Trefoil factor 1 (TFF1) is a potential prognostic biomarker with functional significance in breast cancers

A Genotype-Phenotype Model for Predicting Resistance Training Effects on Leg Press Performance

This study develops a comprehensive genotype-phenotype model for predicting the effects of resistance training on leg press performance. A cohort of physically inactive adults (N=193) underwent 12 weeks of resistance training, and measurements of maximum isokinetic leg press peak force, muscle mass, and thickness were taken before and after the intervention. Whole-genome genotyping was performed, and genome-wide association analysis identified 85 novel SNPs significantly associated with changes in leg press strength after training. A prediction model was constructed using stepwise linear regression, incorporating seven lead SNPs that explained 40.4% of the training effect variance. The polygenic score showed a significant positive correlation with changes in leg press strength. By integrating genomic markers and phenotypic indicators, the comprehensive prediction model explained 75.4% of the variance in the training effect. Additionally, five SNPs were found to potentially impact muscle contraction, metabolism, growth, and development through their association with REACTOME pathways. Individual responses to resistance training varied, with changes in leg press strength ranging from -55.83% to 151.20%. The study highlights the importance of genetic factors in predicting training outcomes and provides insights into the potential biological functions underlying resistance training effects. The comprehensive model offers valuable guidance for personalized fitness programs based on individual genetic profiles and phenotypic characteristics.

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.

Trefoil Family Factor Peptide 1-A New Biomarker in Liquid Biopsies of Retinoblastoma under Therapy

Effective management of retinoblastoma (RB), the most prevalent childhood eye cancer, depends on reliable monitoring and diagnosis. A promising candidate in this context is the secreted trefoil family factor peptide 1 (TFF1), recently discovered as a promising new biomarker in patients with a more advanced subtype of retinoblastoma. The present study investigated TFF1 expression within aqueous humor (AH) of enucleated eyes and compared TFF1 levels in AH and corresponding blood serum samples from RB patients undergoing intravitreal chemotherapy (IVC). TFF1 was consistently detectable in AH, confirming its potential as a biomarker. Crucially, our data confirmed that TFF1-secreting cells within the tumor mass originate from RB tumor cells, not from surrounding stromal cells. IVC-therapy-responsive patients exhibited remarkably reduced TFF1 levels post-therapy. By contrast, RB patients' blood serum displayed low-to-undetectable levels of TFF1 even after sample concentration and no therapy-dependent changes were observed. Our findings suggest that compared with blood serum, AH represents the more reliable source of TFF1 if used for liquid biopsy RB marker analysis in RB patients. Thus, analysis of TFF1 in AH of RB patients potentially provides a minimally invasive tool for monitoring RB therapy efficacy, suggesting its importance for effective treatment regimens.

Multi-omics analysis reveals the chemoresistance mechanism of proliferating tissue-resident macrophages in PDAC via metabolic adaptation

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer that typically demonstrates resistance to chemotherapy. Tumor-associated macrophages (TAMs) are essential in tumor microenvironment (TME) regulation, including promoting chemoresistance. However, the specific TAM subset and mechanisms behind this promotion remain unclear. We employ multi-omics strategies, including single-cell RNA sequencing (scRNA-seq), transcriptomics, multicolor immunohistochemistry (mIHC), flow cytometry, and metabolomics, to analyze chemotherapy-treated samples from both humans and mice. We identify four major TAM subsets within PDAC, among which proliferating resident macrophages (proliferating rMφs) are strongly associated with poor clinical outcomes. These macrophages are able to survive chemotherapy by producing more deoxycytidine (dC) and fewer dC kinases (dCKs) to decrease the absorption of gemcitabine. Moreover, proliferating rMφs promote fibrosis and immunosuppression in PDAC. Eliminating them in the transgenic mouse model alleviates fibrosis and immunosuppression, thereby re-sensitizing PDAC to chemotherapy. Consequently, targeting proliferating rMφs may become a potential treatment strategy for PDAC to enhance chemotherapy.

