Synergistic effect between denosumab and immune checkpoint inhibitors (ICI)? A retrospective study of 268 patients with ICI and bone metastases.

Background: Bone metastasis is a significant concern in advanced solid tumors, contributing to diminished patient survival and quality of life due to skeletal-related events (SREs). Denosumab (DMAB), a monoclonal antibody targeting the receptor activator of nuclear factor kappa-B ligand (RANKL), is used to prevent SREs in such cases. The RANK/RANKL axis, crucial in immunological processes, has garnered attention, especially with the expanding use of immune checkpoint inhibitors (ICI) in modern oncology. Objective: Our study aims to explore the potential synergistic antitumor effects of combining immunotherapy with denosumab, as suggested by anecdotal evidence, small cohort studies, and preclinical research. Methods: We conducted a retrospective analysis using the IMMUCARE database, encompassing patients receiving ICI treatment since 2014 and diagnosed with bone metastases. We examined overall survival (OS), progression-free survival (PFS) and switch of treatment line based on denosumab usage. Patients were stratified into groups: without denosumab, ICI followed by denosumab, and denosumab followed by ICI. Survival curves and multivariate Cox regression analyses were performed. Results: Among the 268 patients with bone metastases, 154 received treatment with ICI alone, while 114 received ICI in combination with denosumab at some point during their oncological history. No significant differences were observed in overall survival (OS) or progression-free survival (PFS) between patients receiving ICI monotherapy and those receiving ICI with denosumab (p = 0.29 and p = 0.79, respectively). However, upon analyzing patients who received denosumab following ICI initiation (17 patients), a notable difference emerged. The group receiving ICI followed by denosumab exhibited a significant advantage compared to those without denosumab (154 patients) or those receiving denosumab before ICI initiation (72 patients) (p = 0.022). Conclusion: This retrospective investigation supports the notion of potential benefits associated with sequential administration of ICI and denosumab, although statistical significance was not achieved. Future studies, including prospective trials or updated retrospective analyses, focusing on cancers treated with first-line immunotherapy, could provide further insights into this therapeutic approach.

SERPINH1 as a Novel Biomarker for Colon Cancer Bone Metastasis with Machine Learning and Immunohistochemistry Validation.

Background: Bone metastasis (BM) is a serious clinical symptom of advanced colorectal cancer. However, there is a lack of effective biomarkers for early diagnosis and treatment. Method: RNA-seq data from public databases (GSE49355, GSE101607) were collected and normalized and batch effects were removed using the combat package. Differential expression analysis was performed to identify significant genes. Robust Rank Aggregation and machine learning algorithms were used to pinpoint candidate biomarkers. These biomarkers were validated using immunohistochemistry and further analyzed for survival rates. Enrichment analysis was conducted to explore biological mechanisms. Additionally, drug sensitivity and immune infiltration analyses were performed to provide insights into potential therapeutic targets. Results: Analysis results revealed 386 genes elevated in primary versus normal tissues and 26 genes varying between primary and BM. Serpin Protease Inhibitor Clade H1 (SERPINH1) as a novel biomarker for colon cancer metastasis. High SERPINH1 expression correlates with poor survival outcomes and is linked to high lymphatic invasion and advanced cancer stages. Additionally, SERPINH1 expression influences immune infiltration and is not predictive of chemotherapy response, but potential new drugs are suggested for high-expression cases. The gene also enriches classical cancer pathways such as Hedgehog and transforming growth factor-ß. Conclusions: We identified novel colon cancer BM markers, including SERPINH1, using machine learning algorithms combined with traditional transcriptomic data and validated their expression through immunohistochemistry. This biomarker could significantly assist clinicians in making more precise treatment decisions.

Prognostic Value of PlGF Upregulation in Prostate Cancer.

