Prognostic utility of the combination of pretreatment monocyte-to-lymphocyte ratio and neutrophil-to-lymphocyte ratio in patients with NMIBC after transurethral resection

Association between polychlorinated biphenyls and circulatory immune markers: results from NHANES 1999-2004

OBJECTIVES

Polychlorinated biphenyls (PCBs), a family of persistent toxic and organic environmental pollutants, were associated with multiple organ damages in humans once accumulating. However, association between PCBs exposure and circulatory immune markers were not clear.

METHODS

Data was collected from participants enrolled in the National Health and Nutrition Examination Survey in 1999-2004. PCBs were categorized by latent class analysis (LCA). Weighted quantile sum (WQS) regression was used to investigate effects of PCBs exposure on circulatory immune markers including leukocyte counts, monocyte-lymphocyte ratio (MLR), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII).

RESULTS

There were 3,109 participants included in the final analysis with blood PCBs levels presented as 3 classes. The high PCBs group had a higher rate of comorbidities. Leukocyte, lymphocyte and neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and system immune-inflammation index (SII) were significantly lower in the high PCBs group than in the low PCBs group (all p-values < 0.05). After adjusting for covariant variables, the low PCBs group was positively associated with SII (p = 0.021) and NLR (p = 0.006) in multivariate regression. Significantly negative correlations between PCBs classification and SII (β = -14.513, p = 0.047), and NLR (β = -0.035, p = 0.017) were found in WQS models. LBX028LA showed the most significant contribution in the associations between PCBs and SII, and LBX128LA contributed most significantly to associations with NLR.

CONCLUSION

Our study adds novel evidence that exposures to PCBs may be adversely associated with the circulatory immune markers, indicating the potential toxic effect of PCBs on the human immune system.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

Preoperative monocyte-to-lymphocyte ratio as a potential predictor of bladder cancer

OBJECTIVES

The aim of this study was to investigate the role of preoperative Monocyte-to-Lymphocyte ratio (MLR) as a potential predictor of bladder cancer (BC).

METHODS

Clinical data of patients who underwent TURBT at our institution between 2017 and 2021 were collected and retrospectively analysed. MLR was obtained from preoperative blood analyses performed within 1 month from hospital admission. The association of MLR with different clinic-pathological features obtained from histological reports was further analysed. Statistical analysis was performed using the Kruskal Wallis test for non-parametric variables, assuming p<0.05 as statistically significant.

RESULTS

510 patients were included in the study (81% males, 19% females), with a mean age of 71.66 ± 11.64 years. Mean MLR was higher in patients with any-type bladder cancer, reporting an MLR of 0.41 ± 0.11 compared to 0.38 ± 0.43 in patients without bladder cancer (p=0.043). In the subsequent comparison among low-grade and high-grade bladder cancer, MLR did not report statistically significant differences, with 0.29 ± 0.12 for low-grade BC and 0.51 ± 0.81 for high-grade BC (p=0.085).

CONCLUSIONS

Our findings reported elevated preoperative MLR should be considered a potential biomarker predicting malignancy for bladder tumours. Furthermore, research are necessary to assess its role in discerning low-grade from high-grade patients.