A gene-expression signature to predict survival in breast cancer across independent data sets

Single-cell analysis of matrisome-related genes in breast invasive carcinoma: new avenues for molecular subtyping and risk estimation

Background

The incidence of breast cancer remains high and severely affects human health. However, given the heterogeneity of tumor cells, identifying additional characteristics of breast cancer cells is essential for accurate treatment.

Purpose

This study aimed to analyze the relevant characteristics of matrix genes in breast cancer through the multigroup data of a breast cancer multi-database.

Methods

The related characteristics of matrix genes in breast cancer were analyzed using multigroup data from the breast cancer multi database in the Cancer Genome Atlas, and the differential genes of breast cancer matrix genes were identified using the elastic net penalty logic regression method. The risk characteristics of matrix genes in breast cancer were determined, and matrix gene expression in different breast cancer cells was evaluated using real-time fluorescent quantitative polymerase chain reaction (PCR). A consensus clustering algorithm was used to identify the biological characteristics of the population based on the matrix molecular subtypes in breast cancer, followed by gene mutation, immune correlation, pathway, and ligand-receptor analyses.

Results

This study reveals the genetic characteristics of cell matrix related to breast cancer. It is found that 18.1% of stromal genes are related to the prognosis of breast cancer, and these genes are mostly concentrated in the biological processes related to metabolism and cytokines in protein. Five different matrix-related molecular subtypes were identified by using the algorithm, and it was found that the five molecular subtypes were obviously different in prognosis, immune infiltration, gene mutation and drug-making gene analysis.

Conclusions

This study involved analyzing the characteristics of cell-matrix genes in breast cancer, guiding the precise prevention and treatment of the disease.

PSMD2 overexpression as a biomarker for resistance and prognosis in renal cell carcinoma treated with immune checkpoint and tyrosine kinase inhibitors

BACKGROUND

Integrated immune checkpoint inhibitors (ICIs) plus tyrosine kinase inhibitors (TKIs) are now the recommended first-line therapy to manage renal cell carcinoma (mRCC). Proteasome 26S subunit non-ATPase 2 (PSMD2) overexpression in tumors has been correlated with tumor progression. Currently, mRCC lacks an established biomarker for the combination of ICI+TKI.

METHODS

This study involved RNA sequencing of RCC patients from two cohorts treated with ICI+TKI (ZS-MRCC and JAVELIN-Renal-101). We utilized immunohistochemistry alongside flow cytometry, aiming at assessing immune cell infiltration and functionality in high-risk localized RCC samples. Response and progression-free survival (PFS) were evaluated relying upon RECIST criteria.

RESULTS

PSMD2 was significantly overexpressed in advanced RCC and among non-responders to ICI+TKI therapy. Overexpressed PSMD2 was correlated with poor PFS in the ZS-MRCC and JAVELIN-101 cohorts. Multivariate Cox analysis validated PSMD2 as an independent PFS predictor. PSMD2 overexpression was related to a reduction in CD8+ T cells, especially GZMB+ CD8+ T cells, besides an increase in PD1+ CD4+ T cells. Additionally, tumors with high PSMD2 levels showed enhanced T cell exhaustion levels and a higher regulatory T cell presence. A Machine Learning (ML) model based on PSMD2 expression and other screened factors was subsequently developed to predict the effectiveness of ICI+TKI.

CONCLUSIONS

Elevated PSMD2 expression is linked to resistance and decreased PFS in mRCC patients undergoing ICI+TKI therapy. High PSMD2 levels are also associated with impaired function and increased exhaustion of tumor-infiltrating lymphocytes. An ML model incorporating PSMD2 expression could potentially identify patients who may have a higher likelihood of benefiting from ICI+TKI.

Txnrd1 as a prognosticator for recurrence, metastasis and response to neoadjuvant chemotherapy and radiotherapy in breast cancer patients

Thioredoxin reductase 1 (Txnrd1) is known to have prognostic significance in a subset of breast cancer patients. Despite the pivotal role of Txnrd1 in regulating several cellular and physiological processes in cancer progression and metastasis, its clinical significance is largely unrecognized. Here, we undertook a retrospective comprehensive meta-analysis of 13,322 breast cancer patients from 43 independent cohorts to assess prognostic and predictive roles of Txnrd1. We observed that Txnrd1 has a positive correlation with tumor grade and size and it is over-expressed in higher-grade and larger tumors. Further, hormone receptor-negative and HER2-positive tumors exhibit elevated Txnrd1 gene expression. Patients with elevated Txnrd1 expression exhibit significant hazards for shorter disease-specific and overall survival. While Txnrd1 has a positive correlation with tumor recurrence and metastasis, it has a negative correlation with time to recurrence and metastasis. Txnrd1High patients exhibit 2.5 years early recurrence and 1.3 years early metastasis as compared to Txnrd1Low cohort. Interestingly, patients with high Txnrd1 gene expression exhibit a pathologic complete response (pCR) to neoadjuvant chemotherapy, but they experience early recurrence after radiotherapy. Txnrd1High MDA-MB-231 cells exhibit significant ROS generation and reduced viability after doxorubicin treatment compared to Txnrd1Low MCF7 cells. Corroborating with findings from meta-analysis, Txnrd1 depletion leads to decreased survival, enhanced sensitivity to radiation induced killing, poor scratch-wound healing, and reduced invasion potential in MDA-MB-231 cells. Thus, Txnrd1 appears to be a potential predictor of recurrence, metastasis and therapy response in breast cancer patients.

Novel 2 Gene Signatures Associated With Breast Cancer Proliferation: Insights From Predictive Differential Gene Expression Analysis

The use of proliferation markers provides valuable information about the rate of tumor growth, which can guide treatment decisions. However, there is still a lack of consensus regarding the optimal molecular markers or tests to use in clinical practice. Integrating gene expression data with clinical and histopathologic parameters enhances our understanding of disease processes, facilitates the identification of precise prognostic predictors, and supports the development of effective therapeutic strategies. The purpose of this study was to apply an integrated approach that combines morphologic, clinical, and bioinformatic data to reveal effective regulators of proliferation. Whole-slide images generated from hematoxylin-and-eosin-stained sections of The Cancer Genome Atlas (TCGA) breast cancer (BC) database (n = 1053) alongside their transcriptomic and clinical data were used to identify genes differentially expressed between tumors with high and low mitotic scores. Genes enriched in the cell-cycle pathway were used to predict the protein-protein interaction (PPI) network. Ten hub genes (ORC6, SKP2, SMC1B, CDKN2A, CDC25B, E2F1, E2F2, ORC1, PTTG1, and CDC25A) were identified using CytoHubba a Cytoscape plugin. In a multivariate Cox regression model, ORC6 and SKP2 were predictors of survival independent of existing methods of proliferation assessment including mitotic score and Ki67. The prognostic ability of these genes was validated using the Molecular Taxonomy of Breast Cancer International Consortium, Nottingham cohort, Uppsala cohort, and a combined multicentric cohort. The protein expression of these 2 genes was investigated on a large cohort of BC cases, and they were significantly associated with poor prognosis and patient outcome. A positive correlation between ORC6 and SKP2 mRNA and protein expression was observed. Our study has identified 2 gene signatures, ORC6 and SKP2, which play a significant role in BC proliferation. These genes surpassed both mitotic scores and Ki67 in multivariate analysis. Their identification provides potential opportunities for the development of targeted treatments for patients with BC.

Autosurv: interpretable deep learning framework for cancer survival analysis incorporating clinical and multi-omics data

Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can also reveal the underlying disease mechanisms at the molecular level. In this study, we developed and validated a deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian-cancer patients using multiple independent multi-omics datasets. Our model achieved significantly better prognosis prediction than the current machine learning and deep learning approaches in various settings. Moreover, an interpretation method was applied to tackle the "black-box" nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that were important to distinguish predicted high- and low-risk patients. The significance of the identified features was partially supported by previous studies.

Deregulated transcription factors in the emerging cancer hallmarks

Cancer progression is a multifaceted process that entails several stages and demands the persistent expression or activation of transcription factors (TFs) to facilitate growth and survival. TFs are a cluster of proteins with DNA-binding domains that attach to promoter or enhancer DNA strands to start the transcription of genes by collaborating with RNA polymerase and other supporting proteins. They are generally acknowledged as the major regulatory molecules that coordinate biological homeostasis and the appropriate functioning of cellular components, subsequently contributing to human physiology. TFs proteins are crucial for controlling transcription during the embryonic stage and development, and the stability of different cell types depends on how they function in different cell types. The development and progression of cancer cells and tumors might be triggered by any anomaly in transcription factor function. It has long been acknowledged that cancer development is accompanied by the dysregulated activity of TF alterations which might result in faulty gene expression. Recent studies have suggested that dysregulated transcription factors play a major role in developing various human malignancies by altering and rewiring metabolic processes, modifying the immune response, and triggering oncogenic signaling cascades. This review emphasizes the interplay between TFs involved in metabolic and epigenetic reprogramming, evading immune attacks, cellular senescence, and the maintenance of cancer stemness in cancerous cells. The insights presented herein will facilitate the development of innovative therapeutic modalities to tackle the dysregulated transcription factors underlying cancer.

