Establishment of Prognostic Nomogram for Male Breast Cancer Patients: A Surveillance, Epidemiology and End Results Database Analysis.

BACKGROUND: Male breast cancer (MBC) represents a rare subtype of breast cancer, with limited prognostic factor studies available. The purpose of this research was to develop a unique nomogram for predicting MBC patient overall survival (OS) and breast cancer-specific survival (BCSS). METHODS: From 2010 to 2020, clinical characteristics of male breast cancer patients were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Following univariate and multivariate analyses, nomograms for OS and BCSS were created. Kaplan-Meier plots were further generated to illustrate the relationship between independent risk variables and survival. The nomogram's ability to discriminate was measured by employing the area under a time-dependent receiver operating characteristic curve (AUC) and calibration curves. Additionally, when the nomogram was used to direct clinical practice, we also used decision curve analysis (DCA) to evaluate the clinical usefulness and net clinical benefits. RESULTS: A total of 2143 patients were included in this research. Univariate and multivariate analysis showed that age, grade, surgery, chemotherapy status, brain metastasis status, subtype, marital status, race, and AJCC-T, AJCC-N, and AJCC-M stages were significantly correlated with OS. Lung metastasis, age, marital status, grade, surgery, and AJCC-T, AJCC-N, and AJCC-M stages were significantly correlated with BCSS. By comprising these variables, a predictive nomogram was constructed in the SEER cohort. Then, it could be validated well in the validation cohort by receiver operating characteristics (ROCs) curve and calibration plot. Furthermore, the nomogram demonstrated better decision curve analysis (DCA) results, indicating the ability to forecast survival probability with greater accuracy. CONCLUSION: We created and validated a unique nomogram that can assist clinicians in identifying MBC patients at high risk and forecasting their OS/BCSS.

The value of second-line anti-HER2 therapy in metastatic HER-2 positive patients: a cost-effectiveness analysis in China.

Background: Breast cancer (BC) is one of the most common cancers worldwide. The inevitability of drug resistance to initial anti-HER-2 therapy necessitates the emergence of second-line anti-HER-2 drugs which exhibit a promising outlook. Consequently, it is imperative to appraise their efficacy through network meta-analysis and ascertain their comparative cost-effectiveness. Methods: The data used in our analysis were acquired from patients enrolled in the EMILIA, DESTINY-Breast03, and PHOEBE phase III randomized clinical trials. A partitioned survival model was used for patients diagnosed with HER-2-positive metastatic Breast cancer. The model was crafted with a time horizon of 10 years, operating on a 21-day cycle and incorporating a 5% discount rate for both costs and outcomes. The willingness-to-pay threshold was set at $36,058.06 per quality-adjusted life year (QALY). The impact of parameter uncertainty on the findings was assessed using a one-way deterministic sensitivity analysis and probability sensitivity analysis. Findings: Within the model encompassing 1782 patients, the utilization of pyrotinib plus capecitabine (PC) treatment yielded an additional 0.70 QALY in comparison to T-DM1, resulting in an incremental cost-effectiveness ratio (ICER) of $31,121.53 per QALY gained. Similarly, the administration of T-DXd treatment led to an additional 0.80 QALY compared to T-DM1, resulting in an ICER of $153,950.19 per QALY gained. The PC strategies are considered more cost-effective than T-DXd when the WTP threshold is set at $36,058.06 per QALY. However, this method is not cost effective for T-DXd. The probability of the PC strategies being cost-effective was 62%, whereas the probability of T-DXd was 0% when compared to T-DM1. Conclusion: PC is a cost-effective therapy for patients afflicted with HER-2-positive metastatic BC compared to T-DM1 from the perspective of China at a WTP threshold of $36,058.06 per QALY. Nevertheless, T-DXd is not as cost-effective as T-DM1, considering its current medication pricing. Therefore, reducing the cost of T-DXd could improve its overall cost-effectiveness.

Disitamab Vedotin (RC48) for HER2-positive advanced breast cancer: a case report and literature review.

