Knockdown of SERPINE1 reverses resistance of triple‑negative breast cancer to paclitaxel via suppression of VEGFA

3D Bioprinted Tumor-Stroma Models of Triple-Negative Breast Cancer Stem Cells for Preclinical Targeted Therapy Evaluation

Breast cancer stem cells (CSCs) play a pivotal role in therapy resistance and tumor relapse, emphasizing the need for reliable in vitro models that recapitulate the complexity of the CSC tumor microenvironment to accelerate drug discovery. We present a bioprinted breast CSC tumor-stroma model incorporating triple-negative breast CSCs (TNB-CSCs) and stromal cells (human breast fibroblasts), within a breast-derived decellularized extracellular matrix bioink. Comparison of molecular signatures in this model with different clinical subtypes of bioprinted tumor-stroma models unveils a unique molecular profile for artificial CSC tumor models. We additionally demonstrate that the model can recapitulate the invasive potential of TNB-CSC. Surface-enhanced Raman scattering imaging allowed us to monitor the invasive potential of tumor cells in deep z-axis planes, thereby overcoming the depth-imaging limitations of confocal fluorescence microscopy. As a proof-of-concept application, we conducted high-throughput drug testing analysis to assess the efficacy of CSC-targeted therapy in combination with conventional chemotherapeutic compounds. The results highlight the usefulness of tumor-stroma models as a promising drug-screening platform, providing insights into therapeutic efficacy against CSC populations resistant to conventional therapies.

Regulation of VEGF-A expression and VEGF-A-targeted therapy in malignant tumors

Vascular endothelial growth factor A (VEGF-A), a highly conserved dimeric glycoprotein, is a key regulatory gene and a marker molecule of angiogenesis. The upregulation of VEGF-A facilitates the process of tumor vascularization, thereby fostering the initiation and progression of malignant neoplasms. Many genes can adjust the angiogenesis of tumors by changing the expression of VEGF-A. In addition, VEGF-A also exhibits immune regulatory properties, which directly or indirectly suppresses the antitumor activity of immune cells. The emergence of VEGF-A-targeted therapy alone or in rational combinations has revolutionized the treatment of various cancers. This review discusses how diverse mechanisms in various tumors regulate VEGF-A expression to promote tumor angiogenesis and the role of VEGF-A in tumor immune microenvironment. The application of drugs targeting VEGF-A in tumor therapy is also summarized including antibody molecule drugs and traditional Chinese medicine.

High-Throughput Transcriptomics of Nontumorigenic Breast Cells Exposed to Environmentally Relevant Chemicals

BACKGROUND

There is a suite of chemicals, including metals, pesticides, and personal care product compounds, which are commonly detected at high levels in US Center for Disease Control's National Health and Nutrition Examination Survey (NHANES) chemical biomarker screens. Whether these chemicals influence development of breast cancer is not well understood.

OBJECTIVES

The objectives were to perform an unbiased concentration-dependent assessment of these chemicals, to quantify differences in cancer-specific genes and pathways, to describe if these differences occur at human population-relevant concentrations, and to specifically test for differences in markers of stemness and cellular plasticity.

METHODS

We treated nontumorigenic mammary epithelial cells, MCF10A, with 21 chemicals at four concentrations (25  nM , 250  nM , 2.5 μ M , and 25 μ M ) for 48 h. We conducted RNA-sequencing for these 408 samples, adapting the plexWell plate-based RNA-sequencing method to analyze differences in gene expression. We calculated gene and biological pathway-specific benchmark concentrations (BMCs) using BMDExpress3, identifying differentially expressed genes and generating the best fit benchmark concentration models for each chemical across all genes. We identified enriched biological processes and pathways for each chemical and tested whether chemical exposures change predicted cell type distributions. We contextualized benchmark concentrations relative to human population biomarker concentrations in NHANES.

RESULTS

We detected chemical concentration-dependent differences in gene expression for thousands of genes. Enrichment and cell type distribution analyses showed benchmark concentration responses correlated with differences in breast cancer-related pathways, including induction of basal-like characteristics for some chemicals, including arsenic, lead, copper, and methyl paraben. Comparison of benchmark data to NHANES chemical biomarker (urine or blood) concentrations indicated an overlap between exposure levels and levels sufficient to cause a gene expression response.

DISCUSSION

These analyses revealed that many of these 21 chemicals resulted in differences in genes and pathways involved in breast cancer in vitro at human exposure-relevant concentrations. https://doi.org/10.1289/EHP12886.

Research and experimental verification on the mechanisms of cellular senescence in triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with high heterogeneity, poor prognosis, and a low 10-year survival rate of less than 50%. Although cellular senescence displays extensive effects on cancer, the comprehensions of cellular senescence-related characteristics in TNBC patients remains obscure.