Dual network analysis of transcriptome data for discovery of new therapeutic targets in non-small cell lung cancer

The drug therapy for non-small cell lung cancer (NSCLC) have always been issues of poisonous side effect, acquired drug resistance and narrow applicable population. In this study, we built a novel network analysis method (difference- correlation- enrichment- causality- node), which was based on the difference analysis, Spearman correlation network analysis, biological function analysis and Bayesian causality network analysis to discover new therapeutic target of NSCLC in the sequencing data of BEAS-2B and 7 NSCLC cell lines. Our results showed that, as a proteasome subunit coding gene in the central of cell cycle network, PSMD2 was associated with prognosis and was an independent prognostic factor for NSCLC patients. Knockout of PSMD2 inhibited the proliferation of NSCLC cells by inducing cell cycle arrest, and exhibited marked increase of cell cycle blocking protein p21, p27 and decrease of cell cycle driven protein CDK4, CDK6, CCND1 and CCNE1. IPA and molecular docking suggested bortezomib has stronger affinity to PSMD2 compared with reported targets PSMB1 and PSMB5. In vitro and In vivo experiments demonstrated the inhibitory effect of bortezomib in NSCLC with different driven mutations or with tyrosine kinase inhibitors resistance. Taken together, bortezomib could target PSMD2, PSMB1 and PSMB5 to inhibit the proteasome degradation of cell cycle check points, to block cell proliferation of NSCLC, which was potential optional drug for NSCLC patients.

Aberrant activation of five embryonic stem cell-specific genes robustly predicts a high risk of relapse in breast cancers

BACKGROUND

In breast cancer, as in all cancers, genetic and epigenetic deregulations can result in out-of-context expressions of a set of normally silent tissue-specific genes. The activation of some of these genes in various cancers empowers tumours cells with new properties and drives enhanced proliferation and metastatic activity, leading to a poor survival prognosis.

RESULTS

In this work, we undertook an unprecedented systematic and unbiased analysis of out-of-context activations of a specific set of tissue-specific genes from testis, placenta and embryonic stem cells, not expressed in normal breast tissue as a source of novel prognostic biomarkers. To this end, we combined a strict machine learning framework of transcriptomic data analysis, and successfully created a new robust tool, validated in several independent datasets, which is able to identify patients with a high risk of relapse. This unbiased approach allowed us to identify a panel of five biomarkers, DNMT3B, EXO1, MCM10, CENPF and CENPE, that are robustly and significantly associated with disease-free survival prognosis in breast cancer. Based on these findings, we created a new Gene Expression Classifier (GEC) that stratifies patients. Additionally, thanks to the identified GEC, we were able to paint the specific molecular portraits of the particularly aggressive tumours, which show characteristics of male germ cells, with a particular metabolic gene signature, associated with an enrichment in pro-metastatic and pro-proliferation gene expression.

CONCLUSIONS

The GEC classifier is able to reliably identify patients with a high risk of relapse at early stages of the disease. We especially recommend to use the GEC tool for patients with the luminal-A molecular subtype of breast cancer, generally considered of a favourable disease-free survival prognosis, to detect the fraction of patients undergoing a high risk of relapse.

Removing the association of random gene sets and survival time in cancers with positive random bias using fixed-point gene set

Cancer research aims to identify genes that cause or control disease progression. Although a wide range of gene sets have been published, they are usually in poor agreement with one another. Furthermore, recent findings from a gene-expression cohort of different cancer types, known as positive random bias, showed that sets of genes chosen randomly are significantly associated with survival time much higher than expected. In this study, we propose a method based on Brouwer's fixed-point theorem that employs significantly survival-associated random gene sets and reveals a small fixed-point gene set for cancers with a positive random bias property. These sets significantly correspond to cancer-related pathways with biological relevance for the progression and metastasis of the cancer types they represent. Our findings show that our proposed significant gene sets are biologically related to each cancer type available in the cancer genome atlas with the positive random bias property, and by using these sets, positive random bias is significantly more reduced in comparison with state-of-the-art methods in this field. The random bias property is removed in 8 of these 17 cancer types, and the number of random sets of genes associated with survival time is significantly reduced in the remaining 9 cancers.

Membrane Protein Modification Modulates Big and Small Extracellular Vesicle Biodistribution and Tumorigenic Potential in Breast Cancers In Vivo

Extracellular vesicles (EVs) are released by cells to mediate intercellular communication under pathological and physiological conditions. While small EVs (sEVs; <100-200 nm, exosomes) are intensely investigated, the properties and functions of medium and large EVs (big EVs (bEVs); >200 nm, microvesicles) are less well explored. Here, bEVs and sEVs are identified as distinct EV populations, and it is determined that bEVs are released in a greater bEV:sEV ratio in the aggressive human triple-negative breast cancer (TNBC) subtype. PalmGRET, bioluminescence-resonance-energy-transfer (BRET)-based EV reporter, reveals dose-dependent EV biodistribution at nonlethal and physiological EV dosages, as compared to lipophilic fluorescent dyes. Remarkably, the bEVs and sEVs exhibit unique biodistribution profiles, yet individually promote in vivo tumor growth in a syngeneic immunocompetent TNBC breast tumor murine model. The bEVs and sEVs share mass-spectrometry-identified tumor-progression-associated EV surface membrane proteins (tpEVSurfMEMs), which include solute carrier family 29 member 1, Cd9, and Cd44. tpEVSurfMEM depletion attenuates EV lung organotropism, alters biodistribution, and reduces protumorigenic potential. This study identifies distinct in vivo property and function of bEVs and sEVs in breast cancer, which suggest the significant role of bEVs in diseases, diagnostic and therapeutic applications.

The Impact of DTYMK as a Prognostic Marker in Colorectal Cancer

Background

Overexpression of deoxythymidylate kinase (DTYMK) has been associated with more aggressiveness and pathological behaviors in hepatocellular carcinoma (HCC) and non-small cell lung cancer (NSCLC). However, the expression of DTYMK and its prognostic significance in colorectal cancer (CRC) patients are yet unknown. The goal of this study was to investigate the DTYMK immunohistochemistry reactivity in CRC tissues and to see how it correlated with various histological and clinical features as well as survival.

Methods

Several bioinformatics databases and two tissue microarrays (TMAs) of 227 cases were used in this study. Immunohistochemistry assay was used to study the protein expression of DTYMK.

Results

Based on the GEPIA, UALCAN, and Oncomine databases, DTYMK expression has increased in tumor tissues at both RNA and protein levels in colorectal adenocarcinoma (COAD) compared to normal tissues. A high DTYMK H-score was found in 122/227 (53%) of the cases, whereas a low DTYMK H-score was found in 105/227. The age at diagnosis (P = 0.036), stage of the disease (P = 0.038), and site of origin (P = 0.032) were all linked to a high DTYMK H-score. Patients with high level of DTYMK had bad overall survival. Interestingly, high DTYMK protein level was associated with PSM2 (P = 0.002) and MSH2 (P = 0.003), but not with MLH2 or MSH6.

Conclusion

This is the first study to cover the expression and prognostic significance of DTYMK in CRC. DTYMK was upregulated in CRC and could be considered as a prognostic biomarker.

Transcriptional factors targeting in cancer stem cells for tumor modulation

Cancer Stem Cells (CSCs) are now considered the primary "seeds" for the onset, development, metastasis, and recurrence of tumors. Despite therapeutic breakthroughs, cancer remains the leading cause of death worldwide. This is because the tumor microenvironment contains a key population of cells known as CSCs, which promote tumor aggression. CSCs are self-renewing cells that aid tumor recurrence by promoting tumor growth and persisting in patients after many traditional cancer treatments. According to reports, numerous transcription factors (TF) play a key role in maintaining CSC pluripotency and its self-renewal property. The understanding of the functions, structures, and interactional dynamics of these transcription factors with DNA has modified the hypothesis, paving the way for novel transcription factor-targeted therapies. These TFs, which are crucial and are required by cancer cells, play a vital function in the etiology of human cancer. Such CSC TFs will help with gene expression profiling, which provides crucial data for predicting the prognosis of patients. To overcome anti-cancer medication resistance and completely eradicate cancer, a potent therapy combining TFs-based CSC targets with traditional chemotherapy may be developed. In order to develop therapies that could eliminate CSCs, we here concentrated on the effect of TFs and other components of signalling pathways on cancer stemness.