Background/aim: Human epidermal growth factor receptor 2 (HER2)-positive breast cancer is associated with a higher risk of metastasis and poorer overall survival (OS) due to HER2 gene overexpression/amplification. Although anti-HER2 targeted therapy has shown survival benefits in HER2-positive advanced breast cancer (ABC) patients, long-term treatment often leads to drug resistance, complicating further treatment options. RC48, an antibody-drug conjugate (ADC), combines the benefits of antibody targeting with the cytotoxic effects of a small molecule drug. Case report: We present a case involving a female patient with HER2-positive ABC who developed drug resistance and disease progression following multi-line anti-HER2 targeted therapy. In this instance, RC48 exhibited anti-tumor activity in an ABC patient resistant to HER2-targeted therapy. After eight treatment cycles with 120 mg of RC48, the tumor size decreased and stabilized. Conclusion: This case report underscores the potential clinical value of RC48 as a promising treatment alternative for patients resistant to HER2 targeted therapies.

Enhancing cord blood stem cell-derived NK cell growth and differentiation through hyperosmosis.

BACKGROUND: Natural killer (NK) cells hold great promise in treating diverse hematopoietic and solid tumors. Despite their availability from peripheral blood and cord blood, stem cell-derived NK cells offer an 'off-the-shelf' solution. Hematopoietic stem and progenitor cells (HSPCs) derived from cord blood pose no risk to the newborn or mother and are virtually ideal sources for NK cell differentiation. METHODS: We developed a modified protocol to differentiate HSPCs to NK cells under serum-free conditions using defined factors. The HSPC-derived NK (HSC-NK) cells could be expanded in a K562 feeder cell-dependent manner. Furthermore, using lentivirus transduction, chimeric antigen receptor (CAR)-modified HSPCs could be differentiated into NK cells, leading to the establishment of CAR-NK cells. RESULTS: The efficiency of NK cell differentiation from HSPCs was increased through the simple modulation of osmotic pressure by the addition of sodium chloride or glucose. Furthermore, the hyperosmosis-primed HSC-NK cells exhibited enhanced proliferation capacity and maintained normal functional characteristics, including transcriptome and antitumor efficacy. The optimized protocol yielded approximately 1.8 million NK cells from a single CD34-positive cell within a 28-day cycle, which signifies more than a ten-fold increase in efficiency relative to the conventional methods. This optimized protocol was also suitable for generating CAR-NK cells with high yields compared to standard conditions. CONCLUSIONS: The results of this study establish high osmotic pressure as a simple yet powerful adjustment that significantly enhances the efficiency and functionality of HSC-NK cells, including CAR-NK cells. This optimized protocol could lead to cost-effective, high-yield NK cell therapies, potentially revolutionizing cancer immunotherapy strategies.

Digital pathology and artificial intelligence as the next chapter in diagnostic hematopathology.

Digital pathology has a crucial role in diagnostic pathology and is increasingly a technological requirement in the field. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond the microscopic slide and enable true integration of knowledge and expertise. There is clear potential for artificial intelligence (AI) breakthroughs in pathology and hematopathology. In this review article, we discuss the approach of using machine learning in the diagnosis, classification, and treatment guidelines of hematolymphoid disease, as well as recent progress of artificial intelligence in flow cytometric analysis of hematolymphoid diseases. We review these topics specifically through the potential clinical applications of CellaVision, an automated digital image analyzer of peripheral blood, and Morphogo, a novel artificial intelligence-based bone marrow analyzing system. Adoption of these new technologies will allow pathologists to streamline workflow and achieve faster turnaround time in diagnosing hematological disease.

Cardiac Biomarkers for the Detection and Management of Cancer Therapy-Related Cardiovascular Toxicity.

Cardiotoxicity is one of the major side effects of anti-cancer therapy affecting the overall prognosis of patients and possibly leading to the discontinuation of chemotherapy. Traditional cardiovascular tests such as electrocardiography and transthoracic echocardiography have limited sensitivity and specificity for the early detection of myocardial injury. Cardiovascular imaging generally detects cancer therapy-related cardiac dysfunction (CTRCD) at advanced stages, whereas biomarkers are inexpensive, easily detected, reproducible, and capable of detecting even minimal cardiomyocyte damage or mild hemodynamic fluctuations. The presence of circulating cardiac biomarkers has been investigated as early indicators of cardiotoxicity and predictors of subsequent CTRCD. Currently, the most frequently used cardiac biomarkers are cardiac troponin (cTn) and natriuretic peptides (NPs). This review presents the evidence gathered so far regarding the usefulness and limitations of cardiac biomarkers in the field of cardio-oncology.

Prognostic value of baseline genetic features and newly identified TP53 mutations in advanced breast cancer.

