The Combination of Plasma Fibrinogen Concentration and Neutrophil Lymphocyte Ratio (F-NLR) as a Prognostic Factor of Epithelial Ovarian Cancer

New prognostic index for neoadjuvant chemotherapy outcome in patients with advanced high-grade serous ovarian cancer

BACKGROUND

A validated prognostic index for the outcome of patients with advanced high-grade serous ovarian cancer (HGSOC) undergoing neoadjuvant chemotherapy (NACT) remains elusive. To address this need, we developed an ovarian neoadjuvant chemotherapy prognostic index (ONCPI) to improve predictive accuracy.

METHODS

We encompassed an analysis of the clinicopathological characteristics of patients with advanced HGSOC who were administered platinum-based NACT. Blood inflammatory composite markers were calculated and converted into binary values using optimal cutoffs. Omental hematoxylin and eosin (H&E) stained slides were selected for the assessment of chemotherapy response score (CRS), which served as a measure of NACT efficacy. Logistic regression analysis and Cox proportional hazards regression model were utilized to construct a prognostic index.

RESULTS

Multivariate logistic analysis showed that both CRS and neutrophil-to-lymphocyte ratio (NLR) independently influenced the response to platinum-based chemotherapy. Meanwhile, Kaplan-Meier and Cox regression analysis revealed that CRS score was significantly correlated with progression-free survival (PFS) and overall survival (OS), and patients with high NLR showed poor OS. We further developed an ovarian neoadjuvant chemotherapy prognostic index (ONCPI) based on the CRS and NLR. The area under the curve (AUC) value of ONCPI was 0.771 (P < 0.001, 95% CI: 0.656-0.887) for the prediction of platinum resistance. This AUC value surpasses that of the individual NLR and CRS, which were 0.670 (P = 0.018, 95% CI: 0.547-0.793) and 0.714 (P = 0.003, 95% CI: 0.590-0.839), respectively. Moreover, survival analysis suggested that patients with ONCPI of 0 and 1 were significantly associated with improved PFS and OS.

CONCLUSIONS

The ONCPI emerges as a significant prognostic marker for predicting NACT outcome in advanced HGSOC patients and holds promise for integration into clinical practice and risk-stratified trial design.

A Neutrophil Extracellular Traps-Related Signature Predicts Clinical Outcomes and Identifies Immune Landscape in Ovarian Cancer

Ovarian cancer (OvCa) is the most lethal gynaecology malignancies worldwide. Neutrophil extracellular traps (NETs), net-like protein structures produced by activated neutrophils and DNA-histone complexes, have a central role in tumours, though haven't been fully explored in OvCa. We obtained transcriptome data from TCGA-OvCa database (n = 376) as training, ICGC-OvCa database (n = 111) as validation and GTEx database (n = 180) as controls. Through LASSO-COX Regression analysis, we identified an eight-gene signature among 87 NETs-related genes, which was significantly related to poor prognosis in both TCGA-OvCa and ICGC-OvCa cohorts (Log-rank p-value = 0.0003 and 0.0014). Next, we constructed and validated a prognostic nomogram, consist of NETs-related signature and clinical features (C-index = 0.82). We evaluated 22 typical immune cell infiltration through CIBERSORT analysis, which implied upregulation of memory CD4 + T cells, follicular helper T cells and neutrophils in high-risk group. Additionally, we predicted therapy sensitivity through TIDE algorithm, indicating that high NETs-riskscore exhibited more sensitivity towards Sorafenib and less sensitivity towards immunotherapy. We initially reported that RAC2 upregulation was associated with NETs formation and poor prognosis (p-value < 0.05) through IHC analysis of tissue microarrays (n = 125). Conclusively, NETs-related signature was reliable for OvCa prognosis prediction and therapy assessment. Especially, RAC2 was predominantly related to NETs formation, thus providing hints towards anti-tumour mechanism of NETs in OvCa.

Association between alteration of neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, cancer antigen-125 and surgical outcomes in advanced stage ovarian cancer patient who received neoadjuvant chemotherapy

Introduction

Optimal resection significantly influences the prognosis of advanced-stage epithelial ovarian cancer (EOC) patients undergoing debulking surgery. In patients who received neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS), the determination of the ideal timing for surgery remains a challenge. Inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and CA-125 levels, have been recognized as potential predictive markers.

Objective

This study aims to evaluate the predictive value of changes in NLR, PLR, and CA-125 levels following NACT, specifically assessing their impact on surgical outcomes during IDS for advanced-stage EOC.