Development of a Cancer-Associated Fibroblast-Related Prognostic Model in Breast Cancer via Bulk and Single-Cell RNA Sequencing

Background

The most numerous cells in the tumor microenvironment, cancer-associated fibroblasts (CAFs) play a crucial role in cancer development. Our objective was to develop a cancer-associated fibroblast breast cancer predictive model.

Methods

We acquire breast cancer (BC) scRNA-seq data from Gene Expression Omnibus (GEO), and "Seurat" was used for data processing, including quality control, filtering, principal component analysis, and t-SNE. Afterward, "singleR" software was used to annotate cells. Seurat's "FindAllMarkers" program is used to locate particular CAF markers. clusterProfiler was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The Cancer Genome Atlas (TCGA) database was utilized to provide univariate Cox regression, least absolute shrinkage operator (LASSO) analysis using bulk RNA-seq data. For model development, multivariate Cox regression studies are used. Utilizing pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms, chemosensitivity and immunotherapy response were predicted. The "rms" software was used to facilitate and simplify modeling.

Results

Integrating the scRNA-seq (GSE176078) dataset yielded 28 cell clusters. In addition, well-known cell types helped identify 12 cell types. We found 193 marker genes that are elevated in CAFs. In addition, a five-gene predictive model associated to CAF was created in the training set. In the training set, the validation set, and the external validation set, greater risk scores were associated with a worse prognosis. And individuals with a higher risk score were more susceptible to immunotherapy and conventional chemotherapy medicines.

Conclusion

In conclusion, we establish a strong prognostic model comprised of 5 genes related with CAF that might serve as a potent prognostic indicator and aid clinicians in making more rational medication choices.

PSMD2 promotes the progression of bladder cancer and is correlated with immune infiltration

Introduction

PSMD2 plays an oncogenic role in multiple human malignancies, while it is still unclear that the potential roles and underlying mechanisms of PSMD2 in BCa.

Methods

The RNA-seq from TCGA and GTEx database was utilized to preliminarily analyze the expression of PSMD2 in BCa tissues, qRT-PCR was adopted to verify the PSMD2 expression in BCa cell lines. Cox regression analyses were applied to assess the prognostic values of PSMD2 in BCa. GSEA analysis was used to explore the underlying mechanisms of PSMD2. In vitro assays such as wound healing and colony formation assays were applied to determine the carcinogenesis of PSMD2 in BCa. xCell and ssGSEA algorithms were applied to analyze the associations of PSMD2 with TIME.

Results

The results revealed that in comparison with normal bladder tissues and cell line, PSMD2 was found to be significantly elevated in BCa tissues and cell lines. Elevated expression of PSMD2 can independently predict unfavorable OS for BCa patients. The PSMD2 expression and other clinicopathologic factors were combined to develop a nomogram, which can help to predict OS for BCa patients. GSEA analyses revealed that PSMD2 is correlated with the cell cycle, antigen processing and presentation, JAK-STAT signaling pathway, Toll like receptor signaling pathway, P53 and MAPK signaling pathway. Knockdown of PSMD2 could remarkably inhibit the wound healing and colony formation efficiency of BCa cells. xCell analysis revealed that overexpressed PSMD2 is positively related to the Th2 cells infiltrates and expression levels of immune escape markers, and negatively associated with the infiltrating levels of NK T cell and CD8+ T cell.

Discussion

In conclusion, overexpressed PSMD2 is tightly linked to the immune infiltrates and promotes the progression of BCa.

Rational Approach to Finding Genes Encoding Molecular Biomarkers: Focus on Breast Cancer

Early detection of cancer facilitates treatment and improves patient survival. We hypothesized that molecular biomarkers of cancer could be rationally predicted based on even partial knowledge of transcriptional regulation, functional pathways and gene co-expression networks. To test our data mining approach, we focused on breast cancer, as one of the best-studied models of this disease. We were particularly interested to check whether such a ‘guilt by association’ approach would lead to pan-cancer markers generally known in the field or whether molecular subtype-specific ‘seed’ markers will yield subtype-specific extended sets of breast cancer markers. The key challenge of this investigation was to utilize a small number of well-characterized, largely intracellular, breast cancer-related proteins to uncover similarly regulated and functionally related genes and proteins with the view to predicting a much-expanded range of disease markers, especially that of extracellular molecular markers, potentially suitable for the early non-invasive detection of the disease. We selected 23 previously characterized proteins specific to three major molecular subtypes of breast cancer and analyzed their established transcription factor networks, their known metabolic and functional pathways and the existing experimentally derived protein co-expression data. Having started with largely intracellular and transmembrane marker ‘seeds’ we predicted the existence of as many as 150 novel biomarker genes to be associated with the selected three major molecular sub-types of breast cancer all coding for extracellularly targeted or secreted proteins and therefore being potentially most suitable for molecular diagnosis of the disease. Of the 150 such predicted protein markers, 114 were predicted to be linked through the combination of regulatory networks to basal breast cancer, 48 to luminal and 7 to Her2-positive breast cancer. The reported approach to mining molecular markers is not limited to breast cancer and therefore offers a widely applicable strategy of biomarker mining.

CmP signaling network unveils novel biomarkers for triple negative breast cancer in African American women

Breast cancer is the most diagnosed cancer worldwide and remains the second leading cause of cancer death. While breast cancer mortality has steadily declined over the past decades through medical advances, an alarming disparity in breast cancer mortality has emerged between African American women (AAW) and Caucasian American women (CAW). New evidence suggests more aggressive behavior of triple-negative breast cancer (TNBC) in AAW may contribute to racial differences in tumor biology and mortality. Progesterone (PRG) can exert its cellular effects through either its classic, non-classic, or combined responses through binding to either classic nuclear PRG receptors (nPRs) or non-classic membrane PRG receptors (mPRs), warranting both pathways equally important in PRG-mediated signaling. In our previous report, we demonstrated that the CCM signaling complex (CSC) consisting of CCM1, CCM2, and CCM3 can couple both nPRs and mPRs signaling cascades to form a CSC-mPRs-PRG-nPRs (CmPn) signaling network in nPR positive(+) breast cancer cells. In this report, we furthered our research by establishing the CSC-mPRs-PRG (CmP) signaling network in nPR(-) breast cancer cells, demonstrating that a common core mechanism exists, regardless of nPR(+⁣/⁣-) status. This is the first report stating that inducible expression patterns exist between CCMs and major mPRs in TNBC cells. Furthermore, we firstly show mPRs in TNBC cells are localized in the nucleus and participate in nucleocytoplasmic shuttling in a coordinately synchronized fashion with CCMs under steroid actions, following the same cellular distribution as other well-defined steroid hormone receptors. Finally, for the first time, we deconvoluted the CmP signalosome by using systems biology and TNBC clinical data, which helped us understand key factors within the CmP network and identify 6 specific biomarkers with potential clinical applications associated with AAW-TNBC tumorigenesis. These novel biomarkers could have immediate clinical implications to dramatically improve health disparities among AAW-TNBCs.

cPLA2 blockade attenuates S100A7-mediated breast tumorigenicity by inhibiting the immunosuppressive tumor microenvironment

BACKGROUND

Molecular mechanisms underlying inflammation-associated breast tumor growth are poorly studied. S100A7, a pro-inflammatory molecule has been shown to enhance breast cancer growth and metastasis. However, the S100A7-mediated molecular mechanisms in enhancing tumor growth and metastasis are unclear.

METHODS

Human breast cancer tissue and plasma samples were used to analyze the expression of S100A7, cPLA2, and PGE2. S100A7-overexpressing or downregulated human metastatic breast cancer cells were used to evaluate the S100A7-mediated downstream signaling mechanisms. Bi-transgenic mS100a7a15 overexpression, TNBC C3 (1)/Tag transgenic, and humanized patient-derived xenograft mouse models and cPLA2 inhibitor (AACOCF3) were used to investigate the role of S100A7/cPLA2/PGE2 signaling in tumor growth and metastasis. Additionally, CODEX, a highly advanced multiplexed imaging was employed to delineate the effects of S100A7/cPLA2 inhibition on the recruitment of various immune cells.

RESULTS

In this study, we found that S100A7 and cPLA2 are highly expressed and correlate with decreased overall survival in breast cancer patients. Further mechanistic studies revealed that S100A7/RAGE signaling promotes the expression of cPLA2 to mediate its oncogenic effects. Pharmacological inhibition of cPLA2 suppressed S100A7-mediated tumor growth and metastasis in multiple pre-clinical models including transgenic and humanized patient-derived xenograft (PDX) mouse models. The attenuation of cPLA2 signaling reduced S100A7-mediated recruitment of immune-suppressive myeloid cells in the tumor microenvironment (TME). Interestingly, we discovered that the S100A7/cPLA2 axis enhances the immunosuppressive microenvironment by increasing prostaglandin E2 (PGE2). Furthermore, CO-Detection by indEXing (CODEX) imaging-based analyses revealed that cPLA2 inhibition increased the infiltration of activated and proliferating CD4⁺ and CD8⁺ T cells in the TME. In addition, CD163⁺ tumor associated-macrophages were positively associated with S100A7 and cPLA2 expression in malignant breast cancer patients.

