Serum albumin and derived neutrophil-to-lymphocyte ratio are potential predictive biomarkers for immune checkpoint inhibitors in small cell lung cancer

Prognostic impact of histopathological features and serum inflammatory markers in patients with gastric cancer undergoing neoadjuvant therapy

BACKGROUND

Neoadjuvant therapies induce tumor regression, resulting in improved surgical resection and pathologic complete response rates, as well as long-term disease-free and overall survival (OS). In addition to the tumor regression score, serum inflammatory markers, including neutrophil, lymphocyte, platelet, and serum albumin levels, are used to determine prognosis.

AIM

To investigate the effect of histological features and serum inflammatory markers on the prognosis of gastric cancer following neoadjuvant treatment.

METHODS

Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and serum albumin levels were retrospectively recorded for 177 patients receiving neoadjuvant 5-fluorouracil, leucovorin, oxaliplatin and docetaxel chemotherapy. Disease-free and OS were analyzed based on tumor histopathological features, type of surgery, regression scores, and serum inflammatory markers.

RESULTS

Patients over 65 years of age, those with lymphovascular or perineural invasion, hypoalbuminemia, and those who did not receive adjuvant therapy were found to be at higher risk for shorter recurrence/relapse intervals [hazard ratio (HR): 1.64, P = 0.04; HR: 4.20, P < 0.001; HR: 1.87, P = 0.03; HR: 3.5, P < 0.001; and HR: 2.73, P = 0.01, respectively]. Lymphovascular invasion, R1 resection, lack of adjuvant treatment, and hypoalbuminemia negatively influenced OS (HR: 3.68, P < 0.003; HR: 2.37, P = 0.01; HR: 3.99, P < 0.001; and HR: 2.50, P = 0.01, respectively). No effect of NLR and PLR was observed.

CONCLUSION

Current neoadjuvant therapies prolong disease-free and OS. The practical application of serum inflammatory markers (NLR and PLR) is limited due to the lack of standard cut-off values. Nutritional status, hypoalbuminemia, and incomplete perioperative chemotherapy have been associated with poor prognosis.

Impact of fibrinogen-to-albumin ratio on the long-term prognosis of patients with advanced HER2-negative gastric cancer receiving immunochemotherapy

BACKGROUND

There is currently no effective targeted therapy for advanced HER2-negative gastric cancer (GC). While immunotherapy combined with chemotherapy is the first-line treatment for GC, patient survival outcomes remain highly heterogeneous, highlighting the urgent need for reliable predictive biomarkers. The fibrinogen-to-albumin ratio (FAR) integrates both inflammation (elevated fibrinogen levels) and nutritional status (reduced albumin levels). Although FAR has been associated with immunotherapy resistance in various solid tumors, its prognostic value in GC patients receiving immunochemotherapy remains unclear.

AIM

To assess the predictive value of the FAR in the long-term prognosis of advanced HER2-negative GC patients receiving sintilimab-based immunotherapy combined with chemotherapy.

METHODS

This retrospective study included 260 patients with unresectable or metastatic HER2-negative GC who received sintilimab plus chemotherapy from 2021 to 2024. Pre-treatment FAR values were calculated, and the optimal cutoff value was determined using receiver operating characteristic curve analysis. The association between the FAR and overall survival (OS) and progression-free survival (PFS) was analyzed using Kaplan-Meier survival curves and Cox proportional hazards models. Independent prognostic factors were identified by multivariate Cox regression analysis based on OS, and a nomogram model was constructed incorporating FAR. The concordance index (C-index) and calibration curves were used to assess the predictive performance and calibration of the model.

