Validated biomarkers: The key to precision treatment in patients with breast cancer

Development of a breast cancer invasion score to predict tumor aggressiveness and prognosis via PI3K/AKT/mTOR pathway analysis

Invasiveness is a key indicator of tumor malignancy and is often linked to poor prognosis in breast cancer (BC). To explore the diverse characteristics of invasive cells, single-cell RNA sequencing (scRNA-seq) data from three ductal carcinoma stages were analyzed, classifying samples into invasion and non-invasion groups. Nine genes (MCTS1, PGK1, PCMT1, C8orf76, TMEM242, QPRT, SLC16A2, AFG1L, and SPINK8) were identified as key discriminators between these groups. A breast cancer invasion score (BCIS) model was developed using LASSO Cox regression, revealed that high BCIS correlated with poorer overall survival in TCGA-BRCA patients and was validated across GSE20685 and METABRIC datasets (five-year and ten-year survival). Functional experiments demonstrated that knockdown of PGK1 or PCMT1 inhibited tumor cell proliferation and reduced the phosphorylation levels of mTORC, P70S6K, S6, and AKT, indicating suppression of the PI3K/AKT/mTOR pathways. High-BCIS tumors exhibited enrichment in protein secretion and PI3K/AKT/mTOR pathways, associated with aggressiveness and therapy resistance. This study introduced the BCIS score, distinguishing invasion from non-invasion cells, linked to PI3K/AKT/mTOR pathways, offering insights into BRCA prognosis and tumor aggressiveness.

ERBB3-related gene PBX1 is associated with prognosis in patients with HER2-positive breast cancer

BACKGROUND

HER2-positive breast cancer (BC) is a subtype of breast cancer. Increased ERBB3 expression has been implicated as a potential cause of resistance to other HER-targeted therapies. Our study aimed to screen and validate prognostic markers associated with ERBB3 expression by bioinformatics and affecting the prognosis of HER2 staging.

METHODS

Analyzing differences in ERBB3-related groups. ERBB3 expression-related differentially expressed genes (DEGs) were identified and intersected with survival status-related DEGs to obtain intersected genes. Three algorithms, LASSO, RandomForest and XGBoost were combined to identify the signature genes. we construct risk models and generate ROC curves for prediction. Furthermore, we delve into the immunological traits, correlations, and expression patterns of signature genes by conducting a comprehensive analysis that encompasses immune infiltration analysis, correlation analysis, and differential expression analysis.

RESULTS

Significant variability in ERBB3 expression and prognosis in high and low ERBB3 expression groups. Twenty-five candidate DEGs were identified by intersecting ERBB3-related DEGs with survival-related DEGs. Utilizing three distinct machine learning algorithms, we identified three signature genes-PBX1, IGHM, and CXCL13-that exhibited significant diagnostic value within the diagnostic model. In addition, the risk model had better prognostic and predictive effects, and the immune infiltration analysis showed that IGHM, CXCL13 might affect the proliferation of BC cells through immune cells. Functional studies demonstrated that interference with PBX1 inhibited the proliferation, migration, and epithelial-mesenchymal transition process of HER2-positive BC cells.

CONCLUSION

PBX1, IGHM and CXCL13 are associated with the expression level of the ERBB3 and are prognostic markers for HER2-positive in BC, which may play an important role in the development and progression of BC.

Epoxy metabolites of linoleic acid promote the development of breast cancer via orchestrating PLEC/NFκB1/CXCL9-mediated tumor growth and metastasis

Breast cancer (BC) is a common malignant tumor in women and requires a comprehensive understanding of its pathogenesis for the development of new therapeutic strategies. Polyunsaturated fatty acids (PUFAs) metabolism-driven inflammation is a causative factor in cancer development. However, the function of PUFAs' metabolism in BC remains largely unknown. Here we report the role and underlying mechanism of epoxyoctadecenoic acids (EpOMEs), the metabolites of linoleic acid mediated by cytochrome P450 (CYP) monooxygenases, in promoting the development of BC, particularly triple-negative BC (TNBC). A metabolomics study identified that EpOMEs were significantly increased in the plasma of BC patients and MMTV-PyMT mice, which accounted for the upregulation of CYP2J2 in BC tumor tissues and tumor cells. Decreased EpOMEs by treatment of CYP monooxygenase inhibitors significantly alleviated tumor development in MMTV-PyMT mice. Treatment with EpOMEs and overexpression of CYP2J2 to increase EpOMEs in TNBC cells significantly promoted cellular proliferation, migration, tumor growth, and metastasis. Whereas knockdown of CYP2J2 to decrease EpOMEs inhibited tumorigenesis and lung metastasis of TNBC, which was reversed by EpOME administration. Transcriptomics and proteomics analyses revealed CXCL9 and PLEC were critical for EpOME-mediated promotion of TNBC. Knockdown of CXCL9 and PLEC inhibited TNBC progression and EpOME-mediated promotion of TNBC. Both overexpression of CYP2J2 and EpOME treatment upregulate PLEC, while PLEC upregulates NFκB1, which is a transcription regulator of CXCL9. This study extends the understanding of the function of PUFAs metabolism in BC development, providing potential therapeutic targets and dietary guidelines for patients with TNBC and other BCs. The illustration of the hypothetical mechanism CYP2J2/EpOMEs promotes the tumorigenesis and metastasis of TNBC via PLEC/NFKB1/CXCL9 signaling pathway.

Down-regulation of ESRP2 inhibits breast cancer cell proliferation via inhibiting cyclinD1

Epithelial splicing regulatory protein 2 (ESRP2),an important alternative splicing protein of mRNA, is reported to have a dual role in tumors, which can promote or inhibit the occurrence and development of tumors. However, the function and mechanism of ESRP2 in breast cancer (BC) remain unclear. The distribution of ESRP2 expression in breast cancer and the correlation between ESRP2 expression and the overall survival rate were detected by The Cancer Genome Atlas (TCGA) database. Gene Ontology(GO)analysis, containing biological process, cellular components, and molecular function, was utilized to evaluate the potential mechanism of ESRP2 in breast cancer. The ESRP2 expression in breast cancer cell lines was detected by real-time quantitative PCR analysis (RT-qPCR) and western blotting. Cell clone was performed to examine the proliferation of ESRP2 knockdown in MCF-7 cells. The cell cycle was measured by flow cytometry assays. The role of ESRP2 knockdown in synergistic effect with chemotherapeutic agents was also determined by MTT assay. Bioinformatics analysis demonstrated that the ESRP2 gene was elevated in breast cancer cells and its overexpression was strongly correlated with shorter overall survival. GO analysis revealed that ESRP2 expression was related to cell proliferation. ESRP2 mRNA and protein expression were elevated in breast cancer cell lines, compared to the normal human breast cell line MCF-10 A. Dwon-regulation of ESRP2 inhibited cell proliferation and promoted the sensitivity of chemotherapy drug, Cisplatin(DDP) and Paclitaxel (TAXOL), in MCF-7 cells.Additionally, ESRP2 knockdown obstructed the cell cycle at the G1 phase and caused a decrease in cyclinD1 protein expression. These findings reveal that ESRP2 is highly expressed in breast cancer and is correlated with poor prognosis in breast cancer patients. ESRP2 knockdown can inhibit MCF-7 cell proliferation by arresting the cell cycle at the G1 phase and promoting the sensitivity of chemotherapy drugs (DDP and TAXOL)in MCF-7 cells. ESRP2 may be required for the regulation of breast cancer progression, as well as a critical target for the clinical treatment of breast cancer.

Copper metabolism-related signature for prognosis prediction and MMP13 served as malignant factor for breast cancer

Objectives

To comprehensively analyze the copper metabolism in Breast cancer, we established a prognostic signature for breast cancer (BC) related to copper metabolism.

