Lipopolysaccharide-Binding Protein Is an Early Biomarker of Cardiac Function After Radiation Therapy for Breast Cancer

Discovering the Radiation Biomarkers in the Plasma of Total-Body Irradiated Leukemia Patients

The increased risk of acute large-scale radiological exposure for the world's population underlines the need for optimal radiation biomarkers. Ionizing radiation triggers a complex response by the genome, proteome, and metabolome, all of which have been reported as suitable indicators of radiation-induced damage in vivo. This study analyzed peripheral blood samples from total-body irradiation (TBI) leukemia patients through mass spectrometry (MS) to identify and quantify differentially regulated proteins in plasma before and after irradiation. In brief, samples were taken from 16 leukemic patients prior to and 24 h after TBI (2 × 2.0 Gy), processed with Tandem Mass Tag isobaric labelling kit (TMTpro-16-plex), and analyzed by MS. In parallel, label-free relative quantification was performed with a RP-nanoLC-ESI-MS/MS system in a Q-Exactive mass spectrometer. Protein identification was done in Proteome Discoverer v.2.2 platform (Thermo). Data is available via ProteomeXchange with identifier PXD043516. Using two different methods, we acquired two datasets of up-regulated (ratio ≥ 1.2) or down-regulated (ratio ≤ 0.83) plasmatic proteins 24 h after irradiation, identifying 356 and 346 proteins in the TMT-16plex and 285 and 308 label-free analyses, respectively (P ≤ 0.05). Combining the two datasets yielded 15 candidates with significant relation to gamma-radiation exposure. The majority of these proteins were associated with the inflammatory response and lipid metabolism. Subsequently, from these, five proteins showed the strongest potential as radiation biomarkers in humans (C-reactive protein, Alpha amylase 1A, Mannose-binding protein C, Phospholipid transfer protein, and Complement C5). These candidate biomarkers might have implications for practical biological dosimetry.

Cardiac markers in left-sided breast cancer patients receiving adjuvant radiotherapy: a prospective study

OBJECTIVES

To investigate the association between radiotherapy (RT) and cardiac biomarkers in women with left-sided breast cancer.

METHODS

This prospective observational study recruited patients with stage I-III left-sided breast cancer without coronary heart disease who required adjuvant RT. High-sensitivity troponin I(hsTnI), N-terminal pro-brain natriuretic peptide(NT-proBNP), and high-sensitivity C-reactive protein(hsCRP) levels were measured pre-RT, immediately after RT, and 3 months post-RT. Cardiac-sparing RT techniques were utilized (Field-in-Field IMRT/VMAT ± voluntary deep inspiration breath-hold). Statistical analyses were performed using non-parametric tests and multivariable quantile regression (QR).

RESULTS

One hundred five patients completed the study, with 63 evaluable at three months post-RT. Pre- and post-RT biomarkers showed no significant differences. Median pre-RT and post-RT values were: hsTnI (0.012ng/mL; 0.012ng/mL), hsCRP (3.1 mg/L; 2.8 mg/L), and NT-proBNP (59pg/mL; 45pg/mL). Three months post-RT, hsTnI, hsCRP and NT-proBNP levels also showed no significant differences. Multivariable QR revealed no association between heart Dmean [median(IQR): 2.87 Gy (2.05-3.94)] and post-RT biomarkers. Age and BMI were associated with hsCRP and NT-proBNP, respectively.

CONCLUSIONS

hsTnI, NT-proBNP, and hsCRP are not correlated with contemporary low cardiac exposure in left-sided breast cancer patients treated with contemporary RT techniques.

