Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools

Development and validation of matrix metalloproteinase for predicting prognosis and tumour microenvironment immune profiles in uterine corpus endometrial carcinoma

Background: Matrix metalloproteinases (MMPs) are involved in many processes of tumour progression and invasion. However, few studies have analysed the effects of MMP expression patterns on endometrial cancer (EC) development from the perspective of the tumour microenvironment (TME). we quantified MMP expression in individual by constructing an MMP score and found MMP score effectively predict the prognosis of EC patients. Methods: MMPs expression profiles were determined based on the differential expression of 12 MMP-related regulators. Principal component analysis (PCA) was used to construct an MMP scoring system which can quantify the MMPs expression patterns individually of EC patients. Kaplan-Meier analysis, the log-rank test, and time-dependent receiver operating characteristic (ROC) curve analysis were used to evaluate the value of MMPs expression in predicting prognosis. Single-cell RNA sequencing (scRNA-seq) dataset was used to verify correlation between MMPs and progression of EC. Gene Ontology (GO) analysis was used to investigate the pathways and functions underlying MMPs expression. Tumour immune dysfunction, exclusion prediction, and pharmacotherapy response analyses were performed to assess the potential response to pharmacotherapy based on MMPs patterns. Results: We downloaded the MMPs expression data, somatic mutation data and corresponding clinical information of EC patients from the TCGA website and ICGC portal. Based on the MMP-related differentially expressed genes (DEGs), the MMP score was constructed, and EC patients were divided into high and low MMP score groups. There was a positive correlation between MMP score and prognosis of EC patients. Patients with high MMP scores had better prognosis, more abundant immune cell infiltration and stronger antitumoor immunity. Although prognosis is worse with the lower group than the high, patients with low MMP score had better response to immunotherapy, which means they could prolong the survival time through Immunological checkpoint blockade (ICB) therapy. scRNA-seq analysis identified significant heterogeneity between MMP score and classical pathways in EC. Conclusion: Our work indicates that the MMP score could be a potential tool to evaluate MMP expression patterns, immune cell infiltration, response to pharmacotherapy, clinicopathological features, and survival outcomes in EC. This will provide the more effective guide to select immunotherapeutic strategies of EC in the future.

Investigating the tissue specificity and prognostic impact of cis-regulatory cancer risk variants

The tissue-specific incidence of cancers and their genetic basis are poorly understood. Although prior studies have shown global correlation across tissues for cancer risk single-nucleotide polymorphisms (SNPs) identified through genome-wide association studies (GWAS), any shared functional regulation of gene expression on a per SNP basis has not been well characterized. We set to quantify cis-mediated gene regulation and tissue sharing for SNPs associated with eight common cancers. We identify significant tissue sharing for individual SNPs and global enrichment for breast, colorectal, and Hodgkin lymphoma cancer risk SNPs in multiple tissues. In addition, we observe increasing tissue sharing for cancer risk SNPs overlapping with super-enhancers for breast cancer and Hodgkin lymphoma providing further evidence of tissue specificity. Finally, for genes under cis-regulation by breast cancer SNPs, we identify a phenotype characterized by low expression of tumor suppressors and negative regulators of the WNT pathway associated with worse freedom from progression and overall survival in patients who eventually develop breast cancer. Our results introduce a paradigm for functionally annotating individual cancer risk SNPs and will inform the design of future translational studies aimed to personalize assessment of inherited cancer risk across tissues.

Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis

Background

Uterine corpus endometrial carcinoma (UCEC) is a common malignant cancer type which affects the health of women worldwide. However, its molecular mechanism has not been elucidated.