Methods

Copper metabolism-related genes were sourced from previous literatures and were selected by the Univariate Cox regression. Cu-enrichment scores were calculated via ssGSEA. Differentially expressed genes were identified with limma between high and low Cu-enrichment scores group, then we used the Random Survival Forest and LASSO to build the CuScore for BC. Kaplan-Meier analysis, ROC curves, and Cox regression were used to evaluate CuScore. Genomic mutations were analyzed with GISTIC. Immune cells were examined using ESTIMATE, ssGSEA and TIMER. Enrichment analysis used clusterProfiler and GSVA. The GDSC database and oncoPredict package analyzed chemotherapeutic sensitivity. MMP13 was selected for in vitro assays.

Results

Four copper metabolism-related genes (UBE2D2, SLC31A1, ATP7A, and MAPK1) with prognostic value were identified. Higher expression levels of these genes were associated with higher Cu-enrichment scores, a factor of malignancy in breast cancer. Among 115 differentially expressed genes, 19 prognostic genes were identified, with three (CEACAM5, MMP13, and CRISP3) highlighted by Random Survival Forest and LASSO. Higher CuScores correlated with worse prognoses and were effective in predicting breast cancer outcomes. CuScore and metastasis were independent prognostic factors. Tumor-infiltrating immune cells were associated with lower CuScores. GO-GSEA analysis indicated six immune-related pathways might be regulated by CuScore. Patients with higher CuScores had lower TMB and were more sensitive to Sapitinib and LCL161, while those with lower CuScores might respond better to anti-PD1 therapy. High MMP13 expression in breast cancer was linked to malignancy, affecting cell proliferation and migration.

Conclusion

The identified copper metabolism-related gene signature has the potential to predict prognosis and guide clinical treatment for BC. Among these genes, MMP13 may act as a malignant factor in BC.

Introducing effective genes in lymph node metastasis of breast cancer patients using SHAP values based on the mRNA expression data

OBJECTIVE

Breast cancer, a global concern predominantly impacting women, poses a significant threat when not identified early. While survival rates for breast cancer patients are typically favorable, the emergence of regional metastases markedly diminishes survival prospects. Detecting metastases and comprehending their molecular underpinnings are crucial for tailoring effective treatments and improving patient survival outcomes.

METHODS

Various artificial intelligence methods and techniques were employed in this study to achieve accurate outcomes. Initially, the data was organized and underwent hold-out cross-validation, data cleaning, and normalization. Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. Finally, the selected features were considered, and the SHAP algorithm was utilized to identify the most significant features for enhancing the decoding of dominant molecular mechanisms in lymph node metastases.

RESULTS

In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. The RF classifier utilized the candidate mRNAs to differentiate between negative and positive categories with an accuracy of 61% and an AUC of 0.6. During the SHAP process, intriguing relationships between the selected mRNAs and positive/negative lymph node status were discovered. The results indicate that GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2 are among the top five most impactful mRNAs based on their SHAP values.

CONCLUSION

The prominent identified mRNAs including GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2, are implicated in lymph node metastasis. This study holds promise in elucidating a thorough insight into key candidate genes that could significantly impact the early detection and tailored therapeutic strategies for lymph node metastasis in patients with breast cancer.

Cellular geometry and epithelial-mesenchymal plasticity intersect with PIEZO1 in breast cancer cells

Differences in shape can be a distinguishing feature between different cell types, but the shape of a cell can also be dynamic. Changes in cell shape are critical when cancer cells escape from the primary tumor and undergo major morphological changes that allow them to squeeze between endothelial cells, enter the vasculature, and metastasize to other areas of the body. A shift from rounded to spindly cellular geometry is a consequence of epithelial-mesenchymal plasticity, which is also associated with changes in gene expression, increased invasiveness, and therapeutic resistance. However, the consequences and functional impacts of cell shape changes and the mechanisms through which they occur are still poorly understood. Here, we demonstrate that altering the morphology of a cell produces a remodeling of calcium influx via the ion channel PIEZO1 and identify PIEZO1 as an inducer of features of epithelial-to-mesenchymal plasticity. Combining automated epifluorescence microscopy and a genetically encoded calcium indicator, we demonstrate that activation of the PIEZO1 force channel with the PIEZO1 agonist, YODA 1, induces features of epithelial-to-mesenchymal plasticity in breast cancer cells. These findings suggest that PIEZO1 is a critical point of convergence between shape-induced changes in cellular signaling and epithelial-mesenchymal plasticity in breast cancer cells.

Mechanism exploration and biomarker identification of glycemic deterioration in patients with diseases of the exocrine pancreas

The damage to the endocrine pancreas among patients with diseases of the exocrine pancreas (DP) leads to reduced glycemic deterioration, ultimately resulting in diabetes of the exocrine pancreas (DEP). The present research aims to investigate the mechanism responsible for glycemic deterioration in DP patients, and to identify useful biomarkers, with the ultimate goal of enhancing clinical practice awareness. Gene expression profiles of patients with DP in this study were acquired from the Gene Expression Omnibus database. The original study defines DP patients to belong in one of three categories: non-diabetic (ND), impaired glucose tolerance (IGT) and DEP, which correspond to normoglycemia, early and late glycemic deterioration, respectively. After ensuring quality control, the discovery cohort included 8 ND, 20 IGT, and 12 DEP, while the validation cohort included 27 ND, 15 IGT, and 20 DEP. Gene set enrichment analysis (GSEA) employed differentially expressed genes (DEGs), while immunocyte infiltration was determined using single sample gene set enrichment analysis (ssGSEA). Additionally, correlation analysis was conducted to establish the link between clinical characteristics and immunocyte infiltration. The least absolute shrinkage and selection operator regression and random forest combined to identify biomarkers indicating glycemic deterioration in DP patients. These biomarkers were further validated through independent cohorts and animal experiments. With glycemic deterioration, biological processes in the pancreatic islets such as nutrient metabolism and complex immune responses are disrupted in DP patients. The expression of ACOT4, B2M, and ACKR2 was upregulated, whereas the expression of CACNA1F was downregulated. Immunocyte infiltration in the islet microenvironment showed a significant positive correlation with the age, body mass index (BMI), HbA1c and glycemia at the 2-h of patients. It was a crucial factor in glycemic deterioration. Additionally, B2M demonstrated a significant positive correlation with immunocyte infiltration and clinical features. Quantitative real-time PCR (qRT-PCR) and western blotting confirmed the upregulation in B2M. Immunofluorescent staining suggested the alteration of B2M was mainly in the alpha cells and beta cells. Overall, the study showed that gradually increased immunocyte infiltration was a significant contributor to glycemic deterioration in patients with DP, and it also highlighted B2M as a biomarker.

Pan-cancer analysis of SERPINE family genes as biomarkers of cancer prognosis and response to therapy

Background: Serine protease inhibitor E (SERPINE) family genes participate in the tumor growth, cancer cell survival and metastasis. However, the SERPINE family members role in the prognosis and their clinical therapeutic potentials in various human cancer types have not been elaborately explored. Methods: We preliminarily analyzed expression levels and prognostic values of SERPINE family genes, and investigated the correlation between SERPINEs expression and tumor microenvironment (TME), Stemness score, clinical characteristic, immune infiltration, tumor mutational burden (TMB), immune subtype, and drug sensitivity in pan-cancer, which based on updated public databases and integrated some bioinformatics analysis methods. In addition, we conducted the enrichment analysis of SERPINEs from DAVID and KOBAS databases. Results: SERPINE1, SERPINE2, and SERPINE3 expression were upregulated in nine cancers, twelve cancers, and six cancers, respectively. The expression of SERPINE family genes was associated with the prognosis in several cancers from The Cancer Genome Atlas (TCGA). Furthermore, SERPINE family genes expression also had a significant relation to stromal and immune scores, and RNA stemness score and DNA stemness score in pan-cancer. SERPINE1 and SERPINE2 expression significantly increased in tumor advanced stage in colon adenocarcinoma (COAD). Results showed that SERPINE1 and SERPINE2 expression were negatively related with B cells and Monocytes, respectively. SERPINE2 expression had a significantly positive relation with B cells and Macrophages. In terms of TMB, SERPINE1, SERPINE2, and SERPINE3 were found to associated with TMB in seven cancers, fourteen cancers, and four cancers, respectively. Moreover, all SERPINE gene family members were significantly correlated with immune subtypes. SERPINE1 expression had a significantly positive or negative correlation with drug sensitivity. Conclusion: The study indicated the great potential of SERPINE family genes as biomarkers for prognosis and provided valuable strategies for further investigation of SERPINE family genes as potential targets in cancer.

