Bioinformatics analysis to screen key prognostic genes in the breast cancer tumor microenvironment

Identification of the prognostic immune subtype in copy-number high endometrial cancer

OBJECTIVE

The TCGA molecular subtype of endometrial cancer (EC) is widely applied, among which the copy-number high (CNH) subtype has the poorest prognosis. However, the heterogeneity of this subtype remains elusive. In this study, we aimed to identify heterogeneous immune subtypes in CNH EC and explore their prognostic significance.

METHODS

We collected 60 CNH EC cases in the TCGA database and performed unsupervised cluster analysis based on the enrichment scores of immune-related gene signatures to identify immune subtypes. We described their immune characteristics and prognoses and conducted differential gene analysis and lasso regression to identify a prognostic biomarker, GZMM. For experimental validation, we performed immunohistochemical staining of GZMM in 39 p53-positive EC surgical samples.

RESULTS

We defined two immune subtypes, immune-hot (IH) and immune-cold (IC), which differed in immune cell infiltration, cytokine and chemokine expression and prognosis. The IH subtype has significantly stronger immune activation than the IC subtype, showing a significant infiltration of immune effector cells and high expression of relevant chemokines, with better prognosis. Moreover, the immunohistochemical staining of GZMM in a cohort of 39 p53-positive EC surgical samples confirmed GZMM as a unique prognostic biomarker, with high expression in both tumor cells and lymphocytes predicting a better prognosis.

CONCLUSION

Our study revealed heterogeneous immune subtypes in CNH EC and identified GZMM as a prognostic biomarker. The stratified classification strategy combining molecular and immune subtypes provides valuable insights for future clinical practice.

KLRB1 is a novel prognostic biomarker in endometrial cancer and is associated with immune infiltration

Background

Endometrial cancer (EC) has the characteristics of high mortality and poor prognosis in the advanced stage, which seriously threatens women's health. Killer cell lectin-like receptor B1 (KLRB1) is a promising immune checkpoint of which the expression level can regulate the killing effect on tumor cells of the immune system, thereby affecting the survival and prognosis of tumor patients. However, it is still unclear whether KLRB1 is associated with survival and prognosis in patients with EC. Therefore, our study focused on the relationship between KLRB1 and immune cells to explore the role of KLRB1 on the immune microenvironment, and to further explore its feasibility as a prognostic marker in EC.

Methods

In this study, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to analyze the messenger RNA (mRNA) expression level of KLRB1 in normal endometrial and EC tissues. The University of Alabama at Birmingham Cancer data analysis Portal (UALCAN) database was used to determine the correlation between KLRB1 mRNA expression and clinical features among the EC patients. KLRB1 expression levels were investigated in the Tumor IMmune Estimation Resource (TIMER) database to reveal its relationship with immune cell infiltration of EC. Finally, using the R package clusterProfiler, enrichment analysis was performed on KLRB1 to study its potential function.

Results

The results suggested that KLRB1 expression varied in different tumor tissues, and the EC group had lower mRNA expression levels than did the control group. It was also found that patients with high expression of KLRB1 had a better prognosis. According to further enrichment and immune infiltration analyses, KLRB1 expression had a closed relationship with the level of infiltration of some immune cell types, such as B cells memory, eosinophils, and Tregs, among others.

Conclusions

KLRB1 expression is associated with the infiltration of immune cells and can be used as a prognostic biomarker in EC.

Mdwgan-gp: data augmentation for gene expression data based on multiple discriminator WGAN-GP

BACKGROUND

Although gene expression data play significant roles in biological and medical studies, their applications are hampered due to the difficulty and high expenses of gathering them through biological experiments. It is an urgent problem to generate high quality gene expression data with computational methods. WGAN-GP, a generative adversarial network-based method, has been successfully applied in augmenting gene expression data. However, mode collapse or over-fitting may take place for small training samples due to just one discriminator is adopted in the method.

RESULTS

In this study, an improved data augmentation approach MDWGAN-GP, a generative adversarial network model with multiple discriminators, is proposed. In addition, a novel method is devised for enriching training samples based on linear graph convolutional network. Extensive experiments were implemented on real biological data.

CONCLUSIONS

The experimental results have demonstrated that compared with other state-of-the-art methods, the MDWGAN-GP method can produce higher quality generated gene expression data in most cases.

Tumor microenvironment: barrier or opportunity towards effective cancer therapy

Tumor microenvironment (TME) is a specialized ecosystem of host components, designed by tumor cells for successful development and metastasis of tumor. With the advent of 3D culture and advanced bioinformatic methodologies, it is now possible to study TME’s individual components and their interplay at higher resolution. Deeper understanding of the immune cell’s diversity, stromal constituents, repertoire profiling, neoantigen prediction of TMEs has provided the opportunity to explore the spatial and temporal regulation of immune therapeutic interventions. The variation of TME composition among patients plays an important role in determining responders and non-responders towards cancer immunotherapy. Therefore, there could be a possibility of reprogramming of TME components to overcome the widely prevailing issue of immunotherapeutic resistance. The focus of the present review is to understand the complexity of TME and comprehending future perspective of its components as potential therapeutic targets. The later part of the review describes the sophisticated 3D models emerging as valuable means to study TME components and an extensive account of advanced bioinformatic tools to profile TME components and predict neoantigens. Overall, this review provides a comprehensive account of the current knowledge available to target TME.