Combinatorial Herbal Extracts Alleviate Alcohol-Induced Hepatic Disorders

Plant-derived compounds can be useful for the management of liver disease. Traditionally, hepatic disorders have been treated with herbal extracts. Although many herbal extracts in Eastern medicine have been shown to possess hepatoprotective activities, single-origin herbal extracts primarily demonstrate either antioxidant or anti-inflammatory activities. The current study investigated the effects of combinatorial herbal extracts on alcohol-induced hepatic disorders in an ethanol-fed mouse model. Sixteen herbal combinations were evaluated as hepatoprotective formulations; the active constituents in these herbal extracts were daidzin, peonidin-3-glucoside, hesperidin, glycyrrhizin, and phosphatidylcholine. RNA sequencing analysis showed that exposure to ethanol altered hepatic gene expression profiles (compared to those of the non-alcohol-fed group), resulting in 79 differentially expressed genes. A majority of the differentially expressed genes in alcohol-induced hepatic disorders were associated with dysfunction of the normal cellular homeostasis in the liver; however, these genes were repressed by treatment with herbal extracts. Moreover, following treatment with herbal extracts, there were neither acute inflammatory responses in the liver tissue nor abnormalities in the cholesterol profile. These results suggest that combinatorial herbal extracts may alleviate alcohol-induced hepatic disorders by modulating the inflammatory response and lipid metabolism in the liver.

CustOmics: A versatile deep-learning based strategy for multi-omics integration

The availability of patient cohorts with several types of omics data opens new perspectives for exploring the disease's underlying biological processes and developing predictive models. It also comes with new challenges in computational biology in terms of integrating high-dimensional and heterogeneous data in a fashion that captures the interrelationships between multiple genes and their functions. Deep learning methods offer promising perspectives for integrating multi-omics data. In this paper, we review the existing integration strategies based on autoencoders and propose a new customizable one whose principle relies on a two-phase approach. In the first phase, we adapt the training to each data source independently before learning cross-modality interactions in the second phase. By taking into account each source's singularity, we show that this approach succeeds at taking advantage of all the sources more efficiently than other strategies. Moreover, by adapting our architecture to the computation of Shapley additive explanations, our model can provide interpretable results in a multi-source setting. Using multiple omics sources from different TCGA cohorts, we demonstrate the performance of the proposed method for cancer on test cases for several tasks, such as the classification of tumor types and breast cancer subtypes, as well as survival outcome prediction. We show through our experiments the great performances of our architecture on seven different datasets with various sizes and provide some interpretations of the results obtained. Our code is available on (https://github.com/HakimBenkirane/CustOmics).

In Silico Analysis of Publicly Available Transcriptomic Data for the Identification of Triple-Negative Breast Cancer-Specific Biomarkers

BACKGROUND

Breast cancer is the most common type of cancer among women and is classified into multiple subtypes. Triple-negative breast cancer (TNBC) is the most aggressive subtype, with high mortality rates and limited treatment options such as chemotherapy and radiation. Due to the heterogeneity and complexity of TNBC, there is a lack of reliable biomarkers that can be used to aid in the early diagnosis and prognosis of TNBC in a non-invasive screening method.

AIM

This study aims to use in silico methods to identify potential biomarkers for TNBC screening and diagnosis, as well as potential therapeutic markers.

METHODS

Publicly available transcriptomic data of breast cancer patients published in the NCBI's GEO database were used in this analysis. Data were analyzed with the online tool GEO2R to identify differentially expressed genes (DEGs). Genes that were differentially expressed in more than 50% of the datasets were selected for further analysis. Metascape, Kaplan-Meier plotter, cBioPortal, and the online tool TIMER were used for functional pathway analysis to identify the biological role and functional pathways associated with these genes. Breast Cancer Gene-Expression Miner v4.7 was used to validify the obtained results in a larger cohort of datasets.

RESULTS

A total of 34 genes were identified as differentially expressed in more than half of the datasets. The DEG GATA3 had the highest degree of regulation, and it plays a role in regulating other genes. The estrogen-dependent pathway was the most enriched pathway, involving four crucial genes, including GATA3. The gene FOXA1 was consistently down-regulated in TNBC in all datasets.

CONCLUSIONS

The shortlisted 34 DEGs will aid clinicians in diagnosing TNBC more accurately as well as developing targeted therapies to improve patient prognosis. In vitro and in vivo studies are further recommended to validate the results of the current study.

Weifuchun alters tongue flora and decreases serum trefoil factor I levels in gastric intestinal metaplasia: A CONSORT-compliant article

OBJECTIVE

To explore the molecular mechanisms of Weifuchun in the treatment of gastric intestinal metaplasia (GIM), we designed a preclinical pilot study to examine potential markers of disease progression based on alterations in the tongue flora.