Methods

A retrospective cohort study enrolled advanced-stage EOC patients who underwent NACT followed by IDS at Vajira Hospital in Thailand from January 2009 to June 2023. Data on clinical, surgical, and inflammatory markers were collected, and the predictive value of these markers for suboptimal resection outcomes was assessed.

Results

Among the 65 patients, 98.5 % exhibited radiologic responses post-NACT, while 29.2 % experienced suboptimal resections. Univariate analysis did not reveal significant associations between suboptimal resection and NLR changes after the first NACT cycle or alterations in NLR, PLR, and CA-125 levels at the end of NACT. Subsequent analysis suggested that an NLR decrease exceeding 70 % after the first cycle and NACT completion might predict suboptimal resection, yet statistical analyses showed limited prognostic efficacy (AuROC = 0.608 and 0.597).

Conclusion

Our study does not support that changes in NLR, PLR, platelet count, and CA-125 levels after NACT reliably predict IDS outcomes. Additional prospective investigations using larger cohorts or a combination of evaluation methods, rather than relying solely on NLR, are recommended.

New use of preoperative fibrinogen in ovarian cancer management

Background

Ovarian cancer (OC) is often diagnosed at an advanced stage due to the absence of specific symptoms in its early stages. And the prognosis greatly depends on when the disease is diagnosed. Thus, we conducted to evaluate the value of preoperative fibrinogen (Fib) levels for the diagnosis of OC in the hope of improving its diagnostic efficiency.

Methods

A total of 126 ovarian tumor patients were retrospectively included in this study. Four candidate OC markers, including cancer antigen 125 (CA125), Fib, platelet (PLT) and homocysteine (Hcy) were employed to establish a diagnosis model for OC. The diagnostic performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC) and Youden index.

Results

All included markers could be used for the diagnosis of OC. The AUCs of CA125, Fib, PLT and Hcy were 0.881, 0.825, 0.676 and 0.647, respectively. The new diagnosis model combining CA125 and Fib (CA125-Fib) had a higher AUC (0.924), Youden index (0.730), and best sensitivity (SN) (74.6%) and specificity (SP) (98.41%). CA125-Fib also had a high value in the diagnosis of stage I-II OC (AUC, Youden index, SN and SP: 0.853, 0.624, 81.48% and 80.95%).

Conclusions

Fib could be used for OC diagnosis. In particular, the combination of Fib and CA125 could further improve the diagnostic efficiency. And the diagnostic value of PLT and Hcy was found to be poor.

Clinical significance and immune infiltration analyses of a novel coagulation-related signature in ovarian cancer

Ovarian cancer (OV) is the most lethal gynecological malignancies worldwide. The coagulation cascade could induce tumor cell infiltration and contribute to OV progression. However, coagulation-related gene (CRG) signature for OV prognosis hasn't been determined yet. In this study, we evaluated the prognostic value of coagulation scores through receiver operating characteristics (ROC) analysis and K-M curves, among OV patients at our institution. Based on the transcriptome data of TCGA-OV cohort, we stratified two coagulation-related subtypes with distinct differences in prognosis and tumor immune microenvironment (p < 0.05). Moreover, from the 6406 differentially-expressed genes (DEGs) between the GTEx (n = 180) and TCGA-OV cohorts (n = 376), we identified 138 potential CRGs. Through LASSO-Cox algorithm, we finally distinguished a 3-gene signature (SERPINA10, CD38, and ZBTB16), with promising prognostic ability in both TCGA (p < 0.001) and ICGC cohorts (p = 0.040). Stepwise, we constructed a nomogram based on the clinical features and coagulation-related signature for overall survival prediction, with the C-index of 0.6761, which was evaluated by calibration curves. Especially, based on tissue microarrays analysis, Quantitative real-time fluorescence PCR (qRT-PCR), and Western Blot, we found that aberrant upregulation of CRGs was related to poor prognosis in OV at both mRNA and protein level (p < 0.05). Collectively, the coagulation-related signature was a robust prognostic biomarker, which could provide therapeutic benefits for chemotherapy/immunotherapy and assist clinical decision in OV patients.

Prognostic analysis of the plasma fibrinogen combined with neutrophil-to-lymphocyte ratio in patients with non-small cell lung cancer after radical resection

BACKGROUND

To investigate the correlation between the fibrinogen combined with neutrophil-to-lymphocyte ratio (F-NLR) and the clinicopathologic features of non-small cell lung cancer (NSCLC) patients who underwent radical resection.

METHODS

This study reviewed the medical records of 289 patients with NSCLC who underwent radical resection. The patients were stratified into three groups based on F-NLR as follows: patients with low NLR and fibrinogen were group A, patients with high NLR or fibrinogen were group B, and patients with high NLR and fibrinogen were group C. Receiver operating characteristic curve and Youden index were used to determine the cutoff value of the NLR and fibrinogen. Survival curves were described by Kaplan-Meier method and compared by log-rank test. The univariate and multivariate analyses were performed with the Cox proportional hazard model to identify the prognostic factors.