RESULTS

Patients with high FAR (≥ 0.08) had significantly shorter median PFS [7.80 months (6.40-8.30) vs 10.00 months (9.30-11.20), P < 0.001] and OS [14.20 months (12.20-16.60) vs 19.50 months (18.80-22.00), P < 0.001] compared to the group with low FAR (< 0.08). Moreover, the group with high FAR had a significantly lower objective response rate (10.22% vs 19.51%, P = 0.034) and disease control rate (34.31% vs 49.59%, P = 0.013). The incidence of adverse events did not significantly differ between the two groups (P > 0.05). Multivariate analysis confirmed the FAR as an independent prognostic factor for OS (HR = 2.33, 95%CI: 1.59-3.43, P < 0.001). The nomogram model, incorporating FAR, Eastern Cooperative Oncology Group performance status, programmed cell death ligand 1 expression, tumor stage, and body mass index, demonstrated strong predictive accuracy, with an internal validation C-index of 0.73 (95%CI: 0.66-0.79). The calibration curve showed a high consistency between predicted and actual survival rates.

CONCLUSION

Patients with low FAR had significantly better prognostic outcomes than those with high FAR when receiving immunochemotherapy. Thus, FAR may serve as a valuable prognostic biomarker for predicting survival outcomes in patients with advanced HER2-negative GC.

The Impact of the Histologic Types of Lung Cancer on CBC-Derived Inflammatory Markers-Current Knowledge and Future Perspectives

Background/Objectives: The analysis of the complete blood count (CBC)-derived inflammatory indexes across different histological subtypes of lung cancer supports the early detection of tumor-induced inflammation and has a good predictive value for severity in cancer patients. The main objective of this article was to assess the variations in CBC-derived inflammatory markers across different histologic subtypes of lung cancer, with the final goal of identifying specific predictors of severity for each histologic subtype of lung cancer. Methods: We conducted a retrospective descriptive study that included 202 patients diagnosed with lung carcinoma at the Clinical County Hospital Mureș. The analyzed parameters were as follows: the histological type, the stage of the tumor, patients' general data, and associated comorbidities. In addition, nine CBC-derived inflammatory indexes, like the neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (d-NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), eosinophil-to-neutrophil ratio (ENR), eosinophil-to-monocyte ratio (EMR), systemic inflammatory index (SII), systemic inflammatory response index (SIRI), and aggregate index of systemic inflammation (AISI), were analyzed as predictors of severity and correlated with histologic findings. Results: The predictors of severity differed across the histologic subtypes. SIRI, d-NLR, and age were predictors of severity in adenocarcinoma patients, while the d-NLR, ENR, leukocyte, and neutrophil count predicted severity in squamous cell carcinoma. For SCLC patients, AISI, SIRI, SII, d-NLR, EMR, ENR, MLR, leukocyte count, lymphocyte count, neutrophil count, platelets count, COPD, smoking, and male gender were predictors for severity. Conclusions: Understanding the complexity and variations in the inflammatory response across different histologic types of lung cancer can personalize treatment regimens and target specific abnormal cellular lines, thus improving the outcome of this highly deadly condition.

Predictive Value of Advanced Lung Cancer Inflammation Index and Development of a Nomogram for Prognosis in Patients with Cervical Cancer Treated with Radiotherapy

Purpose

As an assessment tool of nutritional status and inflammation, the advanced lung cancer inflammation index (ALI) is associated with survival in various cancers. We aimed to investigate the association between the ALI's prognostic value and survival time in patients with the stage IIB-III cervical cancer treated with radiotherapy.

Patients and Methods

We retrospectively screened patients diagnosed with cervical cancer and underwent radiotherapy in a single institution between September 2013 to September 2015. The ALI was calculated as body mass index * serum albumin/neutrophil-to-lymphocyte ratio. The cut-off value of ALI was determined by the receiver operating characteristic (ROC) curves. Overall survival (OS) and progression-free survival (PFS) were evaluated using the Kaplan-Meier method and Cox proportional hazards models. A nomogram was developed using prognostic factors based on multivariate analyses.

Results

A total of 178 patients with cervical cancer were included. The cutoff value of ALI was set at 310.6 by ROC analyses. Kaplan Meier survival curves indicated that patients with low ALI had a significantly poorer OS (log-rank P<0.001) and PFS (log-rank P=0.0056) than those with high ALI. The association between ALI and OS was significant in the patients with obese/overweight and low/normal weight. The Cox regression analysis indicated that patients with low ALI were associated with a decreased OS (Hazard Ratio (HR) = 2.56, 95% Confidence Intervals (CI), 1.35-4.83; P= 0.004) and PFS (HR = 1.83; 95% CI, 1.06-3.17; P = 0.031). The nomogram on OS was created based on ALI with C-index of 0.81. Patients with high nomogram points had worse OS than those with low nomogram points (log rank P<0.0001).