METHODS

Total 27 patients with GIM were treated with Weifuchun for 4 weeks and 26 volunteers as controls. Tongue coating bacteria were profiled using 16S rDNA high-throughput sequencing. Serum pepsinogen I and II levels were detected using the latex immunoturbidimetric assay. The levels of serum trefoil factor I was detected by ELISA. Microplate-based quantification was used to detect serum total bile acid (TBA).

RESULTS

After treatment, the relative abundance of 4 dominant tongue coating genera (Granulicatella, Gemella, Lachnoanaerobaculum, and Neisseria) increased significantly wheras Alloprevotella, [Eubacterium] nodatum group, Prevotell, and Ruminococcaceae UCG-014 decreased (P < .05). The results showed that Alloprevotella and 3 rare tongue coating genera (Lautropia, Treponema 2, and Aliihoeflea) might be potential markers or target flora for the treatment of GIM. Kyoto encyclopedia of genes and genomes (KEGG) function prediction analysis showed that Weifuchun may regulate bile secretion and folate biosynthesis in patients with GIM. The level of serum trefoil factor I decreased significantly in response to Weifuchun treatment, which was consistent with the decrease in folate biosynthesis predicted by KEGG.

CONCLUSION

Weifuchun may restore the balance of tongue flora by decreasing the levels of serum trefoil factor I, thereby providing a new way to measuring the underlying effectiveness and potential mechanisms of action of this traditional Chinese medicinal compound in the treatment of GIM.

Downregulation of the Tumor Suppressor TFF1 Is Required during Induction of Colon Cancer Progression by L1

Simple Summary

Aberrant activation of Wnt/β-catenin signaling and the subsequent induction of downstream target genes is a hallmark of colorectal cancer (CRC) development. Previously, we found that overexpression of the immunoglobulin-like cell adhesion receptor L1CAM (L1), a target of the Wnt/β-catenin pathway, confers enhanced proliferation, motility, tumorigenesis, and liver metastasis in CRC cells. Transcriptomic and proteomic analyses revealed changes in both pro-tumorigenic and potential tumor-suppressor genes in L1-overexpressing CRC cells. We wished to identify such tumor suppressor/s, and found that trefoil family factor 1 (TFF1) was involved in L1-mediated CRC progression. TFF1 overexpression suppressed the growth, motility and tumorigenesis of L1-expressing CRC cells by inhibiting the NF-κB pathway. In human CRC tissue, TFF1-positive staining was evident in goblet cells of the normal mucosa, while in CRC tissue, TFF1 expression was lost in >50% of the tumor samples. Our results support a tumor-suppressor role of TFF1 in human CRC, and we suggest that TFF1 could be used for CRC detection and as a novel therapeutic target in L1-mediated CRC.

Abstract

The immunoglobulin family cell adhesion receptor L1 is induced in CRC cells at the invasive front of the tumor tissue, and confers enhanced proliferation, motility, tumorigenesis, and liver metastasis. To identify putative tumor suppressors whose expression is downregulated in L1-expressing CRC cells, we blocked the L1–ezrin–NF-κB signaling pathway and searched for genes induced under these conditions. We found that TFF1, a protein involved in protecting the mucus epithelial layer of the colon, is downregulated in L1-expressing cells and displays characteristics of a tumor suppressor. Overexpression of TFF1 in L1-transfected human CRC cells blocks the pro-tumorigenic and metastatic properties conferred by L1 by suppressing NF-κB signaling. Immunohistochemical analyses revealed that human CRC tissue samples often lose the expression of TFF1, while the normal mucosa displays TFF1 in goblet cells. Identifying TFF1 as a tumor suppressor in CRC cells could provide a novel marker for L1-mediated CRC development and a potential target for therapy.

Integrated Bioinformatics Analysis for the Screening of Hub Genes and Therapeutic Drugs in Androgen Receptor-Positive TNBC