BACKGROUND: Prostate cancer (PCa) is the second most commonly diagnosed cancer in men worldwide, with metastasis, particularly to bone, being the primary cause of mortality. Currently, prognostic markers like PSA levels and Gleason classification are limited in predicting metastasis, emphasizing the need for novel clinical biomarkers. New molecules predicting tumor progression have been identified over time. Some, such as the immune checkpoint inhibitors (ICIs) PD-1/PD-L1, have become valid markers as theranostic tools essential for prognosis and drug target therapy. However, despite the success of ICIs as an anti-cancer therapy for solid tumors, their efficacy in treating bone metastases has mainly proven ineffective, suggesting intrinsic resistance to this therapy in the bone microenvironment. This study explores the potential of immunological intratumoral biomarkers, focusing on placental growth factor (PlGF), Vascular Endothelial Growth Factor Receptor 1 (VEGFR1), and Programmed Cell Death Protein 1 (PD-1), in predicting bone metastasis formation. METHODS: we analyzed PCa samples from patients with and without metastasis by immunohistochemical analysis. RESULTS: Results revealed that PlGF expression is significantly higher in primary tumors of patients that developed metastasis within five years from the histological diagnosis. Additionally, PlGF expression correlates with increased VEGFR1 and PD-1 levels, as well as the presence of intratumoral M2 macrophages. CONCLUSIONS: These findings suggest that PlGF contributes to an immunosuppressive environment, thus favoring tumor progression and metastatic process. Results here highlight the potential of integrating these molecular markers with existing prognostic tools to enhance the accuracy of metastasis prediction in PCa. By identifying patients at risk for metastasis, clinicians can tailor treatment strategies more effectively, potentially improving survival outcomes and quality of life. This study underscores the importance of further research into the role of intratumoral biomarkers in PCa management.

Bone scintigraphy based on deep learning model and modified growth optimizer.

Bone scintigraphy is recognized as an efficient diagnostic method for whole-body screening for bone metastases. At the moment, whole-body bone scan image analysis is primarily dependent on manual reading by nuclear medicine doctors. However, manual analysis needs substantial experience and is both stressful and time-consuming. To address the aforementioned issues, this work proposed a machine-learning technique that uses phases to detect Bone scintigraphy. The first phase in the proposed model is the feature extraction and it was conducted based on integrating the Mobile Vision Transformer (MobileViT) model in our framework to capture highly complex representations from raw medical imagery using two primary components including ViT and lightweight CNN featuring a limited number of parameters. In addition, the second phase is named feature selection, and it is dependent on the Arithmetic Optimization Algorithm (AOA) being used to improve the Growth Optimizer (GO). We evaluate the performance of the proposed FS model, named GOAOA using a set of 18 UCI datasets. Additionally, the applicability of Bone scintigraphy for real-world application is evaluated using 2800 bone scan images (1400 normal and 1400 abnormal). The results and statistical analysis revealed that the proposed GOAOA algorithm as an FS technique outperforms the other FS algorithms employed in this study.

AI diagnostics in bone oncology for predicting bone metastasis in lung cancer patients using DenseNet-264 deep learning model and radiomics.

This study aims to predict bone metastasis in lung cancer patients using radiomics and deep learning. Early prediction of bone metastasis is crucial for timely intervention and personalized treatment plans. This can improve patient outcomes and quality of life. By integrating advanced imaging techniques with artificial intelligence, this study seeks to enhance predictive accuracy and clinical decision-making. Methods: We included 189 lung cancer patients, comprising 89 with non-bone metastasis and 100 with confirmed bone metastasis. Radiomic features were extracted from CT images, and feature selection was performed using Minimum Redundancy Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO). We developed and validated a radiomics model and a deep learning model using DenseNet-264. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Statistical comparisons were made using the DeLong test. Results: The radiomics model achieved an AUC of 0.815 on the training set and 0.778 on the validation set. The DenseNet-264 model demonstrated superior performance with an AUC of 0.990 on the training set and 0.971 on the validation set. The DeLong test confirmed that the AUC of the DenseNet-264 model was significantly higher than that of the radiomics model (p < 0.05). Conclusions: The DenseNet-264 model significantly outperforms the radiomics model in predicting bone metastasis in lung cancer patients. The early and accurate prediction provided by the deep learning model can facilitate timely interventions and personalized treatment planning, potentially improving patient outcomes. Future studies should focus on validating these findings in larger, multi-center cohorts and integrating clinical data to further enhance predictive accuracy.

