Overexpression of CLEC3A promotes tumor progression and poor prognosis in breast invasive ductal cancer

Development of a novel prognostic signature based on single-cell combined bulk RNA analysis in breast cancer

BACKGROUND

Breast cancer (BC), a malignant tumor, is a significant cause of death and disability among women globally. Recent research indicates that copy number variation plays a crucial role in tumor development. In this study, we employed the Single-Cell Variational Aneuploidy Analysis (SCEVAN) algorithm to differentiate between malignant and non-malignant cells, aiming to identify genetic signatures with prognostic relevance for predicting patient survival.

METHODS

We analyzed gene expression profiles and associated clinical data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Using the SCEVAN algorithm, we distinguished malignant from non-malignant cells and investigated cellular interactions within the tumor microenvironment (TME). We categorized TCGA samples based on differentially expressed genes (DEGs) between these cell types. Subsequent Kyoto Encyclopedia of Genes and Genomes pathway analysis was conducted. Additionally, we developed polygenic models for the DEGs using least absolute shrinkage and selection operator-penalized Cox regression analysis. To assess the prognostic accuracy of these characteristics, we generated Kaplan-Meier and receiver operating characteristic curves from training and validation datasets. We also monitored the expression variations of prognostic genes across the pseudotime of malignant cells. Patients were divided into high-risk and low-risk groups based on median risk scores to compare their TME and identify potential therapeutic agents. Lastly, polymerase chain reaction was used to validate seven pivotal genes.

RESULTS

The SCEVAN algorithm identified distinct malignant and non-malignant cells in GSE180286. Cellchat analysis revealed significantly increased cellular communication, particularly between fibroblasts, endothelial cells and malignant cells. The DEGs were predominantly involved in immune-related pathways. TCGA samples were classified into clusters A and B based on these genes. Cluster A, enriched in immune pathways, was associated with poorer prognosis, whereas cluster B, predominantly involved in circadian rhythm pathways, showed better outcomes. We constructed a 14-gene prognostic signature, validated in a 1:1 internal TCGA cohort and external GEO datasets (GSE42568 and GSE146558). Kaplan-Meier analysis confirmed the prognostic signature's accuracy (p < 0.001). Receiver operating characteristic curve analysis demonstrated the predictive reliability of these prognostic features. Single-cell pseudotime analysis with monocle2 highlighted the distinct expression trends of these genes in malignant cells, underscoring the intratumoral heterogeneity. Furthermore, we explored the differences in TME between high- and low-risk groups and identified 16 significantly correlated drugs.

CONCLUSION

Our findings suggest that the 14-gene prognostic signature could serve as a novel biomarker for forecasting the prognosis of BC patients. Additionally, the immune cells and pathways in different risk groups indicate that immunotherapy may be a crucial component of treatment strategies for BC patients.

MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study

BACKGROUND

There are few prospective studies on the correlations between MRI features and whole RNA-sequencing data in breast cancer according to molecular subtypes. The purpose of our study was to explore the association between genetic profiles and MRI phenotypes of breast cancer and to identify imaging markers that influences the prognosis and treatment according to subtypes.

METHODS

From June 2017 to August 2018, MRIs of 95 women with invasive breast cancer were prospectively analyzed, using the breast imaging-reporting and data system and texture analysis. Whole RNA obtained from surgical specimens was analyzed using next-generation sequencing. The association between MRI features and gene expression profiles was analyzed in the entire tumor and subtypes. Gene networks, enriched functions, and canonical pathways were analyzed using Ingenuity Pathway Analysis. The P value for differential expression was obtained using a parametric F test comparing nested linear models and adjusted for multiple testing by reporting Q value.

