Deficiency in trefoil factor 1 (TFF1) increases tumorigenicity of human breast cancer cells and mammary tumor development in TFF1-knockout mice

Exploring the Latent Information in Spatial Transcriptomics Data via Multi-View Graph Convolutional Network Based on Implicit Contrastive Learning

Latest developments in spatial transcriptomics enable thoroughly profiling of gene expression while preserving tissue microenvironment. Connecting gene expression with spatial arrangement is key for precise spatial domain identification, enhancing the comprehension of tissue microenvironments and biological processes. However, accurately analyzing spatial domains with similar gene expression and histological features is still challenging. This study introduces STMIGCL, a novel framework that leverages a multi-view graph convolutional network and implicit contrastive learning. First, it creates neighbor graphs from gene expression and spatial coordinates, and then combines these with gene expression through multi-view learning to learn low-dimensional representations. To further refine the obtained low-dimensional representations, a graph contrastive learning method with contrastive enhancement in the latent space is employed, aiming to better capture critical information in the data and improve the accuracy and discriminative power of the embeddings. Finally, an attention mechanism is used to adaptively integrate different views, capturing the importance of spots in various views to obtain the final spot representation. Experimental data confirms that STMIGCL significantly enhances spatial domain recognition precision and outperforms all baseline methods in tasks such as trajectory inference and Spatially Variable Genes (SVGs) recognition.

SGTB: A graph representation learning model combining transformer and BERT for optimizing gene expression analysis in spatial transcriptomics data

In recent years, spatial transcriptomics (ST) has emerged as an innovative technology that enables the simultaneous acquisition of gene expression information and its spatial distribution at the single-cell or regional level, providing deeper insights into cellular interactions and tissue organization, this technology provides a more holistic view of tissue organization and intercellular dynamics. However, existing methods still face certain limitations in data representation capabilities, making it challenging to fully capture complex spatial dependencies and global features. To address this, this paper proposes an innovative spatial multi-scale graph convolutional network (SGTB) based on large language models, integrating graph convolutional networks (GCN), Transformer, and BERT language models to optimize the representation of spatial transcriptomics data. The Graph Convolutional Network (GCN) employs a multi-layer architecture to extract features from gene expression matrices. Through iterative aggregation of neighborhood information, it captures spatial dependencies among cells and gene co-expression patterns, thereby constructing hierarchical cell embeddings. Subsequently, the model integrates an attention mechanism to assign weights to critical features and leverages Transformer layers to model global relationships, refining the ability of learned representations to reflect variations in spatial patterns. Finally, the model incorporates the BERT language model, mapping cell embeddings into textual inputs to exploit its deep semantic representation capabilities for high-dimensional feature extraction. These features are then fused with the embeddings generated by the Transformer, further optimizing feature learning for spatial transcriptomics data. This approach holds significant application value in improving the accuracy of tasks such as cell type classification and gene regulatory network construction, providing a novel computational framework for deep mining of spatial multi-scale biological data.

MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning

Spatially resolved transcriptomics (SRT) simultaneously measure spatial location, histology images, and transcriptional profiles of cells or regions in undissociated tissues. Integrative analysis of multi-modal SRT data holds immense potential for understanding biological mechanisms. Here, we present a flexible multi-modal contrastive learning for the integration of SRT data (MuCST), which joins denoising, heterogeneity elimination, and compatible feature learning. MuCST accurately identifies spatial domains and is applicable to diverse datasets platforms. Overall, MuCST provides an alternative for integrative analysis of multi-modal SRT data ( https://github.com/xkmaxidian/MuCST ).

STMGraph: spatial-context-aware of transcriptomes via a dual-remasked dynamic graph attention model

Spatial transcriptomics (ST) technologies enable dissecting the tissue architecture in spatial context. To perceive the global contextual information of gene expression patterns in tissue, the spatial dependence of cells must be fully considered by integrating both local and non-local features by means of spatial-context-aware. However, the current ST integration algorithm ignores for ST dropouts, which impedes the spatial-aware of ST features, resulting in challenges in the accuracy and robustness of microenvironmental heterogeneity detecting, spatial domain clustering, and batch-effects correction. Here, we developed an STMGraph, a universal dual-view dynamic deep learning framework that combines dual-remask (MASK-REMASK) with dynamic graph attention model (DGAT) to exploit ST data outperforming pre-existing tools. The dual-remask mechanism masks the embeddings before encoding and decoding, establishing dual-decoding-view to share features mutually. DGAT leverages self-supervision to update graph linkage relationships from two distinct perspectives, thereby generating a comprehensive representation for each node. Systematic benchmarking against 10 state-of-the-art tools revealed that the STMGraph has the optimal performance with high accuracy and robustness on spatial domain clustering for the datasets of diverse ST platforms from multi- to sub-cellular resolutions. Furthermore, STMGraph aggregates ST information cross regions by dual-remask to realize the batch-effects correction implicitly, allowing for spatial domain clustering of ST multi-slices. STMGraph is platform independent and superior in spatial-context-aware to achieve microenvironmental heterogeneity detection, spatial domain clustering, batch-effects correction, and new biological discovery, and is therefore a desirable novel tool for diverse ST studies.

Interdependency of estradiol-mediated ERα activation and subsequent PR and GREB1 induction to control cell cycle progression

Various groups of chemicals that we encounter in every-day life are known to disrupt the endocrine system, such as estrogen mimics that can disturb normal cellular development and homeostasis. To understand the effect of estrogen on intracellular protein dynamics and how this relates to cell proliferation, we aimed to develop a quantitative description of transcription factor complexes and their regulation of cell cycle progression in response to estrogenic stimulation. We designed a mathematical model that describes the dynamics of three proteins, GREB1, PR and TFF1, that are transcriptionally activated upon binding of 17β-estradiol (E2) to estrogen receptor alpha (ERα). Calibration of this model to imaging data monitoring the expression dynamics of these proteins in MCF7 cells suggests that transcriptional activation of GREB1 and PR depends on the association of the E2-ERα complex with both GREB1 and PR. We subsequently combined this ER signaling model with a previously published cell cycle model and compared this to quantification of cell cycle durations in MCF7 cells following nuclei tracking based on images segmented with deep neural networks. The resulting model predicts the effect of GREB1 and PR knockdown on cell cycle progression, thus providing mechanistic insight in the molecular interactions between ERα-regulated proteins and their relation to cell cycle progression. Our findings form a valuable basis to further investigate the pharmacodynamics of endocrine disrupting chemicals and their influence on cellular behavior.

Spatial analyses revealed S100P + TFF1 + tumor cells in spread through air spaces samples correlated with undesirable therapy response in non-small cell lung cancer

Spread through air spaces (STAS) is a recognized aggressive pattern in lung cancer, serving as a crucial risk factor for postoperative recurrence. However, its phenotype and related spatial structure have remained elusive. To address these limitations, we conducted a comprehensive study based on spatial data, analyzing over 30,000 spots from 14 non-STAS samples and one STAS sample. We observed increased proliferation activities and angiogenesis in STAS, identifying S100P as a potential biomarker for STAS. Furthermore, our investigation into the heterogeneity of STAS tumor cells revealed a subset identified as S100P + TFF1 +, exhibiting a negative impact on patients' survival in public datasets. This subtype exhibited the highest activities in the TGFb and hypoxia, suggesting its potential pro-tumor role within the tumor microenvironment. To assess the role of S100P + TFF1 + tumor cells in therapy response, we included data from two clinical trial cohorts (BPI-7711 for EGFR-TKI therapy and ORIENT-3 for immunotherapy). The presence of S100P + TFF1 + tumor cells correlated with worse responses to both EGFR-TKI therapy and immunotherapy. Notably, TFF1 emerged as a serum marker for predicting EGFR-TKI response. Cell-cell communication analysis revealed that the TGFb signaling pathway was the most activated in S100P + TFF1 + tumor cells, with TGFB2-TGFBR2 identified as the main ligand-receptor pair. This was further validated by multiplex immunofluorescence performed on twenty NSCLC samples. In summary, our study identified S100P as the biomarker for STAS and highlighted the adverse role of S100P + TFF1 + tumor cells in survival outcomes.