CONCLUSIONS

Our study provides new mechanistic insights on the cross-talk between S100A7/cPLA2 in enhancing breast tumor growth and metastasis by generating an immunosuppressive TME that inhibits the infiltration of cytotoxic T cells. Furthermore, our studies indicate that S100A7/cPLA2 could be used as novel prognostic marker and cPLA2 inhibitors as promising drugs against S100A7-overexpressing aggressive breast cancer.

Identification of Prognostic Genes for Colon Cancer through Gene Co-expression Network Analysis

OBJECTIVE

The present study was aimed to identify novel key genes, prognostic biomarkers and molecular pathways implicated in tumorigenesis of colon cancer.

METHODS

The microarray data GSE41328 containing 10 colon cancer samples and 10 adjacent normal tissues was analyzed to identify 4763 differentially expressed genes. Meanwhile, another microarray data GSE17536 was performed for weighted gene co-expression network analysis (WGCNA).

RESULTS

In present study, 12 co-expressed gene modules associated with tumor progression were identified for further studies. The red module showed the highest association with pathological stage by Pearson's correlation analysis. Functional enrichment analysis revealed that genes in red module focused on cell division, cell proliferation, cell cycle and metabolic related pathway. Then, a total of 26 key hub genes were identified, and GEPIA database was subsequently selected for validation. Holliday junction-recognizing protein (HJURP) and cell division cycle 25 homolog C (CDC25C) were identified as effective prognosis biomarkers, which were all detrimental to prognosis. Gene set enrichment analyses (GSEA) found the two hub genes were enriched in "oocyte meiosis", "oocyte maturation that are progesterone-mediated", "p53 signaling pathway", and "cell cycle". Furthermore, the immunohistochemistry and western blotting showed that HJURP was highly expressed in colon cancer tissue.

CONCLUSION

HJURP was identified as a key gene associated with colon cancer progression and prognosis by WGCNA, which might influence the prognosis by regulating cell cycle pathways.

Hepatocyte growth factor pathway expression in breast cancer by race and subtype

BACKGROUND

African American women have the highest risk of breast cancer mortality compared to other racial groups. Differences in tumor characteristics have been implicated as a possible cause; however, the tumor microenvironment may also contribute to this disparity in mortality. Hepatocyte growth factor (HGF) is a stroma-derived marker of the tumor microenvironment that may affect tumor progression differentially by race.

OBJECTIVE

To examine whether an HGF gene expression signature is differentially expressed by race and tumor characteristics.

METHODS

Invasive breast tumors from 1957 patients were assessed for a 38-gene RNA-based HGF gene expression signature. Participants were black (n = 1033) and non-black (n = 924) women from the population-based Carolina Breast Cancer Study (1993-2013). Generalized linear models were used to estimate the relative frequency differences (RFD) in HGF status by race, clinical, and demographic factors.

RESULTS

Thirty-two percent of tumors were positive for the HGF signature. Black women were more likely [42% vs. 21%; RFD = + 19.93% (95% CI 16.00, 23.87)] to have HGF-positive tumors compared to non-black women. Triple-negative patients had a higher frequency of HGF positivity [82% vs. 13% in non-triple-negative; RFD = + 65.85% (95% CI 61.71, 69.98)], and HGF positivity was a defining feature of basal-like subtype [92% vs. 8% in non-basal; RFD = + 81.84% (95% CI 78.84, 84.83)]. HGF positivity was associated with younger age, stage, higher grade, and high genomic risk of recurrence (ROR-PT) score.

CONCLUSION

HGF expression is a defining feature of basal-like tumors, and its association with black race and young women suggests it may be a candidate pathway for understanding breast cancer disparities.

A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models

Introduction

Ovarian cancer (OC) is one of the most frequent gynecologic cancers among women, and high-accuracy risk prediction techniques are essential to effectively select the best intervention strategies and clinical management for OC patients at different risk levels. Current risk prediction models used in OC have low sensitivity, and few of them are able to identify OC patients at high risk of mortality, which would both optimize the treatment of high-risk patients and prevent unnecessary medical intervention in those at low risk.

Objectives

To this end, we have developed a bagging-based algorithm with GA-XGBoost models that predicts the risk of death from OC using gene expression profiles.

Methods

Four gene expression datasets from public sources were used as training (n = 1) or validation (n = 3) sets. The performance of our proposed algorithm was compared with fine-tuning and other existing methods. Moreover, the biological function of selected genetic features was further interpreted, and the response to a panel of approved drugs was predicted for different risk levels.

Results

The proposed algorithm showed good sensitivity (74-100%) in the validation sets, compared with two simple models whose sensitivity only reached 47% and 60%. The prognostic gene signature used in this study was highly connected to AKT, a key component of the PI3K/AKT/mTOR signaling pathway, which influences the tumorigenesis, proliferation, and progression of OC.

Conclusion

These findings demonstrated an improvement in the sensitivity of risk classification of OC patients with our risk prediction models compared with other methods. Ongoing effort is needed to validate the outcomes of this approach for precise clinical treatment.

Surface Expression of Kynurenine 3-Monooxygenase Promotes Proliferation and Metastasis in Triple-Negative Breast Cancers

Kynurenine 3-monooxygenase (KMO) is the pivotal enzyme in the kynurenine pathway and is located on the mitochondrial outer membrane. The dysregulation of KMO leads to various neurodegenerative diseases; however, it is rarely mentioned in cancer progression. Our previous study showed that KMO overexpression in canine mammary gland tumors (cMGT) is associated with poor prognosis in cMGT patients. Surprisingly, it was also found that KMO can be located on the cell membranes of cMGT cells, unlike its location in normal cells, where KMO is expressed only within the cytosol. Since cMGT and human breast cancer share similar morphologies and pathogenesis, this study investigated the possibility of detecting surface KMO in human breast cancers and the role of surface KMO in tumorigenesis. Using immunohistochemistry (IHC), flow cytometry (FC), immunofluorescence assay (IFA), and transmission electron microscopy (TEM), we demonstrated that KMO can be aberrantly and highly expressed on the cell membranes of breast cancer tissues and in an array of cell lines. Masking surface KMO with anti-KMO antibody reduced the cell viability and inhibited the migration and invasion of the triple-negative breast cancer cell line, MDA-MB-231. These results indicated that aberrant surface expression of KMO may be a potential therapeutic target for human breast cancers.

Long non-coding RNA TDRG1 promotes hypoxia-induced glycolysis by targeting the miR-214-5p/SEMA4C axis in cervical cancer cells

Long non-coding RNA (lncRNA) has been demonstrated as vital regulator in human cancer. However, the precise role of lnc-TDRG1 in cervical cancer (CC) remains unclear, so this study was aimed to clarify the role and underlying molecular mechanism of lnc-TDRG1 in CC. The real-time quantitative polymerase chain reaction (RT-qPCR) was conducted to assess the expression levels of lnc-TDRG1, miR-214-5p and Semaphorin 4C (SEMA4C). Under hypoxia condition, the biological behaviors of CC cell, including invasion and glycolysis were determined by transwell assay and Glucose Assay Kit and Lactate Assay Kit, respectively. The Western blot assay was employed to test the expression level of SEMA4C and hexokinase 2 (HK2) expression. The interaction relationship between miR-214-5p and lnc-TDRG1 or SEMA4C was analyzed bioinformatics database and confirmed by dual-luciferase reporter assay, respectively. A xenograft experiment in nude mice was established to clarify the functional role of lnc-TDRG1 in vivo. We found Lnc-TDRG1 was highly expressed in CC tissues and cells and it was upregulated in response to hypoxia. Loss-of-functional experiment suggested that knockdown of lnc-TDRG1 impede invasion, hypoxia-induced glycolysis in vitro and tumor growth in vivo, which was abolished by knockdown of miR-214-5p or overexpression of SEMA4C. Moreover, we confirmed that miR-214-5p specifically bound to SEMA4C and negatively correlated with SEMA4C expression. Collectively, lnc-TDRG1 regulated SEMA4C expression by sponging miR-214-5p in CC. Collectively, mechanistically, lnc-TDRG1 could act as a sponge of miR-214-5p to regulate the expression of SEMA4C, and further regulate invasion and hypoxia-glycolysis in CC cells.