Method

Single-cell RNA sequencing (scRNA-seq) data were analyzed by Seurat package. Scores for cellular senescence-related pathways were computed by single-sample gene set enrichment analysis (ssGSEA). Subsequently, unsupervised consensus clustering was performed for molecular cluster identification. Immune scores of patients in The Cancer Genome Atlas (TCGA) dataset and associated immune cell scores were calculated using Estimation of STromal and Immune cells in MAlignantTumours using Expression data (ESTIMATE) and Microenvironment Cell Populations-counter (MCP-counter), Tumor Immune Estimation Resource (TIMER) and Estimating the Proportion of Immune and Cancer cells (EPIC) methods, respectively. Immunotherapy scores were assessed using TIDE. Furthermore, feature genes were identified by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses; these were used to construct a risk model. Additionally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and transwell assay were conducted for in vitro validation of hub genes.

Result

TNBC was classified into three subtypes based on cellular senescence-related pathways as clusters 1, 2, and 3. Specifically, cluster 1 showed the best prognosis, followed by cluster 2 and cluster 3. The levels of gene expression in cluster 2 were the lowest, whereas these were the highest in cluster 3. Moreover, clusters 1 and 3 showed a high degree of immune infiltration. TIDE scores were higher for cluster 3, suggesting that immune escape was more likely in patients with the cluster 3 subtype who were less likely to benefit from immunotherapy. Next, the TNBC risk model was constructed and validated. RT-qPCR revealed that prognostic risk genes (MMP28, ACP5 and KRT6A) were up-regulated while protective genes (CT83) were down-regulated in TNBC cell lines, validating the results of the bioinformatics analysis. Meanwhile, cellular experiments revealed that ACP5 could promote the migration and invasion abilities in two TNBC cell lines. Finally, we evaluated the validity of prognostic models for assessing TME characteristics and TNBC chemotherapy response.

Conclusion

In conclusion, these findings help to assess the efficacy of targeted therapies in patients with different molecular subtypes, have practical applications for subtype-specific treatment of TNBC patients, and provide information on prognostic factors, as well as guidance for the revelation of the molecular mechanisms by which senescence-associated genes influence TNBC progression.

Construction of a Cuproptosis-Related Gene Signature for Predicting Prognosis in Gastric Cancer

This study aimed to develop and validate a cuproptosis-related gene signature for the prognosis of gastric cancer. The data in TCGA GC TPM format from UCSC were extracted for analysis, and GC samples were randomly divided into training and validation groups. Pearson correlation analysis was used to obtain cuproptosis-related genes co-expressed with 19 Cuproptosis genes. Univariate Cox and Lasso regression analyses were used to obtain cuproptosis-related prognostic genes. Multivariate Cox regression analysis was used to construct the final prognostic risk model. The risk score curve, Kaplan-Meier survival curves, and ROC curve were used to evaluate the predictive ability of Cox risk model. Finally, the functional annotation of the risk model was obtained through enrichment analysis. Then, a six-gene signature was identified in the training cohort and verified among all cohorts using Cox regression analyses and Kaplan-Meier plots, demonstrating its independent prognostic significance for gastric cancer. In addition, ROC analysis confirmed the significant predictive potential of this signature for the prognosis of gastric cancer. Functional enrichment analysis was mainly related to cell-matrix function. Therefore, a new cuproptosis-related six-gene signature (ACLY, FGD6, SERPINE1, SPATA13, RANGAP1, and ADGRE5) was constructed for the prognosis of gastric cancer, allowing for tailored prediction of outcome and the formulation of novel therapeutics for gastric cancer patients.

A phenome-wide approach to identify causal risk factors for deep vein thrombosis

Deep vein thrombosis (DVT) is the formation of a blood clot in a deep vein. DVT can lead to a venous thromboembolism (VTE), the combined term for DVT and pulmonary embolism, a leading cause of death and disability worldwide. Despite the prevalence and associated morbidity of DVT, the underlying causes are not well understood. Our aim was to leverage publicly available genetic summary association statistics to identify causal risk factors for DVT. We conducted a Mendelian randomization phenome-wide association study (MR-PheWAS) using genetic summary association statistics for 973 exposures and DVT (6,767 cases and 330,392 controls in UK Biobank). There was evidence for a causal effect of 57 exposures on DVT risk, including previously reported risk factors (e.g. body mass index-BMI and height) and novel risk factors (e.g. hyperthyroidism and varicose veins). As the majority of identified risk factors were adiposity-related, we explored the molecular link with DVT by undertaking a two-sample MR mediation analysis of BMI-associated circulating proteins on DVT risk. Our results indicate that circulating neurogenic locus notch homolog protein 1 (NOTCH1), inhibin beta C chain (INHBC) and plasminogen activator inhibitor 1 (PAI-1) influence DVT risk, with PAI-1 mediating the BMI-DVT relationship. Using a phenome-wide approach, we provide putative causal evidence that hyperthyroidism, varicose veins and BMI enhance the risk of DVT. Furthermore, the circulating protein PAI-1 has a causal role in DVT aetiology and is involved in mediating the BMI-DVT relationship.

