Identification of Biomarkers for Breast Cancer Using Databases

In Situ Simultaneous Detection of Surface Protein and microRNA in Clustered Extracellular Vesicles from Cancer Cell Lines Using Flow Cytometry

Extracellular vesicles (EVs) are becoming increasingly important in liquid biopsy for cancer because they contain multiple biomarkers, including proteins and RNAs, and circulate throughout the body. Cancer cell-derived EVs are highly heterogeneous, and multiplexed biomarker detection techniques are required to improve the accuracy of diagnosis. In addition, in situ EV biomarker detection increases the efficiency of the detection process because EVs are difficult to handle. In this study, in situ simultaneous detection of EV surface proteins, programmed cell death-ligand 1 (PD-L1), and internal miRNA-21 (miR-21) analyzed by conventional flow cytometry was developed for a breast cancer liquid biopsy. However, the majority of EVs were not recognized by flow cytometry for biomarker detection because the size of EVs was below the detectable size range of the flow cytometer. To solve this problem, the formation of EV clusters was induced by 1,2-distearoyl-sn-glycero-3-phosphoethanolamine (DSPE)-polyethylene glycol-DSPE during biomarker detection. Consequently, both PD-L1 and miR-21 detection signals from cancer cell-derived EVs were drastically increased, making them distinguishable from normal cell-derived EVs. The in situ simultaneous cancer biomarker detection from EV clusters analyzed by flow cytometry contributes to an increase in the sensitivity and accuracy of the EV-based liquid biopsy for cancer.

Preclinical and Clinical Trials of New Treatment Strategies Targeting Cancer Stem Cells in Subtypes of Breast Cancer

Breast cancer (BC) can be classified into various histological subtypes, each associated with different prognoses and treatment options, including surgery, radiation, chemotherapy, and endocrine therapy. Despite advances in this area, many patients still face treatment failure, the risk of metastasis, and disease recurrence, which can ultimately lead to death. Mammary tumors, like other solid tumors, contain a population of small cells known as cancer stem-like cells (CSCs) that have high tumorigenic potential and are involved in cancer initiation, progression, metastasis, tumor recurrence, and resistance to therapy. Therefore, designing therapies specifically targeting at CSCs could help to control the growth of this cell population, leading to increased survival rates for BC patients. In this review, we discuss the characteristics of CSCs, their surface biomarkers, and the active signaling pathways associated with the acquisition of stemness in BC. We also cover preclinical and clinical studies that focus on evaluating new therapy systems targeted at CSCs in BC through various combinations of treatments, targeted delivery systems, and potential new drugs that inhibit the properties that allow these cells to survive and proliferate.

Chemokines in Triple-Negative Breast Cancer Heterogeneity: New Challenges for Clinical Implications

Tumor heterogeneity is a hallmark of cancer and one of the primary causes of resistance to therapies. Triple-negative breast cancer (TNBC), which accounts for 15% to 20% of all breast cancers and is the most aggressive subtype, is very diverse, connected to metastatic potential and response to therapy. It is a very diverse disease at the molecular, pathologic, and clinical levels. TNBC is substantially more likely to recur and has a worse overall survival rate following diagnosis than other breast cancer subtypes. Chemokines, low molecular weight proteins that stimulate chemotaxis, have been shown to control the cues responsible for TNBC heterogeneity. In this review, we have focused on tumor heterogeneity and the role of chemokines in modulating tumor heterogeneity, since this is the most critical issue in treating TNBC. Additionally, we examined numerous cues mediated by chemokine networks that contribute to the heterogeneity of TNBC. Recent developments in our knowledge of the chemokine networks that regulate TNBC heterogeneity may pave the door for developing difficult-to-treat TNBC treatment options.

Identification of KIF4A and its effect on the progression of lung adenocarcinoma based on the bioinformatics analysis

BACKGROUND

Lung adenocarcinoma (LUAD) is the most frequent histological type of lung cancer, and its incidence has displayed an upward trend in recent years. Nevertheless, little is known regarding effective biomarkers for LUAD.