Integrated bioinformatics analyses identifying potential biomarkers for type 2 diabetes mellitus and breast cancer: In SIK1-ness and health

The bidirectional causal relationship between type 2 diabetes mellitus (T2DM) and breast cancer (BC) has been established by numerous epidemiological studies. However, the underlying molecular mechanisms are not yet fully understood. Identification of hub genes implicated in T2DM-BC molecular crosstalk may help elucidate on the causative mechanisms. For this, expression series GSE29231 (T2DM-adipose tissue), GSE70905 (BC- breast adenocarcinoma biopsies) and GSE150586 (diabetes and BC breast biopsies) were extracted from Gene Expression Omnibus (GEO) database, and analyzed to obtain differentially expressed genes (DEGs). The overlapping DEGs were determined using FunRich. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Transcription Factor (TF) analyses were performed on EnrichR software and a protein-protein interaction (PPI) network was constructed using STRING software. The network was analyzed on Cytoscape to determine hub genes and Kaplan-Meier plots were obtained. A total of 94 overlapping DEGs were identified between T2DM and BC samples. These DEGs were mainly enriched for GO terms RNA polymerase II core promoter proximal region sequence and its DNA binding, and cAMP response element binding protein, and KEGG pathways including bladder cancer, thyroid cancer and PI3K-AKT signaling. Eight hub genes were identified: interleukin 6 (IL6), tumor protein 53 (TP53), interleukin 8 (CXCL8), MYC, matrix metalloproteinase 9 (MMP9), beta-catenin 1 (CTNNB1), nitric oxide synthase 3 (NOS3) and interleukin 1 beta (IL1β). MMP9 and MYC associated unfavorably with overall survival (OS) in breast cancer patients, IL6, TP53, IL1β and CTNNB1 associated favorably, whereas NOS3 did not show any correlation with OS. Salt inducible kinase 1 (SIK1) was identified as a significant key DEG for comorbid samples when compared with BC, also dysregulated in T2DM and BC samples (adjusted p <0.05). Furthermore, four of the significant hub genes identified, including IL6, CXCL8, IL1B and MYC were also differentially expressed for comorbid samples, however at p < 0.05. Our study identifies key genes including SIK1, for comorbid state and 8 hub genes that may be implicated in T2DM-BC crosstalk. However, limitations associated with the insilico nature of this study necessitates for subsequent validation in wet lab. Hence, further investigation is crucial to study the molecular mechanisms of action underlying these genes to fully explore their potential as diagnostic and prognostic biomarkers and therapeutic targets for T2DM-BC association.

Radiation-induced circulating microRNAs linked to echocardiography parameters after radiotherapy

Introduction

Patients treated with radiotherapy to the chest region are at risk of cardiac sequelae, however, identification of those with greatest risk of complications remains difficult. Here, we sought to determine whether short-term changes in circulating miRNA expression are related to measures of cardiac dysfunction in follow-up.

Materials and methods

Two parallel patient cohorts were enrolled and followed up for 3 years after completion of RT to treat left-sided breast cancer. In the primary group (N=28) we used a a panel of 752 miRNAs to identify miRNAs associated with radiation and cardiac indices at follow up. In the second, independent cohort (N=56) we validated those candidate miRNAs with a targeted qPCR panel. In both cohorts. serum samples were collected before RT, 24h after the last dose and 1 month after RT; cardiac echocardiography was performed 2.5-3 year after RT.

Results

Seven miRNAs in the primary group showed marked changes in serum miRNAs immediately after RT compared to baseline and associations with cardiopulmonary dose-volume histogram metrics. Among those miRNAs: miR-15b-5p, miR-22-3p, miR-424-5p and miR-451a were confirmed to show significant decrease of expression 24 hours post-RT in the validation cohort. Moreover, miR-29c, miR-451 and miR-424 were correlated with the end-diastolic diameter of the left ventricle, which was also confirmed in multivariable analysis adjusting for RT-associated factors.

Conclusion

We identified a subset of circulating miRNAs predictive for cardiac function impairment in patients treated for left-sided breast cancer, although longer clinical observation could determine if these can be used to predict major clinical endpoints.