Pten knockout affects drug resistance differently in melanoma and kidney cancer

BACKGROUND

PTEN is a tumor suppressor that is often mutated and nonfunctional in many types of cancer. The high heterogeneity of PTEN function between tumor types makes new Pten knockout models necessary to assess its impact on cancer progression and/or treatment outcomes.

METHODS

We aimed to show the effect of CRISPR/Cas9-mediated Pten knockout on murine melanoma (B16 F10) and kidney cancer (Renca) cells. We evaluated the effect of PTEN deregulation on tumor progression in vivo and in vitro, as well as on the effectiveness of drug treatment in vitro. In addition, we studied the molecular changes induced by Pten knockout.

RESULTS

In both models, Pten mutation did not cause significant changes in cell proliferation in vitro or in vivo. Cells with Pten knockout differed in sensitivity to cisplatin treatment: in B16 F10 cells, the lack of PTEN induced sensitivity and, in Renca cells, resistance to drug treatment. Accumulation of pAKT was observed in both cell lines, but only Renca cells showed upregulation of the p53 level after Pten knockout. PTEN deregulation also varied in the way that it altered PAI-1 secretion in the tested models, showing a decrease in PAI-1 in B16 F10 Pten/KO and an increase in Renca Pten/KO cells. In kidney cancer cells, Pten knockout caused changes in epithelial to mesenchymal transition marker expression, with downregulation of E-cadherin and upregulation of Snail, Mmp9, and Acta2 (α-SMA).

CONCLUSIONS

The results confirmed heterogenous cell responses to PTEN loss, which may lead to a better understanding of the role of PTEN in particular types of tumors and points to PTEN as a therapeutic target for personalized medicine.

The predictive effect of immune therapy and chemotherapy under T cell-related gene prognostic index for Gastric cancer

Background: Gastric cancer (GC) is one of the most common malignancies in the human digestive tract. CD4+T cells can eliminate tumor cells directly through the mechanism of cytolysis, they can also indirectly attack tumor cells by regulating the tumor TME. A prognostic model of CD4+T cells is urgently needed to improve treatment strategies and explore the specifics of this interaction between CD4+T cells and gastric cancer cells. Methods: The detailed data of GC samples were downloaded from the Cancer Genome Atlas (TCGA), GSE66229, and GSE84437 datasets. CD4⁺ T cell-related genes were identified to construct a risk-score model by using the Cox regression method and validated with the Gene Expression Omnibus (GEO) dataset. In addition, postoperative pathological tissues of 139 gastric cancer patients were randomly selected for immunohistochemical staining, and their prognostic information were collected for external verification. Immune and molecular characteristics of these samples and their predictive efficacy in immunotherapy and chemotherapy were analysed. Results: The training set and validation set had consistent results, with GC patients of high PROC and SERPINE1 expression having poorer prognosis. In order to improve their clinical application value, we constructed a risk scoring model and established a high-precision nomogram. Low-risk patients had a better overall survival (OS) than high-risk patients, consistent with the results from the GEO cohort. Furthermore, the risk-score model can predict infiltration of immune cells in the tumor microenvironment of GC, as well as the response of immunotherapy. Correlations between the abundance of immune cells with PROC and SERPINE1 genes were shown in the prognostic model according to the training cohort. Finally, sensitive drugs were identified for patients in different risk subgroup. Conclusion: The risk model not only provides a basis for better prognosis in GC patients, but also is a potential prognostic indicator to distinguish the molecular and immune characteristics of the tumor, and its response to immune checkpoint inhibitor (ICI) therapy and chemotherapy.

SERPINE1 and its co-expressed genes are associated with the progression of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma(ccRCC) is a frequently occurring malignant tumor of the urinary system. Despite extensive research, the regulatory mechanisms underlying the pathogenesis and progression of ccRCC remain largely unknown.

METHODS

We downloaded 5 ccRCC expression profiles from the Gene Expression Omnibus (GEO) database and obtained the list of differentially expressed genes (DEGs). Using String and Cytoscape tools, we determined the hub genes of ccRCC, and then analyzed their relationship with ccRCC patient survival. Ultimately, we identified SERPINE1 as a prognostic factor in ccRCC. Meanwhile, we confirmed the role of SERPINE1 in 786-O cells by cell transfection and in vitro experiments.

RESULTS

Our analysis yielded a total of 258 differentially expressed genes, comprising 105 down-regulated genes and 153 up-regulated genes. Survival analysis of SERPINE1 expression in The Cancer Genome Atlas (TCGA) confirmed its association with the increase of tumor grade, lymph node metastasis, and tumor stage, as well as with shorter survival. Furthermore, we found that SERPINE1 expression levels were associated with CD8 + T cells, CD4 + T cells, B cells, macrophages, neutrophils, and dendritic cells. Cell experiments showed that knockdown SERPINE1 expression could inhibit the proliferation, migration and invasion of ccRCC cells. Among the co-expressed genes with the highest correlation, ITGA5, SLC2A3, SLC2A14, SHC1, CEBPB, and ADA were overexpressed and associated with shorter overall survival (OS) in ccRCC.