As the most invasive and lethal subtype of breast cancer (BC), triple-negative breast carcinoma (TNBC) is of increasing interest. However, the androgen receptor (AR) still has an unclear role in TNBC. The current study is aimed at testing the diagnostic and therapeutic performance of novel biomarkers for AR-positive TNBC. The GSE76124 dataset was analyzed by combining WGCNA and other bioinformatics methods. Subsequently, function enrichment analysis was applied to identify the relationships between these differential expression genes (DEGs). Subsequently, the protein-protein interaction network was established, and the hub genes were identified by Cytoscape software. Eventually, the miRNA-hub gene modulate network was developed and the Drug-Gene Interaction Database (DGIdb) was applied to verify the potential drugs for AR-positive TNBC. In the current research, 88 DEGs in total were selected from the intersection of the purple module genes identified by WGCNA and limma package. TFF1, FOXA1, ESR1, AGR2, TFF3, AGR3, GATA3, XBP1, SPDEF, and TOX3 were selected as hub genes by the MCC method, which were all upregulated. The survival analysis suggested that TFF1 was the only one related to significant lower survival rate in TNBC. Ultimately, hsa-miR-520g-3p and hsa-miR-520h were found taking part in the regulation of TFF1, and 2 small molecules were identified as the potential targets for AR-positive TNBC treatment. As a result, our study suggested that hsa-miR-520g-3p, hsa-miR-520h, and TFF1 might have significant potential values for AR-positive TNBC diagnosis and prognosis prediction. TFF1, hsa-miR-520g-3, and hsa-miR-520h may serve as the novel therapeutic targets, and our findings offer further insights into the therapy of AR-positive TNBC.

Estrogens, Cancer and Immunity

Sex hormones are included in many physiological and pathological pathways. Estrogens belong to steroid hormones active in female sex. Estradiol (E2) is the strongest female sex hormone and, with its receptors, contributes to oncogenesis, cancer progression and response to treatment. In recent years, a role of immunosurveillance and suppression of immune response in malignancy has been well defined, forming the basis for cancer immunotherapy. The interplay of sex hormones with cancer immunity, as well as the response to immune checkpoint inhibitors, is of interest. In this review, we investigate the impact of sex hormones on natural immune response with respect to main active elements in anticancer immune surveillance: dendritic cells, macrophages, lymphocytes and checkpoint molecules. We describe the main sex-dependent tumors and the contribution of estrogen in their progression, response to treatment and especially modulation of anticancer immune response.

FOXA1 transcription activates TFF1 to reduce 6‑OHDA‑induced dopaminergic neuron damage

Forkhead box A1 (FOXA1) plays an important role in the central nervous system, and its loss can lead to the downregulation of tyrosine hydroxylase, which directly affects the synthesis of dopamine, thus leading to Parkinson's disease (PD). The present study aimed to explore the specific role of FOXA1 in PD. Blood samples from patients with PD were collected to determine the expression levels of FOXA1 using reverse transcription-quantitative PCR (RT-qPCR). In addition, mouse dopaminergic neuron MES23.5 cells were induced with 6-hydroxydopamine (6-OHDA) to construct an in vitro PD model in order to study the effect of FOXA1 overexpression on cell inflammation, oxidative stress and apoptosis with RT-qPCR, assay kits and TUNEL assays, respectively. Subsequently, the expression of FOXA1 was silenced to assess the effect on the downstream mechanism. The results revealed that the expression level of FOXA1 was downregulated in patients with PD, and FOXA1 overexpression attenuated 6-OHDA-induced inflammation, oxidative stress and apoptosis in MES23.5 cells. Furthermore, FOXA1 could bind to the trefoil factor 1 (TFF1) promoter, and the effects of FOXA1 overexpression on cells were reversed by TFF1 silencing, indicating that TFF1 mediated the mechanism of FOXA1 overexpression in MES23.5 cells. In conclusion, following FOXA1 transcription, TFF1 expression was activated, thereby relieving 6-OHDA-induced cell inflammation, oxidative stress and apoptosis. The present findings suggested that FOXA1 may serve as a target for the treatment of PD.

Diagnostic Value of Dynamic Enhanced Magnetic Resonance Imaging Combined with Serum CA15-3, CYFRA21-1, and TFF1 for Breast Cancer

Objective

To explore the diagnostic value of dynamic enhanced magnetic resonance imaging (MRI) combined with serum CA15-3, CYFRA21-1, and TFF1 for breast cancer.

Methods

By means of a retrospective study, 60 breast cancer patients treated in our hospital from January 2018 to December 2020 were selected as the breast cancer group, 60 patients with benign breast lesions were selected as the benign group, and 60 healthy individuals who received physical examination in our hospital in the same period were selected as the control group. All study subjects received dynamic enhanced MRI scan and serological tests, their serum CA15-3 and CYFRA21-1 levels were measured with the electrochemiluminescence instrument and original auxiliary reagent, and the TFF1 level was measured with enzyme-linked immunosorbent assay (ELISA). The MRI performance variation in breast lesion patients was analyzed, the serum CA15-3, CYFRA21-1, and TFF1 levels of study subjects were compared among the three groups, and the efficacy of single diagnosis by dynamic enhanced MRI, CA15-3, CYFRA21-1, or TFF1 as well as combined diagnosis was explored by ROC curves.

