Independent Prognostic Factor In Breast Cancer Research Articles

Characterization of SUSD3 as a novel prognostic biomarker and therapeutic target for breast cancer.

Breast cancer (BC) remains a significant global health challenge, contributing substantially to cancer-related deaths worldwide. Its prevalence and associated death rates remain alarmingly high, highlighting the persistent public health burden. The objective of this study was to systematically examine the involvement of SUSD3 (Sushi Domain-Containing 3) in BC, highlighting its crucial role in the pathogenesis and progression of this disease. BC-related gene microarray data, along with corresponding clinicopathological information, were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Leveraging TIMER and HPA databases, we conducted comparative analyses to evaluate SUSD3 expression in BC. We then analyzed the association between SUSD3 and clinical traits, as well as the prognostic value of SUSD3. SUSD3-related differential expression genes (DEGs) were sent for analysis utilizing GO, KEGG, and GSEA. We utilized SUSD3 mRNA expression to assess immune cells' scores in BC tissues calculated by single-sample enrichment analyses based on "CIBERSORT" R package. Drug sensitivity analysis was used to screen potential drugs sensitive to SUSD3. R software was used for statistical analyses and graphical representation of the data. Our findings confirmed a significant upregulation of SUSD3 expression in BC, which correlated with a favorable prognosis. Clinical correlation analysis further emphasized the strong association between SUSD3 expression and key clinical parameters like estrogen receptor (ER) status, progesterone receptor (PR) status, stage, and T classification in breast cancer. Univariate and multivariate Cox regression analyses showed that SUSD3 could be used as an independent prognostic factor for BC. Differentially expressed genes (DEGs) co-expressed with SUSD3 were significantly associated with various biological processes, such as the cell cycle, DNA replication, p53 signaling pathway, cancer-related pathways, and Wnt signaling pathway, as indicated by gene set enrichment analysis (GSEA). Furthermore, our analysis demonstrated that SUSD3 generally exhibited negative associations with immune modulators. Drug sensitivity analysis revealed positive correlations between SUSD3 and the efficacy of Fulvestrant, Raloxifene, and Fluphenazine. The research emphasizes the significance of SUSD3 as a potential marker for BC, providing insights into the underlying molecular mechanisms implicated in tumorigenesis. SUSD3 holds promise in helping the classification of breast cancer pathological groups, predicting prognosis, and facilitating targeted therapy.

Identification of SMC2 and SMC4 as prognostic markers in breast cancer through bioinformatics analysis.

Breast cancer (BRCA) is one of the most common malignant tumors. The structural maintenance of chromosome (SMC) gene family has been shown to play an important role in human cancers. However, the role of SMC families in BRCA is unclear. This study aimed to explore the role and potential clinical value of whole SMCs in BRCA. TIMER and UALCAN database were used to analysis the expression level. Genetic variations were analyzed by cBioPortal. Promoter methylation and protein level were analyzed by UCLCAN. GO and KEGG were analyzed by Metascape database. Prognostic value of SMCs was analyzed by Kaplan-Meier and multivariate cox regression analyses. Immune infiltration analysis was conducted by CIBERSORT. Immunotherapy outcome prediction was conducted by Cancer Immunome Atlas. Targeted drug therapy outcome prediction was taken by GDSC and R language. The cell viability was tested by CCK8 and migration was tested by wound healing assay. Xenograft model was used to investigate the in vivo role of SMC2. Expression levels of SMC1A, SMC2, SMC4, SMC5 and SMC6 mRNA were increased in BRCA tissues, and negatively correlated with promoter methylation. Overexpression of SMC2 and SMC4 was negatively correlated with survival. Function of SMCs family regulatory genes was mainly related to ATPase activity. Expression of most SMCs was negatively correlated with immunotherapy and drug therapy outcomes. Interfere SMC2 and SMC4 decreased IC50 values of 5-fluorouracil and oxaliplatin and inhibited the migration of MCF7 cells. Tumor growth and weights were significantly decreased in si-SMC2 groups. Combined bioinformatics and clinical specimen analysis verified SMC2 and SMC4 as independent prognostic factors in BRCA, suggesting their significance for the diagnosis and treatment of BRCA.

A Novel Disulfidptosis-related lncRNAs Prognostic Signature for Prognosis Predicting and Immune Microenvironment Characterization in Breast Cancer.