METHODS

The robust rank aggregation method was used to mine differentially expressed genes (DEGs) from the gene expression omnibus (GEO) datasets. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to extract hub genes from the protein-protein interaction (PPI) network. The expression of the hub genes was validated using expression profiles from TCGA and Oncomine databases and was verified by real-time quantitative PCR (qRT-PCR). The module and survival analyses of the hub genes were determined using Cytoscape and Kaplan-Meier curves. The function of KIF4A as a hub gene was investigated in LUAD cell lines.

RESULTS

The PPI analysis identified seven DEGs including BIRC5, DLGAP5, CENPF, KIF4A, TOP2A, AURKA, and CCNA2, which were significantly upregulated in Oncomine and TCGA LUAD datasets, and were verified by qRT-PCR in our clinical samples. We determined the overall and disease-free survival analysis of the seven hub genes using GEPIA. We further found that CENPF, DLGAP5, and KIF4A expressions were positively correlated with clinical stage. In LUAD cell lines, proliferation and migration were inhibited and apoptosis was promoted by knocking down KIF4A expression.

CONCLUSION

We have identified new DEGs and functional pathways involved in LUAD. KIF4A, as a hub gene, promoted the progression of LUAD and might represent a potential therapeutic target for molecular cancer therapy.

Breast cancer stem cells: A review of their characteristics and the agents that affect them

The evolving concept that cancer stem cells (CSCs) are the driving element in cancer development, evolution and heterogeneity, has overridden the previous model of a tumor consisting of cells all with similar sequentially acquired mutations and a similar potential for renewal, invasion and metastasis. This paradigm shift has focused attention on therapeutically targeting CSCs directly as a means of eradicating the disease. In breast cancers, CSCs can be identified by cell surface markers and are characterized by their ability to self-renew and differentiate, resist chemotherapy and radiation, and initiate new tumors upon serial transplantation in xenografted mice. These functional properties of CSCs are regulated by both intracellular and extracellular factors including pluripotency-related transcription factors, intracellular signaling pathways and external stimuli. Several classes of natural products and synthesized compounds have been studied to target these regulatory elements and force CSCs to lose stemness and/or terminally differentiate and thereby achieve a therapeutic effect. However, realization of an effective treatment for breast cancers, focused on the biological effects of these agents on breast CSCs, their functions and signaling, has not yet been achieved. In this review, we delineate the intrinsic and extrinsic factors identified to date that control or promote stemness in breast CSCs and provide a comprehensive compilation of potential agents that have been studied to target breast CSCs, transcription factors and stemness-related signaling. Our aim is to stimulate further study of these agents that could become the basis for their use as stand-alone treatments or components of combination therapies effective against breast cancers.

Six-gene-based prognostic model predicts overall survival in patients with uveal melanoma

BACKGROUND

Uveal melanoma (UM) is the most common primary intraocular tumor in adults, which has a high mortality rate and worse prognosis. Therefore, early potential molecular detection and prognostic evaluation seem more important for early diagnosis and treatment.

METHODS

Gene expression data were obtained from The Cancer Genome Atlas-Uveal melanomas database. Survival genes were identified by univariate analysis and were regarded to be associated with the overall survival of UM patients. Then, pathway enrichment analysis of these survival genes was performed. Robust likelihood-based survival model and multivariate survival analysis were conducted to identify more reliable genes and the prognostic signature for UM survival prediction. Two internal datasets and another two UM datasets from Gene Expression Omnibus (GEO) were used for the validation of prognostic signature.

RESULTS

Firstly, 2,010 survival genes were screened by univariate survival analysis. GO and KEGG analysis revealed that these genes were mainly involved in pathways such as mRNA processing, RNA splicing, spliceosome and ubiquitin mediated proteolysis. Secondly, a six-gene signature was identified by Robust likelihood-based survival model approach. The gene expression of the six genes can successfully divide UM samples into high- and low-risk groups and have strong survival prediction ability. What's more, the expression of six genes was compared in 80 healthy adipose tissue samples obtained from GTEx (Genotype-Tissue Expression) database and further validated in internal datasets and GEO datasets, which also can predict UM patient survival.

