Low expression of KLRB1 predicts poor survival outcomes and is associated with immune infiltration in breast cancer

Background

KLRB1 is downregulated in various cancer types. Nevertheless, the specific involvement of KLRB1 in the context of breast cancer (BRCA) has not been fully elucidated. This research aimed to explore its clinical value in BRCA.

Methods

A dataset comprising 1,109 BRCA samples and 113 healthy samples was retrieved from The Cancer Genome Atlas (TCGA) database to establish the association between KLRB1 expression and pan-cancer. Subsequently, an analysis was executed to explore the link between KLRB1 and BRCA. T-tests and Chi-squared tests were conducted to assess the expression of KLRB1 and its clinical implications in BRCA. The prognosis-predictive value of KLRB1 in BRCA was assessed using the Kaplan-Meier method and Cox regression analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses screened biological pathways to analyze the association between the immune infiltration level and KLRB1 expression in BRCA. Lastly, the conclusion was validated through quantitative polymerase chain reaction (qPCR), immunohistochemistry (IHC), and Cell Counting Kit-8 (CCK8) assays.

Results

KLRB1 exhibited low expression in patients with BRCA. Furthermore, KLRB1 demonstrated strong diagnostic potential, as indicated by an area under curve (AUC) of 0.712. Kaplan-Meier survival and Cox regression analyses indicated that attenuated expression of KLRB1 was independently linked to unfavorable clinical outcomes. GO and KEGG enrichment analyses were performed on the top 10 genes that exhibited positive and negative correlations with KLRB1. Analysis of genes positively correlated with KLRB1 revealed associations with signaling receptor activator activity, lymphocyte proliferation, mononuclear cell proliferation, leukocyte proliferation, receptor-ligand activity, immunoglobulin binding, and hematopoietic cell lineage signaling pathway. KLRB1 expression exhibited significant correlations with all immune cells. Furthermore, qPCR and IHC outcomes demonstrated that KLRB1 was significantly downregulated in BRCA tissues. CCK8 findings showed a decrease in the proliferation of BRCA MCF7 cells upon knockout of KLRB1.

Conclusions

This research investigated the mechanism and potential therapeutic target of the KLRB1 gene in BRCA. By analyzing the expression and function of the KLRB1 gene, the study aims to find its significant role in the onset and progression of BRCA. This research endeavors to offer novel strategies and approaches for treating BRCA.

A model for predicting clinical prognosis based on brain metastasis-related genes in patients with breast cancer

Background

Brain metastasis (BM) is a clinically relevant cause of death in patients with breast cancer (BRCA). This study was designed to develop a clinical model capable of predicting BRCA patients' prognostic outcomes according to the expression of BM-related genes (BMRGs).

Methods

The public Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases served as data sources. BMRGs of BRCA were selected from previous literature. Differences among BRCA molecular subtypes were compared using R 'limma' package. The impact of BM-related differentially expressed genes (BM_DEGs) on BRCA patients' outcomes was explored with a risk score model, after which the relationship between these risk scores and immune cell infiltration was examined. Risk scores were also used to judge the predicted efficacy of immunotherapeutic interventions. The utility of risk scores in combination with clinicopathological characteristics was evaluated as a predictor of patient's survival through univariate and multivariate analyses.

Results

The R limma package was used to explore differential gene expression, after which 12 BM_DEGs were incorporated into a risk scoring model. The resultant risk scores were able to predict immunotherapeutic treatment efficacy. In addition, a nomogram incorporating risk scores, stage, and age was established. The nomogram was able to reliably predict the overall survival (OS) of BRCA patients, yielding predictive outcomes that aligned well with actual observations.

Conclusions

In summary, a predictive clinical model for BRCA patients was successfully established in this study, providing a valuable tool that may be particularly helpful for the assessment of patients facing a risk of BM development.

Effect of caspase-1 (CASP1) combined with multimodal ultrasound features on the prognosis of breast cancer patients

Background

Breast cancer (BRCA) is the malignant tumor with the highest incidence rate among women in the world, and its mortality rate ranks second. The purpose of our study is to explore the correlation between caspase-1 (CASP1) and the prognosis of BRCA patients and the potential mechanism of action, and to analyze the clinical value of CASP1 combined with multimodal ultrasound features in early screening and prognosis of BRCA.