RESULTS

In 95 participants (mean age, 53 years ± 11 [standard deviation]), mass lesion type was associated with upregulation of CCL3L1 (sevenfold) and irregular mass shape was associated with downregulation of MIR421 (sixfold). In estrogen receptor-positive cancer with mass lesion type, CCL3L1 (21-fold), SNHG12 (11-fold), and MIR206 (sevenfold) were upregulated, and MIR597 (265-fold), MIR126 (12-fold), and SOX17 (fivefold) were downregulated. In triple-negative breast cancer with increased standard deviation of texture analysis on precontrast T1-weighted imaging, CLEC3A (23-fold), SRGN (13-fold), HSPG2 (sevenfold), KMT2D (fivefold), and VMP1 (fivefold) were upregulated, and IGLC2 (73-fold) and PRDX4 (sevenfold) were downregulated (all, P < 0.05 and Q < 0.1). Gene network and functional analysis showed that mass type estrogen receptor-positive cancers were associated with cell growth, anti-estrogen resistance, and poor survival.

CONCLUSION

MRI characteristics are associated with the different expressions of genes related to metastasis, anti-drug resistance, and prognosis, depending on the molecular subtypes of breast cancer.

A novel PD-1/PD-L1 pathway molecular typing-related signature for predicting prognosis and the tumor microenvironment in breast cancer

BACKGROUND

Currently, the development of breast cancer immunotherapy based on the PD-1/PD-L1 pathway is relatively slow, and the specific mechanism affecting the immunotherapy efficacy in breast cancer is still unclear.

METHODS

Weighted correlation network analysis (WGCNA) and the negative matrix factorization (NMF) were used to distinguish subtypes related to the PD-1/PD-L1 pathway in breast cancer. Then univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were used to construct the prognostic signature. A nomogram was established based on the signature. The relationship between the signature gene IFNG and breast cancer tumor microenvironment was analyzed.

RESULTS

Four PD-1/PD-L1 pathway-related subtypes were distinguished. A prognostic signature related to PD-1/PD-L1 pathway typing was constructed to evaluate breast cancer's clinical characteristics and tumor microenvironment. The nomogram based on the RiskScore could be used to accurately predict breast cancer patients' 1-year, 3-year, and 5-year survival probability. The expression of IFNG was positively correlated with CD8+ T cell infiltration in the breast cancer tumor microenvironment.

CONCLUSION

A prognostic signature is constructed based on the PD-1/PD-L1 pathway typing in breast cancer, which can guide the precise treatment of breast cancer. The signature gene IFNG is positively related to CD8+ T cell infiltration in breast cancer.

A signature constructed with mitophagy-related genes to predict the prognosis and therapy response for breast cancer

Over the past decades, the incidence and mortality rates of breast cancer (BC) have increased rapidly; however, molecular biomarkers that can reliably detect BC are yet to be discovered. Our study aimed to identify a novel signature that can predict the prognosis of patients with BC. Data from the TCGA-BRCA cohort were analyzed using univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) analysis was performed to build a stable prognostic model. Subsequently, Kaplan–Meier (K–M) and receiver operating characteristic (ROC) analyses were performed to demonstrate the predictive power of our gene signature. Each patient was assigned to either a low- or high-risk group. Patients with high-risk BC had poorer survival than those with low-risk BC. Cox regression analysis suggested that our signature was an independent prognostic factor. Additionally, decision curve analysis and calibration accurately predicted the capacity of our nomogram. Thus, based on the differentially expressed genes (DEGs) of mitophagy-related tumor classification, we established a 13-gene signature and robust nomogram for predicting BC prognosis, which can be beneficial for the diagnosis and treatment of BC.

Identification of Crucial lncRNAs for Luminal A Breast Cancer through RNA Sequencing

BACKGROUND

The growing body of evidence indicates aberrant expression of long noncoding RNAs (lncRNAs) in breast cancer. Nevertheless, a few studies have focused on identifying key lncRNAs for patients with luminal A breast cancer. In our study, we tried to find key lncRNAs and mRNAs in luminal A breast cancer.