Results

Dynamic enhanced MRI showed that malignant lesion had obscure boundary, irregular margin, and heterogeneity after enhancement, and the time-signal intensity curve presented fast-in fast-out; the benign lesion had a clear boundary and smooth margin, 25 cases showed homogeneity after enhancement, and the time-signal intensity curve presented slow-in slow-out; the CA15-3, CYFRA21-1, and TFF1 levels were significantly different among the breast cancer group, benign group, and control group (33.81 ± 12.46 vs 19.02 ± 6.47 vs 9.55 ± 2.64, 4.08 ± 1.41 vs 1.96 ± 1.19 vs 0.99 ± 0.21, 1.39 ± 0.54 vs 1.04 ± 0.26 vs 0.89 ± 0.12, P < 0.05); 57 breast cancer patients were diagnosed by a combined examination, with a sensitivity of 95.0%, specificity of 83.3%, positive predictive value of 74.0%, negative predictive value of 97.1%, accuracy rate of 87.2%, and AUC (95%CI) = 0.892 (0.840–0.943), indicating a significantly higher diagnostic value of the combined examination than the single examination by CA15-3, CYFRA21-1, TFF1, or MRI.

Conclusion

Combining dynamic enhanced MRI with serum CA15-3, CYFRA21-1, and TFF1 has good efficacy in diagnosing breast cancer, which can be applied in clinical diagnosis of breast cancer.

TFF1 in Aqueous Humor—A Potential New Biomarker for Retinoblastoma

Simple Summary

Retinoblastoma is the most common pediatric intraocular malignancy with high cure rates in developed countries. Nevertheless, useful predictive biomarkers providing reliable evidence for therapy decisions are urgently needed to optimize therapy regimes. TFF1 is a promising candidate as it is expressed in a more advanced subtype of retinoblastoma. Additionally, TFF1 is a naturally secreted peptide. Thus, TFF1 might be detectable in the aqueous humor of RB patients’ eyes, providing the opportunity to determine its expression prior to therapy without the necessity of a tumor biopsy. We therefore investigated for the first time aqueous humor samples of retinoblastoma patients in order to test for the availably and expression status of TFF1 as well as to compare it with the original tumor and established corresponding primary cell cultures.

Abstract

Retinoblastoma (RB) is the most common childhood eye cancer. The expression of trefoil factor family peptide 1 (TFF1), a small secreted peptide, has been correlated with more advanced RB stages and it might be a promising new candidate as a RB biomarker. The study presented addressed the question of if TFF1 is detectable in aqueous humor (AH) of RB patients’ eyes, providing easy accessibility as a diagnostic and/or therapy accompanying predictive biomarker. The TFF1 expression status of 15 retinoblastoma AH samples was investigated by ELISA and Western blot analyses. The results were correlated with the TFF1 expression status in the tumor of origin and compared to TFF1 expression in established corresponding primary tumor cell cultures and supernatants. Nine out of fifteen AH patient samples exhibited TFF1 expression, which correlated well with TFF1 levels of the original tumor. TFF1 expression in most of the corresponding primary cell cultures reflects the levels of the original tumor, although not all TFF1-expressing tumor cells seem to secret into the AH. Together, our findings strongly suggest TFF1 as a reliable new RB biomarker.

An Efficient Algorithm for the Detection of Outliers in Mislabeled Omics Data

High dimensionality and noise have made it difficult to detect related biomarkers in omics data. Through previous study, penalized maximum trimmed likelihood estimation is effective in identifying mislabeled samples in high-dimensional data with mislabeled error. However, the algorithm commonly used in these studies is the concentration step (C-step), and the C-step algorithm that is applied to robust penalized regression does not ensure that the criterion function is gradually optimized iteratively, because the regularized parameters change during the iteration. This makes the C-step algorithm runs very slowly, especially when dealing with high-dimensional omics data. The AR-Cstep (C-step combined with an acceptance-rejection scheme) algorithm is proposed. In simulation experiments, the AR-Cstep algorithm converged faster (the average computation time was only 2% of that of the C-step algorithm) and was more accurate in terms of variable selection and outlier identification than the C-step algorithm. The two algorithms were further compared on triple negative breast cancer (TNBC) RNA-seq data. AR-Cstep can solve the problem of the C-step not converging and ensures that the iterative process is in the direction that improves criterion function. As an improvement of the C-step algorithm, the AR-Cstep algorithm can be extended to other robust models with regularized parameters.

