BAG-1 as a biomarker in early breast cancer prognosis: a systematic review with meta-analyses

Development and Validation of a New BAG-1L-Specific Antibody to Quantify BAG-1L Protein Expression in Advanced Prostate Cancer

BCL-2-associated athanogene-1L (BAG-1L) is a critical co-regulator that binds to and enhances the transactivation function of the androgen receptor, leading to prostate cancer development and progression. Studies investigating the clinical importance of BAG-1L protein expression in advanced prostate cancer have been limited by the paucity of antibodies that specifically recognize the long isoform. In this study, we developed and validated a new BAG-1L-specific antibody using multiple orthogonal methods across several cell lines with and without genomic manipulation of BAG-1L and all BAG-1 isoforms. Following this, we performed exploratory immunohistochemistry to determine BAG-1L protein expression in normal human, matched castration-sensitive prostate cancer (CSPC) and castration-resistant prostate cancer (CRPC), unmatched primary and metastatic CRPC, and early breast cancer tissues. We demonstrated higher BAG-1L protein expression in CRPC metastases than in unmatched, untreated, castration-sensitive prostatectomies from men who remained recurrence-free for 5 years. In contrast, BAG-1L protein expression did not change between matched, same patient, CSPC and CRPC biopsies, suggesting that BAG-1L protein expression may be associated with more aggressive biology and the development of castration resistance. Finally, in a cohort of patients who universally developed CRPC, there was no association between BAG-1L protein expression at diagnosis and time to CRPC or overall survival, and no association between BAG-1L protein expression at CRPC biopsy and clinical outcome from androgen receptor targeting therapies or docetaxel chemotherapy. The limitations of this study include the requirement to validate the reproducibility of the assay developed, the potential influence of pre-analytical factors, timing of CRPC biopsies, relatively small patient numbers, and heterogenous therapies on BAG-1L protein expression, and the clinical outcome analyses performed. We describe a new BAG-1L-specific antibody that the research community can further develop to elucidate the biological and clinical significance of BAG-1L protein expression in malignant and nonmalignant diseases.

Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information

BACKGROUND

Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer.

METHODS

Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262).

RESULTS

We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22-3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03-3.3; p = 3.99 ×10-2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm).

CONCLUSION

We developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer.

SIGNIFICANCE

Transcriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.

Molecular profiling of a real-world breast cancer cohort with genetically inferred ancestries reveals actionable tumor biology differences between European ancestry and African ancestry patient populations

BACKGROUND

Endocrine-resistant HR+/HER2- breast cancer (BC) and triple-negative BC (TNBC) are of interest for molecularly informed treatment due to their aggressive natures and limited treatment profiles. Patients of African Ancestry (AA) experience higher rates of TNBC and mortality than European Ancestry (EA) patients, despite lower overall BC incidence. Here, we compare the molecular landscapes of AA and EA patients with HR+/HER2- BC and TNBC in a real-world cohort to promote equity in precision oncology by illuminating the heterogeneity of potentially druggable genomic and transcriptomic pathways.

METHODS

De-identified records from patients with TNBC or HR+/HER2- BC in the Tempus Database were randomly selected (N = 5000), with most having stage IV disease. Mutations, gene expression, and transcriptional signatures were evaluated from next-generation sequencing data. Genetic ancestry was estimated from DNA-seq. Differences in mutational prevalence, gene expression, and transcriptional signatures between AA and EA were compared. EA patients were used as the reference population for log fold-changes (logFC) in expression.

RESULTS

After applying inclusion criteria, 3433 samples were evaluated (n = 623 AA and n = 2810 EA). Observed patterns of dysregulated pathways demonstrated significant heterogeneity among the two groups. Notably, PIK3CA mutations were significantly lower in AA HR+/HER2- tumors (AA = 34% vs. EA = 42%, P < 0.05) and the overall cohort (AA = 28% vs. EA = 37%, P = 2.08e-05). Conversely, KMT2C mutation was significantly more frequent in AA than EA TNBC (23% vs. 12%, P < 0.05) and HR+/HER2- (24% vs. 15%, P = 3e-03) tumors. Across all subtypes and stages, over 8000 genes were differentially expressed between the two ancestral groups including RPL10 (logFC = 2.26, P = 1.70e-162), HSPA1A (logFC = - 2.73, P = 2.43e-49), ATRX (logFC = - 1.93, P = 5.89e-83), and NUTM2F (logFC = 2.28, P = 3.22e-196). Ten differentially expressed gene sets were identified among stage IV HR+/HER2- tumors, of which four were considered relevant to BC treatment and were significantly enriched in EA: ERBB2_UP.V1_UP (P = 3.95e-06), LTE2_UP.V1_UP (P = 2.90e-05), HALLMARK_FATTY_ACID_METABOLISM (P = 0.0073), and HALLMARK_ANDROGEN_RESPONSE (P = 0.0074).