A case of solitary metastatic colon adenocarcinoma of the sternum: an unusual metastatic site.

Colorectal cancer is a prevalent malignancy; it ranks as the third leading cause of cancer-related deaths globally. Despite the effectiveness of surgical intervention for primary tumors, ~30% of patients develop metastases, commonly in the regional lymph nodes, liver, lungs, and peritoneum. Bone metastasis is relatively rare but can occur, typically affecting vertebrae, pelvis, femur, and humerus. This study presents a 68-year-old patient with a history of locally advanced colon cancer who presented with a rapidly enlarging, painful sternal mass. Imaging and biopsy confirmed metastatic colon adenocarcinoma in the sternum. The patient was treated with radiation therapy, resulting in significant symptomatic relief and tumor reduction. This case highlights the rarity of sternal metastasis from colorectal cancer. Given the poor prognosis associated with skeletal metastases in colorectal cancer, this case emphasizes the need for vigilance in monitoring for atypical metastatic sites and the importance of tailored palliative care strategies.

Development of a predictive model for patients with bone metastases referred to palliative radiotherapy: Secondary analysis of a multicenter study (the PRAIS trial).

BACKGROUND: The decision to administer palliative radiotherapy (RT) to patients with bone metastases (BMs), as well as the selection of treatment protocols (dose, fractionation), requires an accurate assessment of survival expectancy. In this study, we aimed to develop three predictive models (PMs) to estimate short-, intermediate-, and long-term overall survival (OS) for patients in this clinical setting. MATERIALS AND METHODS: This study constitutes a sub-analysis of the PRAIS trial, a longitudinal observational study collecting data from patients referred to participating centers to receive palliative RT for cancer-induced bone pain. Our analysis encompassed 567 patients from the PRAIS trial database. The primary objectives were to ascertain the correlation between clinical and laboratory parameters with the OS rates at three distinct time points (short: 3 weeks; intermediate: 24 weeks; prolonged: 52 weeks) and to construct PMs for prognosis. We employed machine learning techniques, comprising the following steps: (i) identification of reliable prognostic variables and training; (ii) validation and testing of the model using the selected variables. The selection of variables was accomplished using the LASSO method (Least Absolute Shrinkage and Selection Operator). The model performance was assessed using receiver operator characteristic curves (ROC) and the area under the curve (AUC). RESULTS: Our analysis demonstrated a significant impact of clinical parameters (primary tumor site, presence of non-bone metastases, steroids and opioid intake, food intake, and body mass index) and laboratory parameters (interleukin 8 [IL-8], chloride levels, C-reactive protein, white blood cell count, and lymphocyte count) on OS. Notably, different factors were associated with the different times for OS with only IL-8 included both in the PMs for short- and long-term OS. The AUC values for ROC curves for 3-week, 24-week, and 52-week OS were 0.901, 0.767, and 0.806, respectively. CONCLUSIONS: We successfully developed three PMs for OS based on easily accessible clinical and laboratory parameters for patients referred to palliative RT for painful BMs. While our findings are promising, it is important to recognize that this was an exploratory trial. The implementation of these tools into clinical practice warrants further investigation and confirmation through subsequent studies with separate databases.

Lipid rafts, caveolae, and epidermal growth factor receptor family: friends or foes?