CONCLUSIONS

In this study, we identified hub genes that are strongly related to ccRCC, and highlights the potential utility of overexpressed SERPINE1 and its co-expressed genes could be used as prognostic and diagnostic biomarkers in ccRCC.

Development of Clioquinol Platinum(IV) Conjugates as Autophagy-Targeted Antimetastatic Agents

A series of autophagy-targeted antimetastatic clioquinol (CLQ) platinum(IV) conjugates were designed and prepared by incorporating an autophagy activator CLQ into the platinum(IV) system. Complex 5 with the cisplatin core bearing dual CLQ ligands with potent antitumor properties was screened out as a candidate. More importantly, it displayed potent antimetastatic properties both in vitro and in vivo as expected. Mechanism investigation manifested that complex 5 induced serious DNA damage to increase γ-H2AX and P53 expression and caused mitochondria-mediated apoptosis through the Bcl-2/Bax/caspase3 pathway. Then, it promoted prodeath autophagy by suppressing PI3K/AKT/mTOR signaling and activating the HIF-1α/Beclin1 pathway. The T-cell immunity was elevated by restraining the PD-L1 expression and subsequently increasing CD3⁺ and CD8⁺ T cells. Ultimately, metastasis of tumor cells was suppressed by the synergistic effects of DNA damage, autophagy promotion, and immune activation aroused by CLQ platinum(IV) complexes. Key proteins VEGFA, MMP-9, and CD34 tightly associated with angiogenesis and metastasis were downregulated.

Inhibition of IκB Kinase Is a Potential Therapeutic Strategy to Circumvent Resistance to Epidermal Growth Factor Receptor Inhibition in Triple-Negative Breast Cancer Cells

Triple-negative breast cancer (TNBC) remains as an intractable malignancy with limited therapeutic targets. High expression of epidermal growth factor receptor (EGFR) has been associated with a poor prognosis of TNBC; however, EGFR targeting has failed with unfavorable clinical outcomes. Here, we performed a combinatorial screening of fifty-five protein kinase inhibitors with the EGFR inhibitor gefitinib in the TNBC cell line MDA-MB-231 and identified the IκB kinase (IKK) inhibitor IKK16 as a sensitizer of gefitinib. Cell viability and clonogenic survival assays were performed to evaluate the antiproliferative effects of the gefitinib and IKK16 (Gefitinib + IKK16) combination in TNBC cell lines. Western blot analyses were also performed to reveal the potential mode of action of this combination. In addition, next-generation sequencing (NGS) analysis was performed in Gefitinib+IKK16-treated cells. The Gefitinib+IKK16 treatment synergistically reduced cell viability and colony formation of TNBC cell lines such as HS578T, MDA-MB-231, and MDA-MB-468. This combination downregulated p-STAT3, p-AKT, p-mTOR, p-GSK3β, and p-RPS6. In addition, p-NF-κB and the total NF-κB were also regulated by this combination. Furthermore, NGS analysis revealed that NF-κB/RELA targets including CCL2, CXCL8, EDN1, IL-1β, IL-6, and SERPINE1 were further reduced and several potential tumor suppressors, such as FABP3, FADS2, FDFT1, SEMA6A, and PCK2, were synergistically induced by the Gefitinib-+IKK16 treatment. Taken together, we identified the IKK/NF-κB pathway as a potential target in combination of EGFR inhibition for treating TNBC.

Identification of therapeutically potential targets and their ligands for the treatment of OSCC

Recent advancements in cancer biology have revealed molecular changes associated with carcinogenesis and chemotherapeutic exposure. The available information is being gainfully utilized to develop therapies targeting specific molecules involved in cancer cell growth, survival, and chemoresistance. Targeted therapies have dramatically increased overall survival (OS) in many cancers. Therefore, developing such targeted therapies against oral squamous cell carcinoma (OSCC) is anticipated to have significant clinical implications. In the current work, we have identified drug-specific sensitivity-related prognostic biomarkers (BOP1, CCNA2, CKS2, PLAU, and SERPINE1) using gene expression, Cox proportional hazards regression, and machine learning in OSCC. Dysregulation of these markers is significantly associated with OS in many cancers. Their elevated expression is related to cellular proliferation and aggressive malignancy in various cancers. Mechanistically, inhibition of these biomarkers should significantly reduce cellular proliferation and metastasis in OSCC and should result in better OS. It is pertinent to note that no effective small-molecule candidate has been identified against these biomarkers to date. Therefore, a comprehensive in silico drug design strategy assimilating homology modeling, extensive molecular dynamics (MD) simulation, and ensemble molecular docking has been applied to identify potential compounds against identified targets, and potential molecules have been identified. We hope that this study will help in deciphering potential genes having roles in chemoresistance and a significant impact on OS. It will also result in the identification of new targeted therapeutics against OSCC.