Establishment and Validation of a Gene Signature-Based Prognostic Model to Improve Survival Prediction in Adrenocortical Carcinoma Patients

BACKGROUND

The current guideline for the management of adrenocortical carcinoma (ACC) is insufficient for accurate risk prediction to guide adjuvant therapy. Given frequent and severe therapeutic side effects, a better estimate of survival is warranted for risk-specific assignment to adjuvant treatment. We attempted to construct an integrated model based on a prognostic gene signature and clinicopathological features to improve risk stratification and survival prediction in ACC.

METHODS

Using a series of bioinformatic and statistical approaches, a gene-expression signature was established and validated in two independent cohorts. By combining the signature with clinicopathological features, a decision tree was generated to improve risk stratification, and a nomogram was constructed to personalize risk prediction. Time-dependent receiver operating characteristic (tROC) and calibration analysis were performed to evaluate the predictive power and accuracy.

RESULTS

A three-gene signature could discriminate high-risk patients well in both training and validation cohorts. Multivariate regression analysis demonstrated the signature to be an independent predictor of overall survival. The decision tree could identify risk subgroups powerfully, and the nomogram showed high accuracy of survival prediction. Particularly, expression of a gene hitherto unknown to be dysregulated in ACC, TIGD1, was shown to be prognostically relevant.

CONCLUSION

We propose a novel gene signature to guide decision-making about adjuvant therapy in ACC. The score shows unprecedented survival prediction and hence constitutes a huge step towards personalized management. As a secondary important finding, we report the discovery and validation of a new oncogene, TIGD1, which was consistently overexpressed in ACC. TIGD1 might shed further light on the biology of ACC and might give rise to targeted therapies that not only apply to ACC but potentially also to other malignancies.

Omics-wide quantitative B-cell infiltration analyses identify GPR18 for human cancer prognosis with superiority over CD20

Tumor-infiltrating B lymphocyte (TIL-B), and TIL-B-related biomarkers have clinical prognostic values for human cancers. CD20 (encoded by MS4A1) is a widely used TIL-B biomarker. Using TCGA-quantitative multiomics datasets, we first cross-compare prognostic powers of intratumoral CD20 protein, mRNA and TIL-B levels in pan-cancers. Here, we show that MS4A1 and TIL-B are consistently prognostic in 5 cancers (head and neck, lung, cervical, kidney and low-grade glioma), while unexpectedly, CD20 protein levels lack quantitative correlations with MS4A1/TIL-B levels and demonstrate limited prognosticity. Subsequent bioinformatics discovery for TIL-B prognostic gene identifies a single gene, GPR18 with stand-alone prognosticity across 9 cancers (superior over CD20), with further validations in multiple non-TCGA cohorts. GPR18's immune signature denotes major B-cell-T-cell interactions, with its intratumoral expression strongly tied to a "T-cell active", likely cytolytic, status across human cancers, suggesting its functional link to cytolytic T-cell activity in cancer. GPR18 merits biological and clinical utility assessments over CD20.

QuaDMutNetEx: a method for detecting cancer driver genes with low mutation frequency

BACKGROUND

Cancer is caused by genetic mutations, but not all somatic mutations in human DNA drive the emergence or growth of cancers. While many frequently-mutated cancer driver genes have already been identified and are being utilized for diagnostic, prognostic, or therapeutic purposes, identifying driver genes that harbor mutations occurring with low frequency in human cancers is an ongoing endeavor. Typically, mutations that do not confer growth advantage to tumors - passenger mutations - dominate the mutation landscape of tumor cell genome, making identification of low-frequency driver mutations a challenge. The leading approach for discovering new putative driver genes involves analyzing patterns of mutations in large cohorts of patients and using statistical methods to discriminate driver from passenger mutations.

RESULTS

We propose a novel cancer driver gene detection method, QuaDMutNetEx. QuaDMutNetEx discovers cancer drivers with low mutation frequency by giving preference to genes encoding proteins that are connected in human protein-protein interaction networks, and that at the same time show low deviation from the mutual exclusivity pattern that characterizes driver mutations occurring in the same pathway or functional gene group across a cohort of cancer samples.

CONCLUSIONS

Evaluation of QuaDMutNetEx on four different tumor sample datasets show that the proposed method finds biologically-connected sets of low-frequency driver genes, including many genes that are not found if the network connectivity information is not considered. Improved quality and interpretability of the discovered putative driver gene sets compared to existing methods shows that QuaDMutNetEx is a valuable new tool for detecting driver genes. QuaDMutNetEx is available for download from https://github.com/bokhariy/QuaDMutNetExunder the GNU GPLv3 license.

Gene expression based survival prediction for cancer patients-A topic modeling approach

Cancer is one of the leading cause of death, worldwide. Many believe that genomic data will enable us to better predict the survival time of these patients, which will lead to better, more personalized treatment options and patient care. As standard survival prediction models have a hard time coping with the high-dimensionality of such gene expression data, many projects use some dimensionality reduction techniques to overcome this hurdle. We introduce a novel methodology, inspired by topic modeling from the natural language domain, to derive expressive features from the high-dimensional gene expression data. There, a document is represented as a mixture over a relatively small number of topics, where each topic corresponds to a distribution over the words; here, to accommodate the heterogeneity of a patient's cancer, we represent each patient (≈ document) as a mixture over cancer-topics, where each cancer-topic is a mixture over gene expression values (≈ words). This required some extensions to the standard LDA model-e.g., to accommodate the real-valued expression values-leading to our novel discretized Latent Dirichlet Allocation (dLDA) procedure. After using this dLDA to learn these cancer-topics, we can then express each patient as a distribution over a small number of cancer-topics, then use this low-dimensional "distribution vector" as input to a learning algorithm-here, we ran the recent survival prediction algorithm, MTLR, on this representation of the cancer dataset. We initially focus on the METABRIC dataset, which describes each of n = 1,981 breast cancer patients using the r = 49,576 gene expression values, from microarrays. Our results show that our approach (dLDA followed by MTLR) provides survival estimates that are more accurate than standard models, in terms of the standard Concordance measure. We then validate this "dLDA+MTLR" approach by running it on the n = 883 Pan-kidney (KIPAN) dataset, over r = 15,529 gene expression values-here using the mRNAseq modality-and find that it again achieves excellent results. In both cases, we also show that the resulting model is calibrated, using the recent "D-calibrated" measure. These successes, in two different cancer types and expression modalities, demonstrates the generality, and the effectiveness, of this approach. The dLDA+MTLR source code is available at https://github.com/nitsanluke/GE-LDA-Survival.

Action and clinical significance of CCAAT/enhancer-binding protein delta in hepatocellular carcinoma

CCAAT/enhancer-binding protein delta (CEBPD) is associated with the regulation of apoptosis and cell proliferation and is a candidate tumor suppressor gene. Here, we investigated its role in hepatocellular carcinoma (HCC). We observe that CEBPD mRNA expression is significantly downregulated in HCC tumors as compared with adjacent tissues. Protein levels of CEBPD are also lower in tumors relative to adjacent tissues. Reduced expression of CEBPD in the tumor correlates with worse clinical outcome. In both Huh7 and HepG2 cells, shRNA-mediated CEBPD knockdown significantly reduces cell proliferation, single cell colony formation and arrests cells in the G0/G1 phase. Subcutaneous xenografting of Huh7 in nude mice show that CEBPD knockdown results in smaller tumors. Gene expression analysis shows that CEBPD modulates interleukin-1 signaling. We conclude that CEBPD expression uncouples cancer compartment expansion and clinical outcome in HCC, potentially by modulating interleukin-1 signaling. Thus, although our results support the notion that CEBPD acts as a tumor suppressor in HCC, its action does not involve impairing compartment expansion per se but more likely acts through improving anticancer immunity.

Reverse signaling by semaphorin 4C elicits SMAD1/5- and ID1/3-dependent invasive reprogramming in cancer cells

Semaphorins are a family of molecular signals that guide cell migration and are implicated in the regulation of cancer cells. In particular, transmembrane semaphorins are postulated to act as both ligands (“forward” mode) and signaling receptors (“reverse” mode); however, reverse semaphorin signaling in cancer is relatively less understood. Here, we identified a previously unknown function of transmembrane semaphorin 4C (Sema4C), acting in reverse mode, to elicit nonconventional TGF-β/BMP receptor activation and selective SMAD1/5 phosphorylation. Sema4C coimmunoprecipitated with TGFBRII and BMPR1, supporting its role as modifier of this pathway. Sema4C reverse signaling led to the increased abundance of ID1/3 transcriptional factors and to extensive reprogramming of gene expression, which suppressed the typical features of the epithelial-mesenchymal transition in invasive carcinoma cells. This phenotype was nevertheless coupled with burgeoning metastatic behavior in vivo, consistent with evidence that Sema4C expression correlates with metastatic progression in human breast cancers. Thus, Sema4C reverse signaling promoted SMAD1/5- and ID1/3-dependent gene expression reprogramming and phenotypic plasticity in invasive cancer cells.