Expression of Trefoil Factor 1 (TFF1) in Cancer: A Tissue Microarray Study Involving 18,878 Tumors

Background/Objectives: Trefoil factor 1 (TFF1) plays a role in the mucus barrier. Methods: To evaluate the prevalence of TFF1 expression in cancer, a tissue microarray containing 18,878 samples from 149 tumor types and 608 samples of 76 normal tissue types was analyzed through immunohistochemistry (IHC). Results: TFF1 staining was detectable in 65 of 149 tumor categories. The highest rates of TFF1 positivity were found in mucinous ovarian carcinomas (76.2%), colorectal adenomas and adenocarcinomas (47.1-75%), breast neoplasms (up to 72.9%), bilio-pancreatic adenocarcinomas (42.1-62.5%), gastro-esophageal adenocarcinomas (40.4-50.0%), neuroendocrine neoplasms (up to 45.5%), cervical adenocarcinomas (39.1%), and urothelial neoplasms (up to 24.3%). High TFF1 expression was related to a low grade of malignancy in non-invasive urothelial carcinomas of the bladder (p = 0.0225), low grade of malignancy (p = 0.0003), estrogen and progesterone receptor expression (p < 0.0001), non-triple negativity (p = 0.0005) in invasive breast cancer of no special type, and right-sided tumor location (p = 0.0021) in colorectal adenocarcinomas. Conclusions: TFF1 IHC has only limited utility for the discrimination of different tumor entities given its expression in many tumor entities. The link between TFF1 expression and parameters of malignancy argues for a relevant biological role of TFF1 in cancer. TFF1 may represent a suitable therapeutic target due to its expression in only a few normal cell types.

SpaGIC: graph-informed clustering in spatial transcriptomics via self-supervised contrastive learning

Spatial transcriptomics technologies enable the generation of gene expression profiles while preserving spatial context, providing the potential for in-depth understanding of spatial-specific tissue heterogeneity. Leveraging gene and spatial data effectively is fundamental to accurately identifying spatial domains in spatial transcriptomics analysis. However, many existing methods have not yet fully exploited the local neighborhood details within spatial information. To address this issue, we introduce SpaGIC, a novel graph-based deep learning framework integrating graph convolutional networks and self-supervised contrastive learning techniques. SpaGIC learns meaningful latent embeddings of spots by maximizing both edge-wise and local neighborhood-wise mutual information of graph structures, as well as minimizing the embedding distance between spatially adjacent spots. We evaluated SpaGIC on seven spatial transcriptomics datasets across various technology platforms. The experimental results demonstrated that SpaGIC consistently outperformed existing state-of-the-art methods in several tasks, such as spatial domain identification, data denoising, visualization, and trajectory inference. Additionally, SpaGIC is capable of performing joint analyses of multiple slices, further underscoring its versatility and effectiveness in spatial transcriptomics research.

Bioinformatic analysis and experimental validation of six cuproptosis-associated genes as a prognostic signature of breast cancer

BACKGROUND

Breast carcinoma (BRCA) is a life-threatening malignancy in women and shows a poor prognosis. Cuproptosis is a novel mode of cell death but its relationship with BRCA is unclear. This study attempted to develop a cuproptosis-relevant prognostic gene signature for BRCA.

METHODS

Cuproptosis-relevant subtypes of BRCA were obtained by consensus clustering. Differential expression analysis was implemented using the 'limma' package. Univariate Cox and multivariate Cox analyses were performed to determine a cuproptosis-relevant prognostic gene signature. The signature was constructed and validated in distinct datasets. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were also conducted using the prognostic signature to uncover the underlying molecular mechanisms. ESTIMATE and CIBERSORT algorithms were applied to probe the linkage between the gene signature and tumor microenvironment (TME). Immunotherapy responsiveness was assessed using the Tumor Immune Dysfunction and Exclusion (TIDE) web tool. Real-time quantitative PCR (RT-qPCR) was performed to detect the expressions of cuproptosis-relevant prognostic genes in breast cancer cell lines.

RESULTS

Thirty-eight cuproptosis-associated differentially expressed genes (DEGs) in BRCA were mined by consensus clustering and differential expression analysis. Based on univariate Cox and multivariate Cox analyses, six cuproptosis-relevant prognostic genes, namely SAA1, KRT17, VAV3, IGHG1, TFF1, and CLEC3A, were mined to establish a corresponding signature. The signature was validated using external validation sets. GSVA and GSEA showed that multiple cell cycle-linked and immune-related pathways along with biological processes were associated with the signature. The results ESTIMATE and CIBERSORT analyses revealed significantly different TMEs between the two Cusig score subgroups. Finally, RT-qPCR analysis of cell lines further confirmed the expressional trends of SAA1, KRT17, IGHG1, and CLEC3A.

CONCLUSION

Taken together, we constructed a signature for projecting the overall survival of BRCA patients and our findings authenticated the cuproptosis-relevant prognostic genes, which are expected to provide a basis for developing prognostic molecular biomarkers and an in-depth understanding of the relationship between cuproptosis and BRCA.

Identification of a novel five ferroptosis-related gene signature as a promising prognostic model for breast cancer

BACKGROUND

Breast cancer (BCa) is a major challenge for women's health worldwide. Ferroptosis is closely related to tumorigenesis and cancer progression. However, the prognostic value of ferroptosis-related genes in BCa remains unclear, and more accurate prognostic models are urgently needed.

METHODS

Gene expression profiles and clinical information of BCa patients were collected from public databases. LASSO and multivariate Cox regression analysis were utilized to construct the prognostic gene signature. Kaplan-Meier plotter, receiver operating characteristic (ROC) curves, and nomogram were used to validate the prognostic value of the gene signature. Gene set enrichment analysis was performed to explore the molecular functions and signaling pathways.

RESULTS

Differentially expressed ferroptosis-related genes between BCa samples and normal tissues were obtained. A novel five-gene signature including BCL2, SLC40A1, TFF1, APOOL, and PRAME was established for prognosis prediction. Patients stratified into high-risk or low-risk group displayed significantly different survival. Kaplan-Meier and ROC curves showed a good performance for survival prediction in different cohorts. Biological function analysis revealed that the five-gene signature was associated with cancer progression, immune infiltration, immune response, and drug resistance. Nomogram including the five-gene signature was established.

CONCLUSION

A novel five ferroptosis-related gene signature and nomogram could be used for prognostic prediction in BCa.

Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism

MOTIVATION

Recent advances in spatial transcriptomics technologies have enabled gene expression profiles while preserving spatial context. Accurately identifying spatial domains is crucial for downstream analysis and it requires the effective integration of gene expression profiles and spatial information. While increasingly computational methods have been developed for spatial domain detection, most of them cannot adaptively learn the complex relationship between gene expression and spatial information, leading to sub-optimal performance.

RESULTS

To overcome these challenges, we propose a novel deep learning method named Spatial-MGCN for identifying spatial domains, which is a Multi-view Graph Convolutional Network (GCN) with attention mechanism. We first construct two neighbor graphs using gene expression profiles and spatial information, respectively. Then, a multi-view GCN encoder is designed to extract unique embeddings from both the feature and spatial graphs, as well as their shared embeddings by combining both graphs. Finally, a zero-inflated negative binomial decoder is used to reconstruct the original expression matrix by capturing the global probability distribution of gene expression profiles. Moreover, Spatial-MGCN incorporates a spatial regularization constraint into the features learning to preserve spatial neighbor information in an end-to-end manner. The experimental results show that Spatial-MGCN outperforms state-of-the-art methods consistently in several tasks, including spatial clustering and trajectory inference.

Diagnostic utility of trefoil factor families for the early detection of lung cancer and their correlation with tissue expression

Trefoil factors (TFFs) are upregulated in numerous types of cancer, including those of the breast, the colon, the lung and the pancreas, suggesting their potential utility as biomarkers for screening. In the present study, the clinical relevance of serum or urinary TFFs as biomarkers were comprehensively evaluated and the correlation with TFF expression levels in lung cancer tissue was examined. Serum and urine were collected from 199 patients with lung cancer and 198 healthy individuals. Concentrations of serum and urinary TFF1, TFF2 and TFF3 were measured using ELISA and the potential of TFF levels to discriminate between cancer and non-cancer samples was evaluated. In 100 of the cancer cases, expression of TFF1-3 was analyzed using immunohistochemical staining of paraffin sections. Furthermore, the relationship between TFF levels and clinicopathological factors among these cancer cases was analyzed using immunohistochemistry of tissue specimens, quantified and statistically analyzed. While serum levels of all TFFs measured using ELISA were significantly higher in patients with lung cancer compared with those in healthy individuals, urinary TFFs were lower. Areas under the curve (AUC) of the receiver operating characteristic curves for serum/urinary TFF1, TFF2 and TFF3 were 0.709/0.594, 0.722/0.501 and 0.663/0.665, respectively. Furthermore, the combination of serum TFF1, TFF2, TFF3 and urinary TFF1 and TFF3 demonstrated the highest AUC (0.826). In the clinicopathological analysis, serum TFF1 was higher in the early pathological T-stage (pTis/1/2) compared with the later stage (pT3/4) and TFF2 was higher in the pN0/1 than the pN2 group. With regards to the histological types, urinary TFF1 was higher in squamous cell carcinoma than adenocarcinoma (AC), but TFF2 tended to be higher in AC. Using immunohistochemical analysis, although TFF1 and TFF3 expression showed positive correlation with serum concentrations, TFF2 was inversely correlated. In conclusion, serum and urinary TFF levels are promising predictive biomarkers, and their measurements provide a useful in vivo and non-invasive diagnostic screening tool. In particular, TFF1 and TFF3 could be surrogate markers of clinicopathological profiles of human lung cancer.