Expression of Soluble Form of Aurora A as a Predictive Factor for Neoadjuvant Therapy in Breast Cancer Patients: A Single-Center Pilot Study

PURPOSE

To search for new predictive breast cancer biomarkers. We analyzed the serum concentrations of biomarkers involved in carcinogenesis, which can also be targeted by therapy.

METHODS

In a single-center prospective study, the serum levels of Aurora A, thymidine kinase 1, and human epidermal growth factor receptor type 3 (HER3) were determined in 119 women with BC before neoadjuvant treatment using ELISA kits.

RESULTS

The following clinical data were analyzed: age; TNM; the expression of ER, PGR, HER2, and Ki67; histological grade (G); and the response to neoadjuvant treatment (NAT) in the residual tumor burden classification (RCB). A complete pathological response (pCR) was achieved after NAT in 41 patients (34%). The highest proportion of the patients with a confirmed pCR was found for triple negative breast cancer (TNBC) (62.5%); non-luminal HER2-positive (52.6%) cancer subtypes (p = 0.0003); and in the G3 group (50%; p = 0.0078). The patients with higher levels of Aurora A were more likely to achieve pCR (p = 0.039). In the multivariate analysis, the serum Aurora A levels ≥ 4.75 ng/mL correlated with a higher rate of pCR (OR: 3.5; 95% CI: 1.2-10.1; p = 0.023).

CONCLUSIONS

We showed that in a biologically heterogeneous group of BC patients, the pretreatment serum Aurora A levels were of significant value in predicting the response to NAT.

Ketosis Suppression and Ageing (KetoSAge): The Effects of Suppressing Ketosis in Long Term Keto-Adapted Non-Athletic Females

Most studies on ketosis have focused on short-term effects, male athletes, or weight loss. Hereby, we studied the effects of short-term ketosis suppression in healthy women on long-standing ketosis. Ten lean (BMI 20.5 ± 1.4), metabolically healthy, pre-menopausal women (age 32.3 ± 8.9) maintaining nutritional ketosis (NK) for > 1 year (3.9 years ± 2.3) underwent three 21-day phases: nutritional ketosis (NK; P1), suppressed ketosis (SuK; P2), and returned to NK (P3). Adherence to each phase was confirmed with daily capillary D-beta-hydroxybutyrate (BHB) tests (P1 = 1.9 ± 0.7; P2 = 0.1 ± 0.1; and P3 = 1.9 ± 0.6 pmol/L). Ageing biomarkers and anthropometrics were evaluated at the end of each phase. Ketosis suppression significantly increased: insulin, 1.78-fold from 33.60 (± 8.63) to 59.80 (± 14.69) pmol/L (p = 0.0002); IGF1, 1.83-fold from 149.30 (± 32.96) to 273.40 (± 85.66) µg/L (p = 0.0045); glucose, 1.17-fold from 78.6 (± 9.5) to 92.2 (± 10.6) mg/dL (p = 0.0088); respiratory quotient (RQ), 1.09-fold 0.66 (± 0.05) to 0.72 (± 0.06; p = 0.0427); and PAI-1, 13.34 (± 6.85) to 16.69 (± 6.26) ng/mL (p = 0.0428). VEGF, EGF, and monocyte chemotactic protein also significantly increased, indicating a pro-inflammatory shift. Sustained ketosis showed no adverse health effects, and may mitigate hyperinsulinemia without impairing metabolic flexibility in metabolically healthy women.

Clinicopathological Features of Invasive Breast Cancer: A Five-Year Retrospective Study in Southern and South-Western Ethiopia

Background: Breast cancer (BC) is the most common type of cancer in Ethiopia. The incidence of BC is also rising, but the exact figure is still poorly known. Therefore, this study was conducted to address the gap in epidemiological data on BC in southern and southwestern Ethiopia. Materials and Methods: This is a five-year (2015-2019) retrospective study. The demographic and clinicopathological data were collected from biopsy reports of different kinds of breast carcinomas in the pathology department of Jimma University Specialized Hospital and Hawassa University Specialized Referral Hospital. Histopathological grades and stages were conducted using Nottingham grading and TNM staging system, respectively. Collected data were entered and analyzed using SPSS Version-20 software. Results: The mean age of patients at diagnosis was 42.27 (SD = 13.57) years. The pathological stage of most BC patients was stage III, and most of them had tumor sizes greater than 5 cm. Most patients had moderately differentiated tumor grade, and mastectomy was the most common type of surgery at the time of diagnosis. Invasive ductal carcinoma was the most common histological type of BC, followed by invasive lobular carcinoma. Lymph node involvement was seen in 60.5% of cases. Lymph node involvement was associated with tumor size (χ² = 8.55, p = 0.033) and type of surgery (χ² = 39.69, p < 0.001). Conclusions: This study showed that BC patients in southern and southwestern Ethiopia displayed advanced pathological stages, relatively young age at diagnosis, and predominant invasive ductal carcinoma histological patterns.

Spatial Proteomics for the Molecular Characterization of Breast Cancer

Breast cancer (BC) is a major global health issue, affecting a significant proportion of the female population and contributing to high rates of mortality. One of the primary challenges in the treatment of BC is the disease's heterogeneity, which can lead to ineffective therapies and poor patient outcomes. Spatial proteomics, which involves the study of protein localization within cells, offers a promising approach for understanding the biological processes that contribute to cellular heterogeneity within BC tissue. To fully leverage the potential of spatial proteomics, it is critical to identify early diagnostic biomarkers and therapeutic targets, and to understand protein expression levels and modifications. The subcellular localization of proteins is a key factor in their physiological function, making the study of subcellular localization a major challenge in cell biology. Achieving high resolution at the cellular and subcellular level is essential for obtaining an accurate spatial distribution of proteins, which in turn can enable the application of proteomics in clinical research. In this review, we present a comparison of current methods of spatial proteomics in BC, including untargeted and targeted strategies. Untargeted strategies enable the detection and analysis of proteins and peptides without a predetermined molecular focus, whereas targeted strategies allow the investigation of a predefined set of proteins or peptides of interest, overcoming the limitations associated with the stochastic nature of untargeted proteomics. By directly comparing these methods, we aim to provide insights into their strengths and limitations and their potential applications in BC research.

RNA biomarkers for alcohol use disorder

Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking (naltrexone, acamprosate, disulfiram, and some off-label medications), and treating comorbid psychiatric conditions (e.g., depression and anxiety). It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic (or other -omic data, e.g., methylome, proteome, metabolome) can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed.

Circulating Biomarkers in the Management of Breast Cancer

Circulating biomarkers have become a promising modality in the management of many cancers. Similarly, in breast cancer, circulatory biomarkers are useful, non-invasive methods in the diagnosis, prognostication, and evaluation of response to treatment. Invasive surgical biopsies can be potentially replaced by “liquid biopsy,” which involves analysing circulatory biomarkers that may reveal features of primary and metastatic disease. Therefore, providing an insight into the cancer biology can be utilised to monitor treatment response, treatment-induced adaptation and tumour and disease progression through non-invasive means. The objective of this review is to provide an overview of the current status of the circulating biomarkers highlighting their promising impact on the management of patients with breast cancer.