Methods

We analyzed The Cancer Genome Atlas (TCGA) database to confirm that CASP1 was expressed in BRCA patients and determine whether its expression was correlated with patient prognosis. The relationship between CASP1 expression and survival was measured by the clinicopathological parameters. Multivariate analysis was performed using Cox regression, and a nomogram was developed using these results for quality assurance purposes. The correlations between CASP1 and immune cells were investigated using the Tumor Immune Estimation Resource (TIMER) and TCGA databases. Next, we performed gene set enrichment analysis (GSEA) to determine the potential mechanism of action. Finally, to analyze the effect of CASP1 combined with multimodal ultrasonography characteristics on the prognosis of BRCA patients was studied by analyzing the clinical data of patients.

Results

CASP1 expression was lower in BRCA tumor tissues than in the surrounding tissues. Patients with high CASP1 expression had better overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) than those with low CASP1 expression. GSEA suggested that CASP1 may affect the cell cycle, immune environment, inflammation, apoptosis, the HIPPOMERLIN pathway, Natural killer (NK) cell regulation of cytotoxicity, p53 expression, the Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway, the mitogen-activated protein kinase (MAPK) pathway, extracellular matrix, etc., thereby influencing the biological events in BRCA. Among conventional ultrasound features and contrast-enhanced ultrasound (CEUS) features, mass margin status and blood flow grade were associated with the expression of CASP1. Meanwhile, patients with poor ultrasound features tended to have low CASP1 expression.

Conclusions

CASP1 may be a novel predictive marker for BRCA patients. CASP1 combined with multimodal ultrasound features has good clinical value in the early screening and prognostic prediction of BRCA.

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

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Analysis of C-X-C motif chemokine receptors in breast cancer: potential value in immunotherapy and prognostic prediction

Background

The concept of individualized therapy has advanced the development of prognostic biomarkers to manage patients with breast cancer (BRCA). Immunotherapy has shown great potential in treating BRCA, and the C-X-C motif chemokine receptor (CXCR) has generated interest in regulating tumor progression through the immune microenvironment. Although CXCRs were utilized for prognosis prediction in glioma with favourable capability, the prognostic and therapeutic role of CXCR in BRCA is unclear.

Methods

We used The Cancer Genome Atlas (TCGA) database to analyze 1,095 BRCA patients' transcription, mutation, survival time and survival status. Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE), Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), quanTIseq, and Estimating the Proportion of Immune and Cancer cells (EPIC) algorithms were performed to infer the association of CXCR genes with immune cells. We randomly divided the TCGA dataset into a training set and a validation set according to 1:1, constructed a LASSO Cox regression model based on CXCR family genes using the glmnet R package in the training set, assembled clinical variables to draw a visual Nomogram using the R package rms, and validated the model by receiver operating characteristic (ROC) curves, calibration curves with clinical decision curves in the validation set efficacy.

Results

Compared to normal samples, CXCR3/4/5 messenger RNA (mRNA) expression levels were upregulated in BRCA samples, whereas CXCR1/2 mRNA expression levels were downregulated. High CXCR3/5/6 expression was associated with a good prognosis. Subsequently, we divided the CXCRs into 2 molecular subgroups based on their expression patterns and explored prognosis, immune infiltration, functional enrichment, hallmarks, and immune response differentiation between the two subgroups. After LASSO Cox regression modeling, a CXCR score predicting overall survival (OS) was constructed, and the predictive accuracy was assessed. By pooling clinical variables, a nomogram individual risk assessment method was established to measure the identification of genuinely high-risk patients who should receive interventions.

Conclusions

In summary, CXCR genes were associated with immune infiltration and survival in BRCA patients, and our CXCR-based prognostic model could better predict the prognosis of BRCA patients and provide potential immunotherapy targets for clinical purposes.

MiR-93-5p regulates tumorigenesis and tumor immunity by targeting PD-L1/CCND1 in breast cancer

Background

Challenges in medical care posed by rapid tumor progression, individualized responses to therapy, and the heterogeneous characteristics of breast cancer (BRCA) highlight the urgent need for new treatment strategies, as well as therapeutic and prognostic markers. Accumulating evidence has revealed that microRNAs broadly participate in carcinogenesis, but our understanding of the role of miR-93-5p in BRCA remains limited.