CONCLUSIONS

The six genes (SH2D3A, TMEM201, LZTS1, CREG1, NIPA1 and HIST1H4E) model might play a vital role in prognosis of UM, which should be helpful for further insight into the treatment of uveal melanoma.

Highly robust model of transcription regulator activity predicts breast cancer overall survival

BACKGROUND

While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes.

METHODS

Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome.

RESULT

We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients.

CONCLUSION

Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression.

Nanomedicine: A Promising Avenue for the Development of Effective Therapy for Breast Cancer

PURPOSE

Breast cancer is the most probable cancer among women. However, the available treatment is based on targeting different stages of breast cancer viz., radiation therapy, hormonal therapy, chemotherapy, and surgical interventions, which have some limitations. The available chemotherapeutics are associated with problems like low solubility, low permeability, high first-pass metabolism, and P-glycoprotein efflux. Hence, the aforementioned restrictions lead to ineffective treatment. Multiple chemotherapeutics can also cause resistance in tumors. So, the purpose is to develop an effective therapeutic regimen for the treatment of breast cancer by applying a nanomedicinal approach.

METHODS

This review has been conducted on a systematic search strategy, based on relevant literature available on Pub Med, MedlinePlus, Google Scholar, and Sciencedirect up to November 2019 using keywords present in abstract and title of the review. As per our inclusion and exclusion criteria, 226 articles were screened. Among 226, a total of 40 articles were selected for this review.

RESULTS

The significant findings with the currently available treatment is that the drug, besides its distribution to the target-specific site, also distributes to healthy cells, which results in severe side effects. Moreover, the drug is less bioavailable at the site of action; therefore, to overcome this, a high dose is required, which again causes side effects and lower the benefits. Nanomedicinal approaches give an alternative approach to avoid the associated problems of available chemotherapeutics treatment of breast cancer.

CONCLUSION

The nanomedicinal strategies are useful over the conventional treatment of breast cancer and deliver a target-specific drug-using different novel drug delivery approaches.

Associations Between Apparent Diffusion Coefficient Values and the Prognostic Factors of Breast Cancer

OBJECTIVES

Breast imaging can offer great information about breast cancer heterogeneity. The purpose of this study was to analyze the relationship between apparent diffusion coefficient (ADC) values and various prognostic factors and investigate whether ADC values are useful for breast cancer diagnosis, evaluation of treatment response, and determination of prognosis.

METHODS

A total of 111 cases of breast cancer were included in this study. Magnetic resonance findings were recorded according to the Breast Imaging Reporting and Data System magnetic resonance imaging lexicon. Diffusion-weighted imaging rim sign and minimum, maximum, and difference ADC values (ADCdiff) were also evaluated.

RESULTS

ADCdiff was related to all prognostic factors such as histological grade, Ki-67, tumor size, molecular subtype, axillary node metastasis, lymphvascular invasion, internal enhancement pattern, intratumoral high T2 signal, peritumoral edema, and diffusion-weighted imaging rim sign, whereas minimum and maximum ADC values showed variable associations.

CONCLUSIONS

Apparent diffusion coefficient values were shown to be correlated with many proven or possible prognostic factors of breast cancer. In particular, ADCdiff can reflect tumor heterogeneity and showed higher correlation.