Identification of Potential miRNA-mRNA Regulatory Network in the Development of Oral Cancer

Background

Oral cancer is a difficult question in modern medical system, and there are few effective strategies to completely heal these diseases. This research investigated the miRNA-mRNA network in oral cancer development via bioinformatics excavation.

Methods

GSE28100 and GSE23558 in the GEO database were selected for bioinformatics analysis. The datasets were analyzed with GEO2R to obtain the related matrix files. The hot plot and heatmap of the matrix files were drawn with R language. The MiRDIP database was applied to predict and screen the targets of miRNAs. The DEGs in the matrix files were analyzed with the DAVID database and visualized with R language for enrichment analysis. The PPI-network of the DEGs was established with the STRING database and Cytoscape. Besides, the miRNA-mRNA was visualized by Cytoscape.

Results

35 genes were identified as the DEGs in GES28100. 1651 genes were identified as the DEGs in GSE23558. 143 common genes in the targets of miRNAs in GSE28100 and the DEGs in GSE the targets of DEGs in GES28100 and common genes were enriched in the PI3K/AKT pathway, MAPK pathway, etc. The DEGs in GSE28100 and GSE23558 were involved in the regulations of transcription from RNA polymerase II promoter and DNA transcription. The DEGs in GSE28100 and GSE23558 were established with the miRNA-mRNA network.

Conclusion

This research identified miR-15b-5p, miR-199a-3p, miR-21-5p, miR-424-5p, and miR-454-3p as the biomarker of oral cancer and established the miRNA-mRNA network in oral progression.

Establishment and Analysis of an Individualized EMT-Related Gene Signature for the Prognosis of Breast Cancer in Female Patients

Background. The current high mortality rate of female breast cancer (BC) patients emphasizes the necessity of identifying powerful and reliable prognostic signatures in BC patients. Epithelial-mesenchymal transition (EMT) was reported to be associated with the development of BC. The purpose of this study was to identify prognostic biomarkers that predict overall survival (OS) in female BC patients by integrating data from TCGA database. Method. We first downloaded the dataset in TCGA and identified gene signatures by overlapping candidate genes. Differential analysis was performed to find differential EMT-related genes. Univariate regression analysis was then performed to identify candidate prognostic variables. We then developed a prognostic model by multivariate analysis to predict OS. Calibration curves, receiver operating characteristics (ROC) curves, $C$ -index, and decision curve analysis (DCA) were used to test the veracity of the prognostic model. Result. In this study, we identified and validated a prognostic model integrating age and six genes (CD44, P3H1, SDC1, COL4A1, TGFβ1, and SERPINE1). $C$ -index values for BC patients were 0.672 (95% CI 0.611–0.732) and 0.692 (95% CI 0.586–0.798) in the training cohort and test set, respectively. The calibration curve and the DCA curve show the good predictive performance of the model. Conclusion. This study offered a robust predictive model for OS prediction in female BC patients and may provide a more accurate treatment strategy and personalized therapy in the future.

A Comprehensive Analysis for Expression, Diagnosis, and Prognosis of m5C Regulator in Breast Cancer and Its ncRNA–mRNA Regulatory Mechanism

Recent studies have well demonstrated that 5-methylcytosine (m⁵C) regulators play pivotal roles in pathological conditions, including cancer. This study first tried to identify potential 5-methylcytosine (m⁵C) regulators in breast cancer by combination of expression, diagnosis, and survival analyses, and then established an ncRNA–mRNA network accounting for m⁵C regulators’ roles in breast cancer. Among 13 m⁵C regulators, DNMT3B and ALYREF were significantly upregulated in breast cancer and their high expression indicated unfavorable prognosis. Both DNMT3B and ALYREF possessed the statistical abilities to distinguish breast cancer from normal breast samples. Moreover, five potential upstream miRNAs (let-7b-5p, miR-195-5p, miR-29a-3p, miR-26a-5p, and miR-26b-5p) of m⁵C regulators could not only serve as independent prognostic predictors but also together made up a promising miRNA prognostic signature in breast cancer. Next, upstream potential lncRNAs of the five miRNAs were predicted and analyzed. Pathway enrichment analysis revealed that the target genes of these miRNAs were markedly enriched in some cancer-related pathways, and further investigation indicated VEGFA and EZH2 were found to be the most potential target genes in the m⁵C regulators-related ncRNA–mRNA network in breast cancer. These findings comprehensively provided key clues for developing m⁵C regulators-related effective therapeutic targets and promising diagnostic biomarkers in breast cancer.

Identifying drivers of breast cancer metastasis in progressively invasive subpopulations of zebrafish-xenografted MDA-MB-231

Cancer metastasis is the primary cause of the high mortality rate among human cancers. Efforts to identify therapeutic agents targeting cancer metastasis frequently fail to demonstrate efficacy in clinical trials despite strong preclinical evidence. Until recently, most preclinical studies used mouse models to evaluate anti-metastatic agents. Mouse models are time-consuming and expensive. In addition, an important drawback is that mouse models inadequately model the early stages of metastasis which plausibly leads to the poor correlation with clinical outcomes.