METHODS

RNA sequencing was performed to identify differentially expressed mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) in luminal A breast cancer. The protein-protein interaction (PPI), DElncRNA-DEmRNA coexpression, DElncRNA-nearby DEmRNA interaction networks, and functional annotation were performed to uncover the function of DEmRNAs. Online databases were used to validate the RNA sequencing result. The diagnostic value of candidate mRNAs was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

A total number of 1451 DEmRNAs and 272 DElncRNAs were identified. Several hub proteins were identified in the PPI network, including TUBB3, HIST2H3C, MCM2, MYOC, NEK2, LIPE, FN1, FOXJ1, S100A7, and DLK1. In the DElncRNA-DEmRNA coexpression, some hub lncRNAs were identified, including AP001528.2, LINC00968, LINC02202, TRHDE-AS1, LINC01140, AL354707.1, AC097534.1, MIR222HG, and AL662844.4. The mRNA expression result of TFF1, COL10A1, LEP, PLIN1, PGM5-AS1, and TRHDE-AD1 in the GSE98793 was consistent with the RNA sequencing result. The protein expression results of TUBB3, MCM2, MYOC, FN1, S100A7, and TFF1 were consistent with the mRNA expression result COL10A1, LEP, PLIN1, PGM5-AS1, and TRHDE-AD1 were capable of discriminating luminal A breast cancer and normal controls. Four lncRNA-nearby and coexpressed mRNA pairs of HOXC-AS3-HOXC10, AC020907.2-FXYD1, AC026461.1-MT1X, and AC132217.1-IGF2 were identified. AMPK (involved LIPE and LEP) and PPAR (involved PLIN1) were two significantly enriched pathways in luminal A breast cancer.

CONCLUSION

This study could be helpful in unraveling the pathogenesis and providing novel therapeutic strategies for luminal A breast cancer.

Unsupervised Learning Framework With Multidimensional Scaling in Predicting Epithelial-Mesenchymal Transitions

Clustering tumor metastasis samples from gene expression data at the whole genome level remains an arduous challenge, in particular, when the number of experimental samples is small and the number of genes is huge. We focus on the prediction of the epithelial-mesenchymal transition (EMT), which is an underlying mechanism of tumor metastasis, here, rather than tumor metastasis itself, to avoid confounding effects of uncertainties derived from various factors. In this paper, we propose a novel model in predicting EMT based on multidimensional scaling (MDS) strategies and integrating entropy and random matrix detection strategies to determine the optimal reduced number of dimension in low dimensional space. We verified our proposed model with the gene expression data for EMT samples of breast cancer and the experimental results demonstrated the superiority over state-of-the-art clustering methods. Furthermore, we developed a novel feature extraction method for selecting the significant genes and predicting the tumor metastasis. The source code is available at "https://github.com/yushanqiu/yushan.qiu-szu.edu.cn".

Prognostic Biomarker-Based Identification of Drugs for Managing the Treatment of Endometrial Cancer

INTRODUCTION

Uterine corpus endometrial carcinoma (UCEC) causes thousands of deaths per year. To improve the overall survival of patients with UCEC, there is a need to identify prognostic biomarkers and potential drugs.

OBJECTIVES

The aim of this study was twofold: the identification of prognostic gene signatures from expression profiles of pattern recognition receptor (PRR) genes and identification of the most effective existing drugs using the prognostic gene signature.

METHODS

This study was based on the expression profile of PRR genes of 541 patients with UCEC obtained from The Cancer Genome Atlas. Key prognostic signatures were identified using various approaches, including survival analysis, network, and clustering. Hub genes were identified by constructing a co-expression network. Representative genes were identified using k-means and k-medoids-based clustering. Univariate Cox proportional hazard (PH) analysis was used to identify survival-associated genes. 'cmap2' was used to identify potential drugs that can suppress/enhance the expression of prognostic genes.