Approximately 30% of breast cancer (BC) patients suffer from disease relapse after definitive treatment. Monitoring BC at baseline and disease progression using comprehensive genomic profiling would facilitate the prediction of prognosis. We retrospectively studied 101 BC patients ultimately experiencing relapse and/or metastases. The baseline and circulating tumor DNA-monitoring cohorts included patients with baseline tumor tissue and serial plasma samples, respectively. Samples were analyzed with targeted next-generation sequencing of 425 cancer-relevant genes. Of 35 patients in the baseline cohort, patients with TP53 mutations (P < 0.01), or CTCF/GNAS mutations (P < 0.01) displayed inferior disease-free survival, and patients harboring TP53 (P = 0.06) or NOTCH1 (P = 0.06) mutations showed relatively poor overall survival (OS), compared to patients with wild-type counterparts. Of the 59 patients with serial plasma samples, 11 patients who were newly detected with TP53 mutations had worse OS than patients whose TP53 mutational status remained negative (P < 0.01). These results indicate that an inferior prognosis of advanced breast cancer was potentially associated with baseline TP53, CTCF, and NOTCH1 alterations. Newly identified TP53 mutations after relapse and/or metastasis was another potential prognostic biomarker of poor prognosis.

Developing a Machine Learning Algorithm for Identifying Abnormal Urothelial Cells: A Feasibility Study.

INTRODUCTION: Urine cytology plays an important role in diagnosing urothelial carcinoma (UC). However, urine cytology interpretation is subjective and difficult. Morphogo (ALAB, Boston, MA, USA), equipped with automatic acquisition and scanning, optical focusing, and automatic classification with convolutional neural network has been developed for bone marrow aspirate smear analysis of hematopoietic diseases. The goal of this preliminary study was to determine the feasibility of developing a machine learning algorithm on Morphogo for identifying abnormal urothelial cells in urine cytology slides. METHODS: Thirty-seven achieved abnormal urine cytology slides from cases with the diagnosis of atypical urothelial cells and above (suspicions or positive for UC) were obtained from 1 hospital. A pathologist (J.R.) reviewed the slides and manually selected and annotated representative cells to feed into Morphogo with following categories: benign (urothelial cells, squamous cells, degenerated cells, and inflammatory cells), atypical cells, and suspicious cells. Initial validation of the algorithm was performed on a subset of the original 37 cases. Urine samples from additional 12 unknown cases with various histological diagnoses (6 cases of high-grade urothelial carcinoma (HGUC), 1 case of low-grade urothelial carcinoma (LGUC), 1 case of prostate adenocarcinoma, 1 case of renal cell carcinoma, and 4 cases of non-neoplastic conditions) were collected from another hospital for initial blind testing. RESULTS: A total of 1,910 benign and 1,978 abnormal (atypical and suspicious) cells from 37 slides were annotated for developing and training of the algorithm. This algorithm was validated on 27 slides that resulted in identification of at least 1 abnormal cell per slide, with a total of 200 abnormal cells, and an average of 7.4 cells per slide. Of the 12 unknown cases tested, the original cytology was positive for tumor cells in 2 HGUC samples. Morphogo was abnormal (atypical or suspicious) for 6 samples from patients with UC, including one with LGUC and one with prostate adenocarcinoma. CONCLUSION: Morphogo machine learning algorithm is capable of identifying abnormal urothelial cells. Further validation studies with a larger number of urine samples will be needed to determine if it can be used to assist the cytological diagnosis of UC.

Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study.

Bone marrow smear examination is an indispensable diagnostic tool in the evaluation of hematological diseases, but the process of manual differential count is labor extensive. In this study, we developed an automatic system with integrated scanning hardware and machine learning-based software to perform differential cell count on bone marrow smears to assist diagnosis. The initial development of the artificial neural network was based on 3000 marrow smear samples retrospectively archived from Sir Run Run Shaw Hospital affiliated to Zhejiang University School of Medicine between June 2016 and December 2018. The preliminary field validating test of the system was based on 124 marrow smears newly collected from the Second Affiliated Hospital of Harbin Medical University between April 2019 and November 2019. The study was performed in parallel of machine automatic recognition with conventional manual differential count by pathologists using the microscope. We selected representative 600,000 marrow cell images as training set of the algorithm, followed by random captured 30,867 cell images for validation. In validation, the overall accuracy of automatic cell classification was 90.1% (95% CI, 89.8-90.5%). In a preliminary field validating test, the reliability coefficient (ICC) of cell series proportion between the two analysis methods were high (ICC ≥ 0.883, P < 0.0001) and the results by the two analysis methods were consistent for granulocytes and erythrocytes. The system was effective in cell classification and differential cell count on marrow smears. It provides a useful digital tool in the screening and evaluation of various hematological disorders.