Local augmented graph neural network for multi-omics cancer prognosis prediction and analysis

Cancer prognosis prediction and analysis can help patients understand expected life and help clinicians provide correct therapeutic guidance. Thanks to the development of sequencing technology, multi-omics data, and biological networks have been used for cancer prognosis prediction. Besides, graph neural networks can simultaneously consider multi-omics features and molecular interactions in biological networks, becoming mainstream in cancer prognosis prediction and analysis. However, the limited number of neighboring genes in biological networks restricts the accuracy of graph neural networks. To solve this problem, a local augmented graph convolutional network named LAGProg is proposed in this paper for cancer prognosis prediction and analysis. The process follows: first, given a patient's multi-omics data features and biological network, the corresponding augmented conditional variational autoencoder generates features. Then, the generated augmented features and the original features are fed into a cancer prognosis prediction model to complete the cancer prognosis prediction task. The conditional variational autoencoder consists of two parts: encoder-decoder. In the encoding phase, an encoder learns the conditional distribution of the multi-omics data. As a generative model, a decoder takes the conditional distribution and the original feature as inputs to generate the enhanced features. The cancer prognosis prediction model consists of a two-layer graph convolutional neural network and a Cox proportional risk network. The Cox proportional risk network consists of fully connected layers. Extensive experiments on 15 real-world datasets from TCGA demonstrated the effectiveness and efficiency of the proposed method in predicting cancer prognosis. LAGProg improved the C-index values by an average of 8.5% over the state-of-the-art graph neural network method. Moreover, we confirmed that the local augmentation technique could enhance the model's ability to represent multi-omics features, improve the model's robustness to missing multi-omics features, and prevent the model's over-smoothing during training. Finally, based on genes identified through differential expression analysis, we discovered 13 prognostic markers highly associated with breast cancer, among which ten genes have been proved by literature review.

Chromatin Regulator-Related Gene Signature for Predicting Prognosis and Immunotherapy Efficacy in Breast Cancer

BACKGROUND

Many studies have found that chromatin regulators (CRs) are correlated with tumorigenesis and disease prognosis. Here, we attempted to build a new CR-related gene model to predict breast cancer (BC) survival status.

METHODS

First, the CR-related differentially expressed genes (DEGs) were screened in normal and tumor breast tissues, and the potential mechanism of CR-related DEGs was determined by function analysis. Based on the prognostic DEGs, the Cox regression model was applied to build a signature for BC. Then, survival and receiver operating characteristic (ROC) curves were performed to validate the signature's efficacy and identify its independent prognostic value. The CIBERSORT and tumor immune dysfunction and exclusion (TIDE) algorithms were used to assess the immune cells infiltration and immunotherapy efficacy for this signature, respectively. Additionally, a novel nomogram was also built for clinical decisions.

RESULTS

We identified 98 CR-related DEGs in breast tissues and constructed a novel 6 CR-related gene signature (ARID5A, ASCL1, IKZF3, KDM4B, PRDM11, and TFF1) to predict the outcome of BC patients. The prognostic value of this CR-related gene signature was validated with outstanding predictive performance. The TIDE analysis revealed that the high-risk group patients had a better response to immune checkpoint blockade (ICB) therapy.

CONCLUSION

A new CR-related gene signature was built, and this signature could provide the independent predictive capability of prognosis and immunotherapy efficacy for BC patients.

DeepST: identifying spatial domains in spatial transcriptomics by deep learning

Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.

Runx1 regulates Tff1 expression to expedite viability of retinal microvascular endothelial cells in mice with diabetic retinopathy

Diabetic retinopathy (DR) represents a major complication of diabetes, and molecular mechanisms related to vascular dysfunction, particularly endothelial dysfunction, in DR remains unclear. In the present work, we generated a DR animal model using mice and a cell model in mouse retinal microvascular endothelial cells (mRMECs) to examine the role of Trefoil factor family 1 (Tff1) in DR. Tff1 was poorly expressed in DR mice and high glucose (HG)-treated mRMECs. Overexpression of Tff1 significantly attenuated streptozotocin-induced retinal proliferation and angiogenesis in DR mice and reduced the secretion of inflammatory factors. In HG-treated mRMECs, overexpression of Tff1 remarkably reduced the proliferation and angiogenesis of mRMECs. In further experiments, we found that Tff1 was transcriptionally repressed by Runt-related transcription factor 1 (Runx1) directly, and Tff1 expression was indirectly modulated by Runx1 via the core-binding factor subunit beta (CBF-β)/nuclear factor, erythroid 2/microRNA-423-5p axis and the CBF-β/estrogen receptor 1 (ESR1) axis. Moreover, Tff1 could inhibit the activation of NF-κB signaling pathway, which in turn attenuated retinal endothelial cell proliferation and angiogenesis. It was thus proposed that Runx1/Tff1/NF-κB axis may be a potential target for the treatment strategy of DR, and further studies are needed.

Influence of breast cancer risk factors on proliferation and DNA damage in human breast glandular tissues: role of intracellular estrogen levels, oxidative stress and estrogen biotransformation

Breast cancer etiology is associated with both proliferation and DNA damage induced by estrogens. Breast cancer risk factors (BCRF) such as body mass index (BMI), smoking, and intake of estrogen-active drugs were recently shown to influence intratissue estrogen levels. Thus, the aim of the present study was to investigate the influence of BCRF on estrogen-induced proliferation and DNA damage in 41 well-characterized breast glandular tissues derived from women without breast cancer. Influence of intramammary estrogen levels and BCRF on estrogen receptor (ESR) activation, ESR-related proliferation (indicated by levels of marker transcripts), oxidative stress (indicated by levels of GCLC transcript and oxidative derivatives of cholesterol), and levels of transcripts encoding enzymes involved in estrogen biotransformation was identified by multiple linear regression models. Metabolic fluxes to adducts of estrogens with DNA (E-DNA) were assessed by a metabolic network model (MNM) which was validated by comparison of calculated fluxes with data on methoxylated and glucuronidated estrogens determined by GC– and UHPLC–MS/MS. Intratissue estrogen levels significantly influenced ESR activation and fluxes to E-DNA within the MNM. Likewise, all BCRF directly and/or indirectly influenced ESR activation, proliferation, and key flux constraints influencing E-DNA (i.e., levels of estrogens, CYP1B1, SULT1A1, SULT1A2, and GSTP1). However, no unambiguous total effect of BCRF on proliferation became apparent. Furthermore, BMI was the only BCRF to indeed influence fluxes to E-DNA (via congruent adverse influence on levels of estrogens, CYP1B1 and SULT1A2).

Channeling the Force: Piezo1 Mechanotransduction in Cancer Metastasis

Cancer metastasis is one of the leading causes of death worldwide, motivating research into identifying new methods of preventing cancer metastasis. Recently there has been increasing interest in understanding how cancer cells transduce mechanical forces into biochemical signals, as metastasis is a process that consists of a wide range of physical forces. For instance, the circulatory system through which disseminating cancer cells must transit is an environment characterized by variable fluid shear stress due to blood flow. Cancer cells and other cells can transduce physical stimuli into biochemical responses using the mechanosensitive ion channel Piezo1, which is activated by membrane deformations that occur when cells are exposed to physical forces. When active, Piezo1 opens, allowing for calcium flux into the cell. Calcium, as a ubiquitous second-messenger cation, is associated with many signaling pathways involved in cancer metastasis, such as angiogenesis, cell migration, intravasation, and proliferation. In this review, we discuss the roles of Piezo1 in each stage of cancer metastasis in addition to its roles in immune cell activation and cancer cell death.

