tumor samples, while WTAP, RBM15, YTHDC2, YTHDC1, FTO, METTL14, METTL3, ALKBH5, KIAA1429, YTHDF1, and ZC3H13 were significantly downregulated in the cancer specimens compared with those in control samples. A three-gene prognostic signature comprising RBM15, KIAA1429, and FTO could predict overall survival in patients with PTC. In addition, the prognostic signature-based risk score was identified as an independent prognostic indicator for PTC.

CONCLUSIONS

We established a robust m⁶A RNA methylation regulator-based molecular signature for predicting prognosis in patients with PTC with high accuracy; this signature might provide important guidance for therapeutic strategies.

Identification of a five-gene signature for predicting the progression and prognosis of stage I endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is often diagnosed at an early clinical stage based on abnormal vaginal bleeding. However, the prognosis of UCEC is poor. The present study was conducted to identify novel tumor grade-related genes with the potential to predict the prognosis and progression of UCEC. A total of three gene expression microarray datasets were downloaded from the Gene Expression Omnibus database, and one RNA-sequencing dataset with corresponding clinical information of patients with UCEC was obtained from The Cancer Genome Atlas database. In summary, 1,447 differentially expressed genes (DEGs) were identified between endometrial cancerous tissues and normal endometrial tissues. Weighted gene co-expression network analysis was performed to assess the associations between DEGs and clinical traits. In total, five genes were found to be highly associated with the tumorigenesis and prognosis of UCEC. Among them, BUB1 mitotic checkpoint serine/threonine kinase B, cyclin B1, cell-division cycle protein 20 and non-SMC condensing I complex subunit G were involved in cell cycle regulation pathways, and DLG-associated protein 5 was involved in the Notch receptor 3 signaling pathway based on functional enrichment analyses. Of the five genes, four were highly expressed in endometrial cancerous tissues compared with normal endometrial tissues at the protein level. In addition, the higher expression of these genes predicted a higher tumor grade and worse overall survival. In conclusion, the present study revealed a 5-gene signature that can be used to predict the progression of UCEC.

Identification of Potential Key Genes for Pathogenesis and Prognosis in Prostate Cancer by Integrated Analysis of Gene Expression Profiles and the Cancer Genome Atlas

Background: Prostate cancer (PCa)is a malignancy of the urinary system with a high incidence, which is the second most common male cancer in the world. There are still huge challenges in the treatment of prostate cancer. It is urgent to screen out potential key biomarkers for the pathogenesis and prognosis of PCa. Methods: Multiple gene differential expression profile datasets of PCa tissues and normal prostate tissues were integrated analysis by R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the overlapping Differentially Expressed Genes (DEG) were performed. The STRING online database was used in conjunction with Cytospace software for protein-protein interaction (PPI) network analysis to define hub genes. The relative mRNA expression of hub genes was detected in Gene Expression Profiling Interactive Analysis (GEPIA) database. A prognostic gene signature was identified by Univariate and multivariate Cox regression analysis. Results: Three hundred twelve up-regulated genes and 85 down-regulated genes were identified from three gene expression profiles (GSE69223, GSE3325, GSE55945) and The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset. Seven hub genes (FGF2, FLNA, FLNC, VCL, CAV1, ACTC1, and MYLK) further were detected, which related to the pathogenesis of PCa. Seven prognostic genes (BCO1, BAIAP2L2, C7, AP000844.2, ASB9, MKI67P1, and TMEM272) were screened to construct a prognostic gene signature, which shows good predictive power for survival by the ROC curve analysis. Conclusions: We identified a robust set of new potential key genes in PCa, which would provide reliable biomarkers for early diagnosis and prognosis and would promote molecular targeting therapy for PCa.

Comprehensive Analysis of Prognostic Alternative Splicing Signatures in Endometrial Cancer

Background

Alternative splicing (AS) is one of the critical post-transcriptional regulatory mechanisms of various cancers and also plays a crucial role in the development of cancers, including endometrial cancer (EC).

Methods

The splicing data and gene expression profiles of EC were obtained from The Cancer Genome Atlas. The corresponding clinical data were extracted from TCGA-CDR. With univariate Cox regression analysis, least absolute shrinkage and selection operator model, and multivariate Cox regression analysis, the survival-related AS events were selected. Functional enrichment analysis was also performed to investigate the functions of these AS events. Splicing factors and AS regulation network were constructed to understand the correlation among these AS events.