Exploring the prognostic impact of tumor sidedness in ovarian cancer: A population-based survival analysis of over 10,000 patients

Very recently, emerging evidence demonstrated that laterality might be an independent prognostic factor in patients with advanced ovarian cancer (OC). Based on preliminary provocative observations, our study aimed to investigate the prognostic impact of sidedness in a large cohort of women with OC. Survival was estimated based on Kaplan-Meier method and survival curves were compared using Log-rank test. Cox proportional-hazards model was used to study the association between survival and covariates. A total of 10,177 women with OC were included. Mean age at diagnosis was 59.58 years (±13.5); 36.7% OC right-sided, 36.9% were left- sided, and 26.4% had bilateral OC. The median overall survival (OS) for the entire population was 77 months, with the lowest median OS observed in bilateral OC (median OS: 34 months). The prognostic value of OC sidedness was not confirmed at the univariable analysis (HR = 0.958; 95% CI: 0.888-1.033, p = 0.268). However, women with bilateral OC has a 45% higher risk of death as compared with unilateral diagnosis (HR = 1.453; 95% CI: 1.410-1.497; p< 0.001). The independent prognostic value was further confirmed on multivarible analysis after adjusting for covariates including age, marital status, histological type, CA-125 at diagnosis, grade, stage, chemotherapy and surgery (HR = 1.087; 95% CI: 1.043-1.136, p = 0.02). However, the ultimate prognostic significance appeared less prominent, with bilateral OC conferring a relative increase of 8.7% of mortality. Our real-world study demonstrated that impact of tumor sidedness has no prognostic implication (right vs left OC) but bilateral OCs might be marginally more prognostically unfavorable. Prospective validation might be warranted, to confirm the prognostic significance of OC sidedness, including for the presence of key genetic alterations and lymph nodes asymmetry, to better stratify patients with OC and predict outcomes according to tumor sidedness at diagnosis.

SERPINE1 Overexpression Promotes Malignant Progression and Poor Prognosis of Gastric Cancer

The serine protease inhibitor clade E member 1 (SERPINE1) is a major inhibitor of tissue plasminogen activator and urokinase, and has been implicated in the development and progression of a variety of tumors. In this study, mRNA microarray and TCGA database were used to comprehensively analyze the upregulation of SERPINE1 in gastric cancer (GC) tissues compared with the normal stomach tissues. Kaplan-Meier results confirmed that patients with high SERPINE1 expression exhibited worse overall survival and disease-free survival. In addition, cell proliferation, cell scratches, transwell migration and invasion assay showed that SERPINE1 knockdown inhibited the proliferation, migration and invasion of GC ells. Western blot showed that the expression of VEGF and IL-6 was significantly upregulated after overexpression of SERPINE1. Meanwhile, SERPINE1 was positively correlated with the level of immune infiltration using the online analysis tools TISIDB and TIMER. And SERPINE1 expression increased with the increase of malignancy of GC which were detected by Immunohistochemistry. Finally, tumorigenesis experiments in nude mice further demonstrated that SERPINE1 could promote the occurrence and development of GC, while deletion of SERPINE1 inhibited the progression of GC. In summary, SERPINE1 was highly expressed in GC tissues, and SERPINE1 was helpful for differential diagnosis of pathological grade of gastric mucosal lesions. SERPINE1 might regulate the expression of VEGF and IL-6 through the VEGF signaling pathway and JAK-STAT3 inflammatory signaling pathway, thus ultimately affecting the invasion and migration of GC cells.

Epigenetic biomarkers of ageing are predictive of mortality risk in a longitudinal clinical cohort of individuals diagnosed with oropharyngeal cancer

BACKGROUND

Epigenetic clocks are biomarkers of ageing derived from DNA methylation levels at a subset of CpG sites. The difference between age predicted by these clocks and chronological age, termed "epigenetic age acceleration", has been shown to predict age-related disease and mortality. We aimed to assess the prognostic value of epigenetic age acceleration and a DNA methylation-based mortality risk score with all-cause mortality in a prospective clinical cohort of individuals with head and neck cancer: Head and Neck 5000. We investigated two markers of intrinsic epigenetic age acceleration (IEAAHorvath and IEAAHannum), one marker of extrinsic epigenetic age acceleration (EEAA), one optimised to predict physiological dysregulation (AgeAccelPheno), one optimised to predict lifespan (AgeAccelGrim) and a DNA methylation-based predictor of mortality (ZhangScore). Cox regression models were first used to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CI) for associations of epigenetic age acceleration with all-cause mortality in people with oropharyngeal cancer (n = 408; 105 deaths). The added prognostic value of epigenetic markers compared to a clinical model including age, sex, TNM stage and HPV status was then evaluated.

RESULTS

IEAAHannum and AgeAccelGrim were associated with mortality risk after adjustment for clinical and lifestyle factors (HRs per standard deviation [SD] increase in age acceleration = 1.30 [95% CI 1.07, 1.57; p = 0.007] and 1.40 [95% CI 1.06, 1.83; p = 0.016], respectively). There was weak evidence that the addition of AgeAccelGrim to the clinical model improved 3-year mortality prediction (area under the receiver operating characteristic curve: 0.80 vs. 0.77; p value for difference = 0.069).

CONCLUSION

In the setting of a large, clinical cohort of individuals with head and neck cancer, our study demonstrates the potential of epigenetic markers of ageing to enhance survival prediction in people with oropharyngeal cancer, beyond established prognostic factors. Our findings have potential uses in both clinical and non-clinical contexts: to aid treatment planning and improve patient stratification.

Role of miRNA-495 and NRXN-1 and CNTN-1 mRNA Expression and Its Prognostic Importance in Breast Cancer Patients

Breast cancer is a heterogeneous disease in which genetic factors are involved in disease worsening and higher mortality. Epidemiological and clinical research revealed that breast cancer incidence continues to rise. 100 histopathologically confirmed untreated newly diagnosed cases of invasive ductal carcinoma (IDC) of breast and 100 healthy subjects were involved and blood samples were collected in non-EDTA plain vials. Serum was separated by centrifugation, total RNA was extracted from serum, and cDNA synthesis was done to study the miRNA-495 and neurexin-1 (NRXN-1) and contactin 1 (CNTN-1) mRNA expression by QRT-PCR. The expression levels of miRNA-495, NRXN-1, and CNTN-1 were expressed in fold change. The present study observed decreased relative miRNA-495 expression (0.07-fold) while an increase in NRXN-1 (11.61-fold) and CNTN-1 (4.92-fold) was observed among breast cancer patients compared to healthy controls. A significant difference was observed in miRNA-495 expression with menopausal status (p=0.0001) and TNM stages (p=0.02). It was observed that NRXN-1 expression was significantly associated with menopausal status (p=0.03), lymph node involvement (p < 0.0001), estrogen receptor (ER) status (p=0.03), progesterone receptor (PR) status (p=0.005), TNM stages (p < 0.0001), and distant metastases (p < 0.0001). CNTN-1 expression was also found to be associated with lymph node involvement (p=0.01), PR status (p=0.03), HER2 status (p=0.04), TNM stages (p < 0.0001), and distant metastases (p < 0.0001). ROC suggested that NRXN-1 and CNTN-1 could be the important predictive marker for disease advancement and distant organ metastases. The study concluded that the decreased expression of miR-495 observed in breast cancer patients showed a negative correlation with NRXN-1 while the increased expression of NRXN-1 and CNTN-1 was linked with disease advancement and distant metastases and could be the important predictive marker for breast cancer patients.