Multiplex gene quantification as digital markers for extremely rapid evaluation of chemo-drug sensitivity

Current administrations for precision drug uses are limited in evaluation speed. Here, we propose the use of multiplex gene-based digital markers for the extremely rapid personalized prediction of individual sensitivity to cancer drugs. We first screen the transcriptional profiles by applying two to three gene filters and scoring genes by their impact on drug sensitivity and finalize the gene lists by K-nearest neighbors cross-validation. The digital markers are cancer type dependent, are composed of tens to hundreds of gene expressions, and are rapidly quantified by reverse transcription quantitative real-time PCR (qRT-PCR) within 1–3 h after tumor sampling. The area under the receiver operating characteristic curve reached 0.88 when testing the performance of digital markers on organoids derived from colorectal cancer patient tumors. The algorithm and corresponding graphic user interface were developed to demonstrate the promise of digital markers for extremely rapid drug recommendation.

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Non-targeted multiplex genes are screened as digital markers for drug sensitivity

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Transcription level cohort of 10s to 100s genes predicts drug sensitivity

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Digital markers are quantified using qRT-PCR within 1–3 h

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Digital markers guide extremely rapid chemo-drug uses after patient hospitalization

In clinical cancer medicine, many patients require immediate chemotherapy after hospitalization. Current administrations for precision drug uses are limited in evaluation speed, including genomic sequencing and tumor organoid evaluation. An extremely rapid evaluation protocol is in high demand to realize drug recommendation within a few hours after tumor sampling. In this work, we have proposed an approach for extremely rapid and personalized drug recommendation.

TFF-1 Functions to Suppress Multiple Phenotypes Associated with Lung Cancer Progression

Introduction

Trefoil Factor (TFF) is a member of a protein family comprised of three isoforms, of which TFF-1 exhibits antithetical functions; promotion or suppression of cell proliferation, survival and invasion, depending on the cancer type. However, the pathobiological function of TFF-1 in lung carcinoma has been still unclear.

Methods

We examined the expression and secretion of TFF-1 using cultured human lung carcinoma cells by immunoblotting, immunofluorescence, enzyme-linked immunosorbent assay and quantitative real-time PCR analyses. The effects of TFF-1 on various phenotypes were analyzed in two cell lines, including those transfected with cDNA encoding TFF-1. Cell proliferation and death were examined by hemocytometer cell counting and by colorimetric viability/cytotoxicity assay. Cell cycle profile, migration and invasion were also examined by flow cytometry, wound healing assay and Matrigel Transwell assay, respectively. The effect of TFF-1 overexpression was confirmed by additional transfection of TFF-1-specific siRNA.

Results

Endogenous TFF-1 protein expression and secretion into the media were observed exclusively in adenocarcinoma-derived cell lines. Forced overexpression of TFF-1 drove cell cycle transition, while the proliferation decreased by 19% to 25% due to increased cell death. This cell death was predominantly caused by apoptosis, as assessed by the activation of caspase 3/7. Cell migration was also suppressed by 71% to 82% in TFF-1-transfected cells. The suppressive effect of TFF-1 on proliferation and migration was restored by transfection of TFF-1 siRNA. Moreover, invasion was also suppressed to 77% to 83% in TFF-1-transfected cells.

Conclusion

These findings reveal that TFF-1 functions as a suppressor of cancer proliferation by induction of apoptosis, cell migration and invasion and thus may provide a synergistic target for potential treatment strategies for human lung carcinoma.

Trefoil Factor Family (TFF) Peptides and Their Links to Inflammation: A Re-evaluation and New Medical Perspectives

Trefoil factor family peptides (TFF1, TFF2, TFF3), together with mucins, are typical exocrine products of mucous epithelia. Here, they act as a gastric tumor suppressor (TFF1) or they play different roles in mucosal innate immune defense (TFF2, TFF3). Minute amounts are also secreted as endocrine, e.g., by the immune and central nervous systems. As a hallmark, TFF peptides have different lectin activities, best characterized for TFF2, but also TFF1. Pathologically, ectopic expression occurs during inflammation and in various tumors. In this review, the role of TFF peptides during inflammation is discussed on two levels. On the one hand, the expression of TFF1-3 is regulated by inflammatory signals in different ways (upstream links). On the other hand, TFF peptides influence inflammatory processes (downstream links). The latter are recognized best in various Tff-deficient mice, which have completely different phenotypes. In particular, TFF2 is secreted by myeloid cells (e.g., macrophages) and lymphocytes (e.g., memory T cells), where it modulates immune reactions triggering inflammation. As a new concept, in addition to lectin-triggered activation, a hypothetical lectin-triggered inhibition of glycosylated transmembrane receptors by TFF peptides is discussed. Thus, TFFs are promising players in the field of glycoimmunology, such as galectins and C-type lectins.

Trefoil factor-1 upregulation in estrogen-receptor positive breast cancer correlates with an increased risk of bone metastasis

Bone is one of the most preferred sites of metastatic spread from different cancer types, including breast cancer. However, different breast cancer subtypes exhibit distinct metastatic behavior in terms of kinetics and anatomic sites of relapse. Despite advances in the diagnosis, the identification of patients at high-risk of bone recurrence is still an unmet clinical need. We conducted a retrospective analysis, by gene expression and immunohistochemical assays, on 90 surgically resected breast cancer samples collected from patients who experienced no evidence of distant metastasis, bone or visceral metastasis in order to identify a primary tumor-derived marker of bone recurrence. We identified trefoil factor-1 (pS2 or TFF1) as strictly correlated to bone metastasis from ER+ breast cancer. In silico analysis was carried out to confirm this observation, linking gene expression data with clinical characteristics available from public clinical datasets. Then, we investigated TFF1 function in ER+ breast cancer tumorigenesis and bone metastasis through xenograft in vivo models of MCF 7 breast cancer with gain and loss of function of TFF1. As a response to microenvironmental features in primary tumors, TFF1 expression could modulate ER+ breast cancer growth, leading to a less proliferative phenotype. Our results showed it may not play a role in late stages of bone metastasis, however further studies are warranted to understand whether it could contribute in the early-stages of the metastatic cascade. In conclusion, TFF1 upregulation in primary ER+ breast cancer could be useful to identify patients at high-risk of bone metastasis. This could help clinicians in the identification of patients who likely can develop bone metastasis and who could benefit from personalized treatments and follow-up strategies to prevent metastatic disease.

Trefoil Factor Family (TFF) Peptides and Their Diverse Molecular Functions in Mucus Barrier Protection and More: Changing the Paradigm

Trefoil factor family peptides (TFF1, TFF2, TFF3) are typically co-secreted together with mucins. Tff1 represents a gastric tumor suppressor gene in mice. TFFs are also synthesized in minute amounts in the immune and central nervous systems. In mucous epithelia, they support rapid repair by enhancing cell migration ("restitution") via their weak chemotactic and anti-apoptotic effects. For a long time, as a paradigm, this was considered as their major biological function. Within recent years, the formation of disulfide-linked heterodimers was documented for TFF1 and TFF3, e.g., with gastrokine-2 and IgG Fc binding protein (FCGBP). Furthermore, lectin activities were recognized as enabling binding to a lipopolysaccharide of Helicobacter pylori (TFF1, TFF3) or to a carbohydrate moiety of the mucin MUC6 (TFF2). Only recently, gastric TFF1 was demonstrated to occur predominantly in monomeric forms with an unusual free thiol group. Thus, a new picture emerged, pointing to diverse molecular functions for TFFs. Monomeric TFF1 might protect the gastric mucosa as a scavenger for extracellular reactive oxygen/nitrogen species. Whereas, the TFF2/MUC6 complex stabilizes the inner layer of the gastric mucus. In contrast, the TFF3-FCGBP heterodimer (and also TFF1-FCGBP) are likely part of the innate immune defense of mucous epithelia, preventing the infiltration of microorganisms.

Reciprocal expression of trefoil factor-1 and thyroid transcription factor-1 in lung adenocarcinomas

Molecular targeted therapies against EGFR and ALK have improved the quality of life of lung adenocarcinoma patients. However, targetable driver mutations are mainly found in thyroid transcription factor‐1 (TTF‐1)/NK2 homeobox 1 (NKX2‐1)‐positive terminal respiratory unit (TRU) types and rarely in non‐TRU types. To elucidate the molecular characteristics of the major subtypes of non‐TRU‐type adenocarcinomas, we analyzed 19 lung adenocarcinoma cell lines (11 TRU types and 8 non‐TRU types). A characteristic of non‐TRU‐type cell lines was the strong expression of TFF‐1 (trefoil factor‐1), a gastric mucosal protective factor. An immunohistochemical analysis of 238 primary lung adenocarcinomas resected at Jichi Medical University Hospital revealed that TFF‐1 was positive in 31 cases (13%). Expression of TFF‐1 was frequently detected in invasive mucinous (14/15, 93%), enteric (2/2, 100%), and colloid (1/1, 100%) adenocarcinomas, less frequent in acinar (5/24, 21%), papillary (7/120, 6%), and solid (2/43, 5%) adenocarcinomas, and negative in micropapillary (0/1, 0%), lepidic (0/23, 0%), and microinvasive adenocarcinomas or adenocarcinoma in situ (0/9, 0%). Expression of TFF‐1 correlated with the expression of HNF4‐α and MUC5AC (P < .0001, P < .0001, respectively) and inversely correlated with that of TTF‐1/NKX2‐1 (P < .0001). These results indicate that TFF‐1 is characteristically expressed in non‐TRU‐type adenocarcinomas with gastrointestinal features. The TFF‐1‐positive cases harbored KRAS mutations at a high frequency, but no EGFR or ALK mutations. Expression of TFF‐1 correlated with tumor spread through air spaces, and a poor prognosis in advanced stages. Moreover, the knockdown of TFF‐1 inhibited cell proliferation and soft‐agar colony formation and induced apoptosis in a TFF‐1‐high and KRAS‐mutated lung adenocarcinoma cell line. These results indicate that TFF‐1 is not only a biomarker, but also a potential molecular target for non‐TRU‐type lung adenocarcinomas.