Lipid rafts are dynamic microdomains enriched with cholesterol and sphingolipids that play critical roles in cellular processes by organizing and concentrating specific proteins involved in signal transduction. The interplay between lipid rafts, raft-associated caveolae and the human epidermal growth factor receptors has significant implications in cancer biology, particularly in breast and gastric cancer therapy resistance. This review examines the structural and functional characteristics of lipid rafts, their involvement in EGFR and HER2 signaling, and the impact of lipid rafts/CXCL12/CXCR4/HER2 axis on bone metastasis. We also discuss the potential of targeting lipid rafts and caveolin-1 to enhance therapeutic strategies against HER2-positive cancers and the impact of co-localization of trastuzumab or antibody drug conjugates with caveolin-1 on therapy response. Emerging evidence suggests that disrupting lipid raft integrity or silencing caveolin-1, through several strategies including cholesterol-lowering molecules, can influence HER2 availability and internalization, enhancing anti-HER2 targeted therapy and offering a novel approach to counteract drug resistance and improve treatment efficacy.

First-in-human study of dosimetry, safety and efficacy for [177Lu]Lu-P15-073: a novel bisphosphonate-based radioligand therapy (RLT) agent for bone metastases.

PURPOSE: Bisphosphonates are pivotal in managing bone tumors by inhibiting bone resorption. This study investigates the therapeutic potential of [177Lu]Lu-P15-073, a novel bisphosphonate, for radioligand therapy (RLT) in bone metastases. METHODS: Ten patients (age 35 to 75) with confirmed bone metastases underwent therapy with a single dose of [177Lu]Lu-P15-073 (1,225 ± 84 MBq, or 33 ± 2 mCi). Prior to treatment, bone metastases were verified via [99mTc]Tc-MDP bone scans. Serial planar whole-body scans monitored biodistribution over a 14-day period. Dosimetry was assessed for major organs and tumor lesions, while safety was evaluated through blood biomarkers and pain scores. RESULTS: Serial planar whole-body scans demonstrated rapid and substantial accumulation of [177Lu]Lu-P15-073 in bone metastases, with minimal uptake in blood and other organs. The absorbed dose in the critical organ, red marrow, was measured at (0.034 ± 0.010 mSv/MBq), with a notably low normalized effective dose (0.013 ± 0.005 mSv/MBq) compared to other 177Lu-labeled bisphosphonates. Persistent high uptake in bone metastases was observed, resulting in elevated tumor doses (median 3.12 Gy/GBq). Patients exhibited favorable tolerance to [177Lu]Lu-P15-073 therapy, with no new instances of side effects. Additionally, 87.5% (7/8) of patients experienced a significant reduction in pain scale (numerical rating scale, NRS, from 5.1 ± 2.3 to 3.0 ± 1.8). The tumor-background ratio (TBRmean) of [99mTc]Tc-MDP correlated significantly with [177Lu]Lu-P15-073 uptake (P < 0.01), indicating its potential for prediction of absorbed dose. CONCLUSIONS: This study demonstrates the safety, dosimetry, and efficacy of a single therapeutic dose of [177Lu]Lu-P15-073 in bone metastases. The treatment was well-tolerated with no severe adverse events. These findings suggest that [177Lu]Lu-P15-073 holds promise as a novel RLT agent for bone metastases.

Effect of colony­stimulating factor in the mechanism of bone metastasis development (Review).

Bone metastasis (BM) is a common complication of cancer and contributes to a higher mortality rate in patients with cancer. The treatment of BM remains a significant challenge for oncologists worldwide. The colony­stimulating factor (CSF) has an important effect on the metastasis of multiple cancers. In vitro studies have shown that CSF acts as a cytokine, promoting the colony formation of hematopoietic cells by activating granulocytes and macrophages. Other studies have shown that CSF not only promotes cancer aggressiveness but also correlates with the development and prognosis of various types of cancer. In recent years, the effect of CSF on BM has been primarily investigated using cellular and animal models, with limited clinical studies available. The present review discussed the composition and function of CSF, as well as its role in the progression of BM across various types of cancer. The mechanisms by which osteoclast­ and osteoblast­mediated BM occur are comprehensively described. In addition, the mechanisms of action of emerging therapeutic agents are explored for their potential clinical applications. However, further clinical studies are required to validate these findings.