Matrix Metalloproteinase 11 Is a Potential Therapeutic Target in Lung Adenocarcinoma

Lung cancer is one of the leading causes of cancer-associated death, with the etiology largely unknown. The aim of this study was to identify key driver genes with therapeutic potentials in lung adenocarcinoma (LUAD). Transcriptome microarray data from four GEO datasets (GEO: GSE7670, GSE10072, GSE68465, and GSE43458) were jointly analyzed for differentially expressed genes (DEGs). Ontologic analysis showed that most of the upregulated DEGs enriched in collagen catabolic and fibril organization processes were regulated by matrix metalloproteinases (MMPs). Matrix metalloproteinase 11 (MMP11), the highest upregulated MMP family member in LUAD-transformed cells, acted in an autocrine manner and was significantly increased in sera of LUAD patients. MMP11 depletion severely impaired LUAD cell proliferation, migration, and invasion in vitro, in line with retarded tumor growth in xenograft models. Treatment of different human LUAD cell lines with anti-MMP11 antibody significantly retarded cell growth and migration. Administration of anti-MMP11 antibody at a dose of 1 μg/g body weight significantly suppressed tumor growth in xenograft models. These findings indicate that MMP11 is a key cancer driver gene in LUAD and is an appealing target for antibody therapy.

Twenty-gene-based prognostic model predicts lung adenocarcinoma survival

Introduction

Lung adenocarcinoma (LAC) accounts for more than a half of non-small cell lung cancer with high morbidity and mortality. Progression of treatment has not accelerated the improvement of its prognosis. Hence, it is an urgent need to develop novel biomarkers for its early diagnosis and treatment.

Materials and methods

In this study, we proposed to identify LAC survival-related genes through comprehensive analysis of large-scale gene expression profiles. LAC gene expression data sets were obtained from The Cancer Genome Atlas (TCGA). Identification of differentially expressed genes (DEGs) in LAC compared with adjacent normal lung tissues was first performed followed by univariate Cox regression analysis to obtain genes that are significantly associated with LAC survival (SurGenes). Then, we conducted sure independence screening (SIS) for SurGenes to identify more reliable genes and the prognostic signature for LAC survival prediction. Another two lung cancer data sets from TCGA and Gene Expression Omnibus (GEO) were used for the validation of prognostic signature.

Results

A total of 20 genes were obtained, which were significantly associated with the overall survival (OS) of LAC patients. The prognostic signature, a weighted linear combination of the 20 genes, could successfully separate LAC samples with high OS from those with low OS and had robust predictive performance for survival (training set: p-value <2.2×10⁻¹⁶; testing set: p-value =2.04×10⁻⁵, area under the curve (AUC) =0.615). Combined with GEO data set, we obtained four genes, that is, FUT4, SLC25A42, IGFBP1, and KLHDC8B that are found in both the prognostic signature and DEGs of LAC in GEO data set.

Discussion

The prognostic signature combined with multi-gene expression profiles provides a moderate OS prediction for LAC and should be helpful for appropriate treatment method selection.

Developments in Point-of-Care Diagnostic Technology for Cancer Detection

Cancer is the cause of death for one in seven individuals worldwide. It is widely acknowledged that screening and early diagnosis are of vital importance for improving the likelihood of recovery. However, given the costly, time-consuming, and invasive nature of the many methods currently in use, patients often do not take advantage of the services available to them. Consequently, many researchers are exploring the possibility of developing fast, reliable, and non-invasive diagnostic tools that can be used directly or by local physicians at the point-of-care. Herein, we look at the use of established biomarkers in cancer therapy and investigate emerging biomarkers exhibiting future potential. The incorporation of these biomarkers into point-of-care devices could potentially reduce the strain currently experienced by screening programs in hospitals and healthcare systems. Results derived from point-of-care tests should be accurate, sensitive, and generated rapidly to assist in the selection of the best course of treatment for optimal patient care. Essentially, point-of-care diagnostics should enhance the well-being of patients and lead to a reduction in cancer-related deaths.