Odyssey of trefoil factors in cancer: Diagnostic and therapeutic implications

Trefoil factors 1, 2, and 3 (TFFs) are a family of small secretory molecules involved in the protection and repair of the gastrointestinal tract (GI). TFFs maintain and restore epithelial structural integrity via transducing key signaling pathways for epithelial cell migration, proliferation, and invasion. In recent years, TFFs have emerged as key players in the pathogenesis of multiple diseases, especially cancer. Initially recognized as tumor suppressors, emerging evidence demonstrates their key role in tumor progression and metastasis, extending their actions beyond protection. However, to date, a comprehensive understanding of TFFs' mechanism of action in tumor initiation, progression and metastasis remains obscure. The present review discusses the structural, functional and mechanistic implications of all three TFF family members in tumor progression and metastasis. Also, we have garnered information from studies on their structure and expression status in different organs, along with lessons from their specific knockout in mouse models. In addition, we highlight the emerging potential of using TFFs as a biomarker to stratify tumors for better therapeutic intervention.

The Tumor Suppressor TFF1 Occurs in Different Forms and Interacts with Multiple Partners in the Human Gastric Mucus Barrier: Indications for Diverse Protective Functions

TFF1 is a protective peptide of the Trefoil Factor Family (TFF), which is co-secreted with the mucin MUC5AC, gastrokine 2 (GKN2), and IgG Fc binding protein (FCGBP) from gastric surface mucous cells. Tff1-deficient mice obligatorily develop antropyloric adenoma and about 30% progress to carcinomas, indicating that Tff1 is a tumor suppressor. As a hallmark, TFF1 contains seven cysteine residues with three disulfide bonds stabilizing the conserved TFF domain. Here, we systematically investigated the molecular forms of TFF1 in the human gastric mucosa. TFF1 mainly occurs in an unusual monomeric form, but also as a homodimer. Furthermore, minor amounts of TFF1 form heterodimers with GKN2, FCGBP, and an unknown partner protein, respectively. TFF1 also binds to the mucin MUC6 in vitro, as shown by overlay assays with synthetic ¹²⁵I-labeled TFF1 homodimer. The dominant presence of a monomeric form with a free thiol group at Cys-58 is in agreement with previous studies in Xenopus laevis and mouse. Cys-58 is likely highly reactive due to flanking acid residues (PPEEEC⁵⁸EF) and might act as a scavenger for extracellular reactive oxygen/nitrogen species protecting the gastric mucosa from damage by oxidative stress, e.g., H₂O₂ generated by dual oxidase (DUOX).

Molecular Alterations in the Stomach of Tff1-Deficient Mice: Early Steps in Antral Carcinogenesis

TFF1 is a peptide of the gastric mucosa co-secreted with the mucin MUC5AC. It plays a key role in gastric mucosal protection and repair. Tff1-deficient (Tff1^KO) mice obligatorily develop antropyloric adenoma and about 30% progress to carcinomas. Thus, these mice represent a model for gastric tumorigenesis. Here, we compared the expression of selected genes in Tff1^KO mice and the corresponding wild-type animals (RT-PCR analyses). Furthermore, we systematically investigated the different molecular forms of Tff1 and its heterodimer partner gastrokine-2 (Gkn2) in the stomach (Western blot analyses). As a hallmark, a large portion of murine Tff1 occurs in a monomeric form. This is unexpected because of its odd number of seven cysteine residues. Probably the three conserved acid amino acid residues (EEE) flanking the 7th cysteine residue allow monomeric secretion. As a consequence, the free thiol of monomeric Tff1 could have a protective scavenger function, e.g., for reactive oxygen/nitrogen species. Furthermore, a minor subset of Tff1 forms a disulfide-linked heterodimer with IgG Fc binding protein (Fcgbp). Of special note, in Tff1^KO animals a homodimeric form of Gkn2 was observed. In addition, Tff1^KO animals showed strongly reduced Tff2 transcript and protein levels, which might explain their increased sensitivity to Helicobacter pylori infection.

Establishment and characterization of highly osteolytic luminal breast cancer cell lines by intracaudal arterial injection

Bone is one of the most common metastatic sites of breast cancer, and bone metastasis profoundly affects the quality of life of breast cancer patients. Bone metastasis is commonly observed among all the subtypes of breast cancer; however, its molecular mechanism has been analyzed only in triple-negative subtype of breast cancer (TNBC). To characterize the molecular mechanisms of bone metastasis of luminal breast cancer, we established a bone-metastatic model of the MCF7, luminal breast cancer cell line, with enhanced osteolytic activity by intracaudal arterial injection (CAI). Pathological analysis of the established cell lines revealed that they exhibited fierce osteolytic ability by promoting osteoclast differentiation and activity. The signature genes extracted from highly osteolytic MCF7 cell lines were differed from those of bone-metastatic TNBC cell lines. Our results suggest that unique mechanisms of osteolysis in bone-metastatic lesions of luminal breast cancer. In addition, several up-regulated genes in MCF7-BM (Bone Metastasis) 02 cell lines correlated with poor prognosis with luminal breast cancer patients. Our findings support further study on the bone-metastatic mechanisms of luminal breast cancer.

Identification of key genes as potential biomarkers for triple‑negative breast cancer using integrating genomics analysis

Triple-negative breast cancer (TNBC) accounts for the worst prognosis of all types of breast cancers due to a high risk of recurrence and a lack of targeted therapeutic options. Extensive effort is required to identify novel targets for TNBC. In the present study, a robust rank aggregation (RRA) analysis based on genome-wide gene expression datasets involving TNBC patients from the Gene Expression Omnibus (GEO) database was performed to identify key genes associated with TNBC. A total of 194 highly ranked differentially expressed genes (DEGs) were identified in TNBC vs. non-TNBC. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was utilized to explore the biological functions of the identified genes. These DEGs were mainly involved in the biological processes termed positive regulation of transcription from RNA polymerase II promoter, negative regulation of apoptotic process, response to drug, response to estradiol and negative regulation of cell growth. Genes were mainly involved in the KEGG pathway termed estrogen signaling pathway. The aberrant expression of several randomly selected DEGs were further validated in cell lines, clinical tissues and The Cancer Genome Atlas (TCGA) cohort. Furthermore, all the top-ranked DEGs underwent survival analysis using TCGA database, of which overexpression of 4 genes (FABP7, ART3, CT83, and TTYH1) were positively correlated to the life expectancy (P<0.05) of TNBC patients. In addition, a model consisting of two genes (FABP7 and CT83) was identified to be significantly associated with the overall survival (OS) of TNBC patients by means of Cox regression, Kaplan-Meier, and receiver operating characteristic (ROC) analyses. In conclusion, the present study identified a number of key genes as potential biomarkers involved in TNBC, which provide novel insights into the tumorigenesis of TNBC at the gene level and may serve as independent prognostic factors for TNBC prognosis.

p53, miR-34a and EMP1-Newly Identified Targets of TFF3 Signaling in Y79 Retinoblastoma Cells

Trefoil factor family peptide 3 (TFF3) is supposed to have tumor suppressive functions in retinoblastoma (RB), but the functional pathway is not completely understood. In the study presented, we investigated the downstream pathway of TFF3 signaling in Y79 RB cells. Results from pG13-luciferase reporter assays and western blot analyses indicate induced p53 activity with an upregulation of miR-34a after TFF3 overexpression. Expression levels of the predicted miR-34a target epithelial membrane protein 1 (EMP1) are reduced after TFF3 overexpression. As revealed by WST-1 assay, BrdU, and DAPI cell counts viability and proliferation of Y79 cells significantly decrease following EMP1 knockdown, while apoptosis levels significantly increase. Opposite effects on Y79 cells’ growth could be shown after EMP1 overexpression. Caspase assays showed that EMP1 induced apoptosis after overexpression is at least partially caspase-3/7 dependent. Colony formation and soft agarose assays, testing for anchorage independent growth, revealed that EMP1 overexpressing Y79 cells have a significantly higher ability to form colonies. In in ovo chicken chorioallantoic membrane (CAM) assays inoculated EMP1 overexpressing Y79 cells form significantly larger CAM tumors. Moreover, miR-34a overexpression increases sensitivity of Y79 cells towards RB chemotherapeutics, however, without involvement of EMP1. In summary, the TFF3 signaling pathway in Y79 RB cells involves the activation of p53 with downstream induction of miR-34a and subsequent inhibition of EMP1.