Breast cancer (BRCA) is one of the leading causes of cancer-related death in women. The improvement of the BRCA risk assessment method is of positive clinical significance. Although many clues showed the potential role of disulfidptosis in BRCA as a novel type of programmed cell death, whether disulfidptosis is involved in BRCA tumorigenesis remains unclear. We used LASSO-univariate Cox analysis and multivariate Cox analysis to identify six disulfidptosis-related lncRNAs (DPLs) that correlated with BRCA clinical outcome and confirmed that these DPLs were independent prognostic factors for BRCA (YTHDF3-AS1, AC002398.1, AL451085.2, AC092718.4, AC097662.1 and AC053503.5). The BRCA risk prognosis model was subsequently established based on these DPLs. After verifying the model reliability in predicting prognosis, immune infiltration and somatic mutation analysis showed significant differences in the immune microenvironment and mutation of DPLs by risk stratification. Immunotherapy response and drug resistance analysis suggest the reference value of DPLs in clinical individualized therapy. The abnormal expressions of selected DPLs were further validated by the BRCA cell line experiment. Our results shed new light on the role of DPLs in BRCA.

Clinical significance of low expression of CADM3 in breast cancer and preliminary exploration of related mechanisms

BackgroundCell adhesion molecule 3 (CADM3), a transmembrane glycoprotein on cell membranes, plays a role in the way of ligand and receptor interaction. However, there are few studies on CADM3 in tumors, and how it works in breast cancer (BC) remains unclear.MethodsThe Cancer Genome Atlas (TCGA) database and clinical samples were used to analyze CADM3 expression and its correlation with clinicopathological factors and prognosis. Its correlation with immune infiltration was analyzed by TCGA. The effects of CADM3 on proliferation and migration were investigated by cell clonal formation, CCK-8, cell scratch and transwell assay. Protein interaction network was prepared and the function prediction of related genes was conducted. The correlation between CADM3 and MAPK pathway was further explored by western blot experiment.ResultsThe expression of CADM3 in BC tissues were significantly lower than that in adjacent normal tissues. High level of CADM3 was related to better prognosis of BC patients. CADM3 was an independent prognostic factor for BC. Expression of CADM3 was significantly associated with the status of ER and PR, age and PAM50 subtypes. CADM3 positively related to many immune infiltrating cells. Overexpression of CADM3 can notably reduce cell proliferation and migration. CADM3 was related to MAPK pathway and the phosphorylation of ERK1/2 and JNK1 was inhibited in BC cells with high CADM3.ConclusionsOur research reveals the clinical significance of CADM3 in BC and indicates the critical roles of CADM3 in immune infiltration and MAPK pathway.

Multi-omics analysis reveals the unique landscape of DLD in the breast cancer tumor microenvironment and its implications for immune-related prognosis

BackgroundCuproptosis, i.e., copper-induced programmed cell death, has potential implications in cancer therapy. However, the impact of the cuproptosis-related gene (CRG) dihydrolipoyl dehydrogenase (DLD) on breast cancer (BC) prognosis remains underexplored. MethodsWe employed real-time quantitative PCR and multiplexed immunostaining techniques to quantify DLD expression in both BC and the adjacent non-cancerous tissues. Immunofluorescence analysis was employed to assess the influence of DLD on immune cells and immunological checkpoints in the BC microenvironment. DLD knockdown experiments were conducted in BC cell lines MDA-MB-468 and SK-BR-3, with knockdown efficiency validated via western blot. Subsequently, we performed the cell counting kit-8 (CCK-8) assay, clone formation assay, Transwell migration assay, and invasion assay. To construct a prognostic model, we employed a Lasso-Cox regression analysis of immune-related genes associated with DLD. Additionally, we established a competing endogenous RNA network based on CRGs to evaluate potential regulatory pathways. ResultsCompared to the adjacent tissues, BC tissues exhibited markedly elevated DLD expression levels. In vitro experiments demonstrated that DLD knockdown effectively inhibited BC cell migration, invasion, and proliferation. DLD exhibited positive correlations with CD68+ macrophages and PD-L1 in the tumor, as well as with macrophages and CD4+ T cells in the stroma. Tumor regions with high DLD expression were enriched in PD-L1 and macrophages, while stromal regions with high DLD expression contained CD4+ T cells and macrophages. The AUC values for 1-, 3-, and 5-year overall survival in TCGA-BRCA training set were 0.67, 0.66, and 0.66, respectively. A nomogram with a C-index of 0.715 indicated that risk score, tumor stage, and age could serve as independent prognostic factors for BC. ConclusionOur findings underscore the significant predictive significance of DLD in BC and its influence on the tumor microenvironment. DLD represents a promising diagnostic and prognostic marker for BC, offering novel avenues for the identification of therapeutic targets and the enhancement of immunotherapy in BC.