RESULTS

A value of 3.19 was taken as the optimal cutoff value of NLR in this study. A value of 309 was used as the optimal cutoff value of fibrinogen. Cox multivariate analysis showed that tumor, nodes, metastasis (TNM) stage and F-NLR were independent prognostic factors affecting the survival rate of patients. The first-, third-, and fifth-year survival rates in group A were 99.2%, 96.6%, and 95.0%, respectively. The first-, third-, and fifth-year survival rates in group B were 98.4%, 76.6%, and 63.2%, respectively. The first-, third-, and fifth-year survival rates in group C were 91.3%, 41.1%, and 22.8%, respectively. F-NLR was significantly correlated with overall survival in patients with NSCLC (p < 0.001).

CONCLUSIONS

The F-NLR level is markedly related to the prognosis of patients with NSCLC undergoing radical surgery. Therefore, closer attention should be given to patients with NSCLC with a high F-NLR before surgery to provide postoperative adjuvant therapy.

Independent predictive value of blood inflammatory composite markers in ovarian cancer: recent clinical evidence and perspective focusing on NLR and PLR

Ovarian cancer (OC) is one of the deadliest malignant tumors affecting women worldwide. The predictive value of some blood inflammatory composite markers in OC has been extensively reported. They can be used for early detection and differential diagnosis of OC and can be used for predicting survival, treatment response, and recurrence in the affected patients. Here, we reviewed the predictive values of composite inflammatory markers based on complete blood count, namely neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio, and systemic inflammation index and markers based on blood protein, namely C-reactive protein-to-albumin ratio and prognostic nutritional index in OC, with a focus on NLR and PLR. We referred to the clinical studies on these six markers, reviewed the patient population, and summarized the marker cut-off values, significance, and limitations of these studies. All these studies were retrospective and most of them were single-center clinical studies with small sample sizes. We found that the cut-off values of these markers have not been unified, and methods used to determine these values varied among studies. The predictive value of these markers on survival was mainly reflected in the postoperative patients of multiple subtypes of ovarian cancer including epithelial OC, high-grade serous ovarian carcinoma, and ovarian clear cell carcinoma. We focused on NLR and PLR and calculated their pooled hazard ratios. NLR and PLR were reliable in predicting overall and progression-free survivals in patients with OC. Therefore, it is necessary to adjust important confounding factors and conduct a long-term follow-up prospective cohort study to further clarify the cut-off values of NLR and PLR and their clinical applications.

Preoperative prediction model of microvascular invasion in patients with hepatocellular carcinoma

BACKGROUND

Microvascular invasion (MVI) is an adverse factor for the prognosis of patients with hepatocellular carcinoma (HCC). We aimed to construct a preoperative prediction model for MVI, thereby providing a reference for clinicians in formulating treatment options for HCC.

METHODS

A total of 360 patients with non-metastatic HCC were retrospectively enrolled. We used logistic regression analysis to screen out independent risk factors for MVI and further constructed a predictive model for MVI. The performance of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

RESULTS

Logistic regression analysis revealed that fibrinogen (>4 g/L) (OR: 6.529), alpha-fetoprotein (≥ 400 ng/mL) (OR: 2.676), cirrhosis (OR: 2.25), tumor size (OR: 1.239), and poor tumor border (OR: 3.126) were independent risk factors of MVI. The prediction model of MVI had C-index of 0.746 and 0.772 in the training and validation cohorts, respectively. The calibration curves showed good agreement between actual and predicted MVI risk. Finally, DCA reveals that this model has good clinical utility.

CONCLUSION

The nomogram-based model we established can predict the preoperative MVI well and provides reference for surgeons to make clinical treatment decisions.

Artificial intelligence-based preoperative prediction system for diagnosis and prognosis in epithelial ovarian cancer: A multicenter study

Background

Ovarian cancer (OC) is the most lethal gynecological malignancy, with limited early screening methods and poor prognosis. Artificial intelligence technology has made a great breakthrough in cancer diagnosis.

Purpose

We aim to develop a specific interpretable machine learning (ML) prediction model for the diagnosis and prognosis of epithelial ovarian cancer (EOC) based on a variety of biomarkers.