Aberrant DNA Polymerase Beta Enhances H. pylori Infection Induced Genomic Instability and Gastric Carcinogenesis in Mice

H. pylori is a significant risk factor of gastric cancer that induces chronic inflammation and oxidative DNA damage to promote gastric carcinoma. Base excision repair (BER) is required to maintain the genome integrity and prevent oxidative DNA damage. Mutation in DNA polymerase beta (Pol β) impacts BER efficiency and has been reported in approximately 30-40% of gastric carcinoma tumors. In this study, we examined whether reduced BER capacity associated with mutation in the POLB gene, along with increased DNA damage generated by H. pylori infection, accelerates gastric cancer development. By infecting a Pol β mutant mouse model that lacks dRP lyase with H. pylori, we show that reactive oxygen and nitrogen species (RONS) mediated DNA damage is accumulated in Pol β mutant mice (L22P). In addition, H. pylori infection in Leu22Pro (L22P) mice significantly increases inducible nitric oxide synthesis (iNOS) mediated chronic inflammation. Our data show that L22P mice exhibited accelerated H. pylori induced carcinogenesis and increased tumor incidence. This work shows that Pol β mediated DNA repair under chronic inflammation conditions is an important suppressor of H. pylori induced stomach carcinogenesis.

Possible application of trefoil factor family peptides in gastroesophageal reflux and Barrett's esophagus

Gastroesophageal reflux disease (GERD) is a chronic disorder of the digestive tract characterised mainly by a heartburn. Being one of the most common gastrointestinal diseases, the prevalence of GERD reaches up to 25.9% in Europe. Barrett's esophagus (BE) is an acquired condition characterized by the replacement of the normal stratified squamous epithelium with metaplastic columnar epithelium. BE is believed to develop mainly from chronic GERD and is the most important risk factor of esophageal adenocarcinoma. Despite the availability of drugs such as proton pomp inhibitors and antacids, GERD is still a burden to local economy and impairs health-related quality of life in patients. Also, the endoscopic surveillance in patients with BE is burdensome and expensive what drives the need for biomarker of intestinal metaplasia and dysplasia. Trefoil factor family (TFF), consisting of TFF1, TFF2 and TFF3 peptides is gaining more and more attention due to its unique biochemical features and numerous functions. In this review the role of TFF1, TFF2 and TFF3 as potential treatment option and/or biomarker in the upper GI tract is discussed with particular focus on GERD and BE.

The RUNX Transcriptional Coregulator, CBFβ, Suppresses Migration of ER+ Breast Cancer Cells by Repressing ERα-Mediated Expression of the Migratory Factor TFF1

Core binding factor β (CBFβ), the essential coregulator of RUNX transcription factors, is one of the most frequently mutated genes in estrogen receptor-positive (ER⁺) breast cancer. Many of these mutations are nonsense mutations and are predicted to result in loss of function, suggesting a tumor suppressor role for CBFβ. However, the impact of missense mutations and the loss of CBFβ in ER⁺ breast cancer cells have not been determined. Here we demonstrate that missense mutations in CBFβ accumulate near the Runt domain-binding region. These mutations inhibit the ability of CBFβ to form CBFβ-Runx-DNA complexes. We further show that deletion of CBFβ, using CRISPR-Cas9, in ER⁺ MCF7 cells results in an increase in cell migration. This increase in migration is dependent on the presence of ERα. Analysis of the potential mechanism revealed that the increase in migration is driven by the coregulation of Trefoil factor 1 (TFF1) by CBFβ and ERα. RUNX1-CBFβ acts to repress ERα-activated expression of TFF1. TFF1 is a motogen that stimulates migration and we show that knockdown of TFF1 in CBFβ^-/- cells inhibits the migratory phenotype. Our findings reveal a new mechanism by which RUNX1-CBFβ and ERα combine to regulate gene expression and a new role for RUNX1-CBFβ in the prevention of cell migration by suppressing the expression of the motogen TFF1. IMPLICATIONS: Mutations in CBFβ contribute to the development of breast cancer by inducing a metastatic phenotype that is dependent on ER.

TFF1 antagonizes TIMP-1 mediated proliferative functions in gastric cancer

Tissue inhibitor matrix metalloproteinase-1 (TIMP1) is one of four identified members of the TIMP family. We evaluated the role of TIMP1 in gastric cancer using human and mouse tissues along with gastric organoids and in vitro cell models. Using quantitative real-time RT-PCR, we detected significant overexpression of TIMP1 in the human gastric cancer samples, as compared to normal stomach samples (P < 0.01). We also detected overexpression of Timp1 in neoplastic gastric lesions of the Tff1-knockout (KO) mice, as compared to normal stomach tissues. Reconstitution of TFF1 in human gastric cancer cell lines led to a significant decrease in the mRNA expression level of TIMP1 (P < 0.05). In vitro analysis demonstrated that TIMP1 mRNA expression is induced by TNF-α and activation of NF-κB whereas inhibition of NF-κB using BAY11-7082 led to inhibition of NF-κB and downregulation of TIMP1. Western blot analysis confirmed the decrease in TIMP1 protein level following reconstitution of TFF1. By using immunofluorescence, we showed nuclear localization of NF-κB and expression of TIMP1 in gastric organoids established from the Tff1-KO stomach where reconstitution of Tff1 using recombinant protein led to a notable reduction in the expression of both NF-κB and TIMP1. Using EDU assay, as a measure of proliferating cells, we found that TIMP1 promotes cellular proliferation whereas TFF1 reconstitution leads to a significant decrease in cellular proliferation (P < 0.05). In summary, our findings demonstrate overexpression of TIMP1 in mouse and human gastric cancers through NF-kB-dependent mechanism. We also show that TFF1 suppresses NF-κB and inhibits TIMP1-mediated proliferative potential in gastric cancer.

Trefoil factor 1 inhibits epithelial-mesenchymal transition of pancreatic intraepithelial neoplasm

The tumor-suppressive role of trefoil factor family (TFF) members in gastric carcinogenesis has been suggested, but their significance and mechanisms in other digestive diseases remain elusive. To clarify the role of TFF1 in pancreatic carcinogenesis, we performed IHC on human samples, transfected siRNA against TFF1 into pancreatic cancer cell lines, and employed mouse models in which PanIN development and loss of TFF1 occur simultaneously. In human samples, the expression of TFF1 was specifically observed in pancreatic intraepithelial neoplasm (PanIN), but was frequently lost in the invasive component of pancreatic ductal adenocarcinoma (PDAC). When the expression of TFF1 was suppressed in vitro, pancreatic cancer cell lines showed enhanced invasive ability and features of epithelial-mesenchymal transition (EMT), including upregulated Snail expression. TFF1 expression was also observed in PanIN lesions of Pdx-1 Cre; LSL-KRASG12D (KC) mice, a model of pancreatic cancer, and loss of TFF1 in these mice resulted in the expansion of PanIN lesions, an EMT phenotype in PanIN cells, and an accumulation of cancer-associated fibroblasts (CAFs), eventually resulting in the development of invasive adenocarcinoma. This study indicates that the acquisition of TFF1 expression is an early event in pancreatic carcinogenesis and that TFF1 might act as a tumor suppressor to prevent EMT and the invasive transformation of PanIN.

