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Breast cancer metastases are most commonly found in bone, an indication of poor prognosis. Pathway-based biomarkers identification may help elucidate the cellular signature of breast cancer metastasis in bone, further characterizing the etiology and promoting new therapeutic approaches. We extracted gene expression profiles from mouse macrophages from the GEO dataset, GSE152795 using the GEO2R webtool. The differentially expressed genes (DEGs) were filtered by log2 fold-change with threshold 1.5 (FDR < 0.05). STRING database and Enrichr were used for GO-term analysis, miRNA and TF analysis associated with DEGs. Autodock Vienna was exploited to investigate interaction of anti-cancer drugs, Actinomycin-D and Adriamycin. Sensitivity and specificity of DEGs was assessed using receiver operating characteristic (ROC) analyses. A total of 61 DEGs, included 27 down-regulated and 34 up-regulated, were found to be significant in breast cancer bone metastasis. Major DEGs were associated with lipid metabolism and immunological response of tumor tissue. Crucial DEGs, Bcl3, ADGRG7, FABP4, VCAN, and IRF4 were regulated by miRNAs, miR-497, miR-574, miR-138 and TFs, CCDN1, STAT6, IRF8. Docking analysis showed that these genes possessed strong binding with the drugs. ROC analysis demonstrated Bcl3 is specific to metastasis. DEGs Bcl3, ADGRG7, FABP4, IRF4, their regulating miRNAs and TFs have strong impact on proliferation and metastasis of breast cancer in bone tissues. In conclusion, present study revealed that DEGs are directly involved in of breast tumor metastasis in bone tissues. Identified genes, miRNAs, and TFs can be possible drug targets that may be used for the therapeutics. However, further experimental validation is necessary.

Background Bone marrow lesions (BMLs) are a known risk factor for incident knee osteoarthritis (OA), and deep learning (DL) methods can assist in automated segmentation and risk prediction. Purpose To develop and validate a DL model for quantifying tibiofemoral BML volume on MRI scans in knees without radiographic OA and to assess the association between longitudinal BML changes and incident knee OA. Materials and Methods This retrospective study included knee MRI scans from the Osteoarthritis Initiative prospective cohort (February 2004-October 2015). The DL model, developed between August and October 2023, segmented the tibiofemoral joint into 10 subregions and measured BML volume in each subregion. Baseline and 4-year follow-up MRI scans were analyzed. Knees without OA at baseline were categorized into three groups based on 4-year BML volume changes: BML-free, BML regression, and BML progression. The risk of developing radiographic and symptomatic OA over 9 years was compared among these groups. Results Included were 3869 non-OA knees in 2430 participants (mean age, 59.5 years ± 9.0 [SD]; female-to-male ratio, 1.3:1). At 4-year follow-up, 2216 knees remained BML-free, 1106 showed an increase in BML volume, and 547 showed a decrease in BML volume. BML progression was associated with a higher risk of developing radiographic knee OA compared with remaining BML-free (hazard ratio [HR] = 3.0; P < .001) or BML regression (HR = 2.0; P < .001). Knees with BML progression also had a higher risk of developing symptomatic OA compared with BML-free knees (HR = 1.3; P < .001). Larger volume changes in BML progression were associated with a higher risk of developing both radiographic OA (HR = 2.0; P < .001) and symptomatic OA (HR = 1.7; P < .001). In almost all subchondral plates, especially the medial femur and tibia, BML progression was associated with a higher risk of developing both radiographic and symptomatic OA compared with remaining BML-free. Conclusion Knees with BML progression, according to subregion and extent of volume changes, were associated with an increased risk of OA compared with BML-free knees and knees with BML regression, highlighting the potential utility of monitoring BML volume changes in evaluating interventions to prevent OA development. ClinicalTrials.gov Identifier: NCT00080171 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Said and Sakly in this issue.