Single Cell Genetic Profiling of Tumors of Breast Cancer Patients Aged 50 Years and Older Reveals Enormous Intratumor Heterogeneity Independent of Individual Prognosis

PURPOSE

Older breast cancer patients are underrepresented in cancer research even though the majority (81.4%) of women dying of breast cancer are 55 years and older. Here we study a common phenomenon observed in breast cancer which is a large inter- and intratumor heterogeneity; this poses a tremendous clinical challenge, for example with respect to treatment stratification. To further elucidate genomic instability and tumor heterogeneity in older patients, we analyzed the genetic aberration profiles of 39 breast cancer patients aged 50 years and older (median 67 years) with either short (median 2.4 years) or long survival (median 19 years). The analysis was based on copy number enumeration of eight breast cancer-associated genes using multiplex interphase fluorescence in situ hybridization (miFISH) of single cells, and by targeted next-generation sequencing of 563 cancer-related genes.

RESULTS

We detected enormous inter- and intratumor heterogeneity, yet maintenance of common cancer gene mutations and breast cancer specific chromosomal gains and losses. The gain of COX2 was most common (72%), followed by MYC (69%); losses were most prevalent for CDH1 (74%) and TP53 (69%). The degree of intratumor heterogeneity did not correlate with disease outcome. Comparing the miFISH results of diploid with aneuploid tumor samples significant differences were found: aneuploid tumors showed significantly higher average signal numbers, copy number alterations (CNAs) and instability indices. Mutations in PIKC3A were mostly restricted to luminal A tumors. Furthermore, a significant co-occurrence of CNAs of DBC2/MYC, HER2/DBC2 and HER2/TP53 and mutual exclusivity of CNAs of HER2 and PIK3CA mutations and CNAs of CCND1 and PIK3CA mutations were revealed.

CONCLUSION

Our results provide a comprehensive picture of genome instability profiles with a large variety of inter- and intratumor heterogeneity in breast cancer patients aged 50 years and older. In most cases, the distribution of chromosomal aneuploidies was consistent with previous results; however, striking exceptions, such as tumors driven by exclusive loss of chromosomes, were identified.

Platelet-Derived Procoagulant Microparticles as Blood-based Biomarker of Breast Cancer

Objective:

Breast cancer is the main cause of cancer death in women worldwide. Elevated plasma levels of circulating cell-derived microparticles (MPs) have been reported in various types of cancer, including breast cancer, with the ability to mediate inflammation and thrombosis. Microparticles are bioactive agents, and it has been suggested that MPs can be used as a diagnostic, prognostic, or therapeutic biomarker in various diseases. The aim of this study was to investigate the levels of platelet-derived MPs (PMPs) in breast cancer patients.

Materials and Methods:

In this case-control study, 30 patients with breast cancer and 20 normal subjects were sampled after obtaining written consent. MPs were isolated from blood samples by centrifugation technique. CD42b and annexin V markers were used respectively for counting PMPs and procoagulant MPs with flow cytometry.

Results:

Flow cytometry results showed that the number of PMPs and procoagulant annexin V positive MPs was significantly higher in the breast cancer patients than normal subjects (p<0.001). The number of the annexin V MPs differed significantly in patients with high tumor size (T2) compared to the patients with low tumor size (T1) and controls (p<0.001). Significant and positive correlations were found between PMP levels and tissue-based biomarkers, tumor grading, and distant metastasis (P<0.05). Tumor histological type did not correlate with the numbers of PMPs (p=0.065).

Conclusion:

Increased levels of PMPs and activity in terms of hemostasis and having a positive and significant relationship with tumor grading and metastasis may indicate the effective role of PMPs in the pathogenesis and prognosis of breast cancer.

A Narrative Review on Plasminogen Activator Inhibitor-1 and Its (Patho)Physiological Role: To Target or Not to Target?

Plasminogen activator inhibitor-1 (PAI-1) is the main physiological inhibitor of plasminogen activators (PAs) and is therefore an important inhibitor of the plasminogen/plasmin system. Being the fast-acting inhibitor of tissue-type PA (tPA), PAI-1 primarily attenuates fibrinolysis. Through inhibition of urokinase-type PA (uPA) and interaction with biological ligands such as vitronectin and cell-surface receptors, the function of PAI-1 extends to pericellular proteolysis, tissue remodeling and other processes including cell migration. This review aims at providing a general overview of the properties of PAI-1 and the role it plays in many biological processes and touches upon the possible use of PAI-1 inhibitors as therapeutics.

HER2 splice variants in breast cancer: investigating their impact on diagnosis and treatment outcomes

Overexpression of the HER2 receptor occurs in approximately 20% of breast cancer patients. HER2 positivity is associated with poor prognosis and aggressive tumour phenotypes, which led to rapid progress in HER2 targeted therapeutics and diagnostic testing. Whilst these advances have greatly increased patients' chances of survival, resistance to HER2 targeted therapies, be that intrinsic or acquired, remains a problem. Different forms of the HER2 protein exist within tumours in tandem and can display altered biological activities. Interest in HER2 variants in breast cancer increased when links between resistance to anti-HER2 therapies and a particular variant, Δ16-HER2, were identified. Moreover, the P100 variant potentially reduces the efficacy of the anti-HER2 therapy trastuzumab. Another variant, Herstatin, exhibits 'auto-inhibitory' behaviour. More recently, new HER2 variants have been identified and are currently being assessed for their pro- and anti-cancer properties. It is important when directing the care of patients to consider HER2 variants collectively. This review considers HER2 variants in the context of the tumour environment where multiple variants are co-expressed at altered ratios. This study also provides an up to date account of the landscape of HER2 variants and links this to patterns of resistance against HER2 therapies and treatment plans.

Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis

BACKGROUND

This study intended to determine important genes related to the prognosis and recurrence of breast cancer.

METHODS

Gene expression data of breast cancer patients were downloaded from TCGA database. Breast cancer samples with recurrence and death were defined as poor disease-free survival (DFS) group, while samples without recurrence and survival beyond 5 years were defined as better DFS group. Another gene expression profile dataset (GSE45725) of breast cancer was downloaded as the validation data. Differentially expressed genes (DEGs) were screened between better and poor DFS groups, which were then performed function enrichment analysis. The DEGs that were enriched in the GO function and KEGG signaling pathway were selected for cox regression analysis and Logit regression (LR) model analysis. Finally, correlation analysis between LR model classification and survival prognosis was analyzed.

RESULTS

Based on the breast cancer gene expression profile data in TCGA, 540 DEGs were screened between better DFS and poor DFS groups, including 177 downregulated and 363 upregulated DEGs. A total of 283 DEGs were involved in all GO functions and KEGG signaling pathways. Through LR model screening, 10 important feature DEGs were identified and validated, among which, ABCA3, CCL22, FOXJ1, IL1RN, KCNIP3, MAP2K6, and MRPL13, were significantly expressed in both groups in the two data sets. ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 were good prognostic factors, while KCNIP3 and MRPL13 were poor prognostic factors.

CONCLUSION

ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 may serve as good prognostic factors, while KCNIP3 and MRPL13 may be poor prognostic factors for the prognosis of breast cancer.

Breast cancer: An insight into its inflammatory, molecular, pathological and targeted facets with update on investigational drugs

Cancer is a heterogeneous disease, occurs due to transcriptional changes in genetic and epigenetic including numerous genes and proteins. Worldwide, breast cancer (BC) is the life-threatening malignancies in women, is characterized by the occurrence of more than one molecular alteration. The incidence and mortality of BC are growing every day because of the adoption of western living standards, metropolitanization, and more life expectancy. Even though many modern approaches are available for the detection and treatment of BC, despite of these, it remains the topmost cause of death in women. This review highlights various approaches, including the importance of clinical, pathological, and molecular aspects of BC. Moreover, risk factors, biomarkers, immunotherapy, investigational drugs, and their role through tumor targets and immune systems have been discussed for management of BC. Furthermore, various targeting approaches for tumors through nanocarriers and their clinical trials have been elaborated in BC challenges and future perspectives.