CONCLUSIONS

We observed significant differences in mutational spectra, gene expression, and relevant transcriptional signatures between patients with genetically determined African and European ancestries, particularly within the HR+/HER2- BC and TNBC subtypes. These findings could guide future development of treatment strategies by providing opportunities for biomarker-informed research and, ultimately, clinical decisions for precision oncology care in diverse populations.

Mining Potential Drug Targets for Osteoporosis Based on CeRNA Network

OBJECTIVE

To identify key pathological hub genes, micro RNAs (miRNAs), and circular RNAs (circRNAs) of osteoporosis (OP) and construct their ceRNA network in an effort to explore the potential biomarkers and drug targets for OP therapy.

METHODS

GSE7158, GSE201543, and GSE161361 microarray datasets were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by comparing OP patients with healthy controls and hub genes were screened by machine learning algorithms. Target miRNAs and circRNAs were predicted by FunRich and circbank, then ceRNA network were constructed by Cytoscape. Pathways affecting OP were identified by functional enrichment analysis. The hub genes were verified by receiver operating characteristic (ROC) curve and real time quantitative PCR (RT-qPCR). Potential drug molecules related to OP were predicted by DSigDB database and molecular docking was analyzed by autodock vina software.

RESULTS

A total of 179 DEGs were identified. By combining three machine learning algorithms, BAG2, MME, SLC14A1, and TRIM44 were identified as hub genes. Three OP-associated target miRNAs and 362 target circRNAs were predicted to establish ceRNA network. The ROC curves showed that these four hub genes had good diagnostic performance and their differential expression was statistically significant in OP animal model. Benzo[a]pyrene was predicted which could successfully bind to protein receptors related to the hub genes and it was served as the potential drug molecules.

CONCLUSION

An mRNA-miRNA-circRNA network is reported, which provides new ideas for exploring the pathogenesis of OP. Benzo[a]pyrene, as potential drug molecules for OP, may provide guidance for the clinical treatment.

Function and Application of Flavonoids in the Breast Cancer

Breast cancer is one of the top causes of death, particularly among women, and it affects many women. Cancer can also be caused by various factors, including acquiring genetic alteration. Doctors use radiation to detect and treat breast cancer. As a result, breast cancer becomes radiation-resistant, necessitating a new strategy for its treatment. The approach discovered by the researchers is a flavonoid, which is being researched to see if it might help treat radiation-resistant breast cancer more safely than an approved medicine already being used in the field. As a result, this study focuses on the role of flavonoids in breast cancer suppression, breast cancer gene anomalies, and the resulting apoptotic mechanism.

Structured reporting to improve transparency of analyses in prognostic marker studies

BACKGROUND

Factors contributing to the lack of understanding of research studies include poor reporting practices, such as selective reporting of statistically significant findings or insufficient methodological details. Systematic reviews have shown that prognostic factor studies continue to be poorly reported, even for important aspects, such as the effective sample size. The REMARK reporting guidelines support researchers in reporting key aspects of tumor marker prognostic studies. The REMARK profile was proposed to augment these guidelines to aid in structured reporting with an emphasis on including all aspects of analyses conducted.

METHODS

A systematic search of prognostic factor studies was conducted, and fifteen studies published in 2015 were selected, three from each of five oncology journals. A paper was eligible for selection if it included survival outcomes and multivariable models were used in the statistical analyses. For each study, we summarized the key information in a REMARK profile consisting of details about the patient population with available variables and follow-up data, and a list of all analyses conducted.

RESULTS

Structured profiles allow an easy assessment if reporting of a study only has weaknesses or if it is poor because many relevant details are missing. Studies had incomplete reporting of exclusion of patients, missing information about the number of events, or lacked details about statistical analyses, e.g., subgroup analyses in small populations without any information about the number of events. Profiles exhibit severe weaknesses in the reporting of more than 50% of the studies. The quality of analyses was not assessed, but some profiles exhibit several deficits at a glance.

CONCLUSIONS

A substantial part of prognostic factor studies is poorly reported and analyzed, with severe consequences for related systematic reviews and meta-analyses. We consider inadequate reporting of single studies as one of the most important reasons that the clinical relevance of most markers is still unclear after years of research and dozens of publications. We conclude that structured reporting is an important step to improve the quality of prognostic marker research and discuss its role in the context of selective reporting, meta-analysis, study registration, predefined statistical analysis plans, and improvement of marker research.