RESULTS

Models were developed using hub genes and achieved a maximum hazard ratio (HR) of 1.37 (p = 0.294). Then, a clustering-based model was developed using seven genes (HR 9.14; p = 1.49 × 10^-12). Finally, a nine gene-based risk stratification model was developed (CLEC1B, CLEC3A, IRF7, CTSB, FCN1, RIPK2, NLRP10, NLRP9, and SARM1) and achieved HR 10.70; p = 1.1 × 10^-12. The performance of this model improved significantly in combination with the clinical stage and achieved HR 15.23; p = 2.21 × 10^-7. We also developed a model for predicting high-risk patients (survival ≤ 4.3 years) and achieved an area under the receiver operating characteristic curve (AUROC) of 0.86.

CONCLUSION

We identified potential immunotherapeutic agents based on prognostic gene signature: hexamethonium bromide and isoflupredone. Several novel candidate drugs were suggested, including human interferon-α-2b, paclitaxel, imiquimod, MESO-DAP1, and mifamurtide. These biomolecules and repurposed drugs may be utilised for prognosis and treatment for better survival.

Natural cordycepin induces apoptosis and suppresses metastasis in breast cancer cells by inhibiting the Hedgehog pathway

In the study, we investigated the role of the hedgehog (Hh) pathway in cordycepin's effects on human breast cancer cells, with respect to cell growth, apoptosis and metastasis. We found cordycepin to have low toxicity but significant anticancer effects. Cordycepin-induced apoptosis led to increased PUMA, CYTO-C, FAS, DR4/5, and cleaved caspase-3; and decreased BCL-2, XIAP and PDGFR-α. Cordycepin inhibited metastasis, which was associated with up-regulated E-cadherin, and down-regulated N-cadherin, SNAIL, SLUG and ZEB1. Cordycepin also inhibited expression of Hh pathway components and GLI transcriptional activity. Inversely, knockout of GLI blocked cordycepin-mediated effects on the apoptotic, epithelial-mesenchymal transition (EMT) and Notch pathways, which indicates that GLI is crucial for cordycepin's effects against breast cancer. Inhibition of GLI enhanced cordycepin's effect on breast cancer cell growth. To our knowledge, this is the first study of cordycepin's effect on the Hh pathway in breast cancer, and provides preliminary data for the in vivo study, and possible therapeutic use, of cordycepin.

A 17-gene expression-based prognostic signature associated with the prognosis of patients with breast cancer: A STROBE-compliant study

Identification of reliable predictive biomarkers for patients with breast cancer (BC).Univariate Cox proportional hazards regression model was conducted to identify genes correlated with the overall survival (OS) of patients in the TCGA-BRCA cohort. Functional enrichment analysis was conducted to investigate the biological meaning of these survival related genes. Then, patients in TCGA-BCRA were randomly divided into training set and test. Least absolute shrinkage and selection operator (LASSO) penalized Cox regression model was performed and the risk score of BC patients in this model was used to build a prognostic signature. The prognostic performance of the signature was evaluated in the training set, test set, and an independent validation set GSE7390.2519 genes were demonstrated to be significantly associated with the OS of BC patients. Functional annotation of the 2519 genes suggested that these genes were associated with immune response and protein synthesis related gene ontology terms and pathways. 17 genes were identified in the LASSO Cox regression model and used to construct a 17-gene signature. Patients in the 17-gene signature low risk group have better OS and event-free survival compared with those in the 17-gene signature high risk group in the TCGA-BRCA cohort. The prognostic role of the 17-gene signature has been confirmed in the validation cohort. Multivariable Cox proportional hazards regression model suggested the 17-gene signature was an independent prognostic factor in BC.The 17-gene signature we developed could successfully classify patients into high- and low-risk groups, indicating that it might serve as candidate biomarker in BC.