Identification of 9 key genes and small molecule drugs in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a heterogeneous tumor that the underlying molecular mechanisms are largely unclear. This study aimed to elucidate the key candidate genes and pathways in ccRCC by integrated bioinformatics analysis. 1387 differentially expressed genes were identified based on three expression profile datasets, including 673 upregulated genes and 714 downregulated genes. Then we used weighted correlation network analysis to identify 6 modules associated with pathological stage and grade, blue module was the most relevant module. GO and KEGG pathway analyses showed that genes in blue module were enriched in cell cycle and metabolic related pathways. Further, 25 hub genes in blue module were identified as hub genes. Based on GEPIA database, 9 genes were associated with progression and prognosis of ccRCC patients, including PTTG1, RRM2, TOP2A, UHRF1, CEP55, BIRC5, UBE2C, FOXM1 and CDC20. Then multivariate Cox regression showed that the risk score base on 9 key genes signature was a clinically independent prognostic factor for ccRCC patients. Moreover, we screened out several new small molecule drugs that have the potential to treat ccRCC. Few of them were identified as biomarkers in ccRCC. In conclusion, our research identified 9 potential prognostic genes and several candidate small molecule drugs for ccRCC treatment.

Molecular functions of brain expressed X-linked 2 (BEX2) in malignancies

Over the last decade there has been growing evidence that Brain Expressed X-Linked 2 (BEX2) has a significant role in the process of carcinogenesis. Collectively, available studies suggest a pro-oncogenic function for this gene in multiple malignancies, including breast, colorectal and hepatocellular cancers in addition to brain tumors. The identification of BEX2 in breast cancer resulted from gene expression microarray studies. Subsequent studies showed that BEX2 promotes breast cancer cell growth and survival by modulating the mitochondrial apoptotic pathway and G1 cell cycle. In this process, BEX2 has cross-talk with the NF-κB, c-Jun/JNK and ErbB2 pathways. Of note, several studies have found a pro-oncogenic function for BEX2 in other malignancies associated with a similar signaling function to that observed in breast cancer. In brain tumors, BEX2 promotes cell migration and invasion in oligodendroglioma and glioblastoma cells. In addition, BEX2 expression protects glioma cells against apoptosis mediated through the JNK pathway and is required for glioma cell proliferation through the NF-κB p65. Furthermore, it has been shown that BEX2 promotes cell proliferation through the JNK/c-Jun pathway and regulates JNK/c-Jun phosphorylation in colorectal cancer. Most recently, it has been demonstrated that BEX2 expression is required for cell proliferation and Hepatitis B Virus-mediated development of hepatocellular carcinoma. Therefore, a pro-oncogenic function for BEX2 is supported by reproducible data in multiple malignancies and the NF-κB and JNK/c-Jun pathways are commonly regulated by BEX2 in this process. In view of these findings, targeting BEX2 may provide an attractive therapeutic strategy in multiple malignancies.

High CEP55 expression is associated with poor prognosis in non-small-cell lung cancer

Objectives

Lung cancer is the most common and lethal malignancy worldwide. CEP55 was found to be overexpressed in multiple types of cancer. However, the expression pattern of CEP55 and its clinical significance in non-small-cell lung carcinoma (NSCLC) have not been investigated by immunohistochemistry.

Materials and methods

In this study, we analyzed 203 primary NSCLC specimens from Sun Yat-Sen University Cancer Center to investigate the clinical role of CEP55 in lung cancer. Tissue microarray was successfully generated for immunohistochemical evaluation. The correlation between CEP55 expression and clinical characteristics and survival was analyzed statistically. The predictive effect of CEP55 and APOBEC3B (AP3B) coexpression in lung cancer patients’ prognosis was evaluated.

Results

We found that the CEP55 expression was commonly elevated in NSCLC tissues and overexpression of CEP55 was correlated with unfavorable prognosis in the patients with NSCLC. Furthermore, the combination of CEP55 and AP3B expression was significantly predictive of clinical outcome in all NSCLC patients.

Conclusion

CEP55 may act as a useful and novel prognostic biomarker for NSCLC. Further studies into the mechanism of CEP55 are warranted.

PSMD2 regulates breast cancer cell proliferation and cell cycle progression by modulating p21 and p27 proteasomal degradation

Alterations in the ubiquitin-proteasome system (UPS) and UPS-associated proteins have been implicated in the development of many human malignancies. In this study, we investigated the expression profiles of 797 UPS-related genes using HiSeq data from The Cancer Genome Atlas and identified that PSMD2 was markedly upregulated in breast cancer. High PSMD2 expression was significantly correlated with poor prognosis. Gene set enrichment analysis revealed that transcriptome signatures involving proliferation, cell cycle, and apoptosis were critically enriched in specimens with elevated PSMD2. Consistently, PSMD2 knockdown inhibited cell proliferation and arrested cell cycle at G0/G1 phase in vitro, as well as suppressed tumor growth in vivo. Rescue assays demonstrated that the cell cycle arrest caused by silencing PSMD2 partially resulted from increased p21 and/or p27. Mechanically, PSMD2 physically interacted with p21 and p27 and mediated their ubiquitin-proteasome degradation with the cooperation of USP14. Notably, intratumor injection of therapeutic PSMD2 small interfering RNA effectively delayed xenograft tumor growth accompanied by p21 and p27 upregulation. These data provide novel insight into the role of PSMD2 in breast cancer and suggest that PSMD2 may be a potential therapeutic target.

Sema4C/PlexinB2 signaling controls breast cancer cell growth, hormonal dependence and tumorigenic potential

Semaphorin 4C (Sema4C) expression in human breast cancers correlates with poor disease outcome. Surprisingly, upon knock-down of Sema4C or its receptor PlexinB2 in diverse mammary carcinoma cells (but not their normal counterparts), we observed dramatic growth inhibition associated with impairment of G2/M phase transition, cytokinesis defects and the onset of cell senescence. Mechanistically, we demonstrated a Sema4C/PlexinB2/LARG-dependent signaling cascade that is required to maintain critical RhoA-GTP levels in cancer cells. Interestingly, we also found that Sema4C upregulation in luminal-type breast cancer cells drives a dramatic phenotypic change, with disassembly of polarity complexes, mitotic spindle misorientation, cell-cell dissociation and increased migration and invasiveness. We found that this signaling cascade is dependent on the PlexinB2 effectors ErbB2 and RhoA-dependent kinases. Moreover, Sema4C-overexpressing luminal breast cancer cells upregulated the transcription factors Snail, Slug and SOX-2, and formed estrogen-independent metastatic tumors in mice. In sum, our data indicate that Sema4C/PlexinB2 signaling is essential for the growth of breast carcinoma cells, featuring a novel potential therapeutic target. In addition, elevated Sema4C expression enables indolent luminal-type tumors to become resistant to estrogen deprivation, invasive and metastatic in vivo, which could account for its association with a subset of human breast cancers with poor prognosis.

DNA methylome analysis reveals distinct epigenetic patterns of ascending aortic dissection and bicuspid aortic valve

Aims

Epigenetics may mediate the effects of environmental risk factors on disease, including heart disease. Thus, measuring the DNA methylome offers the opportunity to identify novel disease biomarkers and novel insights into disease mechanisms. The DNA methylation landscape of ascending aortic dissection (AD) and bicuspid aortic valve (BAV) with aortic aneurysmal dilatation remain uncharacterized. The present study aimed to explore the genome-wide DNA methylation landscape underpinning these two diseases.

Methods and results

We used Illumina 450k DNA methylation beadarrays to analyse 21 ascending aorta samples, including 10 cases with AD, 5 with BAV and 6 healthy controls. We adjusted for intra-sample cellular heterogeneity, providing the first unbiased genome-wide exploration of the DNA methylation landscape underpinning these two diseases. We discover that both diseases are characterized by loss of DNA methylation at non-CpG sites. We validate this non-CpG hypomethylation signature with pyrosequencing. In contrast to non-CpGs, AD and BAV exhibit distinct DNA methylation landscapes at CpG sites, with BAV characterized mainly by hypermethylation of EZH2 targets. In the case of AD, integrative DNA methylation gene expression analysis reveals that AD is characterized by a dedifferentiated smooth muscle cell phenotype. Our integrative analysis further reveals hypomethylation associated overexpression of RARA in AD, a pattern which is also seen in cells exposed to smoke toxins.

Conclusion

Our data supports a model in which increased cellular proliferation in AD and BAV underpins loss of methylation at non-CpG sites. Our data further supports a model, in which AD is associated with an inflammatory vascular remodeling process, possibly mediated by the epigenome and linked to environmental risk factors such as smoking.

Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification

One of the goals of cancer research is to identify a set of genes that cause or control disease progression. However, although multiple such gene sets were published, these are usually in very poor agreement with each other, and very few of the genes proved to be functional therapeutic targets. Furthermore, recent findings from a breast cancer gene-expression cohort showed that sets of genes selected randomly can be used to predict survival with a much higher probability than expected. These results imply that many of the genes identified in breast cancer gene expression analysis may not be causal of cancer progression, even though they can still be highly predictive of prognosis. We performed a similar analysis on all the cancer types available in the cancer genome atlas (TCGA), namely, estimating the predictive power of random gene sets for survival. Our work shows that most cancer types exhibit the property that random selections of genes are more predictive of survival than expected. In contrast to previous work, this property is not removed by using a proliferation signature, which implies that proliferation may not always be the confounder that drives this property. We suggest one possible solution in the form of data-driven sub-classification to reduce this property significantly. Our results suggest that the predictive power of random gene sets may be used to identify the existence of sub-classes in the data, and thus may allow better understanding of patient stratification. Furthermore, by reducing the observed bias this may allow more direct identification of biologically relevant, and potentially causal, genes.

Multiple gene sets have been published as predictive of cancer progression and metastasis in several cancer types. Although many of these sets proved to be highly predictive of survival, even gene sets for the same cancer (but from different data-sets or different analyses) exhibit very little overlap and to date did not provide functional therapeutic targets. Recent studies found that in breast cancer, even random gene sets can predict survival much better than would be expected, and on average are better than many published gene sets. Together, these results undermine the causal role of the published gene sets and their potential clinical implications. We show that random gene sets predict survival in many cancer types, and that this property no longer exists after splitting the data into subclasses based on data-driven clusters. This suggests that such sub-classification could increase the likelihood to identify causal genes that are potential therapeutic targets, and that this property can be used as an indication that there may be subclasses within the dataset.

Data-driven analysis of immune infiltrate in a large cohort of breast cancer and its association with disease progression, ER activity, and genomic complexity

The tumor microenvironment is now widely recognized for its role in tumor progression, treatment response, and clinical outcome. The intratumoral immunological landscape, in particular, has been shown to exert both pro-tumorigenic and anti-tumorigenic effects. Identifying immunologically active or silent tumors may be an important indication for administration of therapy, and detecting early infiltration patterns may uncover factors that contribute to early risk. Thus far, direct detailed studies of the cell composition of tumor infiltration have been limited; with some studies giving approximate quantifications using immunohistochemistry and other small studies obtaining detailed measurements by isolating cells from excised tumors and sorting them using flow cytometry. Herein we utilize a machine learning based approach to identify lymphocyte markers with which we can quantify the presence of B cells, cytotoxic T-lymphocytes, T-helper 1, and T-helper 2 cells in any gene expression data set and apply it to studies of breast tissue. By leveraging over 2,100 samples from existing large scale studies, we are able to find an inherent cell heterogeneity in clinically characterized immune infiltrates, a strong link between estrogen receptor activity and infiltration in normal and tumor tissues, changes with genomic complexity, and identify characteristic differences in lymphocyte expression among molecular groupings. With our extendable methodology for capturing cell type specific signal we systematically studied immune infiltration in breast cancer, finding an inverse correlation between beneficial lymphocyte infiltration and estrogen receptor activity in normal breast tissue and reduced infiltration in estrogen receptor negative tumors with high genomic complexity.

Identification of pathway-based prognostic gene signatures in patients with multiple myeloma

Molecular profiling is used to extract prognostic gene signatures in different cancers such as multiple myeloma (MM), which is the second most common hematological malignancy. In this study, we utilized gene expression profiles to find biological pathways that could efficiently predict survival time in patients with MM. Four data sets-namely GSE2658 (559 samples), GSE9782 (264 samples), GSE6477 (147 samples), and GSE57317 (55 samples)-were employed. GSE2658 was used as a training data set and the others as validation data sets. The genes significantly associated with survival were identified using the univariate Cox proportional hazards analysis, and their roles in the biological pathways were explored using the Gene-Set Enrichment Analysis (GSEA) in the training data set. Next, the significant genes and their corresponding pathways were used to reconstruct pathway-based prognostic signatures. Thereafter, the significant gene signatures were externally validated in 3 independent cohorts-namely GSE9782, GSE6477, and GSE57317. Our results revealed that 9 pathway-based prognostic signatures were able to efficiently predict survival time in the training data set (Ps < 0.01). The testing of these signatures in the validation data sets demonstrated that 3 signatures-namely MYC targets, spliceosome, and metabolism of RNA-were able to strongly predict the clinical outcome in the 3 cohorts at P values < 0.01. In addition, in the multivariate Cox analysis, the 3 gene signatures remained as independent prognostic factors compared with the routine prognostic variables in MM-namely serum albumin, serum β2-microglobulin, and age. These signatures were by far the most powerful independent prognostic factors (MYC targets: P = 0.009, spliceosome: P = 0.024, and metabolism of RNA: P < 0.001).

BAG-1 as a biomarker in early breast cancer prognosis: a systematic review with meta-analyses

BACKGROUND

The co-chaperone protein Bcl-2-associated athanogene-1 (BAG-1) is overexpressed in breast cancer and has been incorporated in the oncotype DX and PAM50 breast cancer prognostic assays. Bcl-2-associated athanogene-1 exists as multiple protein isoforms that interact with diverse partners, including chaperones Hsc70/Hsp70, Ser/Thr kinase Raf-1 and Bcl-2, to promote cancer cell survival. The BAG-1L isoform specifically binds to and increases the transcriptional activity of oestrogen receptor in cells, and in some, but not all studies, BAG-1 expression is predictive of clinical outcome in breast cancer.

METHODS

A systematic review of published studies reporting BAG-1 (mRNA and/or protein) expression and clinical outcome in early breast cancer. The REporting Recommendations for Tumour MARKer and Prognostic Studies (REMARK) criteria were used as a template against which data were assessed. Meta-analyses were performed for studies that provided a hazard ratio and 95% confidence intervals for clinical outcomes including disease-free survival or breast cancer-specific survival from univariate analysis.

RESULTS

Eighteen studies used differing methodologies and reported on differing outcomes. Meta-analyses were only possible on results from a subset of reported studies. Meta-analyses suggested improved outcome with high BAG-1 mRNA and high BAG-1 nuclear expression by immunohistochemisty.

CONCLUSIONS

Increased levels of BAG-1 are associated with better breast cancer outcomes.

Molecular genetics complexity impeding research progress in breast and ovarian cancers

Breast and ovarian cancer are heterogeneous diseases. While breast cancer accounts for 25% of cancers worldwide, ovarian cancer accounts for 3.5% of all cancers and it is considered to be the most lethal type of cancer among women. In Oman, breast cancer accounts for 25% and ovarian cancer for 4.5% of all cancer cases. Various risk factors, including variable biological and clinical traits, are involved in the onset of breast and ovarian cancer. Although highly developed diagnostic and therapeutic methods have paved the way for better management, targeted therapy against specific biomarkers has not yet shown any significant improvement, particularly in triple-negative breast cancer and epithelial ovarian cancer, which are associated with high mortality rates. Thus, elucidating the mechanisms underlying the pathology of these diseases is expected to improve their prevention, prognosis and management. The aim of the present study was to provide a comprehensive review and updated information on genomics and proteomics alterations associated with cancer pathogenesis, as reported by several research groups worldwide. Furthermore, molecular research in our laboratory, aimed at identifying new pathways involved in the pathogenesis of breast and ovarian cancer using microarray and chromatin immunoprecipitation (ChIP), is discussed. Relevant candidate genes were found to be either up- or downregulated in a cohort of breast cancer cases. Similarly, ChIP analysis revealed that relevant candidate genes were regulated by the E2F5 transcription factor in ovarian cancer tissue. An ongoing study aims to validate these genes with a putative role as biological markers that may contribute to the development of targeted therapies for breast and ovarian cancer.

The C/EBPδ protein is stabilized by estrogen receptor α activity, inhibits SNAI2 expression and associates with good prognosis in breast cancer

Hypoxia and inflammatory cytokines like interleukin-6 (IL-6, IL6) are strongly linked to cancer progression, and signal in part through the transcription factor Ccaat/enhancer-binding protein δ (C/EBPδ, CEBPD), which has been shown to promote mesenchymal features and malignant progression of glioblastoma. Here we report a different role for C/EBPδ in breast cancer. We found that the C/EBPδ protein is expressed in normal breast epithelial cells and in low-grade cancers. C/EBPδ protein (but not mRNA) expression correlates with estrogen receptor (ER+) and progesterone receptor (PGR) expression and longer progression-free survival of breast cancer patients. Specifically in ER+ breast cancers, CEBPD-but not the related CEBPB-mRNA in combination with IL6 correlated with lower risk of progression. Functional studies in cell lines showed that ERα promotes C/EBPδ expression at the level of protein stability by inhibition of the FBXW7 pathway. Furthermore, we found that C/EBPδ attenuates cell growth, motility and invasiveness by inhibiting expression of the SNAI2 (Slug) transcriptional repressor, which leads to expression of the cyclin-dependent kinase inhibitor CDKN1A (p21CIP1/WAF1). These findings identify a molecular mechanism by which ERα signaling reduces the aggressiveness of cancer cells, and demonstrate that C/EBPδ can have different functions in different types of cancer. Furthermore, our results support a potentially beneficial role for the IL-6 pathway specifically in ER+ breast cancer and call for further evaluation of the role of intra-tumoral IL-6 expression and of which cancers might benefit from current attempts to target the IL-6 pathway as a therapeutic strategy.