Prognostic Value of IGFBP6 in Breast Cancer: Focus on Glucometabolism.

IGFBP6, a member of the IGF binding protein (IGFBP) family, is a specific inhibitor of insulin-like growth factor II (IGF-II) and can inhibit the growth of malignant tumors overexpressing IGF-II. Type 2 diabetes (T2D) is a basic disorder of glucose metabolism that can be regulated by IGF-related pathways. We performed bioinformatics analysis of the TCGA database to explore the possible mechanism of IGFBP6 in breast cancer (BC) metabolism and prognosis and collected clinical samples from BC patients with and without T2D to compare and verify the prognostic effect of IGFBP6. In our study, the levels of IGFBP1-6 were positively correlated with overall survival (OS) in patients with breast cancer. IGFBP6 was upregulated in estrogen receptor (ER)-positive BC, and ER-positive and progesterone receptor (PR) positive patients had a higher expression level of IGFBP6 than ER-negative and PR-negative patients. IGFBP6 could be used as an independent prognostic factor in BC. The expression of IGFBP6 was decreased in BC tissue, and BC tissue from patients with T2D had lower IGFBP6 expression levels than BC tissue from patients without T2D. IGFBP6 is mainly involved in the PI3K-Akt and TGF-β signaling pathways and tumor microenvironment regulation. In terms of metabolism, the expression of IGFBP6 was negatively correlated with that of most glucose metabolism-related genes. IGFBP6 expression was mainly correlated with mutations in TP53, PIK3CA, CDH1, and MAP3K1. In addition, the upregulation of IGFBP6 in BC increased the drug sensitivity to docetaxel, paclitaxel and gemcitabine. Overall, these results indicated that high expression of IGFBP6 is associated with a good prognosis in BC patients, especially in those without T2D. It is not only involved in the maintenance of the tumor microenvironment in BC but also inhibits the energy metabolism of cancer cells through glucose metabolism-related pathways. These findings may provide a new perspective on IGFBP6 as a potential prognostic marker for BC.

A ubiquitin-proteasome system-related signature to predict prognosis, immune infiltration, and therapy efficacy for breast cancer.

The ubiquitin-proteasome system (UPS) is an essential regulatory system for maintaining homeostasis, and its dysfunction may cause various diseases. The activity of proteasome and ubiquitin-conjugating enzymes has been found to be greatly increased in breast cancer (BC), indicating that the heterogeneity of UPS may be related to the progression of BC. Gene data was obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases and performed in multiple algorithms to construct a UPS-related signature for BC. Patients in the UPS low-risk group had greater overall and recurrence-free survival probability than those in the UPS high-risk group. This signature was closely associated with functional enrichment. Some high metabolism-related pathways were more active in the UPS high-risk group. The UPS low-risk group had more abundant anti-tumor immune cells, while in the UPS high-risk group, immunosuppressive cells were dominant. More importantly, we found that the UPS low-risk group was more sensitive to immunotherapy, while the UPS high-risk group responded better to radiotherapy. Drug sensitivity analysis identified more effective chemotherapy drugs in different UPS-related risk groups. This UPS-related signature may serve as a novel biomarker and independent prognostic factor for BC. It can effectively predict prognosis, immune infiltration, and therapy efficacy, providing new strategies for individualized treatment.

ANO6 is a reliable prognostic biomarker and correlates to macrophage polarization in breast cancer.