Activation of cytotoxic T lymphocytes by self-differentiated myeloid-derived dendritic cells for killing breast cancer cells expressing folate receptor alpha protein

Adoptive cell transfer (ACT) is a promising approach for cancer treatment. Activation of T lymphocytes by self-differentiated myeloid-derived antigen-presenting-cells reactive against tumor (SmartDC) resulted in specific anti-cancer function. Folate receptor alpha (FRα) is highly expressed in breast cancer (BC) cells and thus potential to be a target antigen for ACT. To explore the SmartDC technology for treatment of BC, we create SmartDC expressing FRα antigen (SmartDC-FRα) for activation of FRα-specific T lymphocytes. Human primary monocytes were transduced with lentiviruses containing tri-cistronic complementary DNA sequences encoding granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin-4 (IL-4), and FRα to generate SmartDC-FRα. Autologous T lymphocytes were activated by SmartDC-FRα by coculture. The activated T lymphocytes exhibited enhanced cytotoxicity against FRα-expressing BC cell cultures. Up to 84.9 ± 6.2% of MDA-MB-231 and 89.7 ± 1.9% of MCF-7 BC cell lines were specifically lysed at an effector-to-target ratio of 20:1. The cytotoxicity of T lymphocytes activated by SmartDC-FRα was also demonstrated in three-dimensional (3D) spheroid culture of FRα-expressing BC cells marked by size reduction and spheroid disruption. This study thus portray the potential development of T lymphocytes activated by SmartDC-FRα as ACT in FRα-expressing BC treatment.

Prognostic and Immune Implications of a Novel Pyroptosis-Related Five-Gene Signature in Breast Cancer

Background

Breast cancer (BC) is the most common cancer among women worldwide, with enormous heterogeneity. Pyroptosis has a significant impact on the development and progression of tumors. Nonetheless, the possible correlation between pyroptosis-related genes (PRGs) and the BC immune microenvironment has yet to be investigated.

Materials and methods

In The Cancer Genome Atlas Breast Cancer cohort, 38 PRGs were shown to be significantly different between malignant and non-malignant breast tissues. The 38 PRGs’ consensus clustering grouped 1,089 individuals into two pyroptosis-related (PR) patterns. Using univariate and LASSO-Cox analyses, a PR five-gene predictive signature was constructed based on the differentially expressed genes between two clusters. The tools estimation of stromal and immune cells in malignant tumours using expression data (ESTIMATE), cell type identification by estimating relative subsets Of RNA transcripts (CIBERSORT), and single-sample gene set enrichment analysis (ssGSEA) were used to investigate the BC tumor microenvironment (TME).

Results

In TME, the two PR clusters displayed distinct clinicopathological characteristics, survival outcomes, and immunocyte infiltration features. The developed five-signature model (SEMA3B, IGKC, KLRB1, BIRC3, and PSME2) classified BC patients into two risk groups based on the estimated median risk score. Patients in the low-scoring category had a higher chance of survival and more extensive immunocyte infiltration. An external validation set can yield similar results.

Conclusion

Our data suggest that PRGs have a significant impact on the BC immunological microenvironment. The PR clusters and associated predictive signature stimulate additional research into pyroptosis in order to optimize therapeutic strategies for BC patients and their responses to immune therapy.

Declined expressions of vast mitochondria-related genes represented by CYCS and transcription factor ESRRA in skeletal muscle aging

Age-related skeletal muscle deterioration (sarcopenia) has a significant effect on the elderly's health and quality of life, but the molecular and gene regulatory mechanisms remain largely unknown. It is necessary to identify the candidate genes related to skeletal muscle aging and prospective therapeutic targets for effective treatments. The age-line-related genes (ALRGs) and age-line-related transcripts (ALRTs) were investigated using the gene expression profiles of GSE47881 and GSE118825 from the Gene Expression Omnibus (GEO) database. The protein-protein interaction (PPI) networks were performed to identify the key molecules with Cytoscape, and Gene Set Enrichment Analysis (GSEA) was used to clarify the potential molecular functions. Two hub molecules were finally obtained and verified with quantitative real-time PCR (qRT-PCR). The results showed that the expression of mitochondria genes involved in mitochondrial electron transport, complex assembly of the respiratory chain, tricarboxylic acid cycle, oxidative phosphorylation, and ATP synthesis were down-regulated in skeletal muscle with aging. We further identified a primary hub gene of CYCS (Cytochrome C) and a key transcription factor of ESRRA (Estrogen-related Receptor Alpha) to be associated closely with skeletal muscle aging. PCR analysis confirmed the expressions of CYCS and ESRRA in gastrocnemius muscles of mice of different ages were significantly different, and decreased gradually with age. In conclusion, the main cause of skeletal muscle aging may be the systematically reduced expression of mitochondrial functional genes. The CYCS and ESRRA may play significant roles in the progression of skeletal muscle aging and serve as potential biomarkers for future diagnosis and treatment.