Morphogo: An Automatic Bone Marrow Cell Classification System on Digital Images Analyzed by Artificial Intelligence.

INTRODUCTION: The nucleated-cell differential count on the bone marrow aspirate smears is required for the clinical diagnosis of hematological malignancy. Manual bone marrow differential count is time consuming and lacks consistency. In this study, a novel artificial intelligence (AI)-based system was developed to perform cell automatic classification of bone marrow cells and determine its potential clinical applications. MATERIALS AND METHODS: Bone marrow aspirate smears were collected from the Xinqiao Hospital of Army Medical University. First, an automated analysis system (Morphogo) scanned and generated whole digital images of bone marrow smears. Then, the nucleated marrow cells in the selected areas of the smears at a magnification of ×1,000 were analyzed by the software utilizing an AI-based platform. The cell classification results were further reviewed and confirmed independently by 2 experienced pathologists. The automatic cell classification performance of the system was evaluated using 3 categories: accuracy, sensitivity, and specificity. Correlation coefficients and linear regression equations between automatic cell classification by the AI-based system and concurrent manual differential count were calculated. RESULTS: In 230 cases, the classification accuracy was above 85.7% for hematopoietic lineage cells. Averages of sensitivity and specificity of the system were found to be 69.4 and 97.2%, respectively. The differential cell percentage of the automated count based on 200-500 cell counts was correlated with differential cell percentage provided by the pathologists for granulocytes, erythrocytes, and lymphocytes (r ≥ 0.762, p < 0.001). DISCUSSION/CONCLUSION: This pilot study confirmed that the Morphogo system is a reliable tool for automatic bone marrow cell differential count analysis and has potential for clinical applications. Current ongoing large-scale multicenter validation studies will provide more information to further confirm the clinical utility of the system.

Cancer patients with potential eligibility for vascular endothelial growth factor antagonists use have an increased risk for cardiovascular diseases comorbidities.

BACKGROUND: Recent studies have reported the prevalence of cardiovascular diseases (CVDs) among cancer patients following the use of the vascular endothelial growth factor (VEGF) signaling inhibitors. However, data for patients with a history of cancer before active cancer treatment are lacking. This study aims to investigate the distribution of CVD-related comorbidities before cancer treatment in potential VEGF antagonists candidates. METHODS: A total of 22 500 newly diagnosed cancer patients registered from 1 January 2011 to 31 December 2017 were included. Cancer patients with colorectal cancer (CRC), renal cell carcinoma (RCC), thyroid cancer, hepatocellular carcinoma (HCC), and lung cancer were selected. RESULTS: Hypertension (HTN), coronary heart diseases, atrial fibrillation, and heart failure were top CVD comorbidities among studied cancers. HTN was the most prevalent CVD (26.0%). The prevalence of HTN in RCC, CRC (33.5 and 29.4% respectively) was significantly higher than that in HCC, lung cancer, and thyroid cancer patients (25.1, 24.5, and 23.1%, respectively). Among cancer patients with HTN, the majority of cancer patients fall in grade III (75.7%) and very high cardiovascular risk level (85.4%). Out of the 5847 HTN patients, 26% were not in antihypertensive use, and 34.2% failed to achieve the target blood pressure. CONCLUSION: Cancer patients carry a high burden of CVD-related comorbidities before the application of VEGF antagonists. HTN is the most prevalent comorbid condition, and cancer patients with HTN constitute substantial cardiovascular risks and a higher co-prevalence of other CVDs.

Efficacy and Safety of Direct Oral Anticoagulants for Secondary Prevention of Cancer-Associated Thrombosis: A Systematic Review and Meta-Analysis of Randomized Controlled Trials and Prospective Cohort Studies.