Prognostic Impact of Bone Metastasis in Patients With Metastatic Urothelial Carcinoma Treated With Durvalumab With or Without Tremelimumab in the DANUBE Study.

INTRODUCTION: Bone metastases (BM) in metastatic urothelial carcinoma (mUC) may impact patient outcomes, but their independent effect with immune checkpoint inhibitors (ICIs) is uncertain. We aimed to assess the impact of BM and PD-L1 status on outcomes in mUC patients treated with ICIs. PATIENTS AND METHODS: This post hoc analysis of the DANUBE study included 1032 mUC patients treated with durvalumab (D), D + tremelimumab (T), or standard chemotherapy (SoC). Patients were categorized by BM status and assessed for median overall survival (mOS) and median progression-free survival (mPFS) stratified by PD-L1 expression and treatment arm.  RESULTS: Among all patients enrolled in the study, those with BM had a lower mOS than those with no BM (8.7 vs. 15.8 months; P < .0001). Patients with BM and high PD-L1 expression, treated with D or D + T, had numerically longer mOS than patients with BM and low PD-L1 expression. In contrast, in the chemotherapy arm, there was no difference in mOS for BM or no BM, based on PD-L1 expression. Patients with BM had shorter mPFS compared to no BM (2.6 vs. 5.4 months; P < .0001). The study is limited by its post hoc nature. CONCLUSION: Presence of BM was associated with worse outcomes across treatment arms. Patients with BM and high PD-L1 expression treated with D or D + T had longer mOS, suggesting potential benefits of ICIs in this subgroup. Consideration of BM and PD-L1 status in treatment decisions for mUC patients receiving ICIs may improve clinical outcomes.

Predicting the survival of patients with painful tumours treated with palliative radiotherapy: a secondary analysis using the 3-variable number-of-risk-factors model.

BACKGROUND: The 3-variable number-of-risk-factors (NRF) model is a prognostic tool for patients undergoing palliative radiotherapy (PRT). However, there is little research on the NRF model for patients with painful non-bone-metastasis tumours treated with PRT, and the efficacy of the NRF model in predicting survival is unclear to date. Therefore, we aimed to assess the prognostic accuracy of a 3-variable NRF model in patients undergoing PRT for bone and non- bone-metastasis tumours. METHODS: This was a secondary analysis of studies on PRT for bone-metastasis (BM) and PRT for miscellaneous painful tumours (MPTs), including non-BM tumours. Patients were grouped in the NRF model and survival was compared between groups. Discrimination was evaluated using a time-independent C-index and a time-dependent area under the receiver operating characteristic curve (AUROC). A calibration curve was used to assess the agreement between predicted and observed survival. RESULTS: We analysed 485 patients in the BM group and 302 patients in the MPT group. The median survival times in the BM group for groups I, II, and III were 35.1, 10.1, and 3.3 months, respectively (P < 0.001), while in the MPT group, they were 22.1, 9.5, and 4.6 months, respectively (P < 0.001). The C-index was 0.689 in the BM group and 0.625 in the MPT group. In the BM group, time-dependent AUROCs over 2 to 24 months ranged from 0.738 to 0.765, while in the MPT group, they ranged from 0.650 to 0.689, with both groups showing consistent accuracy over time. The calibration curve showed a reasonable agreement between the predicted and observed survival. CONCLUSIONS: The NRF model predicted survival moderately well in both the BM and MPT groups.

Multidisciplinary Treatment for Breast Cancer-related Multiple Bone Metastases during Pregnancy Using Bone Metastasis Cancer Boards: A Case Report.