Here, we report an in vivo model based on xenografted zebrafish embryos where we select for progressively invasive subpopulations of MDA-MB-231 breast cancer cells. A subpopulation analogous to circulating tumor cells found in human cancers was selected by injection of MDA-MB-231 cells into the yolk sacs of 2 days post-fertilized zebrafish embryos and selecting cells that migrated to the tail. The selected subpopulation derived from MDA-MB-231 cells were increasingly invasive in zebrafish. Isolation of these subpopulations and propagation in vitro revealed morphological changes consistent with activation of an epithelial-mesenchymal transition program. Differential gene analysis and knockdown of genes identified gene-candidates (DDIT4, MT1X, CTSD, and SERPINE1) as potential targets for anti-metastasis therapeutics. Furthermore, RNA-splicing analysis reinforced the importance of BIRC5 splice variants in breast cancer metastasis. This is the first report using zebrafish to isolate and expand progressively invasive populations of human cancer cells. The model has potential applications in understanding the metastatic process, identification and/or development of therapeutics that specifically target metastatic cells and formulating personalized treatment strategies for individual cancer patients.

Platelet-Related Molecular Subtype to Predict Prognosis in Hepatocellular Carcinoma

Purpose

Complex crosstalk between tumor cells and platelets is closely related to the development, relapse, and drug resistance of hepatocellular carcinoma (HCC). Therefore, an intensive analysis of the relationship between platelet-related genes and the effectiveness of immunotherapy is necessary for improving the poor prognosis of HCC patients.

Methods

Genes associated with platelets in the GeneCards database were collected and were used to identify molecular subtypes using a non-negative matrix decomposition algorithm (NMF) and constructed a platelet-related genes-based prognostic stratification model by the LASSO-Cox regression and stepwise Cox regression analysis. The effect of this feature on the immune microenvironment of HCC and the response to immune checkpoint inhibitors was also explored.

Results

After identifying two molecular subtypes, we constructed a platelet-related genes-based prognostic stratification model that can be effectively used for immune checkpoint inhibitor (PD1, PD-L1, PD-L2, and CTLA4) efficacy and prognosis prediction in HCC patients, which was subsequently validated using patient samples from ICGC, GSE14520 and a small sample size clinical cohort. We also found downregulation of PAFAH1B3 remarkably inhibited the proliferation and migration ability of Hep3B cells by cytological experiments.

Conclusion

We constructed a prognostic classifier based on platelet-related genes that could effectively classify HCC patients for prognostic prediction and provide new light on the selection of optimal individualized antiplatelet therapy for HCC patients in future clinical practice.

BAP1-Related ceRNA (NEAT1/miR-10a-5p/SERPINE1) Promotes Proliferation and Migration of Kidney Cancer Cells

Background

BAP1 is an important tumor suppressor involved in various biological processes and is commonly lost or inactivated in clear-cell renal cell carcinoma (ccRCC). However, the role of the BAP1-deficient tumor competing endogenous RNA (ceRNA) network involved in ccRCC remains unclear. Thus, this study aims to investigate the prognostic BAP1-related ceRNA in ccRCC.

Methods

Raw data was obtained from the TCGA and the differentially expressed genes were screened to establish a BAP1-related ceRNA network. Subsequently, the role of the ceRNA axis was validated using phenotypic experiments. Dual-luciferase reporter assays and fluorescence in situ hybridization (FISH) assays were used to confirm the ceRNA network.

Results

Nuclear enriched abundant transcript 1 (NEAT1) expression was significantly increased in kidney cancer cell lines. NEAT1 knockdown significantly inhibited cell proliferation and migration, which could be reversed by miR-10a-5p inhibitor. Dual-luciferase reporter assay confirmed miR-10a-5p as a common target of NEAT1 and Serine protease inhibitor family E member 1 (SERPINE1). FISH assays revealed the co-localization of NEAT1 and miR-10a-5p in the cytoplasm. Additionally, the methylation level of SERPINE1 in ccRCC was significantly lower than that in normal tissues. Furthermore, SERPINE1 expression was positively correlated with multiple immune cell infiltration levels.

Conclusions

In BAP1-deficient ccRCC, NEAT1 competitively binds to miR-10a-5p, indirectly upregulating SERPINE1 expression to promote kidney cancer cell proliferation. Furthermore, NEAT1/miR-10a-5p/SERPINE1 were found to be independent prognostic factors of ccRCC.

