FKBP4 is a malignant indicator in luminal A subtype of breast cancer

Proteomic profiling of small extracellular vesicles derived from mouse pancreatic cancer and stellate cells: Role in pancreatic cancer

Small extracellular vesicles (sEVs) are cell-derived vesicles evolving as important elements involved in all stages of cancers. sEVs bear unique protein signatures that may serve as biomarkers. Pancreatic cancer (PC) records a very poor survival rate owing to its late diagnosis and several cancer cell-derived proteins have been reported as candidate biomarkers. However, given the pivotal role played by stellate cells (PSCs, which produce the collagenous stroma in PC), it is essential to also assess PSC-sEV cargo in biomarker discovery. Thus, this study aimed to isolate and characterise sEVs from mouse PC cells and PSCs cultured alone or as co-cultures and performed proteomic profiling and pathway analysis. Proteomics confirmed the enrichment of specific markers in the sEVs compared to their cells of origin as well as the proteins that are known to express in each of the culture types. Most importantly, for the first time it was revealed that PSC-sEVs are enriched in proteins (including G6PI, PGAM1, ENO1, ENO3, and LDHA) that mediate pathways related to development of diabetes, such as glucose metabolism and gluconeogenesis revealing a potential role of PSCs in pancreatic cancer-related diabetes (PCRD). PCRD is now considered a harbinger of PC and further research will enable to identify the role of these components in PCRD and may develop as novel candidate biomarkers of PC.

Pan-cancer analysis of the prognostic and immunological role of FKBP4

Objectives

Our previous studies revealed the significant roles of FK506-binding protein 4 (FKBP4) in tumorigenesis, however, there has been no pan-cancer analysis of FKBP4. Using bioinformatics, the current study reported the expression and prognostic role of FKBP4, and the correlation between FKBP4 and clinicopathological parameters, methylation, molecular network, immunological traits and drug sensitivity.

Methods

RNA sequencing data, somatic mutation, and related clinical information were obtained from TCGA using UCSC Xena. The association between FKBP4 expression and clinical features was assessed using TISIDB. The relationships between FKBP4 expression and tumour stage, OS, DSS, DFS, and PFS were analysed using univariate cox regression analysis. The radar plots for TMB and MSI were obtained using "Fmsb" R package. UALCAN was used to explore the effect of FKBP4 methylation on tumour and normal samples. CBioportal was used to analyse copy number mutations in FKBP4 Gene expression and drug sensitivity data were downloaded from the CellMiner database. GO analysis was performed for the high and the low expression of FKBP4 compared with the median level of FKBP4 using clusterProfiler4.0.

Results

FKBP4 expression is significantly upregulated in various types of cancers. Cox regression analysis showed that high FKBP4 levels were correlated with poor OS, DSS, DFS, and PFS in most patients with cancer. Methylation of FKBP4 DNA was upregulated in most cancers, and FKBP4 expression is positively associated with transmethylase expression. FKBP4 and its copy were significantly associated with the expression of immune-infiltrating cells, immune checkpoint genes, immune modulators, TMB, MMR, and MSI. FKBP4 expression levels significantly correlated with 16 different drug sensitivities (all p < 0.05).

Conclusions

Our pan-cancer bioinformatic analysis revealed a potential mechanism underlying the effects of FKBP4 on the prognosis and progression of various cancers.

Single-cell analysis reveals landscape of endometrial cancer response to estrogen and identification of early diagnostic markers

BACKGROUND

The development of endometrial cancer (EC) is closely related to the abnormal activation of the estrogen signaling pathway. Effective diagnostic markers are important for the early detection and treatment of EC.

METHOD

We downloaded single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST) data of EC from public databases. Enrichment scores were calculated for EC cell subpopulations using the "AddModuleScore" function and the AUCell package, respectively. Six predictive models were constructed, including logistic regression (LR), Gaussian naive Bayes (GaussianNB), k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGB), and neural network (NK). Subsequently, receiver-operating characteristics with areas under the curves (AUCs) were used to assess the robustness of the predictive model.

RESULT

We classified EC cell coaggregation into six cell clusters, of which the epithelial, fibroblast and endothelial cell clusters had higher estrogen signaling pathway activity. We founded the epithelial cell subtype Epi cluster1, the fibroblast cell subtype Fib cluster3, and the endothelial cell subtype Endo cluster3 all showed early activation levels of estrogen response. Based on EC cell subtypes, estrogen-responsive early genes, and genes encoding Stage I and para-cancer differentially expressed proteins in EC patients, a total of 24 early diagnostic markers were identified. The AUCs values of all six classifiers were higher than 0.95, which indicates that the early diagnostic markers we screened have superior robustness across different classification algorithms.