Profiling the transcriptome response of Atlantic salmon head kidney to formalin-killed Renibacterium salmoninarum

Renibacterium salmoninarum is a Gram-positive, intracellular bacterial pathogen that causes Bacterial Kidney Disease (BKD) in Atlantic salmon (Salmo salar). The host transcriptomic response to this immune-suppressive pathogen remains poorly understood. To identify R. salmoninarum-responsive genes, Atlantic salmon were intraperitoneally injected with a low (5 × 10⁵ cells/kg, Low-Rs) or high (5 × 10⁷ cells/kg; High-Rs) dose of formalin-killed R. salmoninarum bacterin or phosphate-buffered saline (PBS control); head kidney samples were collected before and 24 h after injection. Using 44K microarray analysis, we identified 107 and 345 differentially expressed probes in response to R. salmoninarum bacterin (i.e. High-Rs vs. PBS control) by Significance Analysis of Microarrays (SAM) and Rank Products (RP), respectively. Twenty-two microarray-identified genes were subjected to qPCR assays, and 17 genes were confirmed as being significantly responsive to the bacterin. There was an up-regulation in expression of genes playing putative roles as immune receptors and antimicrobial effectors. Genes with putative roles as pathogen recognition (e.g. clec12b and tlr5) or immunoregulatory (e.g. tnfrsf6b and tnfrsf11b) receptors were up-regulated in response to R.salmoninarum bacterin. Also, chemokines and a chemokine receptor showed opposite regulation [up-regulation of effectors (i.e. ccl13 and ccl) and down-regulation of cxcr1] in response to the bacterin. The present study identified and validated novel biomarker genes (e.g. ctsl1, lipe, cldn4, ccny) that can be used to assess Atlantic salmon response to R. salmoninarum, and will be valuable in the development of tools to combat BKD.

Suppression of CLEC3A inhibits osteosarcoma cell proliferation and promotes their chemosensitivity through the AKT1/mTOR/HIF1α signaling pathway

Osteosarcoma (OS) is a primary malignant tumor that occurs in bone, and mainly affects children and adolescents. C-type lectin domain family 3 member A (CLEC3A) is a member of the C-type lectin superfamily, which regulates various biological functions of cells. The present study aimed to identify the effects and related mechanisms of CLEC3A in the proliferation and chemosensitivity of OS cells. The expression of CLEC3A in OS was analyzed using the Gene Expression Omnibus data profile GSE99671, and its expression in OS samples was verified using reverse transcription-quantitative PCR (RT-qPCR) and immunohistochemical staining. The relationship between the expression of CLEC3A and clinical traits in patients with OS was also analyzed, including age, tumor size, TNM stage and lymph node metastasis. Cell Counting Kit-8 assays, colony formation assays and cell cycle distribution analysis were used to determine the roles of CLEC3A in the proliferation and chemosensitivity of OS cells. Finally, RT-qPCR and western blotting were used to demonstrate the relationship between CLEC3A and the AKT1/mTOR/hypoxia-inducible factor 1-α (HIF1α) pathway. Both the mRNA and protein expression levels of CLEC3A were increased in OS tissues compared with adjacent non-tumor tissues, and this was positively associated with TNM stage and lymph node metastasis. The genetic knockdown of CLEC3A with small interfering RNA decreased OS cell proliferation and colony formation, and induced G1 phase arrest, whereas the overexpression of CLEC3A increased OS cell proliferation and colony formation, and alleviated G1 phase arrest. The suppression of CLEC3A also promoted enhanced the chemosensitivity of OS cells to doxorubicin (DOX) and cisplatin (CDDP); it also inhibited the expression of AKT1, mTOR and HIF1α, further to the nuclear localization of HIF1α, and HIF1α target gene expression levels, including VEGF, GLUT1 and MCL1 were also decreased. Furthermore, treatment with the AKT activator SC79 blocked the inhibitory effects of CLEC3A silencing in OS cells. In conclusion, these findings suggested that CLEC3A may function as an oncogene in OS, and that the suppression of CLEC3A may inhibit OS cell proliferation and promote chemosensitivity through the AKT1/mTOR/HIF1α signaling pathway.