Methods

A total of 521 patients with EOC and 144 patients with benign gynecological diseases were enrolled including derivation datasets and an external validation cohort. The predicted information was acquired by 9 supervised ML methods, through 34 parameters. Behind predicted reasons for the best ML were improved by using the SHapley Additive exPlanations (SHAP) algorithm. In addition, the prognosis of EOC was analyzed by unsupervised clustering and Kaplan–Meier (KM) survival analysis.

Results

ML technology was superior to conventional logistic regression in predicting EOC diagnosis and XGBoost performed best in the external validation datasets. The AUC values of distinguishing EOC and benign disease patients, determining pathological type, grade and clinical stage were 0.958 (0.926-0.989), 0.792 (0.701-0.8834), 0.819 (0.687-0.950) and 0.68 (0.573-0.788) respectively. For negative CA-125 EOC patients, the AUC performance of XGBoost model was 0.835(0.763-0.907). We used unsupervised cluster analysis to identify EOC subgroups with significantly poor overall survival (p-value <0.0001) and recurrence-free survival (p-value <0.0001).

Conclusions

Based on the preoperative characteristics, we proved that ML algorithm can provide an acceptable diagnosis and prognosis prediction model for EOC patients. Meanwhile, SHAP analysis can improve the interpretability of ML models and contribute to precision medicine.

Combination of preoperative plasma fibrinogen and neutrophil-to-lymphocyte ratio to predict the prognosis for patients undergoing laparoscopic nephrectomy for renal cell carcinoma

This study was conducted to investigate the prognostic significance of a combination of fibrinogen and neutrophil-to-lymphocyte ratio (NLR) named the F-NLR score as a novel indicator and further create nomograms for predicting the prognosis of patients with renal cell carcinoma (RCC) treated with laparoscopic nephrectomy. A total of 425 patients with RCC who underwent laparoscopic nephrectomy were included in this study. Then, we divided the patients based on the cut-off values of their F-NLR score into three categories: F-NLR 2 (both high fibrinogen and NLR), F-NLR 0 (both low fibrinogen and NLR), and F-NLR 1 (remaining patients). Cox regression analysis was performed to investigate the predictive performance of the F-NLR score on overall survival (OS) and cancer-specific survival (CSS). Predictive nomograms of F-NLR were established and internally validated. Time-dependent receiver operating characteristic (ROC) curve analysis was performed to assess the predictive accuracy of the nomogram, NLR, and fibrinogen as prognostic markers. The F-NLR 0, 1, and 2 groups included 226 (53.2%), 147 (34.6%), and 52 (12.2%) patients, respectively. Cox regression analysis showed that a high F-NLR score was significantly associated with poor prognosis and acted as an independent prognostic factor for OS and CSS (all P < 0.05). Predictive nomograms with F-NLR for OS (C-index: 0.773) and CSS (C-index: 0.838) were well developed. Time-dependent ROC results showed that nomograms containing F-NLR had better predictive performance than NLR and fibrinogen. F-NLR score was a novel effective prognostic biomarker for patients with RCC undergoing laparoscopic nephrectomy.

A Nomogram Combining MRI Multisequence Radiomics and Clinical Factors for Predicting Recurrence of High-Grade Serous Ovarian Carcinoma

Objective

To develop a combined nomogram based on preoperative multimodal magnetic resonance imaging (mMRI) and clinical information for predicting recurrence in patients with high-grade serous ovarian carcinoma (HGSOC).

Methods

This retrospective study enrolled 141 patients with clinicopathologically confirmed HGSOC, including 65 patients with recurrence and 76 without recurrence. Radiomics features were extracted from the mMRI images (FS-T2WI, DWI, and T1WI+C). L1 regularization-based least absolute shrinkage and selection operator (LASSO) regression was performed to select radiomics features. A multivariate logistic regression analysis was used to build the classification models. A nomogram was established by incorporating clinical risk factors and radiomics Radscores. The area under the curve (AUC) of receiver operating characteristics, accuracy, and calibration curves were assessed to evaluate the performance of classification models and nomograms in discriminating recurrence. Kaplan-Meier survival analysis was used to evaluate the associations between the Radscore or clinical factors and disease-free survival (DFS).

Results

One clinical factor and seven radiomics signatures were ultimately selected to establish the predictive model for this study. The AUCs for identifying recurrence in the training and validation cohorts were 0.76 (0.68, 0.84) and 0.67 (0.53, 0.81) with the clinical model, 0.78 (0.71, 0.86) and 0.74 (0.61, 0.86) with the multiradiomics model, and 0.83 (0.77, 0.90) and 0.78 (0.65, 0.90) with the combined nomogram, respectively. The DFS was significantly shorter in the high-risk group than in the low-risk group.