Integration of immune and hypoxia gene signatures improves the prediction of radiosensitivity in breast cancer

Immunity and hypoxia are two important factors that affect the response of cancer patients to radiotherapy. At the same time, considering the limited predictive value of a single predictive model and the uncertainty of grouping patients near the cutoff value, we developed and validated a combined model based on immune- and hypoxia-related gene expression profiles to predict the radiosensitivity of breast cancer patients. This study was based on breast cancer data from The Cancer Genome Atlas (TCGA). Spike-and-slab Lasso regression analysis was performed to select three immune-related genes and develop a radiosensitivity model. Lasso Cox regression modeling selected 11 hypoxia-related genes for development of radiosensitivity model. Three independent datasets (Molecular Taxonomy of Breast Cancer International Consortium [METABRIC], E-TABM-158, GSE103746) were used to validate the predictive value of radiosensitivity signatures. In the TCGA dataset, the 10-year survival probabilities of the immune radioresistant (IRR) and hypoxia radioresistant (HRR) groups were 0.189 (0.037, 0.973) and 0.477 (0.293, 0.776), respectively. The 10-year survival probabilities of the immune radiosensitive (IRS) and hypoxia radiosensitive (HRS) groups were 0.778 (0.676, 0.895) and 0.824 (0.723, 0.939), respectively. Based on these two gene signatures, we further constructed a combined model and divided all patients into three groups (IRS/HRS, mixed, IRR/HRR). We identified the IRS/HRS patients most likely to benefit from radiotherapy; the 10-year survival probability was 0.886 (0.806, 0.976). The 10-year survival probability of the IRR/HRR group was 0. In conclusion, a combined model integrating immune- and hypoxia-related gene signatures could effectively predict the radiosensitivity of breast cancer and more accurately identify radiosensitive and radioresistant patients than a single model.

Cancer-Associated Fibroblasts: Mechanisms of Tumor Progression and Novel Therapeutic Targets

Simple Summary

The tumor microenvironment plays an important role in determining the biological behavior of several of the more aggressive malignancies. Among the various cell types evident in the tumor “field”, cancer-associated fibroblasts (CAFs) are a heterogenous collection of activated fibroblasts secreting a wide repertoire of factors that regulate tumor development and progression, inflammation, drug resistance, metastasis and recurrence. Insensitivity to chemotherapeutics and metastatic spread are the major contributors to cancer patient mortality. This review discusses the complex interactions between CAFs and the various populations of normal and neoplastic cells that interact within the dynamic confines of the tumor microenvironment with a focus on the involved pathways and genes.

Abstract

Cancer-associated fibroblasts (CAFs) are a heterogenous population of stromal cells found in solid malignancies that coexist with the growing tumor mass and other immune/nonimmune cellular elements. In certain neoplasms (e.g., desmoplastic tumors), CAFs are the prominent mesenchymal cell type in the tumor microenvironment, where their presence and abundance signal a poor prognosis in multiple cancers. CAFs play a major role in the progression of various malignancies by remodeling the supporting stromal matrix into a dense, fibrotic structure while secreting factors that lead to the acquisition of cancer stem-like characteristics and promoting tumor cell survival, reduced sensitivity to chemotherapeutics, aggressive growth and metastasis. Tumors with high stromal fibrotic signatures are more likely to be associated with drug resistance and eventual relapse. Clarifying the molecular basis for such multidirectional crosstalk among the various normal and neoplastic cell types present in the tumor microenvironment may yield novel targets and new opportunities for therapeutic intervention. This review highlights the most recent concepts regarding the complexity of CAF biology including CAF heterogeneity, functionality in drug resistance, contribution to a progressively fibrotic tumor stroma, the involved signaling pathways and the participating genes.

Molecular Mechanisms, Biomarkers and Emerging Therapies for Chemotherapy Resistant TNBC

Triple-negative breast cancer (TNBC) is associated with high recurrence rates, high incidence of distant metastases, and poor overall survival (OS). Taxane and anthracycline-containing chemotherapy (CT) is currently the main systemic treatment option for TNBC, while platinum-based chemotherapy showed promising results in the neoadjuvant and metastatic settings. An early arising of intrinsic or acquired CT resistance is common and represents the main hurdle for successful TNBC treatment. Numerous mechanisms were uncovered that can lead to the development of chemoresistance. These include cancer stem cells (CSCs) induction after neoadjuvant chemotherapy (NACT), ATP-binding cassette (ABC) transporters, hypoxia and avoidance of apoptosis, single factors such as tyrosine kinase receptors (EGFR, IGFR1), a disintegrin and metalloproteinase 10 (ADAM10), and a few pathological molecular pathways. Some biomarkers capable of predicting resistance to specific chemotherapeutic agents were identified and are expected to be validated in future studies for a more accurate selection of drugs to be employed and for a more tailored approach, both in neoadjuvant and advanced settings. Recently, based on specific biomarkers, some therapies were tailored to TNBC subsets and became available in clinical practice: olaparib and talazoparib for BRCA1/2 germline mutation carriers larotrectinib and entrectinib for neurotrophic tropomyosin receptor kinase (NTRK) gene fusion carriers, and anti-trophoblast cell surface antigen 2 (Trop2) antibody drug conjugate therapy for heavily pretreated metastatic TNBC (mTNBC). Further therapies targeting some pathologic molecular pathways, apoptosis, miRNAS, epidermal growth factor receptor (EGFR), insulin growth factor 1 receptor (IGF-1R), and androgen receptor (AR) are under investigation. Among them, phosphatidylinositol 3 kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) and EGFR inhibitors as well as antiandrogens showed promising results and are under evaluation in Phase II/III clinical trials. Emerging therapies allow to select specific antiblastics that alone or by integrating the conventional therapeutic approach may overcome/hinder chemoresistance.