CONCLUSION

Our study elucidates the potential biological mechanism of EC response to estrogen at single-cell resolution, which provides a new direction for early diagnosis of EC.

FKBP5 associated CD8 T cell infiltration is a novel prognostic biomarker in luminal B breast cancer

OBJECTIVE

To investigate the relationship between FKBP prolyl isomerase 5 (FKBP5) gene expression and CD8 T cells in tumour progression and immunology of the luminal B subtype of breast cancer (LBBC) using bioinformatics analyses.

METHODS

The Gene Expression Profiling Interactive Analysis 2, Human Protein Atlas and breast cancer gene-expression miner v4.5 databases were used for data mining and analysing FKBP5, its co-expressed genes and CD8 T cell-related markers. The Tumor IMmune Estimation Resource 2.0 database was used for analysing the correlation and prognosis of FKBP5 and CD8 T cell infiltration level in LBBC.

RESULTS

Upregulated FKBP5 expression was correlated with improved survival in LBBC. Upregulated FKBP5-related CD8 T cell markers were also demonstrated to be significantly correlated with better survival in LBBC and might play a role in the biological activity of FKBP5.

CONCLUSION

These findings suggest that FKBP5 and its associated CD8 T cell infiltration are potential benign prognostic indicators for LBBC.

Integration of multi-omics data using adaptive graph learning and attention mechanism for patient classification and biomarker identification

With the rapid development and accumulation of high-throughput sequencing technology and omics data, many studies have conducted a more comprehensive understanding of human diseases from a multi-omics perspective. Meanwhile, graph-based methods have been widely used to process multi-omics data due to its powerful expressive ability. However, most existing graph-based methods utilize fixed graphs to learn sample embedding representations, which often leads to sub-optimal results. Furthermore, treating embedding representations of different omics equally usually cannot obtain more reasonable integrated information. In addition, the complex correlation between omics is not fully taken into account. To this end, we propose an end-to-end interpretable multi-omics integration method, named MOGLAM, for disease classification prediction. Dynamic graph convolutional network with feature selection is first utilized to obtain higher quality omic-specific embedding information by adaptively learning the graph structure and discover important biomarkers. Then, multi-omics attention mechanism is applied to adaptively weight the embedding representations of different omics, thereby obtaining more reasonable integrated information. Finally, we propose omic-integrated representation learning to capture complex common and complementary information between omics while performing multi-omics integration. Experimental results on three datasets show that MOGLAM achieves superior performance than other state-of-the-art multi-omics integration methods. Moreover, MOGLAM can identify important biomarkers from different omics data types in an end-to-end manner.

Cyclopropenone, Cyclopropeniminium Ion, and Cyclopropenethione as Novel Electrophilic Warheads for Potential Target Discovery of Triple-Negative Breast Cancer

Because very few targets are currently available for drug development, triple-negative breast cancer (TNBC) has been defined as one of the most difficult diseases for chemotherapy. Herein, we describe a suite of novel electrophilic warheads, which we have used in chemical proteomics studies in a search for potential targets for TNBC. Binding site analysis revealed that these warheads can modify not only highly nucleophilic residues, including cysteine and lysine, but also weakly nucleophilic residues. Cys12 of Kirsten rat sarcoma (KRAS^G12C) was successfully labeled by cyclopropenone and the cyclopropeniminium ions. Moderate inhibitory activity against TNBC cells was achieved with these novel electrophile-based probes. Activity-based protein profiling reveals that these electrophiles can covalently label a series of essential protein targets, including ALDH2, LRPPRC, and FABP5 from MDA-MB-231 cells. Further functional validation experiments demonstrated that FABP5 might be a potential target for TNBC.

Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs

Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can now be used in molecular research. In this study, we achieved this with the construction of a bilayer network of DNA methylation sites and RNAs. We applied the network model to five types of tumor data to identify key genes associated with tumors. Compared with the single network, the proposed bilayer network resulted in more tumor-associated DNA methylation sites and genes, which we verified with prognostic and KEGG enrichment analyses.

Penetrating Exploration of Prognostic Correlations of the FKBP Gene Family with Lung Adenocarcinoma