Immunohistochemical and clinicopathologic features of estrogen receptor-negative, progesterone receptor-positive, HER-2 negative breast carcinomas

OBJECTIVE

Currently, there is an ongoing debate whether progesterone receptor positive and estrogen receptor negative breast carcinomas represent a true distinct subtype of tumor or a mere immunohistochemical artifact. In this study, we conducted an immunohistochemistry panel with the antibodies TFF1, EGFR, and CK5 to reclassify this phenotype in a luminal or basal-like subtype.

METHODS

Tumors estrogen receptor -/progesterone receptor +, Her-2 - from a large population of breast cancer patients were selected to be studied. Immunohistochemistry with the antibodies TFF1, EGFR, and CK5 was performed. Tumors showing positivity for TFF1, regardless of EGFR and CK5 results, were classified as luminal-like carcinomas. Those lesions that were negative for TFF1, but were positive for EGFR and/or CK5, were classified as basal-like triple-negative carcinomas. When the three markers were negative, tumors were classified as undetermined. Clinical pathologic characteristics of patients and tumor recurrence were evaluated.

RESULTS

Out of 1188 breast carcinomas investigated, 30 cases (2.5%) presented the estrogen receptor -/progesterone receptor +/HER2- phenotype. Of them, 27 tumors (90%) were classified as basal-like triple-negative carcinomas, one as luminal-like (3.3%), and two as undetermined tumors (6.7%). The mean follow-up for the study group was 27.7 (2.7 to 50) months. Out of the 26 patients, 6 had cancer recurrence: 2 local and 4 systemic recurrences. The average time for recurrence was 17 (8 to 38) months.

CONCLUSION

Estrogen receptor -/progesterone receptor +/tumors exhibit aggressive behavior, similar to triple-negative tumors. An appropriate categorization of these tumors should be made to improve their therapeutic management.

A network approach reveals driver genes associated with survival of patients with triple-negative breast cancer

We aimed to identify triple-negative breast cancer (TNBC) drivers that regulate survival time as predictive signatures that improve TNBC prognostication. Breast cancer (BrCa) transcriptomic tumor biopsies were analyzed, identifying network communities enriched with TNBC-specific differentially expressed genes (DEGs) and correlated strongly to TNBC status. Two anticorrelated modules correlated strongly to TNBC subtype and survival. Querying module-specific hubs and DEGs revealed transcriptional changes associated with high survival. Transcripts were nominated as biomarkers and tested as combinatoric ratios using receiver operator characteristic (ROC) analysis to assess survival prediction. ROC test rounds integrated genes with established interactions to hubs and DEGs of key modules, improving prediction. Finally, we tested whether integration of literature-derived genes for implicated hallmark cancer processes could improve prediction of survival. Complementary coexpression, differential expression, genetic interaction, and survival stratification integrated by ROC optimization uncovered a panel of “linchpin survival genes” predictive of patient survival, representing gene interactions in hallmark cancer processes.

•

WGCNA identifies coexpression modules predicted to drive TNBC patient survival

•

Module hubs and DEGs reveal transcriptional changes associated with high survival

•

Nine genes act synergistically to influence TNBC progression, relapse, and survival

•

These genes' levels represent reversible changes in TNBC hallmark cancer processes

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.

Recent Discoveries of Macromolecule- and Cell-Based Biomarkers and Therapeutic Implications in Breast Cancer

Breast cancer is the most commonly diagnosed cancer type and the leading cause of cancer-related mortality in women worldwide. Breast cancer is fairly heterogeneous and reveals six molecular subtypes: luminal A, luminal B, HER2+, basal-like subtype (ER−, PR−, and HER2−), normal breast-like, and claudin-low. Breast cancer screening and early diagnosis play critical roles in improving therapeutic outcomes and prognosis. Mammography is currently the main commercially available detection method for breast cancer; however, it has numerous limitations. Therefore, reliable noninvasive diagnostic and prognostic biomarkers are required. Biomarkers used in cancer range from macromolecules, such as DNA, RNA, and proteins, to whole cells. Biomarkers for cancer risk, diagnosis, proliferation, metastasis, drug resistance, and prognosis have been identified in breast cancer. In addition, there is currently a greater demand for personalized or precise treatments; moreover, the identification of novel biomarkers to further the development of new drugs is urgently needed. In this review, we summarize and focus on the recent discoveries of promising macromolecules and cell-based biomarkers for the diagnosis and prognosis of breast cancer and provide implications for therapeutic strategies.