Conclusion

Pretreatment ALI is an independent negative prognostic factor in patients with cervical cancer treated with radiotherapy. The ALI based nomogram can help to identify patients who may have unfavorable outcomes.

Neutrophil-to-Lymphocyte Ratio as a Potential Predictive Marker for Epileptic Seizures: Unveiling the "V"-Shaped Link

Objective: The neutrophil-to-lymphocyte ratio (NLR) is an important marker of inflammation. An increased NLR has been detected in the blood of patients with epilepsy. However, the correlation between varying NLRs and epileptic seizures (ESs) is still unclear. Methods: A retrospective analysis was conducted, and patients were divided into two groups based on whether they had ES upon admission. Comprehensive data were collected, including routine blood tests, demographic information, and medical histories. The NLR was calculated by dividing the percentage of neutrophils by the percentage of lymphocytes. Results: In total, 414 patients were included (284 males, 151 females; aged 18-90 years), with 271 in the experimental group and 143 in the control group. No significant differences in the NLR were found between the groups (p=0.164). Nonetheless, when NLRs were categorized, a "V"-shaped link with ES was observed. An NLR of 2-3 correlated with the lowest seizure incidence. Higher NLRs were associated with increased neutrophil and decreased lymphocyte counts. Patients with an NLR < 2 had the lowest infection rates and the highest cerebrovascular disease exposure, whereas those with an NLR > 3 had the highest infection rates and were the oldest. Conclusions: NLR modifications can serve as potential predictive markers for ES. However, the relationship between the NLR and ES is not linear. The factors contributing to these variations are multiple and complex. An NLR of 2-3 may represent an equilibrium point. An elevated NLR indicates pronounced inflammatory responses, while a low NLR can have more complex causes.

Development of a prognostic prediction model for non-smoking lung adenocarcinoma based on pathological information and laboratory hematologic indicators: a multicenter study

Objective

To develop a simple and practical model to predict the prognostic survival of non-smoking patients with lung adenocarcinoma by combining general pathological information with laboratory hematologic indicators.

Methods

Cox univariate and multivariate analyses were used to identify the variable indicators. A Cox proportional hazards model was constructed based on the selected variables to compare survival outcomes between the high-and low-risk groups of non-smoking patients with lung adenocarcinoma and to validate the model's performance. Subsequently, a nomogram model was established to systematically evaluate the impact of selected variables on prognosis.

Results

Data of non-smoking patients with lung adenocarcinoma from four hospitals were retrospectively collected. We enrolled 1,172 patients, this includes 372 external validation data. Multivariate analysis identified six significant variables (P < 0.05): tumor TNM stage, tumor size, white blood cell count, neutrophil percentage, lymphocyte percentage, and hemoglobin level. We combined these six variables to build a model. The C-index of the training set is 0.811 (0.780-0.842), this value is 0.786 (0.737-0.835) in,test set and 0.810 (0.772-0.847) in validation set. The area under the curve (AUC) results of the predicted 3-years overall survival (OS) of the three data sets were 0.850, 0.819, and 0.860, respectively. These values for 5-years were 0.811, 0.771, and 0.849. Stratified analysis based on tumor staging showed that the model effectively distinguished outcomes (P < 0.0001). High-risk groups demonstrated significantly poorer prognosis compared to low-risk groups (P < 0.001).

Conclusion

The prognostic model based on tumor TNM stage, tumor size, white blood cell count, neutrophil percentage, lymphocyte percentage, and hemoglobin levels effectively predicted the prognosis of non-smoking patients with lung adenocarcinoma. Compared with the more studied blood markers at present, the indicators of our model do not need conversion, Our model provides a useful reference for personalized diagnosis and treatment in clinical practice.