To investigate the value of Anoctamin 6 (ANO6) in breast cancer (BC) by analyzing its expression, prognostic impact, biological function, and its association with immune characteristics. We initially performed the expression and survival analyses, followed by adopting restricted cubic spline to analyze the nonlinear relationship between ANO6 and overall survival (OS). Stratified and interaction analyses were conducted to further evaluate its prognostic value in BC. Next, we performed enrichment analyses to explore the possible pathways regulated by ANO6. Finally, the correlations between ANO6 and immune characteristics were analyzed to reveal its role in immunotherapy. Lower ANO6 expression was observed in BC than that in the normal breast group, but its overexpression independently predicted poor OS among BC patients (P < .05). Restricted cubic spline analysis revealed a linear relationship between ANO6 and OS (P-Nonlinear > 0.05). Interestingly, menopause status was an interactive factor in the correlation between ANO6 and OS (P for interaction = 0.016). Additionally, ANO6 was involved in stroma-associated pathways, and its elevation was significantly linked to high stroma scores and macrophage polarization (P < .05). Moreover, ANO6 was notably correlated with immune checkpoint expression levels, and scores of tumor mutation burden and microsatellite instability (all P < .05). ANO6 was an independent prognostic factor for BC, and might be a potential target for the BC treatment. Besides, ANO6 might affect BC progression via the regulation of stroma-related pathways and macrophage polarization.

The prognosis of breast cancer patients with bone metastasis could be potentially estimated based on blood routine test and biochemical examination at admission

Purpose Due to the poor and unpredictable prognosis of breast cancer (BC) patients with bone metastasis, it is necessary to find convenient and available prognostic predictors. This study aimed to recognize the clinical and prognostic factors related to clinical laboratory examination and to construct a prognostic nomogram for BC bone metastasis. Methods We retrospectively analyzed 32 candidate indicators from clinical features and laboratory examination data of 276 BC patients with bone metastasis. Univariate and multivariate regression analyses were performed to identify significant prognostic factors related to BC with bone metastasis. Nomogram was constructed and estimated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. Results Patients were randomly grouped into training (n = 197) and validation cohorts (n = 79). In training cohort, the multivariate regression analysis revealed that age, other organ metastasis sites, serum level of lactate dehydrogenase, globulin, white blood cell count, mean corpuscular volume, mean corpuscular hemoglobin, and monocyte ratio were independent prognostic factors for BC with bone metastasis. The prognostic nomogram in training cohort exhibited areas under the ROC curve (AUCs) of 0.797, 0.782, and 0.794, respectively, for predicting 1-, 3-, and 5-year overall survival. In validation cohort, the nomogram still showed acceptable discrimination ability (AUCs: 0.723, 0.742, and 0.704) and calibration. Conclusion This study constructed a novel prognostic nomogram for BC patients with bone metastasis. It could serve as a potential tool of survival assessment to help individual treatment decision-making for clinicians.

The relationship between women's body mass index and breast cancer outcomes was U-shaped.

Several studies have analyzed the relationship between body mass index (BMI) and the prognosis of breast cancer (BC). However, whether their relationship is linear or curvilinear remains unclear. This cohort study examined the specific relationship between BMI and BC outcomes. This retrospective cohort study included 1049 BC patients from March 7, 2013 through December 31, 2019 in a hospital. Kaplan-Meier curves, multivariate Cox proportional models, and restricted cubic spline (RCS) was used to analysis the relationship between BMI and overall survival (OS) and breast cancer-specific survival (BCSS) was analyzed. During a median of 4.87 (IQR:3.26-6.84) years of follow-up period, 71 patients (6.77%) died, of which 50 (70.42%) were attributed to BC. RCS analysis revealed a U- shaped relationship between BMI levels and OS and BCSS after adjusting for other variables. The turning points of the U-shaped curves were 23 kg/m2. On the left side of the turning point, the risk of OS (HR, 0.83; 95% CI, 0.70, 0.98) and BCSS (HR, 0.80; 95% CI, 0.65, 0.98) were adversely correlated with BMI. In contrast, to the right of the turning point, the risk of OS (HR, 1.22; 95% CI, 1.10, 1.37) and BCSS (HR, 1.28; 95% CI, 1.13, 1.46) was positively related to BMI. Kaplan-Meier curves and multivariate Cox regression analyses shown consistent results with RCS analyses. BMI was an independent prognostic factor for BC, and had a U-shaped relationship with OS and BCSS. Interventions should be designed to improve patient outcomes based on BMI.

Performance of a PLK1-based immune risk model for prognosis and treatment response prediction in breast cancer.