Plasma Markers for Early Prediction of Radiation-Induced Myocardial Damage

There is no sensitive and effective method to predict radiation-induced myocardial damage (RIMD). The aim of this study was to explore effective plasma biomarkers for early prediction of RIMD after radiotherapy (RT) in lung cancer patients and in a rat model. Biomarker levels were measured in plasma samples collected before and after thoracic RT from 17 lung cancer patients. For the animal model, a single radiation dose of 40 Gy was delivered to the cardiac apex of female Wistar rats. Control rats received sham irradiation (0 Gy). Dynamic plasma biomarker detection and histopathological analysis to confirm RIMD were performed in rats up to 6 months after RT. In lung cancer patients, the plasma caspase-3 concentration was significantly increased after thoracic RT (P = 0.0479), with increasing but nonsignificant trends observed for caspase-1, CCL2, vascular endothelial growth factor (VEGF), interleukin-1β, and IL-6 (P > 0.05). Changes in caspase-3, VEGF, and IL-6 correlated significantly with mean heart dose (P < 0.05). In the RIMD rat model, caspase-1, caspase-3, CCl-2, VEGF, CCl-5, and TGF-β1 levels were significantly elevated in the first week post-RT (P < 0.05), which was earlier than pathological changes. Myocardial tissue of the RIMD rats also showed significant macrophage infiltration at 1 month (P < 0.01) and fibrosis at 6 months postradiation (P < 0.0001). Macrophage infiltration correlated significantly with plasma caspase-3, CCL2, CCL5, VEGF, and TGF-β1 levels from 3 weeks to 2 months post-RT. Increased plasma caspase-1, caspase-3, CCl-2, and VEGF levels were detected before RIMD-related pathological changes, indicating their clinical potential as biomarkers for early prediction of RIMD.

Analysis of the Value of Serum Biomarker LBP in the Diagnosis of Spinal Tuberculosis

Objective

To investigate the correlation between the expression of lipopolysaccharide-binding protein (LBP) in peripheral blood of spinal tuberculosis and clinical diagnosis and to evaluate its value as a diagnostic marker of spinal tuberculosis.

Methods

In the experimental group, clinical history data and peripheral blood were collected from 100 patients with spinal tuberculosis who were admitted to the Department of Spine Surgery, General Hospital of Ningxia Medical University from May 2017 to May 2020, and peripheral blood was collected from 30 healthy volunteers in the control group. Screening of differential LBP expression by proteomics and ELISA to verify its expression in peripheral blood of spinal tuberculosis patients. t-test, Spearman analysis, linear regression and ROC curve were used to evaluate the diagnostic value of LBP in peripheral blood for spinal tuberculosis.

Results

The expression of LBP protein in peripheral blood is significantly higher in patients with spinal tuberculosis than in the normal population; LBP assay values were significantly and positively correlated with CRP and ESR values (P < 0.01); the AUC of LBP in the diagnosis of spinal tuberculosis for pathological examination, bacteriological culture, T-cell spot test for tuberculosis infection (T-SPOT), imaging diagnosis, and acid fast bacillus were, respectively, 0.677 (P < 0.01), 0.707 (P < 0.01), 0.751 (P < 0.01), 0.714 (P < 0.01), and 0.656 (P < 0.05), and there was a correlation between LBP and the diagnostic evaluation of spinal tuberculosis.

Conclusion

LBP could be a new candidate biomarker for the diagnosis of spinal tuberculosis.

Molecular Biomarkers in Cancer

Molecular cancer biomarkers are any measurable molecular indicator of risk of cancer, occurrence of cancer, or patient outcome. They may include germline or somatic genetic variants, epigenetic signatures, transcriptional changes, and proteomic signatures. These indicators are based on biomolecules, such as nucleic acids and proteins, that can be detected in samples obtained from tissues through tumor biopsy or, more easily and non-invasively, from blood (or serum or plasma), saliva, buccal swabs, stool, urine, etc. Detection technologies have advanced tremendously over the last decades, including techniques such as next-generation sequencing, nanotechnology, or methods to study circulating tumor DNA/RNA or exosomes. Clinical applications of biomarkers are extensive. They can be used as tools for cancer risk assessment, screening and early detection of cancer, accurate diagnosis, patient prognosis, prediction of response to therapy, and cancer surveillance and monitoring response. Therefore, they can help to optimize making decisions in clinical practice. Moreover, precision oncology is needed for newly developed targeted therapies, as they are functional only in patients with specific cancer genetic mutations, and biomarkers are the tools used for the identification of these subsets of patients. Improvement in the field of cancer biomarkers is, however, needed to overcome the scientific challenge of developing new biomarkers with greater sensitivity, specificity, and positive predictive value.