Methods

To identify the hub modules and genes in UCEC associated with clinical phenotypes, the RNA sequencing data and clinical data of 543 UCEC samples were obtained from The Cancer Genome Atlas (TCGA) database and then subjected to weighted gene co-expression network analysis (WGCNA). To explore the potential biological function of the hub modules, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Genes differentially expressed in UCEC were screened according to TCGA data using the "gdcDEAnalysis" package in R (The R Foundation for Statistical Computing). After intersecting with hub genes, the shared genes were used for further survival analyses. The relationship between gene expression level and clinical phenotype was analyzed in the TCGA-UCEC cohort in The University of ALabama at Birmingham CANcer data analysis Portal and the Human Protein Atlas. The microarray data set GSE17025 was also analyzed to validate the gene expression profiles.

Results

There were 19 coexpression modules generated by WGCNA. Among them, 2 modules with 198 hub genes were highly correlated with clinical features (especially histologic grade and clinical stage). Meanwhile, 4,003 differentially expressed genes (DEGs) were screened out, and 164 DEGs overlapped with hub genes. Survival analyses revealed that high expression of GINS4 and low expression of ESR1 showed a trend of poor prognosis. Further analyses demonstrated that both messenger RNA (mRNA) and protein expression profiles of GINS4 and ESR1 were significantly associated with UCEC development and progression in TCGA and GSE17025 cohorts.

Conclusions

Based on the integrated bioinformatic analyses, our data indicated that GINS4 and ESR1 might serve as potential prognostic markers and targets for UCEC therapy.

Computational approaches for discovering significant microRNAs, microRNA-mRNA regulatory pathways, and therapeutic protein targets in endometrial cancer

Endometrial cancer (EC) is a urogenital cancer affecting millions of post-menopausal women, globally. This study aims to identify key miRNAs, target genes, and drug targets associated with EC metastasis. The global miRNA and mRNA expression datasets of endometrial tissue biopsies (24 tumors +3 healthy tissues for mRNA and 18 tumor +4 healthy tissues for miRNAs), were extensively analyzed by mapping of DEGs, DEMi, biological pathway enrichment, miRNA-mRNA networking, drug target identification, and survival curve output for differentially expressed genes. Our results reveal the dysregulated expression of 26 miRNAs and their 66 target genes involved in focal adhesions, p53 signaling pathway, ECM-receptor interaction, Hedgehog signaling pathway, fat digestion and absorption, glioma as well as retinol metabolism involved in cell growth, migration, and proliferation of endometrial cancer cells. The subsequent miRNA-mRNA network and expression status analysis have narrowed down to 2 hub miRNAs (hsa-mir-200a, hsa-mir-429) and 6 hub genes (PTCH1, FOSB, PDGFRA, CCND2, ABL1, ALDH1A1). Further investigations with different systems biology methods have prioritized ALDH1A1, ABL1 and CCND2 as potential genes involved in endometrial cancer metastasis owing to their high mutation load and expression status. Interestingly, overexpression of PTCH1, ABL1 and FOSB genes are reported to be associated with a low survival rate among cancer patients. The upregulated hsa-mir-200a-b is associated with the decreased expression of the PTCH1, CCND2, PDGFRA, FOSB and ABL1 genes in endometrial cancer tissue while hsa-mir-429 is correlated with the decreased expression of the ALDH1A1 gene, besides some antibodies, PROTACs and inhibitory molecules. In conclusion, this study identified key miRNAs (hsa-mir-200a, hsa-mir-429) and target genes ALDH1A1, ABL1 and CCND2 as potential biomarkers for metastatic endometrial cancers from large-scale gene expression data using systems biology approaches.

Expression of the Hippo Pathway Core Components in Endometrial Cancer and Its Association with Clinicopathologic Features

BACKGROUND

The Hippo signaling pathway has a key role in tumorigenesis. This study aimed to evaluate the relationship between the expression of core components of the Hippo signaling pathway and its association with clinicopathological features in endometrial cancer.

MATERIALS AND METHODS

We retrospectively collected endometrioid endometrial cancer specimens from 60 patients between January 2002 and December 2009 at Gyeongsang National University Hospital. Relevant clinicopathological data were obtained through electronic medical records of patients. The expression patterns of six core components (YAP, p-YAP, LATS1/2, MST1/2, KIBRA, and Merlin) were identified by immunohistochemistry on tissue microarray sections.