With or without You: Co-Chaperones Mediate Health and Disease by Modifying Chaperone Function and Protein Triage

Heat shock proteins (HSPs) are a family of molecular chaperones that regulate essential protein refolding and triage decisions to maintain protein homeostasis. Numerous co-chaperone proteins directly interact and modify the function of HSPs, and these interactions impact the outcome of protein triage, impacting everything from structural proteins to cell signaling mediators. The chaperone/co-chaperone machinery protects against various stressors to ensure cellular function in the face of stress. However, coding mutations, expression changes, and post-translational modifications of the chaperone/co-chaperone machinery can alter the cellular stress response. Importantly, these dysfunctions appear to contribute to numerous human diseases. Therapeutic targeting of chaperones is an attractive but challenging approach due to the vast functions of HSPs, likely contributing to the off-target effects of these therapies. Current efforts focus on targeting co-chaperones to develop precise treatments for numerous diseases caused by defects in protein quality control. This review focuses on the recent developments regarding selected HSP70/HSP90 co-chaperones, with a concentration on cardioprotection, neuroprotection, cancer, and autoimmune diseases. We also discuss therapeutic approaches that highlight both the utility and challenges of targeting co-chaperones.

A Novel Six Autophagy-Related Genes Signature Associated With Outcomes and Immune Microenvironment in Lower-Grade Glioma

Since autophagy and the immune microenvironment are deeply involved in the tumor development and progression of Lower-grade gliomas (LGG), our study aimed to construct an autophagy-related risk model for prognosis prediction and investigate the relationship between the immune microenvironment and risk signature in LGG. Therefore, we identified six autophagy-related genes (BAG1, PTK6, EEF2, PEA15, ITGA6, and MAP1LC3C) to build in the training cohort (n = 305 patients) and verify the prognostic model in the validation cohort (n = 128) and the whole cohort (n = 433), based on the data from The Cancer Genome Atlas (TCGA). The six-gene risk signature could divide LGG patients into high- and low-risk groups with distinct overall survival in multiple cohorts (all p < 0.001). The prognostic effect was assessed by area under the time-dependent ROC (t-ROC) analysis in the training, validation, and whole cohorts, in which the AUC value at the survival time of 5 years was 0.837, 0.755, and 0.803, respectively. Cox regression analysis demonstrated that the risk model was an independent risk predictor of OS (HR > 1, p < 0.05). A nomogram including the traditional clinical parameters and risk signature was constructed, and t-ROC, C-index, and calibration curves confirmed its robust predictive capacity. KM analysis revealed a significant difference in the subgroup analyses' survival. Functional enrichment analysis revealed that these autophagy-related signatures were mainly involved in the phagosome and immune-related pathways. Besides, we also found significant differences in immune cell infiltration and immunotherapy targets between risk groups. In conclusion, we built a powerful predictive signature and explored immune components (including immune cells and emerging immunotherapy targets) in LGG.

Metastasis Risk Assessment Using BAG2 Expression by Cancer-Associated Fibroblast and Tumor Cells in Patients with Breast Cancer

Simple Summary

Cancer-associated fibroblasts (CAFs) promote tumor progression and play an important role in evading immune surveillance. The previous study showed that BAG2 could be elevated in cancer associated fibroblasts (CAFs). Here, we evaluated BAG2 expression of CAF and tumor cells and assessed metastasis risk in patients with breast cancer. We found that patients with either BAG2-high or BAG2(+) CAF had significantly worse distant metastasis-free survival than those with BAG2-double negative. Evaluation of BAG2 expression on both CAFs and tumor cells could be helpful to estimate the risk of metastasis in breast cancer.

Abstract

Few studies have examined the role of BAG2 in malignancies. We investigated the prognostic value of BAG2-expression in cancer-associated fibroblasts (CAFs) and tumor cells in predicting metastasis-free survival in patients with breast cancer. Tissue-microarray was constructed using human breast cancer tissues obtained by surgical resection between 1992 and 2015. BAG2 expression was evaluated by immunohistochemistry in CAFs or the tumor cells. BAG2 expression in the CAFs and cytoplasm of tumor cells was classified as positive and negative, and low and high, respectively. BAG2-CAF was evaluated in 310 patients and was positive in 67 (21.6%) patients. Kaplan–Meier plots showed that distant metastasis-free survival (DMFS) was lesser in patients with BAG2(+) CAF than in patients with BAG2(−) CAF (p = 0.039). Additionally, we classified the 310 patients into two groups: 109 in either BAG2-high or BAG2(+) CAF and 201 in BAG2-low and BAG2(−) CAF. DMFS was significantly reduced in patients with either BAG2-high or BAG2(+) CAF than in the patients of the other group (p = 0.005). Multivariable analysis demonstrated that DMFS was prolonged in patients with BAG2(−) CAF or BAG2-low. Evaluation of BAG2 expression on both CAFs and tumor cells could help in determining the risk of metastasis in breast cancer.