IBI: Identification of Biomarker Genes in Individual Tumor Samples

Individual patient biomarkers have an important role in personalized treatment. Although various high-throughput sequencing technologies are widely used in biological experiments, these are usually conducted only once or a few times for each patient, which makes it a challenging problem to identify biomarkers in individual patients. At present, there is a lack of effective methods to identify biomarkers in individual sample data. Here, we propose a novel method, IBI, to identify biomarkers in individual tumor samples. Experimental results from several tumor data sets showed that the proposed method could effectively find biomarker genes for individual patients, including common biomarkers related to the mechanisms of the development of cancer, which can be used to predict survival and drug response in patients. In summary, these results demonstrate that the proposed method offers a new perspective for analyzing individual samples.

Anticancer effect of the traditional Chinese medicine herb Maytenus compound via the EGFR/PI3K/AKT/GSK3β pathway

Background

Cancer is a leading cause of death worldwide; folk anticancer medicinal plants have applied for cancer treatment. The Maytenus compound tablet as traditional Chinese compound medicine has been approved for alleviating hyperplasia of mammary glands, whether it can inhibit cancer still unknown. The study was to evaluate the anticancer activity of the Maytenus compound tablet.

Methods

MTS assay evaluated the anti-proliferation effect of the Maytenus compound on H226, A2058, 786O and HeLa cancer cells and two normal epithelial cell lines, namely, 16HBE and Hecate. Nude mouse xenograft tumor model using H226 and HeLa cells examined the drug’s anticancer effect in vivo. Western blot assay studied the possible mechanism.

Results

The Maytenus compound indicated obvious ability to against proliferation in four strains of cancer cells, particularly against H226 cells by an IC50 of 85.47±10.06 µg/mL and against HeLa cells by an IC50 of 128.74±17.46 µg/mL. However, it had a low cytotoxicity in human normal epithelial cell lines 16HBE with an IC50 of 4,555.86±25.21 µg/mL and Hecate with an IC50 of 833.56±181.88 µg/mL. The Maytenus compound at the 2.45 g/kg oral dosages inhibited the proliferation of H226 cells and HeLa cells in nude mouse with inhibitory rates of 36.06% and 26.45%, respectively, and no organ toxicity. The Maytenus compound could significantly downregulate the expression of pEGFR, pPI3K, pAKT, pGSK3β, β-catenin, and c-MYC and upregulate the protein expression of GSK3β.

Conclusions

The Maytenus compound has significant anticancer activities against human cancer H226 and HeLa cells both in vitro and in vivo, highlighting it may be an anticancer medicine.

CLEC3A, MMP7, and LCN2 as novel markers for predicting recurrence in resected G1 and G2 pancreatic neuroendocrine tumors

Although the postoperative recurrence rate for pancreatic neuroendocrine tumors (PNETs) is reported to be 13.5%-30%, the paucity of valuable biomarkers to predict recurrence poses a problem for the early detection of relapse. Hence, this study aimed to identify new biomarkers to predict the recurrence of PNETs. We performed RNA sequencing (RNA-Seq) on RNA isolated from frozen primary tumors sampled from all localized G1/G2 PNETs resected curatively from 1998 to 2015 in our institution. We calculated differentially expressed genes (DEGs) in tumor with and without recurrence (≥3 years) for the propensity-matched cohort. Gene ontology analysis for the identified DEGs was also performed. Furthermore, we evaluated the expression levels of candidate genes as recurrence predictors via immunostaining. Comparison of transcriptional levels in tumors with and without recurrence identified 166 DEGs. Up- and downregulated genes with high significance in these tumors were mainly related to extracellular organization and cell adhesion, respectively. We observed the top three upregulated genes, C-type lectin domain family 3 member A (CLEC3A), matrix metalloproteinase-7 (MMP7), and lipocalin2 (LCN2) immunohistochemically and compared their levels in recurrent and nonrecurrent tumors. Significantly higher recurrence rate was shown in patients with positive expression of CLEC3A (P = 0.028), MMP7 (P = 0.003), and LCN2 (P = 0.040) than that with negative expression. We identified CLEC3A, MMP7, and LCN2 known to be associated with the phosphatidylinositol-3-kinase/Akt pathway, as potential novel markers to predict the postoperative recurrence of PNETs.