The complexity of lung adenocarcinoma (LUAD), the development of which involves many interacting biological processes, makes it difficult to find therapeutic biomarkers for treatment. FK506-binding proteins (FKBPs) are composed of 12 members classified as conservative intracellular immunophilin family proteins, which are often connected to cyclophilin structures by tetratricopeptide repeat domains and have peptidyl prolyl isomerase activity that catalyzes proline from residues and turns the trans form into the cis form. Since FKBPs belong to chaperone molecules and promote protein folding, previous studies demonstrated that FKBP family members significantly contribute to the degradation of damaged, misfolded, abnormal, and foreign proteins. However, transcript expressions of this gene family in LUAD still need to be more fully investigated. In this research, we adopted high-throughput bioinformatics technology to analyze FKBP family genes in LUAD to provide credible information to clinicians and promote the development of novel cancer target drugs in the future. The current data revealed that the messenger (m)RNA levels of FKBP2, FKBP3, FKBP4, FKBP10, FKBP11, and FKBP14 were overexpressed in LUAD, and FKBP10 had connections to poor prognoses among LUAD patients in an overall survival (OS) analysis. Based on the above results, we selected FKBP10 to further conduct a comprehensive analysis of the downstream pathway and network. Through a DAVID analysis, we found that FKBP10 was involved in mitochondrial electron transport, NADH to ubiquinone transport, mitochondrial respiratory chain complex I assembly, etc. The MetaCore pathway analysis also indicated that FKBP10 was involved in "Ubiquinone metabolism", "Translation_(L)-selenoaminoacid incorporation in proteins during translation", and "Transcription_Negative regulation of HIF1A function". Collectively, this study revealed that FKBP family members are both significant prognostic biomarkers for lung cancer progression and promising clinical therapeutic targets, thus providing new targets for treating LUAD patients.

LncRNA MAFG-AS1 deregulated in breast cancer affects autophagy and progression of breast cancer by interacting with miR-3612 and FKBP4 invitro

PURPOSE

We aimed to explore the function and competing endogenous RNA (ceRNA) pathway of MAFG-AS1 in breast cancer.

METHODS

qRT-PCR assay identified the expression of MAFG-AS1, miR-3612 and FKBP4. We used Western blot analysis to test the autophagy related protein levels in breast cancer cells. Functional assays such as Cell Counting Kit-8 (CCK8) assay, BrdU proliferation assay, Caspase-3 activity detection were used to identify the function of MAFG-AS1, miR-3612 and FKBP4 in breast cancer cells. Mechanism assays were used to verify the interacting relationship among MAFG-AS1, miR-3612 and FKBP4, including RNA pull down assay, RNA immunoprecipitation (RIP) assay and luciferase reporter assay.

RESULTS

MAFG-AS1 and FKBP4 were both up-regulated in breast cancer tissues. MAFG-AS1 could function as an oncogene in breast cancer to activate cell proliferation, and inhibit cell apoptosis and autophagy. Meanwhile, MAFG-AS1 could sponge miR-3612 to elevate the expression of FKBP4. Besides, FKBP4 could activate the cell proliferation and inhibit cell apoptosis and autophagy, which could relieve the inhibitory effect of miR-3612 on breast cancer cells.

CONCLUSION

MAFG-AS1 could activate breast cancer progression via modulating miR-3612/FKBP4 axis in vitro.

Comprehensive analysis of FKBP4/NR3C1/TMEM173 signaling pathway in triple-negative breast cancer cell and dendritic cell among tumor microenvironment

TMEM173 is a pattern recognition receptor detecting cytoplasmic nucleic acids and transmits cGAS related signals that activate host innate immune responses. It has also been found to be involved in tumor immunity and tumorigenesis. In this study, we first identified that the FKBP4/NR3C1 axis was a novel negative regulator of TMEM173 in human breast cancer (BC) cells. The effect of FKBP4 appeared to be at the transcriptional level of TMEM173, because it could suppress the promoter activity of TMEM173, thereby affecting TMEM173 at mRNA and protein levels. Past studies, our bioinformatics analysis, and in vitro experiments further implied that FKBP4 regulated TMEM173 via regulating nuclear translocation of NR3C1. We then demonstrated that the FKBP4/NR3C1/TMEM173 signaling pathway could regulate autophagy and proliferation of BC cells as well as dendritic cell (DC) abundance through exosome release. Our study found an unprecedented strategy used by BC to escape from TMEM173 mediated tumor suppression. Identification of the FKBP4/NR3C1 axis as a novel TMEM173 regulator would provide insights for novel anti-tumor strategy against BC among tumor microenvironment.

Naringenin Regulates FKBP4/NR3C1/NRF2 Axis in Autophagy and Proliferation of Breast Cancer and Differentiation and Maturation of Dendritic Cell

NRF2 is an important regulatory transcription factor involved in tumor immunity and tumorigenesis. In this study, we firstly identified that FKBP4/NR3C1 axis was a novel negative regulator of NRF2 in human breast cancer (BC) cells. The effect of FKBP4 appeared to be at protein level of NRF2 since it could not suppress the expression of NRF2 at mRNA level. Bioinformatics analysis and in vitro experiments further demonstrated that FKBP4 regulated NRF2 via regulating nuclear translocation of NR3C1. We then reported that naringenin, a flavonoid, widely distributed in citrus and tomato, could suppress autophagy and proliferation of BC cells through FKBP4/NR3C1/NRF2 signaling pathway in vitro and in vivo. Naringenin was also found to promote dendritic cell (DC) differentiation and maturation through FKBP4/NR3C1/NRF2 axis. Therefore, our study found that naringenin could induce inhibition of autophagy and cell proliferation in BC cells and enhance DC differentiation and maturation, at least in part, though regulation of FKBP4/NR3C1/NRF2 signaling pathway. Identification of FKBP4/NR3C1/NRF2 axis would provide insights for novel anti-tumor strategy against BC among tumor microenvironment.