TFF1 Promotes EMT-Like Changes through an Auto-Induction Mechanism

Trefoil factor 1 (TFF1) is a small secreted protein expressed in the gastrointestinal tract where, together with the other two members of its family, it plays an essential role in mucosal protection and repair against injury. The molecular mechanisms involved in the protective function of all three TFF proteins are not fully elucidated. In this paper, we investigated the role of TFF1 in epithelial to mesenchymal transition (EMT) events. The effects of TFF1 on cellular models in normoxia and/or hypoxia were evaluated by western blot, immunofluorescence, qRT-PCR and trans-well invasion assays. Luciferase reporter assays were used to assess the existence of an auto-regulatory mechanism of TFF1. The methylation status of TFF1 promoter was measured by high-resolution melting (HRM) analysis. We demonstrate a TFF1 auto-induction mechanism with the identification of a specific responsive element located between −583 and −212 bp of its promoter. Our results suggest that TFF1 can regulate its own expression in normoxic, as well as in hypoxic, conditions acting synergistically with the hypoxia-inducible factor 1 (HIF-1α) pathway. Functionally, this auto-induction mechanism seems to promote cell invasion and EMT-like modifications in vitro. Additionally, exogenously added human recombinant TFF1 protein was sufficient to observe similar effects. Together, these findings suggest that the hypoxic conditions, which can be induced by gastric injury, promote TFF1 up-regulation, strengthened by an auto-induction mechanism, and that the trefoil peptide takes part in the epithelial-mesenchymal transition events eventually triggered to repair the damage.

TFF1 and TFF3 mRNAs Are Higher in Blood from Breast Cancer Patients with Metastatic Disease than Those without

Introduction

Breast cancer metastasis occurs when tumor cells dissociate from the primary tumor and migrate to distant organs through the peripheral bloodstream or lymphatic drainage. Circulating tumor cells (CTCs) originate from primary sites or metastases and circulate in the patients' bloodstream. Molecular assays for the detection and molecular characterization of CTCs can serve as a liquid biopsy and can represent an alternative to invasive biopsies as a source of tumor tissue in the metastatic patients.

Patients and Methods

We analyzed the presence of CTCs in the peripheral blood of 50 breast cancer patients by quantitative real-time reverse transcriptase polymerase chain reaction (RT-qPCR) to detect trefoil factor family (TFF) 1 and 3 genes.

Results

We found significant difference in the level of both TFF1 and TFF3 mRNA in the blood of nonmetastatic versus metastatic breast cancer patients (p= 0.001 and p= 0.038, respectively). TFF1 mRNA was detected at higher levels in 34.6% of metastatic breast cancer patients as compared to 0% of nonmetastatic (p= 0.002). As regards TFF3 mRNA, it was detected at higher levels in 46.2% of metastatic breast cancer patients as compared to 4% of nonmetastatic (p= 0.026). Moreover, we found that the high level of both TFF1 and TFF3 mRNA was related to estrogen status of the patients. The detection of high level of TFF1 mRNA in CTCs was associated with bone metastases (77.8%), while that of TFF3 was related to lymph node involvement (75%) and lung metastases (68.8%).

Conclusion

The combined measurement of both TFF1 and TFF3 mRNA level for differentiation of metastatic from nonmetastatic breast cancer gave 57.69% sensitivity and 83.3% specificity.

Differential Expression of TFF Genes and Proteins in Breast Tumors

SUMMARY – The objective of this study was to determine differential expression of TFF1, TFF2 and TFF3 genes and proteins in breast tumor subtypes. In addition, we investigated the correlation between TFF genes within tumor subgroups, and TFF genes with clinical and pathologic characteristics of the tumor. Study group included 122 patients with surgically removed breast tumors. Samples were investigated using qRT-PCR and immunohistochemistry. TFF1 and TFF3 genes and proteins were expressed in breast tumors, while the levels of TFF2 gene and protein expression were very low or undetectable. TFF1 was significantly more expressed in benign tumors, while TFF3 was more expressed in malignant tumors. Gene and protein expression of both TFF1 and TFF3 was greater in lymph node-negative tumors, hormone positive tumors, tumors with moderate levels of Ki67 expression, and in grade II tumors. A strong positive correlation was found between TFF1 and TFF3 genes, and the expression of both negatively correlated with Ki67 and the level of tumor histologic differentiation. Our results suggest that TFF1 and TFF3, but not TFF2, may have a role in breast tumor pathogenesis and could be used in the assessment of tumor differentiation and malignancy.

Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy

INTRODUCTION

Oral mucositis (OM) is a major dose-limiting side effect of chemotherapy and radiation used in cancer treatment. Due to the complex nature of OM, currently available drug-based treatments are of limited efficacy.

OBJECTIVES

Our objectives were (i) to determine genes and molecular pathways associated with OM and wound healing using computational tools and publicly available data and (ii) to identify drugs formulated for topical use targeting the relevant OM molecular pathways.

METHODS

OM and wound healing-associated genes were determined by text mining, and the intersection of the two gene sets was selected for gene ontology analysis using the GeneCodis program. Protein interaction network analysis was performed using STRING-db. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in OM.

RESULTS

Our analysis identified 447 genes common to both the "OM" and "wound healing" text mining concepts. Gene enrichment analysis yielded 20 genes representing six pathways and targetable by a total of 32 drugs which could possibly be formulated for topical application. A manual search on ClinicalTrials.gov confirmed no relevant pathway/drug candidate had been overlooked. Twenty-five of the 32 drugs can directly affect the PTGS2 (COX-2) pathway, the pathway that has been targeted in previous clinical trials with limited success.

CONCLUSIONS

Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.

Comparative study of gastric cancer and chronic gastritis via network analysis

AIM

In this study the significant differentially expressed genes (DEGs) related to gastric cancer (GC) and chronic gastritis were screened to introduce common and distinctive genes between the two diseases.

BACKGROUND

Diagnosis of gastric cancer as a mortal disease and chronic gastritis the stomach disorder which can be considered as risk factor of GCs required safe and effective molecular biomarkers.

METHODS

Microarray profiles were downloaded from Gene Expression Omnibus (GEO) and analyzed via GEO2R. The candidate DEGs plus relevant genes from STRING database were interacted by Cytoscape software version 3.6.0 the central nodes were determined and analyzed.

RESULTS

JUN, GAPDH, FOS, TP53, PRDM10, VEGFA, and CREB1 as central nodes and TFF1 and ERG1 as the top changed expressed genes were determined as critical nodes related to gastric cancer. GAPDH, PRDM10, TP53, JUN, AKT1, EGFR, MAPK1, EGF, DECR1, and MYC were identified as common remarkable genes between GC and chronic gastritis.

CONCLUSION

Identification of distinctive and common genes between GC and chronic gastritis can be useful in the early stage detection of disease and reducing risk of GCs.

TFF1 hypermethylation and decreased expression in esophageal squamous cell carcinoma and histologically normal tumor surrounding esophageal cells

Background

Esophageal squamous cell carcinoma (ESCC) is one of the 10 most incident cancer types in the world, and it is mainly associated with tobacco and alcohol consumption. ESCC mortality rates stand very close to its incidence, which is a direct consequence of a late diagnosis and an inefficient treatment. Although this scenery is quite alarming, the major molecular alterations that drive this carcinogenesis process remain unclear. We have previously shown through the first ESCC methylome analysis that TFF1 promoter is frequently hypermethylated in ESCC. Here, to evaluate TFF1 methylation as a potential biomarker of early ESCC diagnosis, we investigated the status of TFF1 promoter methylation and its expression in ESSC and histologically normal tumor surrounding tissue of ESCC patients in comparison to healthy esophagus of non-cancer individuals.

Results

Analysis of TFF1 promoter methylation, and gene and protein expression in 65 ESCC patients and 88 controls revealed that TFF1 methylation levels were already increased in histologically normal tumor surrounding tissue of ESCC patients when compared to healthy esophagus of non-cancer individuals. This increase in DNA methylation was followed by the reduction of TFF1 mRNA expression. Interestingly, TFF1 expression was capable of distinguishing tumor surrounding normal tissue from normal mucosa of healthy individuals with 92% accuracy. In addition, TFF1 protein was undetectable both in tumor and surrounding mucosa by immunohistochemistry, while submucosa glands of the healthy esophagus showed positive staining. Furthermore, treatment of TE-1 and TE-13 ESCC cell lines with decitabine led to a reduction of promoter methylation and consequent upregulation of TFF1 gene and protein expression. Finally, using TCGA data we showed that TFF1 loss is observed in ESCC, but not in esophageal adenocarcinoma, highlighting the different molecular mechanisms involved in the development of each histological subtype of esophageal cancer.

Conclusions

This study shows that TFF1 expression is silenced in early phases of ESCC development, which seems to be mediated at least in part by promoter hypermethylation, and provides the basis for the use of TFF1 expression as a potential biomarker for early ESCC detection.