Identification of a prognostic chemoresistance-related gene signature associated with immune microenvironment in breast cancer

Breast cancer is the most common form of cancer among women globally, and chemoresistance is a major challenge to disease treatment that is associated with a poor prognosis. This study was formulated to identify a reliable prognostic biosignature capable of predicting the survival of patients with chemoresistant breast cancer (CRBC) and evaluating the associated tumor immune microenvironment. Through a series of protein-protein interaction and weighted correlation network analyses, genes that were significantly associated with breast cancer chemoresistance were identified. Moreover, univariate Cox regression and lasso-penalized Cox regression analyses were employed to generate a prognostic model, and the prognostic utility of this model was then assessed using time-dependent receiver operating characteristic (ROC) and Kaplan-Meier survival curves. Finally, The CIBERSORT and ESTIMATE algorithms were additionally leveraged to assess relationships between the tumor immune microenvironment and patient prognostic signatures. Overall, a multigenic prognostic biosignature capable of predicting CRBC patient risk was successfully developed based on bioinformatics analysis and in vitro experiments. This biosignature was able to stratify CRBC patients into high- and low-risk subgroups. ROC curves also revealed that this biosignature achieved high diagnostic efficiency, and multivariate regression analyses indicated that this risk signature was an independent risk factor linked to CRBC patient outcomes. In addition, this signature was associated with the infiltration of the tumor microenvironment by multiple immune cell types. In conclusion, the chemoresistance-associated prognostic gene signature developed herein was able to effectively evaluate the prognosis of CRBC patients and to reflect the overall composition of the tumor immune microenvironment.

Metabolism-related long non-coding RNAs (lncRNAs) as potential biomarkers for predicting risk of recurrence in breast cancer patients

Metabolism affects the development, progression, and prognosis of various cancers, including breast cancer (BC). Our aim was to develop a metabolism-related long non-coding RNA (lncRNA) signature to assess the prognosis of BC patients in order to optimize treatment. Metabolism-related genes between breast tumors and normal tissues were screened out, and Pearson correlation analysis was used to investigate metabolism-related lncRNAs. In total, five metabolism-related lncRNAs were enrolled to establish prognostic signatures. Kaplan-Meier plots and the receiver operating characteristic (ROC) curves demonstrated good performance in both training and validation groups. Further analysis demonstrated that the signature was an independent prognostic factor for BC. A nomogram incorporating risk score and tumor stage was then constructed to evaluate the 3 - and 5-year recurrence-free survival (RFS) in patients with BC. In conclusion, this study identified a metabolism-related lncRNA signature that can predict RFS of BC patients and established a prognostic nomogram that helps guide the individualized treatment of patients at different risks.

Identification of significant genes and therapeutic agents for breast cancer by integrated genomics

Breast cancer is the most commonly diagnosed malignancy in women; thus, more cancer prevention research is urgently needed. The aim of this study was to predict potential therapeutic agents for breast cancer and determine their molecular mechanisms using integrated bioinformatics. Summary data from a large genome-wide association study of breast cancer was derived from the UK Biobank. The gene expression profile of breast cancer was from the Oncomine database. We performed a network-wide association study and gene set enrichment analysis to identify the significant genes in breast cancer. Then, we performed Gene Ontology analysis using the STRING database and conducted Kyoto Encyclopedia of Genes and Genomes pathway analysis using Cytoscape software. We verified our results using the Gene Expression Profile Interactive Analysis, PROgeneV2, and Human Protein Atlas databases. Connectivity map analysis was used to identify small-molecule compounds that are potential therapeutic agents for breast cancer. We identified 10 significant genes in breast cancer based on the gene expression profile and genome-wide association study. A total of 65 small-molecule compounds were found to be potential therapeutic agents for breast cancer.

Immune cell infiltration landscape and immune marker molecular typing in preeclampsia

Preeclampsia (PE) is an important topic in obstetrics. In this study, we used weighted gene co-expression network analysis (WGCNA) to screen the key modules related to immune cell infiltration and to identify the hub genes for the molecular subtyping of PE. We first downloaded a set of PE transcriptional data (GSE75010; 157 samples: 80 PE and 77 non-PE) from the GEO database. We then analyzed the PE samples and non-PE samples for immune cell infiltration and screened cells with differences in such infiltration. Next, we downloaded the immune-related genes from an immune-related database to screen the expression profile of the immune-related genes. Then, we obtained a candidate gene set by screening the immune-related genes differentially expressed between the two groups. We used WGCNA to construct a weighted co-expression network for these candidate genes, mined co-expression modules, and then calculated the correlation between each module and immune cells with differential infiltration. We screened the modules related to infiltrating immune cells, identified the key modules' hub genes, and determined the key module genes that interacted with each other. Finally, we obtained the hub genes related to the infiltrating immune cells. We classified the preeclampsia patients by unsupervised cluster molecular typing, determined the difference of immune cell infiltration among the different PE subtypes, and calculated the expression of hub genes in these different subtypes. In conclusion, we found 41 hub genes that may be closely related to the molecular typing of PE.