Improved profiling of low molecular weight serum proteome for gastric carcinoma by data-independent acquisition

Low molecular weight proteins (LMWPs) in the bloodstream participate in various biological processes and are closely associated with disease status, whereas identification of serous LMWPs remains a great technical challenge due to the wide dynamic range of protein components. In this study, we constructed an integrated LMWP library by combining the LMWPs obtained by three enrichment methods (50% ACN, 20% ACN + 20 mM ABC, and 30 kDa) and their fractions identified by the data-dependent acquisition method. With this newly constructed library, we comprehensively profiled LMWPs in serum using data-independent acquisition and reliably achieved quantitative results for 75% serous LMWPs. When applying this strategy to quantify LMWPs in human serum samples, we could identify 405 proteins on average per sample, of which 136 proteins were with a MW less than 30 kDa and 293 proteins were with a MW less than 65 kDa. Of note, pre- and post-operative gastric carcinoma (GC) patients showed differentially expressed serous LWMPs, which was also different from the pattern of LWMP expression in healthy controls. In conclusion, our results showed that LMWPs could efficiently distinguish GC patients from healthy controls as well as between pre- and post-operative statuses, and more importantly, our newly developed LMWP profiling platform could be used to discover candidate LMWP biomarkers for disease diagnosis and status monitoring.

Identification of novel biomarkers for acute radiation injury using multi-omics approach and nonhuman primate model

PURPOSE

The availability of validated biomarkers to assess radiation exposure and to assist in developing medical countermeasures remains an unmet need.

METHODS AND MATERIALS

We used a cobalt-60 gamma-irradiated nonhuman primate (NHP) model to delineate a multi-omics-based serum probability index of radiation exposure. Both male and female NHPs were irradiated with different doses ranging from 6.0 to 8.5 Gy, with 0.5 Gy increments between doses. We leveraged high-resolution mass spectrometry for analysis of metabolites, lipids, and proteins at 1, 2, and 6 days post-irradiation in NHP serum.

RESULTS

A logistic regression model was implemented to develop a 4-analyte panel to stratify irradiated NHPs from unirradiated with high accuracy that was agnostic for all doses of gamma-rays tested in the study, up to six days after exposure. This panel was comprised of Serpin Family A9, acetylcarnitine, PC (16:0/22:6), and suberylglycine, which showed 2 - 4-fold elevation in serum abundance upon irradiation in NHPs and can potentially be translated as a molecular diagnostic for human use following larger validation studies.

CONCLUSIONS

Taken together, this study, for the first time, demonstrates the utility of a combinatorial molecular characterization approach using an NHP model for developing minimally invasive assays from small volumes of blood that can be effectively used for radiation exposure assessments.

Advances in Biomarkers for Detecting Early Cancer Treatment-Related Cardiac Dysfunction

With the gradual prolongation of the overall survival of cancer patients, the cardiovascular toxicity associated with oncology drug therapy and radiotherapy has attracted increasing attention. At present, the main methods to identify early cancer treatment-related cardiac dysfunction (CTRCD) include imaging examination and blood biomarkers. In this review, we will summarize the research progress of subclinical CTRCD-related blood biomarkers in detail. At present, common tumor therapies that cause CTRCD include: (1) Chemotherapy—The CTRCD induced by chemotherapy drugs represented by anthracycline showed a dose-dependent characteristic and most of the myocardial damage is irreversible. (2) Targeted therapy—Cardiovascular injury caused by molecular-targeted therapy drugs such as trastuzumab can be partially or completely alleviated via timely intervention. (3) Immunotherapy—Patients developed severe left ventricular dysfunction who received immune checkpoint inhibitors have been reported. (4) Radiotherapy—CTRCD induced by radiotherapy has been shown to be significantly associated with cardiac radiation dose and radiation volume. Numerous reports have shown that elevated troponin and B-type natriuretic peptide after cancer treatment are significantly associated with heart failure and asymptomatic left ventricular dysfunction. In recent years, a few emerging subclinical CTRCD potential biomarkers have attracted attention. C-reactive protein and ST2 have been shown to be associated with CTRCD after chemotherapy and radiation. Galectin-3, myeloperoxidas, placental growth factor, growth differentiation factor 15 and microRNAs have potential value in predicting CTRCD. In this review, we will summarize CTRCD caused by various tumor therapies from the perspective of cardio-oncology, and focus on the latest research progress of subclinical CTRCD biomarkers.