Background: Venous thromboembolism (VTE) is a common complication in patients with cancer. Direct oral anticoagulants (DOACs) have been proved to be effective on anticoagulation therapy in many diseases. However, the efficacy and the safety of DOACs in the secondary prevention of cancer-associated thrombosis (CAT) remain unclear. To assess the value of DOACs in patients with CAT, we performed a systematic review and meta-analysis of randomized controlled trials and prospective cohort studies. Methods: Medline, Embase, and the Cochrane Library were searched from their earliest date through to June 2018. Two investigators independently assessed eligibility. Data were extracted by one investigator and verified by the second investigator. The efficacy outcome of this study was recurrent VTE, whereas the safety outcome was major and clinically relevant nonmajor bleeding. Relative risks (RRs) and their corresponding 95% confidence interval (CI) were determined. To pool the results, the Mantel-Haenszel fixed-effects or random-effects models were used. Results: A total of nine articles (six randomized controlled trials and three prospective studies) involving 2,697 patients with CAT who were prescribed DOACs (apixaban, edoxaban, rivaroxaban, or dabigatran) and 2,852 patients who were prescribed traditional anticoagulants [vitamin K antagonists (VKAs), low molecular weight heparin (LMWH), dalteparin, or enoxaparin] were compared. VTE recurrence in the DOAC group was significantly lower than that observed in the traditional anticoagulant group (RR: 0.60; 95%CI: 0.49-0.75; I 2: 0%; p < 0.00001). No significant difference in bleeding risk between both groups was found (RR: 0.95; 95%CI: 0.67-1.36; I 2: 75%; p = 0.79). Conclusions: Our findings showed that anticoagulant therapy with DOACs may be more effective than traditional anticoagulants to prevent recurrent VTE in patients with CAT, while the safety of DOACs may be equal to that of traditional anticoagulants. These findings support the use of DOACs as the first-line therapy for secondary prevention of CAT in most cancer patients.

Long noncoding RNA TP73-AS1 promotes non-small cell lung cancer progression by competitively sponging miR-449a/EZH2.

Long noncoding RNAs (lncRNAs) are a type of noncoding RNA transcript that are characterized by lack of protein-coding capacity. The vital role of lncRNAs in non-small cell lung cancer (NSCLC) is attracting increasingly more attention. In the present study, we investigate the role of lncRNA antisense RNA of the TP73 gene (TP73-AS1) in NSCLC carcinogenesis. The results demonstrate that TP73-AS1 is markedly upregulated in NSCLC tissues, and functional experiments revealed that TP73-AS1 is significantly increased in NSCLC tissue and cell lines, indicating a possible oncogenic role. In loss-of-function assays, the knockdown of TP73-AS1 inhibited NSCLC cell proliferation, tumor growth and cycle progression in vivo and in vitro. Bioinformatic tools predicted that miR-449a both targeted the 3'-UTR of TP73-AS1 and EZH2, which was confirmed using luciferase reporter assay and AGO2-dependent RNA immunoprecipitate (RIP). TP73-AS1 and miR-449a were in the same RNA-induced silencing complex (RISC). In summary, the results indicate an explicit oncogenic role of TP73-AS1 in the NSCLC tumorigenesis, suggesting a TP73-AS1-miR-449a-EZH2 axis and providing new insight for NSCLC tumorigenesis.

Effective cytotoxic activity of OSW-1 on colon cancer by inducing apoptosis in vitro and in vivo.

As a natural compound, Ornithogalum caudatum Ait is primarily used as an anti-inflammatory and antitumor agent in Chinese folk medicine. In 1992, OSW-1 was isolated from this compound, which is a new member of cholestane saponin family. In numerous recent studies, OSW-1 has been shown to have powerful cytotoxic anticancer effects against various malignant cells. However, the therapeutic efficacy of OSW-1 on colon cancer and the underlying mechanism are not understood. To explore the mechanism underlying OSW-1 in antitumor therapy, a therapeutic function analysis of OSW-1 on colon cancer was performed in vitro and in vivo. It was shown that with low toxicity on normal colonic cells, OSW-1 suppresses colon cancer cells in vitro and this inhibition was via the intrinsic apoptotic pathway, which increased cellular calcium, changed mitochondrial membrane potential, disrupted mitochondrial morphology, and led to the release of cytochrome c and the activation of caspase-3. Furthermore, in a nude mouse model, OSW-1 had a powerful effect on suppressing colon tumor proliferation without significant side effects through the apoptosis pathway. Taken together, these results demonstrate that OSW-1 is a potential drug for colon cancer treatment.

Prediction of chemotherapeutic efficacy using the ratio of neutrophils to lymphocytes in patients with unresectable or recurrent gastric cancer.