Result

A total of 1826 AS events were identified as survival-related events. Functional enrichment analysis showed that these AS events were associated with several immune system-related processes. Then, the prognostic signatures were developed based on these survival-related events and acted as an independent prognostic factor for EC. Splicing factors and AS regulation network were also constructed to understand the regulatory mechanisms of AS events in EC.

Conclusion

This study systematically analyzed the role of AS events in EC and developed the prognostic model for EC.

RNA-Seq analysis of the pathogenesis of STZ-induced male diabetic mouse liver

OBJECTIVE

Diabetes mellitus (DM) is a chronic disease characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. The liver is a key organ involved in glucose metabolism, and the major target proteins' changes in the pathogenesis are still unknown.

METHODS

A diabetic mouse model was induced by intraperitoneal injection of streptozotocin (STZ) solution and the RNA-Seq analysis was used to evaluate the transcription differences in the livers of diabetic mice of this study. And then, the differentially expressed genes were validated between a normal mouse group (n = 6) and a diabetic mouse group (n = 6) using quantitative real-time PCR (qRT-PCR) and Western blotting analysis. In addition, we also constructed protein-protein interacting (PPI) networks of up-regulated and down-regulated genes.

RESULTS

Transcriptome sequencing analysis revealed 370 up-regulated differentially expressed genes and 281 down-regulated differentially expressed genes in the diabetes model. The gene ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis results showed that the differentially expressed genes were mainly involved in immunity, enzyme activity, metabolism, and steroid synthesis. PPI analysis results indicated that the main 15 core differential proteins (Cyp51a1, Acsl4, Ugt1a1, Stat1, Gsta2, Cbr1, Aldh1a1, Fasn, Ces1, Camk2b, Tap1, Egr1, Sqle, Lpin1, Fabp5) were involved in the pathogenesis of diabetes. The qRT-PCR results showed that expression changes of four genes (Acsl4, Stat1, Gsta2, Fabp5) were in different directions from those of RNA-Seq. Western blotting results indicated that Sqle expression change at the protein level was in opposition direction from qRT-PCR, and we speculated that Sqle may be involved in the post-transcriptional modification process.

CONCLUSIONS

Our data speculated that the pathogenesis of diabetes may be mediated mainly through steroid biosynthesis, metabolic processes, and immune responses. Further researches on these pathways may provide new targets for the prevention and treatment of diabetes.

TRPM4 is highly expressed in human colorectal tumor buds and contributes to proliferation, cell cycle, and invasion of colorectal cancer cells

Transient receptor potential melastatin-4 channel (TRPM4) dysregulation contributes to heart conditions, immune diseases, and cervical and prostate cancer. Up to now, the involvement of TRPM4 in colorectal cancer (CRC) pathophysiology remains unknown. Here, we investigated tumor tissue microarrays from 379 CRC patients and analyzed TRPM4 protein expression, tumor characteristics, and clinical outcome. High TRPM4 protein expression was associated with unfavorable tumor features characteristic for epithelial-mesenchymal transition and infiltrative growth patterns, that is, a high number of tumor buds and a low percentage in tumor border configuration. Compared to CRC cells representing early cancer stages, TRPM4 protein expression was the highest in cells representing late-stage metastatic cancer. Investigation of CRC cell line HCT116 and five CRISPR/cas9 TRPM4 knockout clones demonstrated that TRPM4 exhibited large Na+ current densities (~ 60 pA/pF). In addition, CRISPR/cas9 TRPM4 knockout clones showed a tendency toward decreased migration and invasion, cell viability, and proliferation and exhibited a shift in cell cycle when compared to HCT116. Stable overexpression of TRPM4 (TRPM4 wild-type) in two CRISPR/cas9 TRPM4 knockout clones rescued the decrease in cell viability and cell cycle shift. Stable overexpression of a nonconducting, dominant-negative TRPM4 mutant (TRPM4 D894A) did not rescue the decrease in viability or cell cycle shift. Taken together, these findings pointed to TRPM4 ion channel conductivity as the underlying mechanism for decreased viability and cell cycle shift in the TRPM4 knockout clones. Together with previous findings, our present data suggest that TRPM4 plays a versatile role in cancer cell proliferation, cell cycle, and invasion.