Integrative analysis of genomic and epigenetic regulation of endometrial cancer

A stemness-based signature with inspiring indications in discriminating the prognosis, immune response, and somatic mutation of endometrial cancer patients revealed by machine learning

Endometrial cancer (EC) is a fatal gynecologic tumor. Bioinformatic tools are increasingly developed to screen out molecular targets related to EC. Our study aimed to identify stemness-related prognostic biomarkers for new therapeutic strategies in EC. In this study, we explored the prognostic value of cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, and its correlation with immune infiltrates in EC. Transcriptome and somatic mutation profiles of EC were downloaded from TCGA database. Based on their stemness signature and DEGs, EC patients were divided into two subtypes via consensus clustering, and patients in Stemness Subtype I presented significantly better OS and DFS than Stemness Subtype II. Subtype I also displayed better clinicopathological features, and genomic variations demonstrated different somatic mutation from subtype II. Additionally, two stemness subtypes had distinct tumor immune microenvironment patterns. In the end, three machine learning algorithms were applied to construct a 7-gene stemness subtype risk model, which were further validated in an external independent EC cohort in our hospital. This novel stemness-based classification could provide a promising prognostic predictor for EC and may guide physicians in selecting potential responders for preferential use of immunotherapy. This novel stemness-dependent classification method has high value in predicting the prognosis, and also provides a reference for clinicians in selecting sensitive immunotherapy methods for EC patients.

Bioinformatics Analysis and Experimental Verification Define Different Angiogenesis Subtypes in Endometrial Carcinoma and Identify a Prognostic Signature

Increasing evidence indicates that peripheral blood vessels play a pivotal role in regulating tumor growth with the presence of new blood vessels facilitating tumor growth and metastasis. Nevertheless, the impact of specific molecule-mediated angiogenesis on the tumor immune microenvironment (TIME) and individual prognosis of uterine corpus endometrial carcinoma (UCEC) remains uncertain. The transcriptome information on 217 prognostic angiogenesis-related genes was integrated, and the angiogenesis patterns of 506 UCEC patients in The Cancer Genome Atlas (TCGA) cohort were comprehensively evaluated. We identified five angiogenic subtypes, namely, EC1, EC2, EC3, EC4, and EC5, which differed significantly in terms of prognosis, clinicopathological features, cancer hallmarks, genomic mutations, TIME patterns, and immunotherapy responses. Additionally, an angiogenesis-related prognostic risk score (APRS) was constructed to enable an individualized comprehensive evaluation. In multiple cohorts, APRS demonstrated a powerful predictive ability for the prognosis of UCEC patients. Likewise, APRS was confirmed to be associated with clinicopathological features, genomic mutations, cancer hallmarks, and TIME patterns in UCEC patients. The predictability of APRS for immune checkpoint inhibitor (ICI) therapy was also salient. Subsequently, the expression levels of four angiogenesis-related hub genes were verified by qRT-PCR, immunohistochemistry, and single-cell sequencing data analysis. The effects of four representative genes on angiogenesis were validated by Wound-Healing and Transwell assays, tube formation assay in vitro, and tumor xenograft model in vivo. This study proffered a new classification of UCEC patients based on angiogenesis. The established APRS may contribute to individualized prognosis prediction and immunotherapy selections that are better suited for UCEC patients.

Prognostic value of comprehensive typing based on m6A and gene cluster in TNBC

BACKGROUND

Triple-negative breast cancer (TNBC) is resistant to targeted therapy with HER2 monoclonal antibodies and endocrine therapy, because it lacks the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). TNBC is a subtype of breast cancer with the worst prognosis and the highest mortality rate compared to other subtypes. N6-methyladenosine (m6A) modification is significant in cancer and metastasis, because it can alter gene expression and function at numerous levels, such as RNA splicing, stability, translocation, and translation. There are limited investigations into the connection between TNBC and m6A.

MATERIALS AND METHODS

Breast cancer-related data were retrieved from the Cancer Genome Atlas (TCGA) database, and 116 triple-negative breast cancer cases were identified from the data. The GSE31519 data set, which included 68 cases of TNBC, was obtained from the Gene Expression Omnibus (GEO) database. Survival analysis was used to determine the prognosis of distinct m6A types based on their m6A group, gene group, and m6A score. To investigate the potential mechanism, GO and KEGG analyses were performed on the differentially expressed genes.

RESULTS

The expression of m6A-related genes and their impact on prognosis in TNBC patients were studied. According to the findings, m6A was crucial in determining the prognosis of TNBC patients, and the major m6A-linked genes in this process were YTHDF2, RBM15B, IGFBP3, and WTAP. YTHDF2, RBM15B and IGFBP3 are associated with poor prognosis, while WTAP is associated with good prognosis. By cluster analysis, the gene cluster and the m6A cluster were beneficial in predicting the prognosis of TNBC patients. The m6A score based on m6A and gene clusters was more effective in predicting the prognosis of TNBC patients. Furthermore, the tumor microenvironment may play an important role in the process of m6A, influencing TNBC prognosis.