Polo-like kinase 1 (PLK1), a serine/threonine-protein kinase, functions as a potent oncogene in the initiation and progression of tumor. The aim of this study is to assess potential correlations between PLK1 expression and immune infiltration in breast cancer (BRCA) and construct a PLK1-based immune risk model applicable for prognosis and treatment response prediction in BRCA. We collected data on PLK1 gene expression in BRCA patients from The Cancer Genome Atlas (TCGA) database. Thereafter, we analyzed the associations of PLK1 expression with immune cell infiltration and immunomodulators, and established a prognostic risk model based on seven PLK1-associated immunomodulator genes and a nomogram for survival prediction. BRCA prognosis, clinical stage progression, and tumor classification were all shown to be substantially correlated with PLK1 expression. The PLK1 gene was significantly enriched in T cell and B cell receptors and molecules of the chemokine signaling pathways. Specifically, PLK1 expression was positively correlated with the CD8+ T cell and regulatory T cell (Tregs) activation and negatively correlated with M2 macrophage infiltration. The seven-genes-based risk model could serve as an independent prognostic factor of BRCA. The risk model was markedly correlated with the expression of programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1; both p < 0.001) immune checkpoints, and tumor mutation burden (TMB). High- and low-risk BRCA patients identified by the risk model responded differently to anti-PD-1 and/or anti-CTLA4 therapy, as well as common chemotherapy drugs, like cisplatin, paclitaxel, and gemcitabine. This PLK1-based immune risk model can effectively predict the prognosis and tumor progression of BRCA, identify gene mutations, and evaluate patient's response toward immunotherapy and chemotherapy regimens.

Identifying tumour microenvironment-related signature that correlates with prognosis and immunotherapy response in breast cancer

Tumor microenvironment (TME) plays important roles in prognosis and immune evasion. However, the relationship between TME-related genes and clinical prognosis, immune cell infiltration, and immunotherapy response in breast cancer (BRCA) remains unclear. This study described the TME pattern to construct a TME-related prognosis signature, including risk factors PXDNL, LINC02038 and protective factors SLC27A2, KLRB1, IGHV1-12 and IGKV1OR2-108, as an independent prognostic factor for BRCA. We found that the prognosis signature was negatively correlated with the survival time of BRCA patients, infiltration of immune cells and the expression of immune checkpoints, while positively correlated with tumor mutation burden and adverse treatment effects of immunotherapy. Upregulation of PXDNL and LINC02038 and downregulation of SLC27A2, KLRB1, IGHV1-12 and IGKV1OR2-108 in high-risk score group synergistically contribute to immunosuppressive microenvironment which characterized by immunosuppressive neutrophils, impaired cytotoxic T lymphocytes migration and natural killer cell cytotoxicity. In summary, we identified a TME-related prognostic signature in BRCA, which was connected with immune cell infiltration, immune checkpoints, immunotherapy response and could be developed for immunotherapy targets.

Prognostic implication and immunotherapy response prediction of a ubiquitination-related gene signature in breast cancer.

Breast cancer (BC) is one of the most common tumor types and has poor outcomes. In this study, a ubiquitination-related prognostic signature was constructed, and its association with immunotherapy response in BC was explored. A list of ubiquitination-related genes was obtained from the molecular signatures database, and a ubiquitination-related gene signature was obtained by least absolute shrinkage and selection operator Cox regression. The genes, TCN1, DIRAS3, and IZUMO4, had significant influence on BC outcomes. Patients were categorized into two clusters-a high-risk group with poor survival and a low-risk group with greater chances of controlling BC progression. Univariate and multivariate Cox regression analyses revealed that the risk signature was an independent prognostic factor for BC. Gene set enrichment analysis suggested that the high-risk group was enriched in cell cycle and DNA replication pathways. The risk score was positively linked to the tumor microenvironment and negatively correlated with the immunotherapy response. The IC50 values for rapamycin were higher in the low-risk group, whereas those for axitinib, AZD6244, erlotinib, GDC0941, GSK650394, GSK269962A, lapatinib, and PD0325901 were higher in the high-risk group. Therefore, the ubiquitination-related signature is considered a promising tool for predicting a BC patient's immunotherapy response.

Comprehensive analysis of cuproptosis-related long non-coding RNA signature and personalized therapeutic strategy of breast cancer patients.