Prediction of Late Recurrence and Distant Metastasis in Early-stage Breast Cancer: Overview of Current and Emerging Biomarkers

Recently, a significant number of breast cancer (BC) patients have been diagnosed at an early stage. It is therefore critical to accurately predict the risk of recurrence and distant metastasis for better management of BC in this setting. Clinicopathologic patterns, particularly lymph node status, tumor size, and hormonal receptor status are routinely used to identify women at increased risk of recurrence. However, these factors have limitations regarding their predictive ability for late metastasis risk in patients with early BC. Emerging molecular signatures using gene expression-based approaches have improved the prognostic and predictive accuracy for this indication. However, the use of their based-scores for risk assessment has provided contradictory findings. Therefore, developing and using newly emerged alternative predictive and prognostic biomarkers for identifying patients at high- and low-risk is of great importance. The present review discusses some serum biomarkers and multigene profiling scores for predicting late recurrence and distant metastasis in early-stage BC based on recently published studies and clinical trials.

ALDH3A1 driving tumor metastasis is mediated by p53/BAG1 in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a lethal malignancy with metastasis, a major tumor feature that predominantly correlated with progression, but the molecules that mediated tumor metastasis remain elusive. To declare the critical regulatory genes, RNA sequencing data in LUAD patients was acquired from The Cancer Genome Atlas (TCGA) and found that ALDH3A1 was distinctly highly expressed in LUAD patients with metastasis (M1) compared with those without metastasis (M0), linked to the property of cancer stem cell and epithelial-mesenchymal transition (EMT). Besides, high ALDH3A1 expression predicted a poor prognosis. Knockdown of ALDH3A1 showed decreased proliferation, migration, and invasion in A549 cell line. Furthermore, BAG1 was regulated by ALDH3A1 through p53, enhanced cell proliferation, and predicted clinical prognosis. Our findings collectively uncovered a novel mechanism that orchestrates tumor cells' metastasis, and decreasing ALDH3A1 represented a potential therapeutic target for reprogramming metastasis.

Expression of BAG1 is associated with prognosis in kidney renal clear cell carcinoma based on bioinformatics

Background

BCL2 associated Athano-Gene 1 (BAG1) has been described to be involved in the development and progression of cancer. But the role of BAG1 in kidney renal clear cell carcinoma (KIRC) has remained largely unknown.

Methods

We performed bioinformatic analysis of data from TCGA and GEO dataset. The role of BAG1 in KIRC was explored by Logistic and Cox regression model. The molecular mechanisms of BAG1 was revealed by GSEA.

Results

The current study found that the KIRC tumor samples have a low level of BAG1 mRNA expression compared to the matched normal tissues based on TCGA data and GEO databases. Low expression of BAG1 in KIRC was significantly associated with Sex, clinical pathological stage, tumor-node-metastasis (TNM) stage, hemoglobin levels, cancer status and history of neoadjuvant treatment. Kaplan-Meier survival analysis indicated that KIRC patients with BAG1 high expression have a longer survival time than those with BAG1 low expression (p < 0.000). Cox regression analysis showed that BAG1 remained independently associated with overall survival, with a hazard ratio (HR) of 1.75(CI:1.05–2.90; p = 0.029). GSEA indicated that the signaling pathways including fatty acid metabolism and oxidative phosphorylation were differentially enriched in high BAG1 expression phenotype.

Conclusions

These findings suggested that BAG1 expression may act as a potential favorable prognostic marker and challenging therapeutic target.

p53 and BLC2 Immunohistochemical Expression Across Molecular Subtypes in 1099 Early Breast Cancer Patients With Long-Term Follow-up: An Observational Study

INTRODUCTION

p53 and antiapoptotic B-cell leukemia/lymphoma 2 (BLC2) have been proposed as prognostic markers for early breast cancer (BC), although their relationship with conventional parameters and patient prognosis, as well as their distribution within the molecular BC subtypes remains uncertain.

PATIENTS AND METHODS

In this observational study, we analyzed the immunohistochemical expression of p53 and BLC2 in 1099 early BC patients surgically treated between 2000 and 2006 and followed for at least 5 years, also considering their association with pathologic factors and molecular subtypes, as well as their influence on disease-free survival.