TRP Channels Interactome as a Novel Therapeutic Target in Breast Cancer

Breast cancer is one of the most frequent cancer types worldwide and the first cause of cancer-related deaths in women. Although significant therapeutic advances have been achieved with drugs such as tamoxifen and trastuzumab, breast cancer still caused 627,000 deaths in 2018. Since cancer is a multifactorial disease, it has become necessary to develop new molecular therapies that can target several relevant cellular processes at once. Ion channels are versatile regulators of several physiological- and pathophysiological-related mechanisms, including cancer-relevant processes such as tumor progression, apoptosis inhibition, proliferation, migration, invasion, and chemoresistance. Ion channels are the main regulators of cellular functions, conducting ions selectively through a pore-forming structure located in the plasma membrane, protein–protein interactions one of their main regulatory mechanisms. Among the different ion channel families, the Transient Receptor Potential (TRP) family stands out in the context of breast cancer since several members have been proposed as prognostic markers in this pathology. However, only a few approaches exist to block their specific activity during tumoral progress. In this article, we describe several TRP channels that have been involved in breast cancer progress with a particular focus on their binding partners that have also been described as drivers of breast cancer progression. Here, we propose disrupting these interactions as attractive and potential new therapeutic targets for treating this neoplastic disease.

Transcriptome Based Estrogen Related Genes Biomarkers for Diagnosis and Prognosis in Non-small Cell Lung Cancer

Background

Lung cancer is the tumor with the highest morbidity and mortality, and has become a global public health problem. The incidence of lung cancer in men has declined in some countries and regions, while the incidence of lung cancer in women has been slowly increasing. Therefore, the aim is to explore whether estrogen-related genes are associated with the incidence and prognosis of lung cancer.

Methods

We obtained all estrogen receptor genes and estrogen signaling pathway genes in The Cancer Genome Atlas (TCGA), and then compared the expression of each gene in tumor tissues and adjacent normal tissues for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) separately. Survival analysis was performed of the differentially expressed genes in LUAD and LUSC patients separately. The diagnostic and prognostic values of the candidate genes were validated in the Gene Expression Omnibus (GEO) datasets.

Results

We found 5 estrogen receptor genes and 66 estrogen pathway genes in TCGA. A total of 50 genes were differently expressed between tumor tissues and adjacent normal tissues and 6 of the 50 genes were related to the prognosis of LUAD in TCGA. 56 genes were differently expressed between tumor tissues and adjacent normal tissues and none of the 56 genes was related to the prognosis of LUSC in TCGA. GEO datasets validated that the 6 genes (SHC1, FKBP4, NRAS, PRKCD, KRAS, ADCY9) had different expression between tumor tissues and adjacent normal tissues in LUAD, and 3 genes (FKBP4, KRAS, ADCY9) were related to the prognosis of LUAD.

Conclusions

The expressions of FKBP4 and ADCY9 are related to the pathogenesis and prognosis of LUAD. FKBP4 and ADCY9 may serve as biomarkers in LUAD screening and prognosis prediction in clinical settings.

Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development

In order to identify the strategic topics and the thematic evolution structure of data mining applied to healthcare, in this paper, a bibliometric performance and network analysis (BPNA) was conducted. For this purpose, 6138 articles were sourced from the Web of Science covering the period from 1995 to July 2020 and the SciMAT software was used. Our results present a strategic diagram composed of 19 themes, of which the 8 motor themes ('NEURAL-NETWORKS', 'CANCER', 'ELETRONIC-HEALTH-RECORDS', 'DIABETES-MELLITUS', 'ALZHEIMER'S-DISEASE', 'BREAST-CANCER', 'DEPRESSION', and 'RANDOM-FOREST') are depicted in a thematic network. An in-depth analysis was carried out in order to find hidden patterns and to provide a general perspective of the field. The thematic network structure is arranged thusly that its subjects are organized into two different areas, (i) practices and techniques related to data mining in healthcare, and (ii) health concepts and disease supported by data mining, embodying, respectively, the hotspots related to the data mining and medical scopes, hence demonstrating the field's evolution over time. Such results make it possible to form the basis for future research and facilitate decision-making by researchers and practitioners, institutions, and governments interested in data mining in healthcare.