Purpose: To develop a deep learning algorithm capable of extracting pectoralis muscle and adipose measurements and to longitudinally investigate associations between these measurements and incident heart failure (HF) in participants from the Multi-Ethnic Study of Atherosclerosis (MESA)., Materials and Methods: MESA is a prospective study of subclinical cardiovascular disease characteristics and risk factors for progression to clinically overt disease approved by institutional review boards of six participating centers (ClinicalTrials.gov identifier: NCT00005487). All participants with adequate imaging and clinical data from the fifth examination of MESA were included in this study. Hence, in this secondary analysis, manual segmentations of 600 chest CT examinations (between the years 2010 and 2012) were used to train and validate a convolutional neural network, which subsequently extracted pectoralis muscle and adipose (intermuscular adipose tissue (IMAT), perimuscular adipose tissue (PAT), extramyocellular lipids and subcutaneous adipose tissue) area measurements from 3031 CT examinations using individualized thresholds for adipose segmentation. Next, 1781 participants without baseline HF were longitudinally investigated for associations between baseline pectoralis muscle and adipose measurements and incident HF using crude and adjusted Cox proportional hazards models. The full models were adjusted for variables in categories of demographic (age, race, sex, income), clinical/laboratory (including physical activity, BMI, and smoking), CT (coronary artery calcium score), and cardiac MRI (left ventricular ejection fraction and mass (% of predicted)) data., Results: In 1781 participants (median age, 68 (IQR,61, 75) years; 907 [51%] females), 41 incident HF events occurred over a median 6.5-year follow-up. IMAT predicted incident HF in unadjusted (hazard ratio [HR]:1.14; 95% CI: 1.03-1.26) and fully adjusted (HR:1.16, 95% CI: 1.03-1.31) models. PAT also predicted incident HF in crude (HR:1.19; 95% CI: 1.06-1.35) and fully adjusted (HR:1.25; 95% CI: 1.07-1.46) models., Conclusion: The study demonstrates that fast and reliable deep learning-derived pectoralis muscle and adipose measurements are obtainable from conventional chest CT, which may be predictive of incident HF.©RSNA, 2023., Competing Interests: Disclosures of conflicts of interest: Q.H. No relevant relationships. H.A.I. No relevant relationships. D.A.B. Former editor of Radiology. F.P. No relevant relationships. A.K. No relevant relationships. J.G.K. No relevant relationships. R.C. Speaker for Abbott Laboratories; spouse employed at Medtronic. M.A. No relevant relationships. M.J.B. NIH grant to institution. R.G.B. NIH grant to institution; COPD Foundation (unpaid scientific advisory role). W.P. NIH grant. M.A.B. No relevant relationships. J.A.C.L. No relevant relationships. S.D. Serves on the RSNA Publications Council and chairs the policy subcommittee., (© 2023 by the Radiological Society of North America, Inc.)

Background: Missense mutations in the hyperpolarization-activated cyclic nucleotide-modulated (HCN) channel 4 (HCN4) are one of the genetic causes of cardiac sinus bradycardia., Objective: To investigate possible HCN4 channel mutation in a young patient with profound sinus bradycardia., Methods: Direct sequencing of HCN4 and whole-exome sequencing were performed on DNA samples from the indexed patient (P), the patient's son (PS), and a family unrelated healthy long-distance running volunteer (V). Resting heart rate was 31 bpm for P, 67 bpm for PS, and 50 bpm for V. Immunoblots, flow cytometry, and immunocytofluorescence confocal imaging were used to study cellular distribution of channel variants. Patch-clamp electrophysiology was used to investigate the properties of mutant HCN1 channels., Results: In P no missense mutations were found in the HCN4 gene; instead, we found two heterozygous variants in the HCN1 gene: deletion of an N-terminal glycine triplet ( 72 GGG 74 , "N-del") and a novel missense variant, P851A, in the C-terminal region. N-del variant was found before and shared by PS. These two variations were not found in V. Compared to wild type, N-del and P851A reduced cell surface expression and negatively shifted voltage-activation with slower activation kinetics., Conclusion: Decreased channel activity HCN1 mutant channel makes it unable to contribute to early depolarization of sinus node action potential, thus likely a main cause of the profound sinus bradycardia in this patient., Competing Interests: Authors declare no conflict of interest for this article., (© 2021 The Authors. Journal of Arrhythmia published by John Wiley & Sons Australia, Ltd on behalf of Japanese Heart Rhythm Society.)