Breast cancer: Biology, biomarkers, and treatments

During the past recent years, various therapies emerged in the era of breast cancer. Breast cancer is a heterogeneous disease in which genetic and environmental factors are involved. Breast cancer stem cells (BCSCs) are the main player in the aggressiveness of different tumors and also, these cells are the main challenge in cancer treatment. Moreover, the major obstacle to achieve an effective treatment is resistance to therapies. There are various types of treatment for breast cancer (BC) patients. Therefore, in this review, we present the current treatments, novel approaches such as antibody-drug conjugation systems (ADCs), nanoparticles (albumin-, metal-, lipid-, polymer-, micelle-based nanoparticles), and BCSCs-based therapies. Furthermore, prognostic and predictive biomarkers will be discussed also biomarkers that have been applied by some tests such as Oncotype DX, Mamm αPrint, and uPA/PAI-1 are regarded as suitable prognostic and predictive factors in breast cancer.

A Novel Prognostic Signature of mRNA-lncRNA in Breast Cancer

Comprehensive genomic testing will be required to identify appropriate targets for the precision therapy of breast cancer. Although RNA sequencing (RNA-seq) is an unparalleled platform for this purpose, existing molecular-based prognostic signatures are not optimal for RNA-seq data. In this study, we analyzed RNA-seq datasets to generate a novel prognostic gene signature for breast cancer patients. RNA-seq and clinical datasets from breast cancer patients were obtained from The Cancer Genome Atlas and randomly assigned to training (n = 379) and test (n = 378) cohorts. Using the training cohort, sequential univariate Cox analysis, robust likelihood-based survival analysis, and stepwise multivariable Cox analysis identified a five-gene signature composed of one long noncoding RNA gene and four protein-coding genes. The five-gene signature was then used to dichotomize patients into risk groups and validated using Kaplan-Meier and multivariable Cox analyses. In the full test cohort, the high-risk group had worse overall survival (hazard ratio [HR] = 4.74, 95% confidence interval [CI] = 2.33-9.64, p < 0.0001) and worse relapse-free survival (HR = 2.26, 95% CI = 1.11-4.61, p = 0.024) than the low-risk group. Similarly, overall survival was worse in the high-risk group within nearly all clinically important subsets, including early stage disease (I/II) (HR = 7.87, 95% CI = 3.69-16.77, p < 0.0001), and luminal A (HR = 4.23, 95% CI = 1.11-16.12, p = 0.034), luminal B (HR = 12.79, 95% CI = 2.74-59.69, p = 0.001), and basal (HR = 18.11, 95% CI = 3.21-102.05, p = 0.001) subtypes. Notably, the five-gene signature exhibited superior prognostic performance compared with the Oncotype DX 21-gene signature. This novel five-gene signature may therefore be a powerful prognostic tool for personalized treatment of breast cancer patients as part of an integrated RNA-seq clinical sequencing program.

TWIST1 Gene Expression as a Biomarker for Predicting Primary Doxorubicin Resistance in Breast Cancer

Doxorubicin is one of the most commonly used chemotherapeutic agents for adjuvant chemotherapy of breast cancer. In the studies focused on finding biomarkers to predict the response of the patients and tumors to the drugs used, the Twist transcription factor has been suggested as a candidate biomarker for predicting chemo-resistance of breast tumors. In this study, we aimed to investigate the relationship between TWIST transcription factor expression and the effectiveness of doxorubicin treatment on directly taken primary tumor samples from chemotherapy-naive breast cancer patients. Twenty-six primary breast tumor samples taken from 26 different breast cancer patients were included in this study. Adenosine triphosphate tumor chemo-sensitivity assay (ATP-TCA) has been used to determine tumor response to doxorubicin and real-time reverse-transcription polymerase chain reaction (RT-PCR) was used for analyzing the TWIST1 gene expression of tumors. There was a significant difference in TWIST gene expression between responder and non responder tumors (p <0.05). The TWIST gene expression of the drug-resistant group was higher than the responsive group. This difference was not dependent on the histopathological features of tumors. In conclusion, compatible with earlier studies that have been performed with cell lines, the current study supports the role of higher TWIST gene expression as a biomarker for predicting the response of breast tumors to chemo-therapeutic agent doxorubicin.

Plasminogen Activator Inhibitor-1 Promotes the Recruitment and Polarization of Macrophages in Cancer

Plasminogen activator inhibitor-1 (PAI-1) has a pro-tumorigenic function via its pro-angiogenic and anti-apoptotic activities. Here, we demonstrate that PAI-1 promotes the recruitment and M2 polarization of monocytes/macrophages through different structural domains. Its LRP1 interacting domain regulated macrophage migration, while its C-terminal uPA interacting domain promoted M2 macrophage polarization through activation of p38MAPK and nuclear factor κB (NF-κB) and induction of an autocrine interleukin (IL)-6/STAT3 activation pathway. We then show in several experiments in mice that expression of PAI-1 is associated with increased tumorigenicity, increased presence of M2 macrophages, higher levels of IL-6, and increased STAT3 phosphorylation in macrophages. Strong positive correlations between PAI-1, IL-6, and CD163 (M2 marker) expression were also found by meta-analysis of transcriptome data in many human cancers. Altogether, these data provide evidence for a mechanism explaining the paradoxical pro-tumorigenic function of PAI-1 in cancer.

Prediction of fulvestrant efficacy in patients with advanced breast cancer: retrospective-prospective evaluation of the predictive potential of a multigene expression assay

BACKGROUND

Fulvestrant is a selective oestrogen receptor (ER) degrader used as monotherapy and combination therapy for ER positive HER2 negative advanced breast cancer (ABC) in postmenopausal women. The drug response predictor (DRP), is a mathematical algorithm based on the expression of multiple genes in the tumour. The fulvestrant DRP algorithm has previously shown effect in BC. In this study, we investigated the DRP's potential in predicting fulvestrant benefit.

METHOD

Among 695 patients with ABC prospectively included in a Danish Breast Cancer Cooperative Group (DBCG) cohort we retrospectively included 226 patients who received fulvestrant as monotherapy. The DRP result was based on mRNA extracted from tumour biopsies and analysed using Affymetrix array. Primary endpoint was time to progression (TTP).

RESULTS

For patients who received fulvestrant in line one to four and were previously unexposed to adjuvant endocrine therapy, we identified a hazard ratio (HR) of 0.44 (90% confidence interval (90% CI) upper limit of 1.08, one sided p = 0.066) for a predicted positive vs negative outcome. A weaker association was seen when including patients exposed to adjuvant endocrine treatment or received fulvestrant in fifth or later lines. Exploratory analyses showed that the DRP was efficient when using recent biopsies for DRP estimate and among recently treated patients.

CONCLUSION

The DRP showed a potential in predicting fulvestrant treatment but was not significant in the overall analysis. Use of older biopsies, long-term endocrine treatment and multiple therapies between biopsy used for analysis and fulvestrant treatment, probably affect the predictive accuracy.