CONCLUSIONS

N6-adenylic acid methylation (m6A) was important in altering the prognosis of TNBC patients, and the key m6A-associated genes in this process were YTHDF2, RBM15B, IGFBP3, and WTAP. Furthermore, the comprehensive typing based on m6A and gene clusters was useful in predicting TNBC patients' prognosis, showing potential as valuable evaluating tools for TNBC.

Unbiased In Silico Analysis of Gene Expression Pinpoints Circulating miRNAs Targeting KIAA1324, a New Gene Drastically Downregulated in Ovarian Endometriosis

Objective: To identify circulating miRNAs associated with ovarian endometriosis (OMA), and to analyze candidate genes targeted by these miRNAs. Methods: Putative regulating miRNAs were identified through an original bioinformatics approach. We first queried the miRWalk 2.0 database to collect putative miRNA targets. Then, we matched it to a transcriptomic dataset of OMA. Moving from gene expression in the tissue to possible alterations in the patient plasma, a selection of these miRNAs was quantified by qRT-PCR in plasma samples from 93 patients with isolated OMA and 95 patients surgically checked as free from endometriosis. Then, we characterized the genes regulated by more than one miRNA and validated them by immunohistochemistry and transfection experiments on endometrial cell primary cultures obtained from endometrial biopsies of 10 women with and without endometriosis with miRNA mimics. Stromal and epithelial cells were isolated and cultured separately and gene expression levels were measured by RT-qPCR. Results: Eight miRNAs were identified by bioinformatics analysis. Two of them were overexpressed in plasma from OMA patients: let-7b-5p and miR-92a-3p (p < 0.005). Three miRNAs, let-7b and miR-92a-3p, and miR-93-5p potentially targeted KIAA1324, an estrogen-responsive gene and one of the most downregulated genes in OMA. Transfection experiments with mimics of these two miRNAs showed a strong decrease in KIAA1324 expression, up to 40%. Immunohistochemistry revealed a moderate-to-intense staining for KIAA1324 in the eutopic endometrium and a faint-to-moderate staining in the ectopic endometrium for half of the samples, which is concordant with the transcriptomic data. Discussion and Conclusion: Our results suggested that KIAA1324 might be involved in endometriosis through the downregulating action of two circulating miRNAs. As these miRNAs were found to be overexpressed, their quantification in plasma could provide a tool for an early diagnosis of endometriosis.

Prognostic value of comprehensive typing based on m6A and gene cluster.. [DOI: 10.21203/rs.3.rs-1922311/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background Triple-negative breast cancer (TNBC) is resistant to targeted therapy with HER2 monoclonal antibodies and endocrine therapy because it lacks the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). TNBC is a subtype of breast cancer with the worst prognosis and the highest mortality rate compared to other subtypes. N6-methyladenosine (m6A) modification is significant in cancer and metastasis because it can alter gene expression and function at numerous levels, such as RNA splicing, stability, translocation, and translation. There has been limited investigation into the connection between TNBC and m6A. Materials and Methods Breast cancer-related data were retrieved from the Cancer Genome Atlas (TCGA) database, and 116 triple-negative breast cancer cases were identified from the data. The GSE31519 dataset, which included 68 cases of TNBC, was obtained from the Gene Expression Omnibus (GEO) database. Survival analysis was used to determine the prognosis of distinct m6A types based on their m6A group, gene group, and m6A score. To investigate the potential mechanism, GO and KEGG analyses were performed on the differentially expressed genes. Results The expression of m6A-related genes and their impact on prognosis in TNBC patients were studied. According to the findings, m6A was crucial in determining the prognosis of TNBC patients, and the major m6A-linked genes in this process were YTHDF2, RBM15B, IGFBP3, and WTAP. By cluster analysis, the gene cluster and the m6A cluster were beneficial in predicting the prognosis of TNBC patients. The m6A score based on m6A and gene clusters was more effective in predicting the prognosis of TNBC patients. Furthermore, the tumor microenvironment may play an important role in the process of m6A, influencing TNBC prognosis. Conclusion N6-adenylic acid methylation (m6A) was important in altering the prognosis of TNBC patients, and the key m6A-associated genes in this process were YTHDF2, RBM15B, IGFBP3, and WTAP. Furthermore, the comprehensive typing based on m6A and gene clusters was useful in predicting TNBC patients' prognosis, showing potential as a meaningful evaluating tools for TNBC.