Identifying genes as potential prognostic indicators in patients with serous ovarian cancer resistant to carboplatin using integrated bioinformatics analysis

Serous ovarian cancer (SOC) accounts for >50% of all epithelial ovarian cancers. However, patients with SOC present with various degrees of response to platinum‑based chemotherapy and, thus, their survival may differ. The present study aimed to identify the candidate genes involved in the carcinogenesis and drug resistance of SOC by analyzing the microarray datasets GDS1381 and GDS3592. GDS1381 and GDS3592 were downloaded from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/gds/). A total of 219 differentially expressed genes (DEGs) were identified. Potential genes that may predict the response to carboplatin and, thus, the prognosis of SOC were analyzed. The enriched functions and pathways of DEGs included extracellular region, extracellular space and extracellular exosome, among others. Upon screening the upregulated and downregulated genes on the connectivity map, 10 small‑molecule drugs were identified that may be helpful in improving drug sensitivity in patients with ovarian cancer. A total of 30 hub genes were screened for further analysis after constructing the protein‑to‑protein interaction network. Through survival analysis, comparison of genes across numerous analyses, and immunohistochemistry, GNAI1, non‑structural maintenance of chromosomes (non‑SMC) condensin I complex subunit H (NCAPH), matrix metallopeptidase 9 (MMP9), aurora kinase A (AURKA) and enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2) were identified as the key molecules that may be involved in the carcinogenesis and carboplatin resistance of SOC. In conclusion, GNAI1, NCAPH, MMP9, AURKA and EZH2 should be examined in further studies for the possibility of their participation in the carcinogenesis and carboplatin response of SOC.

Tumor Heterogeneity in Breast Cancer

Breast cancer is a heterogeneous disease and differs greatly among different patients (intertumor heterogeneity) and even within each individual tumor (intratumor heterogeneity). Clinical and morphologic intertumor heterogeneity is reflected by staging systems and histopathologic classification of breast cancer. Heterogeneity in the expression of established prognostic and predictive biomarkers, hormone receptors, and human epidermal growth factor receptor 2 oncoprotein is the basis for targeted treatment. Molecular classifications are indicators of genetic tumor heterogeneity, which is probed with multigene assays and can lead to improved stratification into low- and high-risk groups for personalized therapy. Intratumor heterogeneity occurs at the morphologic, genomic, transcriptomic, and proteomic levels, creating diagnostic and therapeutic challenges. Understanding the molecular and cellular mechanisms of tumor heterogeneity that are relevant to the development of treatment resistance is a major area of research. Despite the improved knowledge of the complex genetic and phenotypic features underpinning tumor heterogeneity, there has been only limited advancement in diagnostic, prognostic, or predictive strategies for breast cancer. The current guidelines for reporting of biomarkers aim to maximize patient eligibility for targeted therapy, but do not take into account intratumor heterogeneity. The molecular classification of breast cancer is not implemented in routine clinical practice. Additional studies and in-depth analysis are required to understand the clinical significance of rapidly accumulating data. This review highlights inter- and intratumor heterogeneity of breast carcinoma with special emphasis on pathologic findings, and provides insights into the clinical significance of molecular and cellular mechanisms of heterogeneity.

Transforming Growth Factor-β Drives the Transendothelial Migration of Hepatocellular Carcinoma Cells

The entry of malignant hepatocytes into blood vessels is a key step in the dissemination and metastasis of hepatocellular carcinoma (HCC). The identification of molecular mechanisms involved in the transmigration of malignant hepatocytes through the endothelial barrier is of high relevance for therapeutic intervention and metastasis prevention. In this study, we employed a model of hepatocellular transmigration that mimics vascular invasion using hepatic sinusoidal endothelial cells and malignant hepatocytes evincing a mesenchymal-like, invasive phenotype by transforming growth factor (TGF)-β. Labelling of respective cell populations with various stable isotopes and subsequent mass spectrometry analyses allowed the “real-time” detection of molecular changes in both transmigrating hepatocytes and endothelial cells. Interestingly, the proteome profiling revealed 36 and 559 regulated proteins in hepatocytes and endothelial cells, respectively, indicating significant changes during active transmigration that mostly depends on cell–cell interaction rather than on TGF-β alone. Importantly, matching these in vitro findings with HCC patient data revealed a panel of common molecular alterations including peroxiredoxin-3, epoxide hydrolase, transgelin-2 and collectin 12 that are clinically relevant for the patient’s survival. We conclude that hepatocellular plasticity induced by TGF-β is crucially involved in blood vessel invasion of HCC cells.