Past, Present, and Future of Radiation-Induced Cardiotoxicity: Refinements in Targeting, Surveillance, and Risk Stratification

Radiation therapy is an important component of cancer therapy for many malignancies. With improvements in cardiac-sparing techniques, radiation-induced cardiac dysfunction has decreased but remains a continued concern. In this review, we provide an overview of the evolution of radiotherapy techniques in thoracic cancers and associated reductions in cardiac risk. We also highlight data demonstrating that in some cases radiation doses to specific cardiac substructures correlate with cardiac toxicities and/or survival beyond mean heart dose alone. Advanced cardiac imaging, cardiovascular risk assessment, and potentially even biomarkers can help guide post-radiotherapy patient care. In addition, treatment of ventricular arrhythmias with the use of ablative radiotherapy may inform knowledge of radiation-induced cardiac dysfunction. Future efforts should explore further personalization of radiotherapy to minimize cardiac dysfunction by coupling knowledge derived from enhanced dosimetry to cardiac substructures, post-radiation regional dysfunction seen on advanced cardiac imaging, and more complete cardiac toxicity data.

Development and validation of a five-immune gene prognostic risk model in colon cancer

BACKGROUND

Colon cancer is a common and highly malignant cancer. Its morbidity is rapidly increasing, and its prognosis is poor. Currently, immunotherapy is a rapidly developing therapeutic modality of colon cancer. This study aimed to construct a prognostic risk model based on immune genes for the early diagnosis and accurate prognostic prediction of colon cancer.

METHODS

Transcriptomic data and clinical data were downloaded from The Cancer Genome Atlas database. Immune genes were obtained from the ImmPort database. Differentially expressed (DE) immune genes between 473 colon cancer and 41 adjacent normal tissues were identified. The entire cohort was randomly divided into the training and testing cohort. The training cohort was used to construct the prognostic model. The testing and entire cohorts were used to validate the model. The clinical utility of the model and its correlation with immune cell infiltration were analyzed.

RESULTS

A total of 333 DE immune genes (176 up-regulated and 157 down-regulated) were detected. We developed and validated a five-immune gene model of colon cancer, including LBP, TFR2, UCN, UTS2, and MC1R. This model was approved to be an independent prognostic variable, which was more accurate than age and the pathological stage for predicting overall survival at five years. Besides, as the risk score increased, the content of CD8+ T cells in colon cancer was decreased.

CONCLUSIONS

We developed and validated a five-immune gene model of colon cancer, including LBP, TFR2, UCN, UTS2, and MC1R. This model could be used as an instrumental variable in the prognosis prediction of colon cancer.

Precision Medicine and the Role of Biomarkers of Radiotherapy Response in Breast Cancer

Radiotherapy remains an important treatment modality in nearly two thirds of all cancers, including the primary curative or palliative treatment of breast cancer. Unfortunately, largely due to tumor heterogeneity, tumor radiotherapy response rates can vary significantly, even between patients diagnosed with the same tumor type. Although in recent years significant technological advances have been made in the way radiation can be precisely delivered to tumors, it is proving more difficult to personalize radiotherapy regimens based on cancer biology. Biomarkers that provide prognostic or predictive information regarding a tumor's intrinsic radiosensitivity or its response to treatment could prove valuable in helping to personalize radiation dosing, enabling clinicians to make decisions between different treatment options whilst avoiding radiation-induced toxicity in patients unlikely to gain therapeutic benefit. Studies have investigated numerous ways in which both patient and tumor radiosensitivities can be assessed. Tumor molecular profiling has been used to develop radiosensitivity gene signatures, while the assessment of specific intracellular or secreted proteins, including circulating tumor cells, exosomes and DNA, has been performed to identify prognostic or predictive biomarkers of radiation response. Finally, the investigation of biomarkers related to radiation-induced toxicity could provide another means by which radiotherapy could become personalized. In this review, we discuss studies that have used these methods to identify or develop prognostic/predictive signatures of radiosensitivity, and how such assays could be used in the future as a means of providing personalized radiotherapy.