Methods

The prognosis of miR-93-5p, programmed cell death-ligand 1 (PD-L1) and CCND1 were analyzed by datasets. Freshly excised breast cancer tissues (N=33) and adjacent noncancerous tissues (N=18) were collected to detect the expression of CCND1 and PD-L1 by immunohistochemistry (IHC). Quantitative real-time PCR (qRT-PCR) and Western blot were used to test the expression of miR-93-5p, PD-L1 and CCND1 after transfected mimics or inhibitors. Dual-luciferase reporter assay indicates the direct targeting between miR-93-5p and PD-L1.

Results

Bioinformatics analysis demonstrated that miR-93-5p plays differential roles in various tumors, and further verification using qRT-PCR revealed that the expression levels of miR-93-5p were lower in MDA-MB-231 cells than in noncancerous breast cells. In addition, we confirmed that PD-L1 and CCND1 generated mutual effects, and miR-93-5p directly targets the PD-L1/CCND1 signaling pathway to influence their accumulation and distribution in the cell membrane, nucleus, and cytoplasm, mediating tumor progression and immune regulation in BRCA.

Conclusions

Taken together, miR-93-5p could regulate tumorigenesis and tumor immunity by targeting PD-L1/CCND1 in BRCA and our research provides a rationale for therapy with miR-93-5p to overcome immune escape and improve risk stratification.

Profiles of alternative splicing landscape in breast cancer and their clinical significance: an integrative analysis based on large-sequencing data

Background

Alternative splicing (AS) is closely correlated with the initiation and progression of carcinoma. The systematic analysis of its biological and clinical significance in breast cancer (BRCA) is, however, lacking.

Methods

Clinical data and RNA-seq were obtained from the TCGA dataset and differentially expressed AS (DEAS) events between tumor and paired normal BRCA tissues were identified. Enrichment analysis was then used to reveal the potential biological functions of DEAS events. We performed protein-protein interaction (PPI) analysis of DEAS events by using STRING and the correlation network between splicing factors (SFs) and AS events was constructed. The LASSO Cox model, Kaplan-Meier and log-rank tests were used to construct and evaluate DEAS-related risk signature, and the association between DEAS events and clinicopathological features were then analyzed.

Results

After strict filtering, 35,367 AS events and 973 DEAS events were detected. DEAS corresponding genes were significantly enriched in pivotal pathways including cell adhesion, cytoskeleton organization, and extracellular matrix organization. A total of 103 DEAS events were correlated with disease free survival. The DEAS-related risk signature stratified BRCA patients into two groups and the area under curve (AUC) was 0.754. Moreover, patients in the high-risk group had enriched basel-like subtype, advanced clinical stages, proliferation, and metastasis potency.

Conclusions

Collectively, the profile of DEAS landscape in BRCA revealed the potential biological function and prognostic value of DEAS events.

Tamoxifen therapy benefit predictive signature combining with prognostic signature in surgical-only ER-positive breast cancer

Tamoxifen treatment is important assistant for estrogen-receptor-positive breast cancer (BRCA) after resection. This study aimed to identify signatures for predicting the prognosis of patients with BRCA after tamoxifen treatment. Data of gene-specific DNA methylation (DM), as well as the corresponding clinical data for the patients with BRCA, were obtained from The Cancer Genome Atlas and followed by systematic bioinformatics analyses. After mapping these DM CPG sites onto genes, we finally obtained 352 relapse-free survival (RFS) associated DM genes, with which 61,776 gene pairs were combined, including 1,614 gene pairs related to RFS. An 11 gene-pair signature was identified to cluster the 189 patients with BRCA into the surgical low-risk group (136 patients) and high-risk group (53 patients). Then, we further identified a tamoxifen-predictive signature that could classify surgical high-risk patients with significant differences on RFS. Combining surgical-only prognostic signature and tamoxifen-predictive signature, patients were clustered into surgical-only low-risk group, tamoxifen nonbenefit group, and tamoxifen benefit group. In conclusion, we identified that the gene pair PDHA2-APRT could serve as a potential prognostic biomarker for patients with BRCA after tamoxifen treatment.