A case-control study of Metallothionein-1 expression in breast cancer and breast fibroadenoma

The overexpression of Metallothionein-1 (MT-1) may play an important role in breast cancer; however, few studies have compared MT-1 expression between breast cancer and fibroadenoma. A cross-sectional controlled study was performed in 66 premenopausal women, aged 20–49 years, who had been histologically diagnosed with breast fibroadenoma or breast cancer. The patients were divided into two groups: group A, control (fibroadenoma, n = 36) and group B, study (breast cancer, n = 30). Immunohistochemistry was performed on tissue samples of fibroadenoma and breast cancer patients to evaluate the expression of metallothionein using an anti-MT-1 polyclonal antibody (rabbit polyclonal anti-metallothionein-Catalog Number biorbyt-orb11042) at a dilution of 1:100. The data were analyzed using NOVA (p < 0.05). Microscopic analysis showed a higher concentration of anti-MT-1-stained nuclei in breast cancer tissues than in fibroadenoma tissues. The mean proportion of cells with anti-MT-1-stained nuclei was 26.93% and 9.10%, respectively, in the study and control groups (p < 0.001). Histological grade 3 tumors showed a significantly higher MT-1 expression than hitological grade 1 (p < 0.05), while breast tumors negative for estrogen-, progesterone- and HER2- receptors had a significantly higher MT-1 expression than positive breast tumors positive for these parameters (p < 0.05). MT-1 protein in women of reproductive age was significantly higher in breast cancer than in fibroadenoma in this study. Furthermore, there was higher MT-1 immunoreactivity in more aggressive tumors.

The evolving role of receptors as predictive biomarkers for metastatic breast cancer

INTRODUCTION

In breast cancer, estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) are essential biomarkers to predict response to endocrine and anti-HER2 therapies, respectively. In metastatic breast cancer, the use of these receptors and targeted therapies present additional challenges: temporal heterogeneity, together with limited sampling methodologies, hinders receptor status assessment, and the constant evolution of the disease invariably leads to resistance to treatment. Areas covered: This review summarizes the genomic abnormalities in ER and HER2, such as mutations, amplifications, translocations, and alternative splicing, emerging as novel biomarkers that provide an insight into underlying mechanisms of resistance and hold potential predictive value to inform treatment selection. We also describe how liquid biopsies for sampling of circulating markers and ultrasensitive detection technologies have emerged which complement ongoing efforts for biomarker discovery and analysis. Expert commentary: While evidence suggests that genomic aberrations in ER and HER2 could contribute to meeting the pressing need for better predictive biomarkers, efforts need to be made to standardize assessment methods and better understand the resistance mechanisms these markers denote. Taking advantage of emerging technologies, research in upcoming years should include prospective trials incorporating these predictors into the study design to validate their potential clinical value.

Towards frailty biomarkers: Candidates from genes and pathways regulated in aging and age-related diseases

OBJECTIVE

Use of the frailty index to measure an accumulation of deficits has been proven a valuable method for identifying elderly people at risk for increased vulnerability, disease, injury, and mortality. However, complementary molecular frailty biomarkers or ideally biomarker panels have not yet been identified. We conducted a systematic search to identify biomarker candidates for a frailty biomarker panel.

METHODS

Gene expression databases were searched (http://genomics.senescence.info/genes including GenAge, AnAge, LongevityMap, CellAge, DrugAge, Digital Aging Atlas) to identify genes regulated in aging, longevity, and age-related diseases with a focus on secreted factors or molecules detectable in body fluids as potential frailty biomarkers. Factors broadly expressed, related to several "hallmark of aging" pathways as well as used or predicted as biomarkers in other disease settings, particularly age-related pathologies, were identified. This set of biomarkers was further expanded according to the expertise and experience of the authors. In the next step, biomarkers were assigned to six "hallmark of aging" pathways, namely (1) inflammation, (2) mitochondria and apoptosis, (3) calcium homeostasis, (4) fibrosis, (5) NMJ (neuromuscular junction) and neurons, (6) cytoskeleton and hormones, or (7) other principles and an extensive literature search was performed for each candidate to explore their potential and priority as frailty biomarkers.

RESULTS

A total of 44 markers were evaluated in the seven categories listed above, and 19 were awarded a high priority score, 22 identified as medium priority and three were low priority. In each category high and medium priority markers were identified.

CONCLUSION

Biomarker panels for frailty would be of high value and better than single markers. Based on our search we would propose a core panel of frailty biomarkers consisting of (1) CXCL10 (C-X-C motif chemokine ligand 10), IL-6 (interleukin 6), CX3CL1 (C-X3-C motif chemokine ligand 1), (2) GDF15 (growth differentiation factor 15), FNDC5 (fibronectin type III domain containing 5), vimentin (VIM), (3) regucalcin (RGN/SMP30), calreticulin, (4) PLAU (plasminogen activator, urokinase), AGT (angiotensinogen), (5) BDNF (brain derived neurotrophic factor), progranulin (PGRN), (6) α-klotho (KL), FGF23 (fibroblast growth factor 23), FGF21, leptin (LEP), (7) miRNA (micro Ribonucleic acid) panel (to be further defined), AHCY (adenosylhomocysteinase) and KRT18 (keratin 18). An expanded panel would also include (1) pentraxin (PTX3), sVCAM/ICAM (soluble vascular cell adhesion molecule 1/Intercellular adhesion molecule 1), defensin α, (2) APP (amyloid beta precursor protein), LDH (lactate dehydrogenase), (3) S100B (S100 calcium binding protein B), (4) TGFβ (transforming growth factor beta), PAI-1 (plasminogen activator inhibitor 1), TGM2 (transglutaminase 2), (5) sRAGE (soluble receptor for advanced glycosylation end products), HMGB1 (high mobility group box 1), C3/C1Q (complement factor 3/1Q), ST2 (Interleukin 1 receptor like 1), agrin (AGRN), (6) IGF-1 (insulin-like growth factor 1), resistin (RETN), adiponectin (ADIPOQ), ghrelin (GHRL), growth hormone (GH), (7) microparticle panel (to be further defined), GpnmB (glycoprotein nonmetastatic melanoma protein B) and lactoferrin (LTF). We believe that these predicted panels need to be experimentally explored in animal models and frail cohorts in order to ascertain their diagnostic, prognostic and therapeutic potential.

Blood-based biomarkers in breast cancer: From proteins to circulating tumor cells to circulating tumor DNA

Biomarkers are the key to personalized treatment in patients with breast cancer. While tissue biomarkers are most useful in determining prognosis and upfront predicting response to therapy, circulating protein biomarkers such as CA 15-3 and carcinoembryonic antigen are mainly used in monitoring response to endocrine or chemotherapy in patients with advanced disease. Although several centers measure biomarkers in asymptomatic patients following curative surgery for primary breast cancer, the clinical utility of this practice is unclear. Promising new biomarkers for breast cancer include circulating tumor DNA and circulating tumor cells. In contrast to circulating protein biomarkers, measurement of circulating tumor DNA–based biomarkers is potentially useful in identifying mechanisms of resistance to ongoing therapies as well as identifying new targets for further treatment. To increase clinical utility, both the established and emerging circulating biomarkers should where possible be incorporated into randomized trials evaluating new therapies in patients with breast cancer.

Protein biomarkers for subtyping breast cancer and implications for future research

INTRODUCTION

Breast cancer subtypes are currently defined by a combination of morphologic, genomic, and proteomic characteristics. These subtypes provide a molecular portrait of the tumor that aids diagnosis, prognosis, and treatment escalation/de-escalation options. Gene expression signatures describing intrinsic breast cancer subtypes for predicting risk of recurrence have been rapidly adopted in the clinic. Despite the use of subtype classifications, many patients develop drug resistance, breast cancer recurrence, or therapy failure. Areas covered: This review provides a summary of immunohistochemistry, reverse phase protein array, mass spectrometry, and integrative studies that are revealing differences in biological functions within and between breast cancer subtypes. We conclude with a discussion of rigor and reproducibility for proteomic-based biomarker discovery. Expert commentary: Innovations in proteomics, including implementation of assay guidelines and standards, are facilitating refinement of breast cancer subtypes. Proteomic and phosphoproteomic information distinguish biologically functional subtypes, are predictive of recurrence, and indicate likelihood of drug resistance. Actionable, activated signal transduction pathways can now be quantified and characterized. Proteomic biomarker validation in large, well-designed studies should become a public health priority to capitalize on the wealth of information gleaned from the proteome.