Loss of Tff1 Promotes Pro-Inflammatory Phenotype with Increase in the Levels of RORγt+ T Lymphocytes and Il-17 in Mouse Gastric Neoplasia

Background: TFF1 deficiency induces a mucosal pro-inflammatory phenotype that contributes to gastric tumorigenesis in mouse and human. Methods: We utilized the Tff1-KO mouse model to assess the impact of TFF1 loss on immune cells infiltration in the stomach. We used single cell suspension, flow cytometry, immunohistochemistry, and quantitative PCR (qPCR) assays. Results: The Tff1-KO gastric mucosa demonstrated high chronic inflammatory scores (score: 3-4) at age 2 months, which exacerbated at age 8 months (score: 4-6). We next used single-cell suspensions for flow cytometry analysis of total leukocytes (CD45+ cells), total T lymphocytes (CD45+CD3+cells), T cell subsets (CD4+, CD8+, and CD3+CD4-CD8-cells), and monocytes/macrophages (CD45+F4/80+cells). The results demonstrated an age-dependent (2 → 8 month age) significant increase of leukocytes (p<0.05), T cells (p<0.05), and monocytes/macrophages (p<0.001) in the gastric mucosa of the Tff1-KO mice, as compared to Tff1-WT. A similar increase was observed in blood samples (p<0.05). Using ionomycin to activate CD4⁺ splenocytes, the results indicated that Tff1-KO CD4⁺ splenocytes secreted higher levels of IL-17A (p<0.05 at 2 and p<0.001 at 8 months) and IL-17F (p<0.05 at 2 and 8 months) than Tff1-WT splenocytes. Conversely, Tff1-KO CD8⁺-cells secreted less IL-17F, but comparable levels of IL-17A. In addition, we detected a significant upregulation of Il-17 mRNA expression in gastric tissues in the Tff1-KO, as compared to Tff1-WT (p<0.001). Conclusions: The results identify TFF1 loss as a major pro-inflammatory step that modulates the tumor microenvironment and immune cell infiltration in the stomach. Furthermore, the data suggest that the increase of IL-17A and IL-17F in Th17 cells, derived from CD4⁺ T cells, reflects the chronic inflammation in gastric mucosa, whereas the absence of change of IL-17A and decrease of IL-17F in CD8⁺Tc17 cells suggest loss of cytotoxic function of CD8⁺Tc17 cells during gastric tumorigenesis of the Tff1-KO mice.

Novel immunohistochemistry-based signatures to predict metastatic site of triple-negative breast cancers

BACKGROUND

Although distant metastasis (DM) in breast cancer (BC) is the most lethal form of recurrence and the most common underlying cause of cancer related deaths, the outcome following the development of DM is related to the site of metastasis. Triple negative BC (TNBC) is an aggressive form of BC characterised by early recurrences and high mortality. Athough multiple variables can be used to predict the risk of metastasis, few markers can predict the specific site of metastasis. This study aimed at identifying a biomarker signature to predict particular sites of DM in TNBC.

METHODS

A clinically annotated series of 322 TNBC were immunohistochemically stained with 133 biomarkers relevant to BC, to develop multibiomarker models for predicting metastasis to the bone, liver, lung and brain. Patients who experienced metastasis to each site were compared with those who did not, by gradually filtering the biomarker set via a two-tailed t-test and Cox univariate analyses. Biomarker combinations were finally ranked based on statistical significance, and evaluated in multivariable analyses.

RESULTS

Our final models were able to stratify TNBC patients into high risk groups that showed over 5, 6, 7 and 8 times higher risk of developing metastasis to the bone, liver, lung and brain, respectively, than low-risk subgroups. These models for predicting site-specific metastasis retained significance following adjustment for tumour size, patient age and chemotherapy status.

CONCLUSIONS

Our novel IHC-based biomarkers signatures, when assessed in primary TNBC tumours, enable prediction of specific sites of metastasis, and potentially unravel biomarkers previously unknown in site tropism.

Serum TFF1 and TFF3 but not TFF2 are higher in women with breast cancer than in women without breast cancer

Breast cancer remains a common malignancy in women, but the take-up for breast cancer screening programs in Japan is still low, possibly due to its perceived inconvenience. TFF1 and TFF3 are expressed in both breast cancer tissue and normal breast. Serum trefoil proteins were reported as cancer screening markers for gastric, prostate, lung, pancreatic cancer and cholangio carcinoma. The purpose of this study was to examine whether serum trefoil proteins could be screening biomarkers for breast cancer. Serum trefoil proteins in 94 breast cancer patients and 84 health check females were measured by ELISA. Serum TFF1 and TFF3 were significantly higher and serum TFF2 was significantly lower in breast cancer patients. Area under the curve of receiver operating characteristic of TFF1, TFF2, and TFF3 was 0.69, 0.83, and. 0.72, respectively. AUC of the combination of TFF1, TFF2, and TFF3 was 0.96. Immunohistochemically, TFF1 expression was positive in 56.5% and TFF3 was positive in 73.9% of breast cancers, while TFF2 was negative in all tumors. Serum TFF1 had positive correlation with expression of TFF1 in breast cancer tissue. Serum concentrations of TFF1 and TFF3 but not TFF2 are higher in women with breast cancer than in women without breast cancer.

IK channel activation increases tumor growth and induces differential behavioral responses in two breast epithelial cell lines

Many potassium channel families are over-expressed in cancer, but their mechanistic role in disease progression is poorly understood. Potassium channels modulate membrane potential (Vmem) and thereby influence calcium ion dynamics and other voltage-sensitive signaling mechanisms, potentially acting as transcriptional regulators. This study investigated the differential response to over-expression and activation of a cancer-associated potassium channel, the intermediate conductance calcium-activated potassium channel (IK), on aggressive behaviors in mammary epithelial and breast cancer cell lines. IK was over-expressed in the highly metastatic breast cancer cell line MDA-MB-231 and the spontaneously immortalized breast epithelial cell line MCF-10A, and the effect on cancer-associated behaviors was assessed. IK over-expression increased primary tumor growth and metastasis of MDA-MB-231 in orthotopic xenografts, demonstrating for the first time in any cancer type that increased IK is sufficient to promote cancer aggression. The primary tumors had similar vascularization as determined by CD31 staining and similar histological characteristics. Interestingly, despite the increased in vivo growth and metastasis, neither IK over-expression nor activation with agonist had a significant effect on MDA-MB-231 proliferation, invasion, or migration in vitro. In contrast, IK decreased MCF-10A proliferation and invasion through Matrigel but had no effect on migration in a scratch-wound assay. We conclude that IK activity is sufficient to promote cell aggression in vivo. Our data provide novel evidence supporting IK and downstream signaling networks as potential targets for cancer therapies.

A novel FOXA1/ESR1 interacting pathway: A study of Oncomine™ breast cancer microarrays

Forkhead box protein A1 (FOXA1) is essential for the growth and differentiation of breast epithelium, and has a favorable outcome in breast cancer (BC). Elevated FOXA1 expression in BC also facilitates hormone responsiveness in estrogen receptor (ESR)-positive BC. However, the interaction between these two pathways is not fully understood. FOXA1 and GATA binding protein 3 (GATA3) along with ESR1 expression are responsible for maintaining a luminal phenotype, thus suggesting the existence of a strong association between them. The present study utilized the Oncomine™ microarray database to identify FOXA1:ESR1 and FOXA1:ESR1:GATA3 co-expression co-regulated genes. Oncomine™ analysis revealed 115 and 79 overlapping genes clusters in FOXA1:ESR1 and FOXA1:ESR1:GATA3 microarrays, respectively. Five ESR1 direct target genes [trefoil factor 1 (TFF1/PS2), B-cell lymphoma 2 (BCL2), seven in absentia homolog 2 (SIAH2), cellular myeloblastosis viral oncogene homolog (CMYB) and progesterone receptor (PGR)] were detected in the co-expression clusters. To further investigate the role of FOXA1 in ESR1-positive cells, MCF7 cells were transfected with a FOXA1 expression plasmid, and it was observed that the direct target genes of ESR1 (PS2, BCL2, SIAH2 and PGR) were significantly regulated upon transfection. Analysis of one of these target genes, PS2, revealed the presence of two FOXA1 binding sites in the vicinity of the estrogen response element (ERE), which was confirmed by binding assays. Under estrogen stimulation, FOXA1 protein was recruited to the FOXA1 site and could also bind to the ERE site (although in minimal amounts) in the PS2 promoter. Co-transfection of FOXA1/ESR1 expression plasmids demonstrated a significantly regulation of the target genes identified in the FOXA1/ESR1 multi-arrays compared with only FOXA1 transfection, which was suggestive of a synergistic effect of ESR1 and FOXA1 on the target genes. In summary, the present study identified novel FOXA1, ESR1 and GATA3 co-expressed genes that may be involved in breast tumorigenesis.