Conclusion

By incorporating radiomics Radscores and clinical factors, we created a radiomics nomogram to preoperatively identify patients with HGSOC who have a high risk of recurrence, which may serve as a potential tool to guide personalized treatment.

Neutrophil-to-lymphocyte ratio and chemotherapy response score as prognostic markers in ovarian cancer patients treated with neoadjuvant chemotherapy

BACKGROUND

Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is the recommended approach in patients with advanced epithelial ovarian cancer (EOC). However, most patients eventually relapse despite the initial high response rate to chemotherapy. Neutrophil-to-lymphocyte ratio is a well-known biomarker that reflects severe inflammation, critical illness, and mortality in various diseases. Chemotherapy response score (CRS) and neutrophil-to-lymphocyte ratio (NLR) have been identified as potential biomarkers of platinum resistance and disease prognosis. We retrospectively evaluated 132 patients with stage IIIc or IV ovarian/fallopian tube/primary peritoneal cancer who had received NACT followed by IDS from 01/01/2003 to 31/12/2018. CRS was assessed on omental specimens collected from IDS according to ICCR guidelines.

RESULTS

Median age was 64.57 years (SD: 9.72; range 39.2-87.1). Most ovarian tumors were serous epithelial (90.9%; 120/132). An elevated NLR (defined as > 3) was observed in 72% (95/132) of patients in contrast with 28% (37/132) of patients characterized by low NLR status. Median PFS (mPFS) and median overall survival (mOS) were 13.05 months (95% CI: 11.42-14.67)) and 34.69 months (95% CI: 23.26-46.12) respectively. In univariate analysis, CRS3 score was significantly associated with prolonged mPFS (CRS1/2: 12.79 months vs CRS3: 17.7 months; P = 0.008). CRS score was not associated with mOS (P = 0.876). High NLR was not significantly associated with mPFS (P = 0.128), however it was significantly associated with poor mOS (P = 0.012). In multivariate analysis, only performance of surgery maintained its statistical significance with both PFS (P = 0.001) and OS (P = 0.008).

CONCLUSION

NLR could serve as a useful predictor of OS but not PFS in ovarian cancer patients receiving NACT. In accordance with our previous study, CRS score at omentum was found to be associated with PFS but not OS in ovarian cancer patients treated with NACT and IDS.

Prognostic value of inflammatory and nutritional markers for hepatocellular carcinoma

Many clinical studies have demonstrated that the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and Onodera's prognostic nutritional index (OPNI) are visibly involved in the prognosis of a variety of tumors. In our research, we aim to determin the prognostic impact of NLR, PLR, and OPNI for hepatocellular carcinoma (HCC).Data of hepatocellular carcinoma patients undergoing treatment in Changzhi People's Hospital between 2011 and 2017 were reviewed. 270 patients with HCC were under inclusion criteria. The optimal cut-off points of OPNI, NLR and PLR were determined by using the X-tile program. The overall survival (OS) was analyzed by Kaplan-Meier method. Multivariate analysis was performed using Cox Proportional Hazard Regression model to determine independent prognostic indicators for HCC.As revealed by Univariate and multivariate analysis, OPNI, Treatment, PLR, and BCLC Stage can be used as independent prognostic indicators for HCC. Comparing the P values and hazard ratios, we found out that the OPNI has greatest influence on prognosis in these indexes. The appropriate cut-off points of NLR, PLR, and OPNI were 2.5, 133.3, and 39.5, respectively. High score OPNI group had a better OS. In the analysis between OPNI and clinicopathological characteristics, there were differences in treatment, postoperative therapy, AST, ALBI grade, NLR and PLR between the high OPNI group and the low OPNI group, while others did not.OPNI is a straightforward and effective independent prognostic indicator for HCC.

Single-particle fibrinogen detection using platelet membrane-coated fluorescent polystyrene nanoparticles

Fibrinogen participates in many physiological processes and is a biomarker for a variety of diseases. On this account, the development of a sensitive method for fibrinogen assay is particularly important. Herein, we demonstrate a new color-coded single-particle detection (SPD) method for fibrinogen detection by using platelet membrane-coated fluorescent polystyrene nanoparticles (PNPs) as the probes. Due to the specific interactions between fibrinogen and integrin receptors on platelet membranes, PNPs can form aggregated structures in the presence of fibrinogen. Therefore, colocalization events between green and red PNPs and the corresponding Pearson's correlation coefficient vary with the concentrations of fibrinogen. The sensing ability shows a linear range of 30-300 μg mL^-1 and a limit of detection (LOD) of 3.9 μg mL^-1 (11.3 nM) for fibrinogen detection. Moreover, it has been validated that the proposed biosensor can selectively detect fibrinogen and shows a good performance in real sample applications.