RESULTS

The positive expression ratio was 75.0% for YAP, 73.3% for p-YAP, 26.7% for MST1/2, 16.7% for KIBRA, 15.0% for Merlin, and 15.0% for LATS1/2. YAP expression was negatively correlated with MST 1/2 kinases (p = 0.045) and positively correlated with p-YAP (p = 0.012). Merlin, and MST 1/2 kinases (p = 0.043) showed a positive correlation. A subgroup of patients aged below 60 years (p = 0.004) and with myometrial invasion depth of less than 1/2 (p = 0.041) showed a positive association with YAP expression. p-YAP expression was negatively associated with a subset of patients with primary tumour size ≥4 cm (p = 0.03). Logistic regression analysis showed a significant association between age and YAP expression. The odds ratio of p-YAP expression was significantly lower in the group with tumour size ≥4 cm.

CONCLUSION

Two prognostic factors, age and tumour size, were significantly associated with the expression of YAP and p-YAP in endometrial cancer. Further research should focus on their expression as a marker for prediction of clinicopathological implications in endometrial cancer.

A systematic review of anti-cancer roles and mechanisms of kaempferol as a natural compound

It has been shown in multiple experimental and biological investigations that kaempferol, an edible flavonoid generated from plants, may be used as an anti-cancer drug and has been shown to have anti-cancer properties. Many signaling pathways are altered in cancer cells, resulting in cell growth inhibition and death in various tumor types. Cancer is a multifaceted illness coordinated by multiple external and internal mechanisms. Natural extracts with the fewest side effects have piqued the attention of researchers in recent years, attempting to create cancer medicines based on them. An extensive array of natural product-derived anti-cancer agents have been examined to find a successful method. Numerous fruits and vegetables have high levels of naturally occurring flavonoid kaempferol, and its pharmacological and biological effects have been studied extensively. Certain forms of cancer are sensitive to kaempferol-mediated anti-cancer activity, although complete research is needed. We have endeavored to concentrate our review on controlling carcinogenic pathways by kaempferol in different malignancies. Aside from its extraordinary ability to modify cell processes, we have also discussed how kaempferol has the potential to be an effective therapy for numerous tumors.

In silico Approach for Validating and Unveiling New Applications for Prognostic Biomarkers of Endometrial Cancer

Endometrial cancer (EC) mortality is directly associated with the presence of prognostic factors. Current stratification systems are not accurate enough to predict the outcome of patients. Therefore, identifying more accurate prognostic EC biomarkers is crucial. We aimed to validate 255 prognostic biomarkers identified in multiple studies and explore their prognostic application by analyzing them in TCGA and CPTAC datasets. We analyzed the mRNA and proteomic expression data to assess the statistical prognostic performance of the 255 proteins. Significant biomarkers related to overall survival (OS) and recurrence-free survival (RFS) were combined and signatures generated. A total of 30 biomarkers were associated either to one or more of the following prognostic factors: histological type (n = 15), histological grade (n = 6), FIGO stage (n = 1), molecular classification (n = 16), or they were associated to OS (n = 11), and RFS (n = 5). A prognostic signature composed of 11 proteins increased the accuracy to predict OS (AUC = 0.827). The study validates and identifies new potential applications of 30 proteins as prognostic biomarkers and suggests to further study under-studied biomarkers such as TPX2, and confirms already used biomarkers such as MSH6, MSH2, or L1CAM. These results are expected to advance the quest for biomarkers to accurately assess the risk of EC patients.