CAFÉ-Map: Context Aware Feature Mapping for mining high dimensional biomedical data

Feature selection and ranking is of great importance in the analysis of biomedical data. In addition to reducing the number of features used in classification or other machine learning tasks, it allows us to extract meaningful biological and medical information from a machine learning model. Most existing approaches in this domain do not directly model the fact that the relative importance of features can be different in different regions of the feature space. In this work, we present a context aware feature ranking algorithm called CAFÉ-Map. CAFÉ-Map is a locally linear feature ranking framework that allows recognition of important features in any given region of the feature space or for any individual example. This allows for simultaneous classification and feature ranking in an interpretable manner. We have benchmarked CAFÉ-Map on a number of toy and real world biomedical data sets. Our comparative study with a number of published methods shows that CAFÉ-Map achieves better accuracies on these data sets. The top ranking features obtained through CAFÉ-Map in a gene profiling study correlate very well with the importance of different genes reported in the literature. Furthermore, CAFÉ-Map provides a more in-depth analysis of feature ranking at the level of individual examples.

AVAILABILITY

CAFÉ-Map Python code is available at: http://faculty.pieas.edu.pk/fayyaz/software.html#cafemap . The CAFÉ-Map package supports parallelization and sparse data and provides example scripts for classification. This code can be used to reconstruct the results given in this paper.

Centrosomal Protein of 55 Regulates Glucose Metabolism, Proliferation and Apoptosis of Glioma Cells via the Akt/mTOR Signaling Pathway

Introduction: Glioma is one of the most common and most aggressive brain tumors in humans. The molecular and cellular mechanisms responsible for the onset and the progression of glioma are elusive and controversial. Centrosomal protein of 55 (CEP55) was initially described as a highly coiled-coil protein that plays critical roles in cell division, but was recently identified as being overexpressed in many human cancers. The function of CEP55 has not previously been characterized in glioma. We aim to discover the effect and mechanism of CEP55 in glioma development.

Method: qRT-PCR and immunohistochemistry were used to analyze CEP55 expression. Glucose uptake, western blot, MTS, CCK-8, Caspase-3 activity and TUNEL staining assays were performed to investigate the role and mechanism of CEP55 on glioma cell process.

Results: We found that the levels of CEP55 expression were upregulated in glioma. In addition, CEP55 appeared to regulate glucose metabolism of glioma cells. Furthermore, knockdown of CEP55 inhibited cell proliferation and induced cell apoptosis in glioma. Finally, we provided preliminary evidence that knockdown of CEP55 inhibited glioma development via suppressing the activity of Akt/mTOR signaling.

Conclusions: Our results demonstrated that CEP55 regulates glucose metabolism, proliferation and apoptosis of glioma cells via the Akt/mTOR signaling pathway, and its promotive effect on glioma tumorigenesis can be a potential target for glioma therapy in the future.

Recombinant human neuregulin-1β is protective against radiation-induced myocardial cell injury

The aim of the present study was to investigate the role of recombinant human neuregulin-1β (rhNRG-1β) in the repair of the radiation-induced damage of myocardial cells and the underlying mechanism. Rats were divided into the radiotherapy alone group, the rhNRG-1β group (radiotherapy with rhNRG‑1β treatment) and the Herceptin group (radiotherapy with Herceptin treatment), and their myocardial cells were analyzed. The morphology of the myocardial cells was observed under an optical microscope, and the expression of γ‑H2AX and p53 was analyzed using immunohistochemistry and western blot analysis. Damage to the myocardial cells was identified in the three groups following radiation treatment, which was identified by cell swelling and altered morphology. The integrated optical density values of γ‑H2AX in the radiotherapy alone, rhNRG‑1β and Herceptin groups were 50.96±5.548, 27.63±10.61 and 76.12±2.084, respectively. The OD of the radiotherapy alone group was significantly higher than that of the rhNRG‑1β treated group (P<0.0001), and the value of the Herceptin group was significantly higher than that of the radiotherapy alone group (P<0.0001). The p53 level in the rhNRG‑1β group was less than that of the radiotherapy alone group (P<0.001), and was higher in the Herceptin group compared with the radiotherapy alone group (P<0.0001). Thus, rhNRG‑1β can ameliorate radiotherapy-induced myocardial cell injury, predominantly by enhancing myocardial cell DNA repair, inhibiting cell apoptosis and improving myocardial function. The results of this study in myocardial cells suggest that patients with thoracic cancer may benefit from treatment with rhNRG‑1β for the repair of the radiation-induced damage of myocardial cells.

NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities

The sequential chain of interactions altering the binary state of a biomolecule represents the ‘information flow’ within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein–protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes—network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets.

Interleukin-6 is a potential therapeutic target in interleukin-6 dependent, estrogen receptor-α-positive breast cancer

Introduction

Interleukin-6 (IL-6) is an important growth factor for estrogen receptor-α (ERα)-positive breast cancer, and elevated serum IL-6 is associated with poor prognosis.

Methods

The role of the phosphorylated signal transducer and activator of transcription 3 pathway was investigated in ERα-positive breast cancer. A panel of cell lines was treated with exogenous IL-6. An IL-6 specific gene signature was generated by profiling ten ERα-positive breast cancer cell lines alone or following treatment with 10 ng/mL recombinant IL-6 or human marrow stromal cell-conditioned media, with or without siltuximab (a neutralizing anti-IL-6 antibody) and grown in three-dimensional tumor microenvironment-aligned cultures for 4 days, 5 days, or 6 days. The established IL-6 signature was validated against 36 human ERα-positive breast tumor samples with matched serum. A comparative MCF-7 xenograft murine model was utilized to determine the role of IL-6 in estrogen-supplemented ERα-positive breast cancer to assess the efficacy of anti-IL-6 therapy in vivo.

Results

In eight of nine ERα-positive breast cancer cell lines, recombinant IL-6 increased phosphorylation of tyrosine 705 of STAT3. Differential gene expression analysis identified 17 genes that could be used to determine IL-6 pathway activation by combining their expression intensity into a pathway activation score. The gene signature included a variety of genes involved in immune cell function and migration, cell growth and apoptosis, and the tumor microenvironment. Validation of the IL-6 gene signature in 36 matched human serum and ERα-positive breast tumor samples showed that patients with a high IL-6 pathway activation score were also enriched for elevated serum IL-6 (≥10 pg/mL). When human IL-6 was provided in vivo, MCF-7 cells engrafted without the need for estrogen supplementation, and addition of estrogen to IL-6 did not further enhance engraftment. Subsequently, we prophylactically treated mice at MCF-7 engraftment with siltuximab, fulvestrant, or combination therapy. Siltuximab alone was able to blunt MCF-7 engraftment. Similarly, siltuximab alone induced regressions in 90% (9/10) of tumors, which were established in the presence which were established in the presence of hMSC expressing human IL-6 and estrogen.

Conclusion

Given the established role for IL-6 in ERα-positive breast cancer, these data demonstrate the potential for anti-IL-6 therapeutics in breast cancer.

Iteratively refining breast cancer intrinsic subtypes in the METABRIC dataset

BACKGROUND

Multi-gene lists and single sample predictor models have been currently used to reduce the multidimensional complexity of breast cancers, and to identify intrinsic subtypes. The perceived inability of some models to deal with the challenges of processing high-dimensional data, however, limits the accurate characterisation of these subtypes. Towards the development of robust strategies, we designed an iterative approach to consistently discriminate intrinsic subtypes and improve class prediction in the METABRIC dataset.

FINDINGS

In this study, we employed the CM1 score to identify the most discriminative probes for each group, and an ensemble learning technique to assess the ability of these probes on assigning subtype labels using 24 different classifiers. Our analysis is comprised of an iterative computation of these methods and statistical measures performed on a set of over 2000 samples. The refined labels assigned using this iterative approach revealed to be more consistent and in better agreement with clinicopathological markers and patients' overall survival than those originally provided by the PAM50 method.

CONCLUSIONS

The assignment of intrinsic subtypes has a significant impact in translational research for both understanding and managing breast cancer. The refined labelling, therefore, provides more accurate and reliable information by improving the source of fundamental science prior to clinical applications in medicine.