In cancer patients, the balance between neutrophil (N) and lymphocyte (L) cell counts fluctuates with the tumor load. The objective of the present study was to determine the implications of the chemotherapy effect by the fluctuations of N/L ratio in patients with unresectable or recurrent gastric cancer. The study participants were identified from a prospective cohort of patients with unresectable or recurrent gastric cancer (n=135). The median N/L ratio was 3.23 (range: 0.76-20.45) prior to chemotherapy (pre-chemo-N/L ratio) and 2.55 (range: 1.17-13.45) following 2-4 weeks from when the chemotherapy was completed (post-chemo-N/L ratio), respectively. The median overall survival was 7.9 months. The results demonstrated that the N/L ratio of the post-chemotherapy was significantly reduced compared with the pre-chemotherapy group (P<0.001). The survival rate for the pre-chemo-N/L ratio ≥4.0 group was significantly reduced compared to the N/L <4.0 group (P=0.01). The difference of the pre-chemo-N/L ratio subtracted from the post-chemo-N/L ratio can inflect the chemotherapy effect, respectively. These results indicate that the N/L ratio may be used to predict the potential chemotherapy efficacy in unresectable or recurrent gastric cancer.

Anti-cancer effects of 2-oxoquinoline derivatives on the HCT116 and LoVo human colon cancer cell lines.

The present study demonstrated the anti-tumor effects of the quinoline derivative [5-(3-chloro-oxo-4-phenyl-cyclobutyl)-quinoli-8-yl-oxy] acetic acid hydrazide (CQAH) against colorectal carcinoma. Substantial apoptotic effects of CQAH on HCT116 and LoVo human colon cancer cell lines were observed. Apoptosis was identified based on cell morphological characteristics, including cell shrinkage and chromatin condensation as well as Annexin V/propidium iodide double staining followed by flow cytometric analysis and detection of apoptosis-associated proteins by western blot analysis. CQAH induced caspase-3 and PARP cleavage, reduced the expression of the anti-apoptotic proteins myeloid cell leukemia-1 and B-cell lymphoma (Bcl) extra large protein and elevated the expression of the pro-apoptotic protein Bcl-2 homologous antagonist killer. In addition, pharmacological inhibition of c-Jun N-terminal kinase (JNK), but not extracellular signal-regulated kinase or p38, significantly reduced CQAH-mediated cell death as well as cleavage of caspase-3 and PARP. Co-treatment of CQAH with the commercial chemotherapeutics 5-fluorouracil and camptothecin-11 significantly improved their efficacies. Comparison of the apoptotic effects of CQAH with those of two illustrated structure-activity associations for this compound type, indicating that substitution at position-4 of the azetidine phenyl ring is pivotal for inducing apoptosis. In conclusion, the results of the present study indicated CQAH and its analogues are potent candidate drugs for the treatment of colon carcinoma.

The neutrophil/lymphocyte ratio is an independent prognostic indicator in patients with bone metastasis.

Patients with the most common advanced human cancers such as lung, breast, uterus, and cancers of the digestive system almost always develop bone metastases, with painful and untreatable consequences. This study aimed to determine the prognostic implications of the neutrophil/lymphocyte (N/L) ratio in the peripheral blood of patients with malignant bone metastasis. Study participants were identified from a prospective cohort of cancer patients with bone metastasis. Data for the N/L ratios were obtained from clinical and pathological records and were analyzed together with other known prognostic factors in the multivariate and univariate analyses. The results showed the average N/L ratio of all 497 patients to be 4.25±2.44 (range 0.54-45.50 years). Multivariate analysis revealed that tumor type and a high N/L ratio were significantly associated with poor prognosis. For the high N/L ratio group, the estimated hazard ratio of death was 1.348 [95% confidence interval (CI), 1.062-1.712] compared with the low N/L ratio group. The average N/L ratio of the 225 patients in the surgery group was 2.79±2.46 (range 0.77-22.75 years). Multivariate analysis revealed that a preoperatively high N/L ratio (P=0.013; HR=2.945; 95% CI, 1.256-6.906) was significantly associated with poor prognosis after bone metastasis in the surgery group. In conclusion, the N/L ratio was confirmed to be an independent prognostic factor in patients with bone metastasis. Thus, the N/L ratio may serve as a clinically accessible and useful biomarker for patient survival.