Background: In patients vulnerable to skeletal-related events (SREs), a multidisciplinary approach is required to manage risk and determine the best treatment plan. We have used Bone Metastasis Cancer Boards (BMCBs) to deliver multidisciplinary treatments in our hospital since 2013. Here, we report a case in which we used BMCBs to coordinate multidisciplinary treatment for a pregnant patient with breast cancer and multiple bone metastases. Case: A 41-year-old pregnant woman was admitted to our hospital because low back pain compromised her ability to stand. She was diagnosed with breast cancer-associated multiple bone metastases. Our unit was consulted for rehabilitation therapy, for which we formed a BMCB. The treatment was integrated and performed according to the recommendations of the BMCB. The patient underwent a cesarean section to initiate primary tumor treatment. After evaluating the risk of SREs, we provided her with rehabilitation therapy. Wearing a plastic molded thoracolumbosacral orthosis, she was able to walk with a pick-up walker. The patient continued outpatient chemotherapy and cared for her infant without experiencing any significant adverse events. Discussion: In this case, we formed our BMCB to determine the treatment plan, which we used to support the patient's needs during childbirth and successfully improved her activities of daily living. BMCBs can contribute to preventing SREs and provide effective rehabilitation therapy for patients with bone metastases. We aspire to continually gather experience through our BMCBs and contribute to the establishment of evidence regarding the effectiveness of rehabilitation therapy for patients with bone metastases.

Deep bone oncology Diagnostics: Computed tomography based Machine learning for detection of bone tumors from breast cancer metastasis.

Purpose: The objective of this study is to develop a novel diagnostic tool using deep learning and radiomics to distinguish bone tumors on CT images as metastases from breast cancer. By providing a more accurate and reliable method for identifying metastatic bone tumors, this approach aims to significantly improve clinical decision-making and patient management in the context of breast cancer. Methods: This study utilized CT images of bone tumors from 178 patients, including 78 cases of breast cancer bone metastases and 100 cases of non-breast cancer bone metastases. The dataset was processed using the Medical Image Segmentation via Self-distilling TransUNet (MISSU) model for automated segmentation. Radiomics features were extracted from the segmented tumor regions using the Pyradiomics library, capturing various aspects of tumor phenotype. Feature selection was conducted using LASSO regression to identify the most predictive features. The model's performance was evaluated using ten-fold cross-validation, with metrics including accuracy, sensitivity, specificity, and the Dice similarity coefficient. Results: The developed radiomics model using the SVM algorithm achieved high discriminatory power, with an AUC of 0.936 on the training set and 0.953 on the test set. The model's performance metrics demonstrated strong accuracy, sensitivity, and specificity. Specifically, the accuracy was 0.864 for the training set and 0.853 for the test set. Sensitivity values were 0.838 and 0.789 for the training and test sets, respectively, while specificity values were 0.896 and 0.933 for the training and test sets, respectively. These results indicate that the SVM model effectively distinguishes between bone metastases originating from breast cancer and other origins. Additionally, the average Dice similarity coefficient for the automated segmentation was 0.915, demonstrating a high level of agreement with manual segmentations. Conclusion: This study demonstrates the potential of combining CT-based radiomics and deep learning for the accurate detection of bone metastases from breast cancer. The high-performance metrics indicate that this approach can significantly enhance diagnostic accuracy, aiding in early detection and improving patient outcomes. Future research should focus on validating these findings on larger datasets, integrating the model into clinical workflows, and exploring its use in personalized treatment planning.

Predictive models and treatment efficacy for liver cancer patients with bone metastases: A comprehensive analysis of prognostic factors and nomogram development.