Identification of a novel circular RNA circZNF652/miR-486-5p/SERPINE1 signaling cascade that regulates cancer aggressiveness in glioblastoma (GBM)

Circular RNAs (circRNAs) are closely associated with cancer development in glioblastoma (GBM), and this study aims to explore the molecular mechanisms of a novel circular RNA circZNF652 in regulating GBM aggressiveness. The present study found that CircZNF652 and SERPINE1 were upregulated, while miR-486-5p was downregulated in GBM tissues and cell lines, and GBM patients with high expression of CircZNF652 and SERPINE1, and patients with low expression of miR-486-5p tended to have a worse prognosis. Further results validated that both silencing of circZNF652 and miR-486-5p overexpression suppressed cell growth, migration, invasion, epithelial–mesenchymal transition (EMT) and tumorigenesis in GBM cells in vitro and in vivo. Next, the underlying mechanisms were investigated, and we found that circZNF652 sponged miR-486-5p to upregulate SERPINE1 in GBM cells. Also, we validated that knock-down of circZNF652 regulated the miR-486-5p/SERPINE1 axis to reverse the malignant phenotypes in GBM cells. Interestingly, we noticed that GBM cells derived exosomes were characterized by high-expressed CircZNF652. Collectively, we concluded that targeting the circular RNA circZNF652/miR-486-5p/SERPINE1 axis was a novel and effective strategy to suppress cancer progression in GBM.

Transcriptome study of oleanolic acid in the inhibition of breast tumor growth based on high-throughput sequencing

The function of oleanolic acid (OA) in various types of cancer has been reported frequently, especially for breast cancer. However, the regulation of breast tumor growth in response to OA treatment has not been studied in depth. Here, we first explored the effect of OA treatment on breast tumors in vitro and in vivo and then used RNA-seq technology to study the effect and molecular mechanism of OA treatment of MCF-7 cells, particularly at the level of functional genomics. The results showed that 40 μM OA treatment could significantly inhibit the proliferation and induce the apoptosis of MCF-7 cells. Through analysis of RNA sequencing data quality and differentially expressed genes (DEGs), 67 significantly downregulated genes and 260 significantly upregulated genes were identified to be involved in OA treatment of MCF-7 cells. Among these genes, 43 unique DEGs were enriched in several signaling pathways and Gene Ontology terms, such as p53 signaling pathway, TNF signaling pathway and mTOR signaling pathway. Six downregulated genes, including THBS1, EDN1, CACNG4, CCN2, AXIN2 and BMP4, as well as six upregulated genes, including ATF4, SERPINE1, SESN2, PPARGC1A, EGR1 and JAG1, were selected as target genes in response to OA treatment. The inhibitory effect of OA on breast cancer was also found in the following mouse experiments. Our study provides evidence and molecular support for the treatment of breast cancer with OA.

Transcriptome Analyses of Myometrium from Fibroid Patients Reveals Phenotypic Differences Compared to Non-Diseased Myometrium

Uterine fibroid tissues are often compared to their matched myometrium in an effort to understand their pathophysiology, but it is not clear whether the myometria of uterine fibroid patients represent truly non-disease control tissues. We analyzed the transcriptomes of myometrial samples from non-fibroid patients (M) and compared them with fibroid (F) and matched myometrial (MF) samples to determine whether there is a phenotypic difference between fibroid and non-fibroid myometria. Multidimensional scaling plots revealed that M samples clustered separately from both MF and F samples. A total of 1169 differentially expressed genes (DEGs) (false discovery rate < 0.05) were observed in the MF comparison with M. Overrepresented Gene Ontology terms showed a high concordance of upregulated gene sets in MF compared to M, particularly extracellular matrix and structure organization. Gene set enrichment analyses showed that the leading-edge genes from the TGFβ signaling and inflammatory response gene sets were significantly enriched in MF. Overall comparison of the three tissues by three-dimensional principal component analyses showed that M, MF, and F samples clustered separately from each other and that a total of 732 DEGs from F vs. M were not found in the F vs. MF, which are likely understudied in the pathogenesis of uterine fibroids and could be key genes for future investigation. These results suggest that the transcriptome of fibroid-associated myometrium is different from that of non-diseased myometrium and that fibroid studies should consider using both matched myometrium and non-diseased myometrium as controls.