RESULTS

p53 and BLC2 are distributed differently across molecular subtypes (P < .0001); in particular, p53 positivity and BLC2 negativity seems to be associated with more aggressive conventional tumor phenotypes. Moreover, BLC2 negativity seems to be a significant discriminating factor for disease-free survival (P = .003) according to Kaplan-Meier analysis, while p53 seems to have no discriminating effect. Among patients with discordant p53/BLC2 phenotype, the combination p53⁺BLC2^- seems to be associated with the worst outcomes (P = .007) and significantly influenced the clinical course of node-negative patients treated only with hormone therapy (P = .004).

CONCLUSION

These two biomarkers, in addition to conventional pathologic factors and molecular subtype, could help define the risk and outcome of BC.

Bag-1 stimulates Bad phosphorylation through activation of Akt and Raf kinases to mediate cell survival in breast cancer

BACKGROUND

Bag-1 (Bcl-2-associated athanogene) is a multifunctional anti-apoptotic protein frequently overexpressed in cancer. Bag-1 interacts with a variety of cellular targets including Hsp70/Hsc70 chaperones, Bcl-2, nuclear hormone receptors, Akt and Raf kinases. In this study, we investigated in detail the effects of Bag-1 on major cell survival pathways associated with breast cancer.

METHODS

Using immunoblot analysis, we examined Bag-1 expression profiles in tumor and normal tissues of breast cancer patients with different receptor status. We investigated the effects of Bag-1 on cell proliferation, apoptosis, Akt and Raf kinase pathways, and Bad phosphorylation by implementing ectopic expression or knockdown of Bag-1 in MCF-7, BT-474, MDA-MB-231 and MCF-10A breast cell lines. We also tested these in tumor and normal tissues from breast cancer patients. We investigated the interactions between Bag-1, Akt and Raf kinases in cell lines and tumor tissues by co-immunoprecipitation, and their subcellular localization by immunocytochemistry and immunohistochemistry.

RESULTS

We observed that Bag-1 is overexpressed in breast tumors in all molecular subtypes, i.e., regardless of their ER, PR and Her2 expression profile. Ectopic expression of Bag-1 in breast cancer cell lines results in the activation of B-Raf, C-Raf and Akt kinases, which are also upregulated in breast tumors. Bag-1 forms complexes with B-Raf, C-Raf and Akt in breast cancer cells, enhancing their phosphorylation and activation, and ultimately leading to phosphorylation of the pro-apoptotic Bad protein at Ser112 and Ser136. This causes Bad's re-localization to the nucleus, and inhibits apoptosis in favor of cell survival.

CONCLUSIONS

Overall, Bad inhibition by Bag-1 through activation of Raf and Akt kinases is an effective survival and growth strategy exploited by breast cancer cells. Therefore, targeting the molecular interactions between Bag-1 and these kinases might prove an effective anticancer therapy.

Identification of potential key genes and pathways predicting pathogenesis and prognosis for triple-negative breast cancer

Background

Triple negative breast cancer (TNBC) is a specific subtype of breast cancer with a poor prognosis due to its aggressive biological behaviour and lack of therapeutic targets. We aimed to explore some novel genes and pathways related to TNBC prognosis through bioinformatics methods as well as potential initiation and progression mechanisms.

Methods

Breast cancer mRNA data were obtained from The Cancer Genome Atlas database (TCGA). Differential expression analysis of cancer and adjacent cancer, as well as, triple negative breast cancer and non-triple negative breast cancer were performed using R software. The key genes related to the pathogenesis were identified by functional and pathway enrichment analysis and protein-protein interaction network analysis. Based on univariate and multivariate Cox proportional hazards model analyses, a gene signature was established to predict overall survival. Receiver operating characteristic curve was used to evaluate the prognostic performance of our model.

Results

Based on mRNA expression profiling of breast cancer patients from the TCGA database, 755 differentially expressed overlapping mRNAs were detected between TNBC/non-TNBC samples and normal tissue. We found eight hub genes associated with the cell cycle pathway highly expressed in TNBC. Additionally, a novel six-gene (TMEM252, PRB2, SMCO1, IVL, SMR3B and COL9A3) signature from the 755 differentially expressed mRNAs was constructed and significantly associated with prognosis as an independent prognostic signature. TNBC patients with high-risk scores based on the expression of the 6-mRNAs had significantly shorter survival times compared to patients with low-risk scores (P < 0.0001).

Conclusions

The eight hub genes we identified might be tightly correlated with TNBC pathogenesis. The 6-mRNA signature established might act as an independent biomarker with a potentially good performance in predicting overall survival.