Background: Bone metastasis considerably undermines the prognosis of advanced primary liver cancer patients. Though its impact is well-recognized, the clinical field still lacks robust predictive models that can accurately forecast patient outcomes and aid in treatment effectiveness evaluation. Addressing this gap is paramount for improving patient management and survival. Materials and methods: We conducted an extensive analysis using data from the SEER database (2010-2020). COX regression analysis was applied to identify prognostic factors for primary liver cancer with bone metastasis (PLCBM). Nomograms were developed and validated to predict survival outcomes in PLCBM patients. Additionally, propensity score matching and Kaplan-Meier survival analyses lent additional insight by dissecting the survival advantage conferred by various treatment strategies. Results: A total of 470 patients with PLCBM were included in our study. The median overall survival (OS) and cancer-specific survival (CSS) for these patients were both 5 months. We unveiled several independent prognosticators for OS and CSS, spanning demographic to therapeutic parameters like marital status, cancer grade, histological type, and treatments received. This discovery enabled the formulation of two novel nomograms-now verified to eclipse the predictive prowess of the traditional TNM staging system regarding discrimination and clinical utility. Additionally, propensity score matching analysis showed the effectiveness of surgeries, radiotherapy, and chemotherapy in improving OS and CSS outcomes for PLCBM patients. Conclusions: Our investigation stands out by introducing pioneering nomograms for prognostic evaluation in PLCBM, a leap forward compared to existing tools. Far exceeding mere academic exercise, these nomograms hold immense clinical value, serving as a foundation for nuanced risk stratification systems and delivering dynamic, interactive guides, allowing healthcare professionals and patients to assess individual bone metastasis survival probabilities and personalize treatment selection.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq revealed key genes for bone metastasis and chemoresistance in prostate cancer.

BACKGROUND: Prostate cancer (PCa) is a serious malignancy. The main causes of PCa aggravation and death are unexplained resistance to chemotherapy and bone metastases. OBJECTIVE: This study aimed to investigate the molecular mechanisms associated with the dynamic processes of progression, bone metastasis, and chemoresistance in PCa. METHODS: Through comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data, Gene Expression Omnibus (GEO) tumor progression and metastasis-related genes were identified. These genes were subjected to lasso regression modeling using the Cancer Genome Atlas (TCGA) database. Tartrate-resistant acid phosphatase (TRAP) staining and real-time quantitative PCR (RT-qPCR) were used to evaluate osteoclast differentiation. CellMiner was used to confirm the effect of LDHA on chemoresistance. Finally, the relationship between LDHA and chemoresistance was verified using doxorubicin-resistant PCa cell lines. RESULTS: 7928 genes were identified as genes related to tumor progression and metastasis. Of these, 7 genes were found to be associated with PCa prognosis. The scRNA-seq and TCGA data showed that the expression of LDHA was higher in tumors and associated with poor prognosis of PCa. In addition, upregulation of LDHA in PCa cells induces osteoclast differentiation. Additionally, high LDHA expression was associated with resistance to Epirubicin, Elliptinium acetate, and doxorubicin. Cellular experiments demonstrated that LDHA knockdown inhibited doxorubicin resistance in PCa cells. CONCLUSIONS: LDHA may play a potential contributory role in PCa initiation and development, bone metastasis, and chemoresistance. LDHA is a key target for the treatment of PCa.

MFP-YOLO: a multi-scale feature perception network for CT bone metastasis detection.

Bone metastasis is one of the most common forms of metastasis in the late stages of malignancy. The early detection of bone metastases can help clinicians develop appropriate treatment plans. CT images are essential for diagnosing and assessing bone metastases in clinical practice. However, early bone metastasis lesions occupy a small part of the image and display variable sizes as the condition progresses, which adds complexity to the detection. To improve diagnostic efficiency, this paper proposes a novel algorithm-MFP-YOLO. Building on the YOLOv5 algorithm, this approach introduces a feature extraction module capable of capturing global information and designs a new content-aware feature pyramid structure to improve the network's capability in processing lesions of varying sizes. Moreover, this paper innovatively applies a transformer-structure decoder to bone metastasis detection. A dataset comprising 3921 CT images was created specifically for this task. The proposed method outperforms the baseline model with a 5.5% increase in precision and a 7.7% boost in recall. The experimental results indicate that this method can meet the needs of bone metastasis detection tasks in real scenarios and provide assistance for medical diagnosis.

Single-Cell Analysis of Bone-Marrow-Disseminated Tumour Cells.