Breast cancer (BC) is considered to be one of the primary causes of cancer deaths in women. Cuproptosis was suggested to play an important role in tumor proliferation and tumor immune microenvironment. Therefore, an investigation was conducted to identify the relationship between cuproptosis-related long non-coding RNAs (lncRNAs) and BC prognosis. Based on The Cancer Genome Atlas (TCGA), nine cuproptosis-related lncRNAs were identified by Pearson's analysis and Cox regression analysis to create a cuproptosis-related lncRNA signature. Subsequently, patients with BC were divided into high-risk and low-risk groups. The Kaplan-Meier curves and a time-dependent receiver operating characteristic (ROC) analysis were employed to elucidate the predictive capability of the signature. After that, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted by Gene Set Enrichment Analysis (GSEA), and the lncRNA-mRNA co-expression network was established by Cytoscape software. Furthermore, the ESTIMATE score was calculated, and the immune cell type component analysis was conducted. Eventually, immunotherapy response analysis was applied to identify the predictive power of cuproptosis-related lncRNAs to tumor immunotherapy response, including immune checkpoint gene expression levels, tumor mutational burden (TMB), and microsatellite instability (MSI). Patients with BC in the low-risk groups showed better clinical outcomes. The KEGG pathways in the high-risk groups were mainly enriched in immune response and immune cell activation. Furthermore, the ESTIMATE scores were higher in the low-risk groups, and their immune cell infiltrations were dramatically different from those of the high-risk groups. The low-risk groups were shown to have higher infiltration levels of CD8+ T cells and TMB-high status, resulting in better response to immunotherapies. The findings of this study revealed that the nine-cuproptosis-related lncRNA risk score was an independent prognostic factor for BC. This signature was a potential predictor for BC immunotherapy response. What we found will provide novel insight into immunotherapeutic treatment strategies in BC.

An autophagy-related long non-coding RNA prognostic model and related immune research for female breast cancer.

Breast cancer (BRCA) is the most common malignancy among women worldwide. It was widely accepted that autophagy and the tumor immune microenvironment play an important role in the biological process of BRCA. Long non-coding RNAs (lncRNAs), as vital regulatory molecules, are involved in the occurrence and development of BRCA. The aim of this study was to assess the prognosis of BRCA by constructing an autophagy-related lncRNA (ARlncRNA) prognostic model and to provide individualized guidance for the treatment of BRCA. The clinical data and transcriptome data of patients with BRCA were acquired from the Cancer Genome Atlas database (TCGA), and autophagy-related genes were obtained from the human autophagy database (HADb). ARlncRNAs were identified by conducting co‑expression analysis. Univariate and multivariate Cox regression analysis were performed to construct an ARlncRNA prognostic model. The prognostic model was evaluated by Kaplan-Meier survival analysis, plotting risk curve, Independent prognostic analysis, clinical correlation analysis and plotting ROC curves. Finally, the tumor immune microenvironment of the prognostic model was studied. 10 ARlncRNAs(AC090912.1, LINC01871, AL358472.3, AL122010.1, SEMA3B-AS1, BAIAP2-DT, MAPT-AS1, DNAH10OS, AC015819.1, AC090198.1) were included in the model. Kaplan-Meier survival analysis of the prognostic model showed that the overall survival(OS) of the low-risk group was significantly better than that of the high-risk group (p< 0.001). Multivariate Cox regression analyses suggested that the prognostic model was an independent prognostic factor for BRCA (HR = 1.788, CI = 1.534-2.084, p < 0.001). ROCs of 1-, 3- and 5-year survival revealed that the AUC values of the prognostic model were all > 0.7, with values of 0.779, 0.746, and 0.731, respectively. In addition, Gene Set Enrichment Analysis (GSEA) suggested that several tumor-related pathways were enriched in the high-risk group, while several immune‑related pathways were enriched in the low-risk group. Patients in the low-risk group had higher immune scores and their immune cells and immune pathways were more active. Patients in the low-risk group had higher PD-1 and CTLA-4 levels and received more benefits from immune checkpoint inhibitors (ICIs) therapy. The ARlncRNA prognostic model showed good performance in predicting the prognosis of patients with BRCA and is of great significance to guide the individualized treatment of these patients.

Identification of a basement membrane-related gene signature for predicting prognosis and estimating the tumor immune microenvironment in breast cancer.