Comparison of methods for the detection of outliers and associated biomarkers in mislabeled omics data

Background

Previous studies have reported that labeling errors are not uncommon in omics data. Potential outliers may severely undermine the correct classification of patients and the identification of reliable biomarkers for a particular disease. Three methods have been proposed to address the problem: sparse label-noise-robust logistic regression (Rlogreg), robust elastic net based on the least trimmed square (enetLTS), and Ensemble. Ensemble is an ensembled classification based on distinct feature selection and modeling strategies. The accuracy of biomarker selection and outlier detection of these methods needs to be evaluated and compared so that the appropriate method can be chosen.

Results

The accuracy of variable selection, outlier identification, and prediction of three methods (Ensemble, enetLTS, Rlogreg) were compared for simulated and an RNA-seq dataset. On simulated datasets, Ensemble had the highest variable selection accuracy, as measured by a comprehensive index, and lowest false discovery rate among the three methods. When the sample size was large and the proportion of outliers was ≤5%, the positive selection rate of Ensemble was similar to that of enetLTS. However, when the proportion of outliers was 10% or 15%, Ensemble missed some variables that affected the response variables.

Overall, enetLTS had the best outlier detection accuracy with false positive rates < 0.05 and high sensitivity, and enetLTS still performed well when the proportion of outliers was relatively large. With 1% or 2% outliers, Ensemble showed high outlier detection accuracy, but with higher proportions of outliers Ensemble missed many mislabeled samples. Rlogreg and Ensemble were less accurate in identifying outliers than enetLTS. The prediction accuracy of enetLTS was better than that of Rlogreg. Running Ensemble on a subset of data after removing the outliers identified by enetLTS improved the variable selection accuracy of Ensemble.

Conclusions

When the proportion of outliers is ≤5%, Ensemble can be used for variable selection. When the proportion of outliers is > 5%, Ensemble can be used for variable selection on a subset after removing outliers identified by enetLTS. For outlier identification, enetLTS is the recommended method. In practice, the proportion of outliers can be estimated according to the inaccuracy of the diagnostic methods used.

Identification of key differentially expressed genes between ER-positive/HER2-negative breast cancer and ER-negative/HER2-negative breast cancer using integrated bioinformatics analysis

Background

Treatment strategies for various subtypes of breast cancer (BC) are different based on their distinct molecular characteristics. Therefore, it is very important to identify key differentially expressed genes (DEGs) between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.

Methods

Gene expression profiles of GSE22093 and GSE23988 were obtained from the Gene Expression Omnibus database. There were 74 ER-positive/HER2-negative BC tissues and 85 ER-negative/HER2-negative BC tissues in the two profile datasets. DEGs between ER-positive/HER2-negative tissues and ER-negative/HER2-negative BC tissues were identified by the GEO2R tool. The common DEGs among the two datasets were detected with Venn software online. Next, we made use of the Database for Annotation, Visualization and Integrated Discovery to analyze enriched Kyoto Encyclopedia of Gene and Genome (KEGG) pathways and gene ontology terms. Then, the protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with the Search Tool for the Retrieval of Interacting Genes. Of the proteins in the PPI network, Molecular Complex Detection plug-in analysis identified nine core upregulated genes and one core downregulated gene. UALCAN and Gene Expression Profiling Interactive Analysis were applied to determine the expression of these 10 genes in BC. Furthermore, for the analysis of overall survival among those genes, the Kaplan-Meier method was implemented.

Results

Ninety-three common DEGs (63 upregulated and 30 downregulated) were identified. KEGG pathway enrichment analysis showed that upregulated DEGs were particularly enriched in the progesterone-mediated oocyte maturation pathway. In addition, PGR might be a prognostic biomarker for ER-positive/HER2-negative BC. CCND1 is a poor prognostic biomarker for ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Moreover, TFF1, AGR2 and EGFR might be predictive biomarkers of node metastasis in ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.

Conclusions

CCND1, AGR2, PGR, TFF1 and EGFR are the key DEGs between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Further studies are required to confirm the functions of the identified genes.

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.