Metastasis frequently targets bones, where cancer cells from the primary tumour migrate to the bone marrow, initiating new tumour growth. Not only is bone the most common site for metastasis, but it also often marks the first site of metastatic recurrence. Despite causing over 90% of cancer-related deaths, effective treatments for bone metastasis are lacking, with current approaches mainly focusing on palliative care. Circulating tumour cells (CTCs) are pivotal in metastasis, originating from primary tumours and circulating in the bloodstream. They facilitate metastasis through molecular interactions with the bone marrow environment, involving direct cell-to-cell contacts and signalling molecules. CTCs infiltrate the bone marrow, transforming into disseminated tumour cells (DTCs). While some DTCs remain dormant, others become activated, leading to metastatic growth. The presence of DTCs in the bone marrow strongly correlates with future bone and visceral metastases. Research on CTCs in peripheral blood has shed light on their release mechanisms, yet investigations into bone marrow DTCs have been limited. Challenges include the invasiveness of bone marrow aspiration and the rarity of DTCs, complicating their isolation. However, advancements in single-cell analysis have facilitated insights into these elusive cells. This review will summarize recent advancements in understanding bone marrow DTCs using single-cell analysis techniques.

Prognostic value of [18F]FDG PET/CT in metastatic hormone-sensitive prostate cancer at initial diagnosis: a retrospective cohort study.

INTRODUCTION: This retrospective study aimed to evaluate the prognostic value of [18F]FDG parameters in patients with visceral and bone metastatic hormone-sensitive prostate cancer (mHSPC). PATIENTS AND METHODS: This analysis included the mHSPC patients who underwent [18F]FDG PET/CT at the initial diagnosis. Baseline characteristics were analyzed, and the uptake of [18F]FDG was quantified using SUVmax. Kaplan-Meier and Cox proportional hazard regression analysis were employed to evaluate the correlation between SUVmax and patient survival. RESULTS: Among the 267 patients enrolled, 90 (33.7%) presented with visceral metastases and 177 (66.3%) had bone metastases. The median follow-up for the visceral metastasis group was 35.5 months (IQR 26-53.8 months). The median overall survival for patients with lung, liver, or both metastases were 30, 21 and 17 months, respectively. Patients exhibiting higher [18F]FDG uptake in metastatic lesions experienced shorter overall survival (OS) in comparison to those with lower [18F]FDG uptake, both in the visceral metastases group (17 vs. 31 months, p = 0.002) and the bone metastases group (27.5 vs. 34.5 months, p < 0.001). Cox regression analysis further revealed that increased [18F]FDG uptake in metastatic lesions emerged as a significant risk factor in both OS and progression-free survival (PFS). In contrast, the variability in [18F]FDG uptake in primary lesions did not provide a reliable indicator for predicting prognosis. CONCLUSIONS: In mHSPC patients, higher [18F]FDG uptake in metastatic lesions indicates shorter survival and increased risk of disease progression. The [18F]FDG SUVmax in primary tumors did not show significant prognostic value. Our study underscores the unique prognostic potential of [18F]FDG PET/CT in mHSPC patients, highlighting its importance in the management of both bone and visceral metastases.

Review on effects and mechanisms of plant-derived natural products against breast cancer bone metastasis.

Bone metastasis is the prevalent form of metastasis in breast cancer, resulting in severe pain, pathological fractures, nerve compression, hypercalcemia, and other complications that significantly impair patients' quality of life. The infiltration and colonization of breast cancer (BC) cells in bone tissue disrupt the delicate balance between osteoblasts and osteoclasts within the bone microenvironment, initiating a vicious cycle of bone metastasis. Once bone metastasis occurs, conventional medical therapy with bone-modifying agents is commonly used to alleviate bone-related complications and improve patients' quality of life. However, the utilization of bone-modifying agents may cause severe drug-related adverse effects. Plant-derived natural products such as terpenoids, alkaloids, coumarins, and phenols have anti-tumor, anti-inflammatory, and anti-angiogenic pharmacological properties with minimal side effects. Certain natural products that exhibit both anti-breast cancer and anti-bone metastasis effects are potential therapeutic agents for breast cancer bone metastasis (BCBM). This article reviewed the effects of plant-derived natural products against BCBM and their mechanisms to provide a reference for the research and development of drugs related to BCBM.