New label-free ultrasensitive electrochemical immunosensor-based Au/MoS2/rGO nanocomposites for CA 27-29 breast cancer antigen detection

Early diagnosis and clinical treatments of breast cancer provide a highly successful chance for patients to survive.

Prostate Cancer Markers

Diagnostic biomarkers derived from blood, urine, or prostate tissue provide additional information beyond clinical calculators to determine the risk of detecting high-grade prostate cancer. Once diagnosed, multiple markers leverage prostate cancer biopsy tissue to prognosticate clinical outcomes, including adverse pathology at radical prostatectomy, disease recurrence, and prostate cancer mortality; however the clinical utility of some outcomes to patient decision making is unclear. Markers using tissue from radical prostatectomy specimens provide additional information about the risk of biochemical recurrence, development of metastatic disease, and subsequent mortality beyond existing multivariable clinical calculators (the use of a marker to simply sub-stratify risk groups such as the NCCN groups is of minimal value). No biomarkers currently available for prostate cancer have been prospectively validated to be predict an improved clinical outcome for a specific therapy based on the test result; however, further research and development of these tests may produce a truly predictive biomarker for prostate cancer treatment.

The urokinase-type plasminogen activator system and its role in tumor progression

In the multistage process of carcinogenesis, the key link in the growth and progression of the tumor is the invasion of malignant cells into normal tissue and their distribution and the degree of destruction of tissues. The most important role in the development of these processes is played by the system of urokinase-type plasminogen activator (uPA system), which consists of several components: serine proteinase – uPA, its receptor – uPAR and its two endogenous inhibitors – PAI-1 and PAI-2. The components of the uPA system are expressed by cancer cells to a greater extent than normal tissue cells. uPA converts plasminogen into broad spectrum, polyfunctional protease plasmin, which, in addition to the regulation of fibrinolysis, can hydrolyze a number of components of the connective tissue matrix (СTM), as well as activate the zymogens of secreted matrix metalloproteinases (MMР) – pro-MMР. MMРs together can hydrolyze all the main components of the СTM, and thus play a key role in the development of invasive processes, as well as to perform regulatory functions by activating and releasing from STM a number of biologically active molecules that are involved in the regulation of the main processes of carcinogenesis. The uPA system promotes tumor progression not only through the proteolytic cascade, but also through uPAR, PAI-1 and PAI-2, which are involved in both the regulation of uPA/uPAR activity and are involved in proliferation, apoptosis, chemotaxis, adhesion, migration and activation of epithelial-mesenchymal transition pathways. All of the above processes are aimed at regulating invasion, metastasis and angiogenesis. The components of the uPA system are used as prognostic and diagnostic markers of many cancers, as well as serve as targets for anticancer therapy.

Personalized Computational Models as Biomarkers

Biomarkers are cornerstones of clinical medicine, and personalized medicine, in particular, is highly dependent on reliable and highly accurate biomarkers for individualized diagnosis and treatment choice. Modern omics technologies, such as genome sequencing, allow molecular profiling of individual patients with unprecedented resolution, but biomarkers based on these technologies often lack the dynamic element to follow the progression of a disease or response to therapy. Here, we discuss computational models as a new conceptual approach to biomarker discovery and design. Being able to integrate a large amount of information, including dynamic information, computational models can simulate disease evolution and response to therapy with high sensitivity and specificity. By populating these models with personal data, they can be highly individualized and will provide a powerful new tool in the armory of personalized medicine.

Effect of macrophages on breast cancer cell proliferation, and on expression of hormone receptors, uPAR and HER-2

Malignant tumors, including breast cancers, are frequently infiltrated with innate immune cells and tumor-associated macrophages (TAMs) represent the major inflammatory component in stroma of many tumors. In this study, we examined the immunoreactivity of the macrophage markers CD68 and CD163 as well as the hormone receptors estrogen receptor α (ERα), progesterone receptor (PR), estrogen receptor β1 (ERβ1), human epidermal growth factor receptor 2 (HER-2), matrix metalloproteinase 9 (MMP‑9), urokinase-type plasminogen activator receptor (uPAR) and the proliferations marker Ki67 in 17 breast cancer biopsies. The quantitative score for CD68+ and CD163+ strongly indicate M2 phenotype dominance in the currently investigated biopsies. We found that an increasing level of macrophages was negatively associated with ERα or PR, whereas a positive association was observed for Ki-67 or uPAR. No significant association could be seen between the level of macrophage and HER-2, ERβ1 or MMP-9 expression. Effect of conditioned media (CM) generated from cultured human M1 and M2 macrophage phenotypes were investigated on the proliferation and expression of selected markers in the T47D breast cancer cell line. We found that in contrast to the in vivo situation, in particularly the CM from M1 macrophages decreased the growth and Ki67 expression in T47D, and significantly increased ERβ1 mRNA levels. Moreover, in accordance to the in vivo situation the CM from the macrophages decreased the expression of ERα protein as well as ERα or PR mRNA. In conclusion our results show that macrophages alone have the capability to decrease the tumor cell expression of ERα and PR in vitro. In the tumor environment in vivo macrophages also contribute to an increase in tumor cell expression of uPAR and Ki67, suggesting that macrophages are involved in impairing the prognosis for breast cancer patients.

Pathologic diagnosis of breast cancer patients: evolution of the traditional clinical-pathologic paradigm toward "precision" cancer therapy

We present an updated account of breast cancer treatment and of progress toward "precision" cancer therapy; we focus on new developments in diagnostic molecular pathology and breast cancer that have emerged during the past 2 years. Increasing awareness of new prognostic and predictive methodologies, and introduction of next generation sequencing has increased understanding of both tumor biology and clinical behavior, which offers the possibility of more appropriate therapeutic choices. It remains unclear which of these testing methodologies provides the most informative and cost-effective actionable results for predictive and prognostic pathology. It is likely, however, that an integrated "step-wise" approach that uses the traditional clinical-pathologic paradigms coordinated with molecular characterization of breast tumor tissue, will offer the most comprehensive and cost-effective options for individualized, "precision" therapy for patients with breast cancer.

Gene expression profiles predictive of cold-induced sweetening in potato

Cold storage (2-4 °C) used in potato production to suppress diseases and sprouting during storage can result in cold-induced sweetening (CIS), where reducing sugars accumulate in tuber tissue leading to undesirable browning, production of bitter flavors, and increased levels of acrylamide with frying. Potato exhibits genetic and environmental variation in resistance to CIS. The current study profiles gene expression in post-harvest tubers before cold storage using transcriptome sequencing and identifies genes whose expression is predictive for CIS. A distance matrix for potato clones based on glucose levels after cold storage was constructed and compared to distance matrices constructed using RNA-seq gene expression data. Congruence between glucose and gene expression distance matrices was tested for each gene. Correlation between glucose and gene expression was also tested. Seventy-three genes were found that had significant p values in the congruence and correlation tests. Twelve genes from the list of 73 genes also had a high correlation between glucose and gene expression as measured by Nanostring nCounter. The gene annotations indicated functions in protein degradation, nematode resistance, auxin transport, and gibberellin response. These 12 genes were used to build models for prediction of CIS using multiple linear regression. Nine linear models were constructed that used different combinations of the 12 genes. An F-box protein, cellulose synthase, and a putative Lax auxin transporter gene were most frequently used. The findings of this study demonstrate the utility of gene expression profiles in predictive diagnostics for severity of CIS.