Histological Grade of Endometrioid Endometrial Cancer and Relapse Risk Can Be Predicted with Machine Learning from Gene Expression Data

The tumor grade of endometrioid endometrial cancer is used as an independent marker of prognosis and a key component in clinical decision making. It is reported that between grades 1 and 3, however, the intermediate grade 2 carries limited information; thus, patients with grade 2 tumors are at risk of both under- and overtreatment. We used RNA-sequencing data from the TCGA project and machine learning to develop a model which can correctly classify grade 1 and grade 3 samples. We used the trained model on grade 2 patients to subdivide them into low-risk and high-risk groups. With iterative retraining, we selected the most relevant 12 transcripts to build a simplified model without losing accuracy. Both models had a high AUC of 0.93. In both cases, there was a significant difference in the relapse-free survivals of the newly identified grade 2 subgroups. Both models could identify grade 2 patients that have a higher risk of relapse. Our approach overcomes the subjective components of the histological evaluation. The developed method can be automated to perform a prescreening of the samples before a final decision is made by pathologists. Our translational approach based on machine learning methods could allow for better therapeutic planning for grade 2 endometrial cancer patients.

The Perspectives of Fertility Preservation in Women with Endometrial Cancer

Simple Summary

Endometrial cancer is a common gynecological malignant disease. Its incidence in women of reproductive age in developed countries is increasing. The standard treatment is surgical in the form of hysterectomy and bilateral salpingo-oophorectomy, which has a significant impact on the quality of women’s lives and precludes further fertility. Conservative management to preserve reproductive function and delay final surgery can today be considered in carefully selected women. We analyze the current approaches to select appropriate candidates and current medical regimens for fertility sparing management. We elaborate on the future perspectives of management. With better characterization of the disease and implementation of molecular biomarkers, more women should be able to benefit from conservative approaches to management of endometrial cancer.

Abstract

Endometrial cancer is the most common gynecological cancer in developed countries. The disease is diagnosed with increasing frequency in younger women, commonly also in their reproductive age. The standard treatment of endometrial cancer is surgical in the form of hysterectomy and bilateral salpingo-oophorectomy, and this precludes future fertility in younger women. The current challenge is to identify the group of women with endometrial cancer and low-risk features that would benefit from more conservative treatment options. More focus in management needs to be aimed towards the preservation of quality of life, without jeopardizing oncological outcomes. In this review, we analyze the current approaches to identification of women for conservative management and evaluate the success of different medical options for treatment and surgical techniques that are fertility sparing. We also elaborate on the future perspectives, focusing on the incorporation of molecular characterization of endometrial cancer to fertility preservation algorithms. Future studies should focus specifically on identifying reliable clinical and molecular predictive markers in this group of young women. With improved knowledge and better risk assessment, the precision medicine is the path towards improved understanding of the disease and possibly widening the group of women that could benefit from treatment methods preserving their fertility.

Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis

Background

Colon cancer is a worldwide leading cause of cancer-related mortality, and the prognosis of colon cancer is still needed to be improved. This study aimed to construct a prognostic model for predicting the prognosis of colon cancer.

Methods

The gene expression profile data of colon cancer were obtained from the TCGA, GSE44861, and GSE44076 datasets. The WGCNA module genes and common differentially expressed genes (DEGs) were used to screen out the prognosis-associated DEGs, which were used to construct a prognostic model. The performance of the prognostic model was assessed and validated in the TCGA training and microarray validation sets (GSE38832 and GSE17538). At last, the model and prognosis-associated clinical factors were used for the construction of the nomogram.

Results

Five colon cancer-related WGCNA modules (including 1160 genes) and 1153 DEGs between tumor and normal tissues were identified, inclusive of 556 overlapping DEGs. Stepwise Cox regression analyses identified there were 14 prognosis-associated DEGs, of which 12 DEGs were included in the optimized prognostic gene signature. This prognostic model presented a high forecast ability for the prognosis of colon cancer both in the TCGA training dataset and the validation datasets (GSE38832 and GSE17538; AUC > 0.8). In addition, patients’ age, T classification, recurrence status, and prognostic risk score were associated with the prognosis of TCGA patients with colon cancer. The nomogram was constructed using the above factors, and the predictive 3- and 5-year survival probabilities had high compliance with the actual survival proportions.

Conclusions

The 12-gene signature prognostic model had a high predictive ability for the prognosis of colon cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-020-02116-y.