Genetic Alteration, Prognostic and Immunological Role of Acyl-CoA Synthetase Long-Chain Family Member 4 in a Pan-Cancer Analysis

Acyl-CoA Synthetase long-chain family member 4 (ACSL4) is a member of acyl-CoA synthetase protein long-chain family, which is associated with amino acid synthesis, lipid synthesis and lipid peroxidation dependent iron death. However, the role of ACSL4 in generalized carcinoma remains unclear. We aim to analyze the expression and prognostic value of ACSL4 in pan-cancer, and further explore the correlation between ACSL4 and immune infiltration. Through ONCOMINE, TIMER (Tumor Immune Estimation Resource), GEPIA (Gene expression Profiling Interactive), UALCAN and HPA, ACSL4 expression patterns of in pan-cancer were analyzed. The prognostic value of ACSL4 was analyzed using PrognoScan and Kaplan-Meier Plotter databases. Furthermore, gene variation and epigenetic modification of ACSL4 were analyzed by cBioPortal and GSCA databases. Meanwhile, GEPIA and TIMER databases applied to evaluate the relationship between ACSL4 expression and immune infiltration. These results indicate that ACSL4 expression is down-regulated and associated with prognosis in most tumors. In general, lower ACSL4 expression shows more beneficial prognosis. The most common genetic alteration of ACSL4 is point mutation. ACSL4 is negatively correlated with DNA methylation levels in most cancers. ACSL4 mutations or hypomethylation are associated with poor prognosis. In addition, ACSL4 is positively correlated with immune infiltration in cancers. ACSL4 and immune infiltration are strongly associated with prognosis in BRCA (Breast invasive carcinoma) and SKCM (Skin Cutaneous Melanoma). ACSL4 mutation caused significant changes of immune infiltration in UCEC (Uterine Corpus Endometrial Carcinoma) and SARC (Sarcoma). ACSL4 may be a promising prognostic biomarker for pan-cancer and is closely associated with immune infiltration in the tumor microenvironment.

Relation-Aware Shared Representation Learning for Cancer Prognosis Analysis With Auxiliary Clinical Variables and Incomplete Multi-Modality Data

The integrative analysis of complementary phenotype information contained in multi-modality data (e.g., histopathological images and genomic data) has advanced the prognostic evaluation of cancers. However, multi-modality based prognosis analysis confronts two challenges: (1) how to explore underlying relations inherent in different modalities data for learning compact and discriminative multi-modality representations; (2) how to take full consideration of incomplete multi-modality data for constructing accurate and robust prognostic model, since a host of complete multi-modality data are not always available. Additionally, many existing multi-modality based prognostic methods commonly ignore relevant clinical variables (e.g., grade and stage), which, however, may provide supplemental information to promote the performance of model. In this paper, we propose a relation-aware shared representation learning method for prognosis analysis of cancers, which makes full use of clinical information and incomplete multi-modality data. The proposed method learns multi-modal shared space tailored for prognostic model via a dual mapping. Within the shared space, it equips with relational regularizers to explore the potential relations (i.e., feature-label and feature-feature relations) among multi-modality data for inducing discriminatory representations and simultaneously obtaining extra sparsity for alleviating overfitting. Moreover, it regresses and incorporates multiple auxiliary clinical attributes with dynamic coefficients to meliorate performance. Furthermore, in training stage, a partial mapping strategy is employed to extend and train a more reliable model with incomplete multi-modality data. We have evaluated our method on three public datasets derived from The Cancer Genome Atlas (TCGA) project, and the experimental results demonstrate the superior performance of the proposed method.

Clinicopathologic and Genomic Analysis of TP53-Mutated Endometrial Carcinomas

PURPOSE

Copy number-high endometrial carcinomas were described by The Cancer Genome Atlas as high-grade endometrioid and serous cancers showing frequent copy-number alterations (CNA), low mutational burden (i.e., non-hypermutant), near-universal TP53 mutation, and unfavorable clinical outcomes. We sought to investigate and compare the clinicopathologic and molecular characteristics of non-hypermutant TP53-altered endometrial carcinomas of four histologic types.

EXPERIMENTAL DESIGN

TP53-mutated endometrial carcinomas, defined as TP53-mutant tumors lacking microsatellite instability or pathogenic POLE mutations, were identified (n = 238) in a cohort of 1,239 endometrial carcinomas subjected to clinical massively parallel sequencing of 410-468 cancer-related genes. Somatic mutations and CNAs (n = 238), and clinicopathologic features were determined (n = 185, initial treatment planning at our institution).

RESULTS

TP53-mutated endometrial carcinomas encompassed uterine serous (n = 102, 55.1%), high-grade endometrial carcinoma with ambiguous features/not otherwise specified (EC-NOS; n = 44, 23.8%), endometrioid carcinomas of all tumor grades (n = 28, 15.1%), and clear cell carcinomas (n = 11, 5.9%). PTEN mutations were significantly more frequent in endometrioid carcinomas, SPOP mutations in clear cell carcinomas, and CCNE1 amplification in serous carcinomas/EC-NOS; however, none of these genomic alterations were exclusive to any given histologic type. ERBB2 amplification was present at similar frequencies across TP53-mutated histologic types (7.7%-18.6%). Although overall survival was similar across histologic types, serous carcinomas presented more frequently at stage IV, had more persistent and/or recurrent disease, and reduced disease-free survival.

CONCLUSIONS

TP53-mutated endometrial carcinomas display clinical and molecular similarities across histologic subtypes. Our data provide evidence to suggest performance of ERBB2 assessment in all TP53-mutated endometrial carcinomas. Given the distinct clinical features of serous carcinomas, histologic classification continues to be relevant.