Breast cancer (BC) is the most common malignancy in the world and has a high cancer-related mortality rate. Basement membranes (BMs) guide cell polarity, differentiation, migration and survival, and their functions are closely related to tumor diseases. However, few studies have focused on the association of basement membrane-related genes (BMRGs) with BC. This study aimed to explore the prognostic features of BMRGs in BC and provide new directions for the prevention and treatment of BC. We collected transcriptomic and clinical data of BC patients from TCGA and GEO datasets and constructed a predictive signature for BMRGs by using univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The reliability of the model was further evaluated and validated by Kaplan-Meier survival curves and receiver operating characteristic curves (ROC). Column line plots and corresponding calibration curves were constructed. Possible biological pathways were investigated by enrichment analysis. Afterward, we assessed the mutation status by tumor mutational burden (TMB) analysis and compared different subtypes using cluster analysis. Finally, we examined drug treatment sensitivity and immunological correlation to lay the groundwork for more in-depth studies in this area. The prognostic risk model consisted of 7 genes (FBLN5, ITGB2, LAMC3, MMP1, EVA1B, SDC1, UNC5A). After validation, we found that the model was highly reliable and could accurately predict the prognosis of BC patients. Cluster analysis showed that patients with cluster 1 had more sensitive drugs and had better chances of better clinical outcomes. In addition, TMB, immune checkpoint, immune status, and semi-inhibitory concentrations were significantly different between high and low-risk groups, with lower-risk patients having the better anti-cancer ability. The basement membrane-related gene signature that we established can be applied as an independent prognostic factor for BC and can provide a reference for individualized treatment of BC patients.

Integrated analysis identified prognostic microRNAs in breast cancer

BackgroundMicroRNAs (miRNAs) play pivotal roles in the development and progression of breast cancer (BC). In this study, we attempted to identify miRNAs associated with BC prognosis and progression via integrated analysis.MethodsWe first screened 83 differentially expressed miRNAs (DEMs) in 1249 BC samples and 151 normal samples. We then validated their roles in expression and prognosis of BC, identified two survival-related DEMs, and established a risk model. The prediction efficiency was assessed in both the training and validation groups. Tissue and cell experiments were conducted to verify the regulatory effects of miR-127 in BC.ResultsThe ROC curve indicated good prediction ability with 1-, 3-, and 5-year survival rates of 0.73, 0.72, and 0.72, respectively. Moreover, hsa-miR-127 was found to be an independent prognostic factor of BC. Functional analyses revealed that it is involved in various cancer pathways such as the PI3K-Akt and p53 pathways. miR-127 expression was down-regulated in both BC tissues and cell lines. The knockdown of miR-127 substantially increased, whereas overexpression decreased BC cell proliferation, invasion, and migration. This effect of miR-127 was consistent with its tumorigenic ability and tumor volume in nude mice.ConclusionsThese findings indicate that low expression of miR-127 contributes to BC migration, invasion, and tumorigenesis and that it can be a therapeutic target and prognostic biomarker for BC.

Lactate score predicts survival, immune cell infiltration and response to immunotherapy in breast cancer.

Background: Tumor-derived lactate can modulate the function of infiltrating immune cells to establish an immunosuppressive microenvironment that favors tumor progression. However, possible effects of lactate-related genes (LRGs) on the tumor microenvironment (TME) of breast cancer (BRCA) are still unknown. Methods: LRGs were comprehensively screened from lactate metabolism-related pathways. We correlated the expression of these LRGs with immune cell infiltrating characteristics in the TME and clinicopathological features of patients. We also established a lactate score for quantifying lactate metabolism patterns of cancers and to predict of recurrence-free survival (RFS). Results: We successfully constructed a lactate score that was an independent prognostic factor in BRCA. A low lactate score, which was associated with immune activation with increased CD8+ T cells infiltration levels, indicated an inflamed TME. Consistently, higher expression levels of inhibitory immune checkpoints, including PD-L1, LAG3, CTLA4, and TIM3, as observed from high lactate score subgroup, suggested an immune-desert phenotype as well as poor prognosis. Moreover, a low lactate score predicted the increased chemotherapeutic drug sensitivity and enhanced anti-PD-1 immunotherapy responses. Conclusion: The present study analyzed the potential roles of LRGs in the TME diversity and prognosis. These results will help to improve our understanding of the characteristics of TME immune cell infiltration and guide the development of more effective immunotherapy strategies.

Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer

BackgroundsPyroptosis, a newly pattern of specific programmed cell death, has been reported to participate in several cancers. However, the value of pyroptosis in breast cancer (BRCA) is still not clear.MethodsHerein, we analyzed the data of BRCA from both The Cancer Genome Atlas (TCGA) and GSEA MSigDB database. Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. After that, we performed clustering analysis by ConsensusClusterPlus. The PRGs with significant prognostic value were then screened through univariate cox regression and further evaluate by constructing a risk model by least absolute shrinkage and selection operator (LASSO) Cox regression. The immune and sensitivity to drugs were also predicted by comprehensive algorithms. Finally, real-time quantitative PCR (qPCR) was performed on two of the screened signature PRGs.ResultsA total of 49 PRGs were obtained from public database and 35 of them were significantly differentially expressed genes (DEGs). Cluster analysis was then performed to explore the relationship between DEGs with overall survival. After that, 6 optimal PRGs (GSDMC, IL-18, CHMP3, TP63, GZMB and CHMP6) were screened out to construct a prognostic signature, which divide BRCA patients into two risk groups. Risk scores were then confirmed to be independent prognostic factors in BRCA. Functional enrichment analyses showed that the signature were obviously associated with tumor-related and immune-associated pathways. 79 microenvironmental cells and 11 immune checkpoint genes were found disparate in two groups. Besides, tumor immune dysfunction and exclusion (TIDE) scores revealed that patients with higher risk scores are more sensitive to immune checkpoint blockade treatment. Patients in the low-risk group were more sensitive to Cytarabine, Docetaxel, Gefitinib, Paclitaxel, and Vinblastine. Inversely, patients in the high-risk group were more sensitive to Lapatinib. Finally, we found that, CHMP3 were down-regulated in both BRCA tissues and cell lines, while IL-18 were up-regulated.ConclusionPRGs play important roles in BRCA. Our study fills the gaps of 6 selected PRGs in BRCA, which were worthy for the further study as predict potential biomarkers and therapeutic targets.

Characteristics of metastasis and survival between male and female breast cancer with different molecular subtypes: A population‐based observational study

ObjectiveMale breast cancer (BC) is a rare disease, having different clinicopathological features and survival outcomes from female patients. The aim of this research was to, combine with molecular subtypes, analyze the metastatic patterns, and prognosis between male and female patients, and to determine whether the gender was the independent prognostic factor for BC.MethodsData used in this study were acquired from the SEER database from 2010 to 2016. The clinicopathology features and metastatic patterns were compared by the Chi‐square test and Fisher's exact test. Kaplan–Meier method was performed to compare overall survival (OS) and factors correlated with OS were determined by Cox regression models. Competing risk models were used to ascertain factors related to breast cancer‐specific death (BCSD).ResultsCompared with female BC, the incidence of regional LN (HR 1.849, 95% CI 1.674–2.043, p < 0.001) and distant metastasis (HR 1.421, 95%CI: 1.157–1.744, p < 0.001) was higher in male BC. For regional LN metastasis, hormone receptor (HoR)−/HER2+ subtype occupied the majority in both male (55.56%) and female (36.86%) groups. For distant metastasis, HoR−/HER2− subtype (21.26%), and HoR−/HER2+ (7.67%) were in major in male and female group separately. Male patients shared similar combinations of metastases with female groups as for single‐site, bi‐site, and tri‐site metastasis. Gender was an independent prognostic factor for OS (p < 0.001) but not for BCSD(p = 0.620). In subgroup of patients with HoR+/HER2−(OS: p = 0.003; BCSD: p = 0.606), HoR+/HER2+(OS: p = 0.003; BCSD: p = 0.277), regional LN positive(OS: p = 0.005; BCSD: p = 0.379), or bone metastasis (OS: p = 0.030; BCSD: p = 0.862), the male cohort had poorer OS but similar BCSD with female cohort.ConclusionsCompared with female patients, male BC had different metastasis patterns and prognostic outcomes, and the affection of breast subtypes on metastasis and survivorship was also different. More attention needs to be paid for specific molecular subtype and more personalized